0s autopkgtest [18:24:52]: starting date and time: 2025-03-15 18:24:52+0000 0s autopkgtest [18:24:52]: git checkout: 325255d2 Merge branch 'pin-any-arch' into 'ubuntu/production' 0s autopkgtest [18:24:52]: host juju-7f2275-prod-proposed-migration-environment-15; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.zvam6zm5/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:glibc --apt-upgrade r-cran-rrcov --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=glibc/2.41-1ubuntu2 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest-s390x --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-15@bos03-s390x-3.secgroup --name adt-plucky-s390x-r-cran-rrcov-20250315-182451-juju-7f2275-prod-proposed-migration-environment-15-ff3ae515-a874-4dab-b3dd-10a16d4e54c8 --image adt/ubuntu-plucky-s390x-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-15 --net-id=net_prod-proposed-migration-s390x -e TERM=linux -e ''"'"'http_proxy=http://squid.internal:3128'"'"'' -e ''"'"'https_proxy=http://squid.internal:3128'"'"'' -e ''"'"'no_proxy=127.0.0.1,127.0.1.1,login.ubuntu.com,localhost,localdomain,novalocal,internal,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,keyserver.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,10.24.0.0/24,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com,radosgw.ps5.canonical.com'"'"'' --mirror=http://ftpmaster.internal/ubuntu/ 124s autopkgtest [18:26:56]: testbed dpkg architecture: s390x 124s autopkgtest [18:26:56]: testbed apt version: 2.9.33 125s autopkgtest [18:26:57]: @@@@@@@@@@@@@@@@@@@@ test bed setup 125s autopkgtest [18:26:57]: testbed release detected to be: None 126s autopkgtest [18:26:58]: updating testbed package index (apt update) 126s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed InRelease [126 kB] 126s Hit:2 http://ftpmaster.internal/ubuntu plucky InRelease 126s Hit:3 http://ftpmaster.internal/ubuntu plucky-updates InRelease 126s Hit:4 http://ftpmaster.internal/ubuntu plucky-security InRelease 126s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/universe Sources [379 kB] 127s Get:6 http://ftpmaster.internal/ubuntu plucky-proposed/main Sources [99.7 kB] 127s Get:7 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse Sources [15.8 kB] 127s Get:8 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x Packages [113 kB] 127s Get:9 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x c-n-f Metadata [1824 B] 127s Get:10 http://ftpmaster.internal/ubuntu plucky-proposed/restricted s390x c-n-f Metadata [116 B] 127s Get:11 http://ftpmaster.internal/ubuntu plucky-proposed/universe s390x Packages [320 kB] 127s Get:12 http://ftpmaster.internal/ubuntu plucky-proposed/universe s390x c-n-f Metadata [13.4 kB] 127s Get:13 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse s390x Packages [3776 B] 127s Get:14 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse s390x c-n-f Metadata [240 B] 127s Fetched 1073 kB in 1s (840 kB/s) 128s Reading package lists... 128s + lsb_release --codename --short 128s + RELEASE=plucky 128s + cat 128s + [ plucky != trusty ] 128s + DEBIAN_FRONTEND=noninteractive eatmydata apt-get -y --allow-downgrades -o Dpkg::Options::=--force-confnew dist-upgrade 128s Reading package lists... 129s Building dependency tree... 129s Reading state information... 129s Calculating upgrade... 129s Calculating upgrade... 129s The following packages were automatically installed and are no longer required: 129s libnsl2 libpython3.12-minimal libpython3.12-stdlib libpython3.12t64 129s linux-headers-6.11.0-8 linux-headers-6.11.0-8-generic 129s linux-modules-6.11.0-8-generic linux-tools-6.11.0-8 129s linux-tools-6.11.0-8-generic 129s Use 'sudo apt autoremove' to remove them. 129s The following packages will be upgraded: 129s pinentry-curses python3-jinja2 strace 129s 3 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 129s Need to get 652 kB of archives. 129s After this operation, 27.6 kB of additional disk space will be used. 129s Get:1 http://ftpmaster.internal/ubuntu plucky/main s390x strace s390x 6.13+ds-1ubuntu1 [500 kB] 130s Get:2 http://ftpmaster.internal/ubuntu plucky/main s390x pinentry-curses s390x 1.3.1-2ubuntu3 [42.9 kB] 130s Get:3 http://ftpmaster.internal/ubuntu plucky/main s390x python3-jinja2 all 3.1.5-2ubuntu1 [109 kB] 130s Fetched 652 kB in 1s (731 kB/s) 130s (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 ... 81428 files and directories currently installed.) 130s Preparing to unpack .../strace_6.13+ds-1ubuntu1_s390x.deb ... 130s Unpacking strace (6.13+ds-1ubuntu1) over (6.11-0ubuntu1) ... 130s Preparing to unpack .../pinentry-curses_1.3.1-2ubuntu3_s390x.deb ... 130s Unpacking pinentry-curses (1.3.1-2ubuntu3) over (1.3.1-2ubuntu2) ... 130s Preparing to unpack .../python3-jinja2_3.1.5-2ubuntu1_all.deb ... 130s Unpacking python3-jinja2 (3.1.5-2ubuntu1) over (3.1.5-2) ... 130s Setting up pinentry-curses (1.3.1-2ubuntu3) ... 130s Setting up python3-jinja2 (3.1.5-2ubuntu1) ... 130s Setting up strace (6.13+ds-1ubuntu1) ... 130s Processing triggers for man-db (2.13.0-1) ... 131s + rm /etc/apt/preferences.d/force-downgrade-to-release.pref 131s + /usr/lib/apt/apt-helper analyze-pattern ?true 131s + uname -r 131s + sed s/\./\\./g 131s + running_kernel_pattern=^linux-.*6\.14\.0-10-generic.* 131s + apt list ?obsolete 131s + tail -n+2+ cut -d/ -f1 131s 131s + grep -v ^linux-.*6\.14\.0-10-generic.* 131s + obsolete_pkgs=linux-headers-6.11.0-8-generic 131s linux-headers-6.11.0-8 131s linux-modules-6.11.0-8-generic 131s linux-tools-6.11.0-8-generic 131s linux-tools-6.11.0-8 131s + DEBIAN_FRONTEND=noninteractive eatmydata apt-get -y purge --autoremove linux-headers-6.11.0-8-generic linux-headers-6.11.0-8 linux-modules-6.11.0-8-generic linux-tools-6.11.0-8-generic linux-tools-6.11.0-8 131s Reading package lists... 131s Building dependency tree... 131s Reading state information... 131s Solving dependencies... 131s The following packages will be REMOVED: 131s libnsl2* libpython3.12-minimal* libpython3.12-stdlib* libpython3.12t64* 131s linux-headers-6.11.0-8* linux-headers-6.11.0-8-generic* 131s linux-modules-6.11.0-8-generic* linux-tools-6.11.0-8* 131s linux-tools-6.11.0-8-generic* 132s 0 upgraded, 0 newly installed, 9 to remove and 5 not upgraded. 132s After this operation, 167 MB disk space will be freed. 132s (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 ... 81428 files and directories currently installed.) 132s Removing linux-tools-6.11.0-8-generic (6.11.0-8.8) ... 132s Removing linux-tools-6.11.0-8 (6.11.0-8.8) ... 132s Removing libpython3.12t64:s390x (3.12.9-1) ... 132s Removing libpython3.12-stdlib:s390x (3.12.9-1) ... 132s Removing libnsl2:s390x (1.3.0-3build3) ... 132s Removing libpython3.12-minimal:s390x (3.12.9-1) ... 132s Removing linux-headers-6.11.0-8-generic (6.11.0-8.8) ... 132s Removing linux-headers-6.11.0-8 (6.11.0-8.8) ... 133s Removing linux-modules-6.11.0-8-generic (6.11.0-8.8) ... 133s Processing triggers for libc-bin (2.41-1ubuntu1) ... 133s (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 ... 56328 files and directories currently installed.) 133s Purging configuration files for libpython3.12-minimal:s390x (3.12.9-1) ... 133s Purging configuration files for linux-modules-6.11.0-8-generic (6.11.0-8.8) ... 133s + grep -q trusty /etc/lsb-release 133s + [ ! -d /usr/share/doc/unattended-upgrades ] 133s + [ ! -d /usr/share/doc/lxd ] 133s + [ ! -d /usr/share/doc/lxd-client ] 133s + [ ! -d /usr/share/doc/snapd ] 133s + type iptables 133s + cat 133s + chmod 755 /etc/rc.local 133s + . /etc/rc.local 133s + iptables -w -t mangle -A FORWARD -p tcp --tcp-flags SYN,RST SYN -j TCPMSS --clamp-mss-to-pmtu 133s + iptables -A OUTPUT -d 10.255.255.1/32 -p tcp -j DROP 133s + iptables -A OUTPUT -d 10.255.255.2/32 -p tcp -j DROP 133s + uname -m 133s + [ s390x = ppc64le ] 133s + [ -d /run/systemd/system ] 133s + systemd-detect-virt --quiet --vm 133s + mkdir -p /etc/systemd/system/systemd-random-seed.service.d/ 133s + cat 133s + grep -q lz4 /etc/initramfs-tools/initramfs.conf 133s + echo COMPRESS=lz4 133s autopkgtest [18:27:05]: upgrading testbed (apt dist-upgrade and autopurge) 133s Reading package lists... 133s Building dependency tree... 133s Reading state information... 133s Calculating upgrade...Starting pkgProblemResolver with broken count: 0 133s Starting 2 pkgProblemResolver with broken count: 0 133s Done 134s Entering ResolveByKeep 134s 134s Calculating upgrade... 134s The following packages will be upgraded: 134s libc-bin libc-dev-bin libc6 libc6-dev locales 134s 5 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 134s Need to get 9512 kB of archives. 134s After this operation, 8192 B of additional disk space will be used. 134s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x libc6-dev s390x 2.41-1ubuntu2 [1678 kB] 136s Get:2 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x libc-dev-bin s390x 2.41-1ubuntu2 [24.3 kB] 136s Get:3 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x libc6 s390x 2.41-1ubuntu2 [2892 kB] 138s Get:4 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x libc-bin s390x 2.41-1ubuntu2 [671 kB] 139s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/main s390x locales all 2.41-1ubuntu2 [4246 kB] 143s Preconfiguring packages ... 143s Fetched 9512 kB in 9s (1055 kB/s) 143s (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 ... 56326 files and directories currently installed.) 143s Preparing to unpack .../libc6-dev_2.41-1ubuntu2_s390x.deb ... 143s Unpacking libc6-dev:s390x (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 143s Preparing to unpack .../libc-dev-bin_2.41-1ubuntu2_s390x.deb ... 143s Unpacking libc-dev-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 143s Preparing to unpack .../libc6_2.41-1ubuntu2_s390x.deb ... 143s Unpacking libc6:s390x (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 144s Setting up libc6:s390x (2.41-1ubuntu2) ... 144s (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 ... 56326 files and directories currently installed.) 144s Preparing to unpack .../libc-bin_2.41-1ubuntu2_s390x.deb ... 144s Unpacking libc-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 144s Setting up libc-bin (2.41-1ubuntu2) ... 144s (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 ... 56326 files and directories currently installed.) 144s Preparing to unpack .../locales_2.41-1ubuntu2_all.deb ... 144s Unpacking locales (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 144s Setting up locales (2.41-1ubuntu2) ... 144s Generating locales (this might take a while)... 145s en_US.UTF-8... done 145s Generation complete. 145s Setting up libc-dev-bin (2.41-1ubuntu2) ... 145s Setting up libc6-dev:s390x (2.41-1ubuntu2) ... 145s Processing triggers for man-db (2.13.0-1) ... 146s Processing triggers for systemd (257.3-1ubuntu3) ... 147s Reading package lists... 147s Building dependency tree... 147s Reading state information... 147s Starting pkgProblemResolver with broken count: 0 147s Starting 2 pkgProblemResolver with broken count: 0 147s Done 147s Solving dependencies... 147s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 147s autopkgtest [18:27:19]: rebooting testbed after setup commands that affected boot 167s autopkgtest [18:27:39]: testbed running kernel: Linux 6.14.0-10-generic #10-Ubuntu SMP Wed Mar 12 14:53:49 UTC 2025 169s autopkgtest [18:27:41]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-rrcov 172s Get:1 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (dsc) [2146 B] 172s Get:2 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (tar) [1542 kB] 172s Get:3 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (diff) [3160 B] 173s gpgv: Signature made Fri Sep 6 03:10:50 2024 UTC 173s gpgv: using RSA key 73471499CC60ED9EEE805946C5BD6C8F2295D502 173s gpgv: issuer "plessy@debian.org" 173s gpgv: Can't check signature: No public key 173s dpkg-source: warning: cannot verify inline signature for ./r-cran-rrcov_1.7-6-1.dsc: no acceptable signature found 173s autopkgtest [18:27:45]: testing package r-cran-rrcov version 1.7-6-1 173s autopkgtest [18:27:45]: build not needed 175s autopkgtest [18:27:47]: test run-unit-test: preparing testbed 175s Reading package lists... 175s Building dependency tree... 175s Reading state information... 175s Starting pkgProblemResolver with broken count: 0 175s Starting 2 pkgProblemResolver with broken count: 0 175s Done 176s The following NEW packages will be installed: 176s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono libblas3 176s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libfreetype6 libgfortran5 176s libgomp1 libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 176s libjpeg8 liblapack3 libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 176s libpaper-utils libpaper2 libpixman-1-0 libsharpyuv0 libsm6 libtcl8.6 176s libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 libxcb-shm0 176s libxft2 libxrender1 libxss1 libxt6t64 r-base-core r-cran-deoptimr 176s r-cran-lattice r-cran-mass r-cran-mvtnorm r-cran-pcapp r-cran-robustbase 176s r-cran-rrcov unzip x11-common xdg-utils zip 176s 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. 