0s autopkgtest [03:31:53]: starting date and time: 2026-02-10 03:31:53+0000 0s autopkgtest [03:31:53]: git checkout: 4b346b80 nova: make wait_reboot return success even when a no-op 0s autopkgtest [03:31:53]: host juju-7f2275-prod-proposed-migration-environment-2; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.mwej7ey_/out --timeout-copy=6000 --needs-internet=try --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:lattice --apt-upgrade r-cran-rrcov --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=lattice/0.22-9-1 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest-cpu2-ram4-disk20-amd64 --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-2@sto01-1.secgroup --name adt-resolute-amd64-r-cran-rrcov-20260210-033153-juju-7f2275-prod-proposed-migration-environment-2-ee83cc33-3a65-4a3e-8dd0-8d7afa734ce1 --image adt/ubuntu-resolute-amd64-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-2 --net-id=net_prod-autopkgtest-workers-amd64 -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/ 4s Creating nova instance adt-resolute-amd64-r-cran-rrcov-20260210-033153-juju-7f2275-prod-proposed-migration-environment-2-ee83cc33-3a65-4a3e-8dd0-8d7afa734ce1 from image adt/ubuntu-resolute-amd64-server-20260204.img (UUID fedf54b4-458b-493e-8072-6425c19717b4)... 73s autopkgtest [03:33:06]: testbed dpkg architecture: amd64 74s autopkgtest [03:33:07]: testbed apt version: 3.1.14 74s autopkgtest [03:33:07]: @@@@@@@@@@@@@@@@@@@@ test bed setup 74s autopkgtest [03:33:07]: testbed release detected to be: None 75s autopkgtest [03:33:08]: updating testbed package index (apt update) 75s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [124 kB] 75s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 75s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 75s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 75s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [31.1 kB] 75s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [181 kB] 75s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [1735 kB] 75s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 Packages [268 kB] 75s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/main i386 Packages [220 kB] 75s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/main amd64 c-n-f Metadata [5980 B] 75s Get:11 http://ftpmaster.internal/ubuntu resolute-proposed/restricted amd64 c-n-f Metadata [120 B] 75s Get:12 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 Packages [1791 kB] 75s Get:13 http://ftpmaster.internal/ubuntu resolute-proposed/universe i386 Packages [768 kB] 75s Get:14 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 c-n-f Metadata [32.6 kB] 75s Get:15 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse i386 Packages [5020 B] 76s Get:16 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse amd64 Packages [26.4 kB] 76s Get:17 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse amd64 c-n-f Metadata [996 B] 76s Fetched 5189 kB in 1s (5879 kB/s) 77s Reading package lists... 77s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease 77s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 77s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 77s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 78s Reading package lists... 78s Reading package lists... 78s Building dependency tree... 78s Reading state information... 78s Calculating upgrade... 78s The following package was automatically installed and is no longer required: 78s libpython3.13 78s Use 'sudo apt autoremove' to remove it. 79s The following NEW packages will be installed: 79s gcc-16-base libpython3.14 libpython3.14-minimal libpython3.14-stdlib 79s linux-headers-6.19.0-3 linux-headers-6.19.0-3-generic 79s linux-image-6.19.0-3-generic linux-modules-6.19.0-3-generic 79s linux-tools-6.19.0-3 linux-tools-6.19.0-3-generic 79s The following packages will be upgraded: 79s 3cpio amd64-microcode apt bpftool busybox-initramfs busybox-static 79s cryptsetup-bin dash dbus dbus-bin dbus-daemon dbus-session-bus-common 79s dbus-system-bus-common dbus-user-session debianutils dmsetup dracut-install 79s ethtool findutils gir1.2-girepository-3.0 gir1.2-glib-2.0 hwdata iproute2 79s iptables less libapt-pkg7.0 libatomic1 libattr1 libbpf1 libbsd0 79s libcryptsetup12 libdbus-1-3 libdevmapper1.02.1 libdrm-amdgpu1 libdrm-common 79s libdrm2 libevent-core-2.1-7t64 libgcc-s1 libgdbm-compat4t64 libgdbm6t64 79s libgirepository-2.0-0 libglib2.0-0t64 libglib2.0-data libgpm2 libgudev-1.0-0 79s libidn2-0 libip4tc2 libip6tc2 libjansson4 libkeyutils1 liblsof0 79s libmaxminddb0 libnetfilter-conntrack3 libnpth0t64 libonig5 libpcap0.8t64 79s libpci3 libsensors-config libsensors5 libstdc++6 libusb-1.0-0 libwrap0 79s libxau6 libxkbcommon0 libxtables12 linux-generic linux-headers-generic 79s linux-headers-virtual linux-image-generic linux-image-virtual linux-perf 79s linux-tools-common linux-virtual lsof man-db mawk patch pciutils pnp.ids 79s pollinate python3-linkify-it python3-markdown-it python3-referencing sed 79s shared-mime-info tar tcpdump ubuntu-kernel-accessories ubuntu-standard wget 79s 90 upgraded, 10 newly installed, 0 to remove and 0 not upgraded. 79s Need to get 237 MB of archives. 79s After this operation, 339 MB of additional disk space will be used. 79s Get:1 http://ftpmaster.internal/ubuntu resolute/main amd64 debianutils amd64 5.23.2build1 [93.3 kB] 79s Get:2 http://ftpmaster.internal/ubuntu resolute/main amd64 dash amd64 0.5.12-12ubuntu3 [96.0 kB] 79s Get:3 http://ftpmaster.internal/ubuntu resolute/main amd64 findutils amd64 4.10.0-3build2 [307 kB] 79s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 sed amd64 4.9-2build3 [195 kB] 79s Get:5 http://ftpmaster.internal/ubuntu resolute/main amd64 tar amd64 1.35+dfsg-3.1build2 [257 kB] 79s Get:6 http://ftpmaster.internal/ubuntu resolute/main amd64 libattr1 amd64 1:2.5.2-3build2 [11.4 kB] 79s Get:7 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-16-base amd64 16-20260208-1ubuntu1 [59.7 kB] 79s Get:8 http://ftpmaster.internal/ubuntu resolute/main amd64 libgcc-s1 amd64 16-20260208-1ubuntu1 [80.3 kB] 79s Get:9 http://ftpmaster.internal/ubuntu resolute/main amd64 libbsd0 amd64 0.12.2-2build2 [42.3 kB] 79s Get:10 http://ftpmaster.internal/ubuntu resolute/main amd64 mawk amd64 1.3.4.20260129-1 [133 kB] 79s Get:11 http://ftpmaster.internal/ubuntu resolute/main amd64 libstdc++6 amd64 16-20260208-1ubuntu1 [844 kB] 79s Get:12 http://ftpmaster.internal/ubuntu resolute/main amd64 libapt-pkg7.0 amd64 3.1.15 [1151 kB] 79s Get:13 http://ftpmaster.internal/ubuntu resolute/main amd64 apt amd64 3.1.15 [1479 kB] 79s Get:14 http://ftpmaster.internal/ubuntu resolute/main amd64 dbus-system-bus-common all 1.16.2-2ubuntu3 [55.8 kB] 79s Get:15 http://ftpmaster.internal/ubuntu resolute/main amd64 dbus-session-bus-common all 1.16.2-2ubuntu3 [54.4 kB] 79s Get:16 http://ftpmaster.internal/ubuntu resolute/main amd64 dbus-user-session amd64 1.16.2-2ubuntu3 [9696 B] 79s Get:17 http://ftpmaster.internal/ubuntu resolute/main amd64 dbus-daemon amd64 1.16.2-2ubuntu3 [119 kB] 79s Get:18 http://ftpmaster.internal/ubuntu resolute/main amd64 dbus-bin amd64 1.16.2-2ubuntu3 [40.1 kB] 79s Get:19 http://ftpmaster.internal/ubuntu resolute/main amd64 dbus amd64 1.16.2-2ubuntu3 [24.2 kB] 79s Get:20 http://ftpmaster.internal/ubuntu resolute/main amd64 libdbus-1-3 amd64 1.16.2-2ubuntu3 [185 kB] 79s Get:21 http://ftpmaster.internal/ubuntu resolute/main amd64 libdevmapper1.02.1 amd64 2:1.02.205-2ubuntu3 [142 kB] 79s Get:22 http://ftpmaster.internal/ubuntu resolute/main amd64 dmsetup amd64 2:1.02.205-2ubuntu3 [79.4 kB] 79s Get:23 http://ftpmaster.internal/ubuntu resolute/main amd64 ethtool amd64 1:6.15-3build1 [318 kB] 79s Get:24 http://ftpmaster.internal/ubuntu resolute/main amd64 gir1.2-girepository-3.0 amd64 2.87.2-2 [25.2 kB] 79s Get:25 http://ftpmaster.internal/ubuntu resolute/main amd64 libgirepository-2.0-0 amd64 2.87.2-2 [76.1 kB] 79s Get:26 http://ftpmaster.internal/ubuntu resolute/main amd64 libatomic1 amd64 16-20260208-1ubuntu1 [11.4 kB] 79s Get:27 http://ftpmaster.internal/ubuntu resolute/main amd64 gir1.2-glib-2.0 amd64 2.87.2-2 [182 kB] 79s Get:28 http://ftpmaster.internal/ubuntu resolute/main amd64 libglib2.0-0t64 amd64 2.87.2-2 [1613 kB] 79s Get:29 http://ftpmaster.internal/ubuntu resolute/main amd64 libbpf1 amd64 1:1.6.2-1build1 [184 kB] 79s Get:30 http://ftpmaster.internal/ubuntu resolute/main amd64 iptables amd64 1.8.11-2ubuntu3 [381 kB] 79s Get:31 http://ftpmaster.internal/ubuntu resolute/main amd64 libip4tc2 amd64 1.8.11-2ubuntu3 [24.2 kB] 79s Get:32 http://ftpmaster.internal/ubuntu resolute/main amd64 libip6tc2 amd64 1.8.11-2ubuntu3 [24.4 kB] 79s Get:33 http://ftpmaster.internal/ubuntu resolute/main amd64 libnetfilter-conntrack3 amd64 1.1.1-1 [47.5 kB] 79s Get:34 http://ftpmaster.internal/ubuntu resolute/main amd64 libxtables12 amd64 1.8.11-2ubuntu3 [36.6 kB] 79s Get:35 http://ftpmaster.internal/ubuntu resolute/main amd64 iproute2 amd64 6.18.0-1ubuntu1 [1178 kB] 79s Get:36 http://ftpmaster.internal/ubuntu resolute/main amd64 less amd64 668-1build1 [172 kB] 79s Get:37 http://ftpmaster.internal/ubuntu resolute/main amd64 libcryptsetup12 amd64 2:2.8.0-1ubuntu3 [283 kB] 79s Get:38 http://ftpmaster.internal/ubuntu resolute/main amd64 libglib2.0-data all 2.87.2-2 [58.2 kB] 79s Get:39 http://ftpmaster.internal/ubuntu resolute/main amd64 libidn2-0 amd64 2.3.8-4build1 [67.6 kB] 79s Get:40 http://ftpmaster.internal/ubuntu resolute/main amd64 libkeyutils1 amd64 1.6.3-6ubuntu3 [10.6 kB] 79s Get:41 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-linkify-it all 2.0.3-1ubuntu3 [19.4 kB] 79s Get:42 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-markdown-it all 3.0.0-3build1 [54.4 kB] 79s Get:43 http://ftpmaster.internal/ubuntu resolute/main amd64 shared-mime-info amd64 2.4-5build3 [476 kB] 79s Get:44 http://ftpmaster.internal/ubuntu resolute/main amd64 busybox-static amd64 1:1.37.0-7ubuntu1 [1034 kB] 79s Get:45 http://ftpmaster.internal/ubuntu resolute/main amd64 libdrm-common all 2.4.131-1 [9774 B] 79s Get:46 http://ftpmaster.internal/ubuntu resolute/main amd64 libdrm2 amd64 2.4.131-1 [42.3 kB] 79s Get:47 http://ftpmaster.internal/ubuntu resolute/main amd64 libgdbm6t64 amd64 1.26-1build1 [36.5 kB] 79s Get:48 http://ftpmaster.internal/ubuntu resolute/main amd64 libgpm2 amd64 1.20.7-12build1 [14.4 kB] 79s Get:49 http://ftpmaster.internal/ubuntu resolute/main amd64 libjansson4 amd64 2.14-2build4 [33.2 kB] 79s Get:50 http://ftpmaster.internal/ubuntu resolute/main amd64 lsof amd64 4.99.4+dfsg-2build2 [239 kB] 79s Get:51 http://ftpmaster.internal/ubuntu resolute/main amd64 liblsof0 amd64 4.99.4+dfsg-2build2 [56.5 kB] 79s Get:52 http://ftpmaster.internal/ubuntu resolute/main amd64 libmaxminddb0 amd64 1.12.2-1build2 [18.9 kB] 79s Get:53 http://ftpmaster.internal/ubuntu resolute/main amd64 libpcap0.8t64 amd64 1.10.5-2ubuntu3 [154 kB] 79s Get:54 http://ftpmaster.internal/ubuntu resolute/main amd64 pciutils amd64 1:3.14.0-1build2 [95.5 kB] 79s Get:55 http://ftpmaster.internal/ubuntu resolute/main amd64 libpci3 amd64 1:3.14.0-1build2 [38.1 kB] 79s Get:56 http://ftpmaster.internal/ubuntu resolute/main amd64 libsensors-config all 1:3.6.2-2build1 [6862 B] 79s Get:57 http://ftpmaster.internal/ubuntu resolute/main amd64 libsensors5 amd64 1:3.6.2-2build1 [28.9 kB] 79s Get:58 http://ftpmaster.internal/ubuntu resolute/main amd64 libusb-1.0-0 amd64 2:1.0.29-2build1 [56.9 kB] 79s Get:59 http://ftpmaster.internal/ubuntu resolute/main amd64 libxau6 amd64 1:1.0.11-1build2 [7502 B] 79s Get:60 http://ftpmaster.internal/ubuntu resolute/main amd64 libxkbcommon0 amd64 1.13.1-1 [159 kB] 79s Get:61 http://ftpmaster.internal/ubuntu resolute/main amd64 man-db amd64 2.13.1-1build1 [1392 kB] 79s Get:62 http://ftpmaster.internal/ubuntu resolute/main amd64 tcpdump amd64 4.99.5-2ubuntu3 [477 kB] 79s Get:63 http://ftpmaster.internal/ubuntu resolute/main amd64 wget amd64 1.25.0-2ubuntu4 [353 kB] 79s Get:64 http://ftpmaster.internal/ubuntu resolute/main amd64 ubuntu-standard amd64 1.564 [13.3 kB] 79s Get:65 http://ftpmaster.internal/ubuntu resolute/main amd64 3cpio amd64 0.14.0-1ubuntu1 [285 kB] 79s Get:66 http://ftpmaster.internal/ubuntu resolute/main amd64 bpftool amd64 7.7.0+6.19.0-3.3 [1229 kB] 79s Get:67 http://ftpmaster.internal/ubuntu resolute/main amd64 busybox-initramfs amd64 1:1.37.0-7ubuntu1 [191 kB] 79s Get:68 http://ftpmaster.internal/ubuntu resolute/main amd64 cryptsetup-bin amd64 2:2.8.0-1ubuntu3 [228 kB] 79s Get:69 http://ftpmaster.internal/ubuntu resolute/main amd64 dracut-install amd64 109-11ubuntu1 [45.8 kB] 79s Get:70 http://ftpmaster.internal/ubuntu resolute/main amd64 hwdata all 0.394-1build1 [1566 B] 79s Get:71 http://ftpmaster.internal/ubuntu resolute/main amd64 pnp.ids all 0.394-1build1 [29.6 kB] 79s Get:72 http://ftpmaster.internal/ubuntu resolute/main amd64 libdrm-amdgpu1 amd64 2.4.131-1 [23.2 kB] 79s Get:73 http://ftpmaster.internal/ubuntu resolute/main amd64 libevent-core-2.1-7t64 amd64 2.1.12-stable-10build2 [93.1 kB] 79s Get:74 http://ftpmaster.internal/ubuntu resolute/main amd64 libgdbm-compat4t64 amd64 1.26-1build1 [6796 B] 79s Get:75 http://ftpmaster.internal/ubuntu resolute/main amd64 libgudev-1.0-0 amd64 1:238-7build1 [15.9 kB] 79s Get:76 http://ftpmaster.internal/ubuntu resolute/main amd64 libnpth0t64 amd64 1.8-3build1 [9302 B] 79s Get:77 http://ftpmaster.internal/ubuntu resolute/main amd64 libonig5 amd64 6.9.10-1build1 [174 kB] 79s Get:78 http://ftpmaster.internal/ubuntu resolute/main amd64 libpython3.14-minimal amd64 3.14.2-1 [920 kB] 79s Get:79 http://ftpmaster.internal/ubuntu resolute/main amd64 libpython3.14-stdlib amd64 3.14.2-1 [2398 kB] 79s Get:80 http://ftpmaster.internal/ubuntu resolute/main amd64 libpython3.14 amd64 3.14.2-1 [2568 kB] 79s Get:81 http://ftpmaster.internal/ubuntu resolute/main amd64 libwrap0 amd64 7.6.q-36build2 [48.5 kB] 79s Get:82 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-modules-6.19.0-3-generic amd64 6.19.0-3.3 [171 MB] 83s Get:83 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-image-6.19.0-3-generic amd64 6.19.0-3.3+1 [16.8 MB] 83s Get:84 http://ftpmaster.internal/ubuntu resolute/main amd64 amd64-microcode amd64 3.20251202.1ubuntu1 [459 kB] 83s Get:85 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-generic amd64 6.19.0-3.3 [1698 B] 83s Get:86 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-image-generic amd64 6.19.0-3.3 [12.2 kB] 83s Get:87 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-virtual amd64 6.19.0-3.3 [1700 B] 83s Get:88 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-image-virtual amd64 6.19.0-3.3 [12.1 kB] 83s Get:89 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-headers-virtual amd64 6.19.0-3.3 [1646 B] 83s Get:90 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-headers-6.19.0-3 all 6.19.0-3.3 [14.9 MB] 83s Get:91 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-headers-6.19.0-3-generic amd64 6.19.0-3.3 [4330 kB] 84s Get:92 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-headers-generic amd64 6.19.0-3.3 [12.0 kB] 84s Get:93 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-perf amd64 6.19.0-3.3 [4480 kB] 84s Get:94 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-tools-common all 6.19.0-3.3 [345 kB] 84s Get:95 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-tools-6.19.0-3 amd64 6.19.0-3.3 [1455 kB] 84s Get:96 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-tools-6.19.0-3-generic amd64 6.19.0-3.3 [1612 B] 84s Get:97 http://ftpmaster.internal/ubuntu resolute/main amd64 patch amd64 2.8-2build1 [95.7 kB] 84s Get:98 http://ftpmaster.internal/ubuntu resolute/main amd64 pollinate all 4.33-4ubuntu5 [14.0 kB] 84s Get:99 http://ftpmaster.internal/ubuntu resolute/main amd64 python3-referencing all 0.36.2-1ubuntu2 [22.2 kB] 84s Get:100 http://ftpmaster.internal/ubuntu resolute/main amd64 ubuntu-kernel-accessories amd64 1.564 [13.1 kB] 84s dpkg-preconfigure: unable to re-open stdin: No such file or directory 84s Fetched 237 MB in 5s (46.0 MB/s) 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 84s Preparing to unpack .../debianutils_5.23.2build1_amd64.deb ... 84s Unpacking debianutils (5.23.2build1) over (5.23.2) ... 84s Setting up debianutils (5.23.2build1) ... 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 84s Preparing to unpack .../dash_0.5.12-12ubuntu3_amd64.deb ... 84s Unpacking dash (0.5.12-12ubuntu3) over (0.5.12-12ubuntu2) ... 84s Setting up dash (0.5.12-12ubuntu3) ... 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 84s Preparing to unpack .../findutils_4.10.0-3build2_amd64.deb ... 84s Unpacking findutils (4.10.0-3build2) over (4.10.0-3build1) ... 84s Setting up findutils (4.10.0-3build2) ... 84s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 84s Preparing to unpack .../sed_4.9-2build3_amd64.deb ... 85s Unpacking sed (4.9-2build3) over (4.9-2build2) ... 85s Setting up sed (4.9-2build3) ... 85s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 85s Preparing to unpack .../tar_1.35+dfsg-3.1build2_amd64.deb ... 85s Unpacking tar (1.35+dfsg-3.1build2) over (1.35+dfsg-3.1build1) ... 85s Setting up tar (1.35+dfsg-3.1build2) ... 85s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 85s Preparing to unpack .../libattr1_1%3a2.5.2-3build2_amd64.deb ... 85s Unpacking libattr1:amd64 (1:2.5.2-3build2) over (1:2.5.2-3build1) ... 85s Setting up libattr1:amd64 (1:2.5.2-3build2) ... 85s Selecting previously unselected package gcc-16-base:amd64. 85s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 83957 files and directories currently installed.) 85s Preparing to unpack .../gcc-16-base_16-20260208-1ubuntu1_amd64.deb ... 85s Unpacking gcc-16-base:amd64 (16-20260208-1ubuntu1) ... 85s Setting up gcc-16-base:amd64 (16-20260208-1ubuntu1) ... 85s (Reading database ... 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(7.7.0+6.19.0-3.3) ... 93s Setting up libip6tc2:amd64 (1.8.11-2ubuntu3) ... 93s Setting up liblsof0 (4.99.4+dfsg-2build2) ... 93s Setting up libmaxminddb0:amd64 (1.12.2-1build2) ... 93s Setting up libpython3.14-minimal:amd64 (3.14.2-1) ... 93s Setting up libsensors-config (1:3.6.2-2build1) ... 93s Setting up less (668-1build1) ... 93s Setting up linux-headers-6.19.0-3 (6.19.0-3.3) ... 93s Setting up libidn2-0:amd64 (2.3.8-4build1) ... 93s Setting up amd64-microcode (3.20251202.1ubuntu1) ... 93s amd64-microcode: microcode will be updated at next boot 93s Setting up man-db (2.13.1-1build1) ... 93s Updating database of manual pages ... 94s man-db.service is a disabled or a static unit not running, not starting it. 94s Setting up libjansson4:amd64 (2.14-2build4) ... 94s Setting up libglib2.0-data (2.87.2-2) ... 94s Setting up pollinate (4.33-4ubuntu5) ... 105s Setting up busybox-static (1:1.37.0-7ubuntu1) ... 105s Setting up libwrap0:amd64 (7.6.q-36build2) ... 105s Setting up linux-image-6.19.0-3-generic (6.19.0-3.3+1) ... 106s I: /boot/vmlinuz is now a symlink to vmlinuz-6.19.0-3-generic 106s I: /boot/initrd.img is now a symlink to initrd.img-6.19.0-3-generic 106s Setting up libdbus-1-3:amd64 (1.16.2-2ubuntu3) ... 106s Setting up libatomic1:amd64 (16-20260208-1ubuntu1) ... 106s Setting up patch (2.8-2build1) ... 106s Setting up libsensors5:amd64 (1:3.6.2-2build1) ... 106s Setting up busybox-initramfs (1:1.37.0-7ubuntu1) ... 106s Setting up libxtables12:amd64 (1.8.11-2ubuntu3) ... 106s Setting up lsof (4.99.4+dfsg-2build2) ... 106s Setting up libpci3:amd64 (1:3.14.0-1build2) ... 106s Setting up libdevmapper1.02.1:amd64 (2:1.02.205-2ubuntu3) ... 106s Setting up dracut-install (109-11ubuntu1) ... 106s Setting up dmsetup (2:1.02.205-2ubuntu3) ... 106s Setting up libnetfilter-conntrack3:amd64 (1.1.1-1) ... 106s Setting up pnp.ids (0.394-1build1) ... 106s Setting up dbus-session-bus-common (1.16.2-2ubuntu3) ... 106s Setting up python3-linkify-it (2.0.3-1ubuntu3) ... 106s Setting up libpcap0.8t64:amd64 (1.10.5-2ubuntu3) ... 106s Setting up libcryptsetup12:amd64 (2:2.8.0-1ubuntu3) ... 106s Setting up mawk (1.3.4.20260129-1) ... 106s Setting up libevent-core-2.1-7t64:amd64 (2.1.12-stable-10build2) ... 106s Setting up libusb-1.0-0:amd64 (2:1.0.29-2build1) ... 106s Setting up linux-image-virtual (6.19.0-3.3) ... 106s Setting up dbus-system-bus-common (1.16.2-2ubuntu3) ... 106s Setting up libbsd0:amd64 (0.12.2-2build2) ... 106s Setting up libdrm-common (2.4.131-1) ... 106s Setting up libstdc++6:amd64 (16-20260208-1ubuntu1) ... 106s Setting up dbus-bin (1.16.2-2ubuntu3) ... 106s Setting up libonig5:amd64 (6.9.10-1build1) ... 106s Setting up libbpf1:amd64 (1:1.6.2-1build1) ... 106s Setting up ethtool (1:6.15-3build1) ... 106s Setting up python3-referencing (0.36.2-1ubuntu2) ... 106s Setting up libxkbcommon0:amd64 (1.13.1-1) ... 106s Setting up cryptsetup-bin (2:2.8.0-1ubuntu3) ... 106s Setting up linux-headers-6.19.0-3-generic (6.19.0-3.3) ... 106s Setting up tcpdump (4.99.5-2ubuntu3) ... 106s Setting up linux-image-generic (6.19.0-3.3) ... 106s Setting up wget (1.25.0-2ubuntu4) ... 106s Setting up libpython3.14-stdlib:amd64 (3.14.2-1) ... 106s Setting up iptables (1.8.11-2ubuntu3) ... 106s Setting up iproute2 (6.18.0-1ubuntu1) ... 107s Setting up linux-headers-generic (6.19.0-3.3) ... 107s Setting up dbus-daemon (1.16.2-2ubuntu3) ... 107s Setting up hwdata (0.394-1build1) ... 107s Setting up dbus-user-session (1.16.2-2ubuntu3) ... 107s Setting up libglib2.0-0t64:amd64 (2.87.2-2) ... 107s No schema files found: doing nothing. 107s Setting up dbus (1.16.2-2ubuntu3) ... 107s A reboot is required to replace the running dbus-daemon. 107s Please reboot the system when convenient. 107s Setting up shared-mime-info (2.4-5build3) ... 108s Setting up gir1.2-glib-2.0:amd64 (2.87.2-2) ... 108s Setting up pciutils (1:3.14.0-1build2) ... 108s Setting up python3-markdown-it (3.0.0-3build1) ... 108s Setting up libdrm2:amd64 (2.4.131-1) ... 108s Setting up libpython3.14:amd64 (3.14.2-1) ... 108s Setting up libapt-pkg7.0:amd64 (3.1.15) ... 108s Setting up linux-tools-common (6.19.0-3.3) ... 108s Setting up libgudev-1.0-0:amd64 (1:238-7build1) ... 108s Setting up libdrm-amdgpu1:amd64 (2.4.131-1) ... 108s Setting up apt (3.1.15) ... 108s Setting up linux-headers-virtual (6.19.0-3.3) ... 108s Setting up linux-generic (6.19.0-3.3) ... 108s Setting up libgirepository-2.0-0:amd64 (2.87.2-2) ... 108s Setting up linux-tools-6.19.0-3 (6.19.0-3.3) ... 108s Setting up ubuntu-standard (1.564) ... 108s Setting up gir1.2-girepository-3.0:amd64 (2.87.2-2) ... 108s Setting up linux-virtual (6.19.0-3.3) ... 108s Setting up linux-perf (6.19.0-3.3) ... 108s Setting up linux-tools-6.19.0-3-generic (6.19.0-3.3) ... 108s Processing triggers for debianutils (5.23.2build1) ... 108s Processing triggers for install-info (7.2-5) ... 108s Processing triggers for initramfs-tools (0.150ubuntu7) ... 108s update-initramfs: Generating /boot/initrd.img-6.18.0-9-generic 113s Processing triggers for libc-bin (2.42-2ubuntu4) ... 113s Processing triggers for linux-image-6.19.0-3-generic (6.19.0-3.3+1) ... 113s /etc/kernel/postinst.d/initramfs-tools: 113s update-initramfs: Generating /boot/initrd.img-6.19.0-3-generic 117s /etc/kernel/postinst.d/zz-update-grub: 117s Sourcing file `/etc/default/grub' 117s Sourcing file `/etc/default/grub.d/50-cloudimg-settings.cfg' 117s Sourcing file `/etc/default/grub.d/90-autopkgtest.cfg' 117s Generating grub configuration file ... 117s Found linux image: /boot/vmlinuz-6.19.0-3-generic 117s Found initrd image: /boot/initrd.img-6.19.0-3-generic 117s Found linux image: /boot/vmlinuz-6.18.0-9-generic 117s Found initrd image: /boot/initrd.img-6.18.0-9-generic 117s Warning: os-prober will not be executed to detect other bootable partitions. 117s Systems on them will not be added to the GRUB boot configuration. 117s Check GRUB_DISABLE_OS_PROBER documentation entry. 117s Adding boot menu entry for UEFI Firmware Settings ... 117s done 117s autopkgtest [03:33:50]: upgrading testbed (apt dist-upgrade and autopurge) 118s Reading package lists... 118s Building dependency tree... 118s Reading state information... 118s Calculating upgrade... 118s The following package was automatically installed and is no longer required: 118s libpython3.13 118s Use 'sudo apt autoremove' to remove it. 118s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 119s Reading package lists... 119s Building dependency tree... 119s Reading state information... 119s Solving dependencies... 119s The following packages will be REMOVED: 119s libpython3.13* 119s 0 upgraded, 0 newly installed, 1 to remove and 0 not upgraded. 119s After this operation, 7599 kB disk space will be freed. 119s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 125273 files and directories currently installed.) 119s Removing libpython3.13:amd64 (3.13.11-1) ... 119s Processing triggers for libc-bin (2.42-2ubuntu4) ... 119s autopkgtest [03:33:52]: rebooting testbed after setup commands that affected boot 149s autopkgtest [03:34:22]: testbed running kernel: Linux 6.19.0-3-generic #3-Ubuntu SMP PREEMPT_DYNAMIC Fri Jan 23 20:01:24 UTC 2026 151s autopkgtest [03:34:24]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-rrcov 152s Get:1 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (dsc) [2146 B] 152s Get:2 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (tar) [1542 kB] 152s Get:3 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (diff) [3160 B] 152s gpgv: Signature made Fri Sep 6 03:10:50 2024 UTC 152s gpgv: using RSA key 73471499CC60ED9EEE805946C5BD6C8F2295D502 152s gpgv: issuer "plessy@debian.org" 152s gpgv: Can't check signature: No public key 152s dpkg-source: warning: cannot verify inline signature for ./r-cran-rrcov_1.7-6-1.dsc: no acceptable signature found 152s autopkgtest [03:34:25]: testing package r-cran-rrcov version 1.7-6-1 153s autopkgtest [03:34:26]: build not needed 154s autopkgtest [03:34:27]: test run-unit-test: preparing testbed 154s Reading package lists... 154s Building dependency tree... 154s Reading state information... 154s Solving dependencies... 154s The following NEW packages will be installed: 154s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono libblas3 154s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libgfortran5 libgomp1 154s libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 libjpeg8 154s liblapack3 liblerc4 libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 154s libpaper-utils libpaper2 libpixman-1-0 libsharpyuv0 libsm6 libtcl8.6 154s libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 libxcb-shm0 154s libxft2 libxrender1 libxss1 libxt6t64 r-base-core r-cran-deoptimr 154s r-cran-lattice r-cran-mass r-cran-mvtnorm r-cran-pcapp r-cran-robustbase 154s r-cran-rrcov unzip x11-common xdg-utils zip 154s 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. 154s Need to get 49.5 MB of archives. 154s After this operation, 94.4 MB of additional disk space will be used. 154s Get:1 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-dejavu-mono all 2.37-8build1 [502 kB] 154s Get:2 http://ftpmaster.internal/ubuntu resolute/main amd64 fonts-dejavu-core all 2.37-8build1 [834 kB] 155s Get:3 http://ftpmaster.internal/ubuntu resolute/main amd64 fontconfig-config amd64 2.17.1-3ubuntu1 [38.5 kB] 155s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 libfontconfig1 amd64 2.17.1-3ubuntu1 [144 kB] 155s Get:5 http://ftpmaster.internal/ubuntu resolute/main amd64 fontconfig amd64 2.17.1-3ubuntu1 [180 kB] 155s Get:6 http://ftpmaster.internal/ubuntu resolute/main amd64 libblas3 amd64 3.12.1-7ubuntu1 [260 kB] 155s Get:7 http://ftpmaster.internal/ubuntu resolute/main amd64 libpixman-1-0 amd64 0.46.4-1 [287 kB] 155s Get:8 http://ftpmaster.internal/ubuntu resolute/main amd64 libxcb-render0 amd64 1.17.0-2ubuntu1 [16.2 kB] 155s Get:9 http://ftpmaster.internal/ubuntu resolute/main amd64 libxcb-shm0 amd64 1.17.0-2ubuntu1 [5808 B] 155s Get:10 http://ftpmaster.internal/ubuntu resolute/main amd64 libxrender1 amd64 1:0.9.12-1 [19.8 kB] 155s Get:11 http://ftpmaster.internal/ubuntu resolute/main amd64 libcairo2 amd64 1.18.4-3 [579 kB] 155s Get:12 http://ftpmaster.internal/ubuntu resolute/main amd64 libdatrie1 amd64 0.2.14-1 [19.8 kB] 155s Get:13 http://ftpmaster.internal/ubuntu resolute/main amd64 libdeflate0 amd64 1.23-2build1 [51.6 kB] 155s Get:14 http://ftpmaster.internal/ubuntu resolute/main amd64 libgfortran5 amd64 16-20260208-1ubuntu1 [957 kB] 155s Get:15 http://ftpmaster.internal/ubuntu resolute/main amd64 libgomp1 amd64 16-20260208-1ubuntu1 [162 kB] 155s Get:16 http://ftpmaster.internal/ubuntu resolute/main amd64 libgraphite2-3 amd64 1.3.14-11ubuntu1 [73.7 kB] 155s Get:17 http://ftpmaster.internal/ubuntu resolute/main amd64 libharfbuzz0b amd64 12.3.2-1 [519 kB] 155s Get:18 http://ftpmaster.internal/ubuntu resolute/main amd64 x11-common all 1:7.7+24ubuntu1 [22.4 kB] 155s Get:19 http://ftpmaster.internal/ubuntu resolute/main amd64 libice6 amd64 2:1.1.1-1build1 [44.0 kB] 155s Get:20 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg-turbo8 amd64 2.1.5-4ubuntu3 [156 kB] 155s Get:21 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg8 amd64 8c-2ubuntu12 [2142 B] 155s Get:22 http://ftpmaster.internal/ubuntu resolute/main amd64 liblapack3 amd64 3.12.1-7ubuntu1 [2739 kB] 156s Get:23 http://ftpmaster.internal/ubuntu resolute/main amd64 liblerc4 amd64 4.0.0+ds-5ubuntu2 [207 kB] 156s Get:24 http://ftpmaster.internal/ubuntu resolute/main amd64 libthai-data all 0.1.30-1 [155 kB] 156s Get:25 http://ftpmaster.internal/ubuntu resolute/main amd64 libthai0 amd64 0.1.30-1 [19.2 kB] 156s Get:26 http://ftpmaster.internal/ubuntu resolute/main amd64 libpango-1.0-0 amd64 1.57.0-1 [241 kB] 156s Get:27 http://ftpmaster.internal/ubuntu resolute/main amd64 libpangoft2-1.0-0 amd64 1.57.0-1 [53.3 kB] 156s Get:28 http://ftpmaster.internal/ubuntu resolute/main amd64 libpangocairo-1.0-0 amd64 1.57.0-1 [29.0 kB] 156s Get:29 http://ftpmaster.internal/ubuntu resolute/main amd64 libpaper2 amd64 2.2.5-0.3build1 [17.3 kB] 156s Get:30 http://ftpmaster.internal/ubuntu resolute/main amd64 libpaper-utils amd64 2.2.5-0.3build1 [15.6 kB] 156s Get:31 http://ftpmaster.internal/ubuntu resolute/main amd64 libsharpyuv0 amd64 1.5.0-0.1build1 [17.6 kB] 156s Get:32 http://ftpmaster.internal/ubuntu resolute/main amd64 libsm6 amd64 2:1.2.6-1build1 [16.9 kB] 156s Get:33 http://ftpmaster.internal/ubuntu resolute/main amd64 libtcl8.6 amd64 8.6.17+dfsg-1build1 [1003 kB] 156s Get:34 http://ftpmaster.internal/ubuntu resolute/main amd64 libjbig0 amd64 2.1-6.1ubuntu3 [30.0 kB] 156s Get:35 http://ftpmaster.internal/ubuntu resolute/main amd64 libwebp7 amd64 1.5.0-0.1build1 [264 kB] 156s Get:36 http://ftpmaster.internal/ubuntu resolute/main amd64 libtiff6 amd64 4.7.0-3ubuntu3 [209 kB] 156s Get:37 http://ftpmaster.internal/ubuntu resolute/main amd64 libxft2 amd64 2.3.6-1build2 [45.1 kB] 156s Get:38 http://ftpmaster.internal/ubuntu resolute/main amd64 libxss1 amd64 1:1.2.3-1build4 [7084 B] 156s Get:39 http://ftpmaster.internal/ubuntu resolute/main amd64 libtk8.6 amd64 8.6.17-1 [823 kB] 156s Get:40 http://ftpmaster.internal/ubuntu resolute/main amd64 libxt6t64 amd64 1:1.2.1-1.3 [173 kB] 156s Get:41 http://ftpmaster.internal/ubuntu resolute/main amd64 zip amd64 3.0-15ubuntu3 [175 kB] 156s Get:42 http://ftpmaster.internal/ubuntu resolute/main amd64 unzip amd64 6.0-29ubuntu1 [180 kB] 156s Get:43 http://ftpmaster.internal/ubuntu resolute/main amd64 xdg-utils all 1.2.1-2ubuntu2 [66.1 kB] 156s Get:44 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-base-core amd64 4.5.2-1ubuntu2 [28.8 MB] 158s Get:45 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-cran-deoptimr all 1.1-4-1 [76.7 kB] 158s Get:46 http://ftpmaster.internal/ubuntu resolute-proposed/universe amd64 r-cran-lattice amd64 0.22-9-1 [1399 kB] 158s Get:47 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-cran-mass amd64 7.3-65-1 [1116 kB] 158s Get:48 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-cran-mvtnorm amd64 1.3-3-1build1 [921 kB] 158s Get:49 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-cran-pcapp amd64 2.0-5-1 [368 kB] 158s Get:50 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-cran-robustbase amd64 0.99-7-1 [3074 kB] 158s Get:51 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-cran-rrcov amd64 1.7-6-1 [2407 kB] 158s Preconfiguring packages ... 158s Fetched 49.5 MB in 4s (14.1 MB/s) 158s Selecting previously unselected package fonts-dejavu-mono. 158s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 125269 files and directories currently installed.) 158s Preparing to unpack .../00-fonts-dejavu-mono_2.37-8build1_all.deb ... 158s Unpacking fonts-dejavu-mono (2.37-8build1) ... 158s Selecting previously unselected package fonts-dejavu-core. 158s Preparing to unpack .../01-fonts-dejavu-core_2.37-8build1_all.deb ... 158s Unpacking fonts-dejavu-core (2.37-8build1) ... 158s Selecting previously unselected package fontconfig-config. 158s Preparing to unpack .../02-fontconfig-config_2.17.1-3ubuntu1_amd64.deb ... 158s Unpacking fontconfig-config (2.17.1-3ubuntu1) ... 159s Selecting previously unselected package libfontconfig1:amd64. 159s Preparing to unpack .../03-libfontconfig1_2.17.1-3ubuntu1_amd64.deb ... 159s Unpacking libfontconfig1:amd64 (2.17.1-3ubuntu1) ... 159s Selecting previously unselected package fontconfig. 159s Preparing to unpack .../04-fontconfig_2.17.1-3ubuntu1_amd64.deb ... 159s Unpacking fontconfig (2.17.1-3ubuntu1) ... 159s Selecting previously unselected package libblas3:amd64. 159s Preparing to unpack .../05-libblas3_3.12.1-7ubuntu1_amd64.deb ... 159s Unpacking libblas3:amd64 (3.12.1-7ubuntu1) ... 159s Selecting previously unselected package libpixman-1-0:amd64. 159s Preparing to unpack .../06-libpixman-1-0_0.46.4-1_amd64.deb ... 159s Unpacking libpixman-1-0:amd64 (0.46.4-1) ... 159s Selecting previously unselected package libxcb-render0:amd64. 159s Preparing to unpack .../07-libxcb-render0_1.17.0-2ubuntu1_amd64.deb ... 159s Unpacking libxcb-render0:amd64 (1.17.0-2ubuntu1) ... 159s Selecting previously unselected package libxcb-shm0:amd64. 159s Preparing to unpack .../08-libxcb-shm0_1.17.0-2ubuntu1_amd64.deb ... 159s Unpacking libxcb-shm0:amd64 (1.17.0-2ubuntu1) ... 159s Selecting previously unselected package libxrender1:amd64. 159s Preparing to unpack .../09-libxrender1_1%3a0.9.12-1_amd64.deb ... 159s Unpacking libxrender1:amd64 (1:0.9.12-1) ... 159s Selecting previously unselected package libcairo2:amd64. 159s Preparing to unpack .../10-libcairo2_1.18.4-3_amd64.deb ... 159s Unpacking libcairo2:amd64 (1.18.4-3) ... 159s Selecting previously unselected package libdatrie1:amd64. 159s Preparing to unpack .../11-libdatrie1_0.2.14-1_amd64.deb ... 159s Unpacking libdatrie1:amd64 (0.2.14-1) ... 159s Selecting previously unselected package libdeflate0:amd64. 159s Preparing to unpack .../12-libdeflate0_1.23-2build1_amd64.deb ... 159s Unpacking libdeflate0:amd64 (1.23-2build1) ... 159s Selecting previously unselected package libgfortran5:amd64. 159s Preparing to unpack .../13-libgfortran5_16-20260208-1ubuntu1_amd64.deb ... 159s Unpacking libgfortran5:amd64 (16-20260208-1ubuntu1) ... 159s Selecting previously unselected package libgomp1:amd64. 159s Preparing to unpack .../14-libgomp1_16-20260208-1ubuntu1_amd64.deb ... 159s Unpacking libgomp1:amd64 (16-20260208-1ubuntu1) ... 159s Selecting previously unselected package libgraphite2-3:amd64. 159s Preparing to unpack .../15-libgraphite2-3_1.3.14-11ubuntu1_amd64.deb ... 159s Unpacking libgraphite2-3:amd64 (1.3.14-11ubuntu1) ... 159s Selecting previously unselected package libharfbuzz0b:amd64. 159s Preparing to unpack .../16-libharfbuzz0b_12.3.2-1_amd64.deb ... 159s Unpacking libharfbuzz0b:amd64 (12.3.2-1) ... 159s Selecting previously unselected package x11-common. 159s Preparing to unpack .../17-x11-common_1%3a7.7+24ubuntu1_all.deb ... 159s Unpacking x11-common (1:7.7+24ubuntu1) ... 159s Selecting previously unselected package libice6:amd64. 159s Preparing to unpack .../18-libice6_2%3a1.1.1-1build1_amd64.deb ... 159s Unpacking libice6:amd64 (2:1.1.1-1build1) ... 159s Selecting previously unselected package libjpeg-turbo8:amd64. 159s Preparing to unpack .../19-libjpeg-turbo8_2.1.5-4ubuntu3_amd64.deb ... 159s Unpacking libjpeg-turbo8:amd64 (2.1.5-4ubuntu3) ... 159s Selecting previously unselected package libjpeg8:amd64. 159s Preparing to unpack .../20-libjpeg8_8c-2ubuntu12_amd64.deb ... 159s Unpacking libjpeg8:amd64 (8c-2ubuntu12) ... 159s Selecting previously unselected package liblapack3:amd64. 159s Preparing to unpack .../21-liblapack3_3.12.1-7ubuntu1_amd64.deb ... 159s Unpacking liblapack3:amd64 (3.12.1-7ubuntu1) ... 159s Selecting previously unselected package liblerc4:amd64. 159s Preparing to unpack .../22-liblerc4_4.0.0+ds-5ubuntu2_amd64.deb ... 159s Unpacking liblerc4:amd64 (4.0.0+ds-5ubuntu2) ... 159s Selecting previously unselected package libthai-data. 159s Preparing to unpack .../23-libthai-data_0.1.30-1_all.deb ... 159s Unpacking libthai-data (0.1.30-1) ... 159s Selecting previously unselected package libthai0:amd64. 159s Preparing to unpack .../24-libthai0_0.1.30-1_amd64.deb ... 159s Unpacking libthai0:amd64 (0.1.30-1) ... 159s Selecting previously unselected package libpango-1.0-0:amd64. 159s Preparing to unpack .../25-libpango-1.0-0_1.57.0-1_amd64.deb ... 159s Unpacking libpango-1.0-0:amd64 (1.57.0-1) ... 159s Selecting previously unselected package libpangoft2-1.0-0:amd64. 159s Preparing to unpack .../26-libpangoft2-1.0-0_1.57.0-1_amd64.deb ... 159s Unpacking libpangoft2-1.0-0:amd64 (1.57.0-1) ... 159s Selecting previously unselected package libpangocairo-1.0-0:amd64. 159s Preparing to unpack .../27-libpangocairo-1.0-0_1.57.0-1_amd64.deb ... 159s Unpacking libpangocairo-1.0-0:amd64 (1.57.0-1) ... 159s Selecting previously unselected package libpaper2:amd64. 159s Preparing to unpack .../28-libpaper2_2.2.5-0.3build1_amd64.deb ... 159s Unpacking 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r-cran-mvtnorm (1.3-3-1build1) ... 162s Setting up r-cran-robustbase (0.99-7-1) ... 162s Setting up r-cran-pcapp (2.0-5-1) ... 162s Setting up r-cran-rrcov (1.7-6-1) ... 162s Processing triggers for libc-bin (2.42-2ubuntu4) ... 162s Processing triggers for man-db (2.13.1-1build1) ... 163s Processing triggers for install-info (7.2-5) ... 164s autopkgtest [03:34:37]: test run-unit-test: [----------------------- 164s BEGIN TEST thubert.R 164s 164s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 164s Copyright (C) 2025 The R Foundation for Statistical Computing 164s Platform: x86_64-pc-linux-gnu 164s 164s R is free software and comes with ABSOLUTELY NO WARRANTY. 164s You are welcome to redistribute it under certain conditions. 164s Type 'license()' or 'licence()' for distribution details. 164s 164s R is a collaborative project with many contributors. 164s Type 'contributors()' for more information and 164s 'citation()' on how to cite R or R packages in publications. 164s 164s Type 'demo()' for some demos, 'help()' for on-line help, or 164s 'help.start()' for an HTML browser interface to help. 164s Type 'q()' to quit R. 164s 164s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, 164s + method=c("hubert", "hubert.mcd", "locantore", "cov", "classic", 164s + "grid", "proj")) 164s + { 164s + ## Test the PcaXxx() functions on the literature datasets: 164s + ## 164s + ## Call PcaHubert() and the other functions for all regression 164s + ## data sets available in robustbase/rrcov and print: 164s + ## - execution time (if time == TRUE) 164s + ## - loadings 164s + ## - eigenvalues 164s + ## - scores 164s + ## 164s + 164s + dopca <- function(x, xname, nrep=1){ 164s + 164s + n <- dim(x)[1] 164s + p <- dim(x)[2] 164s + if(method == "hubert.mcd") 164s + pca <- PcaHubert(x, k=p) 164s + else if(method == "hubert") 164s + pca <- PcaHubert(x, mcd=FALSE) 164s + else if(method == "locantore") 164s + pca <- PcaLocantore(x) 164s + else if(method == "cov") 164s + pca <- PcaCov(x) 164s + else if(method == "classic") 164s + pca <- PcaClassic(x) 164s + else if(method == "grid") 164s + pca <- PcaGrid(x) 164s + else if(method == "proj") 164s + pca <- PcaProj(x) 164s + else 164s + stop("Undefined PCA method: ", method) 164s + 164s + 164s + e1 <- getEigenvalues(pca)[1] 164s + e2 <- getEigenvalues(pca)[2] 164s + k <- pca@k 164s + 164s + if(time){ 164s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 164s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f %10.3f\n", dim(x)[1], dim(x)[2], k, e1, e2, xtime) 164s + } 164s + else{ 164s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f\n", dim(x)[1], dim(x)[2], k, e1, e2) 164s + } 164s + lpad<-lname-nchar(xname) 164s + cat(pad.right(xname, lpad), xres) 164s + 164s + if(!short){ 164s + cat("Scores: \n") 164s + print(getScores(pca)) 164s + 164s + if(full){ 164s + cat("-------------\n") 164s + show(pca) 164s + } 164s + cat("----------------------------------------------------------\n") 164s + } 164s + } 164s + 164s + stopifnot(length(nrep) == 1, nrep >= 1) 164s + method <- match.arg(method) 164s + 164s + options(digits = 5) 164s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 164s + 164s + lname <- 20 164s + 164s + ## VT::15.09.2013 - this will render the output independent 164s + ## from the version of the package 164s + suppressPackageStartupMessages(library(rrcov)) 164s + 164s + data(Animals, package = "MASS") 164s + brain <- Animals[c(1:24, 26:25, 27:28),] 164s + 164s + tmp <- sys.call() 164s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 164s + 164s + cat("Data Set n p k e1 e2\n") 164s + cat("==========================================================\n") 164s + dopca(heart[, 1:2], data(heart), nrep) 164s + dopca(starsCYG, data(starsCYG), nrep) 164s + dopca(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 164s + dopca(stack.x, data(stackloss), nrep) 164s + ## dopca(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) # differences between the architectures 164s + dopca(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 164s + ## dopca(data.matrix(subset(wood, select = -y)), data(wood), nrep) # differences between the architectures 164s + dopca(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 164s + 164s + ## dopca(brain, "Animals", nrep) 164s + dopca(milk, data(milk), nrep) 164s + dopca(bushfire, data(bushfire), nrep) 164s + cat("==========================================================\n") 164s + } 164s > 164s > dogen <- function(nrep=1, eps=0.49, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 164s + 164s + dopca <- function(x, nrep=1){ 164s + gc() 164s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 164s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 164s + xtime 164s + } 164s + 164s + set.seed(1234) 164s + 164s + ## VT::15.09.2013 - this will render the output independent 164s + ## from the version of the package 164s + suppressPackageStartupMessages(library(rrcov)) 164s + library(MASS) 164s + 164s + method <- match.arg(method) 164s + 164s + ap <- c(2, 5, 10, 20, 30) 164s + an <- c(100, 500, 1000, 10000, 50000) 164s + 164s + tottime <- 0 164s + cat(" n p Time\n") 164s + cat("=====================\n") 164s + for(i in 1:length(an)) { 164s + for(j in 1:length(ap)) { 164s + n <- an[i] 164s + p <- ap[j] 164s + if(5*p <= n){ 164s + xx <- gendata(n, p, eps) 164s + X <- xx$X 164s + ## print(dimnames(X)) 164s + tottime <- tottime + dopca(X, nrep) 164s + } 164s + } 164s + } 164s + 164s + cat("=====================\n") 164s + cat("Total time: ", tottime*nrep, "\n") 164s + } 164s > 164s > dorep <- function(x, nrep=1, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 164s + 164s + method <- match.arg(method) 164s + for(i in 1:nrep) 164s + if(method == "hubert.mcd") 164s + PcaHubert(x) 164s + else if(method == "hubert") 164s + PcaHubert(x, mcd=FALSE) 164s + else if(method == "locantore") 164s + PcaLocantore(x) 164s + else if(method == "cov") 164s + PcaCov(x) 164s + else 164s + stop("Undefined PCA method: ", method) 164s + } 164s > 164s > #### gendata() #### 164s > # Generates a location contaminated multivariate 164s > # normal sample of n observations in p dimensions 164s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 164s > # where 164s > # m = (b,b,...,b) 164s > # Defaults: eps=0 and b=10 164s > # 164s > gendata <- function(n,p,eps=0,b=10){ 164s + 164s + if(missing(n) || missing(p)) 164s + stop("Please specify (n,p)") 164s + if(eps < 0 || eps >= 0.5) 164s + stop(message="eps must be in [0,0.5)") 164s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 164s + nbad <- as.integer(eps * n) 164s + xind <- vector("numeric") 164s + if(nbad > 0){ 164s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 164s + xind <- sample(n,nbad) 164s + X[xind,] <- Xbad 164s + } 164s + list(X=X, xind=xind) 164s + } 164s > 164s > pad.right <- function(z, pads) 164s + { 164s + ### Pads spaces to right of text 164s + padding <- paste(rep(" ", pads), collapse = "") 164s + paste(z, padding, sep = "") 164s + } 164s > 164s > whatis <- function(x){ 164s + if(is.data.frame(x)) 164s + cat("Type: data.frame\n") 164s + else if(is.matrix(x)) 164s + cat("Type: matrix\n") 164s + else if(is.vector(x)) 164s + cat("Type: vector\n") 164s + else 164s + cat("Type: don't know\n") 164s + } 164s > 164s > ################################################################# 164s > ## VT::27.08.2010 164s > ## bug report from Stephen Milborrow 164s > ## 164s > test.case.1 <- function() 164s + { 164s + X <- matrix(c( 164s + -0.79984, -1.00103, 0.899794, 0.00000, 164s + 0.34279, 0.52832, -1.303783, -1.17670, 164s + -0.79984, -1.00103, 0.899794, 0.00000, 164s + 0.34279, 0.52832, -1.303783, -1.17670, 164s + 0.34279, 0.52832, -1.303783, -1.17670, 164s + 1.48542, 0.66735, 0.716162, 1.17670, 164s + -0.79984, -1.00103, 0.899794, 0.00000, 164s + 1.69317, 1.91864, -0.018363, 1.76505, 164s + -1.00759, -0.16684, -0.385626, 0.58835, 164s + -0.79984, -1.00103, 0.899794, 0.00000), ncol=4, byrow=TRUE) 164s + 164s + cc1 <- PcaHubert(X, k=3) 164s + 164s + cc2 <- PcaLocantore(X, k=3) 164s + cc3 <- PcaCov(X, k=3, cov.control=CovControlSest()) 164s + 164s + cc4 <- PcaProj(X, k=2) # with k=3 will produce warnings in .distances - too small eignevalues 164s + cc5 <- PcaGrid(X, k=2) # dito 164s + 164s + list(cc1, cc2, cc3, cc4, cc5) 164s + } 164s > 164s > ################################################################# 164s > ## VT::05.08.2016 164s > ## bug report from Matthieu Lesnoff 164s > ## 164s > test.case.2 <- function() 164s + { 164s + do.test.case.2 <- function(z) 164s + { 164s + if(missing(z)) 164s + { 164s + set.seed(12345678) 164s + n <- 5 164s + z <- data.frame(v1 = rnorm(n), v2 = rnorm(n), v3 = rnorm(n)) 164s + z 164s + } 164s + 164s + fm <- PcaLocantore(z, k = 2, scale = TRUE) 164s + fm@scale 164s + apply(z, MARGIN = 2, FUN = mad) 164s + scale(z, center = fm@center, scale = fm@scale) 164s + 164s + T <- fm@scores 164s + P <- fm@loadings 164s + E <- scale(z, center = fm@center, scale = fm@scale) - T %*% t(P) 164s + d2 <- apply(E^2, MARGIN = 1, FUN = sum) 164s + ## print(sqrt(d2)); print(fm@od) 164s + print(ret <- all.equal(sqrt(d2), fm@od)) 164s + 164s + ret 164s + } 164s + do.test.case.2() 164s + do.test.case.2(phosphor) 164s + do.test.case.2(stackloss) 164s + do.test.case.2(salinity) 164s + do.test.case.2(hbk) 164s + do.test.case.2(milk) 164s + do.test.case.2(bushfire) 164s + data(rice); do.test.case.2(rice) 164s + data(un86); do.test.case.2(un86) 164s + } 164s > 164s > ## VT::15.09.2013 - this will render the output independent 164s > ## from the version of the package 164s > suppressPackageStartupMessages(library(rrcov)) 164s > 164s > dodata(method="classic") 164s 164s Call: dodata(method = "classic") 164s Data Set n p k e1 e2 164s ========================================================== 164s heart 12 2 2 812.379735 9.084962 164s Scores: 164s PC1 PC2 164s 1 2.7072 1.46576 164s 2 59.9990 -1.43041 164s 3 -3.5619 -1.54067 164s 4 -7.7696 2.52687 164s 5 14.7660 -0.95822 164s 6 -20.0489 6.91079 164s 7 1.4189 2.25961 164s 8 -34.3308 -4.23717 164s 9 -6.0487 -0.97859 164s 10 -33.0102 -3.73143 164s 11 -18.6372 0.25821 164s 12 44.5163 -0.54476 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 28.5023 3.0141 164s ---------------------------------------------------------- 164s starsCYG 47 2 2 0.331279 0.079625 164s Scores: 164s PC1 PC2 164s 1 0.2072999 0.089973 164s 2 0.6855999 0.349644 164s 3 -0.0743007 -0.061028 164s 4 0.6855999 0.349644 164s 5 0.1775161 0.015053 164s 6 0.4223986 0.211351 164s 7 -0.2926077 -0.516156 164s 8 0.2188453 0.293607 164s 9 0.5593696 0.028761 164s 10 0.0983878 0.074540 164s 11 0.8258140 -0.711176 164s 12 0.4167063 0.180244 164s 13 0.3799883 0.225541 164s 14 -0.9105236 -0.432014 164s 15 -0.7418831 -0.125322 164s 16 -0.4432862 0.048287 164s 17 -1.0503005 -0.229623 164s 18 -0.8393302 -0.007831 164s 19 -0.8126742 -0.195952 164s 20 0.9842316 -0.688729 164s 21 -0.6230699 -0.108486 164s 22 -0.7814875 -0.130933 164s 23 -0.6017038 0.025840 164s 24 -0.1857772 0.155474 164s 25 -0.0020261 0.070412 164s 26 -0.3640775 0.059510 164s 27 -0.3458392 -0.069204 164s 28 -0.1208393 0.053577 164s 29 -0.6033482 -0.176391 164s 30 1.1440521 -0.676183 164s 31 -0.5960920 -0.013765 164s 32 0.0519296 0.259855 164s 33 0.1861752 0.167779 164s 34 1.3802755 -0.632611 164s 35 -0.6542566 -0.173505 164s 36 0.5583690 0.392215 164s 37 0.0561384 0.230152 164s 38 0.1861752 0.167779 164s 39 0.1353472 0.241376 164s 40 0.5355195 0.197080 164s 41 -0.3980701 0.014294 164s 42 0.0277576 0.145332 164s 43 0.2979736 0.234120 164s 44 0.3049884 0.184614 164s 45 0.4889809 0.311684 164s 46 -0.0514512 0.134108 164s 47 -0.5224950 0.037063 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 0.57557 0.28218 164s ---------------------------------------------------------- 164s phosphor 18 2 2 220.403422 68.346121 164s Scores: 164s PC1 PC2 164s 1 4.04290 -15.3459 164s 2 -22.30489 -1.0004 164s 3 -24.52683 3.2836 164s 4 -12.54839 -6.0848 164s 5 -19.37044 2.2979 164s 6 15.20366 -19.9424 164s 7 0.44222 -3.1379 164s 8 -10.64042 3.6933 164s 9 -11.67967 5.9670 164s 10 14.26805 -7.0221 164s 11 -4.98832 1.5268 164s 12 8.74986 7.9379 164s 13 12.26290 6.0251 164s 14 6.27607 7.5768 164s 15 17.53246 3.1560 164s 16 -10.17024 -5.8994 164s 17 21.05826 5.4492 164s 18 16.39281 11.5191 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 14.8460 8.2672 164s ---------------------------------------------------------- 164s stackloss 21 3 3 99.576089 19.581136 164s Scores: 164s PC1 PC2 PC3 164s 1 20.15352 -4.359452 0.324585 164s 2 19.81554 -5.300468 0.308294 164s 3 15.45222 -1.599136 -0.203125 164s 4 2.40370 -0.145282 2.370302 164s 5 1.89538 0.070566 0.448061 164s 6 2.14954 -0.037358 1.409182 164s 7 4.43153 5.500810 2.468051 164s 8 4.43153 5.500810 2.468051 164s 9 -1.47521 1.245404 2.511773 164s 10 -5.11183 -4.802083 -2.407870 164s 11 -2.07009 3.667055 -2.261247 164s 12 -2.66223 2.833964 -3.238659 164s 13 -4.43589 -2.920053 -2.375287 164s 14 -0.46404 7.323193 -1.234961 164s 15 -9.31959 6.232579 -0.056064 164s 16 -10.33350 3.409533 -0.104938 164s 17 -14.81094 -9.872607 0.628103 164s 18 -12.44514 -3.285499 0.742143 164s 19 -11.85300 -2.452408 1.719555 164s 20 -5.73994 -2.494520 0.098250 164s 21 9.98843 1.484952 -3.614198 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 9.9788 4.4251 1.8986 164s ---------------------------------------------------------- 164s salinity 28 3 3 11.410736 7.075409 164s Scores: 164s PC1 PC2 PC3 164s 1 -0.937789 -2.40535 0.812909 164s 2 -1.752631 -2.57774 2.004437 164s 3 -6.509364 -0.78762 -1.821906 164s 4 -5.619847 -2.41333 -1.586891 164s 5 -7.268242 1.61012 1.563568 164s 6 -4.316558 -3.20411 0.029376 164s 7 -2.379545 -3.32371 0.703101 164s 8 0.013514 -3.50586 1.260502 164s 9 0.265262 -0.16736 -2.886883 164s 10 1.890755 2.43623 -0.986832 164s 11 0.804196 2.56656 0.387577 164s 12 0.935082 -1.03559 -0.074081 164s 13 1.814839 -1.61087 0.612290 164s 14 3.407535 -0.15880 2.026088 164s 15 1.731273 2.95159 -1.840286 164s 16 -6.129708 7.21368 2.632273 164s 17 -0.645124 1.06260 0.028697 164s 18 -1.307532 -2.54679 -0.280273 164s 19 0.483455 -0.55896 -3.097281 164s 20 2.053267 0.47308 -1.858703 164s 21 3.277664 -1.31002 0.453753 164s 22 4.631644 -0.78005 1.519894 164s 23 1.864403 5.32790 -0.849694 164s 24 0.623899 4.29317 0.056461 164s 25 1.301696 0.37871 -0.646220 164s 26 2.852126 -0.79527 -0.347711 164s 27 4.134051 -0.92756 0.449222 164s 28 4.781679 -0.20467 1.736616 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 3.3780 2.6600 1.4836 164s ---------------------------------------------------------- 164s hbk 75 3 3 216.162129 1.981077 164s Scores: 164s PC1 PC2 PC3 164s 1 26.2072 -0.660756 0.503340 164s 2 27.0406 -0.108506 -0.225059 164s 3 28.8351 -1.683721 0.263078 164s 4 29.9221 -0.812174 -0.674480 164s 5 29.3181 -0.909915 -0.121600 164s 6 27.5360 -0.599697 0.916574 164s 7 27.6617 -0.073753 0.676620 164s 8 26.5576 -0.882312 0.159620 164s 9 28.8726 -1.074223 -0.673462 164s 10 27.6643 -1.463829 -0.868593 164s 11 34.2019 -0.664473 -0.567265 164s 12 35.4805 -2.730949 -0.259320 164s 13 34.7544 1.325449 0.749884 164s 14 38.9522 8.171389 0.034382 164s 15 -5.5375 0.390704 1.679172 164s 16 -7.4319 0.803850 1.925633 164s 17 -8.5880 0.957577 -1.010312 164s 18 -6.6022 -0.425109 0.625148 164s 19 -6.5596 1.154721 -0.640680 164s 20 -5.2525 0.812527 1.377832 164s 21 -6.2771 0.067747 0.958907 164s 22 -6.2501 1.325491 -1.104428 164s 23 -7.2419 0.839808 0.728712 164s 24 -7.6489 1.131606 0.154897 164s 25 -9.0763 -0.670721 -0.167577 164s 26 -5.5967 0.999411 -0.810000 164s 27 -5.1460 -0.339018 1.326712 164s 28 -7.1659 -0.993461 0.125933 164s 29 -8.2104 -0.169338 -0.073569 164s 30 -6.2499 -1.689222 -0.877481 164s 31 -7.3180 -0.225795 1.687204 164s 32 -7.9446 1.473868 -0.541790 164s 33 -6.3604 1.237472 0.061800 164s 34 -8.9812 -0.710662 -0.830422 164s 35 -5.1698 -0.435484 1.102817 164s 36 -5.9995 -0.058135 -0.713550 164s 37 -5.8753 0.852882 -1.610556 164s 38 -8.4501 0.334363 0.404813 164s 39 -8.1751 -1.300317 0.633282 164s 40 -7.4495 0.672712 -0.829815 164s 41 -5.6213 -1.106765 1.395315 164s 42 -6.8571 -0.900977 -1.509937 164s 43 -7.0633 1.987372 -1.079934 164s 44 -6.3763 -1.867647 -0.251224 164s 45 -8.6456 -0.866053 0.630132 164s 46 -6.5356 -1.763526 -0.189838 164s 47 -8.2224 -1.183284 1.615150 164s 48 -5.6136 -1.100704 1.079239 164s 49 -5.9907 0.220336 1.443387 164s 50 -5.2675 0.142923 0.194023 164s 51 -7.9324 0.324710 1.113289 164s 52 -7.5544 -1.033884 1.792496 164s 53 -6.7119 -1.712257 -1.711778 164s 54 -7.4679 1.856542 0.046658 164s 55 -7.4666 1.161504 -0.725948 164s 56 -6.7110 1.574868 0.534288 164s 57 -8.2571 -0.399824 0.521995 164s 58 -5.9781 1.312567 0.926790 164s 59 -5.6960 -0.394338 -0.332938 164s 60 -6.1017 -0.797579 -1.679359 164s 61 -5.2628 0.919128 -1.436156 164s 62 -9.1245 -0.516135 -0.229065 164s 63 -7.7140 1.659145 0.068510 164s 64 -4.9886 0.173613 0.865810 164s 65 -6.6157 -1.479786 0.098390 164s 66 -7.9511 0.772770 -0.998321 164s 67 -7.1856 0.459602 0.216588 164s 68 -8.7345 -0.860784 -1.238576 164s 69 -8.5833 -0.313481 0.832074 164s 70 -5.8642 -0.142883 -0.870064 164s 71 -5.8879 0.186456 0.464467 164s 72 -7.1865 0.497156 -0.826767 164s 73 -6.8671 -0.058606 -1.335842 164s 74 -7.1398 0.727642 -1.422331 164s 75 -7.2696 -1.347832 -1.496927 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 14.70245 1.40751 0.95725 164s ---------------------------------------------------------- 164s milk 86 8 8 15.940298 2.771345 164s Scores: 164s PC1 PC2 PC3 PC4 PC5 PC6 PC7 164s 1 6.471620 1.031110 0.469432 0.5736412 1.0294362 -0.6054039 -0.2005117 164s 2 7.439545 0.320597 0.081922 -0.6305898 0.7128977 -1.1601053 -0.1170582 164s 3 1.240654 -1.840458 0.520870 -0.1717469 0.2752079 -0.3815506 0.6004089 164s 4 5.952685 -1.856375 1.638710 0.3358626 -0.5834205 -0.0665348 -0.1580799 164s 5 -0.706973 0.261795 0.423736 0.2916399 -0.5307716 -0.3325563 -0.0062349 164s 6 2.524050 0.293380 -0.572997 0.2466367 -0.3497882 0.0386014 -0.1418131 164s 7 3.136085 -0.050202 -0.818165 -0.0451560 -0.5226337 -0.1597194 0.1669050 164s 8 3.260390 0.312365 -0.110776 0.4908006 -0.5225353 -0.1972222 -0.1068433 164s 9 -0.808914 -2.355785 1.344204 -0.4743284 -0.1394914 -0.1390080 -0.2620731 164s 10 -2.511226 -0.995321 -0.087218 -0.5950040 0.4268321 0.2561918 0.0891170 164s 11 -9.204096 -0.598364 1.587275 0.0833647 0.1865626 0.0358228 0.0920394 164s 12 -12.946774 1.951332 -0.179186 0.2560603 0.1300954 -0.1179820 -0.0999494 164s 13 -10.011603 0.726323 -2.102423 -1.3105560 0.3291550 0.0660007 -0.0794410 164s 14 -11.983644 0.768224 -0.532227 -0.5161201 -0.0817164 -0.4358934 -0.1734612 164s 15 -10.465714 -0.704271 2.035437 0.3713778 -0.0564830 -0.2696432 -0.1940091 164s 16 -2.527619 -0.286939 0.354497 0.8571223 0.1585009 0.2272835 0.4386955 164s 17 -0.514527 -2.895087 1.657181 0.2208239 0.1961109 0.1280496 -0.0182491 164s 18 -1.763931 0.854269 -0.686282 0.2848209 -0.4813608 -0.2623962 0.4757030 164s 19 -1.538419 -0.866477 1.103818 0.3874507 0.2086661 0.1267277 0.2354264 164s 20 0.732842 -1.455594 1.097358 -0.2530588 -0.0302385 0.2654274 0.6093330 164s 21 -2.530155 1.932885 -0.873095 0.6202295 -0.4153607 0.0048383 0.0067484 164s 22 -0.772646 0.675846 -0.259539 0.4844670 -0.0893266 -0.2785557 -0.0424662 164s 23 0.185417 1.413719 0.066135 1.1014470 0.0468093 0.0288637 0.2539994 164s 24 -0.280536 0.908864 0.113221 1.3370381 0.3289929 0.2588134 -0.0356289 164s 25 -3.503626 1.971233 0.203620 1.1975494 -0.3175317 0.1149685 0.0584396 164s 26 -0.639313 1.175503 0.403906 0.9082134 -0.2648165 -0.1238813 -0.0174853 164s 27 -2.923327 -0.365168 0.149478 0.8201430 -0.1544609 -0.4856934 -0.0058424 164s 28 2.505633 3.050292 -0.554424 2.1416405 -0.0378764 0.1002280 -0.3888580 164s 29 4.649504 1.054863 -0.081018 1.1454466 0.1502080 0.4967323 0.0879775 164s 30 1.049282 1.355215 -0.142701 0.7805566 -0.2059790 0.0193142 0.0815524 164s 31 1.962583 1.595396 -2.050642 0.3556747 0.1384801 0.1197984 0.1608247 164s 32 1.554846 0.095644 -1.423054 -0.3175620 0.4260008 -0.1612463 -0.0567196 164s 33 2.248977 0.010348 -0.062469 0.6388269 0.2098648 0.1330250 0.0906704 164s 34 0.993109 -0.828812 0.284059 0.3446686 0.1899096 -0.0515571 -0.2281197 164s 35 -0.335103 1.614093 -0.920661 1.2502617 0.2435013 0.1264875 0.0469238 164s 36 4.346795 1.208134 0.368889 1.1429977 -0.1362052 -0.0158169 -0.0183852 164s 37 0.992634 2.013738 -1.350619 0.8714694 0.0057776 -0.2122691 0.1760918 164s 38 2.213341 1.706516 -0.705418 1.2670281 -0.0707149 0.0670467 -0.1863588 164s 39 -1.213255 0.644062 0.163988 1.1213961 0.2945355 0.1093574 0.0019574 164s 40 3.942604 -1.704266 0.660327 0.1618506 0.4259076 0.0070193 0.3462765 164s 41 4.262054 1.687193 0.351875 0.5396477 1.0052810 -0.9331689 0.0056063 164s 42 6.865198 -1.091248 1.153585 1.1248797 0.0873276 0.2565221 0.0333265 164s 43 3.476720 0.555449 -1.030771 -0.3015720 -0.1748109 -0.1584968 0.4079902 164s 44 5.691730 -0.141240 0.565189 0.3174238 0.6478440 1.0579977 -0.5387916 164s 45 0.327134 0.152011 -0.394798 0.4998430 0.1599781 0.3159518 0.1623656 164s 46 0.280225 1.569387 -0.100397 1.2800976 0.0446645 0.0946513 0.0461599 164s 47 3.119928 -0.384834 -3.325600 -1.8865310 -0.1334744 0.1249987 -0.2561273 164s 48 0.501542 0.739816 -1.384556 -0.1244721 0.2948958 0.4836170 -0.1182802 164s 49 -1.953218 0.269986 -1.726474 -0.8510637 0.5047958 0.4860651 0.2318735 