176s Need to get 49.4 MB of archives. 176s After this operation, 92.7 MB of additional disk space will be used. 176s Get:1 http://ftpmaster.internal/ubuntu plucky/main s390x libfreetype6 s390x 2.13.3+dfsg-1 [431 kB] 176s Get:2 http://ftpmaster.internal/ubuntu plucky/main s390x fonts-dejavu-mono all 2.37-8 [502 kB] 177s Get:3 http://ftpmaster.internal/ubuntu plucky/main s390x fonts-dejavu-core all 2.37-8 [835 kB] 178s Get:4 http://ftpmaster.internal/ubuntu plucky/main s390x fontconfig-config s390x 2.15.0-2ubuntu1 [37.5 kB] 178s Get:5 http://ftpmaster.internal/ubuntu plucky/main s390x libfontconfig1 s390x 2.15.0-2ubuntu1 [150 kB] 178s Get:6 http://ftpmaster.internal/ubuntu plucky/main s390x fontconfig s390x 2.15.0-2ubuntu1 [191 kB] 178s Get:7 http://ftpmaster.internal/ubuntu plucky/main s390x libblas3 s390x 3.12.1-2 [252 kB] 179s Get:8 http://ftpmaster.internal/ubuntu plucky/main s390x libpixman-1-0 s390x 0.44.0-3 [201 kB] 179s Get:9 http://ftpmaster.internal/ubuntu plucky/main s390x libxcb-render0 s390x 1.17.0-2 [17.0 kB] 179s Get:10 http://ftpmaster.internal/ubuntu plucky/main s390x libxcb-shm0 s390x 1.17.0-2 [5862 B] 179s Get:11 http://ftpmaster.internal/ubuntu plucky/main s390x libxrender1 s390x 1:0.9.10-1.1build1 [20.4 kB] 179s Get:12 http://ftpmaster.internal/ubuntu plucky/main s390x libcairo2 s390x 1.18.2-2 [580 kB] 180s Get:13 http://ftpmaster.internal/ubuntu plucky/main s390x libdatrie1 s390x 0.2.13-3build1 [20.6 kB] 180s Get:14 http://ftpmaster.internal/ubuntu plucky/main s390x libdeflate0 s390x 1.23-1 [46.1 kB] 180s Get:15 http://ftpmaster.internal/ubuntu plucky/main s390x libgfortran5 s390x 15-20250222-0ubuntu1 [620 kB] 180s Get:16 http://ftpmaster.internal/ubuntu plucky/main s390x libgomp1 s390x 15-20250222-0ubuntu1 [152 kB] 180s Get:17 http://ftpmaster.internal/ubuntu plucky/main s390x libgraphite2-3 s390x 1.3.14-2ubuntu1 [79.8 kB] 181s Get:18 http://ftpmaster.internal/ubuntu plucky/main s390x libharfbuzz0b s390x 10.2.0-1 [538 kB] 181s Get:19 http://ftpmaster.internal/ubuntu plucky/main s390x x11-common all 1:7.7+23ubuntu3 [21.7 kB] 181s Get:20 http://ftpmaster.internal/ubuntu plucky/main s390x libice6 s390x 2:1.1.1-1 [45.4 kB] 181s Get:21 http://ftpmaster.internal/ubuntu plucky/main s390x libjpeg-turbo8 s390x 2.1.5-3ubuntu2 [147 kB] 181s Get:22 http://ftpmaster.internal/ubuntu plucky/main s390x libjpeg8 s390x 8c-2ubuntu11 [2146 B] 181s Get:23 http://ftpmaster.internal/ubuntu plucky/main s390x liblapack3 s390x 3.12.1-2 [2971 kB] 184s Get:24 http://ftpmaster.internal/ubuntu plucky/main s390x libthai-data all 0.1.29-2build1 [158 kB] 185s Get:25 http://ftpmaster.internal/ubuntu plucky/main s390x libthai0 s390x 0.1.29-2build1 [20.7 kB] 185s Get:26 http://ftpmaster.internal/ubuntu plucky/main s390x libpango-1.0-0 s390x 1.56.2-1 [253 kB] 185s Get:27 http://ftpmaster.internal/ubuntu plucky/main s390x libpangoft2-1.0-0 s390x 1.56.2-1 [50.2 kB] 185s Get:28 http://ftpmaster.internal/ubuntu plucky/main s390x libpangocairo-1.0-0 s390x 1.56.2-1 [28.2 kB] 185s Get:29 http://ftpmaster.internal/ubuntu plucky/main s390x libpaper2 s390x 2.2.5-0.3 [17.2 kB] 185s Get:30 http://ftpmaster.internal/ubuntu plucky/main s390x libpaper-utils s390x 2.2.5-0.3 [15.3 kB] 185s Get:31 http://ftpmaster.internal/ubuntu plucky/main s390x libsharpyuv0 s390x 1.5.0-0.1 [16.7 kB] 185s Get:32 http://ftpmaster.internal/ubuntu plucky/main s390x libsm6 s390x 2:1.2.4-1 [18.4 kB] 185s Get:33 http://ftpmaster.internal/ubuntu plucky/main s390x libtcl8.6 s390x 8.6.16+dfsg-1 [1034 kB] 186s Get:34 http://ftpmaster.internal/ubuntu plucky/main s390x libjbig0 s390x 2.1-6.1ubuntu2 [33.1 kB] 186s Get:35 http://ftpmaster.internal/ubuntu plucky/main s390x libwebp7 s390x 1.5.0-0.1 [210 kB] 186s Get:36 http://ftpmaster.internal/ubuntu plucky/main s390x libtiff6 s390x 4.5.1+git230720-4ubuntu4 [217 kB] 187s Get:37 http://ftpmaster.internal/ubuntu plucky/main s390x libxft2 s390x 2.3.6-1build1 [49.6 kB] 187s Get:38 http://ftpmaster.internal/ubuntu plucky/main s390x libxss1 s390x 1:1.2.3-1build3 [7396 B] 187s Get:39 http://ftpmaster.internal/ubuntu plucky/main s390x libtk8.6 s390x 8.6.16-1 [830 kB] 188s Get:40 http://ftpmaster.internal/ubuntu plucky/main s390x libxt6t64 s390x 1:1.2.1-1.2build1 [184 kB] 188s Get:41 http://ftpmaster.internal/ubuntu plucky/main s390x zip s390x 3.0-14ubuntu2 [187 kB] 188s Get:42 http://ftpmaster.internal/ubuntu plucky/main s390x unzip s390x 6.0-28ubuntu6 [186 kB] 188s Get:43 http://ftpmaster.internal/ubuntu plucky/main s390x xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 188s Get:44 http://ftpmaster.internal/ubuntu plucky/universe s390x r-base-core s390x 4.4.3-1 [28.6 MB] 219s Get:45 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-deoptimr all 1.1-3-1-1 [76.6 kB] 219s Get:46 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-lattice s390x 0.22-6-1 [1340 kB] 221s Get:47 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-mass s390x 7.3-64-1 [1113 kB] 222s Get:48 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-mvtnorm s390x 1.3-3-1 [924 kB] 222s Get:49 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-pcapp s390x 2.0-5-1 [378 kB] 223s Get:50 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-robustbase s390x 0.99-4-1-1 [3059 kB] 225s Get:51 http://ftpmaster.internal/ubuntu plucky/universe s390x r-cran-rrcov s390x 1.7-6-1 [2416 kB] 227s Preconfiguring packages ... 227s Fetched 49.4 MB in 51s (971 kB/s) 227s Selecting previously unselected package libfreetype6:s390x. 227s (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 ... 56326 files and directories currently installed.) 227s Preparing to unpack .../00-libfreetype6_2.13.3+dfsg-1_s390x.deb ... 227s Unpacking libfreetype6:s390x (2.13.3+dfsg-1) ... 227s Selecting previously unselected package fonts-dejavu-mono. 227s Preparing to unpack .../01-fonts-dejavu-mono_2.37-8_all.deb ... 227s Unpacking fonts-dejavu-mono (2.37-8) ... 227s Selecting previously unselected package fonts-dejavu-core. 227s Preparing to unpack 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(2.41-1ubuntu2) ... 231s Processing triggers for man-db (2.13.0-1) ... 232s Processing triggers for install-info (7.1.1-1) ... 233s autopkgtest [18:28:45]: test run-unit-test: [----------------------- 233s BEGIN TEST thubert.R 233s 233s R version 4.4.3 (2025-02-28) -- "Trophy Case" 233s Copyright (C) 2025 The R Foundation for Statistical Computing 233s Platform: s390x-ibm-linux-gnu 233s 233s R is free software and comes with ABSOLUTELY NO WARRANTY. 233s You are welcome to redistribute it under certain conditions. 233s Type 'license()' or 'licence()' for distribution details. 233s 233s R is a collaborative project with many contributors. 233s Type 'contributors()' for more information and 233s 'citation()' on how to cite R or R packages in publications. 233s 233s Type 'demo()' for some demos, 'help()' for on-line help, or 233s 'help.start()' for an HTML browser interface to help. 233s Type 'q()' to quit R. 233s 233s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, 233s + method=c("hubert", "hubert.mcd", "locantore", "cov", "classic", 233s + "grid", "proj")) 233s + { 233s + ## Test the PcaXxx() functions on the literature datasets: 233s + ## 233s + ## Call PcaHubert() and the other functions for all regression 233s + ## data sets available in robustbase/rrcov and print: 233s + ## - execution time (if time == TRUE) 233s + ## - loadings 233s + ## - eigenvalues 233s + ## - scores 233s + ## 233s + 233s + dopca <- function(x, xname, nrep=1){ 233s + 233s + n <- dim(x)[1] 233s + p <- dim(x)[2] 233s + if(method == "hubert.mcd") 233s + pca <- PcaHubert(x, k=p) 233s + else if(method == "hubert") 233s + pca <- PcaHubert(x, mcd=FALSE) 233s + else if(method == "locantore") 233s + pca <- PcaLocantore(x) 233s + else if(method == "cov") 233s + pca <- PcaCov(x) 233s + else if(method == "classic") 233s + pca <- PcaClassic(x) 233s + else if(method == "grid") 233s + pca <- PcaGrid(x) 233s + else if(method == "proj") 233s + pca <- PcaProj(x) 233s + else 233s + stop("Undefined PCA method: ", method) 233s + 233s + 233s + e1 <- getEigenvalues(pca)[1] 233s + e2 <- getEigenvalues(pca)[2] 233s + k <- pca@k 233s + 233s + if(time){ 233s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 233s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f %10.3f\n", dim(x)[1], dim(x)[2], k, e1, e2, xtime) 233s + } 233s + else{ 233s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f\n", dim(x)[1], dim(x)[2], k, e1, e2) 233s + } 233s + lpad<-lname-nchar(xname) 233s + cat(pad.right(xname, lpad), xres) 233s + 233s + if(!short){ 233s + cat("Scores: \n") 233s + print(getScores(pca)) 233s + 233s + if(full){ 233s + cat("-------------\n") 233s + show(pca) 233s + } 233s + cat("----------------------------------------------------------\n") 233s + } 233s + } 233s + 233s + stopifnot(length(nrep) == 1, nrep >= 1) 233s + method <- match.arg(method) 233s + 233s + options(digits = 5) 233s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 233s + 233s + lname <- 20 233s + 233s + ## VT::15.09.2013 - this will render the output independent 233s + ## from the version of the package 233s + suppressPackageStartupMessages(library(rrcov)) 233s + 233s + data(Animals, package = "MASS") 233s + brain <- Animals[c(1:24, 26:25, 27:28),] 233s + 233s + tmp <- sys.call() 233s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 233s + 233s + cat("Data Set n p k e1 e2\n") 233s + cat("==========================================================\n") 233s + dopca(heart[, 1:2], data(heart), nrep) 233s + dopca(starsCYG, data(starsCYG), nrep) 233s + dopca(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 233s + dopca(stack.x, data(stackloss), nrep) 233s + ## dopca(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) # differences between the architectures 233s + dopca(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 233s + ## dopca(data.matrix(subset(wood, select = -y)), data(wood), nrep) # differences between the architectures 233s + dopca(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 233s + 233s + ## dopca(brain, "Animals", nrep) 233s + dopca(milk, data(milk), nrep) 233s + dopca(bushfire, data(bushfire), nrep) 233s + cat("==========================================================\n") 233s + } 233s > 233s > dogen <- function(nrep=1, eps=0.49, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 233s + 233s + dopca <- function(x, nrep=1){ 233s + gc() 233s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 233s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 233s + xtime 233s + } 233s + 233s + set.seed(1234) 233s + 233s + ## VT::15.09.2013 - this will render the output independent 233s + ## from the version of the package 233s + suppressPackageStartupMessages(library(rrcov)) 233s + library(MASS) 233s + 233s + method <- match.arg(method) 233s + 233s + ap <- c(2, 5, 10, 20, 30) 233s + an <- c(100, 500, 1000, 10000, 50000) 233s + 233s + tottime <- 0 233s + cat(" n p Time\n") 233s + cat("=====================\n") 233s + for(i in 1:length(an)) { 233s + for(j in 1:length(ap)) { 233s + n <- an[i] 233s + p <- ap[j] 233s + if(5*p <= n){ 233s + xx <- gendata(n, p, eps) 233s + X <- xx$X 233s + ## print(dimnames(X)) 233s + tottime <- tottime + dopca(X, nrep) 233s + } 233s + } 233s + } 233s + 233s + cat("=====================\n") 233s + cat("Total time: ", tottime*nrep, "\n") 233s + } 233s > 233s > dorep <- function(x, nrep=1, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 233s + 233s + method <- match.arg(method) 233s + for(i in 1:nrep) 233s + if(method == "hubert.mcd") 233s + PcaHubert(x) 233s + else if(method == "hubert") 233s + PcaHubert(x, mcd=FALSE) 233s + else if(method == "locantore") 233s + PcaLocantore(x) 233s + else if(method == "cov") 233s + PcaCov(x) 233s + else 233s + stop("Undefined PCA method: ", method) 233s + } 233s > 233s > #### gendata() #### 233s > # Generates a location contaminated multivariate 233s > # normal sample of n observations in p dimensions 233s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 233s > # where 233s > # m = (b,b,...,b) 233s > # Defaults: eps=0 and b=10 233s > # 233s > gendata <- function(n,p,eps=0,b=10){ 233s + 233s + if(missing(n) || missing(p)) 233s + stop("Please specify (n,p)") 233s + if(eps < 0 || eps >= 0.5) 233s + stop(message="eps must be in [0,0.5)") 233s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 233s + nbad <- as.integer(eps * n) 233s + xind <- vector("numeric") 233s + if(nbad > 0){ 233s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 233s + xind <- sample(n,nbad) 233s + X[xind,] <- Xbad 233s + } 233s + list(X=X, xind=xind) 233s + } 233s > 233s > pad.right <- function(z, pads) 233s + { 233s + ### Pads spaces to right of text 233s + padding <- paste(rep(" ", pads), collapse = "") 233s + paste(z, padding, sep = "") 233s + } 233s > 233s > whatis <- function(x){ 233s + if(is.data.frame(x)) 233s + cat("Type: data.frame\n") 233s + else if(is.matrix(x)) 233s + cat("Type: matrix\n") 233s + else if(is.vector(x)) 233s + cat("Type: vector\n") 233s + else 233s + cat("Type: don't know\n") 233s + } 233s > 233s > ################################################################# 233s > ## VT::27.08.2010 233s > ## bug report from Stephen Milborrow 233s > ## 233s > test.case.1 <- function() 233s + { 233s + X <- matrix(c( 233s + -0.79984, -1.00103, 0.899794, 0.00000, 233s + 0.34279, 0.52832, -1.303783, -1.17670, 233s + -0.79984, -1.00103, 0.899794, 0.00000, 233s + 0.34279, 0.52832, -1.303783, -1.17670, 233s + 0.34279, 0.52832, -1.303783, -1.17670, 233s + 1.48542, 0.66735, 0.716162, 1.17670, 233s + -0.79984, -1.00103, 0.899794, 0.00000, 233s + 1.69317, 1.91864, -0.018363, 1.76505, 233s + -1.00759, -0.16684, -0.385626, 0.58835, 233s + -0.79984, -1.00103, 0.899794, 0.00000), ncol=4, byrow=TRUE) 233s + 233s + cc1 <- PcaHubert(X, k=3) 233s + 233s + cc2 <- PcaLocantore(X, k=3) 233s + cc3 <- PcaCov(X, k=3, cov.control=CovControlSest()) 233s + 233s + cc4 <- PcaProj(X, k=2) # with k=3 will produce warnings in .distances - too small eignevalues 233s + cc5 <- PcaGrid(X, k=2) # dito 233s + 233s + list(cc1, cc2, cc3, cc4, cc5) 233s + } 233s > 233s > ################################################################# 233s > ## VT::05.08.2016 233s > ## bug report from Matthieu Lesnoff 233s > ## 233s > test.case.2 <- function() 233s + { 233s + do.test.case.2 <- function(z) 233s + { 233s + if(missing(z)) 233s + { 233s + set.seed(12345678) 233s + n <- 5 233s + z <- data.frame(v1 = rnorm(n), v2 = rnorm(n), v3 = rnorm(n)) 233s + z 233s + } 233s + 233s + fm <- PcaLocantore(z, k = 2, scale = TRUE) 233s + fm@scale 233s + apply(z, MARGIN = 2, FUN = mad) 233s + scale(z, center = fm@center, scale = fm@scale) 233s + 233s + T <- fm@scores 233s + P <- fm@loadings 233s + E <- scale(z, center = fm@center, scale = fm@scale) - T %*% t(P) 233s + d2 <- apply(E^2, MARGIN = 1, FUN = sum) 233s + ## print(sqrt(d2)); print(fm@od) 233s + print(ret <- all.equal(sqrt(d2), fm@od)) 233s + 233s + ret 233s + } 233s + do.test.case.2() 233s + do.test.case.2(phosphor) 233s + do.test.case.2(stackloss) 233s + do.test.case.2(salinity) 233s + do.test.case.2(hbk) 233s + do.test.case.2(milk) 233s + do.test.case.2(bushfire) 233s + data(rice); do.test.case.2(rice) 233s + data(un86); do.test.case.2(un86) 233s + } 233s > 233s > ## VT::15.09.2013 - this will render the output independent 233s > ## from the version of the package 233s > suppressPackageStartupMessages(library(rrcov)) 233s > 233s > dodata(method="classic") 233s 233s Call: dodata(method = "classic") 233s Data Set n p k e1 e2 233s ========================================================== 233s heart 12 2 2 812.379735 9.084962 233s Scores: 233s PC1 PC2 233s 1 2.7072 1.46576 233s 2 59.9990 -1.43041 233s 3 -3.5619 -1.54067 233s 4 -7.7696 2.52687 233s 5 14.7660 -0.95822 233s 6 -20.0489 6.91079 233s 7 1.4189 2.25961 233s 8 -34.3308 -4.23717 233s 9 -6.0487 -0.97859 233s 10 -33.0102 -3.73143 233s 11 -18.6372 0.25821 233s 12 44.5163 -0.54476 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 28.5023 3.0141 233s ---------------------------------------------------------- 233s starsCYG 47 2 2 0.331279 0.079625 233s Scores: 233s PC1 PC2 233s 1 0.2072999 0.089973 233s 2 0.6855999 0.349644 233s 3 -0.0743007 -0.061028 233s 4 0.6855999 0.349644 233s 5 0.1775161 0.015053 233s 6 0.4223986 0.211351 233s 7 -0.2926077 -0.516156 233s 8 0.2188453 0.293607 233s 9 0.5593696 0.028761 233s 10 0.0983878 0.074540 233s 11 0.8258140 -0.711176 233s 12 0.4167063 0.180244 233s 13 0.3799883 0.225541 233s 14 -0.9105236 -0.432014 233s 15 -0.7418831 -0.125322 233s 16 -0.4432862 0.048287 233s 17 -1.0503005 -0.229623 233s 18 -0.8393302 -0.007831 233s 19 -0.8126742 -0.195952 233s 20 0.9842316 -0.688729 233s 21 -0.6230699 -0.108486 233s 22 -0.7814875 -0.130933 233s 23 -0.6017038 0.025840 233s 24 -0.1857772 0.155474 233s 25 -0.0020261 0.070412 233s 26 -0.3640775 0.059510 233s 27 -0.3458392 -0.069204 233s 28 -0.1208393 0.053577 233s 29 -0.6033482 -0.176391 233s 30 1.1440521 -0.676183 233s 31 -0.5960920 -0.013765 233s 32 0.0519296 0.259855 233s 33 0.1861752 0.167779 233s 34 1.3802755 -0.632611 233s 35 -0.6542566 -0.173505 233s 36 0.5583690 0.392215 233s 37 0.0561384 0.230152 233s 38 0.1861752 0.167779 233s 39 0.1353472 0.241376 233s 40 0.5355195 0.197080 233s 41 -0.3980701 0.014294 233s 42 0.0277576 0.145332 233s 43 0.2979736 0.234120 233s 44 0.3049884 0.184614 233s 45 0.4889809 0.311684 233s 46 -0.0514512 0.134108 233s 47 -0.5224950 0.037063 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 0.57557 0.28218 233s ---------------------------------------------------------- 233s phosphor 18 2 2 220.403422 68.346121 233s Scores: 233s PC1 PC2 233s 1 4.04290 -15.3459 233s 2 -22.30489 -1.0004 233s 3 -24.52683 3.2836 233s 4 -12.54839 -6.0848 233s 5 -19.37044 2.2979 233s 6 15.20366 -19.9424 233s 7 0.44222 -3.1379 233s 8 -10.64042 3.6933 233s 9 -11.67967 5.9670 233s 10 14.26805 -7.0221 233s 11 -4.98832 1.5268 233s 12 8.74986 7.9379 233s 13 12.26290 6.0251 233s 14 6.27607 7.5768 233s 15 17.53246 3.1560 233s 16 -10.17024 -5.8994 233s 17 21.05826 5.4492 233s 18 16.39281 11.5191 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 14.8460 8.2672 233s ---------------------------------------------------------- 233s stackloss 21 3 3 99.576089 19.581136 233s Scores: 233s PC1 PC2 PC3 233s 1 20.15352 -4.359452 0.324585 233s 2 19.81554 -5.300468 0.308294 233s 3 15.45222 -1.599136 -0.203125 233s 4 2.40370 -0.145282 2.370302 233s 5 1.89538 0.070566 0.448061 233s 6 2.14954 -0.037358 1.409182 233s 7 4.43153 5.500810 2.468051 233s 8 4.43153 5.500810 2.468051 233s 9 -1.47521 1.245404 2.511773 233s 10 -5.11183 -4.802083 -2.407870 233s 11 -2.07009 3.667055 -2.261247 233s 12 -2.66223 2.833964 -3.238659 233s 13 -4.43589 -2.920053 -2.375287 233s 14 -0.46404 7.323193 -1.234961 233s 15 -9.31959 6.232579 -0.056064 233s 16 -10.33350 3.409533 -0.104938 233s 17 -14.81094 -9.872607 0.628103 233s 18 -12.44514 -3.285499 0.742143 233s 19 -11.85300 -2.452408 1.719555 233s 20 -5.73994 -2.494520 0.098250 233s 21 9.98843 1.484952 -3.614198 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 9.9788 4.4251 1.8986 233s ---------------------------------------------------------- 233s salinity 28 3 3 11.410736 7.075409 233s Scores: 233s PC1 PC2 PC3 233s 1 -0.937789 -2.40535 0.812909 233s 2 -1.752631 -2.57774 2.004437 233s 3 -6.509364 -0.78762 -1.821906 233s 4 -5.619847 -2.41333 -1.586891 233s 5 -7.268242 1.61012 1.563568 233s 6 -4.316558 -3.20411 0.029376 233s 7 -2.379545 -3.32371 0.703101 233s 8 0.013514 -3.50586 1.260502 233s 9 0.265262 -0.16736 -2.886883 233s 10 1.890755 2.43623 -0.986832 233s 11 0.804196 2.56656 0.387577 233s 12 0.935082 -1.03559 -0.074081 233s 13 1.814839 -1.61087 0.612290 233s 14 3.407535 -0.15880 2.026088 233s 15 1.731273 2.95159 -1.840286 233s 16 -6.129708 7.21368 2.632273 233s 17 -0.645124 1.06260 0.028697 233s 18 -1.307532 -2.54679 -0.280273 233s 19 0.483455 -0.55896 -3.097281 233s 20 2.053267 0.47308 -1.858703 233s 21 3.277664 -1.31002 0.453753 233s 22 4.631644 -0.78005 1.519894 233s 23 1.864403 5.32790 -0.849694 233s 24 0.623899 4.29317 0.056461 233s 25 1.301696 0.37871 -0.646220 233s 26 2.852126 -0.79527 -0.347711 233s 27 4.134051 -0.92756 0.449222 233s 28 4.781679 -0.20467 1.736616 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 3.3780 2.6600 1.4836 233s ---------------------------------------------------------- 233s hbk 75 3 3 216.162129 1.981077 233s Scores: 233s PC1 PC2 PC3 233s 1 26.2072 -0.660756 0.503340 233s 2 27.0406 -0.108506 -0.225059 233s 3 28.8351 -1.683721 0.263078 233s 4 29.9221 -0.812174 -0.674480 233s 5 29.3181 -0.909915 -0.121600 233s 6 27.5360 -0.599697 0.916574 233s 7 27.6617 -0.073753 0.676620 233s 8 26.5576 -0.882312 0.159620 233s 9 28.8726 -1.074223 -0.673462 233s 10 27.6643 -1.463829 -0.868593 233s 11 34.2019 -0.664473 -0.567265 233s 12 35.4805 -2.730949 -0.259320 233s 13 34.7544 1.325449 0.749884 233s 14 38.9522 8.171389 0.034382 233s 15 -5.5375 0.390704 1.679172 233s 16 -7.4319 0.803850 1.925633 233s 17 -8.5880 0.957577 -1.010312 233s 18 -6.6022 -0.425109 0.625148 233s 19 -6.5596 1.154721 -0.640680 233s 20 -5.2525 0.812527 1.377832 233s 21 -6.2771 0.067747 0.958907 233s 22 -6.2501 1.325491 -1.104428 233s 23 -7.2419 0.839808 0.728712 233s 24 -7.6489 1.131606 0.154897 233s 25 -9.0763 -0.670721 -0.167577 233s 26 -5.5967 0.999411 -0.810000 233s 27 -5.1460 -0.339018 1.326712 233s 28 -7.1659 -0.993461 0.125933 233s 29 -8.2104 -0.169338 -0.073569 233s 30 -6.2499 -1.689222 -0.877481 233s 31 -7.3180 -0.225795 1.687204 233s 32 -7.9446 1.473868 -0.541790 233s 33 -6.3604 1.237472 0.061800 233s 34 -8.9812 -0.710662 -0.830422 233s 35 -5.1698 -0.435484 1.102817 233s 36 -5.9995 -0.058135 -0.713550 233s 37 -5.8753 0.852882 -1.610556 233s 38 -8.4501 0.334363 0.404813 233s 39 -8.1751 -1.300317 0.633282 233s 40 -7.4495 0.672712 -0.829815 233s 41 -5.6213 -1.106765 1.395315 233s 42 -6.8571 -0.900977 -1.509937 233s 43 -7.0633 1.987372 -1.079934 233s 44 -6.3763 -1.867647 -0.251224 233s 45 -8.6456 -0.866053 0.630132 233s 46 -6.5356 -1.763526 -0.189838 233s 47 -8.2224 -1.183284 1.615150 233s 48 -5.6136 -1.100704 1.079239 233s 49 -5.9907 0.220336 1.443387 233s 50 -5.2675 0.142923 0.194023 233s 51 -7.9324 0.324710 1.113289 233s 52 -7.5544 -1.033884 1.792496 233s 53 -6.7119 -1.712257 -1.711778 233s 54 -7.4679 1.856542 0.046658 233s 55 -7.4666 1.161504 -0.725948 233s 56 -6.7110 1.574868 0.534288 233s 57 -8.2571 -0.399824 0.521995 233s 58 -5.9781 1.312567 0.926790 233s 59 -5.6960 -0.394338 -0.332938 233s 60 -6.1017 -0.797579 -1.679359 233s 61 -5.2628 0.919128 -1.436156 233s 62 -9.1245 -0.516135 -0.229065 233s 63 -7.7140 1.659145 0.068510 233s 64 -4.9886 0.173613 0.865810 233s 65 -6.6157 -1.479786 0.098390 233s 66 -7.9511 0.772770 -0.998321 233s 67 -7.1856 0.459602 0.216588 233s 68 -8.7345 -0.860784 -1.238576 233s 69 -8.5833 -0.313481 0.832074 233s 70 -5.8642 -0.142883 -0.870064 233s 71 -5.8879 0.186456 0.464467 233s 72 -7.1865 0.497156 -0.826767 233s 73 -6.8671 -0.058606 -1.335842 233s 74 -7.1398 0.727642 -1.422331 233s 75 -7.2696 -1.347832 -1.496927 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 14.70245 1.40751 0.95725 233s ---------------------------------------------------------- 233s milk 86 8 8 15.940298 2.771345 233s Scores: 233s PC1 PC2 PC3 PC4 PC5 PC6 PC7 233s 1 6.471620 1.031110 0.469432 0.5736412 1.0294362 -0.6054039 -0.2005117 233s 2 7.439545 0.320597 0.081922 -0.6305898 0.7128977 -1.1601053 -0.1170582 233s 3 1.240654 -1.840458 0.520870 -0.1717469 0.2752079 -0.3815506 0.6004089 233s 4 5.952685 -1.856375 1.638710 0.3358626 -0.5834205 -0.0665348 -0.1580799 233s 5 -0.706973 0.261795 0.423736 0.2916399 -0.5307716 -0.3325563 -0.0062349 233s 6 2.524050 0.293380 -0.572997 0.2466367 -0.3497882 0.0386014 -0.1418131 233s 7 3.136085 -0.050202 -0.818165 -0.0451560 -0.5226337 -0.1597194 0.1669050 233s 8 3.260390 0.312365 -0.110776 0.4908006 -0.5225353 -0.1972222 -0.1068433 233s 9 -0.808914 -2.355785 1.344204 -0.4743284 -0.1394914 -0.1390080 -0.2620731 233s 10 -2.511226 -0.995321 -0.087218 -0.5950040 0.4268321 0.2561918 0.0891170 233s 11 -9.204096 -0.598364 1.587275 0.0833647 0.1865626 0.0358228 0.0920394 233s 12 -12.946774 1.951332 -0.179186 0.2560603 0.1300954 -0.1179820 -0.0999494 233s 13 -10.011603 0.726323 -2.102423 -1.3105560 0.3291550 0.0660007 -0.0794410 233s 14 -11.983644 0.768224 -0.532227 -0.5161201 -0.0817164 -0.4358934 -0.1734612 233s 15 -10.465714 -0.704271 2.035437 0.3713778 -0.0564830 -0.2696432 -0.1940091 233s 16 -2.527619 -0.286939 0.354497 0.8571223 0.1585009 0.2272835 0.4386955 233s 17 -0.514527 -2.895087 1.657181 0.2208239 0.1961109 0.1280496 -0.0182491 233s 18 -1.763931 0.854269 -0.686282 0.2848209 -0.4813608 -0.2623962 0.4757030 233s 19 -1.538419 -0.866477 1.103818 0.3874507 0.2086661 0.1267277 0.2354264 233s 20 0.732842 -1.455594 1.097358 -0.2530588 -0.0302385 0.2654274 0.6093330 233s 21 -2.530155 1.932885 -0.873095 0.6202295 -0.4153607 0.0048383 0.0067484 233s 22 -0.772646 0.675846 -0.259539 0.4844670 -0.0893266 -0.2785557 -0.0424662 233s 23 0.185417 1.413719 0.066135 1.1014470 0.0468093 0.0288637 0.2539994 233s 24 -0.280536 0.908864 0.113221 1.3370381 0.3289929 0.2588134 -0.0356289 233s 25 -3.503626 1.971233 0.203620 1.1975494 -0.3175317 0.1149685 0.0584396 233s 26 -0.639313 1.175503 0.403906 0.9082134 -0.2648165 -0.1238813 -0.0174853 233s 27 -2.923327 -0.365168 0.149478 0.8201430 -0.1544609 -0.4856934 -0.0058424 233s 28 2.505633 3.050292 -0.554424 2.1416405 -0.0378764 0.1002280 -0.3888580 233s 29 4.649504 1.054863 -0.081018 1.1454466 0.1502080 0.4967323 0.0879775 233s 30 1.049282 1.355215 -0.142701 0.7805566 -0.2059790 0.0193142 0.0815524 233s 31 1.962583 1.595396 -2.050642 0.3556747 0.1384801 0.1197984 0.1608247 233s 32 1.554846 0.095644 -1.423054 -0.3175620 0.4260008 -0.1612463 -0.0567196 233s 33 2.248977 0.010348 -0.062469 0.6388269 0.2098648 0.1330250 0.0906704 233s 34 0.993109 -0.828812 0.284059 0.3446686 0.1899096 -0.0515571 -0.2281197 233s 35 -0.335103 1.614093 -0.920661 1.2502617 0.2435013 0.1264875 0.0469238 233s 36 4.346795 1.208134 0.368889 1.1429977 -0.1362052 -0.0158169 -0.0183852 233s 37 0.992634 2.013738 -1.350619 0.8714694 0.0057776 -0.2122691 0.1760918 