164s 50 3.706878 -2.400570 1.361047 -0.4949076 0.2180352 0.4080879 0.1156540 164s 51 -1.060358 -0.521609 -1.387412 -1.2767491 -0.0521356 0.1665452 -0.0044412 164s 52 -4.900528 0.157011 -1.015880 -0.9941168 0.2069608 0.3239762 -0.1921715 164s 53 -0.388496 0.062051 -0.643721 -0.8544141 -0.1857141 0.0063293 0.2664606 164s 54 0.109234 -0.018709 -0.242825 -0.2064701 -0.0585165 0.1720867 0.1117397 164s 55 1.176175 0.644539 -0.373694 0.0038605 -0.3436524 0.0194450 -0.0838883 164s 56 0.407259 -0.606637 0.222915 -0.3622451 -0.0737834 0.0228104 0.0297333 164s 57 -1.022756 -0.071860 0.741957 0.2273628 -0.1388444 -0.2396467 -0.2327738 164s 58 0.245419 1.167059 0.225934 0.8318795 -0.5365166 -0.0090816 -0.1680757 164s 59 -1.300617 -1.110325 -0.262740 -0.8857801 -0.0816954 -0.1186886 -0.0928322 164s 60 -1.110561 -0.832357 -0.212713 -0.4754481 -0.4105982 -0.1886992 -0.0602872 164s 61 0.381831 -1.475116 0.601047 -0.6260156 -0.1854501 -0.1749306 -0.0013904 164s 62 2.734462 -1.887861 0.813453 -0.5856987 0.2310656 0.1117041 -0.0293373 164s 63 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 164s 64 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 164s 65 0.004744 -2.712679 1.178987 -0.6677199 0.0208119 0.0621903 -0.0655693 164s 66 -2.014851 -1.060090 -0.099959 -0.7225044 -0.1947648 -0.2282902 -0.0505015 164s 67 0.621739 -1.296106 0.255632 -0.3309504 -0.0880200 0.2524306 0.1465779 164s 68 -0.271385 -1.709161 -1.100349 -2.0937671 0.2166264 0.0191278 0.0114174 164s 69 -0.326350 -0.737232 0.021639 -0.3850383 -0.4338287 0.2156624 0.1597594 164s 70 4.187093 9.708082 4.632803 -4.9751240 -0.0881576 0.2392433 0.0568049 164s 71 -1.868507 -1.600166 0.436353 -0.8078214 -0.1530893 0.0479471 -0.1999893 164s 72 2.768081 -0.556824 -0.148923 -0.3197853 -0.5524427 0.0907804 -0.0694488 164s 73 -1.441846 -2.735114 -0.294134 -1.2172969 0.0109453 -0.0562910 0.1505788 164s 74 -10.995490 0.615992 1.950966 1.1687190 0.2798335 0.2713257 0.0652135 164s 75 0.508992 -2.363945 -0.407064 -0.9522316 0.1040307 0.1088110 -0.7368484 164s 76 -1.015714 -0.307662 -1.088162 -1.0181862 -0.0440888 -0.1362208 0.0271200 164s 77 -8.028891 -0.580763 0.933638 0.4619362 0.3379832 -0.1368644 -0.0669441 164s 78 1.763308 -1.336175 -0.127809 -0.7161775 -0.1904861 -0.0900461 0.0037539 164s 79 0.208944 -0.580698 -0.626297 -0.7620610 -0.0262368 -0.2928202 0.0285908 164s 80 -3.230608 1.251352 0.195280 0.8687004 0.1812011 0.2600692 -0.1516375 164s 81 1.498160 0.669731 -0.266114 0.3772866 -0.2769688 -0.1066593 -0.1608395 164s 82 3.232051 -1.776018 0.485524 0.1170945 0.0557260 0.2219872 0.1187681 164s 83 2.999977 -0.228275 -0.467724 -0.4287672 0.0494902 -0.2337809 -0.0718159 164s 84 1.238083 0.320956 -1.806006 -1.0142266 0.2359630 -0.0857149 0.0593938 164s 85 1.276376 -2.081214 2.540850 0.3745805 -0.2596482 -0.1228412 -0.2199985 164s 86 0.930715 0.836457 -1.385153 -0.6074929 -0.2476354 0.1680713 -0.0117324 164s PC8 164s 1 9.0765e-04 164s 2 2.1811e-04 164s 3 1.1834e-03 164s 4 8.4077e-05 164s 5 9.9209e-04 164s 6 1.6277e-03 164s 7 2.4907e-04 164s 8 6.8383e-04 164s 9 -5.0924e-04 164s 10 3.1215e-04 164s 11 3.0654e-04 164s 12 -1.1951e-03 164s 13 -1.2849e-03 164s 14 -9.0801e-04 164s 15 -1.2686e-03 164s 16 -1.8441e-03 164s 17 -2.1068e-03 164s 18 -5.7816e-04 164s 19 -1.2330e-03 164s 20 3.3857e-05 164s 21 3.8623e-04 164s 22 1.3035e-04 164s 23 -3.8648e-04 164s 24 -1.7400e-04 164s 25 -3.9196e-04 164s 26 -7.6996e-04 164s 27 -4.8042e-04 164s 28 -2.0628e-04 164s 29 -4.5672e-04 164s 30 -1.4716e-04 164s 31 -4.6385e-05 164s 32 -2.0481e-04 164s 33 -3.0020e-04 164s 34 -5.8179e-05 164s 35 1.3870e-04 164s 36 -6.7177e-04 164s 37 -3.0799e-04 164s 38 6.2140e-04 164s 39 4.5912e-04 164s 40 -3.7165e-04 164s 41 -5.4362e-04 164s 42 -1.0155e-03 164s 43 1.3449e-04 164s 44 -5.4761e-04 164s 45 1.0300e-03 164s 46 1.1039e-03 164s 47 -6.4858e-04 164s 48 -7.6886e-05 164s 49 3.2590e-04 164s 50 8.6845e-05 164s 51 4.9423e-04 164s 52 9.2973e-04 164s 53 4.4342e-04 164s 54 4.9888e-04 164s 55 7.2171e-04 164s 56 -3.2133e-05 164s 57 -1.8101e-04 164s 58 -5.4969e-06 164s 59 -8.3841e-04 164s 60 5.9446e-05 164s 61 -6.5683e-05 164s 62 -3.4073e-04 164s 63 -6.5145e-04 164s 64 -6.5145e-04 164s 65 1.4986e-04 164s 66 2.8096e-04 164s 67 -6.5170e-05 164s 68 -1.3775e-04 164s 69 6.8225e-06 164s 70 -1.6290e-04 164s 71 3.9009e-04 164s 72 -1.3981e-04 164s 73 6.2613e-04 164s 74 2.6513e-03 164s 75 3.7088e-04 164s 76 9.9539e-04 164s 77 1.2979e-03 164s 78 5.6500e-04 164s 79 3.0940e-04 164s 80 8.7993e-04 164s 81 -3.1353e-04 164s 82 4.9625e-04 164s 83 -6.3951e-04 164s 84 -4.5582e-04 164s 85 5.9440e-04 164s 86 -3.6234e-04 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 3.99253025 1.66473582 1.10660264 0.96987790 0.33004256 0.29263512 0.20843280 164s [8] 0.00074024 164s ---------------------------------------------------------- 164s bushfire 38 5 5 38435.075910 1035.305774 164s Scores: 164s PC1 PC2 PC3 PC4 PC5 164s 1 -111.9345 4.9970 -1.00881 -1.224361 3.180569 164s 2 -113.4128 7.4784 -0.79170 -0.235184 2.385812 164s 3 -105.8364 10.9615 -3.15662 -0.251662 1.017328 164s 4 -89.1684 8.7232 -6.15080 -0.075611 1.431111 164s 5 -58.7216 -1.9543 -12.70661 -0.151328 1.425570 164s 6 -35.0370 -12.8434 -17.06841 -0.525664 3.499743 164s 7 -250.2123 -49.4348 23.31261 -19.070238 0.647348 164s 8 -292.6877 -69.7708 -21.30815 13.093808 -1.288764 164s 9 -294.0765 -70.9903 -23.96326 14.940985 -0.939076 164s 10 -290.0193 -57.3747 3.51346 1.858995 0.083107 164s 11 -289.8168 -43.3207 16.08046 -1.745099 -1.506042 164s 12 -290.8645 6.2503 40.52173 -7.496479 -0.033767 164s 13 -232.6865 41.8090 37.19429 -1.280348 -0.470837 164s 14 9.8483 25.1954 -14.56970 0.538484 1.772046 164s 15 137.1924 11.8521 -37.12452 -5.130459 -0.586695 164s 16 92.9804 10.3923 -24.97267 -7.551314 -1.867125 164s 17 90.4493 10.5630 -21.92735 -5.669651 -1.001362 164s 18 78.6325 5.2211 -19.74718 -6.107880 -1.939986 164s 19 82.1178 3.6913 -21.37810 -4.259855 -1.278838 164s 20 92.9044 7.1961 -21.22900 -4.125571 -0.127089 164s 21 74.9157 10.2991 -16.60924 -5.660751 -0.406343 164s 22 66.7350 12.0460 -16.73298 -4.669080 1.333436 164s 23 -62.1981 22.7394 6.03613 -5.182356 -0.453624 164s 24 -116.5696 32.3182 12.74846 -1.465657 -0.097851 164s 25 -53.8907 22.4278 -2.18861 -2.742014 -0.990071 164s 26 -60.6384 20.2952 -3.05206 -2.953685 -0.629061 164s 27 -74.7621 28.9067 -0.65817 1.473357 -0.443957 164s 28 -50.2202 37.3457 -1.44989 5.530426 -1.073521 164s 29 -38.7483 50.2749 2.34469 10.156457 -0.416262 164s 30 -93.3887 51.7884 20.08872 8.798781 -1.620216 164s 31 35.3096 41.7158 13.46272 14.464358 -0.475973 164s 32 290.8493 3.5924 7.41501 15.244293 2.141354 164s 33 326.7236 -29.8194 15.64898 2.612061 0.064931 164s 34 322.9095 -30.6372 16.21520 1.248005 -0.711322 164s 35 328.5307 -29.9533 16.49656 1.138916 0.974792 164s 36 325.6791 -30.6990 16.83840 -0.050949 -1.211360 164s 37 323.8136 -30.7474 19.55764 -1.545150 -0.267580 164s 38 325.2991 -30.5350 20.31878 -1.928580 -0.120425 164s ------------- 164s Call: 164s PcaClassic(x = x) 164s 164s Standard deviations: 164s [1] 196.0487 32.1762 18.4819 6.9412 1.3510 164s ---------------------------------------------------------- 164s ========================================================== 164s > dodata(method="hubert.mcd") 164s 164s Call: dodata(method = "hubert.mcd") 164s Data Set n p k e1 e2 164s ========================================================== 164s heart 12 2 2 358.175786 4.590630 164s Scores: 164s PC1 PC2 164s 1 -12.2285 0.86283 164s 2 -68.9906 -7.43256 164s 3 -5.7035 -1.53793 164s 4 -1.8988 2.90891 164s 5 -24.0044 -2.68946 164s 6 9.9115 8.43321 164s 7 -11.0210 1.77484 164s 8 25.1826 -1.31573 164s 9 -3.2809 -0.74345 164s 10 23.8200 -0.93701 164s 11 9.1344 1.67701 164s 12 -53.6607 -5.08826 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 18.9255 2.1426 164s ---------------------------------------------------------- 164s starsCYG 47 2 2 0.280653 0.005921 164s Scores: 164s PC1 PC2 164s 1 -0.285731 -0.0899858 164s 2 -0.819689 0.0153191 164s 3 0.028077 -0.1501882 164s 4 -0.819689 0.0153191 164s 5 -0.234971 -0.1526225 164s 6 -0.527231 -0.0382380 164s 7 0.372118 -0.5195605 164s 8 -0.357448 0.1009508 164s 9 -0.603553 -0.2533541 164s 10 -0.177170 -0.0722541 164s 11 -0.637339 -1.0390758 164s 12 -0.512526 -0.0662337 164s 13 -0.490978 -0.0120517 164s 14 0.936868 -0.2550656 164s 15 0.684479 -0.0125787 164s 16 0.347708 0.0641382 164s 17 1.009966 -0.0202111 164s 18 0.742477 0.1286170 164s 19 0.773105 -0.0588983 164s 20 -0.795247 -1.0648673 164s 21 0.566048 -0.0319223 164s 22 0.723956 -0.0061308 164s 23 0.505616 0.0899297 164s 24 0.069956 0.0896997 164s 25 -0.080090 -0.0462652 164s 26 0.268755 0.0512425 164s 27 0.289710 -0.0770574 164s 28 0.038341 -0.0269216 164s 29 0.567463 -0.1026188 164s 30 -0.951542 -1.1005280 164s 31 0.512064 0.0504528 164s 32 -0.188059 0.1184850 164s 33 -0.288758 -0.0094200 164s 34 -1.190016 -1.1293460 164s 35 0.615197 -0.0846898 164s 36 -0.710930 0.0938781 164s 37 -0.183223 0.0888774 164s 38 -0.288758 -0.0094200 164s 39 -0.262177 0.0759816 164s 40 -0.630957 -0.0855773 164s 41 0.314679 0.0182135 164s 42 -0.130850 0.0163715 164s 43 -0.415248 0.0205825 164s 44 -0.407188 -0.0287636 164s 45 -0.620693 0.0376892 164s 46 -0.051896 0.0292672 164s 47 0.426662 0.0770340 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 0.529767 0.076946 164s ---------------------------------------------------------- 164s phosphor 18 2 2 285.985489 32.152099 164s Scores: 164s PC1 PC2 164s 1 -2.89681 -18.08811 164s 2 21.34021 -0.40854 164s 3 22.98065 4.13006 164s 4 12.33544 -6.72947 164s 5 17.99823 2.47611 164s 6 -13.35773 -24.10967 164s 7 -0.92957 -5.51314 164s 8 9.16061 2.71354 164s 9 9.89243 5.10403 164s 10 -14.12600 -11.17832 164s 11 3.84175 -0.17605 164s 12 -10.61905 4.37646 164s 13 -13.85065 2.01919 164s 14 -8.11927 4.34325 164s 15 -18.69805 -1.51673 164s 16 9.95352 -6.85784 164s 17 -22.49433 0.29387 164s 18 -18.66592 6.92359 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 16.9111 5.6703 164s ---------------------------------------------------------- 164s stackloss 21 3 3 78.703690 19.249085 164s Scores: 164s PC1 PC2 PC3 164s 1 -20.323997 10.26124 0.92041 164s 2 -19.761418 11.08797 0.92383 164s 3 -16.469919 6.43190 0.22593 164s 4 -4.171902 1.68262 2.50695 164s 5 -3.756174 1.40774 0.57004 164s 6 -3.964038 1.54518 1.53850 164s 7 -7.547376 -3.27780 2.48643 164s 8 -7.547376 -3.27780 2.48643 164s 9 -0.763294 -0.63699 2.53518 164s 10 4.214079 4.46296 -2.28315 164s 11 -0.849132 -2.97767 -2.31393 164s 12 -0.078689 -2.28838 -3.27896 164s 13 3.088921 2.80948 -2.28999 164s 14 -3.307313 -6.14718 -1.35916 164s 15 5.552354 -7.34201 -0.32057 164s 16 7.240091 -4.86180 -0.31031 164s 17 14.908334 6.84995 0.70603 164s 18 10.970281 1.06279 0.68209 164s 19 10.199838 0.37350 1.64712 164s 20 4.273564 1.99328 0.14526 164s 21 -11.992249 2.19025 -3.37391 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 8.8715 4.3874 2.1990 164s ---------------------------------------------------------- 164s salinity 28 3 3 11.651966 4.107426 164s Scores: 164s PC1 PC2 PC3 164s 1 1.68712 1.62591 0.19812128 164s 2 2.35772 2.37290 1.24965734 164s 3 6.80132 -2.14412 0.68142276 164s 4 6.41982 -0.61348 -0.31907921 164s 5 6.36697 -1.98030 4.87319903 164s 6 5.22050 1.20864 0.10252555 164s 7 3.34007 2.02950 0.00064329 164s 8 1.06220 2.89801 -0.35658064 164s 9 0.34692 -2.20572 -1.71677710 164s 10 -2.21421 -2.74842 0.76862599 164s 11 -1.40111 -2.16163 2.21124383 164s 12 -0.38242 0.32284 -0.23732191 164s 13 -1.12809 1.33152 -0.28800043 164s 14 -3.24998 1.35943 1.17514969 164s 15 -2.11006 -3.70114 0.45102357 164s 16 3.46920 -5.41242 8.56937909 164s 17 0.46682 -1.46753 1.48992481 164s 18 2.21807 0.99168 -0.61894625 164s 19 0.28525 -2.00333 -2.16450483 164s 20 -1.66639 -1.76768 -1.06946404 164s 21 -2.58106 1.23534 -0.65557612 164s 22 -4.15573 1.71244 0.08170141 164s 23 -3.07670 -4.87628 2.53200755 164s 24 -1.70808 -3.71657 2.99305849 164s 25 -1.08172 -1.05713 0.02468813 164s 26 -2.23187 0.27323 -0.85760867 164s 27 -3.50498 1.07657 -0.68503455 164s 28 -4.49819 1.43219 0.53416609 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 3.4135 2.0267 1.0764 164s ---------------------------------------------------------- 164s hbk 75 3 3 1.459908 1.201048 164s Scores: 164s PC1 PC2 PC3 164s 1 -31.105415 4.714217 10.4566165 164s 2 -31.707650 5.748724 10.7682402 164s 3 -33.366131 4.625897 12.1570167 164s 4 -34.173377 6.069657 12.4466895 164s 5 -33.780418 5.508823 11.9872893 164s 6 -32.493478 4.684595 10.5679819 164s 7 -32.592637 5.235522 10.3765493 164s 8 -31.293363 4.865797 10.9379676 164s 9 -33.160964 5.714260 12.3098920 164s 10 -31.919786 5.384537 12.3374332 164s 11 -38.231962 6.810641 13.5994385 164s 12 -39.290479 5.393906 15.2942554 164s 13 -39.418445 7.326461 11.5194898 164s 14 -43.906584 13.214819 8.3282743 164s 15 -1.906326 -0.716061 -0.8635112 164s 16 -0.263255 -0.926016 -1.9009292 164s 17 1.776489 1.072332 -0.5496140 164s 18 -0.464648 -0.702441 0.0482897 164s 19 -0.267826 1.283779 -0.2925812 164s 20 -2.122108 -0.165970 -0.8924686 164s 21 -0.937217 -0.548532 -0.4132196 164s 22 -0.423273 1.781869 -0.0323061 164s 23 -0.047532 -0.018909 -1.1259327 164s 24 0.490041 0.520202 -1.1065753 164s 25 2.143049 -0.720869 -0.0495474 164s 26 -1.094748 1.459175 0.2226246 164s 27 -2.070705 -0.898573 0.0023229 164s 28 0.294998 -0.830258 0.5929001 164s 29 1.242995 -0.300216 -0.2010507 164s 30 -0.147958 -0.439099 2.0003038 164s 31 -0.170818 -1.440946 -0.9755627 164s 32 0.958531 1.199730 -1.0129867 164s 33 -0.697307 0.874343 -0.7260649 164s 34 2.278946 -0.261106 0.4196544 164s 35 -1.962829 -0.809318 0.2033113 164s 36 -0.626631 0.600666 0.8004036 164s 37 -0.550885 1.881448 0.7382776 164s 38 1.249717 -0.336214 -0.9349845 164s 39 1.106696 -1.569418 0.1869576 164s 40 0.684034 0.939963 -0.1034965 164s 41 -1.559314 -1.551408 0.3660323 164s 42 0.538741 0.447358 1.6361099 164s 43 0.252685 2.080564 -0.7765259 164s 44 -0.217012 -1.027281 1.7015154 164s 45 1.497600 -1.349234 -0.2698932 164s 46 -0.100388 -1.026443 1.5390401 164s 47 0.811117 -2.195271 -0.5208141 164s 48 -1.462210 -1.321318 0.5600144 164s 49 -1.383976 -0.740714 -0.7348906 164s 50 -1.636773 0.215464 0.3195369 164s 51 0.530918 -0.759743 -1.2069247 164s 52 0.109566 -2.107455 -0.5315473 164s 53 0.564334 0.060847 2.3910630 164s 54 0.272234 1.122711 -1.5060028 164s 55 0.608660 1.197219 -0.5255609 164s 56 -0.565430 0.710345 -1.3708230 164s 57 1.115629 -0.888816 -0.4186014 164s 58 -1.351288 0.374815 -1.1980618 164s 59 -0.998016 0.151228 0.9007970 164s 60 -0.124017 0.764846 1.9005963 164s 61 -1.189858 1.905264 0.7721322 164s 62 2.190589 -0.579614 -0.1377914 164s 63 0.518278 0.931130 -1.4534768 164s 64 -2.124566 -0.194391 -0.0327092 164s 65 -0.154218 -1.050861 1.1309885 164s 66 1.197852 1.044147 -0.2265269 164s 67 0.114174 0.094763 -0.5168926 164s 68 2.201115 -0.032271 0.8573493 164s 69 1.307843 -1.104815 -0.7741270 164s 70 -0.691449 0.676665 1.0004603 164s 71 -1.150975 -0.050861 -0.0717068 164s 72 0.457293 0.861871 0.1026350 164s 73 0.392258 0.897451 0.9178065 164s 74 0.584658 1.450471 0.3201857 164s 75 0.972517 0.063777 1.8223995 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 1.2083 1.0959 1.0168 164s ---------------------------------------------------------- 164s milk 86 8 8 5.739740 2.405262 164s Scores: 164s PC1 PC2 PC3 PC4 PC5 PC6 PC7 164s 1 -5.710924 -1.346213 0.01332091 -0.3709242 -0.566813 0.7529298 -1.2525433 164s 2 -6.578612 -0.440749 1.16354746 0.2870685 -0.573207 0.7368064 -1.6101427 164s 3 -0.720902 1.777381 -0.21532020 -0.3213950 0.287603 -0.4764464 -0.5638337 164s 4 -5.545889 1.621147 -0.85212883 0.4380154 0.022241 0.0718035 0.1176140 164s 5 1.323210 -0.143897 -0.78611461 0.5966857 0.043139 -0.0512545 -0.1419726 164s 6 -1.760792 -0.662792 0.46402240 0.2149752 0.130000 0.0797221 0.1916948 164s 7 -2.344198 -0.363657 0.92442296 0.3921371 0.241463 -0.2370967 0.0636268 164s 8 -2.556824 -0.680132 0.04339934 0.4635077 0.154136 0.0371259 0.0260340 164s 9 1.203234 2.712342 -1.00693092 0.1251739 0.170679 0.2231851 -0.0118196 164s 10 3.151858 1.255826 -0.01678562 -0.5087398 -0.087933 0.0115055 -0.0097828 164s 11 9.562891 1.580419 -2.65612113 -0.1748178 -0.153031 -0.0880112 -0.1648752 164s 12 13.617821 -0.999033 -1.92168237 0.0326918 -0.038488 0.0870082 -0.1809687 164s 13 10.958032 -0.097916 0.95915085 -0.2348663 0.147875 0.1219202 0.0419067 164s 14 12.675941 0.158747 -1.04153243 0.3117402 0.302036 0.1187749 -0.2310830 164s 15 10.726828 1.775339 -3.36786799 0.1285422 0.151594 0.0998947 -0.2028458 164s 16 3.042705 0.212589 -1.23921907 -0.5596596 0.277061 -0.5037073 0.0612182 164s 17 0.780071 2.990008 -1.58490147 -0.5441119 0.436485 -0.0603833 0.1016610 164s 18 2.523916 -0.923373 -0.03221722 0.3830822 0.208008 -0.5505270 -0.1252648 164s 19 1.990563 1.062648 -1.42038451 -0.3602257 -0.068006 -0.1932744 -0.1197842 164s 20 -0.243938 1.674555 -0.72225359 -0.1475652 -0.397855 -0.5385123 -0.0559660 164s 21 3.354424 -2.001060 -0.22542149 0.3346180 0.032502 -0.0953825 0.1293148 164s 22 1.477177 -0.777534 -0.35362339 0.1224412 0.203208 0.0514382 -0.2166274 164s 23 0.502055 -1.618511 -0.85013853 -0.1298862 -0.144328 -0.1941806 -0.1923681 164s 24 0.900504 -1.227820 -1.07180474 -0.5851197 0.112657 0.0467164 0.0405544 164s 25 4.161393 -1.869015 -1.54507759 0.2003123 -0.152582 -0.1382908 0.0864320 164s 26 1.277795 -1.185179 -1.13445511 0.2771556 -0.101901 0.0070037 -0.1279016 164s 27 3.447256 0.257652 -1.13407954 -0.0077859 0.853002 -0.1376443 -0.1897380 164s 28 -1.695730 -3.781876 -0.72940594 -0.0956421 0.064475 0.3665470 0.0726448 164s 29 -3.923610 -1.654818 -0.16117226 -0.4242302 -0.303749 -0.0209844 0.1723890 164s 30 -0.309616 -1.564739 -0.39909943 0.1657509 -0.178739 -0.0600221 -0.0571706 164s 31 -0.960838 -2.242733 1.50477679 -0.2957897 0.163758 -0.1034399 0.0257903 164s 32 -0.671285 -0.459839 1.39124475 -0.3669914 0.246127 0.2094780 -0.2681284 164s 33 -1.589089 -0.390812 -0.16505762 -0.3992573 0.086870 -0.0402114 -0.0399923 164s 34 -0.421868 0.636139 -0.42563447 -0.2985726 0.311365 0.2398515 -0.0540852 164s 35 1.118429 -2.116328 -0.22329747 -0.4864401 0.289927 -0.0503006 0.0101706 164s 36 -3.660291 -1.630831 -0.57876280 0.1294792 -0.260224 0.0912904 -0.1565668 164s 37 -0.087686 -2.530609 0.50076931 -0.0319873 0.194898 -0.1233526 -0.2494283 164s 38 -1.418620 -2.303011 -0.09405565 -0.0931745 0.169466 0.1581787 0.0850095 164s 39 1.815225 -0.838968 -1.10222194 -0.4897630 0.180933 0.0096330 -0.0600652 164s 40 -3.420975 1.398516 -0.17143314 -0.5852146 0.090464 -0.2066323 -0.2974177 164s 41 -3.462295 -1.795174 -0.17500650 -0.1610267 -0.595086 0.5981680 -1.5930268 164s 42 -6.401429 0.451242 -0.78723149 -0.4285618 0.055395 -0.0212476 0.0808936 164s 43 -2.583017 -0.871790 1.29937081 0.2422349 -0.190002 -0.2822972 -0.2625721 164s 44 -5.027244 -0.167503 -0.02382957 -0.8288929 -0.852207 0.7399343 0.4606076 164s 45 0.364494 -0.440380 -0.07746564 -0.4552133 0.095711 -0.1662998 0.1566706 164s 46 0.420706 -1.880819 -0.82180986 -0.1823454 -0.022661 -0.0304227 -0.0516440 164s 47 -1.932985 -0.120002 4.00934170 0.0930728 0.295428 0.2787446 0.3766231 164s 48 0.395402 -1.021393 1.07953292 -0.4599764 -0.132386 0.1895780 0.2771755 164s 49 2.886100 -0.276587 1.48851137 -0.6314648 -0.203963 -0.0891955 0.1347804 164s 50 -3.255379 2.479232 -0.37933775 -0.3651497 -0.415000 0.0045750 0.0671055 164s 51 1.939333 0.617579 1.57113225 0.0310866 -0.039226 0.0409183 0.1830694 164s 52 5.727154 0.275898 0.58814711 -0.1739820 -0.222791 0.2553797 0.1959402 164s 53 1.207873 0.131451 0.80899235 0.2872465 -0.353544 -0.1697200 -0.0987230 164s 54 0.612921 0.040062 0.17807459 -0.0053074 -0.202244 -0.0671788 0.0530276 164s 55 -0.399075 -0.727144 0.26196635 0.3657576 -0.192705 0.0903564 0.0641289 164s 56 0.240719 0.733792 -0.05030509 0.0967214 -0.186906 0.0310231 -0.0594812 164s 57 1.589641 0.289427 -1.02478822 0.2723190 -0.048378 0.2599262 -0.2040853 164s 58 0.423483 -1.262515 -0.85026016 0.4749963 -0.082647 0.0752412 0.1352259 164s 59 1.983684 1.335122 0.42593757 0.1345894 0.096456 0.1153107 -0.0385994 164s 60 1.770171 0.935428 0.14901569 0.3641973 0.274015 -0.0280119 0.0690244 164s 61 0.182845 1.706453 -0.18364654 0.2517421 -0.035773 0.0357087 -0.1363470 164s 62 -2.191617 1.966324 -0.03573689 -0.2203900 -0.235704 0.1682332 -0.1145174 164s 63 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 164s 64 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 164s 65 0.407575 2.996346 -0.63021113 -0.1335795 0.087668 0.0627032 0.0486166 164s 66 2.660379 1.322824 0.10122110 0.2420451 0.192938 0.0344019 -0.0771918 164s 67 -0.032273 1.315299 -0.04511689 -0.1293380 -0.025923 -0.1655965 0.1887534 164s 68 1.117637 2.005809 1.97078787 -0.0429209 -0.176568 0.1634287 -0.0916254 164s 69 0.970730 0.837158 0.01621375 0.2347502 -0.071757 -0.2464626 0.2907551 164s 70 -2.688271 -5.335891 -0.64225481 4.1819517 -9.523550 2.0943027 -2.8098426 164s 71 2.428718 1.976051 -0.24749122 0.1308738 0.018276 0.1711292 0.1346284 164s 72 -2.061944 0.405943 0.50472914 0.4393739 -0.056420 -0.0031558 0.2663880 164s 73 2.029606 2.874991 0.68310320 -0.2067254 0.511537 -0.2010371 0.0805608 164s 74 11.293757 0.328931 -3.84783031 -0.4130266 -0.210499 -0.1103148 -0.0381326 164s 75 0.120896 2.287914 0.83639076 -0.2462845 0.551353 0.6629701 0.3789055 164s 76 1.859499 0.422019 1.18435547 0.1546108 0.017266 0.0470615 -0.1071011 164s 77 8.435857 1.147499 -2.19924186 -0.4156770 0.386548 0.0294075 -0.1911399 164s 78 -1.090858 1.311287 0.62897430 0.1727009 0.077341 0.0135972 -0.0096934 164s 79 0.560012 0.623617 0.83727267 0.1680787 0.087477 0.0611949 -0.2588084 164s 80 3.873817 -1.133641 -1.27469019 -0.2717298 -0.165066 0.1696232 0.0635047 164s 81 -0.758664 -0.880260 0.00057124 0.2838720 0.016243 0.1527299 -0.0150514 164s 82 -2.709588 1.464049 -0.12598126 -0.3828567 0.213647 -0.1425385 0.1552827 164s 83 -2.213670 0.059563 0.87565603 0.1255703 -0.082005 0.2189829 -0.2938264 164s 84 -0.242242 -0.483552 2.05089334 -0.0681005 -0.101578 0.1304632 -0.2218093 164s 85 -1.032129 2.375018 -2.19321259 0.2332079 -0.066379 0.1854598 -0.0873859 164s 86 0.015327 -0.948155 1.39530555 0.2701225 -0.268889 0.0578145 0.1608678 164s PC8 164s 1 2.1835e-03 164s 2 1.6801e-03 164s 3 1.6623e-03 164s 4 2.6286e-04 164s 5 9.5884e-04 164s 6 1.4430e-03 164s 7 1.8784e-04 164s 8 6.8473e-04 164s 9 -6.8490e-04 164s 10 1.1565e-04 164s 11 5.6907e-06 164s 12 -1.8395e-03 164s 13 -2.1582e-03 164s 14 -1.6294e-03 164s 15 -1.6964e-03 164s 16 -1.9664e-03 164s 17 -2.2448e-03 164s 18 -6.5884e-04 164s 19 -1.1536e-03 164s 20 2.6887e-04 164s 21 3.3199e-05 164s 22 1.1170e-04 164s 23 -1.7617e-04 164s 24 -2.1577e-04 164s 25 -6.1495e-04 164s 26 -7.2903e-04 164s 27 -6.8773e-04 164s 28 -2.0742e-04 164s 29 -2.6937e-04 164s 30 -6.7472e-05 164s 31 -1.3222e-04 164s 32 -1.6516e-04 164s 33 -1.8836e-04 164s 34 -1.1273e-04 164s 35 3.0703e-05 164s 36 -3.0311e-04 164s 37 -1.9380e-04 164s 38 5.5526e-04 164s 39 4.1987e-04 164s 40 8.4807e-05 164s 41 8.8725e-04 164s 42 -6.5647e-04 164s 43 4.3202e-04 164s 44 -5.3330e-04 164s 45 8.9161e-04 164s 46 1.1588e-03 164s 47 -1.2714e-03 164s 48 -4.0376e-04 164s 49 4.1280e-06 164s 50 3.0116e-04 164s 51 5.8510e-05 164s 52 3.3236e-04 164s 53 4.0982e-04 164s 54 4.0428e-04 164s 55 6.1600e-04 164s 56 -4.0496e-05 164s 57 -1.8342e-04 164s 58 -1.6748e-04 164s 59 -1.0894e-03 164s 60 -2.6876e-04 164s 61 -5.8951e-05 164s 62 -1.5517e-04 164s 63 -7.9933e-04 164s 64 -7.9933e-04 164s 65 2.2592e-05 164s 66 2.4984e-05 164s 67 -2.2714e-04 164s 68 -3.3991e-04 164s 69 -3.0375e-04 164s 70 3.4033e-03 164s 71 2.3288e-05 164s 72 -3.4126e-04 164s 73 2.5528e-04 164s 74 2.2760e-03 164s 75 -2.8985e-04 164s 76 7.9077e-04 164s 77 9.4636e-04 164s 78 4.9099e-04 164s 79 3.0501e-04 164s 80 6.5280e-04 164s 81 -3.6570e-04 164s 82 4.9966e-04 164s 83 -4.3245e-04 164s 84 -4.6152e-04 164s 85 7.4691e-04 164s 86 -6.1103e-04 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 2.39577535 1.55089079 0.92557331 0.33680677 0.19792033 0.17855133 0.16041702 164s [8] 0.00054179 164s ---------------------------------------------------------- 164s bushfire 38 5 5 31248.552973 358.974577 164s Scores: 164s PC1 PC2 PC3 PC4 PC5 164s 1 155.972 1.08098 -23.31135 -1.93015 1.218941 164s 2 157.738 0.35648 -20.95658 -2.42375 0.466415 164s 3 150.667 2.12545 -16.20395 -2.00140 -0.582924 164s 4 133.892 5.25124 -15.88873 -2.78469 -0.275261 164s 5 102.462 13.00611 -21.54096 -4.69409 -0.944176 164s 6 77.694 18.75377 -28.71865 -6.44244 0.446350 164s 7 286.266 -11.36184 -98.67134 10.95233 -3.625338 164s 8 326.627 29.92767 -112.60824 -29.26330 -13.710094 164s 9 327.898 32.39553 -113.34314 -31.65905 -13.830781 164s 10 325.131 5.81628 -105.58927 -13.45695 -8.987971 164s 11 326.458 -7.84562 -94.25242 -6.11547 -8.572845 164s 12 333.171 -37.69907 -50.89207 8.98187 -1.742979 164s 13 279.789 -40.78415 -8.06209 7.65884 0.181748 164s 14 37.714 10.54231 13.46530 -1.55051 2.102662 164s 15 -90.034 34.68964 18.98186 0.69260 0.417573 164s 16 -46.492 23.65086 10.07282 4.36090 -0.748517 164s 17 -43.990 20.36443 9.61049 2.83084 -0.127983 164s 18 -32.938 19.11199 2.64850 2.92879 -1.473988 164s 19 -36.555 20.60142 2.01879 0.63832 -1.235075 164s 20 -46.837 19.89630 6.65142 0.89120 0.271108 164s 21 -28.670 15.29534 6.59311 3.29638 0.402194 164s 22 -20.331 15.06559 7.33721 2.16591 2.006327 164s 23 108.644 -7.92707 -1.45130 6.27388 0.356715 164s 24 163.697 -16.15568 0.61663 4.24231 0.464415 164s 25 100.471 -0.30739 0.87762 2.86452 -0.692735 164s 26 106.922 0.90864 -1.91436 2.54557 -0.565023 164s 27 121.966 -3.29641 4.85626 -0.47676 -0.490047 164s 28 98.650 -4.51455 16.64160 -3.08996 -0.839397 164s 29 88.795 -10.85457 30.46708 -5.37360 0.315657 164s 30 142.981 -27.89100 22.40713 -1.67126 -0.680158 164s 31 14.125 -21.60028 29.80480 -8.25272 -0.019693 164s 32 -244.044 -11.76430 24.53390 -12.52294 2.022312 164s 33 -283.842 -13.21931 -6.23565 -2.63367 -0.080728 164s 34 -280.168 -13.41903 -7.69318 -1.24571 -0.722513 164s 35 -285.666 -13.78452 -6.50318 -1.23756 1.074669 164s 36 -282.938 -13.82281 -7.63902 0.20435 -0.971673 164s 37 -281.129 -16.20408 -8.57154 1.85797 0.234486 164s 38 -282.589 -16.91969 -8.36010 2.35589 0.490630 164s ------------- 164s Call: 164s PcaHubert(x = x, k = p) 164s 164s Standard deviations: 164s [1] 176.77260 18.94662 16.21701 3.95755 0.92761 164s ---------------------------------------------------------- 164s ========================================================== 164s > dodata(method="hubert") 164s 164s Call: dodata(method = "hubert") 164s Data Set n p k e1 e2 164s ========================================================== 164s heart 12 2 1 315.227002 NA 164s Scores: 164s PC1 164s 1 13.2197 164s 2 69.9817 164s 3 6.6946 164s 4 2.8899 164s 5 24.9956 164s 6 -8.9203 164s 7 12.0121 164s 8 -24.1915 164s 9 4.2721 164s 10 -22.8289 164s 11 -8.1433 164s 12 54.6519 164s ------------- 164s Call: 164s PcaHubert(x = x, mcd = FALSE) 164s 164s Standard deviations: 164s [1] 17.755 164s ---------------------------------------------------------- 164s starsCYG 47 2 1 0.308922 NA 164s Scores: 164s PC1 164s 1 0.224695 164s 2 0.758653 164s 3 -0.089113 164s 4 0.758653 164s 5 0.173934 164s 6 0.466195 164s 7 -0.433154 164s 8 0.296411 164s 9 0.542517 164s 10 0.116133 164s 11 0.576303 164s 12 0.451490 164s 13 0.429942 164s 14 -0.997904 164s 15 -0.745515 164s 16 -0.408745 164s 17 -1.071002 164s 18 -0.803514 164s 19 -0.834141 164s 20 0.734210 164s 21 -0.627085 164s 22 -0.784992 164s 23 -0.566652 164s 24 -0.130992 164s 25 0.019053 164s 26 -0.329791 164s 27 -0.350747 164s 28 -0.099378 164s 29 -0.628499 164s 30 0.890506 164s 31 -0.573100 164s 32 0.127022 164s 33 0.227721 164s 34 1.128979 164s 35 -0.676234 164s 36 0.649894 164s 37 0.122186 164s 38 0.227721 164s 39 0.201140 164s 40 0.569920 164s 41 -0.375716 164s 42 0.069814 164s 43 0.354212 164s 44 0.346152 164s 45 0.559656 164s 46 -0.009140 164s 47 -0.487699 164s ------------- 164s Call: 164s PcaHubert(x = x, mcd = FALSE) 164s 164s Standard deviations: 164s [1] 0.55581 164s ---------------------------------------------------------- 164s phosphor 18 2 1 215.172048 NA 164s Scores: 164s PC1 164s 1 1.12634 164s 2 -22.10340 164s 3 -23.49216 164s 4 -13.45927 164s 5 -18.60808 164s 6 11.24086 164s 7 -0.14748 164s 8 -9.77075 164s 9 -10.37022 164s 10 12.71798 164s 11 -4.61857 164s 12 10.07037 164s 13 13.16767 164s 14 7.57254 164s 15 17.81362 164s 16 -11.08799 164s 17 21.70358 164s 18 18.24496 164s ------------- 164s Call: 164s PcaHubert(x = x, mcd = FALSE) 164s 164s Standard deviations: 164s [1] 14.669 164s ---------------------------------------------------------- 164s stackloss 21 3 2 77.038636 18.859777 164s Scores: 164s PC1 PC2 164s 1 -20.334936 10.28081 164s 2 -19.772121 11.10736 164s 3 -16.461573 6.43794 164s 4 -4.258672 1.73213 164s 5 -3.773146 1.41928 164s 6 -4.015909 1.57571 164s 7 -7.635560 -3.22715 164s 8 -7.635560 -3.22715 164s 9 -0.855388 -0.58707 164s 10 4.298129 4.41664 164s 11 -0.767202 -3.02229 164s 12 0.038375 -2.35217 164s 13 3.172500 2.76354 164s 14 -3.261224 -6.17206 164s 15 5.553840 -7.34784 164s 16 7.242284 -4.86820 164s 17 14.878925 6.85989 164s 18 10.939223 1.07406 164s 19 10.133645 0.40394 164s 20 4.267234 1.99501 164s 21 -11.859921 2.12579 164s ------------- 164s Call: 164s PcaHubert(x = x, mcd = FALSE) 164s 164s Standard deviations: 164s [1] 8.7772 4.3428 164s ---------------------------------------------------------- 165s salinity 28 3 2 8.001175 5.858089 165s Scores: 165s PC1 PC2 165s 1 2.858444 1.04359 165s 2 3.807704 1.55974 165s 3 6.220733 -4.32114 165s 4 6.388841 -2.83649 165s 5 6.077450 -3.70092 165s 6 5.974494 -0.67230 165s 7 4.531584 0.78322 165s 8 2.725849 2.41297 165s 9 0.100501 -2.13615 165s 10 -2.358003 -1.49718 165s 11 -1.317688 -1.15391 165s 12 0.434635 0.58230 165s 13 0.116019 1.79022 165s 14 -1.771501 2.71749 165s 15 -2.630757 -2.44003 165s 16 2.289743 -5.51829 165s 17 0.637985 -1.26452 165s 18 3.076147 0.19883 165s 19 0.097381 -1.95868 165s 20 -1.572471 -0.93003 165s 21 -1.284185 2.21858 165s 22 -2.531713 3.30313 165s 23 -3.865359 -3.01230 165s 24 -2.143461 -2.41918 165s 25 -0.714414 -0.41227 165s 26 -1.327781 1.18373 165s 27 -2.201166 2.41566 165s 28 -2.931988 3.20536 165s ------------- 165s Call: 165s PcaHubert(x = x, mcd = FALSE) 165s 165s Standard deviations: 165s [1] 2.8286 2.4203 165s ---------------------------------------------------------- 165s hbk 75 3 3 1.459908 1.201048 165s Scores: 165s PC1 PC2 PC3 165s 1 31.105415 -4.714217 -10.4566165 165s 2 31.707650 -5.748724 -10.7682402 165s 3 33.366131 -4.625897 -12.1570167 165s 4 34.173377 -6.069657 -12.4466895 165s 5 33.780418 -5.508823 -11.9872893 165s 6 32.493478 -4.684595 -10.5679819 165s 7 32.592637 -5.235522 -10.3765493 165s 8 31.293363 -4.865797 -10.9379676 165s 9 33.160964 -5.714260 -12.3098920 165s 10 31.919786 -5.384537 -12.3374332 165s 11 38.231962 -6.810641 -13.5994385 165s 12 39.290479 -5.393906 -15.2942554 165s 13 39.418445 -7.326461 -11.5194898 165s 14 43.906584 -13.214819 -8.3282743 165s 15 1.906326 0.716061 0.8635112 165s 16 0.263255 0.926016 1.9009292 165s 17 -1.776489 -1.072332 0.5496140 165s 18 0.464648 0.702441 -0.0482897 165s 19 0.267826 -1.283779 0.2925812 165s 20 2.122108 0.165970 0.8924686 165s 21 0.937217 0.548532 0.4132196 165s 22 0.423273 -1.781869 0.0323061 165s 23 0.047532 0.018909 1.1259327 165s 24 -0.490041 -0.520202 1.1065753 165s 25 -2.143049 0.720869 0.0495474 165s 26 1.094748 -1.459175 -0.2226246 165s 27 2.070705 0.898573 -0.0023229 165s 28 -0.294998 0.830258 -0.5929001 165s 29 -1.242995 0.300216 0.2010507 165s 30 0.147958 0.439099 -2.0003038 165s 31 0.170818 1.440946 0.9755627 165s 32 -0.958531 -1.199730 1.0129867 165s 33 0.697307 -0.874343 0.7260649 165s 34 -2.278946 0.261106 -0.4196544 165s 35 1.962829 0.809318 -0.2033113 165s 36 0.626631 -0.600666 -0.8004036 165s 37 0.550885 -1.881448 -0.7382776 165s 38 -1.249717 0.336214 0.9349845 165s 39 -1.106696 1.569418 -0.1869576 165s 40 -0.684034 -0.939963 0.1034965 165s 41 1.559314 1.551408 -0.3660323 165s 42 -0.538741 -0.447358 -1.6361099 165s 43 -0.252685 -2.080564 0.7765259 165s 44 0.217012 1.027281 -1.7015154 165s 45 -1.497600 1.349234 0.2698932 165s 46 0.100388 1.026443 -1.5390401 165s 47 -0.811117 2.195271 0.5208141 165s 48 1.462210 1.321318 -0.5600144 165s 49 1.383976 0.740714 0.7348906 165s 50 1.636773 -0.215464 -0.3195369 165s 51 -0.530918 0.759743 1.2069247 165s 52 -0.109566 2.107455 0.5315473 165s 53 -0.564334 -0.060847 -2.3910630 165s 54 -0.272234 -1.122711 1.5060028 165s 55 -0.608660 -1.197219 0.5255609 165s 56 0.565430 -0.710345 1.3708230 165s 57 -1.115629 0.888816 0.4186014 165s 58 1.351288 -0.374815 1.1980618 165s 59 0.998016 -0.151228 -0.9007970 165s 60 0.124017 -0.764846 -1.9005963 165s 61 1.189858 -1.905264 -0.7721322 165s 62 -2.190589 0.579614 0.1377914 165s 63 -0.518278 -0.931130 1.4534768 165s 64 2.124566 0.194391 0.0327092 165s 65 0.154218 1.050861 -1.1309885 165s 66 -1.197852 -1.044147 0.2265269 165s 67 -0.114174 -0.094763 0.5168926 165s 68 -2.201115 0.032271 -0.8573493 165s 69 -1.307843 1.104815 0.7741270 165s 70 0.691449 -0.676665 -1.0004603 165s 71 1.150975 0.050861 0.0717068 165s 72 -0.457293 -0.861871 -0.1026350 165s 73 -0.392258 -0.897451 -0.9178065 165s 74 -0.584658 -1.450471 -0.3201857 165s 75 -0.972517 -0.063777 -1.8223995 165s ------------- 165s Call: 165s PcaHubert(x = x, mcd = FALSE) 165s 165s Standard deviations: 165s [1] 1.2083 1.0959 1.0168 165s ---------------------------------------------------------- 165s milk 86 8 2 6.040806 2.473780 165s Scores: 165s PC1 PC2 165s 1 -5.768003 -0.9174359 165s 2 -6.664422 0.0280812 165s 3 -0.484521 1.7923710 165s 4 -5.211590 2.0747301 165s 5 1.422641 -0.3268437 165s 6 -1.810360 -0.5469828 165s 7 -2.402924 -0.1987041 165s 8 -2.553389 -0.4963662 165s 9 1.583399 2.5410448 165s 10 3.267946 0.9141367 165s 11 9.924771 0.6501301 165s 12 13.628569 -2.3009846 165s 13 10.774550 -1.1628697 165s 14 12.716376 -1.0670330 165s 15 11.176408 0.7403371 165s 16 3.209269 -0.0804317 165s 17 1.256577 2.8931153 165s 18 2.468720 -1.2008647 165s 19 2.253229 0.8379608 165s 20 0.021073 1.6394221 165s 21 3.205298 -2.3518286 165s 22 1.470733 -0.9618655 165s 23 0.475732 -1.7044535 165s 24 0.930144 -1.3288398 165s 25 4.151553 -2.2882554 165s 26 1.314488 -1.3527439 165s 27 3.613405 -0.0813605 165s 28 -1.909178 -3.6473200 165s 29 -3.987263 -1.3255834 165s 30 -0.370601 -1.5855086 165s 31 -1.273254 -2.1892809 165s 32 -0.816634 -0.4514478 165s 33 -1.553394 -0.2792004 165s 34 -0.275027 0.6359374 165s 35 0.980782 -2.2353223 165s 36 -3.678470 -1.3459182 165s 37 -0.327102 -2.5615283 165s 38 -1.563492 -2.2008288 165s 39 1.876146 -1.0292641 165s 40 -3.204182 1.6694332 165s 41 -3.561892 -1.5844770 165s 42 -6.175135 1.0123714 165s 43 -2.736601 -0.7040261 165s 44 -4.981783 0.2434304 165s 45 0.368802 -0.5011413 165s 46 0.369508 -1.9511091 165s 47 -2.306673 -0.0089446 165s 48 0.215195 -1.1000357 165s 49 2.704678 -0.5919929 165s 50 -2.930879 2.7161936 165s 51 1.846250 0.3732500 165s 52 5.661288 -0.3139157 165s 53 1.154929 -0.0575094 165s 54 0.625715 -0.0733934 165s 55 -0.453714 -0.7535924 165s 56 0.343722 0.6460318 165s 57 1.743002 0.0794685 165s 58 0.433705 -1.3500731 165s 59 2.078550 1.0860506 165s 60 1.867913 0.7162287 165s 61 0.392645 1.6184583 165s 62 -1.958732 2.0993596 165s 63 -2.383251 -0.0253919 165s 64 -2.383251 -0.0253919 165s 65 0.780239 2.9018927 165s 66 2.785329 1.0142893 165s 67 0.131210 1.2703167 165s 68 1.110073 1.8140467 165s 69 1.076878 0.6954148 165s 70 -3.260160 -5.6233069 165s 71 2.647036 1.6892084 165s 72 -2.017340 0.5353349 165s 73 2.247524 2.6406249 165s 74 11.649291 -0.7374197 165s 75 0.280544 2.2306959 165s 76 1.791213 0.1796005 165s 77 8.730344 0.3412271 165s 78 -0.987405 1.3467910 165s 79 0.560808 0.5006661 165s 80 3.897879 -1.5270179 165s 81 -0.792759 -0.8649399 165s 82 -2.493611 1.6796838 165s 83 -2.245966 0.1889555 165s 84 -0.468812 -0.5359088 165s 85 -0.538372 2.4105954 165s 86 -0.185347 -1.0176989 165s ------------- 165s Call: 165s PcaHubert(x = x, mcd = FALSE) 165s 165s Standard deviations: 165s [1] 2.4578 1.5728 165s ---------------------------------------------------------- 165s bushfire 38 5 1 38435.075910 NA 165s Scores: 165s PC1 165s 1 -111.9345 165s 2 -113.4128 165s 3 -105.8364 165s 4 -89.1684 165s 5 -58.7216 165s 6 -35.0370 165s 7 -250.2123 165s 8 -292.6877 165s 9 -294.0765 165s 10 -290.0193 165s 11 -289.8168 165s 12 -290.8645 165s 13 -232.6865 165s 14 9.8483 165s 15 137.1924 165s 16 92.9804 165s 17 90.4493 165s 18 78.6325 165s 19 82.1178 165s 20 92.9044 165s 21 74.9157 165s 22 66.7350 165s 23 -62.1981 165s 24 -116.5696 165s 25 -53.8907 165s 26 -60.6384 165s 27 -74.7621 165s 28 -50.2202 165s 29 -38.7483 165s 30 -93.3887 165s 31 35.3096 165s 32 290.8493 165s 33 326.7236 165s 34 322.9095 165s 35 328.5307 165s 36 325.6791 165s 37 323.8136 165s 38 325.2991 165s ------------- 165s Call: 165s PcaHubert(x = x, mcd = FALSE) 165s 165s Standard deviations: 165s [1] 196.05 165s ---------------------------------------------------------- 165s ========================================================== 165s > 165s > dodata(method="locantore") 165s 165s Call: dodata(method = "locantore") 165s Data Set n p k e1 e2 165s ========================================================== 165s heart 12 2 2 1.835912 0.084745 165s Scores: 165s PC1 PC2 165s [1,] 7.3042 1.745289 165s [2,] 64.6474 0.164425 165s [3,] 1.1057 -1.404189 165s [4,] -3.1943 2.565728 165s [5,] 19.4154 -0.401369 165s [6,] -15.5709 6.666752 165s [7,] 5.9980 2.509372 165s [8,] -29.5933 -4.805972 165s [9,] -1.3933 -0.899323 165s [10,] -28.2845 -4.270057 165s [11,] -14.0069 0.048311 165s [12,] 49.1484 0.694598 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 1.35496 0.29111 165s ---------------------------------------------------------- 165s starsCYG 47 2 2 0.779919 0.050341 165s Scores: 165s PC1 PC2 165s [1,] 0.174291 -0.0489127 165s [2,] 0.703776 0.0769650 165s [3,] -0.136954 -0.1212071 165s [4,] 0.703776 0.0769650 165s [5,] 0.125991 -0.1134658 165s [6,] 0.413609 0.0121367 165s [7,] -0.466451 -0.5036094 165s [8,] 0.238569 0.1446547 165s [9,] 0.498194 -0.1998666 165s [10,] 0.065125 -0.0353931 165s [11,] 0.562344 -0.9836936 165s [12,] 0.399997 -0.0164068 165s [13,] 0.376370 0.0369013 165s [14,] -1.041009 -0.2611550 165s [15,] -0.798187 -0.0090880 165s [16,] -0.464636 0.0805967 165s [17,] -1.123135 -0.0293034 165s [18,] -0.861603 0.1297588 165s [19,] -0.884955 -0.0588007 165s [20,] 0.721130 -1.0033585 165s [21,] -0.679097 -0.0238366 165s [22,] -0.837884 -0.0041718 165s [23,] -0.623423 0.1002615 165s [24,] -0.188079 0.1168815 165s [25,] -0.032888 -0.0131784 165s [26,] -0.385242 0.0707643 165s [27,] -0.401220 -0.0582501 165s [28,] -0.151978 0.0015702 165s [29,] -0.677776 -0.0945350 165s [30,] 0.878688 -1.0329475 165s [31,] -0.628339 0.0605648 165s [32,] 0.068629 0.1556245 165s [33,] 0.174199 0.0317098 165s [34,] 1.118098 -1.0525206 165s [35,] -0.726168 -0.0784655 165s [36,] 0.592061 0.1512588 165s [37,] 0.064942 0.1258519 165s [38,] 0.174199 0.0317098 165s [39,] 0.144335 0.1160195 165s [40,] 0.519088 -0.0311555 165s [41,] -0.429855 0.0359837 165s [42,] 0.015412 0.0513747 165s [43,] 0.299435 0.0665821 165s [44,] 0.293289 0.0169612 165s [45,] 0.504064 0.0916219 165s [46,] -0.063981 0.0612071 165s [47,] -0.544029 0.0904291 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 0.88313 0.22437 165s ---------------------------------------------------------- 165s phosphor 18 2 2 0.933905 0.279651 165s Scores: 165s PC1 PC2 165s 1 4.5660 -15.58981 165s 2 -21.2978 -0.38905 165s 3 -23.3783 3.96546 165s 4 -11.7131 -5.79023 165s 5 -18.2569 2.81141 165s 6 15.5702 -20.54935 165s 7 1.3671 -3.27043 165s 8 -9.4859 3.92005 165s 9 -10.4501 6.22662 165s 10 15.0583 -7.60532 165s 11 -3.9078 1.56960 165s 12 10.0330 7.52732 165s 13 13.4815 5.50056 165s 14 7.5487 7.24752 165s 15 18.6543 2.46040 165s 16 -9.3301 -5.68285 165s 17 22.2533 4.63689 165s 18 17.7892 10.85633 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 0.96639 0.52882 165s ---------------------------------------------------------- 165s stackloss 21 3 3 1.137747 0.196704 165s Scores: 165s PC1 PC2 PC3 165s [1,] 19.98046 -6.20875 -3.93576 165s [2,] 19.57014 -7.11509 -4.03666 165s [3,] 15.48729 -3.14247 -3.29600 165s [4,] 3.12341 -1.38969 1.50633 165s [5,] 2.35380 -0.84492 -0.25745 165s [6,] 2.73860 -1.11731 0.62444 165s [7,] 5.58533 4.04837 2.11170 165s [8,] 5.58533 4.04837 2.11170 165s [9,] -0.56851 0.17483 2.46656 165s [10,] -5.36478 -4.80766 -2.64915 165s [11,] -1.67190 3.34943 -1.74110 165s [12,] -2.46702 2.71547 -2.72389 165s [13,] -4.54414 -2.99497 -2.44736 165s [14,] 0.35419 6.70241 -0.45563 165s [15,] -8.28612 5.93369 1.94314 165s [16,] -9.51708 3.21466 1.64046 165s [17,] -14.87676 -9.74652 1.10983 165s [18,] -12.00452 -3.40212 1.81609 165s [19,] -11.20939 -2.76816 2.79887 165s [20,] -5.42808 -2.89367 0.23748 165s [21,] 9.83969 0.74095 -5.30190 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 1.06665 0.44351 0.33935 165s ---------------------------------------------------------- 165s salinity 28 3 3 1.038873 0.621380 165s Scores: 165s PC1 PC2 PC3 165s 1 -2.7215590 -0.98924 0.3594538 165s 2 -3.6251829 -1.03361 1.4973993 165s 3 -6.0588883 4.23861 -1.1012038 165s 4 -6.2741857 2.42372 -1.4875092 165s 5 -5.7274076 5.42190 2.9332011 165s 6 -5.8431892 0.57161 -0.3385363 165s 7 -4.4051377 -0.83292 0.0851817 165s 8 -2.6155827 -2.50739 0.3386166 165s 9 -0.0426575 1.19631 -2.5025726 165s 10 2.5297488 1.65029 -0.0110335 165s 11 1.5528097 1.93255 1.4216262 165s 12 -0.3140451 -0.73269 -0.1961364 165s 13 0.0010783 -1.88658 0.1849912 165s 14 1.9554303 -2.13519 1.8471356 165s 15 2.7897250 2.40211 -0.6327944 165s 16 -1.7665706 8.69449 5.6608836 165s 17 -0.4374125 1.72696 0.7230753 165s 18 -2.9752196 -0.54118 -0.6829760 165s 19 -0.0599346 0.84127 -2.8473543 165s 20 1.6597909 0.34191 -1.4847516 165s 21 1.3857395 -2.43924 0.0039271 165s 22 2.6664754 -3.14291 1.0600254 165s 23 4.1202067 3.81886 1.0608640 165s 24 2.4163743 3.45141 1.6874099 165s 25 0.8493897 0.31424 -0.3073115 165s 26 1.4216265 -1.55310 -0.5455012 165s 27 2.3021676 -2.63392 0.0481451 165s 28 3.0877115 -2.85951 1.4378956 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 1.01925 0.78828 0.36470 165s ---------------------------------------------------------- 165s hbk 75 3 3 1.038833 0.363386 165s Scores: 165s PC1 PC2 PC3 165s 1 32.393698 -3.4318297 0.051248 165s 2 33.103072 -4.4154651 0.294662 165s 3 35.038965 -3.5996035 -0.940929 165s 4 35.955809 -4.9285404 -0.479059 165s 5 35.424918 -4.3076292 -0.366699 165s 6 33.753497 -3.2463136 0.289013 165s 7 33.817375 -3.6819421 0.684167 165s 8 32.717119 -3.7074394 -0.279567 165s 9 34.932190 -4.6939061 -0.738196 165s 10 33.737339 -4.5702346 -1.193206 165s 11 40.202273 -5.4336890 -0.229323 165s 12 41.638189 -4.5304173 -1.996311 165s 13 40.768565 -5.0531048 2.123222 165s 14 44.408749 -8.8448536 8.236462 165s 15 0.977343 1.3057899 0.938694 165s 16 -0.900390 1.6169842 1.382855 165s 17 -2.384467 -0.9835430 0.375495 165s 18 -0.143306 0.7859701 -0.237712 165s 19 -0.344479 -0.9791245 0.733869 165s 20 1.199115 0.8330752 1.216827 165s 21 0.184475 0.8630593 0.351029 165s 22 -0.100389 -1.5084406 0.718236 165s 23 -0.847925 0.4823829 0.958677 165s 24 -1.334366 -0.1021190 1.000300 165s 25 -2.669352 0.4692990 -0.811134 165s 26 0.601538 -1.1984283 0.541627 165s 27 1.373423 1.2098621 0.136249 165s 28 -0.721268 0.6164612 -0.963817 165s 29 -1.832615 0.2543279 -0.297658 165s 30 0.120086 -0.1558590 -1.976558 165s 31 -0.747437 1.7749106 0.342824 165s 32 -1.727558 -0.8325772 1.043088 165s 33 -0.073907 -0.3923823 1.083904 165s 34 -2.646454 -0.1350138 -1.101448 165s 35 1.331096 1.0443905 -0.039328 165s 36 0.281192 -0.6569943 -0.404009 165s 37 0.245349 -1.8406517 0.093656 165s 38 -2.049446 0.5320301 0.347219 165s 39 -1.645547 1.3268749 -1.068792 165s 40 -1.216874 -0.8556007 0.201262 165s 41 0.959445 1.6250030 -0.553881 165s 42 -0.603579 -0.9569812 -1.502730 165s 43 -0.946870 -1.6333180 1.324763 165s 44 0.076217 0.5018427 -1.902369 165s 45 -2.140584 1.2192726 -0.677180 165s 46 -0.081677 0.5389288 -1.785347 165s 47 -1.590461 2.1881067 -0.583771 165s 48 0.931421 1.3321181 -0.669782 165s 49 0.512639 1.2123979 0.683099 165s 50 1.095415 0.0045968 0.143109 165s 51 -1.456417 1.1186245 0.619657 165s 52 -0.917904 2.2084467 -0.366392 165s 53 -0.429654 -0.8524437 -2.326637 165s 54 -1.213858 -0.4996891 1.630709 165s 55 -1.253877 -0.9438354 0.692022 165s 56 -0.390657 -0.0427482 1.571167 165s 57 -1.797537 0.8934866 -0.281980 165s 58 0.396886 0.3227454 1.492494 165s 59 0.646360 -0.2194210 -0.562699 165s 60 0.119900 -1.2480691 -1.459763 165s 61 0.867946 -1.7843458 0.232229 165s 62 -2.733997 0.3604288 -0.692947 165s 63 -1.442683 -0.3732483 1.452800 165s 64 1.444934 0.5727959 0.434633 165s 65 -0.147284 0.7055205 -1.413940 165s 66 -1.739552 -0.9838385 0.220303 165s 67 -0.824644 0.1503195 0.411693 165s 68 -2.437638 -0.4835278 -1.392882 165s 69 -2.091970 1.1865192 -0.088483 165s 70 0.403429 -0.7855276 -0.540161 165s 71 0.507512 0.3152001 0.276885 165s 72 -0.944376 -0.8197825 0.044859 165s 73 -0.648597 -1.1160277 -0.658528 165s 74 -0.979453 -1.4589411 0.029182 165s 75 -0.982282 -0.7226425 -1.917060 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 1.01923 0.60282 0.46137 165s ---------------------------------------------------------- 165s milk 86 8 8 1.175171 0.426506 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 PC6 165s [1,] 6.1907998 0.58762698 0.686510 -0.209679 0.3321757 -1.3424985 165s [2,] 7.0503894 -0.49576086 -0.322697 -0.767415 -0.0165833 -1.4596064 165s [3,] 0.7670594 -1.83556812 0.468814 0.346810 -0.0204610 -0.2115383 165s [4,] 5.4656748 -2.29797862 1.612819 -0.378295 -0.2050232 0.3486957 165s [5,] -1.0291160 0.37303007 0.634604 -0.521527 -0.3299543 0.0859469 165s [6,] 2.2186300 0.39396818 -0.236987 -0.033975 -0.2549238 0.2541221 165s [7,] 2.7938591 -0.01152811 -0.600546 -0.098564 -0.3906602 0.3798516 165s [8,] 2.9544176 0.32646226 0.273051 -0.275073 -0.3982959 0.2377581 165s [9,] -1.3344639 -2.45440308 1.001792 -0.104783 -0.1744718 -0.0887272 165s [10,] -2.9294174 -0.79860558 -0.260533 0.375330 0.3425169 -0.2056682 165s [11,] -9.5810648 -0.09577968 1.565111 -0.112002 0.3143032 -0.3190238 165s [12,] -13.1147240 2.95665890 0.228086 -0.180867 0.0136463 -0.4604390 165s [13,] -10.2989319 1.53220781 -2.244629 0.323950 -0.0398642 -0.3463501 165s [14,] -12.2553418 1.62281167 -0.472862 -0.212983 -0.4124280 -0.4253719 165s [15,] -10.8346894 -0.09781844 2.134079 -0.272304 -0.1090226 -0.3725738 165s [16,] -2.8358474 0.28109809 0.945309 0.603249 0.1615955 0.1762086 165s [17,] -1.0353408 -2.75475311 1.677879 0.598578 0.0078965 0.0228522 165s [18,] -2.0271810 1.25894451 -0.266038 -0.168565 -0.3000200 0.2891774 165s [19,] -1.9279394 -0.68339726 1.264416 0.186749 0.3018226 -0.0869321 165s [20,] 0.2568334 -1.62632029 0.854279 -0.088175 0.5458645 0.2217019 165s [21,] -2.7017404 2.45223507 -0.243639 -0.211402 -0.2102323 0.2140100 165s [22,] -1.0386097 0.99459030 0.188462 -0.033434 -0.2857078 -0.1438517 165s [23,] -0.0198126 1.73285416 0.761979 0.005501 0.1671992 -0.0375468 165s [24,] -0.4909448 1.40982693 0.967440 0.521275 0.1625359 -0.0892501 165s [25,] -3.6632699 2.51414455 0.966410 -0.272694 0.0467958 0.1572715 165s [26,] -0.8733564 1.42247465 0.946038 -0.338985 -0.0804141 -0.0080759 165s [27,] -3.2254798 0.26912538 0.799468 0.372442 -0.6886191 -0.0553515 165s [28,] 2.4675785 3.56128696 0.813964 0.118354 -0.1677073 -0.0303774 165s [29,] 4.4177264 1.13316321 0.613509 0.261488 0.4229929 0.1780620 165s [30,] 0.8240097 1.54163297 0.398148 -0.221825 0.0309586 0.0830110 165s [31,] 1.7735990 2.00615332 -1.399933 0.469158 -0.0740282 0.0692312 165s [32,] 1.2348922 0.28918604 -1.239899 0.470999 -0.1511519 -0.3692504 165s [33,] 1.9407276 0.19123540 0.406623 0.389965 0.0994854 -0.0204286 165s [34,] 0.6225565 -0.65636700 0.565253 0.369897 -0.1612501 -0.1774611 165s [35,] -0.4869219 2.26301333 0.071825 0.588101 -0.0579092 -0.0362009 165s [36,] 4.1117242 1.16638974 0.982790 -0.266009 0.0728797 -0.0018914 165s [37,] 0.8415225 2.46677043 -0.526780 0.167456 -0.2370116 -0.0731483 165s [38,] 2.0528334 2.09648023 0.220912 0.206722 -0.1924842 0.0676382 165s [39,] -1.4493644 1.14916103 0.904194 0.455498 0.0678893 -0.1476540 165s [40,] 3.4867792 -1.82367389 0.730183 0.499859 0.2327704 -0.1518819 165s [41,] 4.0222120 1.34765470 0.580852 -0.453301 0.2482908 -1.5306566 165s [42,] 6.4789035 -1.25599522 1.644194 0.381331 0.1699942 0.1847594 165s [43,] 3.1529354 0.44884526 -0.967114 -0.220364 0.0037036 0.0802727 165s [44,] 5.3344976 -0.47975673 0.642789 0.298705 0.9983145 -0.1310548 165s [45,] 0.0325597 0.49900084 0.076948 0.486521 0.1642679 0.1392696 165s [46,] 0.1014401 1.97657735 0.733879 0.127235 0.0650844 -0.0144271 165s [47,] 2.7217685 -0.37859042 -3.696163 0.355401 -0.4123714 0.2114024 165s [48,] 0.2292225 1.01473918 -1.115726 0.434557 0.2668316 0.0103147 165s [49,] -2.2803784 0.59474034 -1.783003 0.549252 0.4660435 -0.0802352 165s [50,] 3.1560404 -2.84820361 0.913015 0.077151 0.5803961 0.0350246 165s [51,] -1.4680905 -0.43078891 -1.733657 0.074684 0.0026718 0.0819023 165s [52,] -5.2469034 0.48385240 -1.246027 0.081379 0.2380924 -0.1663831 165s [53,] -0.7670982 0.00234561 -0.923030 -0.366820 0.1582141 0.0508747 165s [54,] -0.2428655 0.04714401 -0.217187 -0.059549 0.1762969 0.0806339 165s [55,] 0.8723441 0.66109329 -0.224917 -0.360607 -0.0638127 0.1310131 165s [56,] 0.0019700 -0.67624071 0.081304 -0.182908 0.1045597 -0.0281936 165s [57,] -1.3684663 -0.00045069 0.860560 -0.350684 -0.1443970 -0.2270651 165s [58,] 0.0079047 1.36376727 0.750919 -0.437914 -0.1894910 0.2345556 165s [59,] -1.7430794 -1.06973583 -0.569381 -0.055139 -0.1582790 -0.0873605 165s [60,] -1.5171606 -0.69340281 -0.287048 -0.136559 -0.3871182 0.1606979 165s [61,] -0.0955085 -1.64221260 0.263650 -0.265665 -0.0808644 -0.0476862 165s [62,] 2.2259171 -2.22161516 0.426279 0.027834 0.2924338 -0.1784242 165s [63,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 165s [64,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 165s [65,] -0.5520071 -2.86186682 0.746248 0.109945 0.0556927 -0.0135739 165s [66,] -2.4472964 -0.94969715 -0.329042 -0.113895 -0.2728443 -0.0523337 165s [67,] 0.1790969 -1.29190443 0.146657 0.140234 0.1534048 0.2318353 165s [68,] -0.8017055 -1.93331421 -1.968273 0.017854 0.1287513 -0.2306786 165s [69,] -0.7356418 -0.68868398 -0.075215 -0.156944 0.0302876 0.4232626 165s [70,] 3.8821693 5.16959880 0.215490 -8.985938 5.2189361 -2.8089276 165s [71,] -2.3478937 -1.60220695 0.058822 -0.111845 -0.0539018 0.0087982 165s [72,] 2.3676739 -0.70331436 -0.214457 -0.307311 -0.1582719 0.3995413 165s [73,] -1.9906385 -2.60946629 -0.730312 0.485522 -0.2391998 0.1009341 165s [74,] -11.2435515 1.44868683 2.482678 0.026711 0.4922865 -0.2822136 165s [75,] 0.0044207 -2.29768358 -0.692425 0.538923 -0.4110598 -0.0824903 165s [76,] -1.4045239 -0.22649785 -1.343257 -0.067382 -0.1322233 -0.1072330 165s [77,] -8.3637576 0.14167751 1.267616 0.384528 -0.0728561 -0.4017300 165s [78,] 1.3022939 -1.47457541 -0.394623 -0.068014 -0.1502832 0.0757414 165s [79,] -0.1950676 -0.58254701 -0.824931 -0.088174 -0.2071634 -0.1896613 165s [80,] -3.4432989 1.73593273 0.777996 0.094211 0.2377017 -0.1520088 165s [81,] 1.2167258 0.77512068 0.085803 -0.214850 -0.2201173 0.0432435 165s [82,] 2.7778798 -1.80071342 0.583878 0.465898 0.0648352 0.2148470 165s [83,] 2.6218578 -0.39825539 -0.553372 -0.145721 -0.0977092 -0.2485337 165s [84,] 0.8946018 0.33790104 -1.974267 0.091828 0.0051986 -0.2606274 165s [85,] 0.7759316 -2.34860124 2.423325 -0.384149 -0.0167182 -0.0353374 165s [86,] 0.6266756 0.87099609 -1.407948 -0.237762 0.0361644 0.1675792 165s PC7 PC8 165s [1,] -0.1014312 1.5884e-03 165s [2,] -0.3831443 1.0212e-03 165s [3,] -0.7164683 1.2035e-03 165s [4,] 0.0892864 3.5409e-04 165s [5,] -0.0943992 1.0547e-03 165s [6,] 0.1184847 1.5031e-03 165s [7,] -0.2509793 1.6850e-05 165s [8,] -0.0136880 7.0308e-04 165s [9,] 0.2238736 -1.9164e-04 165s [10,] 0.0754413 1.3614e-04 165s [11,] 0.0784380 3.5175e-04 165s [12,] 0.2033489 -1.3174e-03 165s [13,] 0.2139525 -1.7101e-03 165s [14,] 0.1209735 -9.1070e-04 165s [15,] 0.2119647 -9.2843e-04 165s [16,] -0.3011483 -2.1474e-03 165s [17,] 0.0660858 -1.9036e-03 165s [18,] -0.5199396 -9.4385e-04 165s [19,] -0.1232622 -1.2649e-03 165s [20,] -0.3900208 -2.6927e-04 165s [21,] 0.0264834 7.6074e-05 165s [22,] -0.0736288 1.7240e-04 165s [23,] -0.2156005 -5.5661e-04 165s [24,] 0.1143327 -2.5248e-04 165s [25,] 0.0481580 -6.1531e-04 165s [26,] -0.0084802 -7.5928e-04 165s [27,] -0.2173883 -3.0971e-04 165s [28,] 0.3288873 -1.8975e-04 165s [29,] 0.0788974 -7.2436e-04 165s [30,] -0.0598663 -3.0463e-04 165s [31,] -0.1511658 -4.8751e-04 165s [32,] -0.0532375 -2.5207e-04 165s [33,] -0.0635290 -3.9270e-04 165s [34,] 0.1598240 1.3024e-04 165s [35,] -0.0355175 -8.5374e-05 165s [36,] -0.0174096 -6.3294e-04 165s [37,] -0.2883141 -5.2809e-04 165s [38,] 0.1426412 5.3331e-04 165s [39,] 0.0313308 4.2738e-04 165s [40,] -0.3536195 -3.4170e-04 165s [41,] -0.3925168 1.4588e-04 165s [42,] -0.0056267 -9.1925e-04 165s [43,] -0.4447402 -1.8415e-04 165s [44,] 0.9184385 -5.9685e-04 165s [45,] -0.0340987 7.2924e-04 165s [46,] -0.0162866 9.7800e-04 165s [47,] 0.2428769 -1.1208e-03 165s [48,] 0.3026758 -4.5769e-04 165s [49,] 0.0246345 -2.6207e-04 165s [50,] 0.0857698 7.6439e-05 165s [51,] 0.1136658 1.3013e-04 165s [52,] 0.3993357 6.2796e-04 165s [53,] -0.1765161 1.1329e-04 165s [54,] 0.0016144 2.5870e-04 165s [55,] 0.1064371 5.8188e-04 165s [56,] 0.0207478 -8.7595e-05 165s [57,] 0.1560065 6.3987e-05 165s [58,] 0.1684561 -5.0193e-05 165s [59,] 0.0778732 -8.5458e-04 165s [60,] 0.0037585 1.0429e-05 165s [61,] -0.0296083 3.1526e-05 165s [62,] 0.0913974 -2.2794e-04 165s [63,] 0.0358917 -7.3721e-04 165s [64,] 0.0358917 -7.3721e-04 165s [65,] 0.1209159 2.9398e-04 165s [66,] -0.0027574 2.9380e-04 165s [67,] -0.0091059 -2.7494e-04 165s [68,] 0.0555970 -3.3016e-04 165s [69,] -0.0149255 -3.1228e-04 165s [70,] 0.9282997 4.7859e-05 165s [71,] 0.2630142 4.2617e-04 165s [72,] 0.1063248 -3.0070e-04 165s [73,] -0.1462452 4.9607e-04 165s [74,] 0.2027591 2.6399e-03 165s [75,] 0.6934350 6.0284e-04 165s [76,] -0.0430524 8.1271e-04 165s [77,] 0.0789302 1.4655e-03 165s [78,] -0.0318359 5.2799e-04 165s [79,] -0.1269568 2.9497e-04 165s [80,] 0.2903958 7.8932e-04 165s [81,] 0.0979443 -3.1531e-04 165s [82,] -0.0548155 4.2140e-04 165s [83,] -0.0371550 -5.6653e-04 165s [84,] -0.0835149 -7.0682e-04 165s [85,] 0.1864954 1.0604e-03 165s [86,] 0.1074252 -7.4859e-04 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 1.08405293 0.65307452 0.28970076 0.11162824 0.09072195 0.06659711 0.05888048 165s [8] 0.00022877 165s ---------------------------------------------------------- 165s bushfire 38 5 5 1.464779 0.043290 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 165s [1,] -69.9562 -13.0364 0.98678 1.054123 2.411188 165s [2,] -71.5209 -10.5459 0.31081 1.631208 1.663470 165s [3,] -63.9308 -7.4622 -2.43241 0.671038 0.465836 165s [4,] -47.0413 -9.6343 -3.83609 0.758349 0.683983 165s [5,] -15.9088 -20.1737 -5.55893 1.181744 -0.053563 165s [6,] 8.3484 -30.7646 -5.51541 1.877227 1.338037 165s [7,] -207.7458 -66.2492 34.48519 -5.894885 -1.051729 165s [8,] -246.4327 -97.0433 -9.57057 22.286225 -9.234869 165s [9,] -247.5984 -98.8613 -12.13406 23.948770 -9.250401 165s [10,] -245.8121 -79.2634 12.47990 13.046128 -5.125478 165s [11,] -246.8887 -62.5899 21.21764 9.111011 -5.080985 165s [12,] -251.1354 -9.2115 31.77448 0.236379 0.707528 165s [13,] -194.0239 27.1288 21.05023 0.940913 1.781359 165s [14,] 51.7182 8.5038 -11.22109 -2.132458 1.984807 165s [15,] 180.5597 -4.8151 -21.36630 -9.390663 -0.817036 165s [16,] 135.7246 -5.0756 -11.33517 -10.015567 -1.670831 165s [17,] 133.0151 -4.0344 -8.95540 -7.702087 -0.923277 165s [18,] 121.2619 -9.0627 -5.96042 -7.210971 -2.092872 165s [19,] 124.9038 -10.6649 -7.22555 -5.349553 -1.771009 165s [20,] 135.5410 -6.8146 -7.52834 -5.562769 -0.396924 165s [21,] 117.1950 -3.5643 -4.67473 -6.862117 -0.234551 165s [22,] 108.9944 -2.3344 -5.90349 -5.928299 1.455538 165s [23,] -21.4031 8.0668 6.19525 -4.784890 0.671394 165s [24,] -76.3499 16.7804 6.52545 -1.391250 1.219282 165s [25,] -12.5732 6.1109 -1.45259 -3.512072 -0.375837 165s [26,] -19.1800 3.4685 -2.02243 -3.490028 -0.169127 165s [27,] -33.6733 12.0757 -3.53322 0.048666 0.067468 165s [28,] -9.3966 21.5055 -5.91671 2.650895 -0.449672 165s [29,] 1.4123 35.8559 -5.98222 5.982362 0.613667 165s [30,] -54.2683 39.6029 7.82694 6.759994 0.035048 165s [31,] 74.8866 34.9048 10.03986 12.592158 0.149308 165s [32,] 331.4144 9.3079 27.73391 17.334531 1.015536 165s [33,] 367.6915 -19.5135 48.52753 10.213314 -1.268047 165s [34,] 363.8686 -20.4079 49.32855 8.986581 -1.930673 165s [35,] 369.4371 -19.5074 49.66761 9.001542 -0.179566 165s [36,] 366.5850 -20.2555 50.30290 7.745330 -2.259131 165s [37,] 364.5463 -19.8198 53.00407 6.757796 -1.083372 165s [38,] 365.9709 -19.3753 53.80168 6.467284 -0.854384 165s ------------- 165s Call: 165s PcaLocantore(x = x) 165s 165s Standard deviations: 165s [1] 1.210280 0.208063 0.177790 0.062694 0.014423 165s ---------------------------------------------------------- 165s ========================================================== 165s > dodata(method="cov") 165s 165s Call: dodata(method = "cov") 165s Data Set n p k e1 e2 165s ========================================================== 165s heart 12 2 2 685.776266 13.127306 165s Scores: 165s PC1 PC2 165s 1 8.18562 1.17998 165s 2 65.41185 -2.80723 165s 3 1.86039 -1.70646 165s 4 -2.26910 2.44051 165s 5 20.19603 -1.47331 165s 6 -14.46264 7.05759 165s 7 6.91264 1.99823 165s 8 -28.95436 -3.81624 165s 9 -0.61523 -1.09711 165s 10 -27.62427 -3.33575 165s 11 -13.17788 0.37931 165s 12 49.94879 -1.62675 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 26.1873 3.6232 165s ---------------------------------------------------------- 165s starsCYG 47 2 2 0.280150 0.007389 165s Scores: 165s PC1 PC2 165s 1 0.272263 -0.07964458 165s 2 0.804544 0.03382837 165s 3 -0.040587 -0.14464760 165s 4 0.804544 0.03382837 165s 5 0.222468 -0.14305159 165s 6 0.512941 -0.02420304 165s 7 -0.378928 -0.51924735 165s 8 0.341045 0.11236831 165s 9 0.592550 -0.23812462 165s 10 0.163442 -0.06357822 165s 11 0.638370 -1.02323643 165s 12 0.498667 -0.05242075 165s 13 0.476291 0.00142479 165s 14 -0.947664 -0.26343572 165s 15 -0.699020 -0.01711057 165s 16 -0.363464 0.06475681 165s 17 -1.024352 -0.02972862 165s 18 -0.759174 0.12317995 165s 19 -0.786925 -0.06478250 165s 20 0.796654 -1.04660568 165s 21 -0.580307 -0.03463751 165s 22 -0.738591 -0.01126825 165s 23 -0.521748 0.08812607 165s 24 -0.086135 0.09457052 165s 25 0.065975 -0.03907968 165s 26 -0.284322 0.05307219 165s 27 -0.303309 -0.07553370 165s 28 -0.052738 -0.02155274 165s 29 -0.580638 -0.10534741 165s 30 0.953478 -1.07986770 165s 31 -0.527590 0.04855502 165s 32 0.171408 0.12730538 165s 33 0.274054 0.00095808 165s 34 1.192364 -1.10502882 165s 35 -0.628641 -0.08815176 165s 36 0.694595 0.11071187 165s 37 0.167026 0.09762710 165s 38 0.274054 0.00095808 165s 39 0.246168 0.08594248 165s 40 0.617380 -0.06994769 165s 41 -0.329735 0.01934346 165s 42 0.115770 0.02432733 165s 43 0.400071 0.03289494 165s 44 0.392768 -0.01656886 165s 45 0.605229 0.05314718 165s 46 0.036628 0.03601196 165s 47 -0.442606 0.07644144 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 0.529292 0.085957 165s ---------------------------------------------------------- 165s phosphor 18 2 2 288.018150 22.020514 165s Scores: 165s PC1 PC2 165s 1 2.7987 -19.015683 165s 2 -20.4311 -0.032022 165s 3 -21.8198 4.589809 165s 4 -11.7869 -6.837833 165s 5 -16.9357 2.664785 165s 6 12.9132 -25.602526 165s 7 1.5249 -6.351664 165s 8 -8.0984 2.416616 165s 9 -8.6979 4.843680 165s 10 14.3903 -12.732868 165s 11 -2.9462 -0.760656 165s 12 11.7427 2.991004 165s 13 14.8400 0.459849 165s 14 9.2449 3.095095 165s 15 19.4860 -3.336883 165s 16 -9.4156 -7.096788 165s 17 23.3759 -1.737460 165s 18 19.9173 5.092467 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 16.9711 4.6926 165s ---------------------------------------------------------- 165s stackloss 21 3 3 28.153060 8.925048 165s Scores: 165s PC1 PC2 PC3 165s [1,] 10.538448 13.596944 12.84989 165s [2,] 9.674846 14.098881 12.89733 165s [3,] 8.993255 9.221043 9.94062 165s [4,] 1.744427 3.649104 0.17292 165s [5,] 0.980215 2.223126 1.34874 165s [6,] 1.362321 2.936115 0.76083 165s [7,] 6.926040 0.637480 -0.11170 165s [8,] 6.926040 0.637480 -0.11170 165s [9,] 0.046655 0.977727 -2.46930 165s [10,] -7.909092 0.926343 0.80232 165s [11,] -0.136672 -3.591094 0.37539 165s [12,] -1.382381 -3.802146 1.01074 165s [13,] -6.181887 -0.077532 0.70744 165s [14,] 3.699843 -4.885854 -0.40226 165s [15,] -2.768005 -7.507870 -6.08487 165s [16,] -5.358811 -6.002058 -5.94256 165s [17,] -17.067135 1.738055 -5.86637 165s [18,] -11.021920 -1.775507 -6.19842 165s [19,] -9.776212 -1.564455 -6.83377 165s [20,] -6.075508 0.369252 -2.08345 165s [21,] 6.301743 2.706174 8.79509 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 5.3059 2.9875 1.3020 165s ---------------------------------------------------------- 165s salinity 28 3 3 11.801732 3.961826 165s Scores: 165s PC1 PC2 PC3 165s 1 -1.59888 1.582157 0.135248 165s 2 -2.26975 2.429177 1.107832 165s 3 -6.79543 -2.034636 0.853876 165s 4 -6.36795 -0.602960 -0.267268 165s 5 -6.42044 -1.520259 5.022962 165s 6 -5.13821 1.225470 0.016977 165s 7 -3.24014 1.998671 -0.123418 165s 8 -0.93998 2.789889 -0.515656 165s 9 -0.30856 -2.424345 -1.422752 165s 10 2.20362 -2.800513 1.142127 165s 11 1.38120 -2.076832 2.515630 165s 12 0.44997 0.207439 -0.152835 165s 13 1.21669 1.193701 -0.277116 165s 14 3.31664 1.306627 1.213342 165s 15 2.08484 -3.774814 0.905400 165s 16 -3.64862 -4.677257 9.046484 165s 17 -0.46124 -1.411762 1.706719 165s 18 -2.13038 0.890401 -0.633349 165s 19 -0.23610 -2.262304 -1.885048 165s 20 1.70337 -1.970773 -0.781880 165s 21 2.67273 1.038742 -0.610945 165s 22 4.24561 1.547290 0.108927 165s 23 2.99619 -4.785343 3.094945 165s 24 1.64474 -3.564562 3.432429 165s 25 1.11703 -1.158030 0.237700 165s 26 2.30707 0.069668 -0.735809 165s 27 3.59356 0.860498 -0.611380 165s 28 4.57550 1.300407 0.589307 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 3.43536 1.99043 0.94546 165s ---------------------------------------------------------- 165s hbk 75 3 3 1.436470 1.181766 165s Scores: 165s PC1 PC2 PC3 165s 1 31.105415 -4.714217 10.4566165 165s 2 31.707650 -5.748724 10.7682402 165s 3 33.366131 -4.625897 12.1570167 165s 4 34.173377 -6.069657 12.4466895 165s 5 33.780418 -5.508823 11.9872893 165s 6 32.493478 -4.684595 10.5679819 165s 7 32.592637 -5.235522 10.3765493 165s 8 31.293363 -4.865797 10.9379676 165s 9 33.160964 -5.714260 12.3098920 165s 10 31.919786 -5.384537 12.3374332 165s 11 38.231962 -6.810641 13.5994385 165s 12 39.290479 -5.393906 15.2942554 165s 13 39.418445 -7.326461 11.5194898 165s 14 43.906584 -13.214819 8.3282743 165s 15 1.906326 0.716061 -0.8635112 165s 16 0.263255 0.926016 -1.9009292 165s 17 -1.776489 -1.072332 -0.5496140 165s 18 0.464648 0.702441 0.0482897 165s 19 0.267826 -1.283779 -0.2925812 165s 20 2.122108 0.165970 -0.8924686 165s 21 0.937217 0.548532 -0.4132196 165s 22 0.423273 -1.781869 -0.0323061 165s 23 0.047532 0.018909 -1.1259327 165s 24 -0.490041 -0.520202 -1.1065753 165s 25 -2.143049 0.720869 -0.0495474 165s 26 1.094748 -1.459175 0.2226246 165s 27 2.070705 0.898573 0.0023229 165s 28 -0.294998 0.830258 0.5929001 165s 29 -1.242995 0.300216 -0.2010507 165s 30 0.147958 0.439099 2.0003038 165s 31 0.170818 1.440946 -0.9755627 165s 32 -0.958531 -1.199730 -1.0129867 165s 33 0.697307 -0.874343 -0.7260649 165s 34 -2.278946 0.261106 0.4196544 165s 35 1.962829 0.809318 0.2033113 165s 36 0.626631 -0.600666 0.8004036 165s 37 0.550885 -1.881448 0.7382776 165s 38 -1.249717 0.336214 -0.9349845 165s 39 -1.106696 1.569418 0.1869576 165s 40 -0.684034 -0.939963 -0.1034965 165s 41 1.559314 1.551408 0.3660323 165s 42 -0.538741 -0.447358 1.6361099 165s 43 -0.252685 -2.080564 -0.7765259 165s 44 0.217012 1.027281 1.7015154 165s 45 -1.497600 1.349234 -0.2698932 165s 46 0.100388 1.026443 1.5390401 165s 47 -0.811117 2.195271 -0.5208141 165s 48 1.462210 1.321318 0.5600144 165s 49 1.383976 0.740714 -0.7348906 165s 50 1.636773 -0.215464 0.3195369 165s 51 -0.530918 0.759743 -1.2069247 165s 52 -0.109566 2.107455 -0.5315473 165s 53 -0.564334 -0.060847 2.3910630 165s 54 -0.272234 -1.122711 -1.5060028 165s 55 -0.608660 -1.197219 -0.5255609 165s 56 0.565430 -0.710345 -1.3708230 165s 57 -1.115629 0.888816 -0.4186014 165s 58 1.351288 -0.374815 -1.1980618 165s 59 0.998016 -0.151228 0.9007970 165s 60 0.124017 -0.764846 1.9005963 165s 61 1.189858 -1.905264 0.7721322 165s 62 -2.190589 0.579614 -0.1377914 165s 63 -0.518278 -0.931130 -1.4534768 165s 64 2.124566 0.194391 -0.0327092 165s 65 0.154218 1.050861 1.1309885 165s 66 -1.197852 -1.044147 -0.2265269 165s 67 -0.114174 -0.094763 -0.5168926 165s 68 -2.201115 0.032271 0.8573493 165s 69 -1.307843 1.104815 -0.7741270 165s 70 0.691449 -0.676665 1.0004603 165s 71 1.150975 0.050861 -0.0717068 165s 72 -0.457293 -0.861871 0.1026350 165s 73 -0.392258 -0.897451 0.9178065 165s 74 -0.584658 -1.450471 0.3201857 165s 75 -0.972517 -0.063777 1.8223995 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 1.1985 1.0871 1.0086 165s ---------------------------------------------------------- 165s milk 86 8 8 5.758630 2.224809 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 PC6 165s 1 5.7090867 1.388263 0.0055924 0.3510505 -0.7335114 -1.41950731 165s 2 6.5825186 0.480410 -1.1356236 -0.3250838 -0.7343177 -1.71595400 165s 3 0.7433619 -1.749281 0.2510521 0.3450575 0.2996413 -0.34585702 165s 4 5.5733255 -1.588521 0.8934908 -0.3412408 0.0087626 0.07235942 165s 5 -1.3030839 0.142394 0.8487785 -0.5847851 0.0588053 -0.08968553 165s 6 1.7708705 0.674240 -0.4153759 -0.1915734 0.1382138 0.12454293 165s 7 2.3570866 0.381017 -0.8771357 -0.3739365 0.2918453 0.13437364 165s 8 2.5700714 0.695006 0.0061108 -0.4323695 0.1643797 -0.00469369 165s 9 -1.1725766 -2.713291 1.0677483 -0.0647875 0.1183120 -0.10762785 165s 10 -3.1357225 -1.255175 0.0666017 0.5083690 -0.1096080 -0.00647493 165s 11 -9.5333894 -1.608943 2.7307809 0.1690156 -0.1682415 -0.06597478 165s 12 -13.6028505 0.941083 2.0136258 -0.1076520 -0.0475905 -0.15295614 165s 13 -10.9497471 0.048776 -0.8765307 0.1518572 0.1428294 -0.00064406 165s 14 -12.6558378 -0.219444 1.1396273 -0.3734679 0.2875578 -0.23870524 165s 15 -10.6924790 -1.818075 3.4560731 -0.1177943 0.1101199 -0.19708172 165s 16 -3.0258070 -0.203186 1.2835368 0.5799363 0.3237454 0.23168871 165s 17 -0.7498665 -2.977505 1.6310512 0.6305329 0.3994006 0.06594881 165s 18 -2.5093526 0.924459 0.0899818 -0.4026675 0.2963072 0.11324019 165s 19 -1.9689970 -1.051282 1.4659908 0.3870104 -0.0708083 -0.02148354 165s 20 0.2695886 -1.646440 0.7597630 0.1750131 -0.3418142 0.21515143 165s 21 -3.3470252 1.989939 0.2887021 -0.3599779 0.0771965 0.16867095 165s 22 -1.4659204 0.777242 0.4090149 -0.1248050 0.1916768 -0.23160291 165s 23 -0.4944476 1.634130 0.8915509 0.1222296 -0.1231015 -0.08351169 165s 24 -0.8945477 1.239223 1.1117165 0.6018455 0.0912200 -0.01204668 165s 25 -4.1499992 1.860190 1.6062973 -0.2139736 -0.1140169 0.16632426 165s 26 -1.2647012 1.188058 1.1893430 -0.2740862 -0.0971504 -0.09851714 165s 27 -3.4280131 -0.267150 1.1969552 0.0354366 0.8482718 -0.18977667 165s 28 1.6896630 3.793723 0.7706325 0.1007287 0.0317704 -0.11269816 165s 29 3.9258127 1.691428 0.1850999 0.4485202 -0.2969916 0.16594044 165s 30 0.3178322 1.577233 0.4455231 -0.1687197 -0.1587136 -0.00823174 165s 31 0.9562350 2.258138 -1.4672169 0.2675668 0.1910110 0.03177387 165s 32 0.6738452 0.470764 -1.3496896 0.3524049 0.2008218 -0.36957179 165s 33 1.5980690 0.413899 0.1999664 0.4232293 0.0768479 -0.04627841 165s 34 0.4365091 -0.626490 0.4718364 0.3392252 0.2554060 -0.19018602 165s 35 -1.1184804 2.124234 0.2650931 0.4791171 0.2927791 -0.01579964 165s 36 3.6673986 1.659798 0.6138972 -0.1092158 -0.2705583 -0.16494176 165s 37 0.0867143 2.541765 -0.4572593 0.0024263 0.2163300 -0.20116352 165s 38 1.4191839 2.315690 0.1365887 0.1028375 0.1595780 -0.02049460 165s 39 -1.8062960 0.845438 1.1469588 0.5022406 0.1603011 -0.08751261 165s 40 3.4380914 -1.358545 0.1956896 0.6314649 0.0716078 -0.21591535 165s 41 3.4608782 1.828575 0.2012565 0.1064437 -0.7454169 -1.64629924 165s 42 6.4162310 -0.402642 0.8070441 0.5146855 0.0331594 0.04373032 165s 43 2.5906567 0.897993 -1.2612252 -0.2620162 -0.1432569 -0.10279385 165s 44 5.0299750 0.203721 0.0439110 0.8775684 -0.9536011 0.15153452 165s 45 -0.3555392 0.454930 0.1173992 0.4688991 0.1137820 0.18752442 165s 46 -0.4155426 1.892410 0.8649578 0.1827426 -0.0186113 -0.04029205 165s 47 1.9328817 0.121936 -3.9578157 -0.1135807 0.2971001 0.18733657 165s 48 -0.3947656 1.028405 -1.0370498 0.4467257 -0.1445498 0.16878692 165s 49 -2.8829860 0.279064 -1.4443310 0.5889970 -0.1883118 0.16947945 165s 50 3.2797246 -2.443968 0.4100655 0.4278962 -0.4414712 0.08598366 165s 51 -1.9272930 -0.622137 -1.5136862 -0.0483369 -0.0272502 0.16006066 165s 52 -5.7161590 -0.298434 -0.5216578 0.1385780 -0.2435931 0.10628617 165s 53 -1.1933277 -0.125878 -0.7556261 -0.3129372 -0.3166453 0.03078643 165s 54 -0.5994394 -0.031069 -0.1296378 0.0061490 -0.1869578 0.09839221 165s 55 0.4104586 0.733465 -0.2088065 -0.3645266 -0.1830137 0.04705775 165s 56 -0.2227671 -0.724741 0.1007592 -0.0838897 -0.1939960 -0.04223579 165s 57 -1.5706297 -0.292436 1.0849660 -0.2559591 -0.0917278 -0.27423151 165s 58 -0.4102168 1.263831 0.9082556 -0.4592777 -0.0676902 0.11089798 165s 59 -1.9640736 -1.340173 -0.3652736 -0.1267573 0.0775692 -0.07977644 165s 60 -1.7490968 -0.941370 -0.0849901 -0.3453455 0.2858594 0.06413468 165s 61 -0.1583416 -1.699326 0.2385988 -0.2231496 -0.0513883 -0.12227279 165s 62 2.2124878 -1.942366 0.0743514 0.2627321 -0.2844018 -0.15848039 165s 63 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 165s 64 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 165s 65 -0.3779208 -2.987354 0.6819006 0.1942611 0.0529259 0.01315140 165s 66 -2.6385498 -1.331204 -0.0367809 -0.2327572 0.1845076 -0.08521680 165s 67 0.0526645 -1.301299 0.0912198 0.1634869 -0.0068236 0.24131589 165s 68 -1.1013065 -2.004809 -1.9168056 0.0260663 -0.2029903 -0.12625268 165s 69 -0.9495853 -0.831697 0.0389476 -0.2123483 -0.0202267 0.38463410 165s 70 2.6935893 5.369312 0.6987368 -4.5754846 -9.6833013 -2.32910628 165s 71 -2.4037611 -1.983509 0.3109848 -0.1015686 -0.0071432 0.06410351 165s 72 2.0795505 -0.392730 -0.4534128 -0.4054224 -0.0312781 0.25408988 165s 73 -2.0038405 -2.874605 -0.6269939 0.2408421 0.5184666 0.11140104 165s 74 -11.2683996 -0.361851 3.9219448 0.4045689 -0.2203308 0.05930132 165s 75 -0.1028287 -2.295813 -0.7769187 0.3071821 0.4537196 0.00522380 165s 76 -1.8466137 -0.425825 -1.1261209 -0.1760585 0.0165729 -0.10698465 165s 77 -8.4124493 -1.174820 2.2700712 0.4213953 0.3446597 -0.20636892 165s 78 1.1103236 -1.299480 -0.5787732 -0.1455945 0.0732148 -0.01806218 165s 79 -0.5451834 -0.620170 -0.7830595 -0.1746479 0.0723052 -0.26017118 165s 80 -3.8647223 1.126328 1.3299567 0.2645241 -0.1881443 0.00485531 165s 81 0.7690939 0.887363 0.0513096 -0.2730980 0.0076447 -0.07590882 165s 82 2.7287618 -1.435327 0.1602865 0.4465859 0.2129425 0.16104418 165s 83 2.2241485 -0.042822 -0.8316486 -0.1230697 -0.1193057 -0.35207561 165s 84 0.2452905 0.491732 -2.0050683 0.0286567 -0.1159415 -0.24887542 165s 85 1.0655845 -2.360746 2.2456131 -0.1479972 -0.1186670 -0.14020891 165s 86 -0.0091659 0.952208 -1.3429189 -0.2944676 -0.2433277 0.15354490 165s PC7 PC8 165s 1 -0.09778744 2.3157e-03 165s 2 0.05189698 1.8077e-03 165s 3 0.70506895 1.2838e-03 165s 4 -0.08541140 3.2781e-04 165s 5 0.11768945 8.3496e-04 165s 6 -0.17886391 1.5222e-03 165s 7 0.14143613 1.3261e-04 165s 8 -0.07724578 7.1241e-04 165s 9 -0.12298048 -7.0110e-04 165s 10 0.07569878 2.3093e-05 165s 11 0.29299858 -3.4542e-04 165s 12 0.07764899 -2.1390e-03 165s 13 -0.08945524 -2.2633e-03 165s 14 0.03597787 -1.8891e-03 165s 15 0.11780498 -2.0279e-03 165s 16 0.46501534 -2.3266e-03 165s 17 0.08603290 -2.4073e-03 165s 18 0.52605757 -9.8822e-04 165s 19 0.31007227 -1.3919e-03 165s 20 0.61582059 -2.3549e-05 165s 21 0.01199350 -6.1649e-05 165s 22 0.03654587 1.3302e-05 165s 23 0.27549986 -3.6759e-04 165s 24 -0.04155354 -2.9882e-04 165s 25 0.11473708 -7.9629e-04 165s 26 0.06673183 -8.3728e-04 165s 27 0.16937729 -9.5775e-04 165s 28 -0.41753592 -7.5544e-05 165s 29 -0.03693100 -2.2481e-04 165s 30 0.08461537 -1.3611e-04 165s 31 0.02476253 -1.4319e-04 165s 32 -0.09756048 -1.2234e-04 165s 33 0.06442434 -2.4915e-04 165s 34 -0.17828409 -9.5882e-05 165s 35 0.00881239 -7.1427e-05 165s 36 -0.01041003 -2.8489e-04 165s 37 0.15994729 -3.1472e-04 165s 38 -0.22386895 6.1384e-04 165s 39 0.03666242 2.8506e-04 165s 40 0.35883231 -8.3062e-05 165s 41 0.18521851 8.5509e-04 165s 42 0.00733985 -6.4477e-04 165s 43 0.35466617 3.2923e-04 165s 44 -0.74952524 -7.6869e-05 165s 45 0.09907237 7.9128e-04 165s 46 0.05119980 1.0606e-03 165s 47 -0.48571583 -9.3780e-04 165s 48 -0.27463442 -2.7037e-04 165s 49 0.06787536 -3.0554e-05 165s 50 0.08499400 3.1181e-04 165s 51 -0.09197457 1.1213e-04 165s 52 -0.24513244 3.9100e-04 165s 53 0.24012780 3.2068e-04 165s 54 0.07999888 3.5689e-04 165s 55 -0.09825475 6.6675e-04 165s 56 0.05133674 -7.2984e-05 165s 57 -0.10302363 -2.0693e-04 165s 58 -0.12323360 -1.6620e-04 165s 59 -0.05119989 -1.1016e-03 165s 60 0.00082131 -3.2951e-04 165s 61 0.08128272 -1.1550e-04 165s 62 -0.01789040 -1.1579e-04 165s 63 -0.07188070 -7.8367e-04 165s 64 -0.07188070 -7.8367e-04 165s 65 0.00917085 -2.6800e-05 165s 66 0.03121573 -5.3492e-05 165s 67 0.12202335 -3.0466e-04 165s 68 -0.04764366 -2.6126e-04 165s 69 0.13828337 -3.9331e-04 165s 70 0.10401069 4.2870e-03 165s 71 -0.14369640 3.7669e-05 165s 72 -0.10334451 -2.6456e-04 165s 73 0.17655402 1.0917e-04 165s 74 0.26779696 1.8685e-03 165s 75 -0.75016549 2.1079e-05 165s 76 0.01802016 7.7555e-04 165s 77 0.13081368 6.4286e-04 165s 78 0.01409131 4.9476e-04 165s 79 0.06643384 2.6590e-04 165s 80 -0.12624376 5.9801e-04 165s 81 -0.14074469 -3.2172e-04 165s 82 0.09228230 4.4064e-04 165s 83 -0.06352151 -3.6274e-04 165s 84 -0.02642452 -3.9742e-04 165s 85 -0.03502188 6.9814e-04 165s 86 -0.11749109 -5.1283e-04 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 2.39971451 1.49157920 0.93184037 0.33183258 0.19628996 0.16485446 0.12784351 165s [8] 0.00052622 165s ---------------------------------------------------------- 165s bushfire 38 5 5 11393.979994 197.523453 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 165s 1 -91.383 -16.17804 0.56195 -0.252428 1.261840 165s 2 -93.033 -13.93251 -0.67212 0.042287 0.470924 165s 3 -85.400 -10.72512 -3.09832 -1.224797 -0.504718 165s 4 -68.381 -12.12202 -3.31950 -0.676880 -0.228383 165s 5 -36.742 -21.04171 -1.98872 0.397655 -0.932613 165s 6 -12.095 -30.21719 0.59595 2.100702 0.384714 165s 7 -227.949 -71.40450 35.57308 -7.880296 -2.710415 165s 8 -262.815 -111.81228 -11.04574 2.397832 -13.646407 165s 9 -263.767 -114.13702 -13.71407 3.131736 -13.825200 165s 10 -264.312 -90.69643 9.72320 0.967173 -8.800150 165s 11 -266.681 -72.85993 16.55010 0.291092 -8.373583 165s 12 -274.050 -18.41395 20.74273 -2.464589 -1.505967 165s 13 -218.299 19.16040 7.69765 0.069012 0.054846 165s 14 29.646 10.52526 -7.50754 0.855493 1.966680 165s 15 159.575 3.86633 -6.95837 -2.753953 0.616068 165s 16 114.286 2.47164 0.62690 -3.146317 -0.501623 165s 17 111.289 3.45086 1.97182 -0.303064 -0.094416 165s 18 99.626 -1.80416 4.88197 -0.013096 -1.438397 165s 19 103.353 -3.50426 3.58993 1.578169 -1.317194 165s 20 113.769 0.84544 3.28254 2.204926 0.131167 165s 21 95.186 3.50703 4.97153 0.916181 0.351658 165s 22 86.996 4.00938 2.95209 1.281788 1.920404 165s 23 -44.232 8.50898 6.30689 -1.038871 0.400078 165s 24 -99.527 13.81377 1.75130 -0.260669 0.394804 165s 25 -34.855 5.99709 -0.57224 -1.660513 -0.620158 165s 26 -41.265 2.94659 -1.04825 -2.243950 -0.440017 165s 27 -56.148 10.14428 -5.41858 0.321752 -0.608412 165s 28 -32.366 20.27795 -8.60687 3.806572 -1.267249 165s 29 -22.438 34.73585 -11.19123 8.296154 -0.511610 165s 30 -79.035 37.05713 -1.51591 9.892959 -1.618635 165s 31 49.465 39.37414 5.95714 22.874813 -1.883481 165s 32 304.825 30.19205 37.68900 45.175923 -1.293939 165s 33 341.237 7.04985 65.43451 44.553009 -3.148116 165s 34 337.467 6.16879 66.48222 43.278480 -3.688631 165s 35 342.929 7.38548 66.91291 43.941556 -1.937887 165s 36 340.143 6.70203 67.85433 42.479161 -3.873639 165s 37 337.931 7.43184 70.50828 42.333220 -2.645830 165s 38 339.281 8.07267 71.34405 42.400459 -2.392774 165s ------------- 165s Call: 165s PcaCov(x = x) 165s 165s Standard deviations: 165s [1] 106.7426 14.0543 4.9184 1.8263 1.0193 165s ---------------------------------------------------------- 165s ========================================================== 165s > dodata(method="grid") 165s 165s Call: dodata(method = "grid") 165s Data Set n p k e1 e2 165s ========================================================== 165s heart 12 2 2 516.143549 23.932102 165s Scores: 165s PC1 PC2 165s [1,] 6.4694 3.8179 165s [2,] 61.7387 19.1814 165s [3,] 1.4722 -1.0161 165s [4,] -3.8056 1.5127 165s [5,] 18.6760 5.3303 165s [6,] -16.8411 1.7900 165s [7,] 4.9962 4.1638 165s [8,] -26.8665 -13.3010 165s [9,] -1.0648 -1.2690 165s [10,] -25.7734 -12.4037 165s [11,] -13.3987 -4.0751 165s [12,] 46.7700 15.1272 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 22.719 4.892 165s ---------------------------------------------------------- 165s starsCYG 47 2 2 0.473800 0.026486 165s Scores: 165s PC1 PC2 165s [1,] 0.181489 -0.0300854 165s [2,] 0.695337 0.1492475 165s [3,] -0.120738 -0.1338110 165s [4,] 0.695337 0.1492475 165s [5,] 0.140039 -0.0992368 165s [6,] 0.413314 0.0551030 165s [7,] -0.409428 -0.5478860 165s [8,] 0.225647 0.1690378 165s [9,] 0.519123 -0.1471454 165s [10,] 0.071513 -0.0277935 165s [11,] 0.663045 -0.9203119 165s [12,] 0.402691 0.0253179 165s [13,] 0.373739 0.0759321 165s [14,] -1.005756 -0.3654219 165s [15,] -0.789968 -0.0898580 165s [16,] -0.467328 0.0334465 165s [17,] -1.111148 -0.1431778 165s [18,] -0.867242 0.0417806 165s [19,] -0.871200 -0.1481782 165s [20,] 0.823011 -0.9236455 165s [21,] -0.669994 -0.0923582 165s [22,] -0.829959 -0.0890246 165s [23,] -0.627294 0.0367802 165s [24,] -0.195929 0.0978059 165s [25,] -0.028257 -0.0157122 165s [26,] -0.387346 0.0317797 165s [27,] -0.390054 -0.0981920 165s [28,] -0.148231 -0.0132120 165s [29,] -0.661454 -0.1625514 165s [30,] 0.982767 -0.9369769 165s [31,] -0.628127 -0.0032112 165s [32,] 0.055476 0.1625819 165s [33,] 0.173158 0.0501056 165s [34,] 1.222924 -0.9319795 165s [35,] -0.711235 -0.1515118 165s [36,] 0.576613 0.2117347 165s [37,] 0.054851 0.1325884 165s [38,] 0.173158 0.0501056 165s [39,] 0.134833 0.1309216 165s [40,] 0.522665 0.0228177 165s [41,] -0.428171 -0.0073782 165s [42,] 0.013192 0.0534392 165s [43,] 0.294173 0.0975945 165s [44,] 0.293132 0.0476054 165s [45,] 0.495172 0.1434167 165s [46,] -0.066790 0.0551060 165s [47,] -0.547311 0.0351134 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 0.68833 0.16275 165s ---------------------------------------------------------- 165s phosphor 18 2 2 392.155327 50.657228 165s Scores: 165s PC1 PC2 165s 1 5.6537 -15.2305 165s 2 -21.2150 -1.8862 165s 3 -23.5966 2.3112 165s 4 -11.2742 -6.6000 165s 5 -18.4067 1.5202 165s 6 16.9795 -19.4039 165s 7 1.5964 -3.1666 165s 8 -9.7354 3.2429 165s 9 -10.8594 5.4759 165s 10 15.5585 -6.5279 165s 11 -4.0058 1.2905 165s 12 9.4815 8.2139 165s 13 13.0640 6.4346 165s 14 7.0230 7.7600 165s 15 18.4378 3.7658 165s 16 -8.9047 -6.3253 165s 17 21.8748 6.1900 165s 18 16.9843 12.0801 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 19.8029 7.1174 165s ---------------------------------------------------------- 165s stackloss 21 3 3 109.445054 16.741203 165s Scores: 165s PC1 PC2 PC3 165s [1,] 15.136434 14.82909 -2.0387704 165s [2,] 14.393636 15.46816 -1.8391595 165s [3,] 12.351209 10.12290 -2.3458098 165s [4,] 2.510036 2.07589 1.8251581 165s [5,] 1.767140 1.78527 -0.0088651 165s [6,] 2.138588 1.93058 0.9081465 165s [7,] 6.966825 -1.75851 0.6274924 165s [8,] 6.966825 -1.75851 0.6274924 165s [9,] -0.089513 -1.09062 2.2894224 165s [10,] -7.146340 2.65628 -0.8983590 165s [11,] -0.461157 -3.09532 -2.6948576 165s [12,] -1.575403 -2.60157 -3.4122582 165s [13,] -5.660744 1.37815 -1.2975809 165s [14,] 2.881484 -5.50628 -2.5762898 165s [15,] -4.917360 -9.13772 0.0676942 165s [16,] -7.145755 -7.22052 0.6665270 165s [17,] -17.173481 1.87173 4.3780920 165s [18,] -11.973894 -2.60174 2.9808153 165s [19,] -10.859648 -3.09549 3.6982160 165s [20,] -6.031899 0.15817 1.2270803 165s [21,] 8.451640 4.98077 -5.4038839 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 10.4616 4.0916 2.8271 165s ---------------------------------------------------------- 165s salinity 28 3 3 14.911546 8.034974 165s Scores: 165s PC1 PC2 PC3 165s 1 -2.72400 0.79288 0.688038 165s 2 -3.45684 0.86162 1.941690 165s 3 -5.73471 -4.79507 0.129202 165s 4 -6.17045 -3.04372 -0.352797 165s 5 -4.72453 -5.59543 4.144851 165s 6 -5.75447 -1.07062 0.579975 165s 7 -4.40759 0.47731 0.680203 165s 8 -2.76360 2.30716 0.540271 165s 9 -0.28782 -1.40644 -2.373399 165s 10 2.64361 -1.43362 -0.266957 165s 11 1.91078 -1.66975 1.312215 165s 12 -0.40661 0.68573 -0.200135 165s 13 -0.14911 1.88993 0.044001 165s 14 1.99005 2.43874 1.373229 165s 15 2.88128 -2.21263 -0.863674 165s 16 -0.12935 -8.28831 6.483875 165s 17 -0.16895 -1.68742 0.905190 165s 18 -3.08054 0.23753 -0.269165 165s 19 -0.38685 -1.08501 -2.736860 165s 20 1.45520 -0.33209 -1.686406 165s 21 1.13834 2.53553 -0.381657 165s 22 2.48522 3.42927 0.417050 165s 23 4.56487 -3.36542 0.711908 165s 24 2.94072 -3.08490 1.556939 165s 25 0.82140 -0.26895 -0.406490 165s 26 1.17794 1.61119 -0.863764 165s 27 2.02965 2.80707 -0.489050 165s 28 2.98039 3.21462 0.747622 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 3.86155 2.83460 0.95394 165s ---------------------------------------------------------- 165s hbk 75 3 3 3.714805 3.187126 165s Scores: 165s PC1 PC2 PC3 165s 1 8.423138 24.765818 19.413334 165s 2 7.823138 25.295092 20.356662 165s 3 9.023138 27.411905 20.218454 165s 4 8.223138 28.010236 21.568269 165s 5 8.623138 27.442650 21.123471 165s 6 9.123138 25.601873 20.279943 165s 7 8.823138 25.463855 20.770811 165s 8 8.223138 25.264348 19.451646 165s 9 8.023138 27.373593 20.716984 165s 10 7.623138 26.752275 19.666288 165s 11 9.323138 31.108975 24.313778 165s 12 10.323138 33.179719 23.469966 165s 13 10.323138 29.958667 26.231274 165s 14 9.323138 29.345676 34.207755 165s 15 1.723138 -0.077538 0.754886 165s 16 1.423138 -1.818609 -0.080979 165s 17 -1.676862 -1.872341 -0.686878 165s 18 0.623138 -0.077633 -0.548955 165s 19 -0.876862 -0.576068 0.716574 165s 20 1.423138 -0.016144 1.261078 165s 21 0.923138 -0.223313 0.041619 165s 22 -1.276862 -0.299937 1.038679 165s 23 0.323138 -1.327742 0.057038 165s 24 -0.376862 -1.626860 0.034051 165s 25 -0.676862 -1.550331 -2.266849 165s 26 -0.776862 0.290637 1.184359 165s 27 1.623138 0.750760 0.417361 165s 28 0.123138 -0.016334 -1.346603 165s 29 -0.476862 -1.220468 -1.338846 165s 30 -0.476862 1.387213 -1.339036 165s 31 1.423138 -1.059368 -0.824991 165s 32 -1.176862 -1.833934 0.118433 165s 33 -0.176862 -0.691099 0.908323 165s 34 -1.276862 -1.251213 -2.243862 165s 35 1.423138 0.858128 0.325317 165s 36 -0.576862 0.574335 0.102918 165s 37 -1.576862 0.413330 0.892903 165s 38 -0.176862 -1.841691 -1.085702 165s 39 0.423138 -0.752683 -2.205550 165s 40 -1.176862 -0.905930 -0.211430 165s 41 1.723138 0.819721 -0.479993 165s 42 -1.376862 0.666284 -1.093554 165s 43 -1.576862 -1.304659 1.061761 165s 44 0.123138 1.203126 -1.553772 165s 45 0.223138 -1.358581 -2.151818 165s 46 0.123138 1.003714 -1.569097 165s 47 1.323138 -1.159169 -2.136494 165s 48 1.423138 0.919427 -0.472331 165s 49 1.423138 -0.246300 0.340737 165s 50 0.423138 0.727773 0.716479 165s 51 0.623138 -1.665267 -0.771259 165s 52 1.623138 -0.798657 -1.607314 165s 53 -1.376862 1.310494 -1.645816 165s 54 -0.576862 -1.879908 0.716669 165s 55 -1.176862 -1.235698 0.164407 165s 56 0.123138 -1.296997 0.962055 165s 57 0.123138 -1.304849 -1.545920 165s 58 0.723138 -0.714086 1.207441 165s 59 -0.076862 0.881115 0.026199 165s 60 -1.376862 1.226208 -0.549050 165s 61 -1.276862 0.781504 1.322377 165s 62 -0.776862 -1.657699 -2.174806 165s 63 -0.576862 -1.956627 0.409888 165s 64 1.123138 0.712448 0.915891 165s 65 0.323138 0.689271 -1.392672 165s 66 -1.476862 -1.289430 -0.441492 165s 67 -0.076862 -0.905930 -0.211430 165s 68 -1.576862 -0.852389 -2.213213 165s 69 0.323138 -1.696011 -1.676276 165s 70 -0.676862 0.773747 0.118243 165s 71 0.523138 0.152524 0.371386 165s 72 -1.076862 -0.606812 -0.188443 165s 73 -1.376862 0.114117 -0.433924 165s 74 -1.676862 -0.522431 0.018632 165s 75 -1.376862 0.612552 -1.699453 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 1.9274 1.7853 1.6714 165s ---------------------------------------------------------- 165s milk 86 8 8 9.206694 2.910585 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 PC6 165s [1,] 6.090978 0.590424 1.1644466 -0.3835606 1.0342867 -0.4752288 165s [2,] 6.903009 -0.575027 0.8613622 -1.1221795 0.7221616 -1.3097951 165s [3,] 0.622903 -1.594239 1.2122863 -0.0555128 0.3252629 -0.2799581 165s [4,] 5.282665 -1.815742 2.2543268 0.9824543 -0.5345577 -0.7331037 165s [5,] -1.039753 0.663906 0.3353811 0.3070599 -0.3224317 -0.4056666 165s [6,] 2.247786 0.218255 -0.3382923 0.1270005 -0.0271307 -0.2035021 165s [7,] 2.784293 -0.291678 -0.4897587 0.0198481 0.0752345 -0.5986846 165s [8,] 2.942266 0.315608 0.1603961 0.3568462 -0.0647311 -0.5316127 165s [9,] -1.420086 -1.751212 1.7027572 0.0708340 -0.9226517 0.0738411 165s [10,] -2.921113 -0.727554 0.0113966 -0.3915037 -0.0772913 0.6062573 165s [11,] -9.568075 0.792291 1.0217507 0.2554182 -0.6254883 0.8899897 165s [12,] -12.885166 3.423607 -1.2579351 -0.4300397 -0.4094558 1.1727128 165s [13,] -10.038470 1.274931 -2.6913262 -1.6219658 -0.3284974 1.1228303 165s [14,] -12.044003 2.096254 -1.2859668 -0.9602250 -0.7937418 0.8264019 165s [15,] -10.798341 1.159257 1.4870766 0.3248231 -1.0787537 0.8723637 165s [16,] -2.841629 0.500846 0.4771762 0.5975365 0.3197882 0.5804087 165s [17,] -1.150691 -1.978038 2.3229313 0.5275273 -0.5339514 0.5421631 165s [18,] -1.992369 1.131288 -0.8385615 0.1156462 0.2253010 -0.3393814 165s [19,] -1.999699 -0.252876 1.2229972 0.5081648 0.0082612 0.3373454 165s [20,] 0.091385 -1.439422 1.1836134 0.6297789 0.0961407 -0.2126653 165s [21,] -2.571346 2.280701 -1.2845660 0.1463583 0.0949331 0.0902039 165s [22,] -0.990078 1.087033 -0.1638640 -0.0351472 0.0743205 -0.0040605 165s [23,] -0.010631 1.704171 0.0038808 0.5765418 0.6086460 0.0329995 165s [24,] -0.440350 1.500798 0.2769870 0.5556999 0.4751445 0.6516120 165s [25,] -3.578249 2.672783 -0.3534268 0.7398104 0.1108289 0.2704730 165s [26,] -0.854914 1.626684 0.2301131 0.5530224 0.0662862 -0.0999969 165s [27,] -3.175381 0.762609 0.5101987 0.0849002 -0.2137237 0.2729808 165s [28,] 2.599844 3.370137 -0.5174736 0.7409946 0.6853156 0.2430943 165s [29,] 4.395534 0.823611 0.1610152 0.8184845 0.7665555 0.0779724 165s [30,] 0.843794 1.438263 -0.2366601 0.4600650 0.3424806 -0.1768083 165s [31,] 1.890815 1.266935 -1.8218143 -0.3909337 0.8390127 0.1026821 165s [32,] 1.300145 -0.085976 -0.8965312 -0.8855787 0.4156780 0.1478055 165s [33,] 1.923087 0.137638 0.3487435 0.2958367 0.4245932 0.1566678 165s [34,] 0.615762 -0.390711 0.8107376 0.0295536 -0.1169590 0.2940241 165s [35,] -0.372946 2.037079 -0.7663299 0.1907237 0.6959350 0.5366205 165s [36,] 4.068134 1.129044 0.5492962 0.7640964 0.4799859 -0.4080205 165s [37,] 0.937617 2.048258 -1.2326566 -0.0942856 0.7885267 -0.1004018 165s [38,] 2.141223 1.877022 -0.5178216 0.3750868 0.4767003 0.1240656 165s [39,] -1.403505 1.327163 0.3165610 0.3989824 0.3505825 0.5915956 165s [40,] 3.337528 -1.689495 1.4737175 0.2584843 0.4308444 -0.0810597 165s [41,] 3.938506 1.384908 0.8103687 -0.5875595 1.1616535 -0.6492603 165s [42,] 6.327471 -1.061362 1.9861187 1.1016484 0.3512405 -0.1540592 165s [43,] 3.120160 -0.064108 -0.8370717 -0.2229341 0.5623447 -0.7152184 165s [44,] 5.290520 -0.669008 0.8597130 0.5518503 0.2470856 0.6454703 165s [45,] 0.058291 0.356399 -0.1896007 0.2427518 0.3705541 0.3975085 165s [46,] 0.150881 1.942057 -0.1140726 0.5656469 0.5227623 0.2151825 165s [47,] 2.870881 -1.446283 -2.8450062 -1.7292144 -0.0888429 -0.1347003 165s [48,] 0.335593 0.500884 -1.3154520 -0.3874864 0.3449038 0.5387692 165s [49,] -2.179494 -0.021237 -1.7792344 -0.8445930 0.4435338 0.6547961 165s [50,] 2.968304 -2.588546 1.8552104 0.4590101 -0.1755089 -0.0550378 165s [51,] -1.399208 -0.820296 -1.3660014 -0.8890243 -0.2344105 0.1236943 165s [52,] -5.112989 0.318983 -1.3852993 -0.8461529 -0.3467685 0.7349666 165s [53,] -0.773103 -0.267333 -0.8154896 -0.3783062 0.0113880 -0.3304648 165s [54,] -0.244565 -0.066211 -0.2541557 0.0043037 0.0390890 0.0074067 165s [55,] 0.894921 0.516411 -0.4443369 0.0708354 -0.0637890 -0.2799646 165s [56,] -0.038706 -0.588256 0.3166588 -0.0196663 -0.1793472 -0.1179341 165s [57,] -1.377469 0.428939 0.7502430 0.1458375 -0.3818977 -0.0380258 165s [58,] 0.042787 1.488605 0.0252606 0.6377516 -0.1524172 -0.1898723 165s [59,] -1.734357 -0.966494 -0.1026850 -0.5656888 -0.4831402 0.0308069 165s [60,] -1.501991 -0.544918 -0.0837127 -0.2362486 -0.5382026 -0.1351338 165s [61,] -0.175102 -1.339436 0.8403933 -0.0907428 -0.4846145 -0.2795153 165s [62,] 2.100915 -2.004702 1.3031556 -0.0041957 -0.2067776 -0.0793613 165s [63,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 165s [64,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 165s [65,] -0.665219 -2.325594 1.6287363 0.0607163 -0.6996720 0.1353325 165s [66,] -2.439244 -0.737375 0.0187770 -0.4561269 -0.5425315 -0.0208332 165s [67,] 0.121564 -1.214385 0.4877707 0.1809998 -0.1943262 0.0662506 165s [68,] -0.804267 -2.238327 -0.8547917 -1.3449926 -0.3577254 -0.0293779 165s [69,] -0.761319 -0.676391 -0.0245494 0.2262894 -0.3396872 -0.1166505 165s [70,] 3.385399 4.360467 -0.7946150 -0.0417895 0.4474362 -4.6626174 165s [71,] -2.364955 -1.257673 0.5226907 -0.2346145 -0.7838777 0.1815821 165s [72,] 2.334511 -0.794530 0.0175620 0.1848925 -0.3437761 -0.4522442 165s [73,] -2.023440 -2.449907 0.2525041 -0.6657474 -0.5509480 0.2118442 165s [74,] -11.180192 2.456516 1.1036540 0.8711496 -0.3833194 1.3548314 165s [75,] 0.058297 -2.094811 0.3075211 -0.8052760 -0.9527729 0.5850255 165s [76,] -1.355742 -0.464355 -1.0183333 -0.8525619 -0.1577144 -0.0767323 165s [77,] -8.296881 0.945092 0.8088967 -0.0071463 -0.4527530 1.0614233 165s [78,] 1.251696 -1.460466 0.2511701 -0.2717606 -0.3158308 -0.2964813 165s [79,] -0.192380 -0.662365 -0.3671703 -0.6722658 -0.1243452 -0.2388225 165s [80,] -3.355201 1.915096 -0.1086672 0.3560062 0.0956865 0.6974817 165s [81,] 1.245305 0.736787 -0.1662155 0.1309822 -0.0122872 -0.2182528 165s [82,] 2.679561 -1.666401 1.1576691 0.3960280 -0.0059146 0.0584136 165s [83,] 2.596651 -0.556654 -0.0807307 -0.4468501 0.0964927 -0.3922894 165s [84,] 0.959377 -0.272038 -1.5879803 -1.1153057 0.3412508 -0.1281556 165s [85,] 0.602737 -1.384591 2.8844745 0.9479144 -0.7946454 -0.2014038 165s [86,] 0.698125 0.335743 -1.5248055 -0.4443037 0.0768256 -0.1999790 165s PC7 PC8 165s [1,] 0.9281777 -0.05158594 165s [2,] 0.8397946 -0.04276628 165s [3,] -0.5189230 0.04913688 165s [4,] -0.0178377 0.01578074 165s [5,] -0.0129237 0.01056305 165s [6,] -0.0764270 0.01469518 165s [7,] -0.3059779 0.04237267 165s [8,] -0.0684673 0.02289928 165s [9,] -0.2549733 -0.00832119 165s [10,] -0.0578118 -0.01894694 165s [11,] 0.0415545 -0.03474479 165s [12,] 0.0869267 -0.04485633 165s [13,] -0.2843977 -0.03100709 165s [14,] -0.3375083 -0.02155574 165s [15,] -0.1718828 -0.02996980 165s [16,] -0.4176728 0.03232381 165s [17,] -0.5923252 0.01765700 165s [18,] -0.3190679 0.04476532 165s [19,] -0.0279426 -0.00236626 165s [20,] 0.1299811 0.00586022 165s [21,] 0.0474059 0.00563264 165s [22,] -0.1240299 0.01123557 165s [23,] 0.2232631 0.00551065 165s [24,] 0.0122404 0.00060079 165s [25,] 0.2627442 -0.00824800 165s [26,] 0.2257329 -0.00440907 165s [27,] -0.8496967 0.05266701 165s [28,] 0.3473502 -0.00500580 165s [29,] 0.4172329 -0.00542705 165s [30,] 0.2773880 -0.00014648 165s [31,] -0.1224270 0.02372808 165s [32,] -0.2224748 0.00757892 165s [33,] -0.0633903 0.01236118 165s [34,] -0.2616599 0.00561781 165s [35,] -0.1671986 0.01988458 165s [36,] 0.4502086 -0.00418541 165s [37,] -0.0773232 0.02768282 165s [38,] 0.0464683 0.01134849 165s [39,] -0.0927182 0.00555823 165s [40,] -0.2162796 0.02467605 165s [41,] 0.9440753 -0.04806541 165s [42,] -0.0078920 0.02022925 165s [43,] 0.1152244 0.02074199 165s [44,] 1.0406693 -0.08815111 165s [45,] -0.1376804 0.01424369 165s [46,] 0.1673461 0.00442877 165s [47,] -0.4125225 0.01038694 165s [48,] 0.1556289 -0.02103354 165s [49,] 0.0434415 -0.01782739 165s [50,] 0.2518610 -0.02154540 165s [51,] -0.1186185 -0.00881133 165s [52,] 0.1507435 -0.04523343 165s [53,] 0.2161208 -0.00967982 165s [54,] 0.1374909 -0.00783970 165s [55,] 0.2417108 -0.00895268 165s [56,] 0.1253846 -0.01188643 165s [57,] 0.1390898 -0.01831232 165s [58,] 0.2219634 -0.00364174 165s [59,] -0.2045636 -0.00589047 165s [60,] -0.3679942 0.01673699 165s [61,] -0.0705611 -0.00273407 165s [62,] 0.1447701 -0.02026768 165s [63,] -0.1854788 0.02686899 165s [64,] -0.1854788 0.02686899 165s [65,] -0.2626650 -0.00376657 165s [66,] -0.3044266 0.00484197 165s [67,] -0.1358811 0.00605789 165s [68,] -0.0551482 -0.02379410 165s [69,] -0.0914891 0.00812122 165s [70,] 10.2524854 -0.64367029 165s [71,] -0.1326972 -0.01666774 165s [72,] 0.0051905 0.00656777 165s [73,] -0.8236843 0.03367265 165s [74,] 0.2140104 -0.04092219 165s [75,] -0.5684260 -0.00987116 165s [76,] -0.1225779 -0.00204629 165s [77,] -0.4235612 -0.00450631 165s [78,] -0.1935155 0.00973901 165s [79,] -0.1615883 0.00518643 165s [80,] 0.2915052 -0.02960159 165s [81,] 0.0908823 0.00038216 165s [82,] -0.3392789 0.02605374 165s [83,] 0.1112141 -0.00629308 165s [84,] 0.0510771 -0.00845572 165s [85,] 0.0748700 -0.01174487 165s [86,] 0.2488127 -0.01446339 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 3.034253 1.706044 1.167717 0.670864 0.536071 0.396285 0.266625 0.020768 165s ---------------------------------------------------------- 165s bushfire 38 5 5 38232.614428 1580.825276 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 165s [1,] -67.120 -23.70481 -1.06551 1.129721 1.311630 165s [2,] -69.058 -21.42113 -1.54798 0.983735 0.430774 165s [3,] -61.939 -17.23665 -3.81386 -0.635074 -0.600149 165s [4,] -44.952 -16.53458 -5.16114 0.411753 -0.390518 165s [5,] -12.644 -21.62271 -7.14146 3.519877 -1.211923 165s [6,] 12.820 -27.86930 -7.66114 7.230422 0.040330 165s [7,] -194.634 -100.67730 27.43084 -0.026242 -0.134248 165s [8,] -229.349 -129.75912 -19.46346 25.591651 -18.592601 165s [9,] -230.306 -131.28743 -22.22175 27.251157 -19.214683 165s [10,] -231.118 -115.10815 3.70208 16.303210 -10.573515 165s [11,] -234.540 -100.24984 13.67112 10.325539 -8.727961 165s [12,] -246.507 -51.03515 27.61698 -5.352226 0.514087 165s [13,] -195.712 -5.81324 20.04485 -9.226807 1.721886 165s [14,] 49.881 16.90911 -9.97400 -1.900739 2.190429 165s [15,] 179.545 23.96999 -18.71166 -2.987136 1.332713 165s [16,] 135.356 15.81282 -9.24353 -4.703584 0.971669 165s [17,] 132.350 16.65014 -7.01838 -2.428578 1.346198 165s [18,] 121.499 9.75832 -4.45699 -1.587450 0.131923 165s [19,] 125.222 9.17601 -5.88919 0.582516 -0.061642 165s [20,] 135.112 14.63812 -5.90351 0.411704 1.460488 165s [21,] 116.581 14.47390 -3.04021 -1.842579 2.005998 165s [22,] 108.223 14.62103 -4.47428 -1.196993 3.288463 165s [23,] -22.095 3.26439 6.58391 -6.164581 2.125258 165s [24,] -77.831 3.46616 6.59280 -6.373595 1.545789 165s [25,] -13.092 3.41344 -0.99296 -5.076733 0.299636 165s [26,] -19.206 -0.17007 -1.84209 -4.858675 0.347945 165s [27,] -35.022 6.54155 -3.12767 -3.556587 -0.327873 165s [28,] -12.651 20.14894 -4.61607 -2.025539 -1.214190 165s [29,] -4.404 36.39823 -3.81590 -0.633155 -0.602027 165s [30,] -60.018 30.40980 9.44610 -1.763156 -0.765133 165s [31,] 67.689 47.40087 12.70229 9.791794 -0.671751 165s [32,] 324.134 63.46147 31.52512 30.099817 2.406344 165s [33,] 364.639 38.84260 51.20467 30.648590 3.218678 165s [34,] 361.089 37.09494 52.00522 29.394356 2.861158 165s [35,] 366.403 38.88889 52.31879 29.878844 4.650618 165s [36,] 363.821 37.40859 53.10394 28.286557 2.922632 165s [37,] 361.761 37.21276 55.73012 27.648760 4.477279 165s [38,] 363.106 37.78395 56.56345 27.460078 4.845396 165s ------------- 165s Call: 165s PcaGrid(x = x) 165s 165s Standard deviations: 165s [1] 195.5316 39.7596 11.7329 7.3743 1.7656 165s ---------------------------------------------------------- 165s ========================================================== 165s > 165s > ## IGNORE_RDIFF_BEGIN 165s > dodata(method="proj") 165s 165s Call: dodata(method = "proj") 165s Data Set n p k e1 e2 165s ========================================================== 165s heart 12 2 2 512.772467 29.052346 165s Scores: 165s PC1 PC2 165s [1,] 6.7568 3.2826 165s [2,] 63.0869 14.1293 165s [3,] 1.3852 -1.1318 165s [4,] -3.6709 1.8153 165s [5,] 19.0457 3.8035 165s [6,] -16.6413 3.1452 165s [7,] 5.3163 3.7464 165s [8,] -27.8536 -11.0863 165s [9,] -1.1638 -1.1788 165s [10,] -26.6915 -10.2803 165s [11,] -13.6842 -2.9790 165s [12,] 47.8395 11.2980 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 22.644 5.390 165s ---------------------------------------------------------- 165s starsCYG 47 2 2 0.470874 0.024681 165s Scores: 165s PC1 PC2 165s [1,] 0.181333 -3.1013e-02 165s [2,] 0.696091 1.4569e-01 165s [3,] -0.121421 -1.3319e-01 165s [4,] 0.696091 1.4569e-01 165s [5,] 0.139530 -9.9951e-02 165s [6,] 0.413590 5.2989e-02 165s [7,] -0.412224 -5.4579e-01 165s [8,] 0.226508 1.6788e-01 165s [9,] 0.518364 -1.4980e-01 165s [10,] 0.071370 -2.8159e-02 165s [11,] 0.658332 -9.2369e-01 165s [12,] 0.402815 2.3259e-02 165s [13,] 0.374123 7.4020e-02 165s [14,] -1.007611 -3.6028e-01 165s [15,] -0.790417 -8.5818e-02 165s [16,] -0.467151 3.5835e-02 165s [17,] -1.111866 -1.3750e-01 165s [18,] -0.867017 4.6214e-02 165s [19,] -0.871946 -1.4372e-01 165s [20,] 0.818278 -9.2784e-01 165s [21,] -0.670457 -8.8932e-02 165s [22,] -0.830403 -8.4781e-02 165s [23,] -0.627097 3.9987e-02 165s [24,] -0.195426 9.8806e-02 165s [25,] -0.028337 -1.5568e-02 165s [26,] -0.387178 3.3760e-02 165s [27,] -0.390551 -9.6197e-02 165s [28,] -0.148297 -1.2454e-02 165s [29,] -0.662277 -1.5917e-01 165s [30,] 0.977965 -9.4199e-01 165s [31,] -0.628135 -7.2164e-16 165s [32,] 0.056306 1.6230e-01 165s [33,] 0.173412 4.9220e-02 165s [34,] 1.218143 -9.3822e-01 165s [35,] -0.712000 -1.4787e-01 165s [36,] 0.577688 2.0878e-01 165s [37,] 0.055528 1.3231e-01 165s [38,] 0.173412 4.9220e-02 165s [39,] 0.135501 1.3023e-01 165s [40,] 0.522775 2.0145e-02 165s [41,] -0.428203 -5.1892e-03 165s [42,] 0.013465 5.3371e-02 165s [43,] 0.294668 9.6089e-02 165s [44,] 0.293371 4.6106e-02 165s [45,] 0.495898 1.4088e-01 165s [46,] -0.066508 5.5447e-02 165s [47,] -0.547124 3.7911e-02 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 0.6862 0.1571 165s ---------------------------------------------------------- 165s phosphor 18 2 2 388.639033 51.954664 165s Scores: 165s PC1 PC2 165s 1 5.8164 -15.1691 165s 2 -21.1936 -2.1132 165s 3 -23.6199 2.0585 165s 4 -11.2029 -6.7203 165s 5 -18.4220 1.3231 165s 6 17.1862 -19.2211 165s 7 1.6302 -3.1493 165s 8 -9.7695 3.1385 165s 9 -10.9174 5.3594 165s 10 15.6275 -6.3610 165s 11 -4.0194 1.2476 165s 12 9.3931 8.3149 165s 13 12.9944 6.5741 165s 14 6.9396 7.8348 165s 15 18.3964 3.9629 165s 16 -8.8365 -6.4202 165s 17 21.8073 6.4237 165s 18 16.8541 12.2611 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 19.714 7.208 165s ---------------------------------------------------------- 165s stackloss 21 3 3 97.347030 38.052774 165s Scores: 165s PC1 PC2 PC3 165s [1,] 19.08066 -9.06092 -2.64544 165s [2,] 18.55152 -9.90152 -2.76118 165s [3,] 15.04269 -5.37517 -2.31373 165s [4,] 2.79667 -1.78925 1.70823 165s [5,] 2.21768 -1.17513 -0.10495 165s [6,] 2.50717 -1.48219 0.80164 165s [7,] 5.97151 3.25438 2.40268 165s [8,] 5.97151 3.25438 2.40268 165s [9,] -0.68332 0.30263 2.42495 165s [10,] -5.83478 -4.04630 -2.91819 165s [11,] -1.07253 3.51914 -1.87651 165s [12,] -1.89116 2.98559 -2.89885 165s [13,] -4.77650 -2.36509 -2.68671 165s [14,] 1.33353 6.57450 -0.50696 165s [15,] -7.45351 7.08878 1.37012 165s [16,] -9.04093 4.56697 1.02289 165s [17,] -16.15938 -7.50855 0.30909 165s [18,] -12.45541 -1.62432 1.11929 165s [19,] -11.63677 -1.09077 2.14162 165s [20,] -5.79275 -2.08680 -0.06187 165s [21,] 10.13623 -0.76824 -4.70180 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 9.8665 6.1687 3.2669 165s ---------------------------------------------------------- 165s salinity 28 3 3 12.120566 8.431549 165s Scores: 165s PC1 PC2 PC3 165s 1 -2.52547 1.45945 -1.1943e-01 165s 2 -3.32298 2.15704 8.7594e-01 165s 3 -6.64947 -3.26398 1.0135e+00 165s 4 -6.64427 -1.81382 -1.6392e-01 165s 5 -6.16898 -2.52222 5.1373e+00 165s 6 -5.87594 0.26440 -2.4425e-15 165s 7 -4.23084 1.46250 -2.8008e-01 165s 8 -2.21502 2.76478 -8.3789e-01 165s 9 -0.40186 -2.17785 -1.6702e+00 165s 10 2.27089 -1.84923 7.3391e-01 165s 11 1.37935 -1.29276 2.1418e+00 165s 12 -0.22635 0.60372 -5.0980e-01 165s 13 0.27224 1.73920 -7.0505e-01 165s 14 2.36592 2.40462 6.4320e-01 165s 15 2.37640 -2.83174 5.2669e-01 165s 16 -2.49175 -4.77664 9.0404e+00 165s 17 -0.61250 -1.11672 1.4398e+00 165s 18 -2.91853 0.63310 -8.3666e-01 165s 19 -0.39732 -2.02029 -2.1396e+00 165s 20 1.47554 -1.23407 -1.1712e+00 165s 21 1.70104 1.92401 -1.1292e+00 165s 22 3.14437 2.81928 -5.2415e-01 165s 23 3.62890 -3.51450 2.6740e+00 165s 24 2.04538 -2.63992 3.0718e+00 165s 25 0.77088 -0.54783 -1.3370e-01 165s 26 1.57254 0.89176 -1.2089e+00 165s 27 2.63610 1.97075 -1.1855e+00 165s 28 3.55112 2.67606 -6.0915e-02 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 3.4815 2.9037 1.3810 165s ---------------------------------------------------------- 165s hbk 75 3 3 3.801978 3.574192 165s Scores: 165s PC1 PC2 PC3 165s 1 28.747049 15.134042 2.3959241 165s 2 29.021724 16.318941 2.6207988 165s 3 31.271908 15.869319 3.4420860 165s 4 31.586189 17.508798 3.6246706 165s 5 31.299168 16.838093 3.2402573 165s 6 30.037754 15.591930 2.1421166 165s 7 29.888160 16.139376 1.9750096 165s 8 28.994463 15.350167 2.8226275 165s 9 30.758047 16.820526 3.7269602 165s 10 29.759314 16.079531 4.0486097 165s 11 35.301371 19.637962 3.7433562 165s 12 37.193371 18.709303 4.9915250 165s 13 35.634808 20.497713 1.4740727 165s 14 36.816439 27.523024 -2.3006796 165s 15 1.237203 -0.331072 -1.3801401 165s 16 -0.451166 -1.118847 -1.9707479 165s 17 -2.604733 0.067276 0.0130015 165s 18 0.179177 -0.804398 -0.1285240 165s 19 -0.765512 0.982349 -0.2513990 165s 20 1.236727 0.259123 -1.4210070 165s 21 0.428326 -0.503724 -0.6830690 165s 22 -0.724774 1.507943 -0.0022175 165s 23 -0.745349 -0.330094 -1.0982084 165s 24 -1.407850 -0.011831 -0.8987075 165s 25 -2.190427 -1.732051 0.4497793 165s 26 0.058631 1.444044 0.0446166 165s 27 1.680557 -0.429402 -0.6031146 165s 28 -0.315122 -1.179169 0.5822607 165s 29 -1.563355 -1.026914 0.1040012 165s 30 0.329957 -0.633156 1.8533795 165s 31 -0.110108 -1.617131 -1.0958807 165s 32 -2.035875 0.463421 -0.6346632 165s 33 -0.356033 0.740564 -0.8116369 165s 34 -2.342887 -1.340168 0.9724491 165s 35 1.607131 -0.379763 -0.3747630 165s 36 0.084455 0.486671 0.6551654 165s 37 -0.436144 1.659467 0.7145344 165s 38 -1.754819 -1.076076 -0.6037590 165s 39 -0.904375 -2.161949 0.3436723 165s 40 -1.455274 0.331839 0.1499308 165s 41 1.539788 -1.212921 -0.1715110 165s 42 -0.688338 -0.048173 1.7491184 165s 43 -1.635822 1.539067 -0.5208916 165s 44 0.511762 -1.165641 1.5020865 165s 45 -1.454500 -2.099954 0.0219268 165s 46 0.362645 -1.208389 1.3758464 165s 47 -0.615800 -2.658098 -0.4629006 165s 48 1.426278 -1.027667 0.0582638 165s 49 0.809592 -0.533893 -1.1232120 165s 50 0.996105 0.469082 -0.0988805 165s 51 -1.036368 -1.227376 -1.0843166 165s 52 -0.016464 -2.331540 -0.6477169 165s 53 -0.376625 -0.405855 2.4526088 165s 54 -1.524100 0.621590 -1.2927429 165s 55 -1.588523 0.591668 -0.2559428 165s 56 -0.592710 0.529426 -1.4111404 165s 57 -1.306991 -1.538024 -0.1841717 165s 58 0.275991 0.491888 -1.4739863 165s 59 0.598971 0.196673 0.6208960 165s 60 -0.127953 0.485014 1.8571970 165s 61 0.140584 1.905037 0.5838465 165s 62 -2.305069 -1.617811 0.3880825 165s 63 -1.666479 0.357251 -1.1934779 165s 64 1.480143 0.248671 -0.5959984 165s 65 0.309561 -1.219790 0.9671263 165s 66 -1.986789 0.248245 0.1723620 165s 67 -0.765691 -0.269054 -0.4611368 165s 68 -2.232721 -1.090790 1.3915841 165s 69 -1.502453 -1.813763 -0.4936268 165s 70 0.170883 0.584046 0.8369571 165s 71 0.543623 0.043244 -0.3707674 165s 72 -1.168908 0.341335 0.2837393 165s 73 -0.902885 0.411872 1.0546196 165s 74 -1.425273 0.852445 0.5719123 165s 75 -0.898536 -0.555475 2.0107684 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 1.9499 1.8906 1.2797 165s ---------------------------------------------------------- 165s milk 86 8 8 8.369408 3.530461 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 PC6 165s [1,] 6.337004 -0.245000 0.7704092 -4.9848e-01 -1.6599e-01 1.1763e-01 165s [2,] 7.021899 1.030349 0.2832977 -1.2673e+00 -8.7296e-01 2.0547e-01 165s [3,] 0.600831 1.686247 0.9682032 -3.2663e-02 7.4112e-02 4.7412e-01 165s [4,] 5.206465 2.665956 1.5942253 9.8285e-01 -5.4159e-01 -2.0155e-01 165s [5,] -0.955757 -0.579889 0.3206393 5.1174e-01 -6.1684e-01 -3.8990e-02 165s [6,] 2.198695 0.073770 -0.5712493 1.9440e-01 -1.0237e-01 4.1825e-02 165s [7,] 2.695361 0.644049 -0.8645373 8.1894e-02 -2.6953e-01 1.6884e-01 165s [8,] 2.945361 0.137227 -0.2071463 5.0841e-01 -4.2075e-01 5.8589e-02 165s [9,] -1.539013 1.879894 1.6952390 1.6792e-01 -2.8195e-01 5.0563e-02 165s [10,] -2.977110 0.319666 0.3515636 -5.2496e-01 4.6898e-01 8.5978e-03 165s [11,] -9.375355 -1.638105 1.9026171 4.1237e-01 1.8768e-02 -1.8546e-01 165s [12,] -12.602600 -4.715888 0.0273004 -4.7798e-02 -1.2246e-02 9.6858e-03 165s [13,] -10.114331 -2.487462 -1.6331544 -1.5139e+00 4.1903e-01 2.8313e-01 165s [14,] -11.949336 -3.190157 -0.2146943 -5.0060e-01 -2.9537e-01 3.2160e-01 165s [15,] -10.595396 -1.905517 2.3716887 7.6651e-01 -3.3531e-01 1.9933e-02 165s [16,] -2.735720 -0.748282 0.6750464 7.2415e-01 5.5304e-01 2.2283e-01 165s [17,] -1.248116 2.131195 2.2596886 6.4958e-01 3.5634e-01 2.9021e-01 165s [18,] -1.904210 -1.285804 -0.7746460 3.0198e-01 -2.7407e-01 1.7500e-01 165s [19,] -1.902313 0.095461 1.3824711 5.0369e-01 2.2193e-01 -5.5628e-02 165s [20,] 0.123220 1.399444 1.1517634 3.2546e-01 7.8261e-02 -4.0733e-01 165s [21,] -2.436023 -2.524827 -1.0197416 3.4819e-01 -1.4914e-01 -4.3669e-02 165s [22,] -0.904931 -1.114894 -0.1235807 2.0285e-01 -1.6200e-01 2.5681e-01 165s [23,] 0.220231 -1.767325 0.0482262 6.4418e-01 9.8618e-02 -5.7683e-02 165s [24,] -0.274403 -1.561826 0.3820323 7.0016e-01 5.5220e-01 1.4376e-01 165s [25,] -3.306400 -2.980247 0.0252488 9.4001e-01 -1.0841e-01 -2.5303e-01 165s [26,] -0.658015 -1.625199 0.3021005 7.2702e-01 -3.0299e-01 -1.2339e-01 165s [27,] -3.137066 -0.774218 0.5577497 6.4188e-01 -8.0125e-02 7.7819e-01 165s [28,] 2.867950 -3.099435 -0.6435415 1.0366e+00 1.5908e-01 7.6524e-02 165s [29,] 4.523097 -0.527338 -0.1032516 6.4537e-01 4.7286e-01 -2.7166e-01 165s [30,] 1.002381 -1.376693 -0.2735956 5.0522e-01 -1.2750e-01 -1.6178e-01 165s [31,] 1.894615 -1.296202 -1.9117282 -3.8032e-01 4.6473e-01 3.1085e-01 165s [32,] 1.210291 0.067230 -0.9832930 -8.5379e-01 3.2823e-01 4.9994e-01 165s [33,] 1.964118 0.022175 0.1818518 3.0464e-01 3.5596e-01 1.4985e-01 165s [34,] 0.576738 0.567851 0.6982155 1.8415e-01 1.8695e-01 3.2706e-01 165s [35,] -0.231793 -2.143909 -0.6825523 4.0681e-01 5.4492e-01 3.6259e-01 165s [36,] 4.250883 -0.719760 0.2157706 7.7167e-01 -1.9064e-01 -2.0611e-01 165s [37,] 1.077364 -2.054664 -1.3064867 1.0043e-01 8.6092e-02 3.5416e-01 165s [38,] 2.259260 -1.653588 -0.6730692 5.7300e-01 1.6930e-01 1.6986e-01 165s [39,] -1.251576 -1.451593 0.4671580 5.8957e-01 4.2672e-01 2.2495e-01 165s [40,] 3.304245 1.998193 1.0941231 1.3734e-01 3.7012e-01 2.4142e-01 165s [41,] 4.286315 -1.280951 0.5856744 -6.0980e-01 -4.3090e-01 1.9801e-01 165s [42,] 6.343820 1.801880 1.3481119 1.0355e+00 2.9802e-01 -8.4501e-04 165s [43,] 3.119491 0.214077 -1.1216236 -3.8134e-01 -1.9523e-01 -2.6706e-02 165s [44,] 5.285254 0.938072 0.7440487 1.1539e-02 8.1629e-01 -7.9286e-01 165s [45,] 0.082429 -0.416631 -0.1588203 2.3098e-01 5.1867e-01 9.4503e-02 165s [46,] 0.357862 -1.951997 -0.0731829 7.0393e-01 1.8828e-01 1.5707e-02 165s [47,] 2.428744 1.522538 -3.0467213 -1.9114e+00 2.4638e-01 3.5871e-01 165s [48,] 0.282348 -0.697287 -1.1592508 -5.4929e-01 6.2199e-01 -5.4596e-02 165s [49,] -2.266009 -0.559548 -1.3794914 -1.1300e+00 7.8872e-01 -2.0411e-02 165s [50,] 2.868649 2.860857 1.6128307 6.7382e-02 2.2344e-01 -4.1484e-01 165s [51,] -1.596061 0.546812 -1.1779327 -1.0512e+00 1.3522e-01 -9.4865e-03 165s [52,] -5.186121 -1.000829 -0.7440599 -9.6302e-01 3.0732e-01 -1.7009e-01 165s [53,] -0.800232 0.049087 -0.6946842 -5.8284e-01 -2.1277e-01 -2.7004e-01 165s [54,] -0.246388 -0.030606 -0.1814302 -1.1632e-01 5.7767e-02 -1.8637e-01 165s [55,] 0.914315 -0.428594 -0.4919557 4.5039e-02 -2.7868e-01 -2.2140e-01 165s [56,] -0.061827 0.583572 0.3263056 -1.1589e-01 -1.2973e-01 -1.6518e-01 165s [57,] -1.295979 -0.421943 0.8410805 3.0441e-01 -3.9478e-01 -4.5233e-02 165s [58,] 0.174908 -1.343854 0.0115086 8.0227e-01 -3.9364e-01 -2.2918e-01 165s [59,] -1.869684 0.840823 0.0109543 -5.5536e-01 -1.4155e-01 1.0613e-01 165s [60,] -1.614271 0.557309 -0.0690787 -9.1753e-02 -3.0975e-01 1.6192e-01 165s [61,] -0.258192 1.434984 0.7684636 -1.1998e-01 -3.4662e-01 -4.8808e-02 165s [62,] 2.000275 2.204730 1.1194067 -2.3783e-01 5.9953e-02 -1.5836e-01 165s [63,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 165s [64,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 165s [65,] -0.822201 2.427550 1.5859438 -2.6715e-16 -1.9429e-15 1.0564e-14 165s [66,] -2.545586 0.605953 0.1469837 -3.5318e-01 -2.5871e-01 1.6901e-01 165s [67,] 0.028900 1.253717 0.4474540 5.3595e-02 1.6063e-01 -1.0980e-01 165s [68,] -1.086135 1.968868 -0.7220293 -1.6576e+00 6.2061e-02 -7.0998e-04 165s [69,] -0.836638 0.660453 0.0049966 1.3663e-01 -1.0131e-01 -2.4008e-01 165s [70,] 4.843092 -6.035092 0.8250084 -3.4481e+00 -4.8538e+00 -7.8407e+00 165s [71,] -2.500038 1.146245 0.6967314 -2.4611e-01 -1.4266e-01 -8.2996e-02 165s [72,] 2.220676 1.122951 -0.2444075 1.1066e-01 -3.1540e-01 -2.1344e-01 165s [73,] -2.310518 2.354552 0.2706503 -6.4192e-01 2.0566e-01 4.5520e-01 165s [74,] -10.802799 -3.462655 2.2031446 1.1326e+00 2.8049e-01 -2.9749e-01 165s [75,] -0.301038 2.284366 0.2440764 -6.9450e-01 2.6435e-01 4.3129e-01 165s [76,] -1.477936 0.245154 -0.8869850 -8.9900e-01 -9.8013e-02 1.1983e-01 165s [77,] -8.169236 -1.599780 1.4987144 3.7767e-01 2.4726e-01 3.8246e-01 165s [78,] 1.096654 1.646072 0.0591327 -3.3138e-01 -1.7936e-01 6.2716e-02 165s [79,] -0.289199 0.625796 -0.3974294 -6.6099e-01 -2.0857e-01 2.1190e-01 165s [80,] -3.160557 -2.282579 0.3255355 4.6181e-01 2.7753e-01 -1.5673e-01 165s [81,] 1.284356 -0.548854 -0.2907281 2.4017e-01 -2.5254e-01 -1.4289e-03 165s [82,] 2.562817 2.019485 0.8249162 3.2973e-01 3.3866e-01 1.3889e-01 165s [83,] 2.538825 0.759863 -0.3142506 -5.1028e-01 -2.0539e-01 8.8979e-02 165s [84,] 0.841123 0.110035 -1.5793120 -1.2807e+00 1.2332e-01 1.6224e-01 165s [85,] 0.636271 1.793014 2.6824860 1.0329e+00 -4.8850e-01 -2.3012e-01 165s [86,] 0.633183 -0.426511 -1.4791366 -6.1314e-01 -7.0534e-02 -2.3778e-01 165s PC7 PC8 165s [1,] 1.0196e-01 -1.7180e-03 165s [2,] 2.6131e-01 -8.5191e-03 165s [3,] 6.9637e-01 -8.0573e-03 165s [4,] -1.3548e-01 -1.4969e-03 165s [5,] 3.1443e-02 -2.7307e-03 165s [6,] -2.5079e-01 3.6450e-03 165s [7,] 4.5377e-02 -2.6071e-03 165s [8,] -1.6060e-01 -2.3761e-04 165s [9,] -1.5152e-01 -4.3079e-04 165s [10,] 9.1089e-02 1.9536e-03 165s [11,] 2.5654e-01 -1.4875e-03 165s [12,] -2.3798e-03 -1.0954e-04 165s [13,] -1.3687e-01 2.8402e-03 165s [14,] -6.5248e-02 -1.5114e-03 165s [15,] 3.7695e-02 -2.7827e-03 165s [16,] 3.8131e-01 -3.7990e-03 165s [17,] 4.5661e-02 -1.4965e-03 165s [18,] 3.9910e-01 -7.2703e-03 165s [19,] 2.9353e-01 -3.3342e-03 165s [20,] 6.0915e-01 -6.0837e-03 165s [21,] -1.0079e-01 1.0179e-03 165s [22,] -2.2945e-02 -1.0515e-03 165s [23,] 2.3631e-01 -2.5558e-03 165s [24,] -7.7207e-02 3.4800e-03 165s [25,] 1.4903e-02 -3.2430e-04 165s [26,] 3.8032e-03 -2.1705e-03 165s [27,] 3.7208e-02 -3.0631e-03 165s [28,] -4.8147e-01 6.1089e-03 165s [29,] -4.0388e-02 2.8549e-03 165s [30,] 3.4318e-02 -1.0014e-03 165s [31,] -2.2872e-02 1.8706e-03 165s [32,] -8.4542e-02 1.3368e-03 165s [33,] 4.5274e-02 5.3383e-04 165s [34,] -2.0048e-01 2.4727e-03 165s [35,] -5.6482e-02 2.9923e-03 165s [36,] -2.6046e-02 -1.2910e-03 165s [37,] 9.6038e-02 -1.8897e-03 165s [38,] -2.9035e-01 4.4317e-03 165s [39,] -4.6322e-03 2.4336e-03 165s [40,] 3.8686e-01 -3.9300e-03 165s [41,] 3.7834e-01 -7.8976e-03 165s [42,] -8.2037e-04 -4.3106e-05 165s [43,] 3.3467e-01 -5.2401e-03 165s [44,] -6.2170e-01 1.2840e-02 165s [45,] 5.3557e-02 2.9156e-03 165s [46,] 5.1785e-04 2.0738e-03 165s [47,] -5.2141e-01 5.7206e-03 165s [48,] -2.7669e-01 6.7329e-03 165s [49,] 8.4319e-02 3.8528e-03 165s [50,] 1.4210e-01 1.6961e-04 165s [51,] -1.1871e-01 2.6676e-03 165s [52,] -2.5036e-01 6.4121e-03 165s [53,] 2.2399e-01 -2.8200e-03 165s [54,] 5.6532e-02 4.9304e-04 165s [55,] -1.4343e-01 1.2558e-03 165s [56,] 4.1682e-02 -9.6490e-04 165s [57,] -1.3014e-01 -6.2709e-04 165s [58,] -2.1428e-01 8.2594e-04 165s [59,] -7.9775e-02 -8.9776e-04 165s [60,] -8.6835e-02 -1.0498e-03 165s [61,] 6.2470e-02 -2.7499e-03 165s [62,] 3.3052e-02 -3.2369e-04 165s [63,] -1.7137e-01 -3.1087e-04 165s [64,] -1.7137e-01 -3.1087e-04 165s [65,] 3.5496e-14 2.5975e-12 165s [66,] -2.2016e-02 -1.2206e-03 165s [67,] 8.5160e-02 -1.4837e-04 165s [68,] -2.2535e-03 1.9054e-04 165s [69,] 5.9976e-02 -8.6961e-04 165s [70,] 1.0448e+00 -2.0167e-02 165s [71,] -1.7609e-01 1.9378e-03 165s [72,] -1.7047e-01 2.6076e-04 165s [73,] 1.1885e-01 -8.1624e-04 165s [74,] 2.0942e-01 3.3164e-03 165s [75,] -7.7528e-01 9.9316e-03 165s [76,] -4.6285e-03 2.5153e-04 165s [77,] 7.0218e-02 1.5708e-03 165s [78,] -1.4859e-02 -6.7049e-04 165s [79,] 5.1054e-02 -2.0198e-03 165s [80,] -1.5770e-01 4.9579e-03 165s [81,] -1.9411e-01 4.4401e-04 165s [82,] 6.0634e-02 8.7960e-04 165s [83,] -4.4635e-02 -1.7048e-03 165s [84,] -2.3612e-03 -2.2242e-04 165s [85,] -5.5171e-02 -1.1222e-03 165s [86,] -1.4972e-01 1.4543e-03 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 2.8929930 1.8789522 0.9946460 0.7479403 0.3744197 0.2596328 0.1421387 165s [8] 0.0025753 165s ---------------------------------------------------------- 165s bushfire 38 5 5 37473.439646 1742.633018 165s Scores: 165s PC1 PC2 PC3 PC4 PC5 165s [1,] -67.2152 -2.3010e+01 4.4179e+00 1.0892e+00 1.7536e+00 165s [2,] -69.0225 -2.1417e+01 2.5382e+00 1.1092e+00 9.3919e-01 165s [3,] -61.6651 -1.8580e+01 -6.1022e-01 -8.1124e-01 -1.6462e-01 165s [4,] -44.5883 -1.8234e+01 -3.9899e-01 -5.2145e-01 2.0050e-01 165s [5,] -12.2941 -2.2954e+01 3.5970e+00 1.1037e+00 -2.4384e-01 165s [6,] 13.0282 -2.8133e+01 8.7670e+00 3.4751e+00 1.3728e+00 165s [7,] -199.0774 -7.7956e+01 5.4935e+01 6.3134e+00 -1.9919e+00 165s [8,] -228.2849 -1.3258e+02 2.2340e+01 2.1656e+01 -1.2594e+01 165s [9,] -228.9164 -1.3560e+02 2.0463e+01 2.2625e+01 -1.2743e+01 165s [10,] -232.4703 -1.0661e+02 3.5597e+01 1.7915e+01 -7.7659e+00 165s [11,] -236.7410 -8.8072e+01 3.6632e+01 1.5095e+01 -7.4695e+00 165s [12,] -249.4091 -3.6830e+01 2.4010e+01 4.7317e+00 -1.2986e+00 165s [13,] -197.0450 4.2633e-14 4.9738e-14 1.1657e-13 -1.1369e-13 165s [14,] 50.9487 1.1397e+01 -1.1247e+01 -4.8733e+00 2.4511e+00 165s [15,] 180.7896 1.7571e+01 -8.0454e+00 -1.0582e+01 1.2714e+00 165s [16,] 135.6178 1.4189e+01 -4.9116e-01 -9.2701e+00 1.4021e-01 165s [17,] 132.5344 1.5577e+01 2.2990e-01 -6.4963e+00 7.3370e-01 165s [18,] 121.3422 1.0471e+01 4.5656e+00 -4.9831e+00 -5.2314e-01 165s [19,] 125.2722 9.0272e+00 3.7365e+00 -3.3313e+00 -2.9097e-01 165s [20,] 135.2370 1.4091e+01 2.0639e+00 -3.6800e+00 1.1733e+00 165s [21,] 116.4250 1.5147e+01 2.9085e+00 -4.8084e+00 1.2603e+00 165s [22,] 108.2925 1.4223e+01 7.7165e-01 -4.5065e+00 2.7943e+00 165s [23,] -22.8258 6.4234e+00 2.4654e+00 -3.9627e+00 7.9847e-01 165s [24,] -78.1850 4.6631e+00 -3.6818e+00 -2.7688e+00 5.8508e-01 165s [25,] -13.0417 2.7521e+00 -3.1955e+00 -4.6824e+00 -3.1085e-01 165s [26,] -19.1244 -9.5045e-01 -2.6771e+00 -4.7104e+00 -1.6172e-01 165s [27,] -34.4379 3.2761e+00 -9.2826e+00 -2.9861e+00 -3.3561e-01 165s [28,] -11.5852 1.4506e+01 -1.5649e+01 -1.6260e+00 -8.5347e-01 165s [29,] -2.9366 2.8741e+01 -2.2907e+01 3.9749e-01 3.5861e-02 165s [30,] -59.7518 2.8633e+01 -1.4710e+01 3.5226e+00 -9.9066e-01 165s [31,] 67.8017 4.7241e+01 -9.1255e+00 1.3201e+01 1.3500e-13 165s [32,] 321.9941 7.6188e+01 2.2491e+01 3.1537e+01 3.2368e+00 165s [33,] 359.5155 6.6710e+01 5.6061e+01 3.4541e+01 2.0718e+00 165s [34,] 355.8007 6.5695e+01 5.7430e+01 3.3578e+01 1.4640e+00 165s [35,] 361.1076 6.7577e+01 5.7402e+01 3.3832e+01 3.2618e+00 165s [36,] 358.3592 6.6791e+01 5.8643e+01 3.2720e+01 1.2487e+00 165s [37,] 355.9974 6.8071e+01 6.0927e+01 3.2560e+01 2.4898e+00 165s [38,] 357.2530 6.9073e+01 6.1517e+01 3.2523e+01 2.7558e+00 165s ------------- 165s Call: 165s PcaProj(x = x) 165s 165s Standard deviations: 165s [1] 193.5806 41.7449 16.7665 8.1585 1.6074 165s ---------------------------------------------------------- 165s ========================================================== 165s > ## IGNORE_RDIFF_END 165s > 165s > ## VT::14.11.2018 - commented out - on some platforms PcaHubert will choose only 1 PC 165s > ## and will show difference 165s > ## test.case.1() 165s > 165s > test.case.2() 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s [1] TRUE 165s > 165s BEGIN TEST tlda.R 165s 165s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 165s Copyright (C) 2025 The R Foundation for Statistical Computing 165s Platform: x86_64-pc-linux-gnu 165s 165s R is free software and comes with ABSOLUTELY NO WARRANTY. 165s You are welcome to redistribute it under certain conditions. 165s Type 'license()' or 'licence()' for distribution details. 165s 165s R is a collaborative project with many contributors. 165s Type 'contributors()' for more information and 165s 'citation()' on how to cite R or R packages in publications. 