233s 38 2.213341 1.706516 -0.705418 1.2670281 -0.0707149 0.0670467 -0.1863588 233s 39 -1.213255 0.644062 0.163988 1.1213961 0.2945355 0.1093574 0.0019574 233s 40 3.942604 -1.704266 0.660327 0.1618506 0.4259076 0.0070193 0.3462765 233s 41 4.262054 1.687193 0.351875 0.5396477 1.0052810 -0.9331689 0.0056063 233s 42 6.865198 -1.091248 1.153585 1.1248797 0.0873276 0.2565221 0.0333265 233s 43 3.476720 0.555449 -1.030771 -0.3015720 -0.1748109 -0.1584968 0.4079902 233s 44 5.691730 -0.141240 0.565189 0.3174238 0.6478440 1.0579977 -0.5387916 233s 45 0.327134 0.152011 -0.394798 0.4998430 0.1599781 0.3159518 0.1623656 233s 46 0.280225 1.569387 -0.100397 1.2800976 0.0446645 0.0946513 0.0461599 233s 47 3.119928 -0.384834 -3.325600 -1.8865310 -0.1334744 0.1249987 -0.2561273 233s 48 0.501542 0.739816 -1.384556 -0.1244721 0.2948958 0.4836170 -0.1182802 233s 49 -1.953218 0.269986 -1.726474 -0.8510637 0.5047958 0.4860651 0.2318735 233s 50 3.706878 -2.400570 1.361047 -0.4949076 0.2180352 0.4080879 0.1156540 233s 51 -1.060358 -0.521609 -1.387412 -1.2767491 -0.0521356 0.1665452 -0.0044412 233s 52 -4.900528 0.157011 -1.015880 -0.9941168 0.2069608 0.3239762 -0.1921715 233s 53 -0.388496 0.062051 -0.643721 -0.8544141 -0.1857141 0.0063293 0.2664606 233s 54 0.109234 -0.018709 -0.242825 -0.2064701 -0.0585165 0.1720867 0.1117397 233s 55 1.176175 0.644539 -0.373694 0.0038605 -0.3436524 0.0194450 -0.0838883 233s 56 0.407259 -0.606637 0.222915 -0.3622451 -0.0737834 0.0228104 0.0297333 233s 57 -1.022756 -0.071860 0.741957 0.2273628 -0.1388444 -0.2396467 -0.2327738 233s 58 0.245419 1.167059 0.225934 0.8318795 -0.5365166 -0.0090816 -0.1680757 233s 59 -1.300617 -1.110325 -0.262740 -0.8857801 -0.0816954 -0.1186886 -0.0928322 233s 60 -1.110561 -0.832357 -0.212713 -0.4754481 -0.4105982 -0.1886992 -0.0602872 233s 61 0.381831 -1.475116 0.601047 -0.6260156 -0.1854501 -0.1749306 -0.0013904 233s 62 2.734462 -1.887861 0.813453 -0.5856987 0.2310656 0.1117041 -0.0293373 233s 63 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 233s 64 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 233s 65 0.004744 -2.712679 1.178987 -0.6677199 0.0208119 0.0621903 -0.0655693 233s 66 -2.014851 -1.060090 -0.099959 -0.7225044 -0.1947648 -0.2282902 -0.0505015 233s 67 0.621739 -1.296106 0.255632 -0.3309504 -0.0880200 0.2524306 0.1465779 233s 68 -0.271385 -1.709161 -1.100349 -2.0937671 0.2166264 0.0191278 0.0114174 233s 69 -0.326350 -0.737232 0.021639 -0.3850383 -0.4338287 0.2156624 0.1597594 233s 70 4.187093 9.708082 4.632803 -4.9751240 -0.0881576 0.2392433 0.0568049 233s 71 -1.868507 -1.600166 0.436353 -0.8078214 -0.1530893 0.0479471 -0.1999893 233s 72 2.768081 -0.556824 -0.148923 -0.3197853 -0.5524427 0.0907804 -0.0694488 233s 73 -1.441846 -2.735114 -0.294134 -1.2172969 0.0109453 -0.0562910 0.1505788 233s 74 -10.995490 0.615992 1.950966 1.1687190 0.2798335 0.2713257 0.0652135 233s 75 0.508992 -2.363945 -0.407064 -0.9522316 0.1040307 0.1088110 -0.7368484 233s 76 -1.015714 -0.307662 -1.088162 -1.0181862 -0.0440888 -0.1362208 0.0271200 233s 77 -8.028891 -0.580763 0.933638 0.4619362 0.3379832 -0.1368644 -0.0669441 233s 78 1.763308 -1.336175 -0.127809 -0.7161775 -0.1904861 -0.0900461 0.0037539 233s 79 0.208944 -0.580698 -0.626297 -0.7620610 -0.0262368 -0.2928202 0.0285908 233s 80 -3.230608 1.251352 0.195280 0.8687004 0.1812011 0.2600692 -0.1516375 233s 81 1.498160 0.669731 -0.266114 0.3772866 -0.2769688 -0.1066593 -0.1608395 233s 82 3.232051 -1.776018 0.485524 0.1170945 0.0557260 0.2219872 0.1187681 233s 83 2.999977 -0.228275 -0.467724 -0.4287672 0.0494902 -0.2337809 -0.0718159 233s 84 1.238083 0.320956 -1.806006 -1.0142266 0.2359630 -0.0857149 0.0593938 233s 85 1.276376 -2.081214 2.540850 0.3745805 -0.2596482 -0.1228412 -0.2199985 233s 86 0.930715 0.836457 -1.385153 -0.6074929 -0.2476354 0.1680713 -0.0117324 233s PC8 233s 1 9.0765e-04 233s 2 2.1811e-04 233s 3 1.1834e-03 233s 4 8.4077e-05 233s 5 9.9209e-04 233s 6 1.6277e-03 233s 7 2.4907e-04 233s 8 6.8383e-04 233s 9 -5.0924e-04 233s 10 3.1215e-04 233s 11 3.0654e-04 233s 12 -1.1951e-03 233s 13 -1.2849e-03 233s 14 -9.0801e-04 233s 15 -1.2686e-03 233s 16 -1.8441e-03 233s 17 -2.1068e-03 233s 18 -5.7816e-04 233s 19 -1.2330e-03 233s 20 3.3857e-05 233s 21 3.8623e-04 233s 22 1.3035e-04 233s 23 -3.8648e-04 233s 24 -1.7400e-04 233s 25 -3.9196e-04 233s 26 -7.6996e-04 233s 27 -4.8042e-04 233s 28 -2.0628e-04 233s 29 -4.5672e-04 233s 30 -1.4716e-04 233s 31 -4.6385e-05 233s 32 -2.0481e-04 233s 33 -3.0020e-04 233s 34 -5.8179e-05 233s 35 1.3870e-04 233s 36 -6.7177e-04 233s 37 -3.0799e-04 233s 38 6.2140e-04 233s 39 4.5912e-04 233s 40 -3.7165e-04 233s 41 -5.4362e-04 233s 42 -1.0155e-03 233s 43 1.3449e-04 233s 44 -5.4761e-04 233s 45 1.0300e-03 233s 46 1.1039e-03 233s 47 -6.4858e-04 233s 48 -7.6886e-05 233s 49 3.2590e-04 233s 50 8.6845e-05 233s 51 4.9423e-04 233s 52 9.2973e-04 233s 53 4.4342e-04 233s 54 4.9888e-04 233s 55 7.2171e-04 233s 56 -3.2133e-05 233s 57 -1.8101e-04 233s 58 -5.4969e-06 233s 59 -8.3841e-04 233s 60 5.9446e-05 233s 61 -6.5683e-05 233s 62 -3.4073e-04 233s 63 -6.5145e-04 233s 64 -6.5145e-04 233s 65 1.4986e-04 233s 66 2.8096e-04 233s 67 -6.5170e-05 233s 68 -1.3775e-04 233s 69 6.8225e-06 233s 70 -1.6290e-04 233s 71 3.9009e-04 233s 72 -1.3981e-04 233s 73 6.2613e-04 233s 74 2.6513e-03 233s 75 3.7088e-04 233s 76 9.9539e-04 233s 77 1.2979e-03 233s 78 5.6500e-04 233s 79 3.0940e-04 233s 80 8.7993e-04 233s 81 -3.1353e-04 233s 82 4.9625e-04 233s 83 -6.3951e-04 233s 84 -4.5582e-04 233s 85 5.9440e-04 233s 86 -3.6234e-04 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 3.99253025 1.66473582 1.10660264 0.96987790 0.33004256 0.29263512 0.20843280 233s [8] 0.00074024 233s ---------------------------------------------------------- 233s bushfire 38 5 5 38435.075910 1035.305774 233s Scores: 233s PC1 PC2 PC3 PC4 PC5 233s 1 -111.9345 4.9970 -1.00881 -1.224361 3.180569 233s 2 -113.4128 7.4784 -0.79170 -0.235184 2.385812 233s 3 -105.8364 10.9615 -3.15662 -0.251662 1.017328 233s 4 -89.1684 8.7232 -6.15080 -0.075611 1.431111 233s 5 -58.7216 -1.9543 -12.70661 -0.151328 1.425570 233s 6 -35.0370 -12.8434 -17.06841 -0.525664 3.499743 233s 7 -250.2123 -49.4348 23.31261 -19.070238 0.647348 233s 8 -292.6877 -69.7708 -21.30815 13.093808 -1.288764 233s 9 -294.0765 -70.9903 -23.96326 14.940985 -0.939076 233s 10 -290.0193 -57.3747 3.51346 1.858995 0.083107 233s 11 -289.8168 -43.3207 16.08046 -1.745099 -1.506042 233s 12 -290.8645 6.2503 40.52173 -7.496479 -0.033767 233s 13 -232.6865 41.8090 37.19429 -1.280348 -0.470837 233s 14 9.8483 25.1954 -14.56970 0.538484 1.772046 233s 15 137.1924 11.8521 -37.12452 -5.130459 -0.586695 233s 16 92.9804 10.3923 -24.97267 -7.551314 -1.867125 233s 17 90.4493 10.5630 -21.92735 -5.669651 -1.001362 233s 18 78.6325 5.2211 -19.74718 -6.107880 -1.939986 233s 19 82.1178 3.6913 -21.37810 -4.259855 -1.278838 233s 20 92.9044 7.1961 -21.22900 -4.125571 -0.127089 233s 21 74.9157 10.2991 -16.60924 -5.660751 -0.406343 233s 22 66.7350 12.0460 -16.73298 -4.669080 1.333436 233s 23 -62.1981 22.7394 6.03613 -5.182356 -0.453624 233s 24 -116.5696 32.3182 12.74846 -1.465657 -0.097851 233s 25 -53.8907 22.4278 -2.18861 -2.742014 -0.990071 233s 26 -60.6384 20.2952 -3.05206 -2.953685 -0.629061 233s 27 -74.7621 28.9067 -0.65817 1.473357 -0.443957 233s 28 -50.2202 37.3457 -1.44989 5.530426 -1.073521 233s 29 -38.7483 50.2749 2.34469 10.156457 -0.416262 233s 30 -93.3887 51.7884 20.08872 8.798781 -1.620216 233s 31 35.3096 41.7158 13.46272 14.464358 -0.475973 233s 32 290.8493 3.5924 7.41501 15.244293 2.141354 233s 33 326.7236 -29.8194 15.64898 2.612061 0.064931 233s 34 322.9095 -30.6372 16.21520 1.248005 -0.711322 233s 35 328.5307 -29.9533 16.49656 1.138916 0.974792 233s 36 325.6791 -30.6990 16.83840 -0.050949 -1.211360 233s 37 323.8136 -30.7474 19.55764 -1.545150 -0.267580 233s 38 325.2991 -30.5350 20.31878 -1.928580 -0.120425 233s ------------- 233s Call: 233s PcaClassic(x = x) 233s 233s Standard deviations: 233s [1] 196.0487 32.1762 18.4819 6.9412 1.3510 233s ---------------------------------------------------------- 233s ========================================================== 233s > dodata(method="hubert.mcd") 233s 233s Call: dodata(method = "hubert.mcd") 233s Data Set n p k e1 e2 233s ========================================================== 233s heart 12 2 2 358.175786 4.590630 233s Scores: 233s PC1 PC2 233s 1 -12.2285 0.86283 233s 2 -68.9906 -7.43256 233s 3 -5.7035 -1.53793 233s 4 -1.8988 2.90891 233s 5 -24.0044 -2.68946 233s 6 9.9115 8.43321 233s 7 -11.0210 1.77484 233s 8 25.1826 -1.31573 233s 9 -3.2809 -0.74345 233s 10 23.8200 -0.93701 233s 11 9.1344 1.67701 233s 12 -53.6607 -5.08826 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 18.9255 2.1426 233s ---------------------------------------------------------- 233s starsCYG 47 2 2 0.280653 0.005921 233s Scores: 233s PC1 PC2 233s 1 -0.285731 -0.0899858 233s 2 -0.819689 0.0153191 233s 3 0.028077 -0.1501882 233s 4 -0.819689 0.0153191 233s 5 -0.234971 -0.1526225 233s 6 -0.527231 -0.0382380 233s 7 0.372118 -0.5195605 233s 8 -0.357448 0.1009508 233s 9 -0.603553 -0.2533541 233s 10 -0.177170 -0.0722541 233s 11 -0.637339 -1.0390758 233s 12 -0.512526 -0.0662337 233s 13 -0.490978 -0.0120517 233s 14 0.936868 -0.2550656 233s 15 0.684479 -0.0125787 233s 16 0.347708 0.0641382 233s 17 1.009966 -0.0202111 233s 18 0.742477 0.1286170 233s 19 0.773105 -0.0588983 233s 20 -0.795247 -1.0648673 233s 21 0.566048 -0.0319223 233s 22 0.723956 -0.0061308 233s 23 0.505616 0.0899297 233s 24 0.069956 0.0896997 233s 25 -0.080090 -0.0462652 233s 26 0.268755 0.0512425 233s 27 0.289710 -0.0770574 233s 28 0.038341 -0.0269216 233s 29 0.567463 -0.1026188 233s 30 -0.951542 -1.1005280 233s 31 0.512064 0.0504528 233s 32 -0.188059 0.1184850 233s 33 -0.288758 -0.0094200 233s 34 -1.190016 -1.1293460 233s 35 0.615197 -0.0846898 233s 36 -0.710930 0.0938781 233s 37 -0.183223 0.0888774 233s 38 -0.288758 -0.0094200 233s 39 -0.262177 0.0759816 233s 40 -0.630957 -0.0855773 233s 41 0.314679 0.0182135 233s 42 -0.130850 0.0163715 233s 43 -0.415248 0.0205825 233s 44 -0.407188 -0.0287636 233s 45 -0.620693 0.0376892 233s 46 -0.051896 0.0292672 233s 47 0.426662 0.0770340 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 0.529767 0.076946 233s ---------------------------------------------------------- 233s phosphor 18 2 2 285.985489 32.152099 233s Scores: 233s PC1 PC2 233s 1 -2.89681 -18.08811 233s 2 21.34021 -0.40854 233s 3 22.98065 4.13006 233s 4 12.33544 -6.72947 233s 5 17.99823 2.47611 233s 6 -13.35773 -24.10967 233s 7 -0.92957 -5.51314 233s 8 9.16061 2.71354 233s 9 9.89243 5.10403 233s 10 -14.12600 -11.17832 233s 11 3.84175 -0.17605 233s 12 -10.61905 4.37646 233s 13 -13.85065 2.01919 233s 14 -8.11927 4.34325 233s 15 -18.69805 -1.51673 233s 16 9.95352 -6.85784 233s 17 -22.49433 0.29387 233s 18 -18.66592 6.92359 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 16.9111 5.6703 233s ---------------------------------------------------------- 233s stackloss 21 3 3 78.703690 19.249085 233s Scores: 233s PC1 PC2 PC3 233s 1 -20.323997 10.26124 0.92041 233s 2 -19.761418 11.08797 0.92383 233s 3 -16.469919 6.43190 0.22593 233s 4 -4.171902 1.68262 2.50695 233s 5 -3.756174 1.40774 0.57004 233s 6 -3.964038 1.54518 1.53850 233s 7 -7.547376 -3.27780 2.48643 233s 8 -7.547376 -3.27780 2.48643 233s 9 -0.763294 -0.63699 2.53518 233s 10 4.214079 4.46296 -2.28315 233s 11 -0.849132 -2.97767 -2.31393 233s 12 -0.078689 -2.28838 -3.27896 233s 13 3.088921 2.80948 -2.28999 233s 14 -3.307313 -6.14718 -1.35916 233s 15 5.552354 -7.34201 -0.32057 233s 16 7.240091 -4.86180 -0.31031 233s 17 14.908334 6.84995 0.70603 233s 18 10.970281 1.06279 0.68209 233s 19 10.199838 0.37350 1.64712 233s 20 4.273564 1.99328 0.14526 233s 21 -11.992249 2.19025 -3.37391 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 8.8715 4.3874 2.1990 233s ---------------------------------------------------------- 233s salinity 28 3 3 11.651966 4.107426 233s Scores: 233s PC1 PC2 PC3 233s 1 1.68712 1.62591 0.19812128 233s 2 2.35772 2.37290 1.24965734 233s 3 6.80132 -2.14412 0.68142276 233s 4 6.41982 -0.61348 -0.31907921 233s 5 6.36697 -1.98030 4.87319903 233s 6 5.22050 1.20864 0.10252555 233s 7 3.34007 2.02950 0.00064329 233s 8 1.06220 2.89801 -0.35658064 233s 9 0.34692 -2.20572 -1.71677710 233s 10 -2.21421 -2.74842 0.76862599 233s 11 -1.40111 -2.16163 2.21124383 233s 12 -0.38242 0.32284 -0.23732191 233s 13 -1.12809 1.33152 -0.28800043 233s 14 -3.24998 1.35943 1.17514969 233s 15 -2.11006 -3.70114 0.45102357 233s 16 3.46920 -5.41242 8.56937909 233s 17 0.46682 -1.46753 1.48992481 233s 18 2.21807 0.99168 -0.61894625 233s 19 0.28525 -2.00333 -2.16450483 233s 20 -1.66639 -1.76768 -1.06946404 233s 21 -2.58106 1.23534 -0.65557612 233s 22 -4.15573 1.71244 0.08170141 233s 23 -3.07670 -4.87628 2.53200755 233s 24 -1.70808 -3.71657 2.99305849 233s 25 -1.08172 -1.05713 0.02468813 233s 26 -2.23187 0.27323 -0.85760867 233s 27 -3.50498 1.07657 -0.68503455 233s 28 -4.49819 1.43219 0.53416609 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 3.4135 2.0267 1.0764 233s ---------------------------------------------------------- 233s hbk 75 3 3 