165s 165s Type 'demo()' for some demos, 'help()' for on-line help, or 165s 'help.start()' for an HTML browser interface to help. 165s Type 'q()' to quit R. 165s 165s > ## VT::15.09.2013 - this will render the output independent 165s > ## from the version of the package 165s > suppressPackageStartupMessages(library(rrcov)) 165s > library(MASS) 165s > 165s > ## VT::14.01.2020 165s > ## On some platforms minor differences are shown - use 165s > ## IGNORE_RDIFF_BEGIN 165s > ## IGNORE_RDIFF_END 165s > 165s > dodata <- function(method) { 165s + 165s + options(digits = 5) 165s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 165s + 165s + tmp <- sys.call() 165s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 165s + cat("===================================================\n") 165s + 165s + cat("\nData: ", "hemophilia\n") 165s + data(hemophilia) 165s + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "anorexia\n") 165s + data(anorexia) 165s + show(rlda <- Linda(Treat~., data=anorexia, method=method)) 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "Pima\n") 165s + data(Pima.tr) 165s + show(rlda <- Linda(type~., data=Pima.tr, method=method)) 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "Forest soils\n") 165s + data(soil) 165s + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) 165s + 165s + ## This will not work within the function, of course 165s + ## - comment it out 165s + ## IGNORE_RDIFF_BEGIN 165s + rlda <- Linda(F~., data=soil1983, method=method) 165s + ## show(rlda) 165s + ## IGNORE_RDIFF_END 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "Raven and Miller diabetes data\n") 165s + data(diabetes) 165s + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "iris\n") 165s + data(iris) 165s + if(method != "mcdA") 165s + { 165s + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) 165s + show(predict(rlda)) 165s + } 165s + 165s + cat("\nData: ", "crabs\n") 165s + data(crabs) 165s + show(rlda <- Linda(sp~., data=crabs, method=method)) 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "fish\n") 165s + data(fish) 165s + fish <- fish[-14,] # remove observation #14 containing missing value 165s + 165s + # The height and width are calculated as percentages 165s + # of the third length variable 165s + fish[,5] <- fish[,5]*fish[,4]/100 165s + fish[,6] <- fish[,6]*fish[,4]/100 165s + 165s + ## There is one class with only 6 observations (p=6). Normally 165s + ## Linda will fail, therefore use l1med=TRUE. 165s + ## This works only for methods mcdB and mcdC 165s + 165s + table(fish$Species) 165s + if(method != "mcdA") 165s + { 165s + ## IGNORE_RDIFF_BEGIN 165s + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) 165s + ## show(rlda) 165s + ## IGNORE_RDIFF_END 165s + show(predict(rlda)) 165s + } 165s + 165s + cat("\nData: ", "pottery\n") 165s + data(pottery) 165s + show(rlda <- Linda(origin~., data=pottery, method=method)) 165s + show(predict(rlda)) 165s + 165s + cat("\nData: ", "olitos\n") 165s + data(olitos) 165s + if(method != "mcdA") 165s + { 165s + ## IGNORE_RDIFF_BEGIN 165s + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) 165s + ## show(rlda) 165s + ## IGNORE_RDIFF_END 165s + show(predict(rlda)) 165s + } 165s + 165s + cat("===================================================\n") 165s + } 165s > 165s > 165s > ## -- now do it: 165s > dodata(method="mcdA") 165s 165s Call: dodata(method = "mcdA") 165s =================================================== 165s 165s Data: hemophilia 165s Call: 165s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 165s 165s Prior Probabilities of Groups: 165s carrier normal 165s 0.6 0.4 165s 165s Group means: 165s AHFactivity AHFantigen 165s carrier -0.30795 -0.0059911 165s normal -0.12920 -0.0603000 165s 165s Within-groups Covariance Matrix: 165s AHFactivity AHFantigen 165s AHFactivity 0.018036 0.011853 165s AHFantigen 0.011853 0.019185 165s 165s Linear Coeficients: 165s AHFactivity AHFantigen 165s carrier -28.4029 17.2368 165s normal -8.5834 2.1602 165s 165s Constants: 165s carrier normal 165s -4.8325 -1.4056 165s 165s Apparent error rate 0.1333 165s 165s Classification table 165s Predicted 165s Actual carrier normal 165s carrier 39 6 165s normal 4 26 165s 165s Confusion matrix 165s Predicted 165s Actual carrier normal 165s carrier 0.867 0.133 165s normal 0.133 0.867 165s 165s Data: anorexia 165s Call: 165s Linda(Treat ~ ., data = anorexia, method = method) 165s 165s Prior Probabilities of Groups: 165s CBT Cont FT 165s 0.40278 0.36111 0.23611 165s 165s Group means: 165s Prewt Postwt 165s CBT 82.633 82.950 165s Cont 81.558 81.108 165s FT 84.331 94.762 165s 165s Within-groups Covariance Matrix: 165s Prewt Postwt 165s Prewt 26.9291 3.3862 165s Postwt 3.3862 18.2368 165s 165s Linear Coeficients: 165s Prewt Postwt 165s CBT 2.5563 4.0738 165s Cont 2.5284 3.9780 165s FT 2.5374 4.7250 165s 165s Constants: 165s CBT Cont FT 165s -275.49 -265.45 -332.31 165s 165s Apparent error rate 0.3889 165s 165s Classification table 165s Predicted 165s Actual CBT Cont FT 165s CBT 16 5 8 165s Cont 11 15 0 165s FT 0 4 13 165s 165s Confusion matrix 165s Predicted 165s Actual CBT Cont FT 165s CBT 0.552 0.172 0.276 165s Cont 0.423 0.577 0.000 165s FT 0.000 0.235 0.765 165s 165s Data: Pima 165s Call: 165s Linda(type ~ ., data = Pima.tr, method = method) 165s 165s Prior Probabilities of Groups: 165s No Yes 165s 0.66 0.34 165s 165s Group means: 165s npreg glu bp skin bmi ped age 165s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 165s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 165s 165s Within-groups Covariance Matrix: 165s npreg glu bp skin bmi ped age 165s npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 165s glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 165s bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 165s skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 165s bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 165s ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 165s age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 165s 165s Linear Coeficients: 165s npreg glu bp skin bmi ped age 165s No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 165s Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 165s 165s Constants: 165s No Yes 165s -37.050 -51.586 165s 165s Apparent error rate 0.22 165s 165s Classification table 165s Predicted 165s Actual No Yes 165s No 107 25 165s Yes 19 49 165s 165s Confusion matrix 165s Predicted 165s Actual No Yes 165s No 0.811 0.189 165s Yes 0.279 0.721 165s 165s Data: Forest soils 165s 165s Apparent error rate 0.3103 165s 165s Classification table 165s Predicted 165s Actual 1 2 3 165s 1 7 2 2 165s 2 3 13 7 165s 3 1 3 20 165s 165s Confusion matrix 165s Predicted 165s Actual 1 2 3 165s 1 0.636 0.182 0.182 165s 2 0.130 0.565 0.304 165s 3 0.042 0.125 0.833 165s 165s Data: Raven and Miller diabetes data 165s Call: 165s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 165s 165s Prior Probabilities of Groups: 165s normal chemical overt 165s 0.52414 0.24828 0.22759 165s 165s Group means: 165s insulin glucose sspg 165s normal 163.939 345.8 99.076 165s chemical 299.448 476.9 223.621 165s overt 95.958 1026.4 343.000 165s 165s Within-groups Covariance Matrix: 165s insulin glucose sspg 165s insulin 7582.0 -1263.1 1095.8 165s glucose -1263.1 18952.4 4919.3 165s sspg 1095.8 4919.3 3351.2 165s 165s Linear Coeficients: 165s insulin glucose sspg 165s normal 0.027694 0.023859 -0.014514 165s chemical 0.040288 0.022532 0.020479 165s overt 0.017144 0.048768 0.025158 165s 165s Constants: 165s normal chemical overt 165s -6.3223 -15.0879 -31.6445 165s 165s Apparent error rate 0.1862 165s 165s Classification table 165s Predicted 165s Actual normal chemical overt 165s normal 69 7 0 165s chemical 13 23 0 165s overt 2 5 26 165s 165s Confusion matrix 165s Predicted 165s Actual normal chemical overt 165s normal 0.908 0.092 0.000 165s chemical 0.361 0.639 0.000 165s overt 0.061 0.152 0.788 165s 165s Data: iris 165s 165s Data: crabs 165s Call: 165s Linda(sp ~ ., data = crabs, method = method) 165s 165s Prior Probabilities of Groups: 165s B O 165s 0.5 0.5 165s 165s Group means: 165s sexM index FL RW CL CW BD 165s B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 165s O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 165s 165s Within-groups Covariance Matrix: 165s sexM index FL RW CL CW BD 165s sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 165s index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 165s FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 165s RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 165s CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 165s CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 165s BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 165s 165s Linear Coeficients: 165s sexM index FL RW CL CW BD 165s B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 165s O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 165s 165s Constants: 165s B O 165s -78.317 -159.259 165s 165s Apparent error rate 0 165s 165s Classification table 165s Predicted 165s Actual B O 165s B 100 0 165s O 0 100 165s 165s Confusion matrix 165s Predicted 165s Actual B O 165s B 1 0 165s O 0 1 165s 165s Data: fish 165s 165s Data: pottery 165s Call: 165s Linda(origin ~ ., data = pottery, method = method) 165s 165s Prior Probabilities of Groups: 165s Attic Eritrean 165s 0.48148 0.51852 165s 165s Group means: 165s SI AL FE MG CA TI 165s Attic 55.36 13.73 9.82 5.45 6.03 0.863 165s Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 165s 165s Within-groups Covariance Matrix: 165s SI AL FE MG CA TI 165s SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 165s AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 165s FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 165s MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 165s CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 165s TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 165s 165s Linear Coeficients: 165s SI AL FE MG CA TI 165s Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 165s Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 165s 165s Constants: 165s Attic Eritrean 165s -1578.14 -901.13 165s 165s Apparent error rate 0.1111 165s 165s Classification table 165s Predicted 165s Actual Attic Eritrean 165s Attic 12 1 165s Eritrean 2 12 165s 165s Confusion matrix 165s Predicted 165s Actual Attic Eritrean 165s Attic 0.923 0.077 165s Eritrean 0.143 0.857 165s 165s Data: olitos 165s =================================================== 165s > dodata(method="mcdB") 165s 165s Call: dodata(method = "mcdB") 165s =================================================== 165s 165s Data: hemophilia 165s Call: 165s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 165s 165s Prior Probabilities of Groups: 165s carrier normal 165s 0.6 0.4 165s 165s Group means: 165s AHFactivity AHFantigen 165s carrier -0.31456 -0.014775 165s normal -0.13582 -0.069084 165s 165s Within-groups Covariance Matrix: 165s AHFactivity AHFantigen 165s AHFactivity 0.0125319 0.0086509 165s AHFantigen 0.0086509 0.0182424 165s 165s Linear Coeficients: 165s AHFactivity AHFantigen 165s carrier -36.486 16.4923 165s normal -12.226 2.0107 165s 165s Constants: 165s carrier normal 165s -6.1276 -1.6771 165s 165s Apparent error rate 0.16 165s 165s Classification table 165s Predicted 165s Actual carrier normal 165s carrier 38 7 165s normal 5 25 165s 165s Confusion matrix 165s Predicted 165s Actual carrier normal 165s carrier 0.844 0.156 165s normal 0.167 0.833 165s 165s Data: anorexia 165s Call: 165s Linda(Treat ~ ., data = anorexia, method = method) 165s 165s Prior Probabilities of Groups: 165s CBT Cont FT 165s 0.40278 0.36111 0.23611 165s 165s Group means: 165s Prewt Postwt 165s CBT 83.254 82.381 165s Cont 82.178 80.539 165s FT 84.951 94.193 165s 165s Within-groups Covariance Matrix: 165s Prewt Postwt 165s Prewt 19.1751 8.8546 165s Postwt 8.8546 25.2326 165s 165s Linear Coeficients: 165s Prewt Postwt 165s CBT 3.3822 2.0780 165s Cont 3.3555 2.0144 165s FT 3.2299 2.5996 165s 165s Constants: 165s CBT Cont FT 165s -227.29 -220.01 -261.06 165s 165s Apparent error rate 0.4444 165s 165s Classification table 165s Predicted 165s Actual CBT Cont FT 165s CBT 16 5 8 165s Cont 12 11 3 165s FT 0 4 13 165s 165s Confusion matrix 165s Predicted 165s Actual CBT Cont FT 165s CBT 0.552 0.172 0.276 165s Cont 0.462 0.423 0.115 165s FT 0.000 0.235 0.765 165s 165s Data: Pima 165s Call: 165s Linda(type ~ ., data = Pima.tr, method = method) 165s 165s Prior Probabilities of Groups: 165s No Yes 165s 0.66 0.34 165s 165s Group means: 165s npreg glu bp skin bmi ped age 165s No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 165s Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 165s 165s Within-groups Covariance Matrix: 165s npreg glu bp skin bmi ped age 165s npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 165s glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 165s bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 165s skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 165s bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 165s ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 165s age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 165s 165s Linear Coeficients: 165s npreg glu bp skin bmi ped age 165s No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 165s Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 165s 165s Constants: 165s No Yes 165s -38.774 -53.654 165s 165s Apparent error rate 0.25 165s 165s Classification table 165s Predicted 165s Actual No Yes 165s No 104 28 165s Yes 22 46 165s 165s Confusion matrix 165s Predicted 165s Actual No Yes 165s No 0.788 0.212 165s Yes 0.324 0.676 165s 165s Data: Forest soils 165s 165s Apparent error rate 0.3448 165s 165s Classification table 165s Predicted 165s Actual 1 2 3 165s 1 4 3 4 165s 2 2 14 7 165s 3 2 2 20 165s 165s Confusion matrix 165s Predicted 165s Actual 1 2 3 165s 1 0.364 0.273 0.364 165s 2 0.087 0.609 0.304 165s 3 0.083 0.083 0.833 165s 165s Data: Raven and Miller diabetes data 166s Call: 166s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 166s 166s Prior Probabilities of Groups: 166s normal chemical overt 166s 0.52414 0.24828 0.22759 166s 166s Group means: 166s insulin glucose sspg 166s normal 152.405 346.55 99.387 166s chemical 288.244 478.80 226.226 166s overt 84.754 1028.28 345.605 166s 166s Within-groups Covariance Matrix: 166s insulin glucose sspg 166s insulin 5061.46 289.69 2071.71 166s glucose 289.69 1983.07 385.31 166s sspg 2071.71 385.31 3000.17 166s 166s Linear Coeficients: 166s insulin glucose sspg 166s normal 0.021952 0.17236 -0.0041671 166s chemical 0.034852 0.23217 0.0215200 166s overt -0.045700 0.50940 0.0813292 166s 166s Constants: 166s normal chemical overt 166s -31.976 -64.433 -275.502 166s 166s Apparent error rate 0.0966 166s 166s Classification table 166s Predicted 166s Actual normal chemical overt 166s normal 73 3 0 166s chemical 4 32 0 166s overt 0 7 26 166s 166s Confusion matrix 166s Predicted 166s Actual normal chemical overt 166s normal 0.961 0.039 0.000 166s chemical 0.111 0.889 0.000 166s overt 0.000 0.212 0.788 166s 166s Data: iris 166s Call: 166s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 166s 166s Prior Probabilities of Groups: 166s setosa versicolor virginica 166s 0.33333 0.33333 0.33333 166s 166s Group means: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s setosa 4.9834 3.4153 1.4532 0.22474 166s versicolor 5.8947 2.8149 4.2263 1.35024 166s virginica 6.5255 3.0017 5.4485 2.06756 166s 166s Within-groups Covariance Matrix: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s Sepal.Length 0.201176 0.084299 0.102984 0.037019 166s Sepal.Width 0.084299 0.108394 0.050253 0.031757 166s Petal.Length 0.102984 0.050253 0.120215 0.045016 166s Petal.Width 0.037019 0.031757 0.045016 0.032825 166s 166s Linear Coeficients: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s setosa 22.536 27.422168 -3.6855 -40.0445 166s versicolor 17.559 6.374082 24.1965 -18.0178 166s virginica 16.488 0.015576 29.9586 3.2926 166s 166s Constants: 166s setosa versicolor virginica 166s -96.901 -100.790 -139.937 166s 166s Apparent error rate 0.0267 166s 166s Classification table 166s Predicted 166s Actual setosa versicolor virginica 166s setosa 50 0 0 166s versicolor 0 48 2 166s virginica 0 2 48 166s 166s Confusion matrix 166s Predicted 166s Actual setosa versicolor virginica 166s setosa 1 0.00 0.00 166s versicolor 0 0.96 0.04 166s virginica 0 0.04 0.96 166s 166s Data: crabs 166s Call: 166s Linda(sp ~ ., data = crabs, method = method) 166s 166s Prior Probabilities of Groups: 166s B O 166s 0.5 0.5 166s 166s Group means: 166s sexM index FL RW CL CW BD 166s B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 166s O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 166s 166s Within-groups Covariance Matrix: 166s sexM index FL RW CL CW BD 166s sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 166s index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 166s FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 166s RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 166s CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 166s CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 166s BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 166s 166s Linear Coeficients: 166s sexM index FL RW CL CW BD 166s B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 166s O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 166s 166s Constants: 166s B O 166s -66.688 -134.375 166s 166s Apparent error rate 0 166s 166s Classification table 166s Predicted 166s Actual B O 166s B 100 0 166s O 0 100 166s 166s Confusion matrix 166s Predicted 166s Actual B O 166s B 1 0 166s O 0 1 166s 166s Data: fish 166s 166s Apparent error rate 0.0949 166s 166s Classification table 166s Predicted 166s Actual 1 2 3 4 5 6 7 166s 1 34 0 0 0 0 0 0 166s 2 0 6 0 0 0 0 0 166s 3 0 0 20 0 0 0 0 166s 4 0 0 0 11 0 0 0 166s 5 0 0 0 0 13 0 1 166s 6 0 0 0 0 0 17 0 166s 7 0 13 0 0 1 0 42 166s 166s Confusion matrix 166s Predicted 166s Actual 1 2 3 4 5 6 7 166s 1 1 0.000 0 0 0.000 0 0.000 166s 2 0 1.000 0 0 0.000 0 0.000 166s 3 0 0.000 1 0 0.000 0 0.000 166s 4 0 0.000 0 1 0.000 0 0.000 166s 5 0 0.000 0 0 0.929 0 0.071 166s 6 0 0.000 0 0 0.000 1 0.000 166s 7 0 0.232 0 0 0.018 0 0.750 166s 166s Data: pottery 166s Call: 166s Linda(origin ~ ., data = pottery, method = method) 166s 166s Prior Probabilities of Groups: 166s Attic Eritrean 166s 0.48148 0.51852 166s 166s Group means: 166s SI AL FE MG CA TI 166s Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 166s Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 166s 166s Within-groups Covariance Matrix: 166s SI AL FE MG CA TI 166s SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 166s AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 166s FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 166s MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 166s CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 166s TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 166s 166s Linear Coeficients: 166s SI AL FE MG CA TI 166s Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 166s Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 166s 166s Constants: 166s Attic Eritrean 166s -3252.6 -1961.9 166s 166s Apparent error rate 0.1111 166s 166s Classification table 166s Predicted 166s Actual Attic Eritrean 166s Attic 12 1 166s Eritrean 2 12 166s 166s Confusion matrix 166s Predicted 166s Actual Attic Eritrean 166s Attic 0.923 0.077 166s Eritrean 0.143 0.857 166s 166s Data: olitos 166s 166s Apparent error rate 0.15 166s 166s Classification table 166s Predicted 166s Actual 1 2 3 4 166s 1 44 1 4 1 166s 2 2 23 0 0 166s 3 6 1 26 1 166s 4 1 1 0 9 166s 166s Confusion matrix 166s Predicted 166s Actual 1 2 3 4 166s 1 0.880 0.020 0.080 0.020 166s 2 0.080 0.920 0.000 0.000 166s 3 0.176 0.029 0.765 0.029 166s 4 0.091 0.091 0.000 0.818 166s =================================================== 166s > dodata(method="mcdC") 166s 166s Call: dodata(method = "mcdC") 166s =================================================== 166s 166s Data: hemophilia 166s Call: 166s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 166s 166s Prior Probabilities of Groups: 166s carrier normal 166s 0.6 0.4 166s 166s Group means: 166s AHFactivity AHFantigen 166s carrier -0.32583 -0.011545 166s normal -0.12783 -0.071377 166s 166s Within-groups Covariance Matrix: 166s AHFactivity AHFantigen 166s AHFactivity 0.0120964 0.0075536 166s AHFantigen 0.0075536 0.0164883 166s 166s Linear Coeficients: 166s AHFactivity AHFantigen 166s carrier -37.117 16.30377 166s normal -11.015 0.71742 166s 166s Constants: 166s carrier normal 166s -6.4636 -1.5947 166s 166s Apparent error rate 0.16 166s 166s Classification table 166s Predicted 166s Actual carrier normal 166s carrier 38 7 166s normal 5 25 166s 166s Confusion matrix 166s Predicted 166s Actual carrier normal 166s carrier 0.844 0.156 166s normal 0.167 0.833 166s 166s Data: anorexia 166s Call: 166s Linda(Treat ~ ., data = anorexia, method = method) 166s 166s Prior Probabilities of Groups: 166s CBT Cont FT 166s 0.40278 0.36111 0.23611 166s 166s Group means: 166s Prewt Postwt 166s CBT 82.477 82.073 166s Cont 82.039 80.835 166s FT 85.242 94.750 166s 166s Within-groups Covariance Matrix: 166s Prewt Postwt 166s Prewt 19.6589 8.3891 166s Postwt 8.3891 22.8805 166s 166s Linear Coeficients: 166s Prewt Postwt 166s CBT 3.1590 2.4288 166s Cont 3.1599 2.3743 166s FT 3.0454 3.0245 166s 166s Constants: 166s CBT Cont FT 166s -230.85 -226.60 -274.53 166s 166s Apparent error rate 0.4583 166s 166s Classification table 166s Predicted 166s Actual CBT Cont FT 166s CBT 16 5 8 166s Cont 14 10 2 166s FT 0 4 13 166s 166s Confusion matrix 166s Predicted 166s Actual CBT Cont FT 166s CBT 0.552 0.172 0.276 166s Cont 0.538 0.385 0.077 166s FT 0.000 0.235 0.765 166s 166s Data: Pima 166s Call: 166s Linda(type ~ ., data = Pima.tr, method = method) 166s 166s Prior Probabilities of Groups: 166s No Yes 166s 0.66 0.34 166s 166s Group means: 166s npreg glu bp skin bmi ped age 166s No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 166s Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 166s 166s Within-groups Covariance Matrix: 166s npreg glu bp skin bmi ped age 166s npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 166s glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 166s bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 166s skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 166s bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 166s ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 166s age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 166s 166s Linear Coeficients: 166s npreg glu bp skin bmi ped age 166s No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 166s Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 166s 166s Constants: 166s No Yes 166s -41.716 -55.056 166s 166s Apparent error rate 0.235 166s 166s Classification table 166s Predicted 166s Actual No Yes 166s No 107 25 166s Yes 22 46 166s 166s Confusion matrix 166s Predicted 166s Actual No Yes 166s No 0.811 0.189 166s Yes 0.324 0.676 166s 166s Data: Forest soils 166s 166s Apparent error rate 0.3276 166s 166s Classification table 166s Predicted 166s Actual 1 2 3 166s 1 5 2 4 166s 2 2 13 8 166s 3 1 2 21 166s 166s Confusion matrix 166s Predicted 166s Actual 1 2 3 166s 1 0.455 0.182 0.364 166s 2 0.087 0.565 0.348 166s 3 0.042 0.083 0.875 166s 166s Data: Raven and Miller diabetes data 166s Call: 166s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 166s 166s Prior Probabilities of Groups: 166s normal chemical overt 166s 0.52414 0.24828 0.22759 166s 166s Group means: 166s insulin glucose sspg 166s normal 167.31 348.69 106.44 166s chemical 247.18 478.18 213.36 166s overt 101.83 932.92 322.42 166s 166s Within-groups Covariance Matrix: 166s insulin glucose sspg 166s insulin 4070.84 118.89 1701.54 166s glucose 118.89 2195.95 426.95 166s sspg 1701.54 426.95 2664.49 166s 166s Linear Coeficients: 166s insulin glucose sspg 166s normal 0.041471 0.15888 -0.011992 166s chemical 0.048103 0.21216 0.015359 166s overt -0.013579 0.41323 0.063462 166s 166s Constants: 166s normal chemical overt 166s -31.177 -59.703 -203.775 166s 166s Apparent error rate 0.0828 166s 166s Classification table 166s Predicted 166s Actual normal chemical overt 166s normal 72 4 0 166s chemical 2 34 0 166s overt 0 6 27 166s 166s Confusion matrix 166s Predicted 166s Actual normal chemical overt 166s normal 0.947 0.053 0.000 166s chemical 0.056 0.944 0.000 166s overt 0.000 0.182 0.818 166s 166s Data: iris 166s Call: 166s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 166s 166s Prior Probabilities of Groups: 166s setosa versicolor virginica 166s 0.33333 0.33333 0.33333 166s 166s Group means: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s setosa 5.0163 3.4510 1.4653 0.2449 166s versicolor 5.9435 2.7891 4.2543 1.3239 166s virginica 6.3867 3.0033 5.3767 2.0700 166s 166s Within-groups Covariance Matrix: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s Sepal.Length 0.186186 0.082478 0.094998 0.035445 166s Sepal.Width 0.082478 0.100137 0.049723 0.030678 166s Petal.Length 0.094998 0.049723 0.113105 0.043078 166s Petal.Width 0.035445 0.030678 0.043078 0.030885 166s 166s Linear Coeficients: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s setosa 23.678 30.2896 -3.1124 -44.9900 166s versicolor 20.342 4.6372 27.3265 -23.2006 166s virginica 18.377 -2.0004 31.4235 4.0906 166s 166s Constants: 166s setosa versicolor virginica 166s -104.96 -110.79 -145.49 166s 166s Apparent error rate 0.0333 166s 166s Classification table 166s Predicted 166s Actual setosa versicolor virginica 166s setosa 50 0 0 166s versicolor 0 48 2 166s virginica 0 3 47 166s 166s Confusion matrix 166s Predicted 166s Actual setosa versicolor virginica 166s setosa 1 0.00 0.00 166s versicolor 0 0.96 0.04 166s virginica 0 0.06 0.94 166s 166s Data: crabs 166s Call: 166s Linda(sp ~ ., data = crabs, method = method) 166s 166s Prior Probabilities of Groups: 166s B O 166s 0.5 0.5 166s 166s Group means: 166s sexM index FL RW CL CW BD 166s B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 166s O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 166s 166s Within-groups Covariance Matrix: 166s sexM index FL RW CL CW BD 166s sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 166s index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 166s FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 166s RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 166s CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 166s CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 166s BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 166s 166s Linear Coeficients: 166s sexM index FL RW CL CW BD 166s B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 166s O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 166s 166s Constants: 166s B O 166s -72.687 -142.365 166s 166s Apparent error rate 0 166s 166s Classification table 166s Predicted 166s Actual B O 166s B 100 0 166s O 0 100 166s 166s Confusion matrix 166s Predicted 166s Actual B O 166s B 1 0 166s O 0 1 166s 166s Data: fish 166s 166s Apparent error rate 0.0316 166s 166s Classification table 166s Predicted 166s Actual 1 2 3 4 5 6 7 166s 1 34 0 0 0 0 0 0 166s 2 0 5 0 0 1 0 0 166s 3 0 0 20 0 0 0 0 166s 4 0 0 0 11 0 0 0 166s 5 0 0 0 0 13 0 1 166s 6 0 0 0 0 0 17 0 166s 7 0 0 0 0 3 0 53 166s 166s Confusion matrix 166s Predicted 166s Actual 1 2 3 4 5 6 7 166s 1 1 0.000 0 0 0.000 0 0.000 166s 2 0 0.833 0 0 0.167 0 0.000 166s 3 0 0.000 1 0 0.000 0 0.000 166s 4 0 0.000 0 1 0.000 0 0.000 166s 5 0 0.000 0 0 0.929 0 0.071 166s 6 0 0.000 0 0 0.000 1 0.000 166s 7 0 0.000 0 0 0.054 0 0.946 166s 166s Data: pottery 166s Call: 166s Linda(origin ~ ., data = pottery, method = method) 166s 166s Prior Probabilities of Groups: 166s Attic Eritrean 166s 0.48148 0.51852 166s 166s Group means: 166s SI AL FE MG CA TI 166s Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 166s Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 166s 166s Within-groups Covariance Matrix: 166s SI AL FE MG CA TI 166s SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 166s AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 166s FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 166s MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 166s CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 166s TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 166s 166s Linear Coeficients: 166s SI AL FE MG CA TI 166s Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 166s Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 166s 166s Constants: 166s Attic Eritrean 166s -5924.2 -3802.9 166s 166s Apparent error rate 0.1111 166s 166s Classification table 166s Predicted 166s Actual Attic Eritrean 166s Attic 12 1 166s Eritrean 2 12 166s 166s Confusion matrix 166s Predicted 166s Actual Attic Eritrean 166s Attic 0.923 0.077 166s Eritrean 0.143 0.857 166s 166s Data: olitos 166s 166s Apparent error rate 0.1667 166s 166s Classification table 166s Predicted 166s Actual 1 2 3 4 166s 1 44 1 2 3 166s 2 2 22 0 1 166s 3 5 2 25 2 166s 4 1 1 0 9 166s 166s Confusion matrix 166s Predicted 166s Actual 1 2 3 4 166s 1 0.880 0.020 0.040 0.060 166s 2 0.080 0.880 0.000 0.040 166s 3 0.147 0.059 0.735 0.059 166s 4 0.091 0.091 0.000 0.818 166s =================================================== 166s > dodata(method="mrcd") 166s 166s Call: dodata(method = "mrcd") 166s =================================================== 166s 166s Data: hemophilia 166s Call: 166s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 166s 166s Prior Probabilities of Groups: 166s carrier normal 166s 0.6 0.4 166s 166s Group means: 166s AHFactivity AHFantigen 166s carrier -0.34048 -0.055943 166s normal -0.13566 -0.081191 166s 166s Within-groups Covariance Matrix: 166s AHFactivity AHFantigen 166s AHFactivity 0.0133676 0.0088055 166s AHFantigen 0.0088055 0.0221225 166s 166s Linear Coeficients: 166s AHFactivity AHFantigen 166s carrier -32.264 10.31334 166s normal -10.478 0.50044 166s 166s Constants: 166s carrier normal 166s -5.7149 -1.6067 166s 166s Apparent error rate 0.16 166s 166s Classification table 166s Predicted 166s Actual carrier normal 166s carrier 38 7 166s normal 5 25 166s 166s Confusion matrix 166s Predicted 166s Actual carrier normal 166s carrier 0.844 0.156 166s normal 0.167 0.833 166s 166s Data: anorexia 166s Call: 166s Linda(Treat ~ ., data = anorexia, method = method) 166s 166s Prior Probabilities of Groups: 166s CBT Cont FT 166s 0.40278 0.36111 0.23611 166s 166s Group means: 166s Prewt Postwt 166s CBT 83.114 84.009 166s Cont 80.327 80.125 166s FT 85.161 94.371 166s 166s Within-groups Covariance Matrix: 166s Prewt Postwt 166s Prewt 22.498 11.860 166s Postwt 11.860 20.426 166s 166s Linear Coeficients: 166s Prewt Postwt 166s CBT 2.1994 2.8357 166s Cont 2.1653 2.6654 166s FT 1.9451 3.4907 166s 166s Constants: 166s CBT Cont FT 166s -211.42 -194.77 -248.97 166s 166s Apparent error rate 0.3889 166s 166s Classification table 166s Predicted 166s Actual CBT Cont FT 166s CBT 15 6 8 166s Cont 6 16 4 166s FT 0 4 13 166s 166s Confusion matrix 166s Predicted 166s Actual CBT Cont FT 166s CBT 0.517 0.207 0.276 166s Cont 0.231 0.615 0.154 166s FT 0.000 0.235 0.765 166s 166s Data: Pima 166s Call: 166s Linda(type ~ ., data = Pima.tr, method = method) 166s 166s Prior Probabilities of Groups: 166s No Yes 166s 0.66 0.34 166s 166s Group means: 166s npreg glu bp skin bmi ped age 166s No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 166s Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 166s 166s Within-groups Covariance Matrix: 166s npreg glu bp skin bmi ped age 166s npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 166s glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 166s bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 166s skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 166s bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 166s ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 166s age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 166s 166s Linear Coeficients: 166s npreg glu bp skin bmi ped age 166s No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 166s Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 166s 166s Constants: 166s No Yes 166s -33.429 -45.434 166s 166s Apparent error rate 0.28 166s 166s Classification table 166s Predicted 166s Actual No Yes 166s No 105 27 166s Yes 29 39 166s 166s Confusion matrix 166s Predicted 166s Actual No Yes 166s No 0.795 0.205 166s Yes 0.426 0.574 166s 166s Data: Forest soils 166s 166s Apparent error rate 0.3448 166s 166s Classification table 166s Predicted 166s Actual 1 2 3 166s 1 7 2 2 166s 2 4 14 5 166s 3 3 4 17 166s 166s Confusion matrix 166s Predicted 166s Actual 1 2 3 166s 1 0.636 0.182 0.182 166s 2 0.174 0.609 0.217 166s 3 0.125 0.167 0.708 166s 166s Data: Raven and Miller diabetes data 166s Call: 166s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 166s 166s Prior Probabilities of Groups: 166s normal chemical overt 166s 0.52414 0.24828 0.22759 166s 166s Group means: 166s insulin glucose sspg 166s normal 154.014 346.07 91.606 166s chemical 248.841 451.10 221.936 166s overt 89.766 1064.16 335.100 166s 166s Within-groups Covariance Matrix: 166s insulin glucose sspg 166s insulin 4948.1 1007.61 1471.12 166s glucose 1007.6 2597.38 358.57 166s sspg 1471.1 358.57 3180.04 166s 166s Linear Coeficients: 166s insulin glucose sspg 166s normal 0.00027839 0.13121 0.013882 166s chemical 0.00148074 0.16615 0.050371 166s overt -0.10102404 0.43466 0.103100 166s 166s Constants: 166s normal chemical overt 166s -24.008 -44.642 -245.497 166s 166s Apparent error rate 0.0966 166s 166s Classification table 166s Predicted 166s Actual normal chemical overt 166s normal 71 5 0 166s chemical 2 34 0 166s overt 0 7 26 166s 166s Confusion matrix 166s Predicted 166s Actual normal chemical overt 166s normal 0.934 0.066 0.000 166s chemical 0.056 0.944 0.000 166s overt 0.000 0.212 0.788 166s 166s Data: iris 166s Call: 166s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 166s 166s Prior Probabilities of Groups: 166s setosa versicolor virginica 166s 0.33333 0.33333 0.33333 166s 166s Group means: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s setosa 4.9755 3.3826 1.4608 0.22053 166s versicolor 5.8868 2.7823 4.2339 1.34603 166s virginica 6.5176 2.9691 5.4560 2.06335 166s 166s Within-groups Covariance Matrix: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s Sepal.Length 0.238417 0.136325 0.086377 0.036955 166s Sepal.Width 0.136325 0.148452 0.067500 0.034804 166s Petal.Length 0.086377 0.067500 0.100934 0.035968 166s Petal.Width 0.036955 0.034804 0.035968 0.023856 166s 166s Linear Coeficients: 166s Sepal.Length Sepal.Width Petal.Length Petal.Width 166s setosa 17.106 15.693 7.8806 -52.031 166s versicolor 21.744 -15.813 38.0139 -11.505 166s virginica 23.032 -26.567 43.6391 23.777 166s 166s Constants: 166s setosa versicolor virginica 166s -70.214 -115.832 -180.294 166s 166s Apparent error rate 0.02 166s 166s Classification table 166s Predicted 166s Actual setosa versicolor virginica 166s setosa 50 0 0 166s versicolor 0 49 1 166s virginica 0 2 48 166s 166s Confusion matrix 166s Predicted 166s Actual setosa versicolor virginica 166s setosa 1 0.00 0.00 166s versicolor 0 0.98 0.02 166s virginica 0 0.04 0.96 166s 166s Data: crabs 167s Call: 167s Linda(sp ~ ., data = crabs, method = method) 167s 167s Prior Probabilities of Groups: 167s B O 167s 0.5 0.5 167s 167s Group means: 167s sexM index FL RW CL CW BD 167s B 0 25.5 13.270 12.138 28.102 32.624 11.816 167s O 1 25.5 16.626 12.262 33.688 37.188 15.324 167s 167s Within-groups Covariance Matrix: 167s sexM index FL RW CL CW BD 167s sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 167s index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 167s FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 167s RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 167s CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 167s CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 167s BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 167s 167s Linear Coeficients: 167s sexM index FL RW CL CW BD 167s B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 167s O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 167s 167s Constants: 167s B O 167s -2.1491e+01 -3.2776e+06 167s 167s Apparent error rate 0.5 167s 167s Classification table 167s Predicted 167s Actual B O 167s B 50 50 167s O 50 50 167s 167s Confusion matrix 167s Predicted 167s Actual B O 167s B 0.5 0.5 167s O 0.5 0.5 167s 167s Data: fish 167s 167s Apparent error rate 0.2532 167s 167s Classification table 167s Predicted 167s Actual 1 2 3 4 5 6 7 167s 1 33 0 0 1 0 0 0 167s 2 0 3 0 0 0 0 3 167s 3 0 2 5 0 0 0 13 167s 4 0 0 0 11 0 0 0 167s 5 0 0 0 0 14 0 0 167s 6 0 0 0 0 0 17 0 167s 7 0 19 0 0 2 0 35 167s 167s Confusion matrix 167s Predicted 167s Actual 1 2 3 4 5 6 7 167s 1 0.971 0.000 0.00 0.029 0.000 0 0.000 167s 2 0.000 0.500 0.00 0.000 0.000 0 0.500 167s 3 0.000 0.100 0.25 0.000 0.000 0 0.650 167s 4 0.000 0.000 0.00 1.000 0.000 0 0.000 167s 5 0.000 0.000 0.00 0.000 1.000 0 0.000 167s 6 0.000 0.000 0.00 0.000 0.000 1 0.000 167s 7 0.000 0.339 0.00 0.000 0.036 0 0.625 167s 167s Data: pottery 167s Call: 167s Linda(origin ~ ., data = pottery, method = method) 167s 167s Prior Probabilities of Groups: 167s Attic Eritrean 167s 0.48148 0.51852 167s 167s Group means: 167s SI AL FE MG CA TI 167s Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 167s Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 167s 167s Within-groups Covariance Matrix: 167s SI AL FE MG CA TI 167s SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 167s AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 167s FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 167s MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 167s CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 167s TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 167s 167s Linear Coeficients: 167s SI AL FE MG CA TI 167s Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 167s Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 167s 167s Constants: 167s Attic Eritrean 167s -4000.1 -3776.1 167s 167s Apparent error rate 0.1111 167s 167s Classification table 167s Predicted 167s Actual Attic Eritrean 167s Attic 12 1 167s Eritrean 2 12 167s 167s Confusion matrix 167s Predicted 167s Actual Attic Eritrean 167s Attic 0.923 0.077 167s Eritrean 0.143 0.857 167s 167s Data: olitos 167s 167s Apparent error rate 0.125 167s 167s Classification table 167s Predicted 167s Actual 1 2 3 4 167s 1 44 2 3 1 167s 2 1 23 1 0 167s 3 4 1 27 2 167s 4 0 0 0 11 167s 167s Confusion matrix 167s Predicted 167s Actual 1 2 3 4 167s 1 0.880 0.040 0.060 0.020 167s 2 0.040 0.920 0.040 0.000 167s 3 0.118 0.029 0.794 0.059 167s 4 0.000 0.000 0.000 1.000 167s =================================================== 167s > dodata(method="ogk") 167s 167s Call: dodata(method = "ogk") 167s =================================================== 167s 167s Data: hemophilia 167s Call: 167s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 167s 167s Prior Probabilities of Groups: 167s carrier normal 167s 0.6 0.4 167s 167s Group means: 167s AHFactivity AHFantigen 167s carrier -0.29043 -0.00052902 167s normal -0.12463 -0.06715037 167s 167s Within-groups Covariance Matrix: 167s AHFactivity AHFantigen 167s AHFactivity 0.015688 0.010544 167s AHFantigen 0.010544 0.016633 167s 167s Linear Coeficients: 167s AHFactivity AHFantigen 167s carrier -32.2203 20.3935 167s normal -9.1149 1.7409 167s 167s Constants: 167s carrier normal 167s -5.1843 -1.4259 167s 167s Apparent error rate 0.1467 167s 167s Classification table 167s Predicted 167s Actual carrier normal 167s carrier 38 7 167s normal 4 26 167s 167s Confusion matrix 167s Predicted 167s Actual carrier normal 167s carrier 0.844 0.156 167s normal 0.133 0.867 167s 167s Data: anorexia 167s Call: 167s Linda(Treat ~ ., data = anorexia, method = method) 167s 167s Prior Probabilities of Groups: 167s CBT Cont FT 167s 0.40278 0.36111 0.23611 167s 167s Group means: 167s Prewt Postwt 167s CBT 82.634 82.060 167s Cont 81.605 80.459 167s FT 85.157 93.822 167s 167s Within-groups Covariance Matrix: 167s Prewt Postwt 167s Prewt 15.8294 4.4663 167s Postwt 4.4663 19.6356 167s 167s Linear Coeficients: 167s Prewt Postwt 167s CBT 4.3183 3.1970 167s Cont 4.2734 3.1256 167s FT 4.3080 3.7983 167s 167s Constants: 167s CBT Cont FT 167s -310.50 -301.12 -363.05 167s 167s Apparent error rate 0.4583 167s 167s Classification table 167s Predicted 167s Actual CBT Cont FT 167s CBT 15 5 9 167s Cont 14 11 1 167s FT 0 4 13 167s 167s Confusion matrix 167s Predicted 167s Actual CBT Cont FT 167s CBT 0.517 0.172 0.310 167s Cont 0.538 0.423 0.038 167s FT 0.000 0.235 0.765 167s 167s Data: Pima 167s Call: 167s Linda(type ~ ., data = Pima.tr, method = method) 167s 167s Prior Probabilities of Groups: 167s No Yes 167s 0.66 0.34 167s 167s Group means: 167s npreg glu bp skin bmi ped age 167s No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 167s Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 167s 167s Within-groups Covariance Matrix: 167s npreg glu bp skin bmi ped age 167s npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 167s glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 167s bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 167s skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 167s bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 167s ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 167s age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 167s 167s Linear Coeficients: 167s npreg glu bp skin bmi ped age 167s No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 167s Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 167s 167s Constants: 167s No Yes 167s -43.449 -60.176 167s 167s Apparent error rate 0.23 167s 167s Classification table 167s Predicted 167s Actual No Yes 167s No 108 24 167s Yes 22 46 167s 167s Confusion matrix 167s Predicted 167s Actual No Yes 167s No 0.818 0.182 167s Yes 0.324 0.676 167s 167s Data: Forest soils 167s 167s Apparent error rate 0.3621 167s 167s Classification table 167s Predicted 167s Actual 1 2 3 167s 1 7 3 1 167s 2 4 13 6 167s 3 3 4 17 167s 167s Confusion matrix 167s Predicted 167s Actual 1 2 3 167s 1 0.636 0.273 0.091 167s 2 0.174 0.565 0.261 167s 3 0.125 0.167 0.708 167s 167s Data: Raven and Miller diabetes data 167s Call: 167s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 167s 167s Prior Probabilities of Groups: 167s normal chemical overt 167s 0.52414 0.24828 0.22759 167s 167s Group means: 167s insulin glucose sspg 167s normal 159.540 344.06 99.486 167s chemical 252.992 478.16 219.442 167s overt 79.635 1094.96 338.517 167s 167s Within-groups Covariance Matrix: 167s insulin glucose sspg 167s insulin 3844.877 67.238 1456.55 167s glucose 67.238 2228.396 324.21 167s sspg 1456.548 324.205 2181.73 167s 167s Linear Coeficients: 167s insulin glucose sspg 167s normal 0.040407 0.15379 -0.0042303 167s chemical 0.047858 0.20764 0.0377766 167s overt -0.026158 0.47736 0.1016873 167s 167s Constants: 167s normal chemical overt 167s -30.115 -61.233 -278.996 167s 167s Apparent error rate 0.0966 167s 167s Classification table 167s Predicted 167s Actual normal chemical overt 167s normal 71 5 0 167s chemical 2 34 0 167s overt 0 7 26 167s 167s Confusion matrix 167s Predicted 167s Actual normal chemical overt 167s normal 0.934 0.066 0.000 167s chemical 0.056 0.944 0.000 167s overt 0.000 0.212 0.788 167s 167s Data: iris 167s Call: 167s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 167s 167s Prior Probabilities of Groups: 167s setosa versicolor virginica 167s 0.33333 0.33333 0.33333 167s 167s Group means: 167s Sepal.Length Sepal.Width Petal.Length Petal.Width 167s setosa 4.9654 3.3913 1.4507 0.21639 167s versicolor 5.8767 2.7909 4.2238 1.34189 167s virginica 6.5075 2.9777 5.4459 2.05921 167s 167s Within-groups Covariance Matrix: 167s Sepal.Length Sepal.Width Petal.Length Petal.Width 167s Sepal.Length 0.180280 0.068876 0.101512 0.036096 167s Sepal.Width 0.068876 0.079556 0.047722 0.029409 167s Petal.Length 0.101512 0.047722 0.111492 0.038658 167s Petal.Width 0.036096 0.029409 0.038658 0.029965 167s 167s Linear Coeficients: 167s Sepal.Length Sepal.Width Petal.Length Petal.Width 167s setosa 28.582 46.5236 -13.859 -54.9877 167s versicolor 19.837 11.9601 20.865 -17.7704 167s virginica 16.999 1.9046 29.808 7.9189 167s 167s Constants: 167s setosa versicolor virginica 167s -134.94 -108.22 -148.56 167s 167s Apparent error rate 0.0133 167s 167s Classification table 167s Predicted 167s Actual setosa versicolor virginica 167s setosa 50 0 0 167s versicolor 0 49 1 167s virginica 0 1 49 167s 167s Confusion matrix 167s Predicted 167s Actual setosa versicolor virginica 167s setosa 1 0.00 0.00 167s versicolor 0 0.98 0.02 167s virginica 0 0.02 0.98 167s 167s Data: crabs 167s Call: 167s Linda(sp ~ ., data = crabs, method = method) 167s 167s Prior Probabilities of Groups: 167s B O 167s 0.5 0.5 167s 167s Group means: 167s sexM index FL RW CL CW BD 167s B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 167s O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 167s 167s Within-groups Covariance Matrix: 167s sexM index FL RW CL CW BD 167s sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 167s index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 167s FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 167s RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 167s CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 167s CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 167s BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 167s 167s Linear Coeficients: 167s sexM index FL RW CL CW BD 167s B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 167s O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 167s 167s Constants: 167s B O 167s -77.695 -147.287 167s 167s Apparent error rate 0 167s 167s Classification table 167s Predicted 167s Actual B O 167s B 100 0 167s O 0 100 167s 167s Confusion matrix 167s Predicted 167s Actual B O 167s B 1 0 167s O 0 1 167s 167s Data: fish 167s 167s Apparent error rate 0.0063 167s 167s Classification table 167s Predicted 167s Actual 1 2 3 4 5 6 7 167s 1 34 0 0 0 0 0 0 167s 2 0 6 0 0 0 0 0 167s 3 0 0 20 0 0 0 0 167s 4 0 0 0 11 0 0 0 167s 5 0 0 0 0 14 0 0 167s 6 0 0 0 0 0 17 0 167s 7 0 0 0 0 1 0 55 167s 167s Confusion matrix 167s Predicted 167s Actual 1 2 3 4 5 6 7 167s 1 1 0 0 0 0.000 0 0.000 167s 2 0 1 0 0 0.000 0 0.000 167s 3 0 0 1 0 0.000 0 0.000 167s 4 0 0 0 1 0.000 0 0.000 167s 5 0 0 0 0 1.000 0 0.000 167s 6 0 0 0 0 0.000 1 0.000 167s 7 0 0 0 0 0.018 0 0.982 167s 167s Data: pottery 167s Call: 167s Linda(origin ~ ., data = pottery, method = method) 167s 167s Prior Probabilities of Groups: 167s Attic Eritrean 167s 0.48148 0.51852 167s 167s Group means: 167s SI AL FE MG CA TI 167s Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 167s Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 167s 167s Within-groups Covariance Matrix: 167s SI AL FE MG CA TI 167s SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 167s AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 167s FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 167s MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 167s CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 167s TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 167s 167s Linear Coeficients: 167s SI AL FE MG CA TI 167s Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 167s Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 167s 167s Constants: 167s Attic Eritrean 167s -4346 -4139 167s 167s Apparent error rate 0.1111 167s 167s Classification table 167s Predicted 167s Actual Attic Eritrean 167s Attic 12 1 167s Eritrean 2 12 167s 167s Confusion matrix 167s Predicted 167s Actual Attic Eritrean 167s Attic 0.923 0.077 167s Eritrean 0.143 0.857 167s 167s Data: olitos 167s 167s Apparent error rate 0.1 167s 167s Classification table 167s Predicted 167s Actual 1 2 3 4 167s 1 45 2 2 1 167s 2 0 25 0 0 167s 3 4 1 27 2 167s 4 0 0 0 11 167s 167s Confusion matrix 167s Predicted 167s Actual 1 2 3 4 167s 1 0.900 0.040 0.040 0.020 167s 2 0.000 1.000 0.000 0.000 167s 3 0.118 0.029 0.794 0.059 167s 4 0.000 0.000 0.000 1.000 167s =================================================== 167s > #dodata(method="fsa") 167s > 167s BEGIN TEST tldapp.R 167s 167s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 167s Copyright (C) 2025 The R Foundation for Statistical Computing 167s Platform: x86_64-pc-linux-gnu 167s 167s R is free software and comes with ABSOLUTELY NO WARRANTY. 167s You are welcome to redistribute it under certain conditions. 167s Type 'license()' or 'licence()' for distribution details. 167s 167s R is a collaborative project with many contributors. 167s Type 'contributors()' for more information and 167s 'citation()' on how to cite R or R packages in publications. 167s 167s Type 'demo()' for some demos, 'help()' for on-line help, or 167s 'help.start()' for an HTML browser interface to help. 167s Type 'q()' to quit R. 167s 167s > ## VT::15.09.2013 - this will render the output independent 167s > ## from the version of the package 167s > suppressPackageStartupMessages(library(rrcov)) 167s > library(MASS) 167s > 167s > dodata <- function(method) { 167s + 167s + options(digits = 5) 167s + set.seed(101) 167s + 167s + tmp <- sys.call() 167s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 167s + cat("===================================================\n") 167s + 167s + data(pottery); show(lda <- LdaPP(origin~., data=pottery, method=method)); show(predict(lda)) 167s + data(hemophilia); show(lda <- LdaPP(as.factor(gr)~., data=hemophilia, method=method)); show(predict(lda)) 167s + data(anorexia); show(lda <- LdaPP(Treat~., data=anorexia, method=method)); show(predict(lda)) 167s + data(Pima.tr); show(lda <- LdaPP(type~., data=Pima.tr, method=method)); show(predict(lda)) 167s + data(crabs); show(lda <- LdaPP(sp~., data=crabs, method=method)); show(predict(lda)) 167s + 167s + cat("===================================================\n") 167s + } 167s > 167s > 167s > ## -- now do it: 167s > 167s > ## Commented out - still to slow 167s > ##dodata(method="huber") 167s > ##dodata(method="sest") 167s > 167s > ## VT::14.11.2018 - Commented out: too slow 167s > ## dodata(method="mad") 167s > ## dodata(method="class") 167s > 167s BEGIN TEST tmcd4.R 167s 167s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 167s Copyright (C) 2025 The R Foundation for Statistical Computing 167s Platform: x86_64-pc-linux-gnu 167s 167s R is free software and comes with ABSOLUTELY NO WARRANTY. 167s You are welcome to redistribute it under certain conditions. 167s Type 'license()' or 'licence()' for distribution details. 167s 167s R is a collaborative project with many contributors. 167s Type 'contributors()' for more information and 167s 'citation()' on how to cite R or R packages in publications. 167s 167s Type 'demo()' for some demos, 'help()' for on-line help, or 167s 'help.start()' for an HTML browser interface to help. 167s Type 'q()' to quit R. 167s 167s > ## Test the exact fit property of CovMcd 167s > doexactfit <- function(){ 167s + exact <-function(seed=1234){ 167s + 167s + set.seed(seed) 167s + 167s + n1 <- 45 167s + p <- 2 167s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 167s + x1[,p] <- x1[,p] + 3 167s + n2 <- 55 167s + m1 <- 0 167s + m2 <- 3 167s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 167s + x<-rbind(x1,x2) 167s + colnames(x) <- c("X1","X2") 167s + x 167s + } 167s + print(CovMcd(exact())) 167s + } 167s > 167s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 167s + ##@bdescr 167s + ## Test the function covMcd() on the literature datasets: 167s + ## 167s + ## Call CovMcd() for all regression datasets available in rrcov and print: 167s + ## - execution time (if time == TRUE) 167s + ## - objective fucntion 167s + ## - best subsample found (if short == false) 167s + ## - outliers identified (with cutoff 0.975) (if short == false) 167s + ## - estimated center and covarinance matrix if full == TRUE) 167s + ## 167s + ##@edescr 167s + ## 167s + ##@in nrep : [integer] number of repetitions to use for estimating the 167s + ## (average) execution time 167s + ##@in time : [boolean] whether to evaluate the execution time 167s + ##@in short : [boolean] whether to do short output (i.e. only the 167s + ## objective function value). If short == FALSE, 167s + ## the best subsample and the identified outliers are 167s + ## printed. See also the parameter full below 167s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 167s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 167s + 167s + doest <- function(x, xname, nrep=1){ 167s + n <- dim(x)[1] 167s + p <- dim(x)[2] 167s + if(method == "MASS"){ 167s + mcd<-cov.mcd(x) 167s + quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2) 167s + } 167s + else{ 167s + mcd <- if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 167s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 167s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 167s + else CovMcd(x, trace=FALSE) 167s + quan <- as.integer(mcd@quan) 167s + } 167s + 167s + crit <- mcd@crit 167s + 167s + if(time){ 167s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 167s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 167s + } 167s + else{ 167s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 167s + } 167s + lpad<-lname-nchar(xname) 167s + cat(pad.right(xname,lpad), xres) 167s + 167s + if(!short){ 167s + cat("Best subsample: \n") 167s + if(length(mcd@best) > 150) 167s + cat("Too long... \n") 167s + else 167s + print(mcd@best) 167s + 167s + ibad <- which(mcd@wt==0) 167s + names(ibad) <- NULL 167s + nbad <- length(ibad) 167s + cat("Outliers: ",nbad,"\n") 167s + if(nbad > 0 & nbad < 150) 167s + print(ibad) 167s + else 167s + cat("Too many to print ... \n") 167s + if(full){ 167s + cat("-------------\n") 167s + show(mcd) 167s + } 167s + cat("--------------------------------------------------------\n") 167s + } 167s + } 167s + 167s + options(digits = 5) 167s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 167s + 167s + lname <- 20 167s + 167s + ## VT::15.09.2013 - this will render the output independent 167s + ## from the version of the package 167s + suppressPackageStartupMessages(library(rrcov)) 167s + 167s + method <- match.arg(method) 167s + if(method == "MASS") 167s + library(MASS) 167s + 167s + data(Animals, package = "MASS") 167s + brain <- Animals[c(1:24, 26:25, 27:28),] 167s + 167s + data(fish) 167s + data(pottery) 167s + data(rice) 167s + data(un86) 167s + data(wages) 167s + 167s + tmp <- sys.call() 167s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 167s + 167s + cat("Data Set n p Half LOG(obj) Time\n") 167s + cat("========================================================\n") 167s + 167s + if(method=="exact") 167s + { 167s + ## only small data sets 167s + doest(heart[, 1:2], data(heart), nrep) 167s + doest(starsCYG, data(starsCYG), nrep) 167s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 167s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 167s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 167s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 167s + doest(brain, "Animals", nrep) 167s + doest(lactic, data(lactic), nrep) 167s + doest(pension, data(pension), nrep) 167s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 167s + doest(stack.x, data(stackloss), nrep) 167s + doest(pilot, data(pilot), nrep) 167s + } else 167s + { 167s + doest(heart[, 1:2], data(heart), nrep) 167s + doest(starsCYG, data(starsCYG), nrep) 167s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 167s + doest(stack.x, data(stackloss), nrep) 167s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 167s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 167s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 167s + doest(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 167s + 167s + doest(brain, "Animals", nrep) 167s + ## doest(milk, data(milk), nrep) # difference between 386 and x64 167s + doest(bushfire, data(bushfire), nrep) 167s + 167s + doest(lactic, data(lactic), nrep) 167s + doest(pension, data(pension), nrep) 167s + ## doest(pilot, data(pilot), nrep) # difference between 386 and x64 167s + 167s + if(method != "MRCD") # these two are quite slow for MRCD, especially the second one 167s + { 167s + doest(radarImage, data(radarImage), nrep) 167s + doest(NOxEmissions, data(NOxEmissions), nrep) 167s + } 167s + 167s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 167s + doest(data.matrix(subset(wagnerGrowth, select = -Period)), data(wagnerGrowth), nrep) 167s + 167s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep) 167s + doest(data.matrix(subset(pottery, select = -origin)), data(pottery), nrep) 167s + doest(rice, data(rice), nrep) 167s + doest(un86, data(un86), nrep) 167s + 167s + doest(wages, data(wages), nrep) 167s + 167s + ## from package 'datasets' 167s + doest(airquality[,1:4], data(airquality), nrep) 167s + doest(attitude, data(attitude), nrep) 167s + doest(attenu, data(attenu), nrep) 167s + doest(USJudgeRatings, data(USJudgeRatings), nrep) 167s + doest(USArrests, data(USArrests), nrep) 167s + doest(longley, data(longley), nrep) 167s + doest(Loblolly, data(Loblolly), nrep) 167s + doest(quakes[,1:4], data(quakes), nrep) 167s + } 167s + cat("========================================================\n") 167s + } 167s > 167s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMCD", "MASS")){ 167s + 167s + doest <- function(x, nrep=1){ 167s + gc() 167s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 167s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 167s + xtime 167s + } 167s + 167s + set.seed(1234) 167s + 167s + ## VT::15.09.2013 - this will render the output independent 167s + ## from the version of the package 167s + suppressPackageStartupMessages(library(rrcov)) 167s + 167s + library(MASS) 167s + method <- match.arg(method) 167s + 167s + ap <- c(2, 5, 10, 20, 30) 167s + an <- c(100, 500, 1000, 10000, 50000) 167s + 167s + tottime <- 0 167s + cat(" n p Time\n") 167s + cat("=====================\n") 167s + for(i in 1:length(an)) { 167s + for(j in 1:length(ap)) { 167s + n <- an[i] 167s + p <- ap[j] 167s + if(5*p <= n){ 167s + xx <- gendata(n, p, eps) 167s + X <- xx$X 167s + tottime <- tottime + doest(X, nrep) 167s + } 167s + } 167s + } 167s + 167s + cat("=====================\n") 167s + cat("Total time: ", tottime*nrep, "\n") 167s + } 167s > 167s > docheck <- function(n, p, eps){ 167s + xx <- gendata(n,p,eps) 167s + mcd <- CovMcd(xx$X) 167s + check(mcd, xx$xind) 167s + } 167s > 167s > check <- function(mcd, xind){ 167s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 167s + ## did not get zero weight 167s + mymatch <- xind %in% which(mcd@wt == 0) 167s + length(xind) - length(which(mymatch)) 167s + } 167s > 167s > dorep <- function(x, nrep=1, method=c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 167s + 167s + method <- match.arg(method) 167s + for(i in 1:nrep) 167s + if(method == "MASS") 167s + cov.mcd(x) 167s + else 167s + { 167s + if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 167s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 167s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 167s + else CovMcd(x, trace=FALSE) 167s + } 167s + } 167s > 167s > #### gendata() #### 167s > # Generates a location contaminated multivariate 167s > # normal sample of n observations in p dimensions 167s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 167s > # where 167s > # m = (b,b,...,b) 167s > # Defaults: eps=0 and b=10 167s > # 167s > gendata <- function(n,p,eps=0,b=10){ 167s + 167s + if(missing(n) || missing(p)) 167s + stop("Please specify (n,p)") 167s + if(eps < 0 || eps >= 0.5) 167s + stop(message="eps must be in [0,0.5)") 167s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 167s + nbad <- as.integer(eps * n) 167s + if(nbad > 0){ 167s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 167s + xind <- sample(n,nbad) 167s + X[xind,] <- Xbad 167s + } 167s + list(X=X, xind=xind) 167s + } 167s > 167s > pad.right <- function(z, pads) 167s + { 167s + ### Pads spaces to right of text 167s + padding <- paste(rep(" ", pads), collapse = "") 167s + paste(z, padding, sep = "") 167s + } 167s > 167s > whatis<-function(x){ 167s + if(is.data.frame(x)) 167s + cat("Type: data.frame\n") 167s + else if(is.matrix(x)) 167s + cat("Type: matrix\n") 167s + else if(is.vector(x)) 167s + cat("Type: vector\n") 167s + else 167s + cat("Type: don't know\n") 167s + } 167s > 167s > ## VT::15.09.2013 - this will render the output independent 167s > ## from the version of the package 167s > suppressPackageStartupMessages(library(rrcov)) 167s > 167s > dodata() 167s 167s Call: dodata() 167s Data Set n p Half LOG(obj) Time 167s ======================================================== 167s heart 12 2 7 5.678742 167s Best subsample: 167s [1] 1 3 4 5 7 9 11 167s Outliers: 0 167s Too many to print ... 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s height weight 167s 38.3 33.1 167s 167s Robust Estimate of Covariance: 167s height weight 167s height 135 259 167s weight 259 564 167s -------------------------------------------------------- 167s starsCYG 47 2 25 -8.031215 167s Best subsample: 167s [1] 1 2 4 6 8 10 12 13 16 24 25 26 28 32 33 37 38 39 40 41 42 43 44 45 46 167s Outliers: 7 167s [1] 7 9 11 14 20 30 34 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s log.Te log.light 167s 4.41 4.95 167s 167s Robust Estimate of Covariance: 167s log.Te log.light 167s log.Te 0.0132 0.0394 167s log.light 0.0394 0.2743 167s -------------------------------------------------------- 167s phosphor 18 2 10 6.878847 167s Best subsample: 167s [1] 3 5 8 9 11 12 13 14 15 17 167s Outliers: 3 167s [1] 1 6 10 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s inorg organic 167s 13.4 38.8 167s 167s Robust Estimate of Covariance: 167s inorg organic 167s inorg 129 130 167s organic 130 182 167s -------------------------------------------------------- 167s stackloss 21 3 12 5.472581 167s Best subsample: 167s [1] 4 5 6 7 8 9 10 11 12 13 14 20 167s Outliers: 9 167s [1] 1 2 3 15 16 17 18 19 21 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s Air.Flow Water.Temp Acid.Conc. 167s 59.5 20.8 87.3 167s 167s Robust Estimate of Covariance: 167s Air.Flow Water.Temp Acid.Conc. 167s Air.Flow 6.29 5.85 5.74 167s Water.Temp 5.85 9.23 6.14 167s Acid.Conc. 5.74 6.14 23.25 167s -------------------------------------------------------- 167s coleman 20 5 13 1.286808 167s Best subsample: 167s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 167s Outliers: 7 167s [1] 1 6 9 10 11 15 18 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s salaryP fatherWc sstatus teacherSc motherLev 167s 2.76 48.38 6.12 25.00 6.40 167s 167s Robust Estimate of Covariance: 167s salaryP fatherWc sstatus teacherSc motherLev 167s salaryP 0.253 1.786 -0.266 0.151 0.075 167s fatherWc 1.786 1303.382 330.496 12.604 34.503 167s sstatus -0.266 330.496 119.888 3.833 10.131 167s teacherSc 0.151 12.604 3.833 0.785 0.555 167s motherLev 0.075 34.503 10.131 0.555 1.043 167s -------------------------------------------------------- 167s salinity 28 3 16 1.326364 167s Best subsample: 167s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 167s Outliers: 4 167s [1] 5 16 23 24 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s X1 X2 X3 167s 10.08 2.78 22.78 167s 167s Robust Estimate of Covariance: 167s X1 X2 X3 167s X1 10.44 1.01 -3.19 167s X2 1.01 3.83 -1.44 167s X3 -3.19 -1.44 2.39 167s -------------------------------------------------------- 167s wood 20 5 13 -36.270094 167s Best subsample: 167s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 167s Outliers: 7 167s [1] 4 6 7 8 11 16 19 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s x1 x2 x3 x4 x5 167s 0.587 0.122 0.531 0.538 0.892 167s 167s Robust Estimate of Covariance: 167s x1 x2 x3 x4 x5 167s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 167s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 167s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 167s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 167s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 167s -------------------------------------------------------- 167s hbk 75 3 39 -1.047858 167s Best subsample: 167s [1] 15 16 17 18 19 20 21 22 23 24 26 27 31 32 33 35 36 37 38 40 43 49 50 51 54 167s [26] 55 56 58 59 61 63 64 66 67 70 71 72 73 74 167s Outliers: 14 167s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=39); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s X1 X2 X3 167s 1.54 1.78 1.69 167s 167s Robust Estimate of Covariance: 167s X1 X2 X3 167s X1 1.227 0.055 0.127 167s X2 0.055 1.249 0.153 167s X3 0.127 0.153 1.160 167s -------------------------------------------------------- 167s Animals 28 2 15 14.555543 167s Best subsample: 167s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 167s Outliers: 14 167s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s body brain 167s 18.7 64.9 167s 167s Robust Estimate of Covariance: 167s body brain 167s body 929 1576 167s brain 1576 5646 167s -------------------------------------------------------- 167s bushfire 38 5 22 18.135810 167s Best subsample: 167s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 167s Outliers: 16 167s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=22); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s V1 V2 V3 V4 V5 167s 105 147 274 218 279 167s 167s Robust Estimate of Covariance: 167s V1 V2 V3 V4 V5 167s V1 346 268 -1692 -381 -311 167s V2 268 236 -1125 -230 -194 167s V3 -1692 -1125 9993 2455 1951 167s V4 -381 -230 2455 647 505 167s V5 -311 -194 1951 505 398 167s -------------------------------------------------------- 167s lactic 20 2 11 0.359580 167s Best subsample: 167s [1] 1 2 3 4 5 7 8 9 10 11 12 167s Outliers: 4 167s [1] 17 18 19 20 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s X Y 167s 3.86 5.01 167s 167s Robust Estimate of Covariance: 167s X Y 167s X 10.6 14.6 167s Y 14.6 21.3 167s -------------------------------------------------------- 167s pension 18 2 10 16.675508 167s Best subsample: 167s [1] 1 2 3 4 5 6 8 9 11 12 167s Outliers: 5 167s [1] 14 15 16 17 18 167s ------------- 167s 167s Call: 167s CovMcd(x = x, trace = FALSE) 167s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 167s 167s Robust Estimate of Location: 167s Income Reserves 167s 52.3 560.9 167s 167s Robust Estimate of Covariance: 167s Income Reserves 167s Income 1420 11932 167s Reserves 11932 208643 167s -------------------------------------------------------- 168s radarImage 1573 5 789 36.694425 168s Best subsample: 168s Too long... 168s Outliers: 117 168s [1] 164 237 238 242 261 262 351 450 451 462 480 481 509 516 535 168s [16] 542 572 597 620 643 654 669 697 737 802 803 804 818 832 833 168s [31] 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 1202 1223 168s [46] 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 1376 1393 168s [61] 1394 1402 1403 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1479 1487 168s [76] 1492 1504 1510 1511 1512 1517 1518 1519 1520 1521 1522 1525 1526 1527 1528 168s [91] 1530 1532 1534 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 168s [106] 1557 1558 1561 1562 1564 1565 1566 1567 1569 1570 1571 1573 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=789); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s X.coord Y.coord Band.1 Band.2 Band.3 168s 52.80 35.12 6.77 18.44 8.90 168s 168s Robust Estimate of Covariance: 168s X.coord Y.coord Band.1 Band.2 Band.3 168s X.coord 123.6 23.0 -361.9 -197.1 -22.5 168s Y.coord 23.0 400.6 34.3 -191.1 -39.1 168s Band.1 -361.9 34.3 27167.9 8178.8 473.7 168s Band.2 -197.1 -191.1 8178.8 26021.8 952.4 168s Band.3 -22.5 -39.1 473.7 952.4 4458.4 168s -------------------------------------------------------- 168s NOxEmissions 8088 4 4046 2.474539 168s Best subsample: 168s Too long... 168s Outliers: 2156 168s Too many to print ... 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=4046); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s julday LNOx LNOxEm sqrtWS 168s 168.19 4.73 7.91 1.37 168s 168s Robust Estimate of Covariance: 168s julday LNOx LNOxEm sqrtWS 168s julday 9180.6297 12.0306 0.7219 -10.1273 168s LNOx 12.0306 0.4721 0.1418 -0.1526 168s LNOxEm 0.7219 0.1418 0.2516 0.0438 168s sqrtWS -10.1273 -0.1526 0.0438 0.2073 168s -------------------------------------------------------- 168s vaso 39 2 21 -3.972244 168s Best subsample: 168s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 168s Outliers: 4 168s [1] 1 2 17 31 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s Volume Rate 168s 1.16 1.72 168s 168s Robust Estimate of Covariance: 168s Volume Rate 168s Volume 0.313 -0.167 168s Rate -0.167 0.728 168s -------------------------------------------------------- 168s wagnerGrowth 63 6 35 6.572208 168s Best subsample: 168s [1] 2 3 4 5 6 7 9 10 11 12 13 14 16 17 18 20 23 25 27 31 32 35 36 38 44 168s [26] 48 51 52 53 54 55 56 57 60 62 168s Outliers: 13 168s [1] 1 8 15 21 22 28 29 33 42 43 46 50 63 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=35); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s Region PA GPA HS GHS y 168s 11.00 33.66 -2.00 2.48 0.31 7.48 168s 168s Robust Estimate of Covariance: 168s Region PA GPA HS GHS y 168s Region 35.5615 17.9337 -0.5337 -0.9545 -0.3093 -14.0090 168s PA 17.9337 27.7333 -4.9017 -1.4174 0.0343 -28.7040 168s GPA -0.5337 -4.9017 5.3410 0.2690 -0.1484 4.0006 168s HS -0.9545 -1.4174 0.2690 0.8662 -0.0454 2.9024 168s GHS -0.3093 0.0343 -0.1484 -0.0454 0.1772 0.7457 168s y -14.0090 -28.7040 4.0006 2.9024 0.7457 82.6877 168s -------------------------------------------------------- 168s fish 159 6 82 8.879005 168s Best subsample: 168s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 168s [20] 20 21 22 23 24 25 26 27 28 30 32 35 36 37 42 43 44 45 46 168s [39] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 107 109 110 111 113 168s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 168s [77] 134 135 136 137 138 139 168s Outliers: 63 168s [1] 30 39 40 41 42 62 63 64 65 66 68 69 70 73 74 75 76 77 78 168s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 168s [39] 98 99 100 101 102 103 104 105 141 143 144 145 147 148 149 150 151 152 153 168s [58] 154 155 156 157 158 159 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=82); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s Weight Length1 Length2 Length3 Height Width 168s 329.9 24.5 26.6 29.7 31.1 14.7 168s 168s Robust Estimate of Covariance: 168s Weight Length1 Length2 Length3 Height Width 168s Weight 69082.99 1477.81 1613.64 1992.62 1439.32 -62.12 168s Length1 1477.81 34.68 37.61 45.51 28.82 -1.31 168s Length2 1613.64 37.61 40.88 49.52 31.81 -1.40 168s Length3 1992.62 45.51 49.52 61.16 42.65 -2.25 168s Height 1439.32 28.82 31.81 42.65 46.74 -2.82 168s Width -62.12 -1.31 -1.40 -2.25 -2.82 1.01 168s -------------------------------------------------------- 168s pottery 27 6 17 -10.586933 168s Best subsample: 168s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 168s Outliers: 9 168s [1] 3 8 12 16 17 18 23 24 25 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=17); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s SI AL FE MG CA TI 168s 54.983 15.206 9.700 3.817 5.211 0.859 168s 168s Robust Estimate of Covariance: 168s SI AL FE MG CA TI 168s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 168s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 168s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 168s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 168s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 168s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 168s -------------------------------------------------------- 168s rice 105 6 56 -14.463986 168s Best subsample: 168s [1] 2 4 6 8 10 12 15 18 21 22 24 29 30 31 32 33 34 36 37 168s [20] 38 41 44 45 47 51 52 53 54 55 59 61 65 67 68 69 70 72 76 168s [39] 78 79 80 81 82 83 84 85 86 92 93 94 95 97 98 99 102 105 168s Outliers: 13 168s [1] 9 14 19 28 40 42 49 58 62 71 75 77 89 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=56); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s Favor Appearance Taste Stickiness 168s -0.2731 0.0600 -0.1468 0.0646 168s Toughness Overall_evaluation 168s 0.0894 -0.2192 168s 168s Robust Estimate of Covariance: 168s Favor Appearance Taste Stickiness Toughness 168s Favor 0.388 0.323 0.393 0.389 -0.195 168s Appearance 0.323 0.503 0.494 0.494 -0.270 168s Taste 0.393 0.494 0.640 0.629 -0.361 168s Stickiness 0.389 0.494 0.629 0.815 -0.486 168s Toughness -0.195 -0.270 -0.361 -0.486 0.451 168s Overall_evaluation 0.471 0.575 0.723 0.772 -0.457 168s Overall_evaluation 168s Favor 0.471 168s Appearance 0.575 168s Taste 0.723 168s Stickiness 0.772 168s Toughness -0.457 168s Overall_evaluation 0.882 168s -------------------------------------------------------- 168s un86 73 7 40 17.009322 168s Best subsample: 168s [1] 1 2 9 10 12 14 16 17 18 20 23 24 26 27 31 32 37 39 41 42 45 47 48 49 50 168s [26] 51 52 55 56 60 61 62 63 64 65 67 70 71 72 73 168s Outliers: 30 168s [1] 3 4 5 6 7 8 11 13 15 19 21 22 28 29 30 34 35 36 38 40 43 44 46 53 54 168s [26] 58 59 66 68 69 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s POP MOR CAR DR GNP DEN TB 168s 20.740 71.023 6.435 0.817 1.146 56.754 0.441 168s 168s Robust Estimate of Covariance: 168s POP MOR CAR DR GNP DEN 168s POP 582.4034 224.9343 -12.6722 -1.6729 -3.3664 226.1952 168s MOR 224.9343 2351.3907 -286.9504 -32.0743 -35.5649 -527.4684 168s CAR -12.6722 -286.9504 58.1190 5.7393 6.6365 83.6180 168s DR -1.6729 -32.0743 5.7393 0.8339 0.5977 12.1938 168s GNP -3.3664 -35.5649 6.6365 0.5977 1.4175 13.0709 168s DEN 226.1952 -527.4684 83.6180 12.1938 13.0709 2041.5809 168s TB 0.4002 -1.1807 0.2701 0.0191 0.0058 -0.9346 168s TB 168s POP 0.4002 168s MOR -1.1807 168s CAR 0.2701 168s DR 0.0191 168s GNP 0.0058 168s DEN -0.9346 168s TB 0.0184 168s -------------------------------------------------------- 168s wages 39 10 19 22.994272 168s Best subsample: 168s [1] 1 2 6 7 8 9 10 11 12 13 14 15 17 18 19 25 26 27 28 168s Outliers: 9 168s [1] 4 5 6 24 28 30 32 33 34 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 168s 2153.37 2.87 1129.16 297.53 360.58 6876.58 39.48 2.36 168s RACE SCHOOL 168s 38.88 10.17 168s 168s Robust Estimate of Covariance: 168s HRS RATE ERSP ERNO NEIN ASSET 168s HRS 6.12e+03 1.73e+01 -1.67e+03 -2.06e+03 9.10e+03 2.02e+05 168s RATE 1.73e+01 2.52e-01 2.14e+01 -3.54e+00 5.85e+01 1.37e+03 168s ERSP -1.67e+03 2.14e+01 1.97e+04 7.76e+01 -1.71e+03 -1.41e+04 168s ERNO -2.06e+03 -3.54e+00 7.76e+01 2.06e+03 -2.02e+03 -4.83e+04 168s NEIN 9.10e+03 5.85e+01 -1.71e+03 -2.02e+03 2.02e+04 4.54e+05 168s ASSET 2.02e+05 1.37e+03 -1.41e+04 -4.83e+04 4.54e+05 1.03e+07 168s AGE -6.29e+01 -2.61e-01 4.83e+00 2.44e+01 -1.08e+02 -2.46e+03 168s DEP -6.17e+00 -7.05e-02 -2.13e+01 2.29e+00 -1.30e+01 -3.16e+02 168s RACE -2.17e+03 -9.46e+00 7.19e+02 5.59e+02 -3.95e+03 -8.77e+04 168s SCHOOL 7.12e+01 5.87e-01 5.39e+01 -2.14e+01 1.63e+02 3.79e+03 168s AGE DEP RACE SCHOOL 168s HRS -6.29e+01 -6.17e+00 -2.17e+03 7.12e+01 168s RATE -2.61e-01 -7.05e-02 -9.46e+00 5.87e-01 168s ERSP 4.83e+00 -2.13e+01 7.19e+02 5.39e+01 168s ERNO 2.44e+01 2.29e+00 5.59e+02 -2.14e+01 168s NEIN -1.08e+02 -1.30e+01 -3.95e+03 1.63e+02 168s ASSET -2.46e+03 -3.16e+02 -8.77e+04 3.79e+03 168s AGE 1.01e+00 7.03e-02 2.39e+01 -9.52e-01 168s DEP 7.03e-02 4.62e-02 2.72e+00 -1.94e-01 168s RACE 2.39e+01 2.72e+00 8.74e+02 -3.09e+01 168s SCHOOL -9.52e-01 -1.94e-01 -3.09e+01 1.62e+00 168s -------------------------------------------------------- 168s airquality 153 4 58 18.213499 168s Best subsample: 168s [1] 3 22 24 25 28 29 32 33 35 36 37 38 39 40 41 42 43 44 46 168s [20] 47 48 49 50 52 56 57 58 59 60 64 66 67 68 69 71 72 73 74 168s [39] 76 78 80 82 83 84 86 87 89 90 91 92 93 94 95 97 98 105 109 168s [58] 110 168s Outliers: 14 168s [1] 8 9 15 18 20 21 23 24 28 30 48 62 117 148 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=58); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s Ozone Solar.R Wind Temp 168s 43.2 192.9 9.6 80.5 168s 168s Robust Estimate of Covariance: 168s Ozone Solar.R Wind Temp 168s Ozone 959.69 771.68 -60.92 198.38 168s Solar.R 771.68 7089.72 -1.72 95.75 168s Wind -60.92 -1.72 10.71 -11.96 168s Temp 198.38 95.75 -11.96 62.78 168s -------------------------------------------------------- 168s attitude 30 7 19 24.442803 168s Best subsample: 168s [1] 2 3 4 5 7 8 10 12 15 17 19 20 22 23 25 27 28 29 30 168s Outliers: 10 168s [1] 1 6 9 13 14 16 18 21 24 26 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s rating complaints privileges learning raises critical 168s 67.1 68.0 52.4 57.6 67.2 77.4 168s advance 168s 43.4 168s 168s Robust Estimate of Covariance: 168s rating complaints privileges learning raises critical advance 168s rating 169.34 127.83 40.48 110.26 91.71 -3.59 53.84 168s complaints 127.83 156.80 52.65 110.97 96.56 7.27 76.03 168s privileges 40.48 52.65 136.91 92.38 69.00 9.53 87.98 168s learning 110.26 110.97 92.38 157.77 112.92 6.74 75.51 168s raises 91.71 96.56 69.00 112.92 112.79 4.91 70.22 168s critical -3.59 7.27 9.53 6.74 4.91 52.25 15.00 168s advance 53.84 76.03 87.98 75.51 70.22 15.00 93.11 168s -------------------------------------------------------- 168s attenu 182 5 86 6.440834 168s Best subsample: 168s [1] 68 69 70 71 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 168s [20] 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 115 116 117 118 168s [39] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 168s [58] 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 168s [77] 157 158 159 160 161 162 163 164 165 166 168s Outliers: 61 168s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 168s [20] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 168s [39] 40 45 46 47 54 55 56 57 58 59 60 61 64 65 82 97 98 100 101 168s [58] 102 103 104 105 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=86); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s event mag station dist accel 168s 18.624 5.752 67.861 22.770 0.141 168s 168s Robust Estimate of Covariance: 168s event mag station dist accel 168s event 1.64e+01 -1.22e+00 5.59e+01 9.98e+00 -8.37e-02 168s mag -1.22e+00 4.13e-01 -3.19e+00 1.35e+00 1.22e-02 168s station 5.59e+01 -3.19e+00 1.03e+03 7.00e+01 5.56e-01 168s dist 9.98e+00 1.35e+00 7.00e+01 2.21e+02 -9.24e-01 168s accel -8.37e-02 1.22e-02 5.56e-01 -9.24e-01 9.62e-03 168s -------------------------------------------------------- 168s USJudgeRatings 43 12 28 -47.889993 168s Best subsample: 168s [1] 1 2 3 4 6 9 10 11 15 16 17 18 19 22 24 25 26 27 28 29 32 33 34 36 37 168s [26] 38 41 43 168s Outliers: 14 168s [1] 5 7 8 12 13 14 20 21 23 30 31 35 40 42 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=28); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 168s 7.40 8.19 7.80 7.96 7.74 7.82 7.74 7.73 7.57 7.63 8.25 7.94 168s 168s Robust Estimate of Covariance: 168s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL 168s CONT 0.852 -0.266 -0.422 -0.155 -0.049 -0.074 -0.117 -0.119 -0.177 168s INTG -0.266 0.397 0.537 0.406 0.340 0.325 0.404 0.409 0.430 168s DMNR -0.422 0.537 0.824 0.524 0.458 0.437 0.520 0.504 0.569 168s DILG -0.155 0.406 0.524 0.486 0.426 0.409 0.506 0.515 0.511 168s CFMG -0.049 0.340 0.458 0.426 0.427 0.403 0.466 0.476 0.478 168s DECI -0.074 0.325 0.437 0.409 0.403 0.396 0.449 0.462 0.460 168s PREP -0.117 0.404 0.520 0.506 0.466 0.449 0.552 0.565 0.551 168s FAMI -0.119 0.409 0.504 0.515 0.476 0.462 0.565 0.594 0.571 168s ORAL -0.177 0.430 0.569 0.511 0.478 0.460 0.551 0.571 0.575 168s WRIT -0.159 0.427 0.549 0.515 0.480 0.461 0.556 0.580 0.574 168s PHYS -0.184 0.269 0.362 0.308 0.298 0.307 0.335 0.358 0.369 168s RTEN -0.260 0.472 0.642 0.519 0.467 0.455 0.539 0.554 0.573 168s WRIT PHYS RTEN 168s CONT -0.159 -0.184 -0.260 168s INTG 0.427 0.269 0.472 168s DMNR 0.549 0.362 0.642 168s DILG 0.515 0.308 0.519 168s CFMG 0.480 0.298 0.467 168s DECI 0.461 0.307 0.455 168s PREP 0.556 0.335 0.539 168s FAMI 0.580 0.358 0.554 168s ORAL 0.574 0.369 0.573 168s WRIT 0.580 0.365 0.567 168s PHYS 0.365 0.300 0.378 168s RTEN 0.567 0.378 0.615 168s -------------------------------------------------------- 168s USArrests 50 4 27 15.391648 168s Best subsample: 168s [1] 4 7 9 12 13 14 15 16 19 21 23 26 27 29 30 32 34 35 36 38 41 42 43 45 46 168s [26] 49 50 168s Outliers: 11 168s [1] 2 3 5 6 10 18 24 28 33 37 47 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s Murder Assault UrbanPop Rape 168s 6.71 145.42 65.06 17.88 168s 168s Robust Estimate of Covariance: 168s Murder Assault UrbanPop Rape 168s Murder 16.1 269.3 20.3 25.2 168s Assault 269.3 6613.0 567.8 453.7 168s UrbanPop 20.3 567.8 225.4 47.7 168s Rape 25.2 453.7 47.7 50.9 168s -------------------------------------------------------- 168s longley 16 7 12 12.747678 168s Best subsample: 168s [1] 5 6 7 8 9 10 11 12 13 14 15 16 168s Outliers: 4 168s [1] 1 2 3 4 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s GNP.deflator GNP Unemployed Armed.Forces Population 168s 106.5 430.6 328.2 295.0 120.2 168s Year Employed 168s 1956.5 66.9 168s 168s Robust Estimate of Covariance: 168s GNP.deflator GNP Unemployed Armed.Forces Population 168s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 168s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 168s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 168s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 168s Population 81.4 803.4 929.1 -327.4 63.5 168s Year 51.6 504.3 595.6 -216.7 39.7 168s Employed 34.2 344.1 323.6 -149.5 26.2 168s Year Employed 168s GNP.deflator 51.6 34.2 168s GNP 504.3 344.1 168s Unemployed 595.6 323.6 168s Armed.Forces -216.7 -149.5 168s Population 39.7 26.2 168s Year 25.1 16.7 168s Employed 16.7 12.4 168s -------------------------------------------------------- 168s Loblolly 84 3 44 4.898174 168s Best subsample: 168s [1] 1 2 4 7 8 10 13 14 19 20 21 25 26 28 31 32 33 34 37 38 39 40 43 44 45 168s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 168s Outliers: 31 168s [1] 5 6 11 12 15 17 18 23 24 29 30 35 36 41 42 47 48 53 54 59 60 65 66 70 71 168s [26] 72 76 77 78 83 84 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=44); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s height age Seed 168s 20.44 8.19 7.72 168s 168s Robust Estimate of Covariance: 168s height age Seed 168s height 247.8 79.5 11.9 168s age 79.5 25.7 3.0 168s Seed 11.9 3.0 17.1 168s -------------------------------------------------------- 168s quakes 1000 4 502 8.274369 168s Best subsample: 168s Too long... 168s Outliers: 265 168s Too many to print ... 168s ------------- 168s 168s Call: 168s CovMcd(x = x, trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=502); nsamp = 500; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s lat long depth mag 168s -21.31 182.48 361.35 4.54 168s 168s Robust Estimate of Covariance: 168s lat long depth mag 168s lat 1.47e+01 3.53e+00 1.34e+02 -2.52e-01 168s long 3.53e+00 4.55e+00 -3.63e+02 4.36e-02 168s depth 1.34e+02 -3.63e+02 4.84e+04 -1.29e+01 168s mag -2.52e-01 4.36e-02 -1.29e+01 1.38e-01 168s -------------------------------------------------------- 168s ======================================================== 168s > dodata(method="deterministic") 168s 168s Call: dodata(method = "deterministic") 168s Data Set n p Half LOG(obj) Time 168s ======================================================== 168s heart 12 2 7 5.678742 168s Best subsample: 168s [1] 1 3 4 5 7 9 11 168s Outliers: 0 168s Too many to print ... 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=7) 168s 168s Robust Estimate of Location: 168s height weight 168s 38.3 33.1 168s 168s Robust Estimate of Covariance: 168s height weight 168s height 135 259 168s weight 259 564 168s -------------------------------------------------------- 168s starsCYG 47 2 25 -8.028718 168s Best subsample: 168s [1] 1 6 10 12 13 16 23 24 25 26 28 31 32 33 37 38 39 40 41 42 43 44 45 46 47 168s Outliers: 7 168s [1] 7 9 11 14 20 30 34 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=25) 168s 168s Robust Estimate of Location: 168s log.Te log.light 168s 4.41 4.95 168s 168s Robust Estimate of Covariance: 168s log.Te log.light 168s log.Te 0.0132 0.0394 168s log.light 0.0394 0.2743 168s -------------------------------------------------------- 168s phosphor 18 2 10 7.732906 168s Best subsample: 168s [1] 2 4 5 7 8 9 11 12 14 16 168s Outliers: 1 168s [1] 6 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 168s 168s Robust Estimate of Location: 168s inorg organic 168s 12.5 40.8 168s 168s Robust Estimate of Covariance: 168s inorg organic 168s inorg 124 101 168s organic 101 197 168s -------------------------------------------------------- 168s stackloss 21 3 12 6.577286 168s Best subsample: 168s [1] 4 5 6 7 8 9 11 13 16 18 19 20 168s Outliers: 2 168s [1] 1 2 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 168s 168s Robust Estimate of Location: 168s Air.Flow Water.Temp Acid.Conc. 168s 58.4 20.5 86.1 168s 168s Robust Estimate of Covariance: 168s Air.Flow Water.Temp Acid.Conc. 168s Air.Flow 56.28 13.33 26.68 168s Water.Temp 13.33 8.28 6.98 168s Acid.Conc. 26.68 6.98 37.97 168s -------------------------------------------------------- 168s coleman 20 5 13 2.149184 168s Best subsample: 168s [1] 3 4 5 7 8 12 13 14 16 17 18 19 20 168s Outliers: 2 168s [1] 6 10 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 168s 168s Robust Estimate of Location: 168s salaryP fatherWc sstatus teacherSc motherLev 168s 2.76 41.08 2.76 25.01 6.27 168s 168s Robust Estimate of Covariance: 168s salaryP fatherWc sstatus teacherSc motherLev 168s salaryP 0.391 2.956 2.146 0.447 0.110 168s fatherWc 2.956 1358.640 442.724 12.235 32.842 168s sstatus 2.146 442.724 205.590 6.464 11.382 168s teacherSc 0.447 12.235 6.464 1.179 0.510 168s motherLev 0.110 32.842 11.382 0.510 0.919 168s -------------------------------------------------------- 168s salinity 28 3 16 1.940763 168s Best subsample: 168s [1] 1 8 10 12 13 14 15 17 18 20 21 22 25 26 27 28 168s Outliers: 2 168s [1] 5 16 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=16) 168s 168s Robust Estimate of Location: 168s X1 X2 X3 168s 10.50 2.58 23.12 168s 168s Robust Estimate of Covariance: 168s X1 X2 X3 168s X1 10.90243 -0.00457 -1.46156 168s X2 -0.00457 3.85051 -1.94604 168s X3 -1.46156 -1.94604 3.21424 168s -------------------------------------------------------- 168s wood 20 5 13 -35.240819 168s Best subsample: 168s [1] 1 2 3 5 9 11 12 13 14 15 17 18 20 168s Outliers: 4 168s [1] 4 6 8 19 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 168s 168s Robust Estimate of Location: 168s x1 x2 x3 x4 x5 168s 0.582 0.125 0.530 0.534 0.888 168s 168s Robust Estimate of Covariance: 168s x1 x2 x3 x4 x5 168s x1 1.05e-02 1.81e-03 2.08e-03 -6.41e-04 -9.61e-04 168s x2 1.81e-03 5.55e-04 8.76e-04 -2.03e-04 -4.70e-05 168s x3 2.08e-03 8.76e-04 5.60e-03 -1.11e-03 -1.26e-05 168s x4 -6.41e-04 -2.03e-04 -1.11e-03 4.27e-03 2.60e-03 168s x5 -9.61e-04 -4.70e-05 -1.26e-05 2.60e-03 2.95e-03 168s -------------------------------------------------------- 168s hbk 75 3 39 -1.045501 168s Best subsample: 168s [1] 15 17 18 19 20 21 22 23 24 26 27 28 29 32 33 35 36 38 40 41 43 48 49 50 51 168s [26] 54 55 56 58 59 63 64 66 67 70 71 72 73 74 168s Outliers: 14 168s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=39) 168s 168s Robust Estimate of Location: 168s X1 X2 X3 168s 1.54 1.78 1.69 168s 168s Robust Estimate of Covariance: 168s X1 X2 X3 168s X1 1.227 0.055 0.127 168s X2 0.055 1.249 0.153 168s X3 0.127 0.153 1.160 168s -------------------------------------------------------- 168s Animals 28 2 15 14.555543 168s Best subsample: 168s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 168s Outliers: 14 168s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=15) 168s 168s Robust Estimate of Location: 168s body brain 168s 18.7 64.9 168s 168s Robust Estimate of Covariance: 168s body brain 168s body 929 1576 168s brain 1576 5646 168s -------------------------------------------------------- 168s bushfire 38 5 22 18.135810 168s Best subsample: 168s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 168s Outliers: 16 168s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=22) 168s 168s Robust Estimate of Location: 168s V1 V2 V3 V4 V5 168s 105 147 274 218 279 168s 168s Robust Estimate of Covariance: 168s V1 V2 V3 V4 V5 168s V1 346 268 -1692 -381 -311 168s V2 268 236 -1125 -230 -194 168s V3 -1692 -1125 9993 2455 1951 168s V4 -381 -230 2455 647 505 168s V5 -311 -194 1951 505 398 168s -------------------------------------------------------- 168s lactic 20 2 11 0.359580 168s Best subsample: 168s [1] 1 2 3 4 5 7 8 9 10 11 12 168s Outliers: 4 168s [1] 17 18 19 20 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=11) 168s 168s Robust Estimate of Location: 168s X Y 168s 3.86 5.01 168s 168s Robust Estimate of Covariance: 168s X Y 168s X 10.6 14.6 168s Y 14.6 21.3 168s -------------------------------------------------------- 168s pension 18 2 10 16.675508 168s Best subsample: 168s [1] 1 2 3 4 5 6 8 9 11 12 168s Outliers: 5 168s [1] 14 15 16 17 18 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 168s 168s Robust Estimate of Location: 168s Income Reserves 168s 52.3 560.9 168s 168s Robust Estimate of Covariance: 168s Income Reserves 168s Income 1420 11932 168s Reserves 11932 208643 168s -------------------------------------------------------- 168s radarImage 1573 5 789 36.694865 168s Best subsample: 168s Too long... 168s Outliers: 114 168s [1] 164 237 238 242 261 262 351 450 451 462 463 480 481 509 516 168s [16] 535 542 572 597 620 643 654 669 679 697 737 802 803 804 818 168s [31] 832 833 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 168s [46] 1202 1223 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 168s [61] 1376 1393 1394 1402 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1504 168s [76] 1510 1511 1512 1518 1519 1520 1521 1522 1525 1526 1527 1528 1530 1532 1534 168s [91] 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1557 1558 1561 168s [106] 1562 1564 1565 1566 1567 1569 1570 1571 1573 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=789) 168s 168s Robust Estimate of Location: 168s X.coord Y.coord Band.1 Band.2 Band.3 168s 52.78 35.37 7.12 18.81 9.09 168s 168s Robust Estimate of Covariance: 168s X.coord Y.coord Band.1 Band.2 Band.3 168s X.coord 123.2 21.5 -363.9 -200.1 -24.3 168s Y.coord 21.5 410.7 46.5 -177.3 -33.4 168s Band.1 -363.9 46.5 27051.1 8138.9 469.3 168s Band.2 -200.1 -177.3 8138.9 25938.0 946.2 168s Band.3 -24.3 -33.4 469.3 946.2 4470.1 168s -------------------------------------------------------- 168s NOxEmissions 8088 4 4046 2.474536 168s Best subsample: 168s Too long... 168s Outliers: 2152 168s Too many to print ... 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=4046) 168s 168s Robust Estimate of Location: 168s julday LNOx LNOxEm sqrtWS 168s 168.20 4.73 7.91 1.37 168s 168s Robust Estimate of Covariance: 168s julday LNOx LNOxEm sqrtWS 168s julday 9176.2934 12.0355 0.7022 -10.1387 168s LNOx 12.0355 0.4736 0.1430 -0.1528 168s LNOxEm 0.7022 0.1430 0.2527 0.0436 168s sqrtWS -10.1387 -0.1528 0.0436 0.2074 168s -------------------------------------------------------- 168s vaso 39 2 21 -3.972244 168s Best subsample: 168s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 168s Outliers: 4 168s [1] 1 2 17 31 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=21) 168s 168s Robust Estimate of Location: 168s Volume Rate 168s 1.16 1.72 168s 168s Robust Estimate of Covariance: 168s Volume Rate 168s Volume 0.313 -0.167 168s Rate -0.167 0.728 168s -------------------------------------------------------- 168s wagnerGrowth 63 6 35 6.511864 168s Best subsample: 168s [1] 2 3 4 5 6 7 9 10 11 12 13 16 17 18 20 23 25 27 31 32 35 36 38 41 44 168s [26] 48 51 52 53 54 55 56 57 60 62 168s Outliers: 15 168s [1] 1 8 15 21 22 28 29 33 39 42 43 46 49 50 63 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=35) 168s 168s Robust Estimate of Location: 168s Region PA GPA HS GHS y 168s 10.91 33.65 -2.05 2.43 0.31 6.98 168s 168s Robust Estimate of Covariance: 168s Region PA GPA HS GHS y 168s Region 35.1365 17.7291 -1.4003 -0.6554 -0.4728 -14.9305 168s PA 17.7291 28.4297 -5.5245 -1.2444 -0.0452 -29.6181 168s GPA -1.4003 -5.5245 5.2170 0.3954 -0.2152 3.8252 168s HS -0.6554 -1.2444 0.3954 0.7273 -0.0107 2.1514 168s GHS -0.4728 -0.0452 -0.2152 -0.0107 0.1728 0.8440 168s y -14.9305 -29.6181 3.8252 2.1514 0.8440 79.0511 168s -------------------------------------------------------- 168s fish 159 6 82 8.880459 168s Best subsample: 168s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 168s [20] 20 21 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 168s [39] 50 51 52 53 54 55 56 57 58 59 60 106 107 108 109 110 111 112 113 168s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 168s [77] 134 135 136 137 138 139 168s Outliers: 64 168s [1] 30 39 40 41 62 63 64 65 66 68 69 70 73 74 75 76 77 78 79 168s [20] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 168s [39] 99 100 101 102 103 104 105 141 142 143 144 145 146 147 148 149 150 151 152 168s [58] 153 154 155 156 157 158 159 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=82) 168s 168s Robust Estimate of Location: 168s Weight Length1 Length2 Length3 Height Width 168s 316.3 24.1 26.3 29.3 31.0 14.7 168s 168s Robust Estimate of Covariance: 168s Weight Length1 Length2 Length3 Height Width 168s Weight 64662.19 1412.34 1541.95 1917.21 1420.83 -61.15 168s Length1 1412.34 34.14 37.04 45.07 29.25 -1.26 168s Length2 1541.95 37.04 40.26 49.04 32.21 -1.34 168s Length3 1917.21 45.07 49.04 60.82 43.03 -2.15 168s Height 1420.83 29.25 32.21 43.03 46.50 -2.66 168s Width -61.15 -1.26 -1.34 -2.15 -2.66 1.02 168s -------------------------------------------------------- 168s pottery 27 6 17 -10.586933 168s Best subsample: 168s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 168s Outliers: 9 168s [1] 3 8 12 16 17 18 23 24 25 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=17) 168s 168s Robust Estimate of Location: 168s SI AL FE MG CA TI 168s 54.983 15.206 9.700 3.817 5.211 0.859 168s 168s Robust Estimate of Covariance: 168s SI AL FE MG CA TI 168s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 168s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 168s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 168s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 168s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 168s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 168s -------------------------------------------------------- 168s rice 105 6 56 -14.423048 168s Best subsample: 168s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 168s [20] 38 44 45 47 51 52 53 55 59 60 65 66 67 70 72 74 76 78 79 168s [39] 80 81 82 83 84 85 86 90 92 93 94 95 97 98 99 100 101 105 168s Outliers: 13 168s [1] 9 19 28 40 42 43 49 58 62 64 71 75 77 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=56) 168s 168s Robust Estimate of Location: 168s Favor Appearance Taste Stickiness 168s -0.2950 0.0799 -0.1555 0.0363 168s Toughness Overall_evaluation 168s 0.0530 -0.2284 168s 168s Robust Estimate of Covariance: 168s Favor Appearance Taste Stickiness Toughness 168s Favor 0.466 0.389 0.471 0.447 -0.198 168s Appearance 0.389 0.610 0.592 0.570 -0.293 168s Taste 0.471 0.592 0.760 0.718 -0.356 168s Stickiness 0.447 0.570 0.718 0.820 -0.419 168s Toughness -0.198 -0.293 -0.356 -0.419 0.400 168s Overall_evaluation 0.557 0.669 0.838 0.846 -0.425 168s Overall_evaluation 168s Favor 0.557 168s Appearance 0.669 168s Taste 0.838 168s Stickiness 0.846 168s Toughness -0.425 168s Overall_evaluation 0.987 168s -------------------------------------------------------- 168s un86 73 7 40 17.117142 168s Best subsample: 168s [1] 2 9 10 12 14 16 17 18 19 20 23 24 25 26 27 31 32 33 37 39 42 48 49 50 51 168s [26] 52 55 56 57 60 61 62 63 64 65 67 70 71 72 73 168s Outliers: 30 168s [1] 3 4 5 6 7 8 11 13 15 21 22 28 29 30 35 36 38 40 41 43 44 45 46 53 54 168s [26] 58 59 66 68 69 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=40) 168s 168s Robust Estimate of Location: 168s POP MOR CAR DR GNP DEN TB 168s 17.036 68.512 6.444 0.877 1.134 64.140 0.433 168s 168s Robust Estimate of Covariance: 168s POP MOR CAR DR GNP DEN 168s POP 3.61e+02 1.95e+02 -6.28e+00 -1.91e-02 -2.07e+00 5.79e+01 168s MOR 1.95e+02 2.39e+03 -2.79e+02 -3.37e+01 -3.39e+01 -9.21e+02 168s CAR -6.28e+00 -2.79e+02 5.76e+01 5.77e+00 6.59e+00 7.81e+01 168s DR -1.91e-02 -3.37e+01 5.77e+00 9.07e-01 5.66e-01 1.69e+01 168s GNP -2.07e+00 -3.39e+01 6.59e+00 5.66e-01 1.42e+00 9.28e+00 168s DEN 5.79e+01 -9.21e+02 7.81e+01 1.69e+01 9.28e+00 3.53e+03 168s TB -6.09e-02 -9.93e-01 2.50e-01 1.98e-02 6.82e-03 -9.75e-01 168s TB 168s POP -6.09e-02 168s MOR -9.93e-01 168s CAR 2.50e-01 168s DR 1.98e-02 168s GNP 6.82e-03 168s DEN -9.75e-01 168s TB 1.64e-02 168s -------------------------------------------------------- 168s wages 39 10 19 23.119456 168s Best subsample: 168s [1] 1 2 5 6 7 9 10 11 12 13 14 15 19 21 23 25 26 27 28 168s Outliers: 9 168s [1] 4 5 9 24 25 26 28 32 34 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 168s 168s Robust Estimate of Location: 168s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 168s 2161.89 2.95 1114.21 297.68 374.00 7269.37 39.13 2.43 168s RACE SCHOOL 168s 36.13 10.39 168s 168s Robust Estimate of Covariance: 168s HRS RATE ERSP ERNO NEIN ASSET 168s HRS 3.53e+03 8.31e+00 -5.96e+03 -6.43e+02 5.15e+03 1.12e+05 168s RATE 8.31e+00 1.78e-01 8.19e+00 2.70e+00 3.90e+01 8.94e+02 168s ERSP -5.96e+03 8.19e+00 1.90e+04 1.13e+03 -4.73e+03 -9.49e+04 168s ERNO -6.43e+02 2.70e+00 1.13e+03 1.80e+03 -3.56e+02 -7.33e+03 168s NEIN 5.15e+03 3.90e+01 -4.73e+03 -3.56e+02 1.38e+04 3.00e+05 168s ASSET 1.12e+05 8.94e+02 -9.49e+04 -7.33e+03 3.00e+05 6.62e+06 168s AGE -3.33e+01 -6.55e-02 8.33e+01 1.50e+00 -3.28e+01 -7.55e+02 168s DEP 4.50e+00 -4.01e-02 -2.77e+01 1.31e+00 -8.09e+00 -1.61e+02 168s RACE -1.30e+03 -6.06e+00 1.80e+03 1.48e+02 -2.58e+03 -5.59e+04 168s SCHOOL 3.01e+01 3.58e-01 -5.57e+00 2.84e+00 9.26e+01 2.10e+03 168s AGE DEP RACE SCHOOL 168s HRS -3.33e+01 4.50e+00 -1.30e+03 3.01e+01 168s RATE -6.55e-02 -4.01e-02 -6.06e+00 3.58e-01 168s ERSP 8.33e+01 -2.77e+01 1.80e+03 -5.57e+00 168s ERNO 1.50e+00 1.31e+00 1.48e+02 2.84e+00 168s NEIN -3.28e+01 -8.09e+00 -2.58e+03 9.26e+01 168s ASSET -7.55e+02 -1.61e+02 -5.59e+04 2.10e+03 168s AGE 6.57e-01 -1.64e-01 1.13e+01 -2.67e-01 168s DEP -1.64e-01 9.20e-02 2.38e-01 -6.01e-02 168s RACE 1.13e+01 2.38e-01 5.73e+02 -1.67e+01 168s SCHOOL -2.67e-01 -6.01e-02 -1.67e+01 7.95e-01 168s -------------------------------------------------------- 168s airquality 153 4 58 18.316848 168s Best subsample: 168s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 168s [20] 47 48 49 50 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 168s [39] 81 82 84 86 87 89 90 91 92 95 97 98 100 101 105 106 108 109 110 168s [58] 111 168s Outliers: 10 168s [1] 8 9 15 18 24 30 48 62 117 148 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=58) 168s 168s Robust Estimate of Location: 168s Ozone Solar.R Wind Temp 168s 40.80 189.37 9.66 78.81 168s 168s Robust Estimate of Covariance: 168s Ozone Solar.R Wind Temp 168s Ozone 935.54 857.76 -56.30 220.48 168s Solar.R 857.76 8507.83 1.36 155.13 168s Wind -56.30 1.36 9.90 -11.61 168s Temp 220.48 155.13 -11.61 84.00 168s -------------------------------------------------------- 168s attitude 30 7 19 24.464288 168s Best subsample: 168s [1] 2 3 4 5 7 8 10 11 12 15 17 19 21 22 23 25 27 28 29 168s Outliers: 8 168s [1] 6 9 13 14 16 18 24 26 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 168s 168s Robust Estimate of Location: 168s rating complaints privileges learning raises critical 168s 64.4 65.2 51.0 55.5 65.9 77.4 168s advance 168s 43.2 168s 168s Robust Estimate of Covariance: 168s rating complaints privileges learning raises critical advance 168s rating 199.95 162.36 115.83 160.44 128.87 -13.55 66.20 168s complaints 162.36 204.84 130.33 170.66 150.19 16.28 96.66 168s privileges 115.83 130.33 181.31 152.63 106.56 4.52 91.44 168s learning 160.44 170.66 152.63 213.06 156.57 9.92 88.31 168s raises 128.87 150.19 106.56 156.57 152.05 23.10 84.00 168s critical -13.55 16.28 4.52 9.92 23.10 80.22 27.15 168s advance 66.20 96.66 91.44 88.31 84.00 27.15 95.51 168s -------------------------------------------------------- 168s attenu 182 5 86 6.593068 168s Best subsample: 168s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 168s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 168s [39] 115 116 117 119 120 121 122 124 125 126 127 128 129 130 131 132 133 134 135 168s [58] 136 137 138 139 140 141 144 145 146 147 148 149 150 151 152 153 154 155 156 168s [77] 157 158 159 160 161 162 163 164 165 166 168s Outliers: 49 168s [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 168s [20] 23 24 25 27 28 29 30 31 32 33 40 45 47 59 60 61 64 65 78 168s [39] 82 83 97 98 100 101 102 103 104 105 117 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=86) 168s 168s Robust Estimate of Location: 168s event mag station dist accel 168s 17.122 5.798 63.461 25.015 0.131 168s 168s Robust Estimate of Covariance: 168s event mag station dist accel 168s event 2.98e+01 -1.58e+00 9.49e+01 -8.36e+00 -3.59e-02 168s mag -1.58e+00 4.26e-01 -3.88e+00 3.13e+00 5.30e-03 168s station 9.49e+01 -3.88e+00 1.10e+03 2.60e+01 5.38e-01 168s dist -8.36e+00 3.13e+00 2.60e+01 2.66e+02 -9.23e-01 168s accel -3.59e-02 5.30e-03 5.38e-01 -9.23e-01 7.78e-03 168s -------------------------------------------------------- 168s USJudgeRatings 43 12 28 -47.886937 168s Best subsample: 168s [1] 2 3 4 6 9 10 11 15 16 18 19 22 24 25 26 27 28 29 30 32 33 34 36 37 38 168s [26] 40 41 43 168s Outliers: 14 168s [1] 1 5 7 8 12 13 14 17 20 21 23 31 35 42 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=28) 168s 168s Robust Estimate of Location: 168s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 168s 7.46 8.26 7.88 8.06 7.85 7.92 7.84 7.83 7.67 7.74 8.31 8.03 168s 168s Robust Estimate of Covariance: 168s CONT INTG DMNR DILG CFMG DECI PREP FAMI 168s CONT 0.7363 -0.2916 -0.4193 -0.1943 -0.0555 -0.0690 -0.1703 -0.1727 168s INTG -0.2916 0.4179 0.5511 0.4167 0.3176 0.3102 0.4247 0.4279 168s DMNR -0.4193 0.5511 0.8141 0.5256 0.4092 0.3934 0.5294 0.5094 168s DILG -0.1943 0.4167 0.5256 0.4820 0.3904 0.3819 0.5054 0.5104 168s CFMG -0.0555 0.3176 0.4092 0.3904 0.3595 0.3368 0.4180 0.4206 168s DECI -0.0690 0.3102 0.3934 0.3819 0.3368 0.3310 0.4135 0.4194 168s PREP -0.1703 0.4247 0.5294 0.5054 0.4180 0.4135 0.5647 0.5752 168s FAMI -0.1727 0.4279 0.5094 0.5104 0.4206 0.4194 0.5752 0.6019 168s ORAL -0.2109 0.4453 0.5646 0.5054 0.4200 0.4121 0.5575 0.5735 168s WRIT -0.2033 0.4411 0.5466 0.5087 0.4222 0.4147 0.5592 0.5787 168s PHYS -0.1624 0.2578 0.3163 0.2833 0.2268 0.2362 0.3108 0.3284 168s RTEN -0.2622 0.4872 0.6324 0.5203 0.4145 0.4081 0.5488 0.5595 168s ORAL WRIT PHYS RTEN 168s CONT -0.2109 -0.2033 -0.1624 -0.2622 168s INTG 0.4453 0.4411 0.2578 0.4872 168s DMNR 0.5646 0.5466 0.3163 0.6324 168s DILG 0.5054 0.5087 0.2833 0.5203 168s CFMG 0.4200 0.4222 0.2268 0.4145 168s DECI 0.4121 0.4147 0.2362 0.4081 168s PREP 0.5575 0.5592 0.3108 0.5488 168s FAMI 0.5735 0.5787 0.3284 0.5595 168s ORAL 0.5701 0.5677 0.3283 0.5688 168s WRIT 0.5677 0.5715 0.3268 0.5645 168s PHYS 0.3283 0.3268 0.2302 0.3308 168s RTEN 0.5688 0.5645 0.3308 0.6057 168s -------------------------------------------------------- 168s USArrests 50 4 27 15.438912 168s Best subsample: 168s [1] 4 7 12 13 14 15 16 19 21 23 25 26 27 29 30 32 34 35 36 38 41 43 45 46 48 168s [26] 49 50 168s Outliers: 7 168s [1] 2 5 6 10 24 28 33 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=27) 168s 168s Robust Estimate of Location: 168s Murder Assault UrbanPop Rape 168s 6.91 150.10 65.88 18.75 168s 168s Robust Estimate of Covariance: 168s Murder Assault UrbanPop Rape 168s Murder 17.9 285.4 17.6 25.0 168s Assault 285.4 6572.8 524.9 465.0 168s UrbanPop 17.6 524.9 211.9 50.5 168s Rape 25.0 465.0 50.5 56.4 168s -------------------------------------------------------- 168s longley 16 7 12 12.747678 168s Best subsample: 168s [1] 5 6 7 8 9 10 11 12 13 14 15 16 168s Outliers: 4 168s [1] 1 2 3 4 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 168s 168s Robust Estimate of Location: 168s GNP.deflator GNP Unemployed Armed.Forces Population 168s 106.5 430.6 328.2 295.0 120.2 168s Year Employed 168s 1956.5 66.9 168s 168s Robust Estimate of Covariance: 168s GNP.deflator GNP Unemployed Armed.Forces Population 168s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 168s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 168s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 168s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 168s Population 81.4 803.4 929.1 -327.4 63.5 168s Year 51.6 504.3 595.6 -216.7 39.7 168s Employed 34.2 344.1 323.6 -149.5 26.2 168s Year Employed 168s GNP.deflator 51.6 34.2 168s GNP 504.3 344.1 168s Unemployed 595.6 323.6 168s Armed.Forces -216.7 -149.5 168s Population 39.7 26.2 168s Year 25.1 16.7 168s Employed 16.7 12.4 168s -------------------------------------------------------- 168s Loblolly 84 3 44 4.898174 168s Best subsample: 168s [1] 1 2 4 7 8 10 13 14 19 20 21 25 26 28 31 32 33 34 37 38 39 40 43 44 45 168s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 168s Outliers: 31 168s [1] 5 6 11 12 15 17 18 23 24 29 30 35 36 41 42 47 48 53 54 59 60 65 66 70 71 168s [26] 72 76 77 78 83 84 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=44) 168s 168s Robust Estimate of Location: 168s height age Seed 168s 20.44 8.19 7.72 168s 168s Robust Estimate of Covariance: 168s height age Seed 168s height 247.8 79.5 11.9 168s age 79.5 25.7 3.0 168s Seed 11.9 3.0 17.1 168s -------------------------------------------------------- 168s quakes 1000 4 502 8.274209 168s Best subsample: 168s Too long... 168s Outliers: 266 168s Too many to print ... 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 168s -> Method: Deterministic MCD(alpha=0.5 ==> h=502) 168s 168s Robust Estimate of Location: 168s lat long depth mag 168s -21.34 182.47 360.58 4.54 168s 168s Robust Estimate of Covariance: 168s lat long depth mag 168s lat 1.50e+01 3.58e+00 1.37e+02 -2.66e-01 168s long 3.58e+00 4.55e+00 -3.61e+02 4.64e-02 168s depth 1.37e+02 -3.61e+02 4.84e+04 -1.36e+01 168s mag -2.66e-01 4.64e-02 -1.36e+01 1.34e-01 168s -------------------------------------------------------- 168s ======================================================== 168s > dodata(method="exact") 168s 168s Call: dodata(method = "exact") 168s Data Set n p Half LOG(obj) Time 168s ======================================================== 168s heart 12 2 7 5.678742 168s Best subsample: 168s [1] 1 3 4 5 7 9 11 168s Outliers: 0 168s Too many to print ... 168s ------------- 168s 168s Call: 168s CovMcd(x = x, nsamp = "exact", trace = FALSE) 168s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = exact; (n,k)mini = (300,5) 168s 168s Robust Estimate of Location: 168s height weight 168s 38.3 33.1 168s 168s Robust Estimate of Covariance: 168s height weight 168s height 135 259 168s weight 259 564 168s -------------------------------------------------------- 169s starsCYG 47 2 25 -8.031215 169s Best subsample: 169s [1] 1 2 4 6 8 10 12 13 16 24 25 26 28 32 33 37 38 39 40 41 42 43 44 45 46 169s Outliers: 7 169s [1] 7 9 11 14 20 30 34 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s log.Te log.light 169s 4.41 4.95 169s 169s Robust Estimate of Covariance: 169s log.Te log.light 169s log.Te 0.0132 0.0394 169s log.light 0.0394 0.2743 169s -------------------------------------------------------- 169s phosphor 18 2 10 6.878847 169s Best subsample: 169s [1] 3 5 8 9 11 12 13 14 15 17 169s Outliers: 3 169s [1] 1 6 10 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s inorg organic 169s 13.4 38.8 169s 169s Robust Estimate of Covariance: 169s inorg organic 169s inorg 129 130 169s organic 130 182 169s -------------------------------------------------------- 169s coleman 20 5 13 1.286808 169s Best subsample: 169s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 169s Outliers: 7 169s [1] 1 6 9 10 11 15 18 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s salaryP fatherWc sstatus teacherSc motherLev 169s 2.76 48.38 6.12 25.00 6.40 169s 169s Robust Estimate of Covariance: 169s salaryP fatherWc sstatus teacherSc motherLev 169s salaryP 0.253 1.786 -0.266 0.151 0.075 169s fatherWc 1.786 1303.382 330.496 12.604 34.503 169s sstatus -0.266 330.496 119.888 3.833 10.131 169s teacherSc 0.151 12.604 3.833 0.785 0.555 169s motherLev 0.075 34.503 10.131 0.555 1.043 169s -------------------------------------------------------- 169s salinity 28 3 16 1.326364 169s Best subsample: 169s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 169s Outliers: 4 169s [1] 5 16 23 24 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s X1 X2 X3 169s 10.08 2.78 22.78 169s 169s Robust Estimate of Covariance: 169s X1 X2 X3 169s X1 10.44 1.01 -3.19 169s X2 1.01 3.83 -1.44 169s X3 -3.19 -1.44 2.39 169s -------------------------------------------------------- 169s wood 20 5 13 -36.270094 169s Best subsample: 169s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 169s Outliers: 7 169s [1] 4 6 7 8 11 16 19 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s x1 x2 x3 x4 x5 169s 0.587 0.122 0.531 0.538 0.892 169s 169s Robust Estimate of Covariance: 169s x1 x2 x3 x4 x5 169s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 169s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 169s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 169s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 169s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 169s -------------------------------------------------------- 169s Animals 28 2 15 14.555543 169s Best subsample: 169s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 169s Outliers: 14 169s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s body brain 169s 18.7 64.9 169s 169s Robust Estimate of Covariance: 169s body brain 169s body 929 1576 169s brain 1576 5646 169s -------------------------------------------------------- 169s lactic 20 2 11 0.359580 169s Best subsample: 169s [1] 1 2 3 4 5 7 8 9 10 11 12 169s Outliers: 4 169s [1] 17 18 19 20 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s X Y 169s 3.86 5.01 169s 169s Robust Estimate of Covariance: 169s X Y 169s X 10.6 14.6 169s Y 14.6 21.3 169s -------------------------------------------------------- 169s pension 18 2 10 16.675508 169s Best subsample: 169s [1] 1 2 3 4 5 6 8 9 11 12 169s Outliers: 5 169s [1] 14 15 16 17 18 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s Income Reserves 169s 52.3 560.9 169s 169s Robust Estimate of Covariance: 169s Income Reserves 169s Income 1420 11932 169s Reserves 11932 208643 169s -------------------------------------------------------- 169s vaso 39 2 21 -3.972244 169s Best subsample: 169s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 169s Outliers: 4 169s [1] 1 2 17 31 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s Volume Rate 169s 1.16 1.72 169s 169s Robust Estimate of Covariance: 169s Volume Rate 169s Volume 0.313 -0.167 169s Rate -0.167 0.728 169s -------------------------------------------------------- 169s stackloss 21 3 12 5.472581 169s Best subsample: 169s [1] 4 5 6 7 8 9 10 11 12 13 14 20 169s Outliers: 9 169s [1] 1 2 3 15 16 17 18 19 21 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s Air.Flow Water.Temp Acid.Conc. 169s 59.5 20.8 87.3 169s 169s Robust Estimate of Covariance: 169s Air.Flow Water.Temp Acid.Conc. 169s Air.Flow 6.29 5.85 5.74 169s Water.Temp 5.85 9.23 6.14 169s Acid.Conc. 5.74 6.14 23.25 169s -------------------------------------------------------- 169s pilot 20 2 11 6.487287 169s Best subsample: 169s [1] 2 3 6 7 9 12 15 16 17 18 20 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMcd(x = x, nsamp = "exact", trace = FALSE) 169s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 169s 169s Robust Estimate of Location: 169s X Y 169s 101.1 67.7 169s 169s Robust Estimate of Covariance: 169s X Y 169s X 3344 1070 169s Y 1070 343 169s -------------------------------------------------------- 169s ======================================================== 169s > dodata(method="MRCD") 169s 169s Call: dodata(method = "MRCD") 169s Data Set n p Half LOG(obj) Time 169s ======================================================== 169s heart 12 2 6 7.446266 169s Best subsample: 169s [1] 1 3 4 7 9 11 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=6) 169s 169s Robust Estimate of Location: 169s height weight 169s 38.8 33.0 169s 169s Robust Estimate of Covariance: 169s height weight 169s height 47.4 75.2 169s weight 75.2 155.4 169s -------------------------------------------------------- 169s starsCYG 47 2 24 -5.862050 169s Best subsample: 169s [1] 1 6 10 12 13 16 23 24 25 26 28 31 33 37 38 39 40 41 42 43 44 45 46 47 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=24) 169s 169s Robust Estimate of Location: 169s log.Te log.light 169s 4.44 5.05 169s 169s Robust Estimate of Covariance: 169s log.Te log.light 169s log.Te 0.00867 0.02686 169s log.light 0.02686 0.41127 169s -------------------------------------------------------- 169s phosphor 18 2 9 9.954788 169s Best subsample: 169s [1] 4 7 8 9 11 12 13 14 16 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 169s 169s Robust Estimate of Location: 169s inorg organic 169s 12.5 39.0 169s 169s Robust Estimate of Covariance: 169s inorg organic 169s inorg 236 140 169s organic 140 172 169s -------------------------------------------------------- 169s stackloss 21 3 11 7.991165 169s Best subsample: 169s [1] 4 5 6 7 8 9 10 13 18 19 20 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=11) 169s 169s Robust Estimate of Location: 169s Air.Flow Water.Temp Acid.Conc. 169s 58.2 21.4 85.2 169s 169s Robust Estimate of Covariance: 169s Air.Flow Water.Temp Acid.Conc. 169s Air.Flow 49.8 17.2 42.7 169s Water.Temp 17.2 13.8 25.2 169s Acid.Conc. 42.7 25.2 58.2 169s -------------------------------------------------------- 169s coleman 20 5 10 5.212156 169s Best subsample: 169s [1] 3 4 5 7 8 9 14 16 19 20 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 169s 169s Robust Estimate of Location: 169s salaryP fatherWc sstatus teacherSc motherLev 169s 2.78 59.44 9.28 25.41 6.70 169s 169s Robust Estimate of Covariance: 169s salaryP fatherWc sstatus teacherSc motherLev 169s salaryP 0.1582 -0.2826 0.4112 0.1754 0.0153 169s fatherWc -0.2826 902.9210 201.5815 -2.1236 18.8736 169s sstatus 0.4112 201.5815 65.4580 -0.3876 4.7794 169s teacherSc 0.1754 -2.1236 -0.3876 0.7233 -0.0322 169s motherLev 0.0153 18.8736 4.7794 -0.0322 0.5417 169s -------------------------------------------------------- 169s salinity 28 3 14 3.586919 169s Best subsample: 169s [1] 1 7 8 12 13 14 18 20 21 22 25 26 27 28 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 169s 169s Robust Estimate of Location: 169s X1 X2 X3 169s 10.95 3.71 21.99 169s 169s Robust Estimate of Covariance: 169s X1 X2 X3 169s X1 14.153 0.718 -3.359 169s X2 0.718 3.565 -0.722 169s X3 -3.359 -0.722 1.607 169s -------------------------------------------------------- 169s wood 20 5 10 -33.100492 169s Best subsample: 169s [1] 1 2 3 5 11 14 15 17 18 20 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 169s 169s Robust Estimate of Location: 169s x1 x2 x3 x4 x5 169s 0.572 0.120 0.504 0.545 0.899 169s 169s Robust Estimate of Covariance: 169s x1 x2 x3 x4 x5 169s x1 0.007543 0.001720 0.000412 -0.001230 -0.001222 169s x2 0.001720 0.000568 0.000355 -0.000533 -0.000132 169s x3 0.000412 0.000355 0.002478 0.000190 0.000811 169s x4 -0.001230 -0.000533 0.000190 0.002327 0.000967 169s x5 -0.001222 -0.000132 0.000811 0.000967 0.001894 169s -------------------------------------------------------- 169s hbk 75 3 38 1.539545 169s Best subsample: 169s [1] 15 17 18 19 20 21 22 23 24 26 27 29 32 33 35 36 38 40 41 43 48 49 50 51 54 169s [26] 55 56 58 59 63 64 66 67 70 71 72 73 74 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=38) 169s 169s Robust Estimate of Location: 169s X1 X2 X3 169s 1.60 2.37 1.64 169s 169s Robust Estimate of Covariance: 169s X1 X2 X3 169s X1 2.810 0.124 1.248 169s X2 0.124 1.017 0.208 169s X3 1.248 0.208 2.218 169s -------------------------------------------------------- 169s Animals 28 2 14 16.278395 169s Best subsample: 169s [1] 1 3 4 5 10 11 18 19 20 21 22 23 26 27 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 169s 169s Robust Estimate of Location: 169s body brain 169s 19.5 56.8 169s 169s Robust Estimate of Covariance: 169s body brain 169s body 2802 5179 169s brain 5179 13761 169s -------------------------------------------------------- 169s bushfire 38 5 19 28.483413 169s Best subsample: 169s [1] 1 2 3 4 5 14 15 16 17 18 19 20 21 22 23 24 25 26 27 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=19) 169s 169s Robust Estimate of Location: 169s V1 V2 V3 V4 V5 169s 103 145 287 221 281 169s 169s Robust Estimate of Covariance: 169s V1 V2 V3 V4 V5 169s V1 366 249 -1993 -503 -396 169s V2 249 252 -1223 -291 -233 169s V3 -1993 -1223 14246 3479 2718 169s V4 -503 -291 3479 1083 748 169s V5 -396 -233 2718 748 660 169s -------------------------------------------------------- 169s lactic 20 2 10 2.593141 169s Best subsample: 169s [1] 1 2 3 4 5 7 8 9 10 11 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 169s 169s Robust Estimate of Location: 169s X Y 169s 2.60 3.63 169s 169s Robust Estimate of Covariance: 169s X Y 169s X 8.13 13.54 169s Y 13.54 24.17 169s -------------------------------------------------------- 169s pension 18 2 9 18.931204 169s Best subsample: 169s [1] 2 3 4 5 6 8 9 11 12 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 169s 169s Robust Estimate of Location: 169s Income Reserves 169s 45.7 466.9 169s 169s Robust Estimate of Covariance: 169s Income Reserves 169s Income 2127 23960 169s Reserves 23960 348275 169s -------------------------------------------------------- 169s vaso 39 2 20 -1.864710 169s Best subsample: 169s [1] 3 4 8 14 18 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=20) 169s 169s Robust Estimate of Location: 169s Volume Rate 169s 1.14 1.77 169s 169s Robust Estimate of Covariance: 169s Volume Rate 169s Volume 0.44943 -0.00465 169s Rate -0.00465 0.34480 