1.459908 1.201048 233s Scores: 233s PC1 PC2 PC3 233s 1 -31.105415 4.714217 10.4566165 233s 2 -31.707650 5.748724 10.7682402 233s 3 -33.366131 4.625897 12.1570167 233s 4 -34.173377 6.069657 12.4466895 233s 5 -33.780418 5.508823 11.9872893 233s 6 -32.493478 4.684595 10.5679819 233s 7 -32.592637 5.235522 10.3765493 233s 8 -31.293363 4.865797 10.9379676 233s 9 -33.160964 5.714260 12.3098920 233s 10 -31.919786 5.384537 12.3374332 233s 11 -38.231962 6.810641 13.5994385 233s 12 -39.290479 5.393906 15.2942554 233s 13 -39.418445 7.326461 11.5194898 233s 14 -43.906584 13.214819 8.3282743 233s 15 -1.906326 -0.716061 -0.8635112 233s 16 -0.263255 -0.926016 -1.9009292 233s 17 1.776489 1.072332 -0.5496140 233s 18 -0.464648 -0.702441 0.0482897 233s 19 -0.267826 1.283779 -0.2925812 233s 20 -2.122108 -0.165970 -0.8924686 233s 21 -0.937217 -0.548532 -0.4132196 233s 22 -0.423273 1.781869 -0.0323061 233s 23 -0.047532 -0.018909 -1.1259327 233s 24 0.490041 0.520202 -1.1065753 233s 25 2.143049 -0.720869 -0.0495474 233s 26 -1.094748 1.459175 0.2226246 233s 27 -2.070705 -0.898573 0.0023229 233s 28 0.294998 -0.830258 0.5929001 233s 29 1.242995 -0.300216 -0.2010507 233s 30 -0.147958 -0.439099 2.0003038 233s 31 -0.170818 -1.440946 -0.9755627 233s 32 0.958531 1.199730 -1.0129867 233s 33 -0.697307 0.874343 -0.7260649 233s 34 2.278946 -0.261106 0.4196544 233s 35 -1.962829 -0.809318 0.2033113 233s 36 -0.626631 0.600666 0.8004036 233s 37 -0.550885 1.881448 0.7382776 233s 38 1.249717 -0.336214 -0.9349845 233s 39 1.106696 -1.569418 0.1869576 233s 40 0.684034 0.939963 -0.1034965 233s 41 -1.559314 -1.551408 0.3660323 233s 42 0.538741 0.447358 1.6361099 233s 43 0.252685 2.080564 -0.7765259 233s 44 -0.217012 -1.027281 1.7015154 233s 45 1.497600 -1.349234 -0.2698932 233s 46 -0.100388 -1.026443 1.5390401 233s 47 0.811117 -2.195271 -0.5208141 233s 48 -1.462210 -1.321318 0.5600144 233s 49 -1.383976 -0.740714 -0.7348906 233s 50 -1.636773 0.215464 0.3195369 233s 51 0.530918 -0.759743 -1.2069247 233s 52 0.109566 -2.107455 -0.5315473 233s 53 0.564334 0.060847 2.3910630 233s 54 0.272234 1.122711 -1.5060028 233s 55 0.608660 1.197219 -0.5255609 233s 56 -0.565430 0.710345 -1.3708230 233s 57 1.115629 -0.888816 -0.4186014 233s 58 -1.351288 0.374815 -1.1980618 233s 59 -0.998016 0.151228 0.9007970 233s 60 -0.124017 0.764846 1.9005963 233s 61 -1.189858 1.905264 0.7721322 233s 62 2.190589 -0.579614 -0.1377914 233s 63 0.518278 0.931130 -1.4534768 233s 64 -2.124566 -0.194391 -0.0327092 233s 65 -0.154218 -1.050861 1.1309885 233s 66 1.197852 1.044147 -0.2265269 233s 67 0.114174 0.094763 -0.5168926 233s 68 2.201115 -0.032271 0.8573493 233s 69 1.307843 -1.104815 -0.7741270 233s 70 -0.691449 0.676665 1.0004603 233s 71 -1.150975 -0.050861 -0.0717068 233s 72 0.457293 0.861871 0.1026350 233s 73 0.392258 0.897451 0.9178065 233s 74 0.584658 1.450471 0.3201857 233s 75 0.972517 0.063777 1.8223995 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 1.2083 1.0959 1.0168 233s ---------------------------------------------------------- 233s milk 86 8 8 5.739740 2.405262 233s Scores: 233s PC1 PC2 PC3 PC4 PC5 PC6 PC7 233s 1 -5.710924 -1.346213 0.01332091 -0.3709242 -0.566813 0.7529298 -1.2525433 233s 2 -6.578612 -0.440749 1.16354746 0.2870685 -0.573207 0.7368064 -1.6101427 233s 3 -0.720902 1.777381 -0.21532020 -0.3213950 0.287603 -0.4764464 -0.5638337 233s 4 -5.545889 1.621147 -0.85212883 0.4380154 0.022241 0.0718035 0.1176140 233s 5 1.323210 -0.143897 -0.78611461 0.5966857 0.043139 -0.0512545 -0.1419726 233s 6 -1.760792 -0.662792 0.46402240 0.2149752 0.130000 0.0797221 0.1916948 233s 7 -2.344198 -0.363657 0.92442296 0.3921371 0.241463 -0.2370967 0.0636268 233s 8 -2.556824 -0.680132 0.04339934 0.4635077 0.154136 0.0371259 0.0260340 233s 9 1.203234 2.712342 -1.00693092 0.1251739 0.170679 0.2231851 -0.0118196 233s 10 3.151858 1.255826 -0.01678562 -0.5087398 -0.087933 0.0115055 -0.0097828 233s 11 9.562891 1.580419 -2.65612113 -0.1748178 -0.153031 -0.0880112 -0.1648752 233s 12 13.617821 -0.999033 -1.92168237 0.0326918 -0.038488 0.0870082 -0.1809687 233s 13 10.958032 -0.097916 0.95915085 -0.2348663 0.147875 0.1219202 0.0419067 233s 14 12.675941 0.158747 -1.04153243 0.3117402 0.302036 0.1187749 -0.2310830 233s 15 10.726828 1.775339 -3.36786799 0.1285422 0.151594 0.0998947 -0.2028458 233s 16 3.042705 0.212589 -1.23921907 -0.5596596 0.277061 -0.5037073 0.0612182 233s 17 0.780071 2.990008 -1.58490147 -0.5441119 0.436485 -0.0603833 0.1016610 233s 18 2.523916 -0.923373 -0.03221722 0.3830822 0.208008 -0.5505270 -0.1252648 233s 19 1.990563 1.062648 -1.42038451 -0.3602257 -0.068006 -0.1932744 -0.1197842 233s 20 -0.243938 1.674555 -0.72225359 -0.1475652 -0.397855 -0.5385123 -0.0559660 233s 21 3.354424 -2.001060 -0.22542149 0.3346180 0.032502 -0.0953825 0.1293148 233s 22 1.477177 -0.777534 -0.35362339 0.1224412 0.203208 0.0514382 -0.2166274 233s 23 0.502055 -1.618511 -0.85013853 -0.1298862 -0.144328 -0.1941806 -0.1923681 233s 24 0.900504 -1.227820 -1.07180474 -0.5851197 0.112657 0.0467164 0.0405544 233s 25 4.161393 -1.869015 -1.54507759 0.2003123 -0.152582 -0.1382908 0.0864320 233s 26 1.277795 -1.185179 -1.13445511 0.2771556 -0.101901 0.0070037 -0.1279016 233s 27 3.447256 0.257652 -1.13407954 -0.0077859 0.853002 -0.1376443 -0.1897380 233s 28 -1.695730 -3.781876 -0.72940594 -0.0956421 0.064475 0.3665470 0.0726448 233s 29 -3.923610 -1.654818 -0.16117226 -0.4242302 -0.303749 -0.0209844 0.1723890 233s 30 -0.309616 -1.564739 -0.39909943 0.1657509 -0.178739 -0.0600221 -0.0571706 233s 31 -0.960838 -2.242733 1.50477679 -0.2957897 0.163758 -0.1034399 0.0257903 233s 32 -0.671285 -0.459839 1.39124475 -0.3669914 0.246127 0.2094780 -0.2681284 233s 33 -1.589089 -0.390812 -0.16505762 -0.3992573 0.086870 -0.0402114 -0.0399923 233s 34 -0.421868 0.636139 -0.42563447 -0.2985726 0.311365 0.2398515 -0.0540852 233s 35 1.118429 -2.116328 -0.22329747 -0.4864401 0.289927 -0.0503006 0.0101706 233s 36 -3.660291 -1.630831 -0.57876280 0.1294792 -0.260224 0.0912904 -0.1565668 233s 37 -0.087686 -2.530609 0.50076931 -0.0319873 0.194898 -0.1233526 -0.2494283 233s 38 -1.418620 -2.303011 -0.09405565 -0.0931745 0.169466 0.1581787 0.0850095 233s 39 1.815225 -0.838968 -1.10222194 -0.4897630 0.180933 0.0096330 -0.0600652 233s 40 -3.420975 1.398516 -0.17143314 -0.5852146 0.090464 -0.2066323 -0.2974177 233s 41 -3.462295 -1.795174 -0.17500650 -0.1610267 -0.595086 0.5981680 -1.5930268 233s 42 -6.401429 0.451242 -0.78723149 -0.4285618 0.055395 -0.0212476 0.0808936 233s 43 -2.583017 -0.871790 1.29937081 0.2422349 -0.190002 -0.2822972 -0.2625721 233s 44 -5.027244 -0.167503 -0.02382957 -0.8288929 -0.852207 0.7399343 0.4606076 233s 45 0.364494 -0.440380 -0.07746564 -0.4552133 0.095711 -0.1662998 0.1566706 233s 46 0.420706 -1.880819 -0.82180986 -0.1823454 -0.022661 -0.0304227 -0.0516440 233s 47 -1.932985 -0.120002 4.00934170 0.0930728 0.295428 0.2787446 0.3766231 233s 48 0.395402 -1.021393 1.07953292 -0.4599764 -0.132386 0.1895780 0.2771755 233s 49 2.886100 -0.276587 1.48851137 -0.6314648 -0.203963 -0.0891955 0.1347804 233s 50 -3.255379 2.479232 -0.37933775 -0.3651497 -0.415000 0.0045750 0.0671055 233s 51 1.939333 0.617579 1.57113225 0.0310866 -0.039226 0.0409183 0.1830694 233s 52 5.727154 0.275898 0.58814711 -0.1739820 -0.222791 0.2553797 0.1959402 233s 53 1.207873 0.131451 0.80899235 0.2872465 -0.353544 -0.1697200 -0.0987230 233s 54 0.612921 0.040062 0.17807459 -0.0053074 -0.202244 -0.0671788 0.0530276 233s 55 -0.399075 -0.727144 0.26196635 0.3657576 -0.192705 0.0903564 0.0641289 233s 56 0.240719 0.733792 -0.05030509 0.0967214 -0.186906 0.0310231 -0.0594812 233s 57 1.589641 0.289427 -1.02478822 0.2723190 -0.048378 0.2599262 -0.2040853 233s 58 0.423483 -1.262515 -0.85026016 0.4749963 -0.082647 0.0752412 0.1352259 233s 59 1.983684 1.335122 0.42593757 0.1345894 0.096456 0.1153107 -0.0385994 233s 60 1.770171 0.935428 0.14901569 0.3641973 0.274015 -0.0280119 0.0690244 233s 61 0.182845 1.706453 -0.18364654 0.2517421 -0.035773 0.0357087 -0.1363470 233s 62 -2.191617 1.966324 -0.03573689 -0.2203900 -0.235704 0.1682332 -0.1145174 233s 63 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 233s 64 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 233s 65 0.407575 2.996346 -0.63021113 -0.1335795 0.087668 0.0627032 0.0486166 233s 66 2.660379 1.322824 0.10122110 0.2420451 0.192938 0.0344019 -0.0771918 233s 67 -0.032273 1.315299 -0.04511689 -0.1293380 -0.025923 -0.1655965 0.1887534 233s 68 1.117637 2.005809 1.97078787 -0.0429209 -0.176568 0.1634287 -0.0916254 233s 69 0.970730 0.837158 0.01621375 0.2347502 -0.071757 -0.2464626 0.2907551 233s 70 -2.688271 -5.335891 -0.64225481 4.1819517 -9.523550 2.0943027 -2.8098426 233s 71 2.428718 1.976051 -0.24749122 0.1308738 0.018276 0.1711292 0.1346284 233s 72 -2.061944 0.405943 0.50472914 0.4393739 -0.056420 -0.0031558 0.2663880 233s 73 2.029606 2.874991 0.68310320 -0.2067254 0.511537 -0.2010371 0.0805608 233s 74 11.293757 0.328931 -3.84783031 -0.4130266 -0.210499 -0.1103148 -0.0381326 233s 75 0.120896 2.287914 0.83639076 -0.2462845 0.551353 0.6629701 0.3789055 233s 76 1.859499 0.422019 1.18435547 0.1546108 0.017266 0.0470615 -0.1071011 233s 77 8.435857 1.147499 -2.19924186 -0.4156770 0.386548 0.0294075 -0.1911399 233s 78 -1.090858 1.311287 0.62897430 0.1727009 0.077341 0.0135972 -0.0096934 233s 79 0.560012 0.623617 0.83727267 0.1680787 0.087477 0.0611949 -0.2588084 233s 80 3.873817 -1.133641 -1.27469019 -0.2717298 -0.165066 0.1696232 0.0635047 233s 81 -0.758664 -0.880260 0.00057124 0.2838720 0.016243 0.1527299 -0.0150514 233s 82 -2.709588 1.464049 -0.12598126 -0.3828567 0.213647 -0.1425385 0.1552827 233s 83 -2.213670 0.059563 0.87565603 0.1255703 -0.082005 0.2189829 -0.2938264 233s 84 -0.242242 -0.483552 2.05089334 -0.0681005 -0.101578 0.1304632 -0.2218093 233s 85 -1.032129 2.375018 -2.19321259 0.2332079 -0.066379 0.1854598 -0.0873859 233s 86 0.015327 -0.948155 1.39530555 0.2701225 -0.268889 0.0578145 0.1608678 233s PC8 233s 1 2.1835e-03 233s 2 1.6801e-03 233s 3 1.6623e-03 233s 4 2.6286e-04 233s 5 9.5884e-04 233s 6 1.4430e-03 233s 7 1.8784e-04 233s 8 6.8473e-04 233s 9 -6.8490e-04 233s 10 1.1565e-04 233s 11 5.6907e-06 233s 12 -1.8395e-03 233s 13 -2.1582e-03 233s 14 -1.6294e-03 233s 15 -1.6964e-03 233s 16 -1.9664e-03 233s 17 -2.2448e-03 233s 18 -6.5884e-04 233s 19 -1.1536e-03 233s 20 2.6887e-04 233s 21 3.3199e-05 233s 22 1.1170e-04 233s 23 -1.7617e-04 233s 24 -2.1577e-04 233s 25 -6.1495e-04 233s 26 -7.2903e-04 233s 27 -6.8773e-04 233s 28 -2.0742e-04 233s 29 -2.6937e-04 233s 30 -6.7472e-05 233s 31 -1.3222e-04 233s 32 -1.6516e-04 233s 33 -1.8836e-04 233s 34 -1.1273e-04 233s 35 3.0703e-05 233s 36 -3.0311e-04 233s 37 -1.9380e-04 233s 38 5.5526e-04 233s 39 4.1987e-04 233s 40 8.4807e-05 233s 41 8.8725e-04 233s 42 -6.5647e-04 233s 43 4.3202e-04 233s 44 -5.3330e-04 233s 45 8.9161e-04 233s 46 1.1588e-03 233s 47 -1.2714e-03 233s 48 -4.0376e-04 233s 49 4.1280e-06 233s 50 3.0116e-04 233s 51 5.8510e-05 233s 52 3.3236e-04 233s 53 4.0982e-04 233s 54 4.0428e-04 233s 55 6.1600e-04 233s 56 -4.0496e-05 233s 57 -1.8342e-04 233s 58 -1.6748e-04 233s 59 -1.0894e-03 233s 60 -2.6876e-04 233s 61 -5.8951e-05 233s 62 -1.5517e-04 233s 63 -7.9933e-04 233s 64 -7.9933e-04 233s 65 2.2592e-05 233s 66 2.4984e-05 233s 67 -2.2714e-04 233s 68 -3.3991e-04 233s 69 -3.0375e-04 233s 70 3.4033e-03 233s 71 2.3288e-05 233s 72 -3.4126e-04 233s 73 2.5528e-04 233s 74 2.2760e-03 233s 75 -2.8985e-04 233s 76 7.9077e-04 233s 77 9.4636e-04 233s 78 4.9099e-04 233s 79 3.0501e-04 233s 80 6.5280e-04 233s 81 -3.6570e-04 233s 82 4.9966e-04 233s 83 -4.3245e-04 233s 84 -4.6152e-04 233s 85 7.4691e-04 233s 86 -6.1103e-04 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 2.39577535 1.55089079 0.92557331 0.33680677 0.19792033 0.17855133 0.16041702 233s [8] 0.00054179 233s ---------------------------------------------------------- 233s bushfire 38 5 5 31248.552973 358.974577 233s Scores: 233s PC1 PC2 PC3 PC4 PC5 233s 1 155.972 1.08098 -23.31135 -1.93015 1.218941 233s 2 157.738 0.35648 -20.95658 -2.42375 0.466415 233s 3 150.667 2.12545 -16.20395 -2.00140 -0.582924 233s 4 133.892 5.25124 -15.88873 -2.78469 -0.275261 233s 5 102.462 13.00611 -21.54096 -4.69409 -0.944176 233s 6 77.694 18.75377 -28.71865 -6.44244 0.446350 233s 7 286.266 -11.36184 -98.67134 10.95233 -3.625338 233s 8 326.627 29.92767 -112.60824 -29.26330 -13.710094 233s 9 327.898 32.39553 -113.34314 -31.65905 -13.830781 233s 10 325.131 5.81628 -105.58927 -13.45695 -8.987971 233s 11 326.458 -7.84562 -94.25242 -6.11547 -8.572845 233s 12 333.171 -37.69907 -50.89207 8.98187 -1.742979 233s 13 279.789 -40.78415 -8.06209 7.65884 0.181748 233s 14 37.714 10.54231 13.46530 -1.55051 2.102662 233s 15 -90.034 34.68964 18.98186 0.69260 0.417573 233s 16 -46.492 23.65086 10.07282 4.36090 -0.748517 233s 17 -43.990 20.36443 9.61049 2.83084 -0.127983 233s 18 -32.938 19.11199 2.64850 2.92879 -1.473988 233s 19 -36.555 20.60142 2.01879 0.63832 -1.235075 233s 20 -46.837 19.89630 6.65142 0.89120 0.271108 233s 21 -28.670 15.29534 6.59311 3.29638 0.402194 233s 22 -20.331 15.06559 7.33721 2.16591 2.006327 233s 23 108.644 -7.92707 -1.45130 6.27388 0.356715 233s 24 163.697 -16.15568 0.61663 4.24231 0.464415 233s 25 100.471 -0.30739 0.87762 2.86452 -0.692735 233s 26 106.922 0.90864 -1.91436 2.54557 -0.565023 233s 27 121.966 -3.29641 4.85626 -0.47676 -0.490047 233s 28 98.650 -4.51455 16.64160 -3.08996 -0.839397 233s 29 88.795 -10.85457 30.46708 -5.37360 0.315657 233s 30 142.981 -27.89100 22.40713 -1.67126 -0.680158 233s 31 14.125 -21.60028 29.80480 -8.25272 -0.019693 233s 32 -244.044 -11.76430 24.53390 -12.52294 2.022312 233s 33 -283.842 -13.21931 -6.23565 -2.63367 -0.080728 233s 34 -280.168 -13.41903 -7.69318 -1.24571 -0.722513 233s 35 -285.666 -13.78452 -6.50318 -1.23756 1.074669 233s 36 -282.938 -13.82281 -7.63902 0.20435 -0.971673 233s 37 -281.129 -16.20408 -8.57154 1.85797 0.234486 233s 38 -282.589 -16.91969 -8.36010 2.35589 0.490630 233s ------------- 233s Call: 233s PcaHubert(x = x, k = p) 233s 233s Standard deviations: 233s [1] 176.77260 18.94662 16.21701 3.95755 0.92761 233s ---------------------------------------------------------- 233s ========================================================== 233s > dodata(method="hubert") 233s 233s Call: dodata(method = "hubert") 233s Data Set n p k e1 e2 233s ========================================================== 233s heart 12 2 1 315.227002 NA 233s Scores: 233s PC1 233s 1 13.2197 233s 2 69.9817 233s 3 6.6946 233s 4 2.8899 233s 5 24.9956 233s 6 -8.9203 233s 7 12.0121 233s 8 -24.1915 233s 9 4.2721 233s 10 -22.8289 233s 11 -8.1433 233s 12 54.6519 233s ------------- 233s Call: 233s PcaHubert(x = x, mcd = FALSE) 233s 233s Standard deviations: 233s [1] 17.755 233s ---------------------------------------------------------- 233s starsCYG 47 2 1 0.308922 NA 233s Scores: 233s PC1 233s 1 0.224695 233s 2 0.758653 233s 3 -0.089113 233s 4 0.758653 233s 5 0.173934 233s 6 0.466195 233s 7 -0.433154 233s 8 0.296411 233s 9 0.542517 233s 10 0.116133 233s 11 0.576303 233s 12 0.451490 233s 13 0.429942 233s 14 -0.997904 233s 15 -0.745515 233s 16 -0.408745 233s 17 -1.071002 233s 18 -0.803514 233s 19 -0.834141 233s 20 0.734210 233s 21 -0.627085 233s 22 -0.784992 233s 23 -0.566652 233s 24 -0.130992 233s 25 0.019053 233s 26 -0.329791 233s 27 -0.350747 233s 28 -0.099378 233s 29 -0.628499 233s 30 0.890506 233s 31 -0.573100 233s 32 0.127022 233s 33 0.227721 233s 34 1.128979 233s 35 -0.676234 233s 36 0.649894 233s 37 0.122186 233s 38 0.227721 233s 39 0.201140 233s 40 0.569920 233s 41 -0.375716 233s 42 0.069814 233s 43 0.354212 233s 44 0.346152 233s 45 0.559656 233s 46 -0.009140 233s 47 -0.487699 233s ------------- 233s Call: 233s PcaHubert(x = x, mcd = FALSE) 233s 233s Standard deviations: 233s [1] 0.55581 233s ---------------------------------------------------------- 233s phosphor 18 2 1 215.172048 NA 233s Scores: 233s PC1 233s 1 1.12634 233s 2 -22.10340 233s 3 -23.49216 233s 4 -13.45927 233s 5 -18.60808 233s 6 11.24086 233s 7 -0.14748 233s 8 -9.77075 233s 9 -10.37022 233s 10 12.71798 233s 11 -4.61857 233s 12 10.07037 233s 13 13.16767 233s 14 7.57254 233s 15 17.81362 233s 16 -11.08799 233s 17 21.70358 233s 18 18.24496 233s ------------- 233s Call: 233s PcaHubert(x = x, mcd = FALSE) 233s 233s Standard deviations: 233s [1] 14.669 233s ---------------------------------------------------------- 233s stackloss 21 3 2 77.038636 18.859777 233s Scores: 233s PC1 PC2 233s 1 -20.334936 10.28081 233s 2 -19.772121 11.10736 233s 3 -16.461573 6.43794 233s 4 -4.258672 1.73213 233s 5 -3.773146 1.41928 233s 6 -4.015909 1.57571 233s 7 -7.635560 -3.22715 233s 8 -7.635560 -3.22715 233s 9 -0.855388 -0.58707 233s 10 4.298129 4.41664 233s 11 -0.767202 -3.02229 233s 12 0.038375 -2.35217 233s 13 3.172500 2.76354 233s 14 -3.261224 -6.17206 233s 15 5.553840 -7.34784 233s 16 7.242284 -4.86820 233s 17 14.878925 6.85989 233s 18 10.939223 1.07406 233s 19 10.133645 0.40394 233s 20 4.267234 1.99501 233s 21 -11.859921 2.12579 233s ------------- 233s Call: 233s PcaHubert(x = x, mcd = FALSE) 233s 233s Standard deviations: 233s [1] 8.7772 4.3428 233s ---------------------------------------------------------- 233s salinity 28 3 2 8.001175 5.858089 233s Scores: 233s PC1 PC2 233s 1 2.858444 1.04359 233s 2 3.807704 1.55974 233s 3 6.220733 -4.32114 233s 4 6.388841 -2.83649 233s 5 6.077450 -3.70092 233s 6 5.974494 -0.67230 233s 7 4.531584 0.78322 233s 8 2.725849 2.41297 233s 9 0.100501 -2.13615 233s 10 -2.358003 -1.49718 233s 11 -1.317688 -1.15391 233s 12 0.434635 0.58230 233s 13 0.116019 1.79022 233s 14 -1.771501 2.71749 233s 15 -2.630757 -2.44003 233s 16 2.289743 -5.51829 233s 17 0.637985 -1.26452 233s 18 3.076147 0.19883 233s 19 0.097381 -1.95868 233s 20 -1.572471 -0.93003 233s 21 -1.284185 2.21858 233s 22 -2.531713 3.30313 233s 23 -3.865359 -3.01230 233s 24 -2.143461 -2.41918 233s 25 -0.714414 -0.41227 233s 26 -1.327781 1.18373 233s 27 -2.201166 2.41566 233s 28 -2.931988 3.20536 233s ------------- 233s Call: 233s PcaHubert(x = x, mcd = FALSE) 233s 233s Standard deviations: 233s [1] 2.8286 2.4203 233s ---------------------------------------------------------- 234s hbk 75 3 3 1.459908 1.201048 234s Scores: 234s PC1 PC2 PC3 234s 1 31.105415 -4.714217 -10.4566165 234s 2 31.707650 -5.748724 -10.7682402 234s 3 33.366131 -4.625897 -12.1570167 234s 4 34.173377 -6.069657 -12.4466895 234s 5 33.780418 -5.508823 -11.9872893 234s 6 32.493478 -4.684595 -10.5679819 234s 7 32.592637 -5.235522 -10.3765493 234s 8 31.293363 -4.865797 -10.9379676 234s 9 33.160964 -5.714260 -12.3098920 234s 10 31.919786 -5.384537 -12.3374332 234s 11 38.231962 -6.810641 -13.5994385 234s 12 39.290479 -5.393906 -15.2942554 234s 13 39.418445 -7.326461 -11.5194898 234s 14 43.906584 -13.214819 -8.3282743 234s 15 1.906326 0.716061 0.8635112 234s 16 0.263255 0.926016 1.9009292 234s 17 -1.776489 -1.072332 0.5496140 234s 18 0.464648 0.702441 -0.0482897 234s 19 0.267826 -1.283779 0.2925812 234s 20 2.122108 0.165970 0.8924686 234s 21 0.937217 0.548532 0.4132196 234s 22 0.423273 -1.781869 0.0323061 234s 23 0.047532 0.018909 1.1259327 234s 24 -0.490041 -0.520202 1.1065753 234s 25 -2.143049 0.720869 0.0495474 234s 26 1.094748 -1.459175 -0.2226246 234s 27 2.070705 0.898573 -0.0023229 234s 28 -0.294998 0.830258 -0.5929001 234s 29 -1.242995 0.300216 0.2010507 234s 30 0.147958 0.439099 -2.0003038 234s 31 0.170818 1.440946 0.9755627 234s 32 -0.958531 -1.199730 1.0129867 234s 33 0.697307 -0.874343 0.7260649 234s 34 -2.278946 0.261106 -0.4196544 234s 35 1.962829 0.809318 -0.2033113 234s 36 0.626631 -0.600666 -0.8004036 234s 37 0.550885 -1.881448 -0.7382776 234s 38 -1.249717 0.336214 0.9349845 234s 39 -1.106696 1.569418 -0.1869576 234s 40 -0.684034 -0.939963 0.1034965 234s 41 1.559314 1.551408 -0.3660323 234s 42 -0.538741 -0.447358 -1.6361099 234s 43 -0.252685 -2.080564 0.7765259 234s 44 0.217012 1.027281 -1.7015154 234s 45 -1.497600 1.349234 0.2698932 234s 46 0.100388 1.026443 -1.5390401 234s 47 -0.811117 2.195271 0.5208141 234s 48 1.462210 1.321318 -0.5600144 234s 49 1.383976 0.740714 0.7348906 234s 50 1.636773 -0.215464 -0.3195369 234s 51 -0.530918 0.759743 1.2069247 234s 52 -0.109566 2.107455 0.5315473 234s 53 -0.564334 -0.060847 -2.3910630 234s 54 -0.272234 -1.122711 1.5060028 234s 55 -0.608660 -1.197219 0.5255609 234s 56 0.565430 -0.710345 1.3708230 234s 57 -1.115629 0.888816 0.4186014 234s 58 1.351288 -0.374815 1.1980618 234s 59 0.998016 -0.151228 -0.9007970 234s 60 0.124017 -0.764846 -1.9005963 234s 61 1.189858 -1.905264 -0.7721322 234s 62 -2.190589 0.579614 0.1377914 234s 63 -0.518278 -0.931130 1.4534768 234s 64 2.124566 0.194391 0.0327092 234s 65 0.154218 1.050861 -1.1309885 234s 66 -1.197852 -1.044147 0.2265269 234s 67 -0.114174 -0.094763 0.5168926 234s 68 -2.201115 0.032271 -0.8573493 234s 69 -1.307843 1.104815 0.7741270 234s 70 0.691449 -0.676665 -1.0004603 234s 71 1.150975 0.050861 0.0717068 234s 72 -0.457293 -0.861871 -0.1026350 234s 73 -0.392258 -0.897451 -0.9178065 234s 74 -0.584658 -1.450471 -0.3201857 234s 75 -0.972517 -0.063777 -1.8223995 234s ------------- 234s Call: 234s PcaHubert(x = x, mcd = FALSE) 234s 234s Standard deviations: 234s [1] 1.2083 1.0959 1.0168 234s ---------------------------------------------------------- 234s milk 86 8 2 6.040806 2.473780 234s Scores: 234s PC1 PC2 234s 1 -5.768003 -0.9174359 234s 2 -6.664422 0.0280812 234s 3 -0.484521 1.7923710 234s 4 -5.211590 2.0747301 234s 5 1.422641 -0.3268437 234s 6 -1.810360 -0.5469828 234s 7 -2.402924 -0.1987041 234s 8 -2.553389 -0.4963662 234s 9 1.583399 2.5410448 234s 10 3.267946 0.9141367 234s 11 9.924771 0.6501301 234s 12 13.628569 -2.3009846 234s 13 10.774550 -1.1628697 234s 14 12.716376 -1.0670330 234s 15 11.176408 0.7403371 234s 16 3.209269 -0.0804317 234s 17 1.256577 2.8931153 234s 18 2.468720 -1.2008647 234s 19 2.253229 0.8379608 234s 20 0.021073 1.6394221 234s 21 3.205298 -2.3518286 234s 22 1.470733 -0.9618655 234s 23 0.475732 -1.7044535 234s 24 0.930144 -1.3288398 234s 25 4.151553 -2.2882554 234s 26 1.314488 -1.3527439 234s 27 3.613405 -0.0813605 234s 28 -1.909178 -3.6473200 234s 29 -3.987263 -1.3255834 234s 30 -0.370601 -1.5855086 234s 31 -1.273254 -2.1892809 234s 32 -0.816634 -0.4514478 234s 33 -1.553394 -0.2792004 234s 34 -0.275027 0.6359374 234s 35 0.980782 -2.2353223 234s 36 -3.678470 -1.3459182 234s 37 -0.327102 -2.5615283 234s 38 -1.563492 -2.2008288 234s 39 1.876146 -1.0292641 234s 40 -3.204182 1.6694332 234s 41 -3.561892 -1.5844770 234s 42 -6.175135 1.0123714 234s 43 -2.736601 -0.7040261 234s 44 -4.981783 0.2434304 234s 45 0.368802 -0.5011413 234s 46 0.369508 -1.9511091 234s 47 -2.306673 -0.0089446 234s 48 0.215195 -1.1000357 234s 49 2.704678 -0.5919929 234s 50 -2.930879 2.7161936 234s 51 1.846250 0.3732500 234s 52 5.661288 -0.3139157 234s 53 1.154929 -0.0575094 234s 54 0.625715 -0.0733934 234s 55 -0.453714 -0.7535924 234s 56 0.343722 0.6460318 234s 57 1.743002 0.0794685 234s 58 0.433705 -1.3500731 234s 59 2.078550 1.0860506 234s 60 1.867913 0.7162287 234s 61 0.392645 1.6184583 234s 62 -1.958732 2.0993596 234s 63 -2.383251 -0.0253919 234s 64 -2.383251 -0.0253919 234s 65 0.780239 2.9018927 234s 66 2.785329 1.0142893 234s 67 0.131210 1.2703167 234s 68 1.110073 1.8140467 234s 69 1.076878 0.6954148 234s 70 -3.260160 -5.6233069 234s 71 2.647036 1.6892084 234s 72 -2.017340 0.5353349 234s 73 2.247524 2.6406249 234s 74 11.649291 -0.7374197 234s 75 0.280544 2.2306959 234s 76 1.791213 0.1796005 234s 77 8.730344 0.3412271 234s 78 -0.987405 1.3467910 234s 79 0.560808 0.5006661 234s 80 3.897879 -1.5270179 234s 81 -0.792759 -0.8649399 234s 82 -2.493611 1.6796838 234s 83 -2.245966 0.1889555 234s 84 -0.468812 -0.5359088 234s 85 -0.538372 2.4105954 234s 86 -0.185347 -1.0176989 234s ------------- 234s Call: 234s PcaHubert(x = x, mcd = FALSE) 234s 234s Standard deviations: 234s [1] 2.4578 1.5728 234s ---------------------------------------------------------- 234s bushfire 38 5 1 38435.075910 NA 234s Scores: 234s PC1 234s 1 -111.9345 234s 2 -113.4128 234s 3 -105.8364 234s 4 -89.1684 234s 5 -58.7216 234s 6 -35.0370 234s 7 -250.2123 234s 8 -292.6877 234s 9 -294.0765 234s 10 -290.0193 234s 11 -289.8168 234s 12 -290.8645 234s 13 -232.6865 234s 14 9.8483 234s 15 137.1924 234s 16 92.9804 234s 17 90.4493 234s 18 78.6325 234s 19 82.1178 234s 20 92.9044 234s 21 74.9157 234s 22 66.7350 234s 23 -62.1981 234s 24 -116.5696 234s 25 -53.8907 234s 26 -60.6384 234s 27 -74.7621 234s 28 -50.2202 234s 29 -38.7483 234s 30 -93.3887 234s 31 35.3096 234s 32 290.8493 234s 33 326.7236 234s 34 322.9095 234s 35 328.5307 234s 36 325.6791 234s 37 323.8136 234s 38 325.2991 234s ------------- 234s Call: 234s PcaHubert(x = x, mcd = FALSE) 234s 234s Standard deviations: 234s [1] 196.05 234s ---------------------------------------------------------- 234s ========================================================== 234s > 234s > dodata(method="locantore") 234s 234s Call: dodata(method = "locantore") 234s Data Set n p k e1 e2 234s ========================================================== 234s heart 12 2 2 1.835912 0.084745 234s Scores: 234s PC1 PC2 234s [1,] 7.3042 1.745289 234s [2,] 64.6474 0.164425 234s [3,] 1.1057 -1.404189 234s [4,] -3.1943 2.565728 234s [5,] 19.4154 -0.401369 234s [6,] -15.5709 6.666752 234s [7,] 5.9980 2.509372 234s [8,] -29.5933 -4.805972 234s [9,] -1.3933 -0.899323 234s [10,] -28.2845 -4.270057 234s [11,] -14.0069 0.048311 234s [12,] 49.1484 0.694598 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 1.35496 0.29111 234s ---------------------------------------------------------- 234s starsCYG 47 2 2 0.779919 0.050341 234s Scores: 234s PC1 PC2 234s [1,] 0.174291 -0.0489127 234s [2,] 0.703776 0.0769650 234s [3,] -0.136954 -0.1212071 234s [4,] 0.703776 0.0769650 234s [5,] 0.125991 -0.1134658 234s [6,] 0.413609 0.0121367 234s [7,] -0.466451 -0.5036094 234s [8,] 0.238569 0.1446547 234s [9,] 0.498194 -0.1998666 234s [10,] 0.065125 -0.0353931 234s [11,] 0.562344 -0.9836936 234s [12,] 0.399997 -0.0164068 234s [13,] 0.376370 0.0369013 234s [14,] -1.041009 -0.2611550 234s [15,] -0.798187 -0.0090880 234s [16,] -0.464636 0.0805967 234s [17,] -1.123135 -0.0293034 234s [18,] -0.861603 0.1297588 234s [19,] -0.884955 -0.0588007 234s [20,] 0.721130 -1.0033585 234s [21,] -0.679097 -0.0238366 234s [22,] -0.837884 -0.0041718 234s [23,] -0.623423 0.1002615 