169s -------------------------------------------------------- 169s wagnerGrowth 63 6 32 9.287760 169s Best subsample: 169s [1] 2 3 4 5 6 7 9 10 11 12 16 18 20 23 25 27 31 32 35 36 38 41 44 48 52 169s [26] 53 54 55 56 57 60 62 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=32) 169s 169s Robust Estimate of Location: 169s Region PA GPA HS GHS y 169s 10.719 33.816 -2.144 2.487 0.293 4.918 169s 169s Robust Estimate of Covariance: 169s Region PA GPA HS GHS y 169s Region 56.7128 17.4919 -2.9710 -0.6491 -0.4545 -10.4287 169s PA 17.4919 29.9968 -7.6846 -1.3141 0.5418 -35.6434 169s GPA -2.9710 -7.6846 6.3238 1.1257 -0.4757 12.4707 169s HS -0.6491 -1.3141 1.1257 1.1330 -0.0915 3.3617 169s GHS -0.4545 0.5418 -0.4757 -0.0915 0.1468 -1.1228 169s y -10.4287 -35.6434 12.4707 3.3617 -1.1228 67.4215 169s -------------------------------------------------------- 169s fish 159 6 79 22.142828 169s Best subsample: 169s [1] 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 169s [20] 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 50 51 169s [39] 52 53 54 55 56 57 58 59 60 71 105 106 107 109 110 111 113 114 115 169s [58] 116 117 118 119 120 122 123 124 125 126 127 128 129 130 131 132 134 135 136 169s [77] 137 138 139 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=79) 169s 169s Robust Estimate of Location: 169s Weight Length1 Length2 Length3 Height Width 169s 291.7 23.8 25.9 28.9 30.4 14.7 169s 169s Robust Estimate of Covariance: 169s Weight Length1 Length2 Length3 Height Width 169s Weight 77155.07 1567.55 1713.74 2213.16 1912.62 -103.97 169s Length1 1567.55 45.66 41.57 52.14 38.66 -2.39 169s Length2 1713.74 41.57 54.26 56.77 42.72 -2.55 169s Length3 2213.16 52.14 56.77 82.57 58.84 -3.65 169s Height 1912.62 38.66 42.72 58.84 70.51 -3.80 169s Width -103.97 -2.39 -2.55 -3.65 -3.80 1.19 169s -------------------------------------------------------- 169s pottery 27 6 14 -6.897459 169s Best subsample: 169s [1] 1 2 4 5 6 10 11 13 14 15 19 21 22 26 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 169s 169s Robust Estimate of Location: 169s SI AL FE MG CA TI 169s 54.39 14.93 9.78 3.82 5.11 0.86 169s 169s Robust Estimate of Covariance: 169s SI AL FE MG CA TI 169s SI 17.47469 -0.16656 0.39943 4.48192 -0.71153 0.06515 169s AL -0.16656 3.93154 -0.35738 -2.29899 0.14770 -0.02050 169s FE 0.39943 -0.35738 0.20434 0.37562 -0.22460 0.00943 169s MG 4.48192 -2.29899 0.37562 2.82339 -0.16027 0.02943 169s CA -0.71153 0.14770 -0.22460 -0.16027 0.88443 -0.01711 169s TI 0.06515 -0.02050 0.00943 0.02943 -0.01711 0.00114 169s -------------------------------------------------------- 169s rice 105 6 53 -8.916472 169s Best subsample: 169s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 169s [20] 38 44 45 47 51 52 53 54 55 59 60 65 67 70 72 76 79 80 81 169s [39] 82 83 84 85 86 90 92 93 94 95 97 98 99 101 105 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=53) 169s 169s Robust Estimate of Location: 169s Favor Appearance Taste Stickiness 169s -0.1741 0.0774 -0.0472 0.1868 169s Toughness Overall_evaluation 169s -0.0346 -0.0683 169s 169s Robust Estimate of Covariance: 169s Favor Appearance Taste Stickiness Toughness 169s Favor 0.402 0.306 0.378 0.364 -0.134 169s Appearance 0.306 0.508 0.474 0.407 -0.146 169s Taste 0.378 0.474 0.708 0.611 -0.258 169s Stickiness 0.364 0.407 0.611 0.795 -0.320 169s Toughness -0.134 -0.146 -0.258 -0.320 0.302 169s Overall_evaluation 0.453 0.536 0.746 0.745 -0.327 169s Overall_evaluation 169s Favor 0.453 169s Appearance 0.536 169s Taste 0.746 169s Stickiness 0.745 169s Toughness -0.327 169s Overall_evaluation 0.963 169s -------------------------------------------------------- 169s un86 73 7 37 19.832993 169s Best subsample: 169s [1] 9 10 12 14 16 17 18 20 23 24 25 26 27 31 32 33 37 39 42 48 49 50 51 52 55 169s [26] 56 57 60 62 63 64 65 67 70 71 72 73 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=37) 169s 169s Robust Estimate of Location: 169s POP MOR CAR DR GNP DEN TB 169s 14.462 66.892 6.670 0.858 1.251 55.518 0.429 169s 169s Robust Estimate of Covariance: 169s POP MOR CAR DR GNP DEN 169s POP 3.00e+02 1.58e+02 9.83e+00 2.74e+00 5.51e-01 6.87e+01 169s MOR 1.58e+02 2.96e+03 -4.24e+02 -4.72e+01 -5.40e+01 -1.01e+03 169s CAR 9.83e+00 -4.24e+02 9.12e+01 8.71e+00 1.13e+01 1.96e+02 169s DR 2.74e+00 -4.72e+01 8.71e+00 1.25e+00 1.03e+00 2.74e+01 169s GNP 5.51e-01 -5.40e+01 1.13e+01 1.03e+00 2.31e+00 2.36e+01 169s DEN 6.87e+01 -1.01e+03 1.96e+02 2.74e+01 2.36e+01 3.12e+03 169s TB 2.04e-02 -1.81e+00 3.42e-01 2.57e-02 2.09e-02 -6.88e-01 169s TB 169s POP 2.04e-02 169s MOR -1.81e+00 169s CAR 3.42e-01 169s DR 2.57e-02 169s GNP 2.09e-02 169s DEN -6.88e-01 169s TB 2.59e-02 169s -------------------------------------------------------- 169s wages 39 10 14 35.698016 169s Best subsample: 169s [1] 1 2 5 6 9 10 11 13 15 19 23 25 26 28 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 169s 169s Robust Estimate of Location: 169s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 169s 2167.71 2.96 1113.50 300.43 382.29 7438.00 39.06 2.41 169s RACE SCHOOL 169s 33.00 10.45 169s 169s Robust Estimate of Covariance: 169s HRS RATE ERSP ERNO NEIN ASSET 169s HRS 1.97e+03 -4.14e-01 -4.71e+03 -6.58e+02 1.81e+03 3.84e+04 169s RATE -4.14e-01 1.14e-01 1.79e+01 3.08e+00 1.40e+01 3.57e+02 169s ERSP -4.71e+03 1.79e+01 1.87e+04 2.33e+03 -2.06e+03 -3.57e+04 169s ERNO -6.58e+02 3.08e+00 2.33e+03 5.36e+02 -3.42e+02 -5.56e+03 169s NEIN 1.81e+03 1.40e+01 -2.06e+03 -3.42e+02 5.77e+03 1.10e+05 169s ASSET 3.84e+04 3.57e+02 -3.57e+04 -5.56e+03 1.10e+05 2.86e+06 169s AGE -1.83e+01 1.09e-02 6.69e+01 8.78e+00 -5.07e+00 -1.51e+02 169s DEP 4.82e+00 -3.14e-02 -2.52e+01 -2.96e+00 -5.33e+00 -1.03e+02 169s RACE -5.67e+02 -1.33e+00 1.21e+03 1.81e+02 -9.13e+02 -1.96e+04 169s SCHOOL 5.33e+00 1.87e-01 1.86e+01 3.12e+00 3.20e+01 7.89e+02 169s AGE DEP RACE SCHOOL 169s HRS -1.83e+01 4.82e+00 -5.67e+02 5.33e+00 169s RATE 1.09e-02 -3.14e-02 -1.33e+00 1.87e-01 169s ERSP 6.69e+01 -2.52e+01 1.21e+03 1.86e+01 169s ERNO 8.78e+00 -2.96e+00 1.81e+02 3.12e+00 169s NEIN -5.07e+00 -5.33e+00 -9.13e+02 3.20e+01 169s ASSET -1.51e+02 -1.03e+02 -1.96e+04 7.89e+02 169s AGE 5.71e-01 -1.56e-01 4.58e+00 -5.00e-02 169s DEP -1.56e-01 8.08e-02 -3.02e-01 -4.47e-02 169s RACE 4.58e+00 -3.02e-01 2.36e+02 -4.54e+00 169s SCHOOL -5.00e-02 -4.47e-02 -4.54e+00 4.23e-01 169s -------------------------------------------------------- 169s airquality 153 4 56 21.136376 169s Best subsample: 169s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 169s [20] 47 48 49 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 81 169s [39] 82 84 86 87 89 90 91 92 96 97 98 100 101 105 106 109 110 111 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=56) 169s 169s Robust Estimate of Location: 169s Ozone Solar.R Wind Temp 169s 41.84 197.21 8.93 80.39 169s 169s Robust Estimate of Covariance: 169s Ozone Solar.R Wind Temp 169s Ozone 1480.7 1562.8 -99.9 347.3 169s Solar.R 1562.8 11401.2 -35.2 276.8 169s Wind -99.9 -35.2 11.4 -23.5 169s Temp 347.3 276.8 -23.5 107.7 169s -------------------------------------------------------- 169s attitude 30 7 15 27.040805 169s Best subsample: 169s [1] 2 3 4 5 7 8 10 12 15 19 22 23 25 27 28 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=15) 169s 169s Robust Estimate of Location: 169s rating complaints privileges learning raises critical 169s 65.8 66.5 50.1 56.1 66.7 78.1 169s advance 169s 41.7 169s 169s Robust Estimate of Covariance: 169s rating complaints privileges learning raises critical advance 169s rating 138.77 80.02 59.22 107.33 95.83 -1.24 54.36 169s complaints 80.02 97.23 50.59 99.50 79.15 -2.71 42.81 169s privileges 59.22 50.59 84.92 90.03 60.88 22.39 44.93 169s learning 107.33 99.50 90.03 187.67 128.71 15.48 63.67 169s raises 95.83 79.15 60.88 128.71 123.94 -1.46 49.98 169s critical -1.24 -2.71 22.39 15.48 -1.46 61.23 12.88 169s advance 54.36 42.81 44.93 63.67 49.98 12.88 48.61 169s -------------------------------------------------------- 169s attenu 182 5 83 9.710111 169s Best subsample: 169s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 169s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 169s [39] 115 116 117 121 122 124 125 126 127 128 129 130 131 132 133 134 135 136 137 169s [58] 138 139 140 141 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 169s [77] 160 161 162 163 164 165 166 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=83) 169s 169s Robust Estimate of Location: 169s event mag station dist accel 169s 18.940 5.741 67.988 23.365 0.124 169s 169s Robust Estimate of Covariance: 169s event mag station dist accel 169s event 2.86e+01 -2.31e+00 1.02e+02 2.68e+01 -1.99e-01 169s mag -2.31e+00 6.17e-01 -7.03e+00 4.67e-01 2.59e-02 169s station 1.02e+02 -7.03e+00 1.66e+03 1.62e+02 7.96e-02 169s dist 2.68e+01 4.67e-01 1.62e+02 3.61e+02 -1.23e+00 169s accel -1.99e-01 2.59e-02 7.96e-02 -1.23e+00 9.42e-03 169s -------------------------------------------------------- 169s USJudgeRatings 43 12 22 -23.463708 169s Best subsample: 169s [1] 2 3 4 6 9 11 15 16 18 19 24 25 26 27 28 29 32 33 34 36 37 38 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=22) 169s 169s Robust Estimate of Location: 169s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 169s 7.24 8.42 8.10 8.19 7.95 8.00 7.96 7.96 7.81 7.89 8.40 8.20 169s 169s Robust Estimate of Covariance: 169s CONT INTG DMNR DILG CFMG DECI PREP 169s CONT 0.61805 -0.05601 -0.09540 0.00694 0.09853 0.06261 0.03939 169s INTG -0.05601 0.23560 0.27537 0.20758 0.16603 0.17281 0.21128 169s DMNR -0.09540 0.27537 0.55349 0.28872 0.24014 0.24293 0.28886 169s DILG 0.00694 0.20758 0.28872 0.34099 0.23502 0.23917 0.29672 169s CFMG 0.09853 0.16603 0.24014 0.23502 0.31649 0.23291 0.27651 169s DECI 0.06261 0.17281 0.24293 0.23917 0.23291 0.30681 0.27737 169s PREP 0.03939 0.21128 0.28886 0.29672 0.27651 0.27737 0.42020 169s FAMI 0.04588 0.20388 0.26072 0.29037 0.27179 0.27737 0.34857 169s ORAL 0.03000 0.21379 0.29606 0.28764 0.27338 0.27424 0.33503 169s WRIT 0.03261 0.20258 0.26931 0.27962 0.26382 0.26610 0.32677 169s PHYS -0.04485 0.13598 0.17659 0.16834 0.14554 0.16467 0.18948 169s RTEN 0.01543 0.22654 0.32117 0.27307 0.23826 0.24669 0.29450 169s FAMI ORAL WRIT PHYS RTEN 169s CONT 0.04588 0.03000 0.03261 -0.04485 0.01543 169s INTG 0.20388 0.21379 0.20258 0.13598 0.22654 169s DMNR 0.26072 0.29606 0.26931 0.17659 0.32117 169s DILG 0.29037 0.28764 0.27962 0.16834 0.27307 169s CFMG 0.27179 0.27338 0.26382 0.14554 0.23826 169s DECI 0.27737 0.27424 0.26610 0.16467 0.24669 169s PREP 0.34857 0.33503 0.32677 0.18948 0.29450 169s FAMI 0.47232 0.33762 0.33420 0.19759 0.29015 169s ORAL 0.33762 0.40361 0.32208 0.19794 0.29544 169s WRIT 0.33420 0.32208 0.38733 0.19276 0.28184 169s PHYS 0.19759 0.19794 0.19276 0.20284 0.18097 169s RTEN 0.29015 0.29544 0.28184 0.18097 0.36877 169s -------------------------------------------------------- 169s USArrests 50 4 25 17.834643 169s Best subsample: 169s [1] 4 7 12 13 14 15 16 19 21 23 25 26 27 29 30 32 34 35 36 38 41 45 46 49 50 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=25) 169s 169s Robust Estimate of Location: 169s Murder Assault UrbanPop Rape 169s 5.38 121.68 63.80 16.33 169s 169s Robust Estimate of Covariance: 169s Murder Assault UrbanPop Rape 169s Murder 17.8 316.3 48.5 31.1 169s Assault 316.3 6863.0 1040.0 548.9 169s UrbanPop 48.5 1040.0 424.8 93.6 169s Rape 31.1 548.9 93.6 63.8 169s -------------------------------------------------------- 169s longley 16 7 8 31.147844 169s Best subsample: 169s [1] 5 6 7 9 10 11 13 14 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=8) 169s 169s Robust Estimate of Location: 169s GNP.deflator GNP Unemployed Armed.Forces Population 169s 104.3 410.8 278.8 300.1 118.2 169s Year Employed 169s 1955.4 66.5 169s 169s Robust Estimate of Covariance: 169s GNP.deflator GNP Unemployed Armed.Forces Population 169s GNP.deflator 85.0 652.3 784.4 -370.7 48.7 169s GNP 652.3 7502.9 7328.6 -3414.2 453.9 169s Unemployed 784.4 7328.6 10760.3 -4646.7 548.1 169s Armed.Forces -370.7 -3414.2 -4646.7 2824.3 -253.9 169s Population 48.7 453.9 548.1 -253.9 40.2 169s Year 33.5 312.7 378.8 -176.1 23.4 169s Employed 23.9 224.8 263.6 -128.3 16.8 169s Year Employed 169s GNP.deflator 33.5 23.9 169s GNP 312.7 224.8 169s Unemployed 378.8 263.6 169s Armed.Forces -176.1 -128.3 169s Population 23.4 16.8 169s Year 18.9 11.7 169s Employed 11.7 10.3 169s -------------------------------------------------------- 169s Loblolly 84 3 42 11.163448 169s Best subsample: 169s [1] 3 4 5 6 10 21 22 23 24 28 29 33 34 35 36 39 40 41 42 45 46 47 48 51 52 169s [26] 53 54 57 58 59 63 64 65 66 70 71 76 77 81 82 83 84 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=42) 169s 169s Robust Estimate of Location: 169s height age Seed 169s 44.20 17.26 6.76 169s 169s Robust Estimate of Covariance: 169s height age Seed 169s height 326.74 139.18 3.50 169s age 139.18 68.48 -2.72 169s Seed 3.50 -2.72 25.43 169s -------------------------------------------------------- 169s quakes 1000 4 500 11.802478 169s Best subsample: 169s Too long... 169s Outliers: 0 169s Too many to print ... 169s ------------- 169s 169s Call: 169s CovMrcd(x = x, trace = FALSE) 169s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=500) 169s 169s Robust Estimate of Location: 169s lat long depth mag 169s -20.59 182.13 432.46 4.42 169s 169s Robust Estimate of Covariance: 169s lat long depth mag 169s lat 15.841 5.702 -106.720 -0.441 169s long 5.702 7.426 -577.189 -0.136 169s depth -106.720 -577.189 66701.479 3.992 169s mag -0.441 -0.136 3.992 0.144 169s -------------------------------------------------------- 169s ======================================================== 169s > ##doexactfit() 169s > 170s BEGIN TEST tmest4.R 170s 170s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 170s Copyright (C) 2025 The R Foundation for Statistical Computing 170s Platform: x86_64-pc-linux-gnu 170s 170s R is free software and comes with ABSOLUTELY NO WARRANTY. 170s You are welcome to redistribute it under certain conditions. 170s Type 'license()' or 'licence()' for distribution details. 170s 170s R is a collaborative project with many contributors. 170s Type 'contributors()' for more information and 170s 'citation()' on how to cite R or R packages in publications. 170s 170s Type 'demo()' for some demos, 'help()' for on-line help, or 170s 'help.start()' for an HTML browser interface to help. 170s Type 'q()' to quit R. 170s 170s > ## VT::15.09.2013 - this will render the output independent 170s > ## from the version of the package 170s > suppressPackageStartupMessages(library(rrcov)) 170s > 170s > library(MASS) 170s > dodata <- function(nrep = 1, time = FALSE, full = TRUE) { 170s + domest <- function(x, xname, nrep = 1) { 170s + n <- dim(x)[1] 170s + p <- dim(x)[2] 170s + mm <- CovMest(x) 170s + crit <- log(mm@crit) 170s + ## c1 <- mm@psi@c1 170s + ## M <- mm$psi@M 170s + 170s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 170s + lpad <- lname-nchar(xname) 170s + cat(pad.right(xname,lpad), xres) 170s + 170s + dist <- getDistance(mm) 170s + quantiel <- qchisq(0.975, p) 170s + ibad <- which(dist >= quantiel) 170s + names(ibad) <- NULL 170s + nbad <- length(ibad) 170s + cat("Outliers: ",nbad,"\n") 170s + if(nbad > 0) 170s + print(ibad) 170s + cat("-------------\n") 170s + show(mm) 170s + cat("--------------------------------------------------------\n") 170s + } 170s + 170s + options(digits = 5) 170s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 170s + 170s + lname <- 20 170s + 170s + data(heart) 170s + data(starsCYG) 170s + data(phosphor) 170s + data(stackloss) 170s + data(coleman) 170s + data(salinity) 170s + data(wood) 170s + data(hbk) 170s + 170s + data(Animals, package = "MASS") 170s + brain <- Animals[c(1:24, 26:25, 27:28),] 170s + data(milk) 170s + data(bushfire) 170s + 170s + tmp <- sys.call() 170s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 170s + 170s + cat("Data Set n p c1 M LOG(det) Time\n") 170s + cat("======================================================================\n") 170s + domest(heart[, 1:2], data(heart), nrep) 170s + domest(starsCYG, data(starsCYG), nrep) 170s + domest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 170s + domest(stack.x, data(stackloss), nrep) 170s + domest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 170s + domest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 170s + domest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 170s + domest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 170s + 170s + 170s + domest(brain, "Animals", nrep) 170s + domest(milk, data(milk), nrep) 170s + domest(bushfire, data(bushfire), nrep) 170s + cat("======================================================================\n") 170s + } 170s > 170s > # generate contaminated data using the function gendata with different 170s > # number of outliers and check if the M-estimate breaks - i.e. the 170s > # largest eigenvalue is larger than e.g. 5. 170s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 170s > # outliers grater than 20. 170s > dogen <- function(){ 170s + eig <- vector("numeric",26) 170s + for(i in 0:25) { 170s + gg <- gendata(eps=i) 170s + mm <- CovMest(gg$x, t0=gg$tgood, S0=gg$sgood, arp=0.001) 170s + eig[i+1] <- ev <- getEvals(mm)[1] 170s + # cat(i, ev, "\n") 170s + 170s + stopifnot(ev < 5 || i > 20) 170s + } 170s + # plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 170s + } 170s > 170s > # 170s > # generate data 50x10 as multivariate normal N(0,I) and add 170s > # eps % outliers by adding d=5.0 to each component. 170s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 170s > # - if eps >= 1, it is the number of outliers 170s > # - use the center and cov of the good data as good start 170s > # - use the center and the cov of all data as a bad start 170s > # If using a good start, the M-estimate must iterate to 170s > # the good solution: the largest eigenvalue is less then e.g. 5 170s > # 170s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 170s + 170s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 170s + stop("eps is out of range") 170s + 170s + library(MASS) 170s + 170s + x <- mvrnorm(n, rep(0,p), diag(p)) 170s + bad <- vector("numeric") 170s + nbad = if(eps < 1) eps*n else eps 170s + if(nbad > 0){ 170s + bad <- sample(n, nbad) 170s + x[bad,] <- x[bad,] + d 170s + } 170s + cov1 <- cov.wt(x) 170s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 170s + 170s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 170s + } 170s > 170s > pad.right <- function(z, pads) 170s + { 170s + ## Pads spaces to right of text 170s + padding <- paste(rep(" ", pads), collapse = "") 170s + paste(z, padding, sep = "") 170s + } 170s > 170s > 170s > ## -- now do it: 170s > dodata() 170s 170s Call: dodata() 170s Data Set n p c1 M LOG(det) Time 170s ====================================================================== 170s heart 12 2 7.160341 170s Outliers: 3 170s [1] 2 6 12 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s height weight 170s 34.9 27.0 170s 170s Robust Estimate of Covariance: 170s height weight 170s height 102 155 170s weight 155 250 170s -------------------------------------------------------- 170s starsCYG 47 2 -5.994588 170s Outliers: 7 170s [1] 7 9 11 14 20 30 34 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s log.Te log.light 170s 4.42 4.95 170s 170s Robust Estimate of Covariance: 170s log.Te log.light 170s log.Te 0.0169 0.0587 170s log.light 0.0587 0.3523 170s -------------------------------------------------------- 170s phosphor 18 2 8.867522 170s Outliers: 3 170s [1] 1 6 10 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s inorg organic 170s 15.4 39.1 170s 170s Robust Estimate of Covariance: 170s inorg organic 170s inorg 169 213 170s organic 213 308 170s -------------------------------------------------------- 170s stackloss 21 3 7.241400 170s Outliers: 9 170s [1] 1 2 3 15 16 17 18 19 21 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s Air.Flow Water.Temp Acid.Conc. 170s 59.5 20.8 87.3 170s 170s Robust Estimate of Covariance: 170s Air.Flow Water.Temp Acid.Conc. 170s Air.Flow 9.34 8.69 8.52 170s Water.Temp 8.69 13.72 9.13 170s Acid.Conc. 8.52 9.13 34.54 170s -------------------------------------------------------- 170s coleman 20 5 2.574752 170s Outliers: 7 170s [1] 2 6 9 10 12 13 15 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s salaryP fatherWc sstatus teacherSc motherLev 170s 2.82 48.44 5.30 25.19 6.51 170s 170s Robust Estimate of Covariance: 170s salaryP fatherWc sstatus teacherSc motherLev 170s salaryP 0.2850 0.1045 1.7585 0.3074 0.0355 170s fatherWc 0.1045 824.8305 260.7062 3.7507 17.7959 170s sstatus 1.7585 260.7062 105.6135 4.1140 5.7714 170s teacherSc 0.3074 3.7507 4.1140 0.6753 0.1563 170s motherLev 0.0355 17.7959 5.7714 0.1563 0.4147 170s -------------------------------------------------------- 170s salinity 28 3 3.875096 170s Outliers: 9 170s [1] 3 5 10 11 15 16 17 23 24 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s X1 X2 X3 170s 10.02 3.21 22.36 170s 170s Robust Estimate of Covariance: 170s X1 X2 X3 170s X1 15.353 1.990 -5.075 170s X2 1.990 5.210 -0.769 170s X3 -5.075 -0.769 2.314 170s -------------------------------------------------------- 170s wood 20 5 -35.156305 170s Outliers: 7 170s [1] 4 6 7 8 11 16 19 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s x1 x2 x3 x4 x5 170s 0.587 0.122 0.531 0.538 0.892 170s 170s Robust Estimate of Covariance: 170s x1 x2 x3 x4 x5 170s x1 6.45e-03 1.21e-03 2.03e-03 -3.77e-04 -1.05e-03 170s x2 1.21e-03 3.12e-04 8.16e-04 -3.34e-05 1.52e-05 170s x3 2.03e-03 8.16e-04 4.27e-03 -5.60e-04 2.27e-04 170s x4 -3.77e-04 -3.34e-05 -5.60e-04 1.83e-03 1.18e-03 170s x5 -1.05e-03 1.52e-05 2.27e-04 1.18e-03 1.78e-03 170s -------------------------------------------------------- 170s hbk 75 3 1.432485 170s Outliers: 14 170s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s X1 X2 X3 170s 1.54 1.78 1.69 170s 170s Robust Estimate of Covariance: 170s X1 X2 X3 170s X1 1.6485 0.0739 0.1709 170s X2 0.0739 1.6780 0.2049 170s X3 0.1709 0.2049 1.5584 170s -------------------------------------------------------- 170s Animals 28 2 18.194822 170s Outliers: 10 170s [1] 2 6 7 9 12 14 15 16 25 28 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s body brain 170s 18.7 64.9 170s 170s Robust Estimate of Covariance: 170s body brain 170s body 4993 8466 170s brain 8466 30335 170s -------------------------------------------------------- 170s milk 86 8 -25.041802 170s Outliers: 20 170s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s X1 X2 X3 X4 X5 X6 X7 X8 170s 1.03 35.88 33.04 26.11 25.09 25.02 123.12 14.39 170s 170s Robust Estimate of Covariance: 170s X1 X2 X3 X4 X5 X6 X7 170s X1 4.89e-07 9.64e-05 1.83e-04 1.76e-04 1.57e-04 1.48e-04 6.53e-04 170s X2 9.64e-05 2.05e+00 3.38e-01 2.37e-01 1.70e-01 2.71e-01 1.91e+00 170s X3 1.83e-04 3.38e-01 1.16e+00 8.56e-01 8.48e-01 8.31e-01 8.85e-01 170s X4 1.76e-04 2.37e-01 8.56e-01 6.83e-01 6.55e-01 6.40e-01 6.91e-01 170s X5 1.57e-04 1.70e-01 8.48e-01 6.55e-01 6.93e-01 6.52e-01 6.90e-01 170s X6 1.48e-04 2.71e-01 8.31e-01 6.40e-01 6.52e-01 6.61e-01 6.95e-01 170s X7 6.53e-04 1.91e+00 8.85e-01 6.91e-01 6.90e-01 6.95e-01 4.40e+00 170s X8 5.56e-06 2.60e-01 1.98e-01 1.29e-01 1.12e-01 1.19e-01 4.12e-01 170s X8 170s X1 5.56e-06 170s X2 2.60e-01 170s X3 1.98e-01 170s X4 1.29e-01 170s X5 1.12e-01 170s X6 1.19e-01 170s X7 4.12e-01 170s X8 1.65e-01 170s -------------------------------------------------------- 170s bushfire 38 5 23.457490 170s Outliers: 15 170s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 170s ------------- 170s 170s Call: 170s CovMest(x = x) 170s -> Method: M-Estimates 170s 170s Robust Estimate of Location: 170s V1 V2 V3 V4 V5 170s 107 147 263 215 277 170s 170s Robust Estimate of Covariance: 170s V1 V2 V3 V4 V5 170s V1 775 560 -4179 -925 -759 170s V2 560 478 -2494 -510 -431 170s V3 -4179 -2494 27433 6441 5196 170s V4 -925 -510 6441 1607 1276 170s V5 -759 -431 5196 1276 1020 170s -------------------------------------------------------- 170s ====================================================================== 170s > dogen() 170s > #cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 170s > 170s BEGIN TEST tmve4.R 170s 170s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 170s Copyright (C) 2025 The R Foundation for Statistical Computing 170s Platform: x86_64-pc-linux-gnu 170s 170s R is free software and comes with ABSOLUTELY NO WARRANTY. 170s You are welcome to redistribute it under certain conditions. 170s Type 'license()' or 'licence()' for distribution details. 170s 170s R is a collaborative project with many contributors. 170s Type 'contributors()' for more information and 170s 'citation()' on how to cite R or R packages in publications. 170s 170s Type 'demo()' for some demos, 'help()' for on-line help, or 170s 'help.start()' for an HTML browser interface to help. 170s Type 'q()' to quit R. 170s 170s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMVE","MASS")){ 170s + ##@bdescr 170s + ## Test the function covMve() on the literature datasets: 170s + ## 170s + ## Call covMve() for all regression datasets available in rrco/robustbasev and print: 170s + ## - execution time (if time == TRUE) 170s + ## - objective fucntion 170s + ## - best subsample found (if short == false) 170s + ## - outliers identified (with cutoff 0.975) (if short == false) 170s + ## - estimated center and covarinance matrix if full == TRUE) 170s + ## 170s + ##@edescr 170s + ## 170s + ##@in nrep : [integer] number of repetitions to use for estimating the 170s + ## (average) execution time 170s + ##@in time : [boolean] whether to evaluate the execution time 170s + ##@in short : [boolean] whether to do short output (i.e. only the 170s + ## objective function value). If short == FALSE, 170s + ## the best subsample and the identified outliers are 170s + ## printed. See also the parameter full below 170s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 170s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 170s + 170s + domve <- function(x, xname, nrep=1){ 170s + n <- dim(x)[1] 170s + p <- dim(x)[2] 170s + alpha <- 0.5 170s + h <- h.alpha.n(alpha, n, p) 170s + if(method == "MASS"){ 170s + mve <- cov.mve(x, quantile.used=h) 170s + quan <- h #default: floor((n+p+1)/2) 170s + crit <- mve$crit 170s + best <- mve$best 170s + mah <- mahalanobis(x, mve$center, mve$cov) 170s + quantiel <- qchisq(0.975, p) 170s + wt <- as.numeric(mah < quantiel) 170s + } 170s + else{ 170s + mve <- CovMve(x, trace=FALSE) 170s + quan <- as.integer(mve@quan) 170s + crit <- log(mve@crit) 170s + best <- mve@best 170s + wt <- mve@wt 170s + } 170s + 170s + 170s + if(time){ 170s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 170s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 170s + } 170s + else{ 170s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 170s + } 170s + 170s + lpad<-lname-nchar(xname) 170s + cat(pad.right(xname,lpad), xres) 170s + 170s + if(!short){ 170s + cat("Best subsample: \n") 170s + print(best) 170s + 170s + ibad <- which(wt == 0) 170s + names(ibad) <- NULL 170s + nbad <- length(ibad) 170s + cat("Outliers: ", nbad, "\n") 170s + if(nbad > 0) 170s + print(ibad) 170s + if(full){ 170s + cat("-------------\n") 170s + show(mve) 170s + } 170s + cat("--------------------------------------------------------\n") 170s + } 170s + } 170s + 170s + options(digits = 5) 170s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 170s + 170s + lname <- 20 170s + 170s + ## VT::15.09.2013 - this will render the output independent 170s + ## from the version of the package 170s + suppressPackageStartupMessages(library(rrcov)) 170s + 170s + method <- match.arg(method) 170s + if(method == "MASS") 170s + library(MASS) 170s + 170s + 170s + data(heart) 170s + data(starsCYG) 170s + data(phosphor) 170s + data(stackloss) 170s + data(coleman) 170s + data(salinity) 170s + data(wood) 170s + 170s + data(hbk) 170s + 170s + data(Animals, package = "MASS") 170s + brain <- Animals[c(1:24, 26:25, 27:28),] 170s + data(milk) 170s + data(bushfire) 170s + 170s + tmp <- sys.call() 170s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 170s + 170s + cat("Data Set n p Half LOG(obj) Time\n") 170s + cat("========================================================\n") 170s + domve(heart[, 1:2], data(heart), nrep) 170s + domve(starsCYG, data(starsCYG), nrep) 170s + domve(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 170s + domve(stack.x, data(stackloss), nrep) 170s + domve(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 170s + domve(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 170s + domve(data.matrix(subset(wood, select = -y)), data(wood), nrep) 170s + domve(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 170s + 170s + domve(brain, "Animals", nrep) 170s + domve(milk, data(milk), nrep) 170s + domve(bushfire, data(bushfire), nrep) 170s + cat("========================================================\n") 170s + } 170s > 170s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMVE", "MASS")){ 170s + 170s + domve <- function(x, nrep=1){ 170s + gc() 170s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 170s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 170s + xtime 170s + } 170s + 170s + set.seed(1234) 170s + 170s + ## VT::15.09.2013 - this will render the output independent 170s + ## from the version of the package 170s + suppressPackageStartupMessages(library(rrcov)) 170s + library(MASS) 170s + 170s + method <- match.arg(method) 170s + 170s + ap <- c(2, 5, 10, 20, 30) 170s + an <- c(100, 500, 1000, 10000, 50000) 170s + 170s + tottime <- 0 170s + cat(" n p Time\n") 170s + cat("=====================\n") 170s + for(i in 1:length(an)) { 170s + for(j in 1:length(ap)) { 170s + n <- an[i] 170s + p <- ap[j] 170s + if(5*p <= n){ 170s + xx <- gendata(n, p, eps) 170s + X <- xx$X 170s + tottime <- tottime + domve(X, nrep) 170s + } 170s + } 170s + } 170s + 170s + cat("=====================\n") 170s + cat("Total time: ", tottime*nrep, "\n") 170s + } 170s > 170s > docheck <- function(n, p, eps){ 170s + xx <- gendata(n,p,eps) 170s + mve <- CovMve(xx$X) 170s + check(mve, xx$xind) 170s + } 170s > 170s > check <- function(mcd, xind){ 170s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 170s + ## did not get zero weight 170s + mymatch <- xind %in% which(mcd@wt == 0) 170s + length(xind) - length(which(mymatch)) 170s + } 170s > 170s > dorep <- function(x, nrep=1, method=c("FASTMVE","MASS")){ 170s + 170s + method <- match.arg(method) 170s + for(i in 1:nrep) 170s + if(method == "MASS") 170s + cov.mve(x) 170s + else 170s + CovMve(x) 170s + } 170s > 170s > #### gendata() #### 170s > # Generates a location contaminated multivariate 170s > # normal sample of n observations in p dimensions 170s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 170s > # where 170s > # m = (b,b,...,b) 170s > # Defaults: eps=0 and b=10 170s > # 170s > gendata <- function(n,p,eps=0,b=10){ 170s + 170s + if(missing(n) || missing(p)) 170s + stop("Please specify (n,p)") 170s + if(eps < 0 || eps >= 0.5) 170s + stop(message="eps must be in [0,0.5)") 170s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 170s + nbad <- as.integer(eps * n) 170s + if(nbad > 0){ 170s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 170s + xind <- sample(n,nbad) 170s + X[xind,] <- Xbad 170s + } 170s + list(X=X, xind=xind) 170s + } 170s > 170s > pad.right <- function(z, pads) 170s + { 170s + ### Pads spaces to right of text 170s + padding <- paste(rep(" ", pads), collapse = "") 170s + paste(z, padding, sep = "") 170s + } 170s > 170s > whatis<-function(x){ 170s + if(is.data.frame(x)) 170s + cat("Type: data.frame\n") 170s + else if(is.matrix(x)) 170s + cat("Type: matrix\n") 170s + else if(is.vector(x)) 170s + cat("Type: vector\n") 170s + else 170s + cat("Type: don't know\n") 170s + } 170s > 170s > ## VT::15.09.2013 - this will render the output independent 170s > ## from the version of the package 170s > suppressPackageStartupMessages(library(rrcov)) 170s > 170s > dodata() 170s 170s Call: dodata() 170s Data Set n p Half LOG(obj) Time 170s ======================================================== 170s heart 12 2 7 3.827606 170s Best subsample: 170s [1] 1 4 7 8 9 10 11 170s Outliers: 3 170s [1] 2 6 12 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s height weight 170s 34.9 27.0 170s 170s Robust Estimate of Covariance: 170s height weight 170s height 142 217 170s weight 217 350 170s -------------------------------------------------------- 170s starsCYG 47 2 25 -2.742997 170s Best subsample: 170s [1] 2 4 6 8 12 13 16 23 24 25 26 28 31 32 33 37 38 39 41 42 43 44 45 46 47 170s Outliers: 7 170s [1] 7 9 11 14 20 30 34 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s log.Te log.light 170s 4.41 4.93 170s 170s Robust Estimate of Covariance: 170s log.Te log.light 170s log.Te 0.0173 0.0578 170s log.light 0.0578 0.3615 170s -------------------------------------------------------- 170s phosphor 18 2 10 4.443101 170s Best subsample: 170s [1] 3 5 8 9 11 12 13 14 15 17 170s Outliers: 3 170s [1] 1 6 10 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s inorg organic 170s 15.2 39.4 170s 170s Robust Estimate of Covariance: 170s inorg organic 170s inorg 188 230 170s organic 230 339 170s -------------------------------------------------------- 170s stackloss 21 3 12 3.327582 170s Best subsample: 170s [1] 4 5 6 7 8 9 10 11 12 13 14 20 170s Outliers: 3 170s [1] 1 2 3 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s Air.Flow Water.Temp Acid.Conc. 170s 56.7 20.2 85.5 170s 170s Robust Estimate of Covariance: 170s Air.Flow Water.Temp Acid.Conc. 170s Air.Flow 34.31 11.07 23.54 170s Water.Temp 11.07 9.23 7.85 170s Acid.Conc. 23.54 7.85 47.35 170s -------------------------------------------------------- 170s coleman 20 5 13 2.065143 170s Best subsample: 170s [1] 1 3 4 5 7 8 11 14 16 17 18 19 20 170s Outliers: 5 170s [1] 2 6 9 10 13 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s salaryP fatherWc sstatus teacherSc motherLev 170s 2.79 44.26 3.59 25.08 6.38 170s 170s Robust Estimate of Covariance: 170s salaryP fatherWc sstatus teacherSc motherLev 170s salaryP 0.2920 1.1188 2.0421 0.3487 0.0748 170s fatherWc 1.1188 996.7540 338.6587 7.1673 23.1783 170s sstatus 2.0421 338.6587 148.2501 4.4894 7.8135 170s teacherSc 0.3487 7.1673 4.4894 0.9082 0.3204 170s motherLev 0.0748 23.1783 7.8135 0.3204 0.6024 170s -------------------------------------------------------- 170s salinity 28 3 16 2.002555 170s Best subsample: 170s [1] 1 7 8 9 12 13 14 18 19 20 21 22 25 26 27 28 170s Outliers: 5 170s [1] 5 11 16 23 24 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s X1 X2 X3 170s 10.2 3.1 22.4 170s 170s Robust Estimate of Covariance: 170s X1 X2 X3 170s X1 14.387 1.153 -4.072 170s X2 1.153 5.005 -0.954 170s X3 -4.072 -0.954 2.222 170s -------------------------------------------------------- 170s wood 20 5 13 -5.471407 170s Best subsample: 170s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 170s Outliers: 5 170s [1] 4 6 8 11 19 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s x1 x2 x3 x4 x5 170s 0.576 0.123 0.531 0.538 0.889 170s 170s Robust Estimate of Covariance: 170s x1 x2 x3 x4 x5 170s x1 7.45e-03 1.11e-03 1.83e-03 -2.90e-05 -5.65e-04 170s x2 1.11e-03 3.11e-04 7.68e-04 3.37e-05 3.85e-05 170s x3 1.83e-03 7.68e-04 4.30e-03 -9.96e-04 -6.27e-05 170s x4 -2.90e-05 3.37e-05 -9.96e-04 3.02e-03 1.91e-03 170s x5 -5.65e-04 3.85e-05 -6.27e-05 1.91e-03 2.25e-03 170s -------------------------------------------------------- 170s hbk 75 3 39 1.096831 170s Best subsample: 170s [1] 15 17 18 19 20 21 24 27 28 30 32 33 35 36 40 41 42 43 44 46 48 49 50 53 54 170s [26] 55 56 58 59 64 65 66 67 70 71 72 73 74 75 170s Outliers: 14 170s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s X1 X2 X3 170s 1.48 1.86 1.73 170s 170s Robust Estimate of Covariance: 170s X1 X2 X3 170s X1 1.695 0.230 0.265 170s X2 0.230 1.679 0.119 170s X3 0.265 0.119 1.683 170s -------------------------------------------------------- 170s Animals 28 2 15 8.945423 170s Best subsample: 170s [1] 1 3 4 5 10 11 17 18 21 22 23 24 26 27 28 170s Outliers: 9 170s [1] 2 6 7 9 12 14 15 16 25 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s body brain 170s 48.3 127.3 170s 170s Robust Estimate of Covariance: 170s body brain 170s body 10767 16872 170s brain 16872 46918 170s -------------------------------------------------------- 170s milk 86 8 47 -1.160085 170s Best subsample: 170s [1] 4 5 7 8 9 10 11 19 21 22 23 24 26 30 31 33 34 35 36 37 38 39 42 43 45 170s [26] 46 54 56 57 59 60 61 62 63 64 65 66 67 69 72 76 78 79 81 82 83 85 170s Outliers: 18 170s [1] 1 2 3 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s X1 X2 X3 X4 X5 X6 X7 X8 170s 1.03 35.91 33.02 26.08 25.06 24.99 122.93 14.38 170s 170s Robust Estimate of Covariance: 170s X1 X2 X3 X4 X5 X6 X7 170s X1 6.00e-07 1.51e-04 3.34e-04 3.09e-04 2.82e-04 2.77e-04 1.09e-03 170s X2 1.51e-04 2.03e+00 3.83e-01 3.04e-01 2.20e-01 3.51e-01 2.18e+00 170s X3 3.34e-04 3.83e-01 1.58e+00 1.21e+00 1.18e+00 1.20e+00 1.60e+00 170s X4 3.09e-04 3.04e-01 1.21e+00 9.82e-01 9.39e-01 9.53e-01 1.36e+00 170s X5 2.82e-04 2.20e-01 1.18e+00 9.39e-01 9.67e-01 9.52e-01 1.34e+00 170s X6 2.77e-04 3.51e-01 1.20e+00 9.53e-01 9.52e-01 9.92e-01 1.38e+00 170s X7 1.09e-03 2.18e+00 1.60e+00 1.36e+00 1.34e+00 1.38e+00 6.73e+00 170s X8 3.33e-05 2.92e-01 2.65e-01 1.83e-01 1.65e-01 1.76e-01 5.64e-01 170s X8 170s X1 3.33e-05 170s X2 2.92e-01 170s X3 2.65e-01 170s X4 1.83e-01 170s X5 1.65e-01 170s X6 1.76e-01 170s X7 5.64e-01 170s X8 1.80e-01 170s -------------------------------------------------------- 170s bushfire 38 5 22 5.644315 170s Best subsample: 170s [1] 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 170s Outliers: 15 170s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 170s ------------- 170s 170s Call: 170s CovMve(x = x, trace = FALSE) 170s -> Method: Minimum volume ellipsoid estimator 170s 170s Robust Estimate of Location: 170s V1 V2 V3 V4 V5 170s 107 147 263 215 277 170s 170s Robust Estimate of Covariance: 170s V1 V2 V3 V4 V5 170s V1 519 375 -2799 -619 -509 170s V2 375 320 -1671 -342 -289 170s V3 -2799 -1671 18373 4314 3480 170s V4 -619 -342 4314 1076 854 170s V5 -509 -289 3480 854 683 170s -------------------------------------------------------- 170s ======================================================== 170s > 170s BEGIN TEST togk4.R 171s 171s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 171s Copyright (C) 2025 The R Foundation for Statistical Computing 171s Platform: x86_64-pc-linux-gnu 171s 171s R is free software and comes with ABSOLUTELY NO WARRANTY. 171s You are welcome to redistribute it under certain conditions. 171s Type 'license()' or 'licence()' for distribution details. 171s 171s R is a collaborative project with many contributors. 171s Type 'contributors()' for more information and 171s 'citation()' on how to cite R or R packages in publications. 171s 171s Type 'demo()' for some demos, 'help()' for on-line help, or 171s 'help.start()' for an HTML browser interface to help. 171s Type 'q()' to quit R. 171s 171s > ## VT::15.09.2013 - this will render the output independent 171s > ## from the version of the package 171s > suppressPackageStartupMessages(library(rrcov)) 171s > 171s > ## VT::14.01.2020 171s > ## On some platforms minor differences are shown - use 171s > ## IGNORE_RDIFF_BEGIN 171s > ## IGNORE_RDIFF_END 171s > 171s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS")){ 171s + domcd <- function(x, xname, nrep=1){ 171s + n <- dim(x)[1] 171s + p <- dim(x)[2] 171s + 171s + mcd<-CovOgk(x) 171s + 171s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 171s + 171s + lpad<-lname-nchar(xname) 171s + cat(pad.right(xname,lpad), xres) 171s + 171s + dist <- getDistance(mcd) 171s + quantiel <- qchisq(0.975, p) 171s + ibad <- which(dist >= quantiel) 171s + names(ibad) <- NULL 171s + nbad <- length(ibad) 171s + cat("Outliers: ",nbad,"\n") 171s + if(nbad > 0) 171s + print(ibad) 171s + cat("-------------\n") 171s + show(mcd) 171s + cat("--------------------------------------------------------\n") 171s + } 171s + 171s + lname <- 20 171s + 171s + ## VT::15.09.2013 - this will render the output independent 171s + ## from the version of the package 171s + suppressPackageStartupMessages(library(rrcov)) 171s + 171s + method <- match.arg(method) 171s + 171s + data(heart) 171s + data(starsCYG) 171s + data(phosphor) 171s + data(stackloss) 171s + data(coleman) 171s + data(salinity) 171s + data(wood) 171s + 171s + data(hbk) 171s + 171s + data(Animals, package = "MASS") 171s + brain <- Animals[c(1:24, 26:25, 27:28),] 171s + data(milk) 171s + data(bushfire) 171s + 171s + tmp <- sys.call() 171s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 171s + 171s + cat("Data Set n p Half LOG(obj) Time\n") 171s + cat("========================================================\n") 171s + domcd(heart[, 1:2], data(heart), nrep) 171s + ## This will not work within the function, of course 171s + ## - comment it out 171s + ## IGNORE_RDIFF_BEGIN 171s + ## domcd(starsCYG,data(starsCYG), nrep) 171s + ## IGNORE_RDIFF_END 171s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 171s + domcd(stack.x,data(stackloss), nrep) 171s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 171s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 171s + ## IGNORE_RDIFF_BEGIN 171s + ## domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 171s + ## IGNORE_RDIFF_END 171s + domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 171s + 171s + domcd(brain, "Animals", nrep) 171s + domcd(milk, data(milk), nrep) 171s + domcd(bushfire, data(bushfire), nrep) 171s + cat("========================================================\n") 171s + } 171s > 171s > pad.right <- function(z, pads) 171s + { 171s + ### Pads spaces to right of text 171s + padding <- paste(rep(" ", pads), collapse = "") 171s + paste(z, padding, sep = "") 171s + } 171s > 171s > dodata() 171s 171s Call: dodata() 171s Data Set n p Half LOG(obj) Time 171s ======================================================== 171s heart 12 2 171s Outliers: 5 171s [1] 2 6 8 10 12 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s height weight 171s 39.76 35.71 171s 171s Robust Estimate of Covariance: 171s height weight 171s height 15.88 32.07 171s weight 32.07 78.28 171s -------------------------------------------------------- 171s phosphor 18 2 171s Outliers: 2 171s [1] 1 6 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s inorg organic 171s 13.31 40.00 171s 171s Robust Estimate of Covariance: 171s inorg organic 171s inorg 92.82 93.24 171s organic 93.24 152.62 171s -------------------------------------------------------- 171s stackloss 21 3 171s Outliers: 2 171s [1] 1 2 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s Air.Flow Water.Temp Acid.Conc. 171s 57.72 20.50 85.78 171s 171s Robust Estimate of Covariance: 171s Air.Flow Water.Temp Acid.Conc. 171s Air.Flow 38.423 11.306 18.605 171s Water.Temp 11.306 6.806 5.889 171s Acid.Conc. 18.605 5.889 29.840 171s -------------------------------------------------------- 171s coleman 20 5 171s Outliers: 3 171s [1] 1 6 10 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s salaryP fatherWc sstatus teacherSc motherLev 171s 2.723 43.202 2.912 25.010 6.290 171s 171s Robust Estimate of Covariance: 171s salaryP fatherWc sstatus teacherSc motherLev 171s salaryP 0.12867 2.80048 0.92026 0.15118 0.06413 171s fatherWc 2.80048 678.72549 227.36415 9.30826 16.15102 171s sstatus 0.92026 227.36415 101.39094 3.38013 5.63283 171s teacherSc 0.15118 9.30826 3.38013 0.57112 0.27701 171s motherLev 0.06413 16.15102 5.63283 0.27701 0.44801 171s -------------------------------------------------------- 171s salinity 28 3 171s Outliers: 3 171s [1] 3 5 16 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s X1 X2 X3 171s 10.74 2.68 22.99 171s 171s Robust Estimate of Covariance: 171s X1 X2 X3 171s X1 8.1047 -0.6365 -0.4720 171s X2 -0.6365 3.0976 -1.3520 171s X3 -0.4720 -1.3520 2.3648 171s -------------------------------------------------------- 171s hbk 75 3 171s Outliers: 14 171s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s X1 X2 X3 171s 1.538 1.780 1.687 171s 171s Robust Estimate of Covariance: 171s X1 X2 X3 171s X1 1.11350 0.04992 0.11541 171s X2 0.04992 1.13338 0.13843 171s X3 0.11541 0.13843 1.05261 171s -------------------------------------------------------- 171s Animals 28 2 171s Outliers: 12 171s [1] 2 6 7 9 12 14 15 16 17 24 25 28 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s body brain 171s 39.65 105.83 171s 171s Robust Estimate of Covariance: 171s body brain 171s body 3981 7558 171s brain 7558 16594 171s -------------------------------------------------------- 171s milk 86 8 171s Outliers: 22 171s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 50 70 74 75 77 85 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s X1 X2 X3 X4 X5 X6 X7 X8 171s 1.03 35.80 33.10 26.15 25.13 25.06 123.06 14.39 171s 171s Robust Estimate of Covariance: 171s X1 X2 X3 X4 X5 X6 X7 171s X1 4.074e-07 5.255e-05 1.564e-04 1.506e-04 1.340e-04 1.234e-04 5.308e-04 171s X2 5.255e-05 1.464e+00 3.425e-01 2.465e-01 1.847e-01 2.484e-01 1.459e+00 171s X3 1.564e-04 3.425e-01 1.070e+00 7.834e-01 7.665e-01 7.808e-01 7.632e-01 171s X4 1.506e-04 2.465e-01 7.834e-01 6.178e-01 5.868e-01 5.959e-01 5.923e-01 171s X5 1.340e-04 1.847e-01 7.665e-01 5.868e-01 6.124e-01 5.967e-01 5.868e-01 171s X6 1.234e-04 2.484e-01 7.808e-01 5.959e-01 5.967e-01 6.253e-01 5.819e-01 171s X7 5.308e-04 1.459e+00 7.632e-01 5.923e-01 5.868e-01 5.819e-01 3.535e+00 171s X8 1.990e-07 1.851e-01 1.861e-01 1.210e-01 1.041e-01 1.116e-01 3.046e-01 171s X8 171s X1 1.990e-07 171s X2 1.851e-01 171s X3 1.861e-01 171s X4 1.210e-01 171s X5 1.041e-01 171s X6 1.116e-01 171s X7 3.046e-01 171s X8 1.292e-01 171s -------------------------------------------------------- 171s bushfire 38 5 171s Outliers: 17 171s [1] 7 8 9 10 11 12 28 29 30 31 32 33 34 35 36 37 38 171s ------------- 171s 171s Call: 171s CovOgk(x = x) 171s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 171s 171s Robust Estimate of Location: 171s V1 V2 V3 V4 V5 171s 104.5 146.0 275.6 217.8 279.3 171s 171s Robust Estimate of Covariance: 171s V1 V2 V3 V4 V5 171s V1 266.8 203.2 -1380.7 -311.1 -252.2 171s V2 203.2 178.4 -910.9 -185.9 -155.9 171s V3 -1380.7 -910.9 8279.7 2035.5 1615.4 171s V4 -311.1 -185.9 2035.5 536.5 418.6 171s V5 -252.2 -155.9 1615.4 418.6 329.2 171s -------------------------------------------------------- 171s ======================================================== 171s > 171s BEGIN TEST tqda.R 171s 171s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 171s Copyright (C) 2025 The R Foundation for Statistical Computing 171s Platform: x86_64-pc-linux-gnu 171s 171s R is free software and comes with ABSOLUTELY NO WARRANTY. 171s You are welcome to redistribute it under certain conditions. 171s Type 'license()' or 'licence()' for distribution details. 171s 171s R is a collaborative project with many contributors. 171s Type 'contributors()' for more information and 171s 'citation()' on how to cite R or R packages in publications. 171s 171s Type 'demo()' for some demos, 'help()' for on-line help, or 171s 'help.start()' for an HTML browser interface to help. 171s Type 'q()' to quit R. 171s 171s > ## VT::15.09.2013 - this will render the output independent 171s > ## from the version of the package 171s > suppressPackageStartupMessages(library(rrcov)) 171s > 171s > dodata <- function(method) { 171s + 171s + options(digits = 5) 171s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 171s + 171s + tmp <- sys.call() 171s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 171s + cat("===================================================\n") 171s + 171s + data(hemophilia); show(QdaCov(as.factor(gr)~., data=hemophilia, method=method)) 171s + data(anorexia, package="MASS"); show(QdaCov(Treat~., data=anorexia, method=method)) 171s + data(Pima.tr, package="MASS"); show(QdaCov(type~., data=Pima.tr, method=method)) 171s + data(iris); # show(QdaCov(Species~., data=iris, method=method)) 171s + data(crabs, package="MASS"); # show(QdaCov(sp~., data=crabs, method=method)) 171s + 171s + show(QdaClassic(as.factor(gr)~., data=hemophilia)) 171s + show(QdaClassic(Treat~., data=anorexia)) 171s + show(QdaClassic(type~., data=Pima.tr)) 171s + show(QdaClassic(Species~., data=iris)) 171s + ## show(QdaClassic(sp~., data=crabs)) 171s + cat("===================================================\n") 171s + } 171s > 171s > 171s > ## -- now do it: 171s > dodata(method="mcd") 171s 171s Call: dodata(method = "mcd") 171s =================================================== 171s Call: 171s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.30795 -0.0059911 171s normal -0.12920 -0.0603000 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.023784 0.015376 171s AHFantigen 0.015376 0.024035 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.0057546 0.0042606 171s AHFantigen 0.0042606 0.0084914 171s Call: 171s QdaCov(Treat ~ ., data = anorexia, method = method) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.633 82.950 171s Cont 81.558 81.108 171s FT 84.331 94.762 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 9.8671 8.6611 171s Postwt 8.6611 11.8966 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 32.5705 -4.3705 171s Postwt -4.3705 22.5079 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 33.056 10.814 171s Postwt 10.814 14.265 171s Call: 171s QdaCov(type ~ ., data = Pima.tr, method = method) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 171s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 2.221983 -0.18658 1.86507 -0.44427 0.1725348 -0.0683616 2.63439 171s glu -0.186582 471.88789 45.28021 8.95404 30.6551510 -0.6359899 3.50218 171s bp 1.865066 45.28021 110.09787 26.11192 14.4739180 -0.2104074 13.23392 171s skin -0.444272 8.95404 26.11192 118.30521 52.3115719 -0.2995751 8.65861 171s bmi 0.172535 30.65515 14.47392 52.31157 43.3140415 0.0079866 6.75720 171s ped -0.068362 -0.63599 -0.21041 -0.29958 0.0079866 0.0587710 -0.18683 171s age 2.634387 3.50218 13.23392 8.65861 6.7572019 -0.1868284 12.09493 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 17.875215 -13.740021 9.03580 4.498580 1.787458 0.079504 26.92283 171s glu -13.740021 917.719003 55.30399 27.976265 10.755113 0.092673 38.94970 171s bp 9.035798 55.303991 129.97953 34.130200 10.104275 0.198342 32.95351 171s skin 4.498580 27.976265 34.13020 101.842647 30.297210 0.064739 3.59427 171s bmi 1.787458 10.755113 10.10428 30.297210 22.529467 0.084369 -6.64317 171s ped 0.079504 0.092673 0.19834 0.064739 0.084369 0.066667 0.11199 171s age 26.922828 38.949697 32.95351 3.594266 -6.643165 0.111992 143.69752 171s Call: 171s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.30795 -0.0059911 171s normal -0.13487 -0.0778567 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.023784 0.015376 171s AHFantigen 0.015376 0.024035 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.020897 0.015515 171s AHFantigen 0.015515 0.017920 171s Call: 171s QdaClassic(Treat ~ ., data = anorexia) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.690 85.697 171s Cont 81.558 81.108 171s FT 83.229 90.494 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 23.479 19.910 171s Postwt 19.910 69.755 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 32.5705 -4.3705 171s Postwt -4.3705 22.5079 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 25.167 22.883 171s Postwt 22.883 71.827 171s Call: 171s QdaClassic(type ~ ., data = Pima.tr) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 171s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 171s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 171s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 171s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 171s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 171s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 171s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 171s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 171s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 171s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 171s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 171s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 171s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 171s Call: 171s QdaClassic(Species ~ ., data = iris) 171s 171s Prior Probabilities of Groups: 171s setosa versicolor virginica 171s 0.33333 0.33333 0.33333 171s 171s Group means: 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s setosa 5.006 3.428 1.462 0.246 171s versicolor 5.936 2.770 4.260 1.326 171s virginica 6.588 2.974 5.552 2.026 171s 171s Group: setosa 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 171s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 171s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 171s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 171s 171s Group: versicolor 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.266433 0.085184 0.182898 0.055780 171s Sepal.Width 0.085184 0.098469 0.082653 0.041204 171s Petal.Length 0.182898 0.082653 0.220816 0.073102 171s Petal.Width 0.055780 0.041204 0.073102 0.039106 171s 171s Group: virginica 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.404343 0.093763 0.303290 0.049094 171s Sepal.Width 0.093763 0.104004 0.071380 0.047629 171s Petal.Length 0.303290 0.071380 0.304588 0.048824 171s Petal.Width 0.049094 0.047629 0.048824 0.075433 171s =================================================== 171s > dodata(method="m") 171s 171s Call: dodata(method = "m") 171s =================================================== 171s Call: 171s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.29810 -0.0028222 171s normal -0.13081 -0.0675283 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.026018 0.017653 171s AHFantigen 0.017653 0.030128 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.0081933 0.0065737 171s AHFantigen 0.0065737 0.0118565 171s Call: 171s QdaCov(Treat ~ ., data = anorexia, method = method) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.436 82.631 171s Cont 81.559 80.272 171s FT 85.120 94.657 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 23.630 25.128 171s Postwt 25.128 38.142 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 35.8824 -8.2405 171s Postwt -8.2405 23.7416 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 33.805 18.206 171s Postwt 18.206 24.639 171s Call: 171s QdaCov(type ~ ., data = Pima.tr, method = method) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.5225 111.26 68.081 26.640 30.801 0.40452 26.306 171s Yes 5.0702 144.32 75.088 31.982 34.267 0.47004 37.140 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 5.74219 14.47051 6.63766 4.98559 0.826570 -0.128106 10.71303 171s glu 14.47051 591.08717 68.81219 44.73311 40.658393 -0.545716 38.01918 171s bp 6.63766 68.81219 121.02716 30.46466 16.789801 -0.320065 25.29371 171s skin 4.98559 44.73311 30.46466 136.52176 56.604475 -0.250711 19.73259 171s bmi 0.82657 40.65839 16.78980 56.60447 47.859747 0.046358 6.94523 171s ped -0.12811 -0.54572 -0.32006 -0.25071 0.046358 0.061485 -0.34653 171s age 10.71303 38.01918 25.29371 19.73259 6.945227 -0.346527 35.66101 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.98861 -1.2430 10.48556 9.05947 2.425316 0.162453 30.149872 171s glu -1.24304 867.1076 46.43838 25.92297 5.517382 1.044360 31.152650 171s bp 10.48556 46.4384 130.12536 17.21407 6.343942 -0.235121 41.091494 171s skin 9.05947 25.9230 17.21407 85.96314 26.089017 0.170061 14.562516 171s bmi 2.42532 5.5174 6.34394 26.08902 22.051976 0.097786 -5.297971 171s ped 0.16245 1.0444 -0.23512 0.17006 0.097786 0.057112 0.055286 171s age 30.14987 31.1527 41.09149 14.56252 -5.297971 0.055286 137.440921 171s Call: 171s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.30795 -0.0059911 171s normal -0.13487 -0.0778567 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.023784 0.015376 171s AHFantigen 0.015376 0.024035 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.020897 0.015515 171s AHFantigen 0.015515 0.017920 171s Call: 171s QdaClassic(Treat ~ ., data = anorexia) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.690 85.697 171s Cont 81.558 81.108 171s FT 83.229 90.494 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 23.479 19.910 171s Postwt 19.910 69.755 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 32.5705 -4.3705 171s Postwt -4.3705 22.5079 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 25.167 22.883 171s Postwt 22.883 71.827 171s Call: 171s QdaClassic(type ~ ., data = Pima.tr) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 171s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 171s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 171s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 171s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 171s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 171s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 171s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 171s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 171s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 171s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 171s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 171s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 171s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 171s Call: 171s QdaClassic(Species ~ ., data = iris) 171s 171s Prior Probabilities of Groups: 171s setosa versicolor virginica 171s 0.33333 0.33333 0.33333 171s 171s Group means: 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s setosa 5.006 3.428 1.462 0.246 171s versicolor 5.936 2.770 4.260 1.326 171s virginica 6.588 2.974 5.552 2.026 171s 171s Group: setosa 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 171s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 171s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 171s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 171s 171s Group: versicolor 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.266433 0.085184 0.182898 0.055780 171s Sepal.Width 0.085184 0.098469 0.082653 0.041204 171s Petal.Length 0.182898 0.082653 0.220816 0.073102 171s Petal.Width 0.055780 0.041204 0.073102 0.039106 171s 171s Group: virginica 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.404343 0.093763 0.303290 0.049094 171s Sepal.Width 0.093763 0.104004 0.071380 0.047629 171s Petal.Length 0.303290 0.071380 0.304588 0.048824 171s Petal.Width 0.049094 0.047629 0.048824 0.075433 171s =================================================== 171s > dodata(method="ogk") 171s 171s Call: dodata(method = "ogk") 171s =================================================== 171s Call: 171s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.29324 0.00033953 171s normal -0.12744 -0.06628182 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.019260 0.013026 171s AHFantigen 0.013026 0.021889 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.0049651 0.0039707 171s AHFantigen 0.0039707 0.0066084 171s Call: 171s QdaCov(Treat ~ ., data = anorexia, method = method) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.587 