234s [24,] -0.188079 0.1168815 234s [25,] -0.032888 -0.0131784 234s [26,] -0.385242 0.0707643 234s [27,] -0.401220 -0.0582501 234s [28,] -0.151978 0.0015702 234s [29,] -0.677776 -0.0945350 234s [30,] 0.878688 -1.0329475 234s [31,] -0.628339 0.0605648 234s [32,] 0.068629 0.1556245 234s [33,] 0.174199 0.0317098 234s [34,] 1.118098 -1.0525206 234s [35,] -0.726168 -0.0784655 234s [36,] 0.592061 0.1512588 234s [37,] 0.064942 0.1258519 234s [38,] 0.174199 0.0317098 234s [39,] 0.144335 0.1160195 234s [40,] 0.519088 -0.0311555 234s [41,] -0.429855 0.0359837 234s [42,] 0.015412 0.0513747 234s [43,] 0.299435 0.0665821 234s [44,] 0.293289 0.0169612 234s [45,] 0.504064 0.0916219 234s [46,] -0.063981 0.0612071 234s [47,] -0.544029 0.0904291 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 0.88313 0.22437 234s ---------------------------------------------------------- 234s phosphor 18 2 2 0.933905 0.279651 234s Scores: 234s PC1 PC2 234s 1 4.5660 -15.58981 234s 2 -21.2978 -0.38905 234s 3 -23.3783 3.96546 234s 4 -11.7131 -5.79023 234s 5 -18.2569 2.81141 234s 6 15.5702 -20.54935 234s 7 1.3671 -3.27043 234s 8 -9.4859 3.92005 234s 9 -10.4501 6.22662 234s 10 15.0583 -7.60532 234s 11 -3.9078 1.56960 234s 12 10.0330 7.52732 234s 13 13.4815 5.50056 234s 14 7.5487 7.24752 234s 15 18.6543 2.46040 234s 16 -9.3301 -5.68285 234s 17 22.2533 4.63689 234s 18 17.7892 10.85633 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 0.96639 0.52882 234s ---------------------------------------------------------- 234s stackloss 21 3 3 1.137747 0.196704 234s Scores: 234s PC1 PC2 PC3 234s [1,] 19.98046 -6.20875 -3.93576 234s [2,] 19.57014 -7.11509 -4.03666 234s [3,] 15.48729 -3.14247 -3.29600 234s [4,] 3.12341 -1.38969 1.50633 234s [5,] 2.35380 -0.84492 -0.25745 234s [6,] 2.73860 -1.11731 0.62444 234s [7,] 5.58533 4.04837 2.11170 234s [8,] 5.58533 4.04837 2.11170 234s [9,] -0.56851 0.17483 2.46656 234s [10,] -5.36478 -4.80766 -2.64915 234s [11,] -1.67190 3.34943 -1.74110 234s [12,] -2.46702 2.71547 -2.72389 234s [13,] -4.54414 -2.99497 -2.44736 234s [14,] 0.35419 6.70241 -0.45563 234s [15,] -8.28612 5.93369 1.94314 234s [16,] -9.51708 3.21466 1.64046 234s [17,] -14.87676 -9.74652 1.10983 234s [18,] -12.00452 -3.40212 1.81609 234s [19,] -11.20939 -2.76816 2.79887 234s [20,] -5.42808 -2.89367 0.23748 234s [21,] 9.83969 0.74095 -5.30190 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 1.06665 0.44351 0.33935 234s ---------------------------------------------------------- 234s salinity 28 3 3 1.038873 0.621380 234s Scores: 234s PC1 PC2 PC3 234s 1 -2.7215590 -0.98924 0.3594538 234s 2 -3.6251829 -1.03361 1.4973993 234s 3 -6.0588883 4.23861 -1.1012038 234s 4 -6.2741857 2.42372 -1.4875092 234s 5 -5.7274076 5.42190 2.9332011 234s 6 -5.8431892 0.57161 -0.3385363 234s 7 -4.4051377 -0.83292 0.0851817 234s 8 -2.6155827 -2.50739 0.3386166 234s 9 -0.0426575 1.19631 -2.5025726 234s 10 2.5297488 1.65029 -0.0110335 234s 11 1.5528097 1.93255 1.4216262 234s 12 -0.3140451 -0.73269 -0.1961364 234s 13 0.0010783 -1.88658 0.1849912 234s 14 1.9554303 -2.13519 1.8471356 234s 15 2.7897250 2.40211 -0.6327944 234s 16 -1.7665706 8.69449 5.6608836 234s 17 -0.4374125 1.72696 0.7230753 234s 18 -2.9752196 -0.54118 -0.6829760 234s 19 -0.0599346 0.84127 -2.8473543 234s 20 1.6597909 0.34191 -1.4847516 234s 21 1.3857395 -2.43924 0.0039271 234s 22 2.6664754 -3.14291 1.0600254 234s 23 4.1202067 3.81886 1.0608640 234s 24 2.4163743 3.45141 1.6874099 234s 25 0.8493897 0.31424 -0.3073115 234s 26 1.4216265 -1.55310 -0.5455012 234s 27 2.3021676 -2.63392 0.0481451 234s 28 3.0877115 -2.85951 1.4378956 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 1.01925 0.78828 0.36470 234s ---------------------------------------------------------- 234s hbk 75 3 3 1.038833 0.363386 234s Scores: 234s PC1 PC2 PC3 234s 1 32.393698 -3.4318297 0.051248 234s 2 33.103072 -4.4154651 0.294662 234s 3 35.038965 -3.5996035 -0.940929 234s 4 35.955809 -4.9285404 -0.479059 234s 5 35.424918 -4.3076292 -0.366699 234s 6 33.753497 -3.2463136 0.289013 234s 7 33.817375 -3.6819421 0.684167 234s 8 32.717119 -3.7074394 -0.279567 234s 9 34.932190 -4.6939061 -0.738196 234s 10 33.737339 -4.5702346 -1.193206 234s 11 40.202273 -5.4336890 -0.229323 234s 12 41.638189 -4.5304173 -1.996311 234s 13 40.768565 -5.0531048 2.123222 234s 14 44.408749 -8.8448536 8.236462 234s 15 0.977343 1.3057899 0.938694 234s 16 -0.900390 1.6169842 1.382855 234s 17 -2.384467 -0.9835430 0.375495 234s 18 -0.143306 0.7859701 -0.237712 234s 19 -0.344479 -0.9791245 0.733869 234s 20 1.199115 0.8330752 1.216827 234s 21 0.184475 0.8630593 0.351029 234s 22 -0.100389 -1.5084406 0.718236 234s 23 -0.847925 0.4823829 0.958677 234s 24 -1.334366 -0.1021190 1.000300 234s 25 -2.669352 0.4692990 -0.811134 234s 26 0.601538 -1.1984283 0.541627 234s 27 1.373423 1.2098621 0.136249 234s 28 -0.721268 0.6164612 -0.963817 234s 29 -1.832615 0.2543279 -0.297658 234s 30 0.120086 -0.1558590 -1.976558 234s 31 -0.747437 1.7749106 0.342824 234s 32 -1.727558 -0.8325772 1.043088 234s 33 -0.073907 -0.3923823 1.083904 234s 34 -2.646454 -0.1350138 -1.101448 234s 35 1.331096 1.0443905 -0.039328 234s 36 0.281192 -0.6569943 -0.404009 234s 37 0.245349 -1.8406517 0.093656 234s 38 -2.049446 0.5320301 0.347219 234s 39 -1.645547 1.3268749 -1.068792 234s 40 -1.216874 -0.8556007 0.201262 234s 41 0.959445 1.6250030 -0.553881 234s 42 -0.603579 -0.9569812 -1.502730 234s 43 -0.946870 -1.6333180 1.324763 234s 44 0.076217 0.5018427 -1.902369 234s 45 -2.140584 1.2192726 -0.677180 234s 46 -0.081677 0.5389288 -1.785347 234s 47 -1.590461 2.1881067 -0.583771 234s 48 0.931421 1.3321181 -0.669782 234s 49 0.512639 1.2123979 0.683099 234s 50 1.095415 0.0045968 0.143109 234s 51 -1.456417 1.1186245 0.619657 234s 52 -0.917904 2.2084467 -0.366392 234s 53 -0.429654 -0.8524437 -2.326637 234s 54 -1.213858 -0.4996891 1.630709 234s 55 -1.253877 -0.9438354 0.692022 234s 56 -0.390657 -0.0427482 1.571167 234s 57 -1.797537 0.8934866 -0.281980 234s 58 0.396886 0.3227454 1.492494 234s 59 0.646360 -0.2194210 -0.562699 234s 60 0.119900 -1.2480691 -1.459763 234s 61 0.867946 -1.7843458 0.232229 234s 62 -2.733997 0.3604288 -0.692947 234s 63 -1.442683 -0.3732483 1.452800 234s 64 1.444934 0.5727959 0.434633 234s 65 -0.147284 0.7055205 -1.413940 234s 66 -1.739552 -0.9838385 0.220303 234s 67 -0.824644 0.1503195 0.411693 234s 68 -2.437638 -0.4835278 -1.392882 234s 69 -2.091970 1.1865192 -0.088483 234s 70 0.403429 -0.7855276 -0.540161 234s 71 0.507512 0.3152001 0.276885 234s 72 -0.944376 -0.8197825 0.044859 234s 73 -0.648597 -1.1160277 -0.658528 234s 74 -0.979453 -1.4589411 0.029182 234s 75 -0.982282 -0.7226425 -1.917060 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 1.01923 0.60282 0.46137 234s ---------------------------------------------------------- 234s milk 86 8 8 1.175171 0.426506 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 PC6 234s [1,] 6.1907998 0.58762698 0.686510 -0.209679 0.3321757 -1.3424985 234s [2,] 7.0503894 -0.49576086 -0.322697 -0.767415 -0.0165833 -1.4596064 234s [3,] 0.7670594 -1.83556812 0.468814 0.346810 -0.0204610 -0.2115383 234s [4,] 5.4656748 -2.29797862 1.612819 -0.378295 -0.2050232 0.3486957 234s [5,] -1.0291160 0.37303007 0.634604 -0.521527 -0.3299543 0.0859469 234s [6,] 2.2186300 0.39396818 -0.236987 -0.033975 -0.2549238 0.2541221 234s [7,] 2.7938591 -0.01152811 -0.600546 -0.098564 -0.3906602 0.3798516 234s [8,] 2.9544176 0.32646226 0.273051 -0.275073 -0.3982959 0.2377581 234s [9,] -1.3344639 -2.45440308 1.001792 -0.104783 -0.1744718 -0.0887272 234s [10,] -2.9294174 -0.79860558 -0.260533 0.375330 0.3425169 -0.2056682 234s [11,] -9.5810648 -0.09577968 1.565111 -0.112002 0.3143032 -0.3190238 234s [12,] -13.1147240 2.95665890 0.228086 -0.180867 0.0136463 -0.4604390 234s [13,] -10.2989319 1.53220781 -2.244629 0.323950 -0.0398642 -0.3463501 234s [14,] -12.2553418 1.62281167 -0.472862 -0.212983 -0.4124280 -0.4253719 234s [15,] -10.8346894 -0.09781844 2.134079 -0.272304 -0.1090226 -0.3725738 234s [16,] -2.8358474 0.28109809 0.945309 0.603249 0.1615955 0.1762086 234s [17,] -1.0353408 -2.75475311 1.677879 0.598578 0.0078965 0.0228522 234s [18,] -2.0271810 1.25894451 -0.266038 -0.168565 -0.3000200 0.2891774 234s [19,] -1.9279394 -0.68339726 1.264416 0.186749 0.3018226 -0.0869321 234s [20,] 0.2568334 -1.62632029 0.854279 -0.088175 0.5458645 0.2217019 234s [21,] -2.7017404 2.45223507 -0.243639 -0.211402 -0.2102323 0.2140100 234s [22,] -1.0386097 0.99459030 0.188462 -0.033434 -0.2857078 -0.1438517 234s [23,] -0.0198126 1.73285416 0.761979 0.005501 0.1671992 -0.0375468 234s [24,] -0.4909448 1.40982693 0.967440 0.521275 0.1625359 -0.0892501 234s [25,] -3.6632699 2.51414455 0.966410 -0.272694 0.0467958 0.1572715 234s [26,] -0.8733564 1.42247465 0.946038 -0.338985 -0.0804141 -0.0080759 234s [27,] -3.2254798 0.26912538 0.799468 0.372442 -0.6886191 -0.0553515 234s [28,] 2.4675785 3.56128696 0.813964 0.118354 -0.1677073 -0.0303774 234s [29,] 4.4177264 1.13316321 0.613509 0.261488 0.4229929 0.1780620 234s [30,] 0.8240097 1.54163297 0.398148 -0.221825 0.0309586 0.0830110 234s [31,] 1.7735990 2.00615332 -1.399933 0.469158 -0.0740282 0.0692312 234s [32,] 1.2348922 0.28918604 -1.239899 0.470999 -0.1511519 -0.3692504 234s [33,] 1.9407276 0.19123540 0.406623 0.389965 0.0994854 -0.0204286 234s [34,] 0.6225565 -0.65636700 0.565253 0.369897 -0.1612501 -0.1774611 234s [35,] -0.4869219 2.26301333 0.071825 0.588101 -0.0579092 -0.0362009 234s [36,] 4.1117242 1.16638974 0.982790 -0.266009 0.0728797 -0.0018914 234s [37,] 0.8415225 2.46677043 -0.526780 0.167456 -0.2370116 -0.0731483 234s [38,] 2.0528334 2.09648023 0.220912 0.206722 -0.1924842 0.0676382 234s [39,] -1.4493644 1.14916103 0.904194 0.455498 0.0678893 -0.1476540 234s [40,] 3.4867792 -1.82367389 0.730183 0.499859 0.2327704 -0.1518819 234s [41,] 4.0222120 1.34765470 0.580852 -0.453301 0.2482908 -1.5306566 234s [42,] 6.4789035 -1.25599522 1.644194 0.381331 0.1699942 0.1847594 234s [43,] 3.1529354 0.44884526 -0.967114 -0.220364 0.0037036 0.0802727 234s [44,] 5.3344976 -0.47975673 0.642789 0.298705 0.9983145 -0.1310548 234s [45,] 0.0325597 0.49900084 0.076948 0.486521 0.1642679 0.1392696 234s [46,] 0.1014401 1.97657735 0.733879 0.127235 0.0650844 -0.0144271 234s [47,] 2.7217685 -0.37859042 -3.696163 0.355401 -0.4123714 0.2114024 234s [48,] 0.2292225 1.01473918 -1.115726 0.434557 0.2668316 0.0103147 234s [49,] -2.2803784 0.59474034 -1.783003 0.549252 0.4660435 -0.0802352 234s [50,] 3.1560404 -2.84820361 0.913015 0.077151 0.5803961 0.0350246 234s [51,] -1.4680905 -0.43078891 -1.733657 0.074684 0.0026718 0.0819023 234s [52,] -5.2469034 0.48385240 -1.246027 0.081379 0.2380924 -0.1663831 234s [53,] -0.7670982 0.00234561 -0.923030 -0.366820 0.1582141 0.0508747 234s [54,] -0.2428655 0.04714401 -0.217187 -0.059549 0.1762969 0.0806339 234s [55,] 0.8723441 0.66109329 -0.224917 -0.360607 -0.0638127 0.1310131 234s [56,] 0.0019700 -0.67624071 0.081304 -0.182908 0.1045597 -0.0281936 234s [57,] -1.3684663 -0.00045069 0.860560 -0.350684 -0.1443970 -0.2270651 234s [58,] 0.0079047 1.36376727 0.750919 -0.437914 -0.1894910 0.2345556 234s [59,] -1.7430794 -1.06973583 -0.569381 -0.055139 -0.1582790 -0.0873605 234s [60,] -1.5171606 -0.69340281 -0.287048 -0.136559 -0.3871182 0.1606979 234s [61,] -0.0955085 -1.64221260 0.263650 -0.265665 -0.0808644 -0.0476862 234s [62,] 2.2259171 -2.22161516 0.426279 0.027834 0.2924338 -0.1784242 234s [63,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 234s [64,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 234s [65,] -0.5520071 -2.86186682 0.746248 0.109945 0.0556927 -0.0135739 234s [66,] -2.4472964 -0.94969715 -0.329042 -0.113895 -0.2728443 -0.0523337 234s [67,] 0.1790969 -1.29190443 0.146657 0.140234 0.1534048 0.2318353 234s [68,] -0.8017055 -1.93331421 -1.968273 0.017854 0.1287513 -0.2306786 234s [69,] -0.7356418 -0.68868398 -0.075215 -0.156944 0.0302876 0.4232626 234s [70,] 3.8821693 5.16959880 0.215490 -8.985938 5.2189361 -2.8089276 234s [71,] -2.3478937 -1.60220695 0.058822 -0.111845 -0.0539018 0.0087982 234s [72,] 2.3676739 -0.70331436 -0.214457 -0.307311 -0.1582719 0.3995413 234s [73,] -1.9906385 -2.60946629 -0.730312 0.485522 -0.2391998 0.1009341 234s [74,] -11.2435515 1.44868683 2.482678 0.026711 0.4922865 -0.2822136 234s [75,] 0.0044207 -2.29768358 -0.692425 0.538923 -0.4110598 -0.0824903 234s [76,] -1.4045239 -0.22649785 -1.343257 -0.067382 -0.1322233 -0.1072330 234s [77,] -8.3637576 0.14167751 1.267616 0.384528 -0.0728561 -0.4017300 234s [78,] 1.3022939 -1.47457541 -0.394623 -0.068014 -0.1502832 0.0757414 234s [79,] -0.1950676 -0.58254701 -0.824931 -0.088174 -0.2071634 -0.1896613 234s [80,] -3.4432989 1.73593273 0.777996 0.094211 0.2377017 -0.1520088 234s [81,] 1.2167258 0.77512068 0.085803 -0.214850 -0.2201173 0.0432435 234s [82,] 2.7778798 -1.80071342 0.583878 0.465898 0.0648352 0.2148470 234s [83,] 2.6218578 -0.39825539 -0.553372 -0.145721 -0.0977092 -0.2485337 234s [84,] 0.8946018 0.33790104 -1.974267 0.091828 0.0051986 -0.2606274 234s [85,] 0.7759316 -2.34860124 2.423325 -0.384149 -0.0167182 -0.0353374 234s [86,] 0.6266756 0.87099609 -1.407948 -0.237762 0.0361644 0.1675792 