82.709 171s Cont 81.558 81.108 171s FT 85.110 94.470 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 10.452 15.115 171s Postwt 15.115 37.085 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 31.3178 -4.2024 171s Postwt -4.2024 21.6422 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 5.5309 1.4813 171s Postwt 1.4813 7.5501 171s Call: 171s QdaCov(type ~ ., data = Pima.tr, method = method) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.4286 110.35 67.495 25.905 30.275 0.39587 26.248 171s Yes 5.1964 142.71 75.357 32.732 34.809 0.48823 37.607 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 3.97823 8.70612 4.58776 4.16463 0.250612 -0.117238 8.21769 171s glu 8.70612 448.91392 51.71120 38.66213 21.816345 -0.296524 24.29370 171s bp 4.58776 51.71120 99.41188 24.27574 10.491311 -0.290753 20.02975 171s skin 4.16463 38.66213 24.27574 98.61950 41.682404 -0.335213 16.60454 171s bmi 0.25061 21.81634 10.49131 41.68240 35.237101 -0.019774 5.12042 171s ped -0.11724 -0.29652 -0.29075 -0.33521 -0.019774 0.051431 -0.36275 171s age 8.21769 24.29370 20.02975 16.60454 5.120417 -0.362748 31.32916 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.26499 6.30612 3.01913 3.76690 0.94825 0.12076 22.64860 171s glu 6.30612 688.16837 22.22704 12.81633 3.55791 0.68833 32.28061 171s bp 3.01913 22.22704 103.97959 9.95281 2.09860 0.45672 31.17602 171s skin 3.76690 12.81633 9.95281 67.51754 19.51489 0.59831 -2.35523 171s bmi 0.94825 3.55791 2.09860 19.51489 17.20331 0.24347 -6.88221 171s ped 0.12076 0.68833 0.45672 0.59831 0.24347 0.05933 0.43798 171s age 22.64860 32.28061 31.17602 -2.35523 -6.88221 0.43798 111.16709 171s Call: 171s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.30795 -0.0059911 171s normal -0.13487 -0.0778567 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.023784 0.015376 171s AHFantigen 0.015376 0.024035 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.020897 0.015515 171s AHFantigen 0.015515 0.017920 171s Call: 171s QdaClassic(Treat ~ ., data = anorexia) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.690 85.697 171s Cont 81.558 81.108 171s FT 83.229 90.494 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 23.479 19.910 171s Postwt 19.910 69.755 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 32.5705 -4.3705 171s Postwt -4.3705 22.5079 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 25.167 22.883 171s Postwt 22.883 71.827 171s Call: 171s QdaClassic(type ~ ., data = Pima.tr) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 171s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 171s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 171s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 171s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 171s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 171s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 171s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 171s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 171s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 171s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 171s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 171s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 171s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 171s Call: 171s QdaClassic(Species ~ ., data = iris) 171s 171s Prior Probabilities of Groups: 171s setosa versicolor virginica 171s 0.33333 0.33333 0.33333 171s 171s Group means: 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s setosa 5.006 3.428 1.462 0.246 171s versicolor 5.936 2.770 4.260 1.326 171s virginica 6.588 2.974 5.552 2.026 171s 171s Group: setosa 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 171s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 171s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 171s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 171s 171s Group: versicolor 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.266433 0.085184 0.182898 0.055780 171s Sepal.Width 0.085184 0.098469 0.082653 0.041204 171s Petal.Length 0.182898 0.082653 0.220816 0.073102 171s Petal.Width 0.055780 0.041204 0.073102 0.039106 171s 171s Group: virginica 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.404343 0.093763 0.303290 0.049094 171s Sepal.Width 0.093763 0.104004 0.071380 0.047629 171s Petal.Length 0.303290 0.071380 0.304588 0.048824 171s Petal.Width 0.049094 0.047629 0.048824 0.075433 171s =================================================== 171s > dodata(method="sde") 171s 171s Call: dodata(method = "sde") 171s =================================================== 171s Call: 171s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.29834 -0.0032286 171s normal -0.12944 -0.0676930 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.025398 0.017810 171s AHFantigen 0.017810 0.030639 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.0083435 0.0067686 171s AHFantigen 0.0067686 0.0119565 171s Call: 171s QdaCov(Treat ~ ., data = anorexia, method = method) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.949 83.323 171s Cont 81.484 80.840 171s FT 84.596 93.835 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 22.283 17.084 171s Postwt 17.084 25.308 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 37.1864 -8.8896 171s Postwt -8.8896 31.1930 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 20.7108 3.1531 171s Postwt 3.1531 25.7046 171s Call: 171s QdaCov(type ~ ., data = Pima.tr, method = method) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.2567 109.91 67.538 25.484 30.355 0.38618 25.628 171s Yes 5.2216 141.64 75.048 32.349 34.387 0.47742 37.634 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 4.396832 10.20629 5.43346 4.38313 7.9891e-01 -0.09389257 7.45638 171s glu 10.206286 601.12211 56.62047 49.67071 3.3829e+01 -0.31896741 31.64508 171s bp 5.433462 56.62047 120.38052 34.38984 1.4817e+01 -0.21784446 26.44853 171s skin 4.383134 49.67071 34.38984 136.94931 6.1576e+01 -0.47532490 17.74141 171s bmi 0.798908 33.82928 14.81668 61.57578 5.1441e+01 0.00061983 8.56856 171s ped -0.093893 -0.31897 -0.21784 -0.47532 6.1983e-04 0.06012655 -0.26872 171s age 7.456376 31.64508 26.44853 17.74141 8.5686e+00 -0.26872005 29.93856 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.91978 7.7491 7.24229 10.46802 5.40627 0.320434 25.88314 171s glu 7.74907 856.4955 58.59554 29.65331 11.44745 1.388745 38.24430 171s bp 7.24229 58.5955 89.66288 21.36597 6.46859 0.764486 36.30462 171s skin 10.46802 29.6533 21.36597 86.79253 26.22071 0.620654 5.28665 171s bmi 5.40627 11.4475 6.46859 26.22071 20.12351 0.211701 0.71583 171s ped 0.32043 1.3887 0.76449 0.62065 0.21170 0.062727 0.93743 171s age 25.88314 38.2443 36.30462 5.28665 0.71583 0.937430 136.24335 171s Call: 171s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 171s 171s Prior Probabilities of Groups: 171s carrier normal 171s 0.6 0.4 171s 171s Group means: 171s AHFactivity AHFantigen 171s carrier -0.30795 -0.0059911 171s normal -0.13487 -0.0778567 171s 171s Group: carrier 171s AHFactivity AHFantigen 171s AHFactivity 0.023784 0.015376 171s AHFantigen 0.015376 0.024035 171s 171s Group: normal 171s AHFactivity AHFantigen 171s AHFactivity 0.020897 0.015515 171s AHFantigen 0.015515 0.017920 171s Call: 171s QdaClassic(Treat ~ ., data = anorexia) 171s 171s Prior Probabilities of Groups: 171s CBT Cont FT 171s 0.40278 0.36111 0.23611 171s 171s Group means: 171s Prewt Postwt 171s CBT 82.690 85.697 171s Cont 81.558 81.108 171s FT 83.229 90.494 171s 171s Group: CBT 171s Prewt Postwt 171s Prewt 23.479 19.910 171s Postwt 19.910 69.755 171s 171s Group: Cont 171s Prewt Postwt 171s Prewt 32.5705 -4.3705 171s Postwt -4.3705 22.5079 171s 171s Group: FT 171s Prewt Postwt 171s Prewt 25.167 22.883 171s Postwt 22.883 71.827 171s Call: 171s QdaClassic(type ~ ., data = Pima.tr) 171s 171s Prior Probabilities of Groups: 171s No Yes 171s 0.66 0.34 171s 171s Group means: 171s npreg glu bp skin bmi ped age 171s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 171s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 171s 171s Group: No 171s npreg glu bp skin bmi ped age 171s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 171s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 171s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 171s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 171s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 171s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 171s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 171s 171s Group: Yes 171s npreg glu bp skin bmi ped age 171s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 171s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 171s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 171s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 171s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 171s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 171s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 171s Call: 171s QdaClassic(Species ~ ., data = iris) 171s 171s Prior Probabilities of Groups: 171s setosa versicolor virginica 171s 0.33333 0.33333 0.33333 171s 171s Group means: 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s setosa 5.006 3.428 1.462 0.246 171s versicolor 5.936 2.770 4.260 1.326 171s virginica 6.588 2.974 5.552 2.026 171s 171s Group: setosa 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 171s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 171s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 171s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 171s 171s Group: versicolor 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.266433 0.085184 0.182898 0.055780 171s Sepal.Width 0.085184 0.098469 0.082653 0.041204 171s Petal.Length 0.182898 0.082653 0.220816 0.073102 171s Petal.Width 0.055780 0.041204 0.073102 0.039106 171s 171s Group: virginica 171s Sepal.Length Sepal.Width Petal.Length Petal.Width 171s Sepal.Length 0.404343 0.093763 0.303290 0.049094 171s Sepal.Width 0.093763 0.104004 0.071380 0.047629 171s Petal.Length 0.303290 0.071380 0.304588 0.048824 171s Petal.Width 0.049094 0.047629 0.048824 0.075433 171s =================================================== 171s > 171s BEGIN TEST tsde.R 172s 172s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 172s Copyright (C) 2025 The R Foundation for Statistical Computing 172s Platform: x86_64-pc-linux-gnu 172s 172s R is free software and comes with ABSOLUTELY NO WARRANTY. 172s You are welcome to redistribute it under certain conditions. 172s Type 'license()' or 'licence()' for distribution details. 172s 172s R is a collaborative project with many contributors. 172s Type 'contributors()' for more information and 172s 'citation()' on how to cite R or R packages in publications. 172s 172s Type 'demo()' for some demos, 'help()' for on-line help, or 172s 'help.start()' for an HTML browser interface to help. 172s Type 'q()' to quit R. 172s 172s > ## Test for singularity 172s > doexact <- function(){ 172s + exact <-function(){ 172s + n1 <- 45 172s + p <- 2 172s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 172s + x1[,p] <- x1[,p] + 3 172s + ## library(MASS) 172s + ## x1 <- mvrnorm(n=n1, mu=c(0,3), Sigma=diag(1,nrow=p)) 172s + 172s + n2 <- 55 172s + m1 <- 0 172s + m2 <- 3 172s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 172s + x<-rbind(x1,x2) 172s + colnames(x) <- c("X1","X2") 172s + x 172s + } 172s + print(CovSde(exact())) 172s + } 172s > 172s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE){ 172s + 172s + domcd <- function(x, xname, nrep=1){ 172s + n <- dim(x)[1] 172s + p <- dim(x)[2] 172s + 172s + mcd<-CovSde(x) 172s + 172s + if(time){ 172s + xtime <- system.time(dorep(x, nrep))[1]/nrep 172s + xres <- sprintf("%3d %3d %3d\n", dim(x)[1], dim(x)[2], xtime) 172s + } 172s + else{ 172s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 172s + } 172s + lpad<-lname-nchar(xname) 172s + cat(pad.right(xname,lpad), xres) 172s + 172s + if(!short){ 172s + 172s + ibad <- which(mcd@wt==0) 172s + names(ibad) <- NULL 172s + nbad <- length(ibad) 172s + cat("Outliers: ",nbad,"\n") 172s + if(nbad > 0) 172s + print(ibad) 172s + if(full){ 172s + cat("-------------\n") 172s + show(mcd) 172s + } 172s + cat("--------------------------------------------------------\n") 172s + } 172s + } 172s + 172s + options(digits = 5) 172s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 172s + 172s + lname <- 20 172s + 172s + ## VT::15.09.2013 - this will render the output independent 172s + ## from the version of the package 172s + suppressPackageStartupMessages(library(rrcov)) 172s + 172s + data(heart) 172s + data(starsCYG) 172s + data(phosphor) 172s + data(stackloss) 172s + data(coleman) 172s + data(salinity) 172s + data(wood) 172s + 172s + data(hbk) 172s + 172s + data(Animals, package = "MASS") 172s + brain <- Animals[c(1:24, 26:25, 27:28),] 172s + data(milk) 172s + data(bushfire) 172s + 172s + tmp <- sys.call() 172s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 172s + 172s + cat("Data Set n p Half LOG(obj) Time\n") 172s + cat("========================================================\n") 172s + domcd(heart[, 1:2], data(heart), nrep) 172s + domcd(starsCYG, data(starsCYG), nrep) 172s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 172s + domcd(stack.x, data(stackloss), nrep) 172s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 172s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 172s + domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 172s + domcd(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 172s + 172s + domcd(brain, "Animals", nrep) 172s + domcd(milk, data(milk), nrep) 172s + domcd(bushfire, data(bushfire), nrep) 172s + ## VT::19.07.2010: test the univariate SDE 172s + for(i in 1:ncol(bushfire)) 172s + domcd(bushfire[i], data(bushfire), nrep) 172s + cat("========================================================\n") 172s + } 172s > 172s > dogen <- function(nrep=1, eps=0.49){ 172s + 172s + library(MASS) 172s + domcd <- function(x, nrep=1){ 172s + gc() 172s + xtime <- system.time(dorep(x, nrep))[1]/nrep 172s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 172s + xtime 172s + } 172s + 172s + set.seed(1234) 172s + 172s + ## VT::15.09.2013 - this will render the output independent 172s + ## from the version of the package 172s + suppressPackageStartupMessages(library(rrcov)) 172s + 172s + ap <- c(2, 5, 10, 20, 30) 172s + an <- c(100, 500, 1000, 10000, 50000) 172s + 172s + tottime <- 0 172s + cat(" n p Time\n") 172s + cat("=====================\n") 172s + for(i in 1:length(an)) { 172s + for(j in 1:length(ap)) { 172s + n <- an[i] 172s + p <- ap[j] 172s + if(5*p <= n){ 172s + xx <- gendata(n, p, eps) 172s + X <- xx$X 172s + tottime <- tottime + domcd(X, nrep) 172s + } 172s + } 172s + } 172s + 172s + cat("=====================\n") 172s + cat("Total time: ", tottime*nrep, "\n") 172s + } 172s > 172s > docheck <- function(n, p, eps){ 172s + xx <- gendata(n,p,eps) 172s + mcd <- CovSde(xx$X) 172s + check(mcd, xx$xind) 172s + } 172s > 172s > check <- function(mcd, xind){ 172s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 172s + ## did not get zero weight 172s + mymatch <- xind %in% which(mcd@wt == 0) 172s + length(xind) - length(which(mymatch)) 172s + } 172s > 172s > dorep <- function(x, nrep=1){ 172s + 172s + for(i in 1:nrep) 172s + CovSde(x) 172s + } 172s > 172s > #### gendata() #### 172s > # Generates a location contaminated multivariate 172s > # normal sample of n observations in p dimensions 172s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 172s > # where 172s > # m = (b,b,...,b) 172s > # Defaults: eps=0 and b=10 172s > # 172s > gendata <- function(n,p,eps=0,b=10){ 172s + 172s + if(missing(n) || missing(p)) 172s + stop("Please specify (n,p)") 172s + if(eps < 0 || eps >= 0.5) 172s + stop(message="eps must be in [0,0.5)") 172s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 172s + nbad <- as.integer(eps * n) 172s + if(nbad > 0){ 172s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 172s + xind <- sample(n,nbad) 172s + X[xind,] <- Xbad 172s + } 172s + list(X=X, xind=xind) 172s + } 172s > 172s > pad.right <- function(z, pads) 172s + { 172s + ### Pads spaces to right of text 172s + padding <- paste(rep(" ", pads), collapse = "") 172s + paste(z, padding, sep = "") 172s + } 172s > 172s > whatis<-function(x){ 172s + if(is.data.frame(x)) 172s + cat("Type: data.frame\n") 172s + else if(is.matrix(x)) 172s + cat("Type: matrix\n") 172s + else if(is.vector(x)) 172s + cat("Type: vector\n") 172s + else 172s + cat("Type: don't know\n") 172s + } 172s > 172s > ## VT::15.09.2013 - this will render the output independent 172s > ## from the version of the package 172s > suppressPackageStartupMessages(library(rrcov)) 172s > 172s > dodata() 172s 172s Call: dodata() 172s Data Set n p Half LOG(obj) Time 172s ======================================================== 172s heart 12 2 172s Outliers: 5 172s [1] 2 6 8 10 12 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s height weight 172s 39.8 35.7 172s 172s Robust Estimate of Covariance: 172s height weight 172s height 38.2 77.1 172s weight 77.1 188.1 172s -------------------------------------------------------- 172s starsCYG 47 2 172s Outliers: 7 172s [1] 7 9 11 14 20 30 34 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s log.Te log.light 172s 4.42 4.96 172s 172s Robust Estimate of Covariance: 172s log.Te log.light 172s log.Te 0.0163 0.0522 172s log.light 0.0522 0.3243 172s -------------------------------------------------------- 172s phosphor 18 2 172s Outliers: 2 172s [1] 1 6 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s inorg organic 172s 13.3 39.7 172s 172s Robust Estimate of Covariance: 172s inorg organic 172s inorg 133 134 172s organic 134 204 172s -------------------------------------------------------- 172s stackloss 21 3 172s Outliers: 6 172s [1] 1 2 3 15 17 21 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s Air.Flow Water.Temp Acid.Conc. 172s 57.8 20.7 86.4 172s 172s Robust Estimate of Covariance: 172s Air.Flow Water.Temp Acid.Conc. 172s Air.Flow 39.7 15.6 25.0 172s Water.Temp 15.6 13.0 11.9 172s Acid.Conc. 25.0 11.9 40.3 172s -------------------------------------------------------- 172s coleman 20 5 172s Outliers: 8 172s [1] 1 2 6 10 11 12 15 18 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s salaryP fatherWc sstatus teacherSc motherLev 172s 2.78 58.64 9.09 25.37 6.69 172s 172s Robust Estimate of Covariance: 172s salaryP fatherWc sstatus teacherSc motherLev 172s salaryP 0.2556 -1.0144 0.6599 0.2673 0.0339 172s fatherWc -1.0144 1615.9192 382.7846 -4.8287 36.0227 172s sstatus 0.6599 382.7846 108.1781 -0.7904 9.1027 172s teacherSc 0.2673 -4.8287 -0.7904 0.9346 -0.0239 172s motherLev 0.0339 36.0227 9.1027 -0.0239 0.9155 172s -------------------------------------------------------- 172s salinity 28 3 172s Outliers: 9 172s [1] 3 4 5 9 11 16 19 23 24 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s X1 X2 X3 172s 10.84 3.35 22.48 172s 172s Robust Estimate of Covariance: 172s X1 X2 X3 172s X1 10.75 -1.62 -2.05 172s X2 -1.62 4.21 -1.43 172s X3 -2.05 -1.43 2.63 172s -------------------------------------------------------- 172s wood 20 5 172s Outliers: 11 172s [1] 4 6 7 8 9 10 12 13 16 19 20 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s x1 x2 x3 x4 x5 172s 0.573 0.119 0.517 0.549 0.904 172s 172s Robust Estimate of Covariance: 172s x1 x2 x3 x4 x5 172s x1 0.025185 0.004279 -0.001262 -0.000382 -0.001907 172s x2 0.004279 0.001011 0.000197 -0.000117 0.000247 172s x3 -0.001262 0.000197 0.003042 0.002034 0.001773 172s x4 -0.000382 -0.000117 0.002034 0.007943 0.001137 172s x5 -0.001907 0.000247 0.001773 0.001137 0.005392 172s -------------------------------------------------------- 172s hbk 75 3 172s Outliers: 15 172s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 53 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s X1 X2 X3 172s 1.59 1.79 1.67 172s 172s Robust Estimate of Covariance: 172s X1 X2 X3 172s X1 1.6354 0.0793 0.2284 172s X2 0.0793 1.6461 0.3186 172s X3 0.2284 0.3186 1.5673 172s -------------------------------------------------------- 172s Animals 28 2 172s Outliers: 13 172s [1] 2 6 7 8 9 12 13 14 15 16 24 25 28 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s body brain 172s 18.7 64.9 172s 172s Robust Estimate of Covariance: 172s body brain 172s body 4702 7973 172s brain 7973 28571 172s -------------------------------------------------------- 172s milk 86 8 172s Outliers: 21 172s [1] 1 2 3 6 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s X1 X2 X3 X4 X5 X6 X7 X8 172s 1.03 35.90 33.04 26.11 25.10 25.02 123.06 14.37 172s 172s Robust Estimate of Covariance: 172s X1 X2 X3 X4 X5 X6 X7 172s X1 4.73e-07 6.57e-05 1.79e-04 1.71e-04 1.62e-04 1.42e-04 6.85e-04 172s X2 6.57e-05 1.57e+00 1.36e-01 9.28e-02 4.18e-02 1.30e-01 1.58e+00 172s X3 1.79e-04 1.36e-01 1.12e+00 8.20e-01 8.27e-01 8.00e-01 6.66e-01 172s X4 1.71e-04 9.28e-02 8.20e-01 6.57e-01 6.41e-01 6.18e-01 5.47e-01 172s X5 1.62e-04 4.18e-02 8.27e-01 6.41e-01 6.93e-01 6.44e-01 5.71e-01 172s X6 1.42e-04 1.30e-01 8.00e-01 6.18e-01 6.44e-01 6.44e-01 5.55e-01 172s X7 6.85e-04 1.58e+00 6.66e-01 5.47e-01 5.71e-01 5.55e-01 4.17e+00 172s X8 1.40e-05 2.33e-01 1.74e-01 1.06e-01 9.44e-02 9.86e-02 3.54e-01 172s X8 172s X1 1.40e-05 172s X2 2.33e-01 172s X3 1.74e-01 172s X4 1.06e-01 172s X5 9.44e-02 172s X6 9.86e-02 172s X7 3.54e-01 172s X8 1.57e-01 172s -------------------------------------------------------- 172s bushfire 38 5 172s Outliers: 23 172s [1] 1 5 6 7 8 9 10 11 12 13 15 16 28 29 30 31 32 33 34 35 36 37 38 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s V1 V2 V3 V4 V5 172s 105 148 287 223 283 172s 172s Robust Estimate of Covariance: 172s V1 V2 V3 V4 V5 172s V1 1964 1712 -10230 -2504 -2066 172s V2 1712 1526 -8732 -2145 -1763 172s V3 -10230 -8732 56327 13803 11472 172s V4 -2504 -2145 13803 3509 2894 172s V5 -2066 -1763 11472 2894 2404 172s -------------------------------------------------------- 172s bushfire 38 1 172s Outliers: 2 172s [1] 13 30 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s V1 172s 98.5 172s 172s Robust Estimate of Covariance: 172s V1 172s V1 431 172s -------------------------------------------------------- 172s bushfire 38 1 172s Outliers: 6 172s [1] 33 34 35 36 37 38 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s V2 172s 141 172s 172s Robust Estimate of Covariance: 172s V2 172s V2 528 172s -------------------------------------------------------- 172s bushfire 38 1 172s Outliers: 0 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s V3 172s 238 172s 172s Robust Estimate of Covariance: 172s V3 172s V3 37148 172s -------------------------------------------------------- 172s bushfire 38 1 172s Outliers: 9 172s [1] 8 9 32 33 34 35 36 37 38 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s V4 172s 210 172s 172s Robust Estimate of Covariance: 172s V4 172s V4 2543 172s -------------------------------------------------------- 172s bushfire 38 1 172s Outliers: 9 172s [1] 8 9 32 33 34 35 36 37 38 172s ------------- 172s 172s Call: 172s CovSde(x = x) 172s -> Method: Stahel-Donoho estimator 172s 172s Robust Estimate of Location: 172s V5 172s 273 172s 172s Robust Estimate of Covariance: 172s V5 172s V5 1575 172s -------------------------------------------------------- 172s ======================================================== 172s > ##doexact() 172s > 172s BEGIN TEST tsest.R 172s 172s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 172s Copyright (C) 2025 The R Foundation for Statistical Computing 172s Platform: x86_64-pc-linux-gnu 172s 172s R is free software and comes with ABSOLUTELY NO WARRANTY. 172s You are welcome to redistribute it under certain conditions. 172s Type 'license()' or 'licence()' for distribution details. 172s 172s R is a collaborative project with many contributors. 172s Type 'contributors()' for more information and 172s 'citation()' on how to cite R or R packages in publications. 172s 172s Type 'demo()' for some demos, 'help()' for on-line help, or 172s 'help.start()' for an HTML browser interface to help. 172s Type 'q()' to quit R. 172s 172s > ## VT::15.09.2013 - this will render the output independent 172s > ## from the version of the package 172s > suppressPackageStartupMessages(library(rrcov)) 172s > 172s > library(MASS) 172s > 172s > dodata <- function(nrep = 1, time = FALSE, full = TRUE, method) { 172s + doest <- function(x, xname, nrep = 1, method=c("sfast", "surreal", "bisquare", "rocke", "suser", "MM", "sdet")) { 172s + 172s + method <- match.arg(method) 172s + 172s + lname <- 20 172s + n <- dim(x)[1] 172s + p <- dim(x)[2] 172s + 172s + mm <- if(method == "MM") CovMMest(x) else CovSest(x, method=method) 172s + 172s + crit <- log(mm@crit) 172s + 172s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 172s + lpad <- lname-nchar(xname) 172s + cat(pad.right(xname,lpad), xres) 172s + 172s + dist <- getDistance(mm) 172s + quantiel <- qchisq(0.975, p) 172s + ibad <- which(dist >= quantiel) 172s + names(ibad) <- NULL 172s + nbad <- length(ibad) 172s + cat("Outliers: ",nbad,"\n") 172s + if(nbad > 0) 172s + print(ibad) 172s + cat("-------------\n") 172s + show(mm) 172s + cat("--------------------------------------------------------\n") 172s + } 172s + 172s + options(digits = 5) 172s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 172s + 172s + data(heart) 172s + data(starsCYG) 172s + data(phosphor) 172s + data(stackloss) 172s + data(coleman) 172s + data(salinity) 172s + data(wood) 172s + data(hbk) 172s + 172s + data(Animals, package = "MASS") 172s + brain <- Animals[c(1:24, 26:25, 27:28),] 172s + data(milk) 172s + data(bushfire) 172s + ### 172s + data(rice) 172s + data(hemophilia) 172s + data(fish) 172s + 172s + tmp <- sys.call() 172s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 172s + 172s + cat("Data Set n p LOG(det) Time\n") 172s + cat("===================================================\n") 172s + doest(heart[, 1:2], data(heart), nrep, method=method) 172s + doest(starsCYG, data(starsCYG), nrep, method=method) 172s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep, method=method) 172s + doest(stack.x, data(stackloss), nrep, method=method) 172s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep, method=method) 172s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep, method=method) 172s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep, method=method) 172s + doest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep, method=method) 172s + 172s + 172s + doest(brain, "Animals", nrep, method=method) 172s + doest(milk, data(milk), nrep, method=method) 172s + doest(bushfire, data(bushfire), nrep, method=method) 172s + 172s + doest(data.matrix(subset(rice, select = -Overall_evaluation)), data(rice), nrep, method=method) 172s + doest(data.matrix(subset(hemophilia, select = -gr)), data(hemophilia), nrep, method=method) 172s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep, method=method) 172s + 172s + ## from package 'datasets' 172s + doest(airquality[,1:4], data(airquality), nrep, method=method) 172s + doest(attitude, data(attitude), nrep, method=method) 172s + doest(attenu, data(attenu), nrep, method=method) 172s + doest(USJudgeRatings, data(USJudgeRatings), nrep, method=method) 172s + doest(USArrests, data(USArrests), nrep, method=method) 172s + doest(longley, data(longley), nrep, method=method) 172s + doest(Loblolly, data(Loblolly), nrep, method=method) 172s + doest(quakes[,1:4], data(quakes), nrep, method=method) 172s + 172s + cat("===================================================\n") 172s + } 172s > 172s > # generate contaminated data using the function gendata with different 172s > # number of outliers and check if the M-estimate breaks - i.e. the 172s > # largest eigenvalue is larger than e.g. 5. 172s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 172s > # outliers grater than 20. 172s > dogen <- function(){ 172s + eig <- vector("numeric",26) 172s + for(i in 0:25) { 172s + gg <- gendata(eps=i) 172s + mm <- CovSRocke(gg$x, t0=gg$tgood, S0=gg$sgood) 172s + eig[i+1] <- ev <- getEvals(mm)[1] 172s + cat(i, ev, "\n") 172s + 172s + ## stopifnot(ev < 5 || i > 20) 172s + } 172s + plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 172s + } 172s > 172s > # 172s > # generate data 50x10 as multivariate normal N(0,I) and add 172s > # eps % outliers by adding d=5.0 to each component. 172s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 172s > # - if eps >= 1, it is the number of outliers 172s > # - use the center and cov of the good data as good start 172s > # - use the center and the cov of all data as a bad start 172s > # If using a good start, the M-estimate must iterate to 172s > # the good solution: the largest eigenvalue is less then e.g. 5 172s > # 172s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 172s + 172s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 172s + stop("eps is out of range") 172s + 172s + library(MASS) 172s + 172s + x <- mvrnorm(n, rep(0,p), diag(p)) 172s + bad <- vector("numeric") 172s + nbad = if(eps < 1) eps*n else eps 172s + if(nbad > 0){ 172s + bad <- sample(n, nbad) 172s + x[bad,] <- x[bad,] + d 172s + } 172s + cov1 <- cov.wt(x) 172s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 172s + 172s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 172s + } 172s > 172s > pad.right <- function(z, pads) 172s + { 172s + ## Pads spaces to right of text 172s + padding <- paste(rep(" ", pads), collapse = "") 172s + paste(z, padding, sep = "") 172s + } 172s > 172s > 172s > ## -- now do it: 172s > dodata(method="sfast") 172s 172s Call: dodata(method = "sfast") 172s Data Set n p LOG(det) Time 172s =================================================== 172s heart 12 2 2.017701 172s Outliers: 3 172s [1] 2 6 12 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 36.1 29.5 172s 172s Robust Estimate of Covariance: 172s height weight 172s height 129 210 172s weight 210 365 172s -------------------------------------------------------- 172s starsCYG 47 2 -1.450032 172s Outliers: 7 172s [1] 7 9 11 14 20 30 34 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 4.42 4.97 172s 172s Robust Estimate of Covariance: 172s log.Te log.light 172s log.Te 0.0176 0.0617 172s log.light 0.0617 0.3880 172s -------------------------------------------------------- 172s phosphor 18 2 2.320721 172s Outliers: 2 172s [1] 1 6 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 14.1 38.8 172s 172s Robust Estimate of Covariance: 172s inorg organic 172s inorg 174 190 172s organic 190 268 172s -------------------------------------------------------- 172s stackloss 21 3 1.470031 172s Outliers: 3 172s [1] 1 2 3 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 57.5 20.5 86.0 172s 172s Robust Estimate of Covariance: 172s Air.Flow Water.Temp Acid.Conc. 172s Air.Flow 38.94 11.66 22.89 172s Water.Temp 11.66 9.96 7.81 172s Acid.Conc. 22.89 7.81 40.48 172s -------------------------------------------------------- 172s coleman 20 5 0.491419 172s Outliers: 2 172s [1] 6 10 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 2.77 45.58 4.13 25.13 6.39 172s 172s Robust Estimate of Covariance: 172s salaryP fatherWc sstatus teacherSc motherLev 172s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 172s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 172s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 172s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 172s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 172s -------------------------------------------------------- 172s salinity 28 3 0.734619 172s Outliers: 4 172s [1] 5 16 23 24 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 10.31 3.07 22.60 172s 172s Robust Estimate of Covariance: 172s X1 X2 X3 172s X1 13.200 0.784 -3.611 172s X2 0.784 4.441 -1.658 172s X3 -3.611 -1.658 2.877 172s -------------------------------------------------------- 172s wood 20 5 -3.202636 172s Outliers: 2 172s [1] 7 9 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 0.551 0.135 0.496 0.511 0.916 172s 172s Robust Estimate of Covariance: 172s x1 x2 x3 x4 x5 172s x1 0.011361 -0.000791 0.005473 0.004204 -0.004747 172s x2 -0.000791 0.000701 -0.000534 -0.001452 0.000864 172s x3 0.005473 -0.000534 0.004905 0.002960 -0.001914 172s x4 0.004204 -0.001452 0.002960 0.005154 -0.002187 172s x5 -0.004747 0.000864 -0.001914 -0.002187 0.003214 172s -------------------------------------------------------- 172s hbk 75 3 0.283145 172s Outliers: 14 172s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 1.53 1.83 1.66 172s 172s Robust Estimate of Covariance: 172s X1 X2 X3 172s X1 1.8091 0.0479 0.2446 172s X2 0.0479 1.8190 0.2513 172s X3 0.2446 0.2513 1.7288 172s -------------------------------------------------------- 172s Animals 28 2 4.685129 172s Outliers: 10 172s [1] 2 6 7 9 12 14 15 16 24 25 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 30.8 84.2 172s 172s Robust Estimate of Covariance: 172s body brain 172s body 14806 28767 172s brain 28767 65195 172s -------------------------------------------------------- 172s milk 86 8 -1.437863 172s Outliers: 15 172s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 172s 172s Robust Estimate of Covariance: 172s X1 X2 X3 X4 X5 X6 X7 172s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 172s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 172s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 172s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 172s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 172s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 172s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 172s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 172s X8 172s X1 7.45e-05 172s X2 3.33e-01 172s X3 2.82e-01 172s X4 2.01e-01 172s X5 1.80e-01 172s X6 1.91e-01 172s X7 6.38e-01 172s X8 2.01e-01 172s -------------------------------------------------------- 172s bushfire 38 5 2.443148 172s Outliers: 13 172s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 108 149 266 216 278 172s 172s Robust Estimate of Covariance: 172s V1 V2 V3 V4 V5 172s V1 911 688 -3961 -856 -707 172s V2 688 587 -2493 -492 -420 172s V3 -3961 -2493 24146 5765 4627 172s V4 -856 -492 5765 1477 1164 172s V5 -707 -420 4627 1164 925 172s -------------------------------------------------------- 172s rice 105 5 -0.724874 172s Outliers: 7 172s [1] 9 40 42 49 57 58 71 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 172s 172s Robust Estimate of Covariance: 172s Favor Appearance Taste Stickiness Toughness 172s Favor 0.423 0.345 0.427 0.405 -0.202 172s Appearance 0.345 0.592 0.570 0.549 -0.316 172s Taste 0.427 0.570 0.739 0.706 -0.393 172s Stickiness 0.405 0.549 0.706 0.876 -0.497 172s Toughness -0.202 -0.316 -0.393 -0.497 0.467 172s -------------------------------------------------------- 172s hemophilia 75 2 -1.868949 172s Outliers: 2 172s [1] 11 36 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] -0.2126 -0.0357 172s 172s Robust Estimate of Covariance: 172s AHFactivity AHFantigen 172s AHFactivity 0.0317 0.0112 172s AHFantigen 0.0112 0.0218 172s -------------------------------------------------------- 172s fish 159 6 1.285876 172s Outliers: 21 172s [1] 61 62 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 172s [20] 103 142 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 358.3 24.7 26.9 29.7 30.0 14.7 172s 172s Robust Estimate of Covariance: 172s Weight Length1 Length2 Length3 Height Width 172s Weight 1.33e+05 3.09e+03 3.34e+03 3.78e+03 1.72e+03 2.24e+02 172s Length1 3.09e+03 7.92e+01 8.54e+01 9.55e+01 4.04e+01 7.43e+00 172s Length2 3.34e+03 8.54e+01 9.22e+01 1.03e+02 4.49e+01 8.07e+00 172s Length3 3.78e+03 9.55e+01 1.03e+02 1.18e+02 5.92e+01 7.65e+00 172s Height 1.72e+03 4.04e+01 4.49e+01 5.92e+01 7.12e+01 8.51e-01 172s Width 2.24e+02 7.43e+00 8.07e+00 7.65e+00 8.51e-01 3.57e+00 172s -------------------------------------------------------- 172s airquality 153 4 2.684374 172s Outliers: 7 172s [1] 7 14 23 30 34 77 107 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 39.34 192.12 9.67 78.71 172s 172s Robust Estimate of Covariance: 172s Ozone Solar.R Wind Temp 172s Ozone 973.104 894.011 -61.856 243.560 172s Solar.R 894.011 9677.269 0.388 179.429 172s Wind -61.856 0.388 11.287 -14.310 172s Temp 243.560 179.429 -14.310 96.714 172s -------------------------------------------------------- 172s attitude 30 7 2.091968 172s Outliers: 4 172s [1] 14 16 18 24 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 172s 172s Robust Estimate of Covariance: 172s rating complaints privileges learning raises critical advance 172s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 172s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 172s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 172s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 172s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 172s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 172s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 172s -------------------------------------------------------- 172s attenu 182 5 1.148032 172s Outliers: 31 172s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 172s [20] 29 30 31 32 64 65 80 94 95 96 97 100 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 16.432 5.849 60.297 27.144 0.134 172s 172s Robust Estimate of Covariance: 172s event mag station dist accel 172s event 54.9236 -3.0733 181.0954 -49.4194 -0.0628 172s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 172s station 181.0954 -8.4388 1689.7161 -114.6319 0.7285 172s dist -49.4194 6.7388 -114.6319 597.3606 -1.7988 172s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 172s -------------------------------------------------------- 172s USJudgeRatings 43 12 -1.683847 172s Outliers: 7 172s [1] 5 7 12 13 14 23 31 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 7.43 8.16 7.75 7.89 7.68 7.76 7.67 7.67 7.51 7.58 8.19 7.86 172s 172s Robust Estimate of Covariance: 172s CONT INTG DMNR DILG CFMG DECI PREP FAMI 172s CONT 0.8710 -0.3019 -0.4682 -0.1893 -0.0569 -0.0992 -0.1771 -0.1975 172s INTG -0.3019 0.6401 0.8598 0.6955 0.5732 0.5439 0.7091 0.7084 172s DMNR -0.4682 0.8598 1.2412 0.9107 0.7668 0.7305 0.9292 0.9158 172s DILG -0.1893 0.6955 0.9107 0.8554 0.7408 0.7036 0.8865 0.8791 172s CFMG -0.0569 0.5732 0.7668 0.7408 0.6994 0.6545 0.7788 0.7721 172s DECI -0.0992 0.5439 0.7305 0.7036 0.6545 0.6342 0.7492 0.7511 172s PREP -0.1771 0.7091 0.9292 0.8865 0.7788 0.7492 0.9541 0.9556 172s FAMI -0.1975 0.7084 0.9158 0.8791 0.7721 0.7511 0.9556 0.9785 172s ORAL -0.2444 0.7453 0.9939 0.8917 0.7842 0.7551 0.9554 0.9680 172s WRIT -0.2344 0.7319 0.9649 0.8853 0.7781 0.7511 0.9498 0.9668 172s PHYS -0.1983 0.4676 0.6263 0.5629 0.5073 0.5039 0.5990 0.6140 172s RTEN -0.3152 0.8000 1.0798 0.9234 0.7952 0.7663 0.9637 0.9693 172s ORAL WRIT PHYS RTEN 172s CONT -0.2444 -0.2344 -0.1983 -0.3152 172s INTG 0.7453 0.7319 0.4676 0.8000 172s DMNR 0.9939 0.9649 0.6263 1.0798 172s DILG 0.8917 0.8853 0.5629 0.9234 172s CFMG 0.7842 0.7781 0.5073 0.7952 172s DECI 0.7551 0.7511 0.5039 0.7663 172s PREP 0.9554 0.9498 0.5990 0.9637 172s FAMI 0.9680 0.9668 0.6140 0.9693 172s ORAL 0.9853 0.9744 0.6280 1.0032 172s WRIT 0.9744 0.9711 0.6184 0.9870 172s PHYS 0.6280 0.6184 0.4716 0.6520 172s RTEN 1.0032 0.9870 0.6520 1.0622 172s -------------------------------------------------------- 172s USArrests 50 4 2.411726 172s Outliers: 4 172s [1] 2 28 33 39 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 7.05 150.66 64.66 19.37 172s 172s Robust Estimate of Covariance: 172s Murder Assault UrbanPop Rape 172s Murder 23.8 380.8 19.2 29.7 172s Assault 380.8 8436.2 605.6 645.3 172s UrbanPop 19.2 605.6 246.5 78.8 172s Rape 29.7 645.3 78.8 77.3 172s -------------------------------------------------------- 172s longley 16 7 1.038316 172s Outliers: 5 172s [1] 1 2 3 4 5 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 107.6 440.8 339.7 292.5 121.0 1957.1 67.2 172s 172s Robust Estimate of Covariance: 172s GNP.deflator GNP Unemployed Armed.Forces Population 172s GNP.deflator 100.6 954.7 1147.1 -507.6 74.2 172s GNP 954.7 9430.9 10115.8 -4616.5 730.1 172s Unemployed 1147.1 10115.8 19838.3 -6376.9 850.6 172s Armed.Forces -507.6 -4616.5 -6376.9 3240.2 -351.3 172s Population 74.2 730.1 850.6 -351.3 57.5 172s Year 46.4 450.8 539.5 -233.0 35.3 172s Employed 30.8 310.5 274.0 -160.8 23.3 172s Year Employed 172s GNP.deflator 46.4 30.8 172s GNP 450.8 310.5 172s Unemployed 539.5 274.0 172s Armed.Forces -233.0 -160.8 172s Population 35.3 23.3 172s Year 21.9 14.6 172s Employed 14.6 11.2 172s -------------------------------------------------------- 172s Loblolly 84 3 1.481317 172s Outliers: 14 172s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 172s ------------- 172s 172s Call: 172s CovSest(x = x, method = method) 172s -> Method: S-estimates: S-FAST 172s 172s Robust Estimate of Location: 172s [1] 24.22 9.65 7.50 172s 172s Robust Estimate of Covariance: 172s height age Seed 172s height 525.08 179.21 14.27 172s age 179.21 61.85 2.94 172s Seed 14.27 2.94 25.86 172s -------------------------------------------------------- 173s quakes 1000 4 1.576855 173s Outliers: 223 173s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 173s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 173s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 173s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 173s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 173s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 173s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 173s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 173s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 173s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 173s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 173s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 173s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 173s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 173s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: S-FAST 173s 173s Robust Estimate of Location: 173s [1] -21.54 182.35 369.21 4.54 173s 173s Robust Estimate of Covariance: 173s lat long depth mag 173s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 173s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 173s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 173s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 173s -------------------------------------------------------- 173s =================================================== 173s > dodata(method="sdet") 173s 173s Call: dodata(method = "sdet") 173s Data Set n p LOG(det) Time 173s =================================================== 173s heart 12 2 2.017701 173s Outliers: 3 173s [1] 2 6 12 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 36.1 29.5 173s 173s Robust Estimate of Covariance: 173s height weight 173s height 129 210 173s weight 210 365 173s -------------------------------------------------------- 173s starsCYG 47 2 -1.450032 173s Outliers: 7 173s [1] 7 9 11 14 20 30 34 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 4.42 4.97 173s 173s Robust Estimate of Covariance: 173s log.Te log.light 173s log.Te 0.0176 0.0617 173s log.light 0.0617 0.3880 173s -------------------------------------------------------- 173s phosphor 18 2 2.320721 173s Outliers: 2 173s [1] 1 6 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 14.1 38.8 173s 173s Robust Estimate of Covariance: 173s inorg organic 173s inorg 174 190 173s organic 190 268 173s -------------------------------------------------------- 173s stackloss 21 3 1.470031 173s Outliers: 3 173s [1] 1 2 3 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 57.5 20.5 86.0 173s 173s Robust Estimate of Covariance: 173s Air.Flow Water.Temp Acid.Conc. 173s Air.Flow 38.94 11.66 22.89 173s Water.Temp 11.66 9.96 7.81 173s Acid.Conc. 22.89 7.81 40.48 173s -------------------------------------------------------- 173s coleman 20 5 0.491419 173s Outliers: 2 173s [1] 6 10 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 2.77 45.58 4.13 25.13 6.39 173s 173s Robust Estimate of Covariance: 173s salaryP fatherWc sstatus teacherSc motherLev 173s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 173s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 173s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 173s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 173s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 173s -------------------------------------------------------- 173s salinity 28 3 0.734619 173s Outliers: 4 173s [1] 5 16 23 24 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 10.31 3.07 22.60 173s 173s Robust Estimate of Covariance: 173s X1 X2 X3 173s X1 13.200 0.784 -3.611 173s X2 0.784 4.441 -1.658 173s X3 -3.611 -1.658 2.877 173s -------------------------------------------------------- 173s wood 20 5 -3.220754 173s Outliers: 4 173s [1] 4 6 8 19 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 0.580 0.123 0.530 0.538 0.890 173s 173s Robust Estimate of Covariance: 173s x1 x2 x3 x4 x5 173s x1 8.16e-03 1.39e-03 1.97e-03 -2.82e-04 -7.61e-04 173s x2 1.39e-03 4.00e-04 8.14e-04 -8.51e-05 -5.07e-06 173s x3 1.97e-03 8.14e-04 4.74e-03 -9.59e-04 2.06e-05 173s x4 -2.82e-04 -8.51e-05 -9.59e-04 3.09e-03 1.87e-03 173s x5 -7.61e-04 -5.07e-06 2.06e-05 1.87e-03 2.28e-03 173s -------------------------------------------------------- 173s hbk 75 3 0.283145 173s Outliers: 14 173s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 1.53 1.83 1.66 173s 173s Robust Estimate of Covariance: 173s X1 X2 X3 173s X1 1.8091 0.0479 0.2446 173s X2 0.0479 1.8190 0.2513 173s X3 0.2446 0.2513 1.7288 173s -------------------------------------------------------- 173s Animals 28 2 4.685129 173s Outliers: 10 173s [1] 2 6 7 9 12 14 15 16 24 25 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 30.8 84.2 173s 173s Robust Estimate of Covariance: 173s body brain 173s body 14806 28767 173s brain 28767 65194 173s -------------------------------------------------------- 173s milk 86 8 -1.437863 173s Outliers: 15 173s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 173s 173s Robust Estimate of Covariance: 173s X1 X2 X3 X4 X5 X6 X7 173s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 173s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 173s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 173s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 173s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 173s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 173s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 173s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 173s X8 173s X1 7.45e-05 173s X2 3.33e-01 173s X3 2.82e-01 173s X4 2.01e-01 173s X5 1.80e-01 173s X6 1.91e-01 173s X7 6.38e-01 173s X8 2.01e-01 173s -------------------------------------------------------- 173s bushfire 38 5 2.443148 173s Outliers: 13 173s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 173s ------------- 173s 173s Call: 173s CovSest(x = x, method = method) 173s -> Method: S-estimates: DET-S 173s 173s Robust Estimate of Location: 173s [1] 108 149 266 216 278 173s 173s Robust Estimate of Covariance: 173s V1 V2 V3 V4 V5 173s V1 911 688 -3961 -856 -707 173s V2 688 587 -2493 -492 -420 173s V3 -3961 -2493 24146 5765 4627 173s V4 -856 -492 5765 1477 1164 173s V5 -707 -420 4627 1164 925 173s -------------------------------------------------------- 174s rice 105 5 -0.724874 174s Outliers: 7 174s [1] 9 40 42 49 57 58 71 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 174s 174s Robust Estimate of Covariance: 174s Favor Appearance Taste Stickiness Toughness 174s Favor 0.423 0.345 0.427 0.405 -0.202 174s Appearance 0.345 0.592 0.570 0.549 -0.316 174s Taste 0.427 0.570 0.739 0.706 -0.393 174s Stickiness 0.405 0.549 0.706 0.876 -0.497 174s Toughness -0.202 -0.316 -0.393 -0.497 0.467 174s -------------------------------------------------------- 174s hemophilia 75 2 -1.868949 174s Outliers: 2 174s [1] 11 36 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] -0.2126 -0.0357 174s 174s Robust Estimate of Covariance: 174s AHFactivity AHFantigen 174s AHFactivity 0.0317 0.0112 174s AHFantigen 0.0112 0.0218 174s -------------------------------------------------------- 174s fish 159 6 1.267294 174s Outliers: 33 174s [1] 61 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 174s [20] 91 92 93 94 95 96 97 98 99 100 101 102 103 142 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] 381.2 25.6 27.8 30.8 31.0 14.9 174s 174s Robust Estimate of Covariance: 174s Weight Length1 Length2 Length3 Height Width 174s Weight 148372.04 3260.48 3508.71 3976.93 1507.43 127.94 174s Length1 3260.48 77.00 82.52 92.18 27.56 3.29 174s Length2 3508.71 82.52 88.57 99.20 30.83 3.43 174s Length3 3976.93 92.18 99.20 113.97 45.50 2.21 174s Height 1507.43 27.56 30.83 45.50 70.54 -4.95 174s Width 127.94 3.29 3.43 2.21 -4.95 2.28 174s -------------------------------------------------------- 174s airquality 153 4 2.684374 174s Outliers: 7 174s [1] 7 14 23 30 34 77 107 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] 39.34 192.12 9.67 78.71 174s 174s Robust Estimate of Covariance: 174s Ozone Solar.R Wind Temp 174s Ozone 973.104 894.011 -61.856 243.560 174s Solar.R 894.011 9677.269 0.388 179.429 174s Wind -61.856 0.388 11.287 -14.310 174s Temp 243.560 179.429 -14.310 96.714 174s -------------------------------------------------------- 174s attitude 30 7 2.091968 174s Outliers: 4 174s [1] 14 16 18 24 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 174s 174s Robust Estimate of Covariance: 174s rating complaints privileges learning raises critical advance 174s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 174s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 174s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 174s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 174s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 174s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 174s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 174s -------------------------------------------------------- 174s attenu 182 5 1.148032 174s Outliers: 31 174s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 174s [20] 29 30 31 32 64 65 80 94 95 96 97 100 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] 16.432 5.849 60.297 27.144 0.134 174s 174s Robust Estimate of Covariance: 174s event mag station dist accel 174s event 54.9236 -3.0733 181.0954 -49.4195 -0.0628 174s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 174s station 181.0954 -8.4388 1689.7161 -114.6321 0.7285 174s dist -49.4195 6.7388 -114.6321 597.3609 -1.7988 174s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 174s -------------------------------------------------------- 174s USJudgeRatings 43 12 -1.683847 174s Outliers: 7 174s [1] 5 7 12 13 14 23 31 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] 7.43 8.16 7.75 7.89 7.68 7.76 7.67 7.67 7.51 7.58 8.19 7.86 174s 174s Robust Estimate of Covariance: 174s CONT INTG DMNR DILG CFMG DECI PREP FAMI 174s CONT 0.8715 -0.3020 -0.4683 -0.1894 -0.0569 -0.0993 -0.1772 -0.1976 174s INTG -0.3020 0.6403 0.8600 0.6956 0.5733 0.5440 0.7093 0.7086 174s DMNR -0.4683 0.8600 1.2416 0.9109 0.7669 0.7307 0.9295 0.9161 174s DILG -0.1894 0.6956 0.9109 0.8555 0.7410 0.7037 0.8867 0.8793 174s CFMG -0.0569 0.5733 0.7669 0.7410 0.6995 0.6546 0.7789 0.7723 174s DECI -0.0993 0.5440 0.7307 0.7037 0.6546 0.6343 0.7493 0.7513 174s PREP -0.1772 0.7093 0.9295 0.8867 0.7789 0.7493 0.9543 0.9559 174s FAMI -0.1976 0.7086 0.9161 0.8793 0.7723 0.7513 0.9559 0.9788 174s ORAL -0.2445 0.7456 0.9942 0.8919 0.7844 0.7553 0.9557 0.9683 174s WRIT -0.2345 0.7321 0.9652 0.8856 0.7783 0.7513 0.9501 0.9671 174s PHYS -0.1986 0.4676 0.6264 0.5628 0.5072 0.5038 0.5990 0.6140 174s RTEN -0.3154 0.8002 1.0801 0.9236 0.7954 0.7665 0.9639 0.9695 174s ORAL WRIT PHYS RTEN 174s CONT -0.2445 -0.2345 -0.1986 -0.3154 174s INTG 0.7456 0.7321 0.4676 0.8002 174s DMNR 0.9942 0.9652 0.6264 1.0801 174s DILG 0.8919 0.8856 0.5628 0.9236 174s CFMG 0.7844 0.7783 0.5072 0.7954 174s DECI 0.7553 0.7513 0.5038 0.7665 174s PREP 0.9557 0.9501 0.5990 0.9639 174s FAMI 0.9683 0.9671 0.6140 0.9695 174s ORAL 0.9856 0.9748 0.6281 1.0035 174s WRIT 0.9748 0.9714 0.6184 0.9873 174s PHYS 0.6281 0.6184 0.4713 0.6520 174s RTEN 1.0035 0.9873 0.6520 1.0624 174s -------------------------------------------------------- 174s USArrests 50 4 2.411726 174s Outliers: 4 174s [1] 2 28 33 39 174s ------------- 174s 174s Call: 174s CovSest(x = x, method = method) 174s -> Method: S-estimates: DET-S 174s 174s Robust Estimate of Location: 174s [1] 7.05 150.66 64.66 19.37 174s 174s Robust Estimate of Covariance: 174s Murder Assault UrbanPop Rape 174s Murder 23.8 380.8 19.2 29.7 174s Assault 380.8 8436.2 605.6 645.3 174s UrbanPop 19.2 605.6 246.5 78.8 174s Rape 29.7 645.3 78.8 77.3 174s -------------------------------------------------------- 175s longley 16 7 1.143113 175s Outliers: 4 175s [1] 1 2 3 4 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: DET-S 175s 175s Robust Estimate of Location: 175s [1] 107 435 334 293 120 1957 67 175s 175s Robust Estimate of Covariance: 175s GNP.deflator GNP Unemployed Armed.Forces Population 175s GNP.deflator 89.2 850.1 1007.4 -404.4 66.2 175s GNP 850.1 8384.4 9020.8 -3692.0 650.5 175s Unemployed 1007.4 9020.8 16585.4 -4990.7 752.5 175s Armed.Forces -404.4 -3692.0 -4990.7 2474.2 -280.9 175s Population 66.2 650.5 752.5 -280.9 51.2 175s Year 41.9 407.6 481.9 -186.4 31.9 175s Employed 27.9 279.7 255.6 -128.8 21.1 175s Year Employed 175s GNP.deflator 41.9 27.9 175s GNP 407.6 279.7 175s Unemployed 481.9 255.6 175s Armed.Forces -186.4 -128.8 175s Population 31.9 21.1 175s Year 20.2 13.4 175s Employed 13.4 10.1 175s -------------------------------------------------------- 175s Loblolly 84 3 1.481317 175s Outliers: 14 175s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: DET-S 175s 175s Robust Estimate of Location: 175s [1] 24.22 9.65 7.50 175s 175s Robust Estimate of Covariance: 175s height age Seed 175s height 525.08 179.21 14.27 175s age 179.21 61.85 2.94 175s Seed 14.27 2.94 25.86 175s -------------------------------------------------------- 175s quakes 1000 4 1.576855 175s Outliers: 223 175s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 175s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 175s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 175s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 175s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 175s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 175s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 175s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 175s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 175s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 175s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 175s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 175s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 175s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 175s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: DET-S 175s 175s Robust Estimate of Location: 175s [1] -21.54 182.35 369.21 4.54 175s 175s Robust Estimate of Covariance: 175s lat long depth mag 175s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 175s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 175s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 175s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 175s -------------------------------------------------------- 175s =================================================== 175s > ##dodata(method="suser") 175s > ##dodata(method="surreal") 175s > dodata(method="bisquare") 175s 175s Call: dodata(method = "bisquare") 175s Data Set n p LOG(det) Time 175s =================================================== 175s heart 12 2 7.721793 175s Outliers: 3 175s [1] 2 6 12 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s height weight 175s 36.1 29.4 175s 175s Robust Estimate of Covariance: 175s height weight 175s height 109 177 175s weight 177 307 175s -------------------------------------------------------- 175s starsCYG 47 2 -5.942108 175s Outliers: 7 175s [1] 7 9 11 14 20 30 34 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s log.Te log.light 175s 4.42 4.97 175s 175s Robust Estimate of Covariance: 175s log.Te log.light 175s log.Te 0.0164 0.0574 175s log.light 0.0574 0.3613 175s -------------------------------------------------------- 175s phosphor 18 2 9.269096 175s Outliers: 2 175s [1] 1 6 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s inorg organic 175s 14.1 38.7 175s 175s Robust Estimate of Covariance: 175s inorg organic 175s inorg 173 189 175s organic 189 268 175s -------------------------------------------------------- 175s stackloss 21 3 8.411100 175s Outliers: 3 175s [1] 1 2 3 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s Air.Flow Water.Temp Acid.Conc. 175s 57.5 20.5 86.0 175s 175s Robust Estimate of Covariance: 175s Air.Flow Water.Temp Acid.Conc. 175s Air.Flow 33.82 10.17 20.02 175s Water.Temp 10.17 8.70 6.84 175s Acid.Conc. 20.02 6.84 35.51 175s -------------------------------------------------------- 175s coleman 20 5 4.722046 175s Outliers: 2 175s [1] 6 10 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s salaryP fatherWc sstatus teacherSc motherLev 175s 2.77 45.59 4.14 25.13 6.39 175s 175s Robust Estimate of Covariance: 175s salaryP fatherWc sstatus teacherSc motherLev 175s salaryP 0.2135 1.8732 1.3883 0.2547 0.0648 175s fatherWc 1.8732 905.6704 296.1916 7.9820 21.0848 175s sstatus 1.3883 296.1916 128.9536 4.0196 7.1917 175s teacherSc 0.2547 7.9820 4.0196 0.7321 0.2799 175s motherLev 0.0648 21.0848 7.1917 0.2799 0.5592 175s -------------------------------------------------------- 175s salinity 28 3 4.169963 175s Outliers: 4 175s [1] 5 16 23 24 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s X1 X2 X3 175s 10.30 3.07 22.59 175s 175s Robust Estimate of Covariance: 175s X1 X2 X3 175s X1 12.234 0.748 -3.369 175s X2 0.748 4.115 -1.524 175s X3 -3.369 -1.524 2.655 175s -------------------------------------------------------- 175s wood 20 5 -33.862485 175s Outliers: 5 175s [1] 4 6 8 11 19 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s x1 x2 x3 x4 x5 175s 0.580 0.123 0.530 0.538 0.890 175s 175s Robust Estimate of Covariance: 175s x1 x2 x3 x4 x5 175s x1 5.88e-03 9.96e-04 1.43e-03 -1.96e-04 -5.46e-04 175s x2 9.96e-04 2.86e-04 5.89e-04 -5.78e-05 -2.24e-06 175s x3 1.43e-03 5.89e-04 3.42e-03 -6.95e-04 1.43e-05 175s x4 -1.96e-04 -5.78e-05 -6.95e-04 2.23e-03 1.35e-03 175s x5 -5.46e-04 -2.24e-06 1.43e-05 1.35e-03 1.65e-03 175s -------------------------------------------------------- 175s hbk 75 3 1.472421 175s Outliers: 14 175s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s X1 X2 X3 175s 1.53 1.83 1.66 175s 175s Robust Estimate of Covariance: 175s X1 X2 X3 175s X1 1.6775 0.0447 0.2268 175s X2 0.0447 1.6865 0.2325 175s X3 0.2268 0.2325 1.6032 175s -------------------------------------------------------- 175s Animals 28 2 18.528307 175s Outliers: 11 175s [1] 2 6 7 9 12 14 15 16 24 25 28 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s body brain 175s 30.7 84.1 175s 175s Robust Estimate of Covariance: 175s body brain 175s body 13278 25795 175s brain 25795 58499 175s -------------------------------------------------------- 175s milk 86 8 -24.816943 175s Outliers: 19 175s [1] 1 2 3 11 12 13 14 15 16 17 20 27 41 44 47 70 74 75 77 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s X1 X2 X3 X4 X5 X6 X7 X8 175s 1.03 35.81 32.96 26.04 25.02 24.94 122.79 14.35 175s 175s Robust Estimate of Covariance: 175s X1 X2 X3 X4 X5 X6 X7 175s X1 6.80e-07 2.20e-04 3.70e-04 3.35e-04 3.27e-04 3.30e-04 1.21e-03 175s X2 2.20e-04 1.80e+00 3.96e-01 3.03e-01 2.45e-01 3.27e-01 2.00e+00 175s X3 3.70e-04 3.96e-01 1.27e+00 9.68e-01 9.49e-01 9.56e-01 1.37e+00 175s X4 3.35e-04 3.03e-01 9.68e-01 7.86e-01 7.55e-01 7.57e-01 1.15e+00 175s X5 3.27e-04 2.45e-01 9.49e-01 7.55e-01 7.88e-01 7.61e-01 1.14e+00 175s X6 3.30e-04 3.27e-01 9.56e-01 7.57e-01 7.61e-01 7.90e-01 1.17e+00 175s X7 1.21e-03 2.00e+00 1.37e+00 1.15e+00 1.14e+00 1.17e+00 5.71e+00 175s X8 6.57e-05 2.71e-01 2.30e-01 1.64e-01 1.48e-01 1.57e-01 5.27e-01 175s X8 175s X1 6.57e-05 175s X2 2.71e-01 175s X3 2.30e-01 175s X4 1.64e-01 175s X5 1.48e-01 175s X6 1.57e-01 175s X7 5.27e-01 175s X8 1.62e-01 175s -------------------------------------------------------- 175s bushfire 38 5 21.704243 175s Outliers: 13 175s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s V1 V2 V3 V4 V5 175s 108 149 266 216 278 175s 175s Robust Estimate of Covariance: 175s V1 V2 V3 V4 V5 175s V1 528 398 -2298 -497 -410 175s V2 398 340 -1445 -285 -244 175s V3 -2298 -1445 14026 3348 2687 175s V4 -497 -285 3348 857 676 175s V5 -410 -244 2687 676 537 175s -------------------------------------------------------- 175s rice 105 5 -7.346939 175s Outliers: 8 175s [1] 9 14 40 42 49 57 58 71 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s Favor Appearance Taste Stickiness Toughness 175s -0.2480 0.1203 -0.1213 0.0710 0.0644 175s 175s Robust Estimate of Covariance: 175s Favor Appearance Taste Stickiness Toughness 175s Favor 0.415 0.338 0.419 0.398 -0.198 175s Appearance 0.338 0.580 0.559 0.539 -0.310 175s Taste 0.419 0.559 0.725 0.693 -0.386 175s Stickiness 0.398 0.539 0.693 0.859 -0.487 175s Toughness -0.198 -0.310 -0.386 -0.487 0.457 175s -------------------------------------------------------- 175s hemophilia 75 2 -7.465173 175s Outliers: 2 175s [1] 11 36 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s AHFactivity AHFantigen 175s -0.2128 -0.0366 175s 175s Robust Estimate of Covariance: 175s AHFactivity AHFantigen 175s AHFactivity 0.0321 0.0115 175s AHFantigen 0.0115 0.0220 175s -------------------------------------------------------- 175s fish 159 6 13.465134 175s Outliers: 35 175s [1] 38 61 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 175s [20] 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 142 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s Weight Length1 Length2 Length3 Height Width 175s 381.4 25.6 27.8 30.8 31.0 14.9 175s 175s Robust Estimate of Covariance: 175s Weight Length1 Length2 Length3 Height Width 175s Weight 111094.92 2440.81 2626.59 2976.92 1129.78 95.85 175s Length1 2440.81 57.63 61.75 68.98 20.67 2.46 175s Length2 2626.59 61.75 66.28 74.24 23.13 2.57 175s Length3 2976.92 68.98 74.24 85.29 34.11 1.65 175s Height 1129.78 20.67 23.13 34.11 52.75 -3.70 175s Width 95.85 2.46 2.57 1.65 -3.70 1.71 175s -------------------------------------------------------- 175s airquality 153 4 21.282926 175s Outliers: 8 175s [1] 7 11 14 23 30 34 77 107 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s Ozone Solar.R Wind Temp 175s 39.40 192.29 9.66 78.74 175s 175s Robust Estimate of Covariance: 175s Ozone Solar.R Wind Temp 175s Ozone 930.566 849.644 -59.157 232.459 175s Solar.R 849.644 9207.569 0.594 168.122 175s Wind -59.157 0.594 10.783 -13.645 175s Temp 232.459 168.122 -13.645 92.048 175s -------------------------------------------------------- 175s attitude 30 7 28.084183 175s Outliers: 6 175s [1] 6 9 14 16 18 24 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s rating complaints privileges learning raises critical 175s 65.7 66.8 51.9 56.1 66.4 76.7 175s advance 175s 43.0 175s 175s Robust Estimate of Covariance: 175s rating complaints privileges learning raises critical advance 175s rating 143.88 114.95 64.97 105.69 83.95 6.96 41.78 175s complaints 114.95 143.84 79.28 115.00 101.48 19.69 66.13 175s privileges 64.97 79.28 126.38 94.70 73.87 5.37 61.07 175s learning 105.69 115.00 94.70 146.14 110.50 21.67 68.49 175s raises 83.95 101.48 73.87 110.50 115.01 24.91 77.16 175s critical 6.96 19.69 5.37 21.67 24.91 71.74 25.88 175s advance 41.78 66.13 61.07 68.49 77.16 25.88 97.71 175s -------------------------------------------------------- 175s attenu 182 5 10.109049 175s Outliers: 35 175s [1] 2 4 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 175s [20] 28 29 30 31 32 64 65 80 93 94 95 96 97 98 99 100 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s event mag station dist accel 175s 16.418 5.850 60.243 27.307 0.134 175s 175s Robust Estimate of Covariance: 175s event mag station dist accel 175s event 41.9000 -2.3543 137.8110 -39.0321 -0.0447 175s mag -2.3543 0.4978 -6.4461 5.2644 0.0118 175s station 137.8110 -6.4461 1283.9675 -90.1657 0.5554 175s dist -39.0321 5.2644 -90.1657 462.3898 -1.3672 175s accel -0.0447 0.0118 0.5554 -1.3672 0.0114 175s -------------------------------------------------------- 175s USJudgeRatings 43 12 -43.367499 175s Outliers: 10 175s [1] 5 7 8 12 13 14 20 23 31 35 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 175s 7.43 8.16 7.75 7.89 7.69 7.76 7.68 7.67 7.52 7.59 8.19 7.87 175s 175s Robust Estimate of Covariance: 175s CONT INTG DMNR DILG CFMG DECI PREP FAMI 175s CONT 0.6895 -0.2399 -0.3728 -0.1514 -0.0461 -0.0801 -0.1419 -0.1577 175s INTG -0.2399 0.5021 0.6746 0.5446 0.4479 0.4254 0.5564 0.5558 175s DMNR -0.3728 0.6746 0.9753 0.7128 0.5992 0.5715 0.7289 0.7181 175s DILG -0.1514 0.5446 0.7128 0.6691 0.5789 0.5501 0.6949 0.6892 175s CFMG -0.0461 0.4479 0.5992 0.5789 0.5468 0.5118 0.6100 0.6049 175s DECI -0.0801 0.4254 0.5715 0.5501 0.5118 0.4965 0.5872 0.5890 175s PREP -0.1419 0.5564 0.7289 0.6949 0.6100 0.5872 0.7497 0.7511 175s FAMI -0.1577 0.5558 0.7181 0.6892 0.6049 0.5890 0.7511 0.7696 175s ORAL -0.1950 0.5848 0.7798 0.6990 0.6143 0.5921 0.7508 0.7610 175s WRIT -0.1866 0.5747 0.7575 0.6946 0.6101 0.5895 0.7470 0.7607 175s PHYS -0.1620 0.3640 0.4878 0.4361 0.3927 0.3910 0.4655 0.4779 175s RTEN -0.2522 0.6268 0.8462 0.7220 0.6210 0.5991 0.7553 0.7599 175s ORAL WRIT PHYS RTEN 175s CONT -0.1950 -0.1866 -0.1620 -0.2522 175s INTG 0.5848 0.5747 0.3640 0.6268 175s DMNR 0.7798 0.7575 0.4878 0.8462 175s DILG 0.6990 0.6946 0.4361 0.7220 175s CFMG 0.6143 0.6101 0.3927 0.6210 175s DECI 0.5921 0.5895 0.3910 0.5991 175s PREP 0.7508 0.7470 0.4655 0.7553 175s FAMI 0.7610 0.7607 0.4779 0.7599 175s ORAL 0.7745 0.7665 0.4893 0.7866 175s WRIT 0.7665 0.7645 0.4823 0.7745 175s PHYS 0.4893 0.4823 0.3620 0.5062 175s RTEN 0.7866 0.7745 0.5062 0.8313 175s -------------------------------------------------------- 175s USArrests 50 4 19.266763 175s Outliers: 4 175s [1] 2 28 33 39 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s Murder Assault UrbanPop Rape 175s 7.04 150.55 64.64 19.34 175s 175s Robust Estimate of Covariance: 175s Murder Assault UrbanPop Rape 175s Murder 23.7 378.9 19.1 29.5 175s Assault 378.9 8388.2 601.3 639.7 175s UrbanPop 19.1 601.3 245.3 77.9 175s Rape 29.5 639.7 77.9 76.3 175s -------------------------------------------------------- 175s longley 16 7 13.789499 175s Outliers: 4 175s [1] 1 2 3 4 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s GNP.deflator GNP Unemployed Armed.Forces Population 175s 107 435 333 293 120 175s Year Employed 175s 1957 67 175s 175s Robust Estimate of Covariance: 175s GNP.deflator GNP Unemployed Armed.Forces Population 175s GNP.deflator 65.05 619.75 734.33 -294.02 48.27 175s GNP 619.75 6112.14 6578.12 -2684.52 474.26 175s Unemployed 734.33 6578.12 12075.90 -3627.79 548.58 175s Armed.Forces -294.02 -2684.52 -3627.79 1797.05 -204.25 175s Population 48.27 474.26 548.58 -204.25 37.36 175s Year 30.58 297.29 351.44 -135.53 23.29 175s Employed 20.36 203.96 186.62 -93.64 15.42 175s Year Employed 175s GNP.deflator 30.58 20.36 175s GNP 297.29 203.96 175s Unemployed 351.44 186.62 175s Armed.Forces -135.53 -93.64 175s Population 23.29 15.42 175s Year 14.70 9.80 175s Employed 9.80 7.36 175s -------------------------------------------------------- 175s Loblolly 84 3 8.518440 175s Outliers: 14 175s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s height age Seed 175s 24.14 9.62 7.51 175s 175s Robust Estimate of Covariance: 175s height age Seed 175s height 464.64 158.43 12.83 175s age 158.43 54.62 2.67 175s Seed 12.83 2.67 22.98 175s -------------------------------------------------------- 175s quakes 1000 4 11.611413 175s Outliers: 234 175s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 175s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 175s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 175s [46] 163 166 170 174 192 205 222 226 230 239 243 250 251 252 254 175s [61] 258 263 267 268 271 283 292 297 300 301 305 311 312 318 320 175s [76] 321 325 328 330 331 334 352 357 360 365 368 376 381 382 384 175s [91] 389 399 400 402 408 413 416 417 418 419 426 429 437 441 443 175s [106] 453 456 467 474 477 490 492 496 504 507 508 509 517 524 527 175s [121] 528 531 532 534 536 538 539 541 542 543 544 545 546 547 552 175s [136] 553 558 560 570 571 581 583 587 593 594 596 597 605 612 613 175s [151] 618 620 625 629 638 642 647 649 653 655 656 672 675 681 686 175s [166] 699 701 702 712 714 716 721 725 726 735 744 753 754 756 759 175s [181] 765 766 769 779 781 782 785 787 797 804 813 825 827 837 840 175s [196] 844 852 853 857 860 865 866 869 870 872 873 883 884 887 888 175s [211] 890 891 893 908 909 912 915 916 921 927 930 952 962 963 969 175s [226] 974 980 982 986 987 988 992 997 1000 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: bisquare 175s 175s Robust Estimate of Location: 175s lat long depth mag 175s -21.54 182.35 369.29 4.54 175s 175s Robust Estimate of Covariance: 175s lat long depth mag 175s lat 2.18e+01 4.82e+00 2.53e+02 -3.54e-01 175s long 4.82e+00 5.87e+00 -4.63e+02 7.45e-02 175s depth 2.53e+02 -4.63e+02 6.51e+04 -2.10e+01 175s mag -3.54e-01 7.45e-02 -2.10e+01 1.83e-01 175s -------------------------------------------------------- 175s =================================================== 175s > dodata(method="rocke") 175s 175s Call: dodata(method = "rocke") 175s Data Set n p LOG(det) Time 175s =================================================== 175s heart 12 2 7.285196 175s Outliers: 3 175s [1] 2 6 12 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s height weight 175s 34.3 26.1 175s 175s Robust Estimate of Covariance: 175s height weight 175s height 105 159 175s weight 159 256 175s -------------------------------------------------------- 175s starsCYG 47 2 -5.929361 175s Outliers: 7 175s [1] 7 9 11 14 20 30 34 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s log.Te log.light 175s 4.42 4.93 175s 175s Robust Estimate of Covariance: 175s log.Te log.light 175s log.Te 0.0193 0.0709 175s log.light 0.0709 0.3987 175s -------------------------------------------------------- 175s phosphor 18 2 8.907518 175s Outliers: 3 175s [1] 1 6 10 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s inorg organic 175s 15.8 39.4 175s 175s Robust Estimate of Covariance: 175s inorg organic 175s inorg 196 252 175s organic 252 360 175s -------------------------------------------------------- 175s stackloss 21 3 8.143313 175s Outliers: 4 175s [1] 1 2 3 21 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s Air.Flow Water.Temp Acid.Conc. 175s 56.8 20.2 86.4 175s 175s Robust Estimate of Covariance: 175s Air.Flow Water.Temp Acid.Conc. 175s Air.Flow 29.26 9.62 14.78 175s Water.Temp 9.62 8.54 6.25 175s Acid.Conc. 14.78 6.25 29.70 175s -------------------------------------------------------- 175s coleman 20 5 4.001659 175s Outliers: 5 175s [1] 2 6 9 10 13 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s salaryP fatherWc sstatus teacherSc motherLev 175s 2.81 40.27 2.11 25.01 6.27 175s 175s Robust Estimate of Covariance: 175s salaryP fatherWc sstatus teacherSc motherLev 175s salaryP 0.2850 1.1473 2.0254 0.3536 0.0737 175s fatherWc 1.1473 798.0714 278.0145 6.4590 18.6357 175s sstatus 2.0254 278.0145 128.7601 4.0666 6.3845 175s teacherSc 0.3536 6.4590 4.0666 0.8749 0.2980 175s motherLev 0.0737 18.6357 6.3845 0.2980 0.4948 175s -------------------------------------------------------- 175s salinity 28 3 3.455146 175s Outliers: 9 175s [1] 3 5 10 11 15 16 17 23 24 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s X1 X2 X3 175s 9.89 3.10 22.46 175s 175s Robust Estimate of Covariance: 175s X1 X2 X3 175s X1 12.710 1.868 -4.135 175s X2 1.868 4.710 -0.663 175s X3 -4.135 -0.663 1.907 175s -------------------------------------------------------- 175s wood 20 5 -35.020244 175s Outliers: 7 175s [1] 4 6 7 8 11 16 19 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s x1 x2 x3 x4 x5 175s 0.588 0.123 0.534 0.535 0.891 175s 175s Robust Estimate of Covariance: 175s x1 x2 x3 x4 x5 175s x1 6.60e-03 1.25e-03 2.16e-03 -3.73e-04 -1.10e-03 175s x2 1.25e-03 3.30e-04 8.91e-04 -1.23e-05 2.62e-05 175s x3 2.16e-03 8.91e-04 4.55e-03 -4.90e-04 1.93e-04 175s x4 -3.73e-04 -1.23e-05 -4.90e-04 2.01e-03 1.36e-03 175s x5 -1.10e-03 2.62e-05 1.93e-04 1.36e-03 1.95e-03 175s -------------------------------------------------------- 175s hbk 75 3 1.413303 175s Outliers: 14 175s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s X1 X2 X3 175s 1.56 1.77 1.68 175s 175s Robust Estimate of Covariance: 175s X1 X2 X3 175s X1 1.6483 0.0825 0.2133 175s X2 0.0825 1.6928 0.2334 175s X3 0.2133 0.2334 1.5334 175s -------------------------------------------------------- 175s Animals 28 2 17.787210 175s Outliers: 11 175s [1] 2 6 7 9 12 14 15 16 24 25 28 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s body brain 175s 60.6 150.2 175s 175s Robust Estimate of Covariance: 175s body brain 175s body 10670 19646 175s brain 19646 41147 175s -------------------------------------------------------- 175s milk 86 8 -25.169970 175s Outliers: 22 175s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 28 41 44 47 70 73 74 75 77 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s X1 X2 X3 X4 X5 X6 X7 X8 175s 1.03 35.87 33.14 26.19 25.17 25.11 123.16 14.41 175s 175s Robust Estimate of Covariance: 175s X1 X2 X3 X4 X5 X6 X7 175s X1 4.47e-07 1.77e-04 1.94e-04 1.79e-04 1.60e-04 1.45e-04 6.45e-04 175s X2 1.77e-04 2.36e+00 4.03e-01 3.08e-01 2.08e-01 3.45e-01 2.18e+00 175s X3 1.94e-04 4.03e-01 1.13e+00 8.31e-01 8.08e-01 7.79e-01 9.83e-01 175s X4 1.79e-04 3.08e-01 8.31e-01 6.62e-01 6.22e-01 5.95e-01 7.82e-01 175s X5 1.60e-04 2.08e-01 8.08e-01 6.22e-01 6.51e-01 5.93e-01 7.60e-01 175s X6 1.45e-04 3.45e-01 7.79e-01 5.95e-01 5.93e-01 5.88e-01 7.81e-01 175s X7 6.45e-04 2.18e+00 9.83e-01 7.82e-01 7.60e-01 7.81e-01 4.81e+00 175s X8 2.47e-05 2.57e-01 2.00e-01 1.37e-01 1.13e-01 1.28e-01 4.38e-01 175s X8 175s X1 2.47e-05 175s X2 2.57e-01 175s X3 2.00e-01 175s X4 1.37e-01 175s X5 1.13e-01 175s X6 1.28e-01 175s X7 4.38e-01 175s X8 1.61e-01 175s -------------------------------------------------------- 175s bushfire 38 5 21.641566 175s Outliers: 13 175s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s V1 V2 V3 V4 V5 175s 111 150 256 214 276 175s 175s Robust Estimate of Covariance: 175s V1 V2 V3 V4 V5 175s V1 554 408 -2321 -464 -393 175s V2 408 343 -1361 -244 -215 175s V3 -2321 -1361 14690 3277 2684 175s V4 -464 -244 3277 783 629 175s V5 -393 -215 2684 629 509 175s -------------------------------------------------------- 175s rice 105 5 -7.208835 175s Outliers: 8 175s [1] 9 14 40 42 49 57 58 71 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s Favor Appearance Taste Stickiness Toughness 175s -0.21721 0.20948 -0.04581 0.15355 -0.00254 175s 175s Robust Estimate of Covariance: 175s Favor Appearance Taste Stickiness Toughness 175s Favor 0.432 0.337 0.417 0.382 -0.201 175s Appearance 0.337 0.591 0.553 0.510 -0.295 175s Taste 0.417 0.553 0.735 0.683 -0.385 175s Stickiness 0.382 0.510 0.683 0.834 -0.462 175s Toughness -0.201 -0.295 -0.385 -0.462 0.408 175s -------------------------------------------------------- 175s hemophilia 75 2 -7.453807 175s Outliers: 2 175s [1] 46 53 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s AHFactivity AHFantigen 175s -0.2276 -0.0637 175s 175s Robust Estimate of Covariance: 175s AHFactivity AHFantigen 175s AHFactivity 0.0405 0.0221 175s AHFantigen 0.0221 0.0263 175s -------------------------------------------------------- 175s fish 159 6 13.110263 175s Outliers: 47 175s [1] 38 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 175s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 175s [39] 98 99 100 101 102 103 104 140 142 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s Weight Length1 Length2 Length3 Height Width 175s 452.1 27.2 29.5 32.6 30.8 15.0 175s 175s Robust Estimate of Covariance: 175s Weight Length1 Length2 Length3 Height Width 175s Weight 132559.85 2817.97 3035.69 3369.07 1231.68 112.19 175s Length1 2817.97 64.16 68.74 75.36 22.52 2.37 175s Length2 3035.69 68.74 73.77 81.12 25.57 2.47 175s Length3 3369.07 75.36 81.12 91.65 37.39 1.40 175s Height 1231.68 22.52 25.57 37.39 50.91 -3.92 175s Width 112.19 2.37 2.47 1.40 -3.92 1.87 175s -------------------------------------------------------- 175s airquality 153 4 21.181656 175s Outliers: 13 175s [1] 6 7 11 14 17 20 23 30 34 53 63 77 107 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s Ozone Solar.R Wind Temp 175s 40.21 198.33 9.76 79.35 175s 175s Robust Estimate of Covariance: 175s Ozone Solar.R Wind Temp 175s Ozone 885.7 581.1 -57.3 226.4 175s Solar.R 581.1 8870.9 26.2 -15.1 175s Wind -57.3 26.2 11.8 -13.4 175s Temp 226.4 -15.1 -13.4 89.4 175s -------------------------------------------------------- 175s attitude 30 7 27.836398 175s Outliers: 8 175s [1] 1 9 13 14 17 18 24 26 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s rating complaints privileges learning raises critical 175s 64.0 65.4 50.5 54.9 63.1 72.6 175s advance 175s 40.5 175s 175s Robust Estimate of Covariance: 175s rating complaints privileges learning raises critical advance 175s rating 180.10 153.16 42.04 128.90 90.25 18.75 39.81 175s complaints 153.16 192.38 58.32 142.48 94.29 8.13 45.33 175s privileges 42.04 58.32 113.65 82.31 69.53 23.13 61.96 175s learning 128.90 142.48 82.31 156.99 101.74 13.22 49.64 175s raises 90.25 94.29 69.53 101.74 110.85 47.84 55.76 175s critical 18.75 8.13 23.13 13.22 47.84 123.00 36.97 175s advance 39.81 45.33 61.96 49.64 55.76 36.97 53.59 175s -------------------------------------------------------- 175s attenu 182 5 9.726797 175s Outliers: 44 175s [1] 1 2 4 5 6 7 8 9 10 11 13 15 16 19 20 21 22 23 24 175s [20] 25 27 28 29 30 31 32 40 45 60 61 64 65 78 80 81 93 94 95 175s [39] 96 97 98 99 100 108 175s ------------- 175s 175s Call: 175s CovSest(x = x, method = method) 175s -> Method: S-estimates: Rocke type 175s 175s Robust Estimate of Location: 175s event mag station dist accel 175s 16.39 5.82 60.89 27.97 0.12 175s 175s Robust Estimate of Covariance: 175s event mag station dist accel 175s event 4.20e+01 -1.97e+00 1.44e+02 -3.50e+01 4.05e-02 175s mag -1.97e+00 5.05e-01 -4.78e+00 4.63e+00 4.19e-03 175s station 1.44e+02 -4.78e+00 1.47e+03 -5.74e+01 7.88e-01 175s dist -3.50e+01 4.63e+00 -5.74e+01 3.99e+02 -1.18e+00 175s accel 4.05e-02 4.19e-03 7.88e-01 -1.18e+00 7.71e-03 175s -------------------------------------------------------- 176s USJudgeRatings 43 12 -46.356873 176s Outliers: 15 176s [1] 1 5 7 8 12 13 14 17 20 21 23 30 31 35 42 176s ------------- 176s 176s Call: 176s CovSest(x = x, method = method) 176s -> Method: S-estimates: Rocke type 176s 176s Robust Estimate of Location: 176s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 176s 7.56 8.12 7.70 7.91 7.74 7.82 7.66 7.66 7.50 7.58 8.22 7.86 176s 176s Robust Estimate of Covariance: 176s CONT INTG DMNR DILG CFMG DECI PREP 176s CONT 0.63426 -0.20121 -0.31858 -0.09578 0.00521 -0.00436 -0.07140 176s INTG -0.20121 0.28326 0.37540 0.27103 0.20362 0.19838 0.25706 176s DMNR -0.31858 0.37540 0.58265 0.33615 0.25649 0.24804 0.31696 176s DILG -0.09578 0.27103 0.33615 0.32588 0.27022 0.26302 0.32236 176s CFMG 0.00521 0.20362 0.25649 0.27022 0.25929 0.24217 0.27784 176s DECI -0.00436 0.19838 0.24804 0.26302 0.24217 0.23830 0.27284 176s PREP -0.07140 0.25706 0.31696 0.32236 0.27784 0.27284 0.35071 176s FAMI -0.07118 0.25858 0.29511 0.32582 0.27863 0.27657 0.35941 176s ORAL -0.11149 0.27055 0.33919 0.31768 0.27339 0.26739 0.34200 176s WRIT -0.10050 0.26857 0.32570 0.32327 0.27860 0.27201 0.34399 176s PHYS -0.09693 0.15339 0.18416 0.17089 0.13837 0.14895 0.18472 176s RTEN -0.15643 0.31793 0.40884 0.33863 0.27073 0.26854 0.34049 176s FAMI ORAL WRIT PHYS RTEN 176s CONT -0.07118 -0.11149 -0.10050 -0.09693 -0.15643 176s INTG 0.25858 0.27055 0.26857 0.15339 0.31793 176s DMNR 0.29511 0.33919 0.32570 0.18416 0.40884 176s DILG 0.32582 0.31768 0.32327 0.17089 0.33863 176s CFMG 0.27863 0.27339 0.27860 0.13837 0.27073 176s DECI 0.27657 0.26739 0.27201 0.14895 0.26854 176s PREP 0.35941 0.34200 0.34399 0.18472 0.34049 176s FAMI 0.38378 0.35617 0.36094 0.19998 0.35048 176s ORAL 0.35617 0.34918 0.34808 0.19759 0.35217 176s WRIT 0.36094 0.34808 0.35242 0.19666 0.35090 176s PHYS 0.19998 0.19759 0.19666 0.14770 0.20304 176s RTEN 0.35048 0.35217 0.35090 0.20304 0.39451 176s -------------------------------------------------------- 176s USArrests 50 4 19.206310 176s Outliers: 4 176s [1] 2 28 33 39 176s ------------- 176s 176s Call: 176s CovSest(x = x, method = method) 176s -> Method: S-estimates: Rocke type 176s 176s Robust Estimate of Location: 176s Murder Assault UrbanPop Rape 176s 7.55 160.94 65.10 19.97 176s 176s Robust Estimate of Covariance: 176s Murder Assault UrbanPop Rape 176s Murder 25.6 409.5 23.4 32.1 176s Assault 409.5 8530.9 676.9 669.4 176s UrbanPop 23.4 676.9 269.9 76.6 176s Rape 32.1 669.4 76.6 76.6 176s -------------------------------------------------------- 176s longley 16 7 13.387132 176s Outliers: 4 176s [1] 1 2 3 4 176s ------------- 176s 176s Call: 176s CovSest(x = x, method = method) 176s -> Method: S-estimates: Rocke type 176s 176s Robust Estimate of Location: 176s GNP.deflator GNP Unemployed Armed.Forces Population 176s 105.5 422.4 318.3 299.7 119.5 176s Year Employed 176s 1956.1 66.5 176s 176s Robust Estimate of Covariance: 176s GNP.deflator GNP Unemployed Armed.Forces Population 176s GNP.deflator 59.97 582.66 694.99 -237.75 46.12 176s GNP 582.66 5849.82 6383.68 -2207.26 461.15 176s Unemployed 694.99 6383.68 11155.03 -3104.18 534.25 176s Armed.Forces -237.75 -2207.26 -3104.18 1429.11 -171.28 176s Population 46.12 461.15 534.25 -171.28 36.79 176s Year 29.01 287.48 340.95 -112.61 22.85 176s Employed 18.99 193.66 186.31 -76.88 14.94 176s Year Employed 176s GNP.deflator 29.01 18.99 176s GNP 287.48 193.66 176s Unemployed 340.95 186.31 176s Armed.Forces -112.61 -76.88 176s Population 22.85 14.94 176s Year 14.36 9.45 176s Employed 9.45 6.90 176s -------------------------------------------------------- 176s Loblolly 84 3 7.757906 176s Outliers: 27 176s [1] 5 6 11 12 18 23 24 29 30 35 36 41 42 47 48 53 54 59 60 65 66 71 72 77 78 176s [26] 83 84 176s ------------- 176s 176s Call: 176s CovSest(x = x, method = method) 176s -> Method: S-estimates: Rocke type 176s 176s Robust Estimate of Location: 176s height age Seed 176s 21.72 8.60 7.58 176s 176s Robust Estimate of Covariance: 176s height age Seed 176s height 316.590 102.273 5.939 176s age 102.273 33.465 -0.121 176s Seed 5.939 -0.121 27.203 176s -------------------------------------------------------- 176s quakes 1000 4 11.473431 176s Outliers: 237 176s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 176s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 176s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 176s [46] 163 166 170 174 176 192 205 222 226 230 239 243 244 250 251 176s [61] 252 254 258 263 267 268 271 283 292 297 300 301 305 311 312 176s [76] 318 320 321 325 328 330 331 334 352 357 360 365 368 376 381 176s [91] 382 384 389 399 400 402 408 410 413 416 417 418 419 426 429 176s [106] 437 441 443 453 456 467 474 477 490 492 496 504 507 508 509 176s [121] 517 524 527 528 531 532 534 536 538 539 541 542 543 544 545 176s [136] 546 547 552 553 558 560 570 571 581 583 587 593 594 596 597 176s [151] 605 612 613 618 620 625 629 638 642 647 649 653 655 656 672 176s [166] 675 681 686 699 701 702 712 714 716 721 725 726 735 744 753 176s [181] 754 756 759 765 766 769 779 781 782 785 787 797 804 813 825 176s [196] 827 837 840 844 852 853 857 860 865 866 869 870 872 873 883 176s [211] 884 887 888 890 891 893 908 909 912 915 916 921 927 930 952 176s [226] 962 963 969 974 980 982 986 987 988 992 997 1000 176s ------------- 176s 176s Call: 176s CovSest(x = x, method = method) 176s -> Method: S-estimates: Rocke type 176s 176s Robust Estimate of Location: 176s lat long depth mag 176s -21.45 182.54 351.18 4.55 176s 176s Robust Estimate of Covariance: 176s lat long depth mag 176s lat 2.10e+01 4.66e+00 2.45e+02 -3.38e-01 176s long 4.66e+00 5.88e+00 -4.63e+02 9.36e-02 176s depth 2.45e+02 -4.63e+02 6.38e+04 -2.02e+01 176s mag -3.38e-01 9.36e-02 -2.02e+01 1.78e-01 176s -------------------------------------------------------- 176s =================================================== 176s > dodata(method="MM") 176s 176s Call: dodata(method = "MM") 176s Data Set n p LOG(det) Time 176s =================================================== 176s heart 12 2 2.017701 176s Outliers: 1 176s [1] 6 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s height weight 176s 40.0 37.7 176s 176s Robust Estimate of Covariance: 176s height weight 176s height 99.2 205.7 176s weight 205.7 458.9 176s -------------------------------------------------------- 176s starsCYG 47 2 -1.450032 176s Outliers: 7 176s [1] 7 9 11 14 20 30 34 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s log.Te log.light 176s 4.41 4.94 176s 176s Robust Estimate of Covariance: 176s log.Te log.light 176s log.Te 0.0180 0.0526 176s log.light 0.0526 0.3217 176s -------------------------------------------------------- 176s phosphor 18 2 2.320721 176s Outliers: 1 176s [1] 6 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s inorg organic 176s 12.3 41.4 176s 176s Robust Estimate of Covariance: 176s inorg organic 176s inorg 94.2 67.2 176s organic 67.2 162.1 176s -------------------------------------------------------- 176s stackloss 21 3 1.470031 176s Outliers: 0 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s Air.Flow Water.Temp Acid.Conc. 176s 60.2 21.0 86.4 176s 176s Robust Estimate of Covariance: 176s Air.Flow Water.Temp Acid.Conc. 176s Air.Flow 81.13 21.99 23.15 176s Water.Temp 21.99 10.01 6.43 176s Acid.Conc. 23.15 6.43 27.22 176s -------------------------------------------------------- 176s coleman 20 5 0.491419 176s Outliers: 1 176s [1] 10 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s salaryP fatherWc sstatus teacherSc motherLev 176s 2.74 43.14 3.65 25.07 6.32 176s 176s Robust Estimate of Covariance: 176s salaryP fatherWc sstatus teacherSc motherLev 176s salaryP 0.1878 2.0635 1.0433 0.2721 0.0582 176s fatherWc 2.0635 670.2232 211.0609 4.3625 15.6083 176s sstatus 1.0433 211.0609 92.8743 2.6532 5.1816 176s teacherSc 0.2721 4.3625 2.6532 1.2757 0.1613 176s motherLev 0.0582 15.6083 5.1816 0.1613 0.4192 176s -------------------------------------------------------- 176s salinity 28 3 0.734619 176s Outliers: 2 176s [1] 5 16 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s X1 X2 X3 176s 10.46 2.66 23.15 176s 176s Robust Estimate of Covariance: 176s X1 X2 X3 176s X1 10.079 -0.024 -1.899 176s X2 -0.024 3.466 -1.817 176s X3 -1.899 -1.817 3.665 176s -------------------------------------------------------- 176s wood 20 5 -3.202636 176s Outliers: 0 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s x1 x2 x3 x4 x5 176s 0.550 0.133 0.506 0.511 0.909 176s 176s Robust Estimate of Covariance: 176s x1 x2 x3 x4 x5 176s x1 0.008454 -0.000377 0.003720 0.002874 -0.003065 176s x2 -0.000377 0.000516 -0.000399 -0.000933 0.000645 176s x3 0.003720 -0.000399 0.004186 0.001720 -0.001714 176s x4 0.002874 -0.000933 0.001720 0.003993 -0.001028 176s x5 -0.003065 0.000645 -0.001714 -0.001028 0.002744 176s -------------------------------------------------------- 176s hbk 75 3 0.283145 176s Outliers: 14 176s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s X1 X2 X3 176s 1.54 1.79 1.68 176s 176s Robust Estimate of Covariance: 176s X1 X2 X3 176s X1 1.8016 0.0739 0.2000 176s X2 0.0739 1.8301 0.2295 176s X3 0.2000 0.2295 1.7101 176s -------------------------------------------------------- 176s Animals 28 2 4.685129 176s Outliers: 10 176s [1] 2 6 7 9 12 14 15 16 24 25 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s body brain 176s 82 148 176s 176s Robust Estimate of Covariance: 176s body brain 176s body 21050 24534 176s brain 24534 35135 176s -------------------------------------------------------- 176s milk 86 8 -1.437863 176s Outliers: 12 176s [1] 1 2 3 12 13 17 41 44 47 70 74 75 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s X1 X2 X3 X4 X5 X6 X7 X8 176s 1.03 35.73 32.87 25.96 24.94 24.85 122.55 14.33 176s 176s Robust Estimate of Covariance: 176s X1 X2 X3 X4 X5 X6 X7 176s X1 1.08e-06 5.36e-04 6.80e-04 5.96e-04 5.87e-04 5.91e-04 2.22e-03 176s X2 5.36e-04 2.42e+00 7.07e-01 5.51e-01 4.89e-01 5.70e-01 3.08e+00 176s X3 6.80e-04 7.07e-01 1.64e+00 1.28e+00 1.25e+00 1.26e+00 2.38e+00 176s X4 5.96e-04 5.51e-01 1.28e+00 1.05e+00 1.01e+00 1.02e+00 2.01e+00 176s X5 5.87e-04 4.89e-01 1.25e+00 1.01e+00 1.05e+00 1.02e+00 1.96e+00 176s X6 5.91e-04 5.70e-01 1.26e+00 1.02e+00 1.02e+00 1.05e+00 2.01e+00 176s X7 2.22e-03 3.08e+00 2.38e+00 2.01e+00 1.96e+00 2.01e+00 9.22e+00 176s X8 1.68e-04 4.13e-01 3.37e-01 2.53e-01 2.34e-01 2.43e-01 8.81e-01 176s X8 176s X1 1.68e-04 176s X2 4.13e-01 176s X3 3.37e-01 176s X4 2.53e-01 176s X5 2.34e-01 176s X6 2.43e-01 176s X7 8.81e-01 176s X8 2.11e-01 176s -------------------------------------------------------- 176s bushfire 38 5 2.443148 176s Outliers: 12 176s [1] 8 9 10 11 31 32 33 34 35 36 37 38 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s V1 V2 V3 V4 V5 176s 109 149 258 215 276 176s 176s Robust Estimate of Covariance: 176s V1 V2 V3 V4 V5 176s V1 708 538 -2705 -558 -464 176s V2 538 497 -1376 -248 -216 176s V3 -2705 -1376 20521 4833 3914 176s V4 -558 -248 4833 1217 969 176s V5 -464 -216 3914 969 778 176s -------------------------------------------------------- 176s rice 105 5 -0.724874 176s Outliers: 5 176s [1] 9 42 49 58 71 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s Favor Appearance Taste Stickiness Toughness 176s -0.2653 0.0969 -0.1371 0.0483 0.0731 176s 176s Robust Estimate of Covariance: 176s Favor Appearance Taste Stickiness Toughness 176s Favor 0.421 0.349 0.427 0.405 -0.191 176s Appearance 0.349 0.605 0.565 0.553 -0.316 176s Taste 0.427 0.565 0.725 0.701 -0.378 176s Stickiness 0.405 0.553 0.701 0.868 -0.484 176s Toughness -0.191 -0.316 -0.378 -0.484 0.464 176s -------------------------------------------------------- 176s hemophilia 75 2 -1.868949 176s Outliers: 2 176s [1] 11 36 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s AHFactivity AHFantigen 176s -0.2342 -0.0333 176s 176s Robust Estimate of Covariance: 176s AHFactivity AHFantigen 176s AHFactivity 0.0309 0.0122 176s AHFantigen 0.0122 0.0231 176s -------------------------------------------------------- 176s fish 159 6 1.285876 176s Outliers: 20 176s [1] 61 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 176s [20] 142 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s Weight Length1 Length2 Length3 Height Width 176s 352.7 24.3 26.4 29.2 29.7 14.6 176s 176s Robust Estimate of Covariance: 176s Weight Length1 Length2 Length3 Height Width 176s Weight 1.20e+05 2.89e+03 3.12e+03 3.51e+03 1.49e+03 2.83e+02 176s Length1 2.89e+03 7.73e+01 8.35e+01 9.28e+01 3.73e+01 9.26e+00 176s Length2 3.12e+03 8.35e+01 9.04e+01 1.01e+02 4.16e+01 1.01e+01 176s Length3 3.51e+03 9.28e+01 1.01e+02 1.14e+02 5.37e+01 1.01e+01 176s Height 1.49e+03 3.73e+01 4.16e+01 5.37e+01 6.75e+01 3.22e+00 176s Width 2.83e+02 9.26e+00 1.01e+01 1.01e+01 3.22e+00 4.18e+00 176s -------------------------------------------------------- 176s airquality 153 4 2.684374 176s Outliers: 6 176s [1] 7 14 23 30 34 77 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s Ozone Solar.R Wind Temp 176s 40.35 186.21 9.86 78.09 176s 176s Robust Estimate of Covariance: 176s Ozone Solar.R Wind Temp 176s Ozone 951.0 959.9 -62.5 224.6 176s Solar.R 959.9 8629.9 -28.1 244.9 176s Wind -62.5 -28.1 11.6 -15.8 176s Temp 224.6 244.9 -15.8 93.1 176s -------------------------------------------------------- 176s attitude 30 7 2.091968 176s Outliers: 4 176s [1] 14 16 18 24 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s rating complaints privileges learning raises critical 176s 65.0 66.5 52.4 56.2 65.3 75.6 176s advance 176s 42.7 176s 176s Robust Estimate of Covariance: 176s rating complaints privileges learning raises critical advance 176s rating 143.5 123.4 62.4 92.5 79.2 17.7 28.2 176s complaints 123.4 159.8 83.9 99.7 96.0 27.3 44.0 176s privileges 62.4 83.9 133.5 78.6 62.0 13.4 46.4 176s learning 92.5 99.7 78.6 136.0 90.9 18.9 62.6 176s raises 79.2 96.0 62.0 90.9 107.6 34.6 63.3 176s critical 17.7 27.3 13.4 18.9 34.6 84.9 25.9 176s advance 28.2 44.0 46.4 62.6 63.3 25.9 94.4 176s -------------------------------------------------------- 176s attenu 182 5 1.148032 176s Outliers: 21 176s [1] 2 7 8 9 10 11 15 16 24 25 28 29 30 31 32 64 65 94 95 176s [20] 96 100 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s event mag station dist accel 176s 15.36 5.95 58.11 33.56 0.14 176s 176s Robust Estimate of Covariance: 176s event mag station dist accel 176s event 4.88e+01 -2.74e+00 1.53e+02 -1.14e+02 5.95e-02 176s mag -2.74e+00 5.32e-01 -6.29e+00 1.10e+01 9.37e-03 176s station 1.53e+02 -6.29e+00 1.29e+03 -2.95e+02 1.04e+00 176s dist -1.14e+02 1.10e+01 -2.95e+02 1.13e+03 -2.41e+00 176s accel 5.95e-02 9.37e-03 1.04e+00 -2.41e+00 1.70e-02 176s -------------------------------------------------------- 176s USJudgeRatings 43 12 -1.683847 176s Outliers: 7 176s [1] 5 7 12 13 14 23 31 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 176s 7.45 8.15 7.74 7.87 7.67 7.74 7.65 7.65 7.50 7.57 8.17 7.85 176s 176s Robust Estimate of Covariance: 176s CONT INTG DMNR DILG CFMG DECI PREP FAMI 176s CONT 0.9403 -0.2500 -0.3953 -0.1418 -0.0176 -0.0620 -0.1304 -0.1517 176s INTG -0.2500 0.6314 0.8479 0.6889 0.5697 0.5386 0.7007 0.6985 176s DMNR -0.3953 0.8479 1.2186 0.9027 0.7613 0.7232 0.9191 0.9055 176s DILG -0.1418 0.6889 0.9027 0.8474 0.7344 0.6949 0.8751 0.8655 176s CFMG -0.0176 0.5697 0.7613 0.7344 0.6904 0.6442 0.7683 0.7594 176s DECI -0.0620 0.5386 0.7232 0.6949 0.6442 0.6219 0.7362 0.7360 176s PREP -0.1304 0.7007 0.9191 0.8751 0.7683 0.7362 0.9370 0.9357 176s FAMI -0.1517 0.6985 0.9055 0.8655 0.7594 0.7360 0.9357 0.9547 176s ORAL -0.1866 0.7375 0.9841 0.8816 0.7747 0.7433 0.9400 0.9496 176s WRIT -0.1881 0.7208 0.9516 0.8711 0.7646 0.7357 0.9302 0.9439 176s PHYS -0.1407 0.4673 0.6261 0.5661 0.5105 0.5039 0.5996 0.6112 176s RTEN -0.2494 0.7921 1.0688 0.9167 0.7902 0.7585 0.9533 0.9561 176s ORAL WRIT PHYS RTEN 176s CONT -0.1866 -0.1881 -0.1407 -0.2494 176s INTG 0.7375 0.7208 0.4673 0.7921 176s DMNR 0.9841 0.9516 0.6261 1.0688 176s DILG 0.8816 0.8711 0.5661 0.9167 176s CFMG 0.7747 0.7646 0.5105 0.7902 176s DECI 0.7433 0.7357 0.5039 0.7585 176s PREP 0.9400 0.9302 0.5996 0.9533 176s FAMI 0.9496 0.9439 0.6112 0.9561 176s ORAL 0.9712 0.9558 0.6271 0.9933 176s WRIT 0.9558 0.9483 0.6135 0.9725 176s PHYS 0.6271 0.6135 0.4816 0.6549 176s RTEN 0.9933 0.9725 0.6549 1.0540 176s -------------------------------------------------------- 176s USArrests 50 4 2.411726 176s Outliers: 3 176s [1] 2 33 39 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s Murder Assault UrbanPop Rape 176s 7.52 163.86 65.66 20.64 176s 176s Robust Estimate of Covariance: 176s Murder Assault UrbanPop Rape 176s Murder 19.05 295.96 8.32 23.40 176s Assault 295.96 6905.03 396.53 523.49 176s UrbanPop 8.32 396.53 202.98 62.81 176s Rape 23.40 523.49 62.81 79.10 176s -------------------------------------------------------- 176s longley 16 7 1.038316 176s Outliers: 5 176s [1] 1 2 3 4 5 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s GNP.deflator GNP Unemployed Armed.Forces Population 176s 107.5 440.4 339.4 293.0 120.9 176s Year Employed 176s 1957.0 67.2 176s 176s Robust Estimate of Covariance: 176s GNP.deflator GNP Unemployed Armed.Forces Population 176s GNP.deflator 100.4 953.8 1140.8 -501.8 74.3 176s GNP 953.8 9434.3 10084.3 -4573.8 731.3 176s Unemployed 1140.8 10084.3 19644.6 -6296.3 848.4 176s Armed.Forces -501.8 -4573.8 -6296.3 3192.3 -348.5 176s Population 74.3 731.3 848.4 -348.5 57.7 176s Year 46.3 450.7 537.0 -230.7 35.3 176s Employed 30.8 310.2 273.8 -159.4 23.3 176s Year Employed 176s GNP.deflator 46.3 30.8 176s GNP 450.7 310.2 176s Unemployed 537.0 273.8 176s Armed.Forces -230.7 -159.4 176s Population 35.3 23.3 176s Year 21.9 14.6 176s Employed 14.6 11.2 176s -------------------------------------------------------- 176s Loblolly 84 3 1.481317 176s Outliers: 0 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s height age Seed 176s 31.93 12.79 7.48 176s 176s Robust Estimate of Covariance: 176s height age Seed 176s height 440.644 165.652 6.958 176s age 165.652 63.500 0.681 176s Seed 6.958 0.681 16.564 176s -------------------------------------------------------- 176s quakes 1000 4 1.576855 176s Outliers: 218 176s [1] 7 12 15 17 22 27 32 37 40 41 45 48 53 63 64 176s [16] 73 78 87 91 92 94 99 108 110 117 118 119 120 121 122 176s [31] 126 133 136 141 143 145 148 152 154 155 157 159 160 163 170 176s [46] 192 205 222 226 230 239 243 250 251 252 254 258 263 267 268 176s [61] 271 283 292 300 301 305 311 312 318 320 321 325 328 330 334 176s [76] 352 357 360 365 381 382 384 389 400 402 408 413 416 417 419 176s [91] 429 437 441 443 453 456 467 474 477 490 492 496 504 507 508 176s [106] 509 517 524 527 528 531 532 534 536 538 539 541 542 543 544 176s [121] 545 546 547 552 553 560 571 581 583 587 593 594 596 597 605 176s [136] 612 613 618 620 625 629 638 642 647 649 653 655 656 672 675 176s [151] 681 686 699 701 702 712 714 716 721 725 726 735 744 754 756 176s [166] 759 765 766 769 779 781 782 785 787 797 804 813 825 827 837 176s [181] 840 844 852 853 857 860 865 866 869 870 872 873 883 884 887 176s [196] 888 890 891 893 908 909 912 915 916 921 927 930 962 963 969 176s [211] 974 980 982 986 987 988 997 1000 176s ------------- 176s 176s Call: 176s CovMMest(x = x) 176s -> Method: MM-estimates 176s 176s Robust Estimate of Location: 176s lat long depth mag 176s -21.74 182.37 356.37 4.56 176s 176s Robust Estimate of Covariance: 176s lat long depth mag 176s lat 2.97e+01 6.53e+00 3.46e+02 -4.66e-01 176s long 6.53e+00 6.92e+00 -5.05e+02 5.62e-02 176s depth 3.46e+02 -5.05e+02 7.39e+04 -2.51e+01 176s mag -4.66e-01 5.62e-02 -2.51e+01 2.32e-01 176s -------------------------------------------------------- 176s =================================================== 176s > ##dogen() 176s > ##cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 176s > 176s autopkgtest [03:34:49]: test run-unit-test: -----------------------] 177s autopkgtest [03:34:50]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 177s run-unit-test PASS 177s autopkgtest [03:34:50]: test pkg-r-autopkgtest: preparing testbed 178s Reading package lists... 178s Building dependency tree... 178s Reading state information... 178s Solving dependencies... 178s The following NEW packages will be installed: 178s build-essential cpp cpp-15 cpp-15-x86-64-linux-gnu cpp-x86-64-linux-gnu 178s dctrl-tools g++ g++-15 g++-15-x86-64-linux-gnu g++-x86-64-linux-gnu gcc 178s gcc-15 gcc-15-x86-64-linux-gnu gcc-x86-64-linux-gnu gfortran gfortran-15 178s gfortran-15-x86-64-linux-gnu gfortran-x86-64-linux-gnu icu-devtools libasan8 178s libblas-dev libbz2-dev libc-dev-bin libc6-dev libcc1-0 libcrypt-dev 178s libdeflate-dev libgcc-15-dev libgfortran-15-dev libhwasan0 libicu-dev 178s libisl23 libitm1 libjpeg-dev libjpeg-turbo8-dev libjpeg8-dev liblapack-dev 178s liblsan0 liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 libpcre2-32-0 178s libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev libquadmath0 178s libreadline-dev libstdc++-15-dev libtirpc-dev libtsan2 libubsan1 libzstd-dev 178s linux-libc-dev pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev rpcsvc-proto 178s zlib1g-dev 178s 0 upgraded, 61 newly installed, 0 to remove and 0 not upgraded. 178s Need to get 112 MB of archives. 178s After this operation, 402 MB of additional disk space will be used. 178s Get:1 http://ftpmaster.internal/ubuntu resolute/main amd64 libc-dev-bin amd64 2.42-2ubuntu4 [23.3 kB] 178s Get:2 http://ftpmaster.internal/ubuntu resolute/main amd64 linux-libc-dev amd64 6.19.0-3.3 [1846 kB] 178s Get:3 http://ftpmaster.internal/ubuntu resolute/main amd64 libcrypt-dev amd64 1:4.5.1-1 [122 kB] 178s Get:4 http://ftpmaster.internal/ubuntu resolute/main amd64 rpcsvc-proto amd64 1.4.3-1build1 [68.3 kB] 178s Get:5 http://ftpmaster.internal/ubuntu resolute/main amd64 libc6-dev amd64 2.42-2ubuntu4 [2207 kB] 179s Get:6 http://ftpmaster.internal/ubuntu resolute/main amd64 libisl23 amd64 0.27-1build1 [691 kB] 179s Get:7 http://ftpmaster.internal/ubuntu resolute/main amd64 libmpc3 amd64 1.3.1-2 [54.8 kB] 179s Get:8 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp-15-x86-64-linux-gnu amd64 15.2.0-12ubuntu1 [12.9 MB] 179s Get:9 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp-15 amd64 15.2.0-12ubuntu1 [1034 B] 179s Get:10 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [5746 B] 179s Get:11 http://ftpmaster.internal/ubuntu resolute/main amd64 cpp amd64 4:15.2.0-4ubuntu1 [22.4 kB] 179s Get:12 http://ftpmaster.internal/ubuntu resolute/main amd64 libcc1-0 amd64 16-20260208-1ubuntu1 [51.2 kB] 179s Get:13 http://ftpmaster.internal/ubuntu resolute/main amd64 libitm1 amd64 16-20260208-1ubuntu1 [30.2 kB] 179s Get:14 http://ftpmaster.internal/ubuntu resolute/main amd64 libasan8 amd64 16-20260208-1ubuntu1 [3182 kB] 179s Get:15 http://ftpmaster.internal/ubuntu resolute/main amd64 liblsan0 amd64 16-20260208-1ubuntu1 [1392 kB] 179s Get:16 http://ftpmaster.internal/ubuntu resolute/main amd64 libtsan2 amd64 16-20260208-1ubuntu1 [2838 kB] 179s Get:17 http://ftpmaster.internal/ubuntu resolute/main amd64 libubsan1 amd64 16-20260208-1ubuntu1 [1238 kB] 179s Get:18 http://ftpmaster.internal/ubuntu resolute/main amd64 libhwasan0 amd64 16-20260208-1ubuntu1 [1729 kB] 179s Get:19 http://ftpmaster.internal/ubuntu resolute/main amd64 libquadmath0 amd64 16-20260208-1ubuntu1 [155 kB] 179s Get:20 http://ftpmaster.internal/ubuntu resolute/main amd64 libgcc-15-dev amd64 15.2.0-12ubuntu1 [2866 kB] 180s Get:21 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-15-x86-64-linux-gnu amd64 15.2.0-12ubuntu1 [25.4 MB] 180s Get:22 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-15 amd64 15.2.0-12ubuntu1 [530 kB] 180s Get:23 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [1208 B] 180s Get:24 http://ftpmaster.internal/ubuntu resolute/main amd64 gcc amd64 4:15.2.0-4ubuntu1 [5024 B] 180s Get:25 http://ftpmaster.internal/ubuntu resolute/main amd64 libstdc++-15-dev amd64 15.2.0-12ubuntu1 [2553 kB] 180s Get:26 http://ftpmaster.internal/ubuntu resolute/main amd64 g++-15-x86-64-linux-gnu amd64 15.2.0-12ubuntu1 [14.4 MB] 180s Get:27 http://ftpmaster.internal/ubuntu resolute/main amd64 g++-15 amd64 15.2.0-12ubuntu1 [25.3 kB] 180s Get:28 http://ftpmaster.internal/ubuntu resolute/main amd64 g++-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [966 B] 180s Get:29 http://ftpmaster.internal/ubuntu resolute/main amd64 g++ amd64 4:15.2.0-4ubuntu1 [1100 B] 180s Get:30 http://ftpmaster.internal/ubuntu resolute/main amd64 build-essential amd64 12.12ubuntu2 [5256 B] 180s Get:31 http://ftpmaster.internal/ubuntu resolute/main amd64 dctrl-tools amd64 2.24-3build4 [104 kB] 180s Get:32 http://ftpmaster.internal/ubuntu resolute/main amd64 libgfortran-15-dev amd64 15.2.0-12ubuntu1 [973 kB] 180s Get:33 http://ftpmaster.internal/ubuntu resolute/main amd64 gfortran-15-x86-64-linux-gnu amd64 15.2.0-12ubuntu1 [13.6 MB] 181s Get:34 http://ftpmaster.internal/ubuntu resolute/main amd64 gfortran-15 amd64 15.2.0-12ubuntu1 [18.1 kB] 181s Get:35 http://ftpmaster.internal/ubuntu resolute/main amd64 gfortran-x86-64-linux-gnu amd64 4:15.2.0-4ubuntu1 [1014 B] 181s Get:36 http://ftpmaster.internal/ubuntu resolute/main amd64 gfortran amd64 4:15.2.0-4ubuntu1 [1172 B] 181s Get:37 http://ftpmaster.internal/ubuntu resolute/main amd64 icu-devtools amd64 78.2-1ubuntu1 [214 kB] 181s Get:38 http://ftpmaster.internal/ubuntu resolute/main amd64 libblas-dev amd64 3.12.1-7ubuntu1 [235 kB] 181s Get:39 http://ftpmaster.internal/ubuntu resolute/main amd64 libbz2-dev amd64 1.0.8-6build2 [36.2 kB] 181s Get:40 http://ftpmaster.internal/ubuntu resolute/main amd64 libdeflate-dev amd64 1.23-2build1 [58.8 kB] 181s Get:41 http://ftpmaster.internal/ubuntu resolute/main amd64 libicu-dev amd64 78.2-1ubuntu1 [12.5 MB] 181s Get:42 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg-turbo8-dev amd64 2.1.5-4ubuntu3 [300 kB] 181s Get:43 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg8-dev amd64 8c-2ubuntu12 [1480 B] 181s Get:44 http://ftpmaster.internal/ubuntu resolute/main amd64 libjpeg-dev amd64 8c-2ubuntu12 [1480 B] 181s Get:45 http://ftpmaster.internal/ubuntu resolute/main amd64 liblapack-dev amd64 3.12.1-7ubuntu1 [5433 kB] 181s Get:46 http://ftpmaster.internal/ubuntu resolute/main amd64 libncurses-dev amd64 6.6+20251231-1 [389 kB] 181s Get:47 http://ftpmaster.internal/ubuntu resolute/main amd64 libpcre2-16-0 amd64 10.46-1 [243 kB] 181s Get:48 http://ftpmaster.internal/ubuntu resolute/main amd64 libpcre2-32-0 amd64 10.46-1 [230 kB] 181s Get:49 http://ftpmaster.internal/ubuntu resolute/main amd64 libpcre2-posix3 amd64 10.46-1 [7354 B] 181s Get:50 http://ftpmaster.internal/ubuntu resolute/main amd64 libpcre2-dev amd64 10.46-1 [832 kB] 181s Get:51 http://ftpmaster.internal/ubuntu resolute/main amd64 libpkgconf3 amd64 1.8.1-4build1 [32.8 kB] 181s Get:52 http://ftpmaster.internal/ubuntu resolute/main amd64 zlib1g-dev amd64 1:1.3.dfsg+really1.3.1-1ubuntu2 [898 kB] 181s Get:53 http://ftpmaster.internal/ubuntu resolute/main amd64 libpng-dev amd64 1.6.54-1 [273 kB] 181s Get:54 http://ftpmaster.internal/ubuntu resolute/main amd64 libreadline-dev amd64 8.3-3 [189 kB] 181s Get:55 http://ftpmaster.internal/ubuntu resolute/main amd64 libzstd-dev amd64 1.5.7+dfsg-3 [376 kB] 181s Get:56 http://ftpmaster.internal/ubuntu resolute/main amd64 liblzma-dev amd64 5.8.2-2 [179 kB] 181s Get:57 http://ftpmaster.internal/ubuntu resolute/main amd64 pkgconf-bin amd64 1.8.1-4build1 [21.7 kB] 181s Get:58 http://ftpmaster.internal/ubuntu resolute/main amd64 pkgconf amd64 1.8.1-4build1 [16.8 kB] 181s Get:59 http://ftpmaster.internal/ubuntu resolute/main amd64 libtirpc-dev amd64 1.3.6+ds-1 [195 kB] 181s Get:60 http://ftpmaster.internal/ubuntu resolute/universe amd64 r-base-dev all 4.5.2-1ubuntu2 [1880 B] 181s Get:61 http://ftpmaster.internal/ubuntu resolute/universe amd64 pkg-r-autopkgtest all 20250812 [6158 B] 182s Fetched 112 MB in 3s (33.0 MB/s) 182s Selecting previously unselected package libc-dev-bin. 182s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 127937 files and directories currently installed.) 182s Preparing to unpack .../00-libc-dev-bin_2.42-2ubuntu4_amd64.deb ... 182s Unpacking libc-dev-bin (2.42-2ubuntu4) ... 182s Selecting previously unselected package linux-libc-dev:amd64. 182s Preparing to unpack .../01-linux-libc-dev_6.19.0-3.3_amd64.deb ... 182s Unpacking linux-libc-dev:amd64 (6.19.0-3.3) ... 182s Selecting previously unselected package libcrypt-dev:amd64. 182s Preparing to unpack .../02-libcrypt-dev_1%3a4.5.1-1_amd64.deb ... 182s Unpacking libcrypt-dev:amd64 (1:4.5.1-1) ... 182s Selecting previously unselected package rpcsvc-proto. 182s Preparing to unpack .../03-rpcsvc-proto_1.4.3-1build1_amd64.deb ... 182s Unpacking rpcsvc-proto (1.4.3-1build1) ... 182s Selecting previously unselected package libc6-dev:amd64. 182s Preparing to unpack .../04-libc6-dev_2.42-2ubuntu4_amd64.deb ... 182s Unpacking libc6-dev:amd64 (2.42-2ubuntu4) ... 182s Selecting previously unselected package libisl23:amd64. 182s Preparing to unpack .../05-libisl23_0.27-1build1_amd64.deb ... 182s Unpacking libisl23:amd64 (0.27-1build1) ... 182s Selecting previously unselected package libmpc3:amd64. 182s Preparing to unpack .../06-libmpc3_1.3.1-2_amd64.deb ... 182s Unpacking libmpc3:amd64 (1.3.1-2) ... 182s Selecting previously unselected package cpp-15-x86-64-linux-gnu. 182s Preparing to unpack .../07-cpp-15-x86-64-linux-gnu_15.2.0-12ubuntu1_amd64.deb ... 182s Unpacking cpp-15-x86-64-linux-gnu (15.2.0-12ubuntu1) ... 182s Selecting previously unselected package cpp-15. 182s Preparing to unpack .../08-cpp-15_15.2.0-12ubuntu1_amd64.deb ... 182s Unpacking cpp-15 (15.2.0-12ubuntu1) ... 182s Selecting previously unselected package cpp-x86-64-linux-gnu. 182s Preparing to unpack .../09-cpp-x86-64-linux-gnu_4%3a15.2.0-4ubuntu1_amd64.deb ... 182s Unpacking cpp-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 182s Selecting previously unselected package cpp. 182s Preparing to unpack .../10-cpp_4%3a15.2.0-4ubuntu1_amd64.deb ... 182s Unpacking cpp (4:15.2.0-4ubuntu1) ... 182s Selecting previously unselected package libcc1-0:amd64. 182s Preparing to unpack .../11-libcc1-0_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking libcc1-0:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package libitm1:amd64. 182s Preparing to unpack .../12-libitm1_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking libitm1:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package libasan8:amd64. 182s Preparing to unpack .../13-libasan8_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking libasan8:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package liblsan0:amd64. 182s Preparing to unpack .../14-liblsan0_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking liblsan0:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package libtsan2:amd64. 182s Preparing to unpack .../15-libtsan2_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking libtsan2:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package libubsan1:amd64. 182s Preparing to unpack .../16-libubsan1_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking libubsan1:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package libhwasan0:amd64. 182s Preparing to unpack .../17-libhwasan0_16-20260208-1ubuntu1_amd64.deb ... 182s Unpacking libhwasan0:amd64 (16-20260208-1ubuntu1) ... 182s Selecting previously unselected package libquadmath0:amd64. 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.../28-g++_4%3a15.2.0-4ubuntu1_amd64.deb ... 183s Unpacking g++ (4:15.2.0-4ubuntu1) ... 183s Selecting previously unselected package build-essential. 183s Preparing to unpack .../29-build-essential_12.12ubuntu2_amd64.deb ... 183s Unpacking build-essential (12.12ubuntu2) ... 183s Selecting previously unselected package dctrl-tools. 183s Preparing to unpack .../30-dctrl-tools_2.24-3build4_amd64.deb ... 183s Unpacking dctrl-tools (2.24-3build4) ... 183s Selecting previously unselected package libgfortran-15-dev:amd64. 183s Preparing to unpack .../31-libgfortran-15-dev_15.2.0-12ubuntu1_amd64.deb ... 183s Unpacking libgfortran-15-dev:amd64 (15.2.0-12ubuntu1) ... 183s Selecting previously unselected package gfortran-15-x86-64-linux-gnu. 183s Preparing to unpack .../32-gfortran-15-x86-64-linux-gnu_15.2.0-12ubuntu1_amd64.deb ... 183s Unpacking gfortran-15-x86-64-linux-gnu (15.2.0-12ubuntu1) ... 183s Selecting previously unselected package gfortran-15. 183s Preparing to unpack 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.../43-libjpeg-dev_8c-2ubuntu12_amd64.deb ... 183s Unpacking libjpeg-dev:amd64 (8c-2ubuntu12) ... 183s Selecting previously unselected package liblapack-dev:amd64. 184s Preparing to unpack .../44-liblapack-dev_3.12.1-7ubuntu1_amd64.deb ... 184s Unpacking liblapack-dev:amd64 (3.12.1-7ubuntu1) ... 184s Selecting previously unselected package libncurses-dev:amd64. 184s Preparing to unpack .../45-libncurses-dev_6.6+20251231-1_amd64.deb ... 184s Unpacking libncurses-dev:amd64 (6.6+20251231-1) ... 184s Selecting previously unselected package libpcre2-16-0:amd64. 184s Preparing to unpack .../46-libpcre2-16-0_10.46-1_amd64.deb ... 184s Unpacking libpcre2-16-0:amd64 (10.46-1) ... 184s Selecting previously unselected package libpcre2-32-0:amd64. 184s Preparing to unpack .../47-libpcre2-32-0_10.46-1_amd64.deb ... 184s Unpacking libpcre2-32-0:amd64 (10.46-1) ... 184s Selecting previously unselected package libpcre2-posix3:amd64. 184s Preparing to unpack .../48-libpcre2-posix3_10.46-1_amd64.deb ... 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... 184s Selecting previously unselected package libzstd-dev:amd64. 184s Preparing to unpack .../54-libzstd-dev_1.5.7+dfsg-3_amd64.deb ... 184s Unpacking libzstd-dev:amd64 (1.5.7+dfsg-3) ... 184s Selecting previously unselected package liblzma-dev:amd64. 184s Preparing to unpack .../55-liblzma-dev_5.8.2-2_amd64.deb ... 184s Unpacking liblzma-dev:amd64 (5.8.2-2) ... 184s Selecting previously unselected package pkgconf-bin. 184s Preparing to unpack .../56-pkgconf-bin_1.8.1-4build1_amd64.deb ... 184s Unpacking pkgconf-bin (1.8.1-4build1) ... 184s Selecting previously unselected package pkgconf:amd64. 184s Preparing to unpack .../57-pkgconf_1.8.1-4build1_amd64.deb ... 184s Unpacking pkgconf:amd64 (1.8.1-4build1) ... 184s Selecting previously unselected package libtirpc-dev:amd64. 184s Preparing to unpack .../58-libtirpc-dev_1.3.6+ds-1_amd64.deb ... 184s Unpacking libtirpc-dev:amd64 (1.3.6+ds-1) ... 184s Selecting previously unselected package r-base-dev. 184s Preparing to unpack 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184s Setting up libhwasan0:amd64 (16-20260208-1ubuntu1) ... 184s Setting up libcrypt-dev:amd64 (1:4.5.1-1) ... 184s Setting up libasan8:amd64 (16-20260208-1ubuntu1) ... 184s Setting up libtsan2:amd64 (16-20260208-1ubuntu1) ... 184s Setting up libisl23:amd64 (0.27-1build1) ... 184s Setting up libc-dev-bin (2.42-2ubuntu4) ... 184s Setting up libdeflate-dev:amd64 (1.23-2build1) ... 184s Setting up libcc1-0:amd64 (16-20260208-1ubuntu1) ... 184s Setting up liblsan0:amd64 (16-20260208-1ubuntu1) ... 184s Setting up libblas-dev:amd64 (3.12.1-7ubuntu1) ... 184s update-alternatives: using /usr/lib/x86_64-linux-gnu/blas/libblas.so to provide /usr/lib/x86_64-linux-gnu/libblas.so (libblas.so-x86_64-linux-gnu) in auto mode 184s Setting up dctrl-tools (2.24-3build4) ... 184s Setting up libitm1:amd64 (16-20260208-1ubuntu1) ... 184s Setting up libgcc-15-dev:amd64 (15.2.0-12ubuntu1) ... 184s Setting up cpp-15-x86-64-linux-gnu (15.2.0-12ubuntu1) ... 184s Setting up libgfortran-15-dev:amd64 (15.2.0-12ubuntu1) ... 184s Setting up pkgconf:amd64 (1.8.1-4build1) ... 184s Setting up gcc-15-x86-64-linux-gnu (15.2.0-12ubuntu1) ... 184s Setting up liblapack-dev:amd64 (3.12.1-7ubuntu1) ... 184s update-alternatives: using /usr/lib/x86_64-linux-gnu/lapack/liblapack.so to provide /usr/lib/x86_64-linux-gnu/liblapack.so (liblapack.so-x86_64-linux-gnu) in auto mode 184s Setting up cpp-15 (15.2.0-12ubuntu1) ... 184s Setting up libc6-dev:amd64 (2.42-2ubuntu4) ... 184s Setting up libicu-dev:amd64 (78.2-1ubuntu1) ... 184s Setting up libbz2-dev:amd64 (1.0.8-6build2) ... 184s Setting up cpp-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 184s Setting up gfortran-15-x86-64-linux-gnu (15.2.0-12ubuntu1) ... 184s Setting up libjpeg-turbo8-dev:amd64 (2.1.5-4ubuntu3) ... 184s Setting up libncurses-dev:amd64 (6.6+20251231-1) ... 184s Setting up libpcre2-dev:amd64 (10.46-1) ... 184s Setting up gcc-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 184s Setting up libreadline-dev:amd64 (8.3-3) ... 184s Setting up gfortran-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 184s Setting up gcc-15 (15.2.0-12ubuntu1) ... 184s Setting up libstdc++-15-dev:amd64 (15.2.0-12ubuntu1) ... 184s Setting up zlib1g-dev:amd64 (1:1.3.dfsg+really1.3.1-1ubuntu2) ... 184s Setting up cpp (4:15.2.0-4ubuntu1) ... 184s Setting up libjpeg8-dev:amd64 (8c-2ubuntu12) ... 184s Setting up gfortran-15 (15.2.0-12ubuntu1) ... 184s Setting up g++-15-x86-64-linux-gnu (15.2.0-12ubuntu1) ... 184s Setting up libpng-dev:amd64 (1.6.54-1) ... 184s Setting up libjpeg-dev:amd64 (8c-2ubuntu12) ... 184s Setting up gcc (4:15.2.0-4ubuntu1) ... 184s Setting up g++-x86-64-linux-gnu (4:15.2.0-4ubuntu1) ... 184s Setting up g++-15 (15.2.0-12ubuntu1) ... 184s Setting up gfortran (4:15.2.0-4ubuntu1) ... 184s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 184s update-alternatives: warning: skip creation of /usr/share/man/man1/f95.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f95) doesn't exist 184s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 184s update-alternatives: warning: skip creation of /usr/share/man/man1/f77.1.gz because associated file /usr/share/man/man1/gfortran.1.gz (of link group f77) doesn't exist 184s Setting up g++ (4:15.2.0-4ubuntu1) ... 184s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 184s Setting up build-essential (12.12ubuntu2) ... 184s Setting up r-base-dev (4.5.2-1ubuntu2) ... 184s Setting up pkg-r-autopkgtest (20250812) ... 184s Processing triggers for libc-bin (2.42-2ubuntu4) ... 184s Processing triggers for man-db (2.13.1-1build1) ... 185s Processing triggers for install-info (7.2-5) ... 185s autopkgtest [03:34:58]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 185s autopkgtest [03:34:58]: test pkg-r-autopkgtest: [----------------------- 185s Test: Try to load the R library rrcov 185s 185s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 185s Copyright (C) 2025 The R Foundation for Statistical Computing 185s Platform: x86_64-pc-linux-gnu 185s 185s R is free software and comes with ABSOLUTELY NO WARRANTY. 185s You are welcome to redistribute it under certain conditions. 185s Type 'license()' or 'licence()' for distribution details. 185s 185s R is a collaborative project with many contributors. 185s Type 'contributors()' for more information and 185s 'citation()' on how to cite R or R packages in publications. 185s 185s Type 'demo()' for some demos, 'help()' for on-line help, or 185s 'help.start()' for an HTML browser interface to help. 185s Type 'q()' to quit R. 185s 186s > library('rrcov') 186s Loading required package: robustbase 186s Scalable Robust Estimators with High Breakdown Point (version 1.7-6) 186s 186s > 186s autopkgtest [03:34:59]: test pkg-r-autopkgtest: -----------------------] 186s pkg-r-autopkgtest PASS 186s autopkgtest [03:34:59]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 187s autopkgtest [03:35:00]: @@@@@@@@@@@@@@@@@@@@ summary 187s run-unit-test PASS 187s pkg-r-autopkgtest PASS