234s PC7 PC8 234s [1,] -0.1014312 1.5884e-03 234s [2,] -0.3831443 1.0212e-03 234s [3,] -0.7164683 1.2035e-03 234s [4,] 0.0892864 3.5409e-04 234s [5,] -0.0943992 1.0547e-03 234s [6,] 0.1184847 1.5031e-03 234s [7,] -0.2509793 1.6850e-05 234s [8,] -0.0136880 7.0308e-04 234s [9,] 0.2238736 -1.9164e-04 234s [10,] 0.0754413 1.3614e-04 234s [11,] 0.0784380 3.5175e-04 234s [12,] 0.2033489 -1.3174e-03 234s [13,] 0.2139525 -1.7101e-03 234s [14,] 0.1209735 -9.1070e-04 234s [15,] 0.2119647 -9.2843e-04 234s [16,] -0.3011483 -2.1474e-03 234s [17,] 0.0660858 -1.9036e-03 234s [18,] -0.5199396 -9.4385e-04 234s [19,] -0.1232622 -1.2649e-03 234s [20,] -0.3900208 -2.6927e-04 234s [21,] 0.0264834 7.6074e-05 234s [22,] -0.0736288 1.7240e-04 234s [23,] -0.2156005 -5.5661e-04 234s [24,] 0.1143327 -2.5248e-04 234s [25,] 0.0481580 -6.1531e-04 234s [26,] -0.0084802 -7.5928e-04 234s [27,] -0.2173883 -3.0971e-04 234s [28,] 0.3288873 -1.8975e-04 234s [29,] 0.0788974 -7.2436e-04 234s [30,] -0.0598663 -3.0463e-04 234s [31,] -0.1511658 -4.8751e-04 234s [32,] -0.0532375 -2.5207e-04 234s [33,] -0.0635290 -3.9270e-04 234s [34,] 0.1598240 1.3024e-04 234s [35,] -0.0355175 -8.5374e-05 234s [36,] -0.0174096 -6.3294e-04 234s [37,] -0.2883141 -5.2809e-04 234s [38,] 0.1426412 5.3331e-04 234s [39,] 0.0313308 4.2738e-04 234s [40,] -0.3536195 -3.4170e-04 234s [41,] -0.3925168 1.4588e-04 234s [42,] -0.0056267 -9.1925e-04 234s [43,] -0.4447402 -1.8415e-04 234s [44,] 0.9184385 -5.9685e-04 234s [45,] -0.0340987 7.2924e-04 234s [46,] -0.0162866 9.7800e-04 234s [47,] 0.2428769 -1.1208e-03 234s [48,] 0.3026758 -4.5769e-04 234s [49,] 0.0246345 -2.6207e-04 234s [50,] 0.0857698 7.6439e-05 234s [51,] 0.1136658 1.3013e-04 234s [52,] 0.3993357 6.2796e-04 234s [53,] -0.1765161 1.1329e-04 234s [54,] 0.0016144 2.5870e-04 234s [55,] 0.1064371 5.8188e-04 234s [56,] 0.0207478 -8.7595e-05 234s [57,] 0.1560065 6.3987e-05 234s [58,] 0.1684561 -5.0193e-05 234s [59,] 0.0778732 -8.5458e-04 234s [60,] 0.0037585 1.0429e-05 234s [61,] -0.0296083 3.1526e-05 234s [62,] 0.0913974 -2.2794e-04 234s [63,] 0.0358917 -7.3721e-04 234s [64,] 0.0358917 -7.3721e-04 234s [65,] 0.1209159 2.9398e-04 234s [66,] -0.0027574 2.9380e-04 234s [67,] -0.0091059 -2.7494e-04 234s [68,] 0.0555970 -3.3016e-04 234s [69,] -0.0149255 -3.1228e-04 234s [70,] 0.9282997 4.7859e-05 234s [71,] 0.2630142 4.2617e-04 234s [72,] 0.1063248 -3.0070e-04 234s [73,] -0.1462452 4.9607e-04 234s [74,] 0.2027591 2.6399e-03 234s [75,] 0.6934350 6.0284e-04 234s [76,] -0.0430524 8.1271e-04 234s [77,] 0.0789302 1.4655e-03 234s [78,] -0.0318359 5.2799e-04 234s [79,] -0.1269568 2.9497e-04 234s [80,] 0.2903958 7.8932e-04 234s [81,] 0.0979443 -3.1531e-04 234s [82,] -0.0548155 4.2140e-04 234s [83,] -0.0371550 -5.6653e-04 234s [84,] -0.0835149 -7.0682e-04 234s [85,] 0.1864954 1.0604e-03 234s [86,] 0.1074252 -7.4859e-04 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 1.08405293 0.65307452 0.28970076 0.11162824 0.09072195 0.06659711 0.05888048 234s [8] 0.00022877 234s ---------------------------------------------------------- 234s bushfire 38 5 5 1.464779 0.043290 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 234s [1,] -69.9562 -13.0364 0.98678 1.054123 2.411188 234s [2,] -71.5209 -10.5459 0.31081 1.631208 1.663470 234s [3,] -63.9308 -7.4622 -2.43241 0.671038 0.465836 234s [4,] -47.0413 -9.6343 -3.83609 0.758349 0.683983 234s [5,] -15.9088 -20.1737 -5.55893 1.181744 -0.053563 234s [6,] 8.3484 -30.7646 -5.51541 1.877227 1.338037 234s [7,] -207.7458 -66.2492 34.48519 -5.894885 -1.051729 234s [8,] -246.4327 -97.0433 -9.57057 22.286225 -9.234869 234s [9,] -247.5984 -98.8613 -12.13406 23.948770 -9.250401 234s [10,] -245.8121 -79.2634 12.47990 13.046128 -5.125478 234s [11,] -246.8887 -62.5899 21.21764 9.111011 -5.080985 234s [12,] -251.1354 -9.2115 31.77448 0.236379 0.707528 234s [13,] -194.0239 27.1288 21.05023 0.940913 1.781359 234s [14,] 51.7182 8.5038 -11.22109 -2.132458 1.984807 234s [15,] 180.5597 -4.8151 -21.36630 -9.390663 -0.817036 234s [16,] 135.7246 -5.0756 -11.33517 -10.015567 -1.670831 234s [17,] 133.0151 -4.0344 -8.95540 -7.702087 -0.923277 234s [18,] 121.2619 -9.0627 -5.96042 -7.210971 -2.092872 234s [19,] 124.9038 -10.6649 -7.22555 -5.349553 -1.771009 234s [20,] 135.5410 -6.8146 -7.52834 -5.562769 -0.396924 234s [21,] 117.1950 -3.5643 -4.67473 -6.862117 -0.234551 234s [22,] 108.9944 -2.3344 -5.90349 -5.928299 1.455538 234s [23,] -21.4031 8.0668 6.19525 -4.784890 0.671394 234s [24,] -76.3499 16.7804 6.52545 -1.391250 1.219282 234s [25,] -12.5732 6.1109 -1.45259 -3.512072 -0.375837 234s [26,] -19.1800 3.4685 -2.02243 -3.490028 -0.169127 234s [27,] -33.6733 12.0757 -3.53322 0.048666 0.067468 234s [28,] -9.3966 21.5055 -5.91671 2.650895 -0.449672 234s [29,] 1.4123 35.8559 -5.98222 5.982362 0.613667 234s [30,] -54.2683 39.6029 7.82694 6.759994 0.035048 234s [31,] 74.8866 34.9048 10.03986 12.592158 0.149308 234s [32,] 331.4144 9.3079 27.73391 17.334531 1.015536 234s [33,] 367.6915 -19.5135 48.52753 10.213314 -1.268047 234s [34,] 363.8686 -20.4079 49.32855 8.986581 -1.930673 234s [35,] 369.4371 -19.5074 49.66761 9.001542 -0.179566 234s [36,] 366.5850 -20.2555 50.30290 7.745330 -2.259131 234s [37,] 364.5463 -19.8198 53.00407 6.757796 -1.083372 234s [38,] 365.9709 -19.3753 53.80168 6.467284 -0.854384 234s ------------- 234s Call: 234s PcaLocantore(x = x) 234s 234s Standard deviations: 234s [1] 1.210280 0.208063 0.177790 0.062694 0.014423 234s ---------------------------------------------------------- 234s ========================================================== 234s > dodata(method="cov") 234s 234s Call: dodata(method = "cov") 234s Data Set n p k e1 e2 234s ========================================================== 234s heart 12 2 2 685.776266 13.127306 234s Scores: 234s PC1 PC2 234s 1 8.18562 1.17998 234s 2 65.41185 -2.80723 234s 3 1.86039 -1.70646 234s 4 -2.26910 2.44051 234s 5 20.19603 -1.47331 234s 6 -14.46264 7.05759 234s 7 6.91264 1.99823 234s 8 -28.95436 -3.81624 234s 9 -0.61523 -1.09711 234s 10 -27.62427 -3.33575 234s 11 -13.17788 0.37931 234s 12 49.94879 -1.62675 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 26.1873 3.6232 234s ---------------------------------------------------------- 234s starsCYG 47 2 2 0.280150 0.007389 234s Scores: 234s PC1 PC2 234s 1 0.272263 -0.07964458 234s 2 0.804544 0.03382837 234s 3 -0.040587 -0.14464760 234s 4 0.804544 0.03382837 234s 5 0.222468 -0.14305159 234s 6 0.512941 -0.02420304 234s 7 -0.378928 -0.51924735 234s 8 0.341045 0.11236831 234s 9 0.592550 -0.23812462 234s 10 0.163442 -0.06357822 234s 11 0.638370 -1.02323643 234s 12 0.498667 -0.05242075 234s 13 0.476291 0.00142479 234s 14 -0.947664 -0.26343572 234s 15 -0.699020 -0.01711057 234s 16 -0.363464 0.06475681 234s 17 -1.024352 -0.02972862 234s 18 -0.759174 0.12317995 234s 19 -0.786925 -0.06478250 234s 20 0.796654 -1.04660568 234s 21 -0.580307 -0.03463751 234s 22 -0.738591 -0.01126825 234s 23 -0.521748 0.08812607 234s 24 -0.086135 0.09457052 234s 25 0.065975 -0.03907968 234s 26 -0.284322 0.05307219 234s 27 -0.303309 -0.07553370 234s 28 -0.052738 -0.02155274 234s 29 -0.580638 -0.10534741 234s 30 0.953478 -1.07986770 234s 31 -0.527590 0.04855502 234s 32 0.171408 0.12730538 234s 33 0.274054 0.00095808 234s 34 1.192364 -1.10502882 234s 35 -0.628641 -0.08815176 234s 36 0.694595 0.11071187 234s 37 0.167026 0.09762710 234s 38 0.274054 0.00095808 234s 39 0.246168 0.08594248 234s 40 0.617380 -0.06994769 234s 41 -0.329735 0.01934346 234s 42 0.115770 0.02432733 234s 43 0.400071 0.03289494 234s 44 0.392768 -0.01656886 234s 45 0.605229 0.05314718 234s 46 0.036628 0.03601196 234s 47 -0.442606 0.07644144 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 0.529292 0.085957 234s ---------------------------------------------------------- 234s phosphor 18 2 2 288.018150 22.020514 234s Scores: 234s PC1 PC2 234s 1 2.7987 -19.015683 234s 2 -20.4311 -0.032022 234s 3 -21.8198 4.589809 234s 4 -11.7869 -6.837833 234s 5 -16.9357 2.664785 234s 6 12.9132 -25.602526 234s 7 1.5249 -6.351664 234s 8 -8.0984 2.416616 234s 9 -8.6979 4.843680 234s 10 14.3903 -12.732868 234s 11 -2.9462 -0.760656 234s 12 11.7427 2.991004 234s 13 14.8400 0.459849 234s 14 9.2449 3.095095 234s 15 19.4860 -3.336883 234s 16 -9.4156 -7.096788 234s 17 23.3759 -1.737460 234s 18 19.9173 5.092467 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 16.9711 4.6926 234s ---------------------------------------------------------- 234s stackloss 21 3 3 28.153060 8.925048 234s Scores: 234s PC1 PC2 PC3 234s [1,] 10.538448 13.596944 12.84989 234s [2,] 9.674846 14.098881 12.89733 234s [3,] 8.993255 9.221043 9.94062 234s [4,] 1.744427 3.649104 0.17292 234s [5,] 0.980215 2.223126 1.34874 234s [6,] 1.362321 2.936115 0.76083 234s [7,] 6.926040 0.637480 -0.11170 234s [8,] 6.926040 0.637480 -0.11170 234s [9,] 0.046655 0.977727 -2.46930 234s [10,] -7.909092 0.926343 0.80232 234s [11,] -0.136672 -3.591094 0.37539 234s [12,] -1.382381 -3.802146 1.01074 234s [13,] -6.181887 -0.077532 0.70744 234s [14,] 3.699843 -4.885854 -0.40226 234s [15,] -2.768005 -7.507870 -6.08487 234s [16,] -5.358811 -6.002058 -5.94256 234s [17,] -17.067135 1.738055 -5.86637 234s [18,] -11.021920 -1.775507 -6.19842 234s [19,] -9.776212 -1.564455 -6.83377 234s [20,] -6.075508 0.369252 -2.08345 234s [21,] 6.301743 2.706174 8.79509 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 5.3059 2.9875 1.3020 234s ---------------------------------------------------------- 234s salinity 28 3 3 11.801732 3.961826 234s Scores: 234s PC1 PC2 PC3 234s 1 -1.59888 1.582157 0.135248 234s 2 -2.26975 2.429177 1.107832 234s 3 -6.79543 -2.034636 0.853876 234s 4 -6.36795 -0.602960 -0.267268 234s 5 -6.42044 -1.520259 5.022962 234s 6 -5.13821 1.225470 0.016977 234s 7 -3.24014 1.998671 -0.123418 234s 8 -0.93998 2.789889 -0.515656 234s 9 -0.30856 -2.424345 -1.422752 234s 10 2.20362 -2.800513 1.142127 234s 11 1.38120 -2.076832 2.515630 234s 12 0.44997 0.207439 -0.152835 234s 13 1.21669 1.193701 -0.277116 234s 14 3.31664 1.306627 1.213342 234s 15 2.08484 -3.774814 0.905400 234s 16 -3.64862 -4.677257 9.046484 234s 17 -0.46124 -1.411762 1.706719 234s 18 -2.13038 0.890401 -0.633349 234s 19 -0.23610 -2.262304 -1.885048 234s 20 1.70337 -1.970773 -0.781880 234s 21 2.67273 1.038742 -0.610945 234s 22 4.24561 1.547290 0.108927 234s 23 2.99619 -4.785343 3.094945 234s 24 1.64474 -3.564562 3.432429 234s 25 1.11703 -1.158030 0.237700 234s 26 2.30707 0.069668 -0.735809 234s 27 3.59356 0.860498 -0.611380 234s 28 4.57550 1.300407 0.589307 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 3.43536 1.99043 0.94546 234s ---------------------------------------------------------- 234s hbk 75 3 3 1.436470 1.181766 234s Scores: 234s PC1 PC2 PC3 234s 1 31.105415 -4.714217 10.4566165 234s 2 31.707650 -5.748724 10.7682402 234s 3 33.366131 -4.625897 12.1570167 234s 4 34.173377 -6.069657 12.4466895 234s 5 33.780418 -5.508823 11.9872893 234s 6 32.493478 -4.684595 10.5679819 234s 7 32.592637 -5.235522 10.3765493 234s 8 31.293363 -4.865797 10.9379676 234s 9 33.160964 -5.714260 12.3098920 234s 10 31.919786 -5.384537 12.3374332 234s 11 38.231962 -6.810641 13.5994385 234s 12 39.290479 -5.393906 15.2942554 234s 13 39.418445 -7.326461 11.5194898 234s 14 43.906584 -13.214819 8.3282743 234s 15 1.906326 0.716061 -0.8635112 234s 16 0.263255 0.926016 -1.9009292 234s 17 -1.776489 -1.072332 -0.5496140 234s 18 0.464648 0.702441 0.0482897 234s 19 0.267826 -1.283779 -0.2925812 234s 20 2.122108 0.165970 -0.8924686 234s 21 0.937217 0.548532 -0.4132196 234s 22 0.423273 -1.781869 -0.0323061 234s 23 0.047532 0.018909 -1.1259327 234s 24 -0.490041 -0.520202 -1.1065753 234s 25 -2.143049 0.720869 -0.0495474 234s 26 1.094748 -1.459175 0.2226246 234s 27 2.070705 0.898573 0.0023229 234s 28 -0.294998 0.830258 0.5929001 234s 29 -1.242995 0.300216 -0.2010507 234s 30 0.147958 0.439099 2.0003038 234s 31 0.170818 1.440946 -0.9755627 234s 32 -0.958531 -1.199730 -1.0129867 234s 33 0.697307 -0.874343 -0.7260649 234s 34 -2.278946 0.261106 0.4196544 234s 35 1.962829 0.809318 0.2033113 234s 36 0.626631 -0.600666 0.8004036 234s 37 0.550885 -1.881448 0.7382776 234s 38 -1.249717 0.336214 -0.9349845 234s 39 -1.106696 1.569418 0.1869576 234s 40 -0.684034 -0.939963 -0.1034965 234s 41 1.559314 1.551408 0.3660323 234s 42 -0.538741 -0.447358 1.6361099 234s 43 -0.252685 -2.080564 -0.7765259 234s 44 0.217012 1.027281 1.7015154 234s 45 -1.497600 1.349234 -0.2698932 234s 46 0.100388 1.026443 1.5390401 234s 47 -0.811117 2.195271 -0.5208141 234s 48 1.462210 1.321318 0.5600144 234s 49 1.383976 0.740714 -0.7348906 234s 50 1.636773 -0.215464 0.3195369 234s 51 -0.530918 0.759743 -1.2069247 234s 52 -0.109566 2.107455 -0.5315473 234s 53 -0.564334 -0.060847 2.3910630 234s 54 -0.272234 -1.122711 -1.5060028 234s 55 -0.608660 -1.197219 -0.5255609 234s 56 0.565430 -0.710345 -1.3708230 234s 57 -1.115629 0.888816 -0.4186014 234s 58 1.351288 -0.374815 -1.1980618 234s 59 0.998016 -0.151228 0.9007970 234s 60 0.124017 -0.764846 1.9005963 234s 61 1.189858 -1.905264 0.7721322 234s 62 -2.190589 0.579614 -0.1377914 234s 63 -0.518278 -0.931130 -1.4534768 234s 64 2.124566 0.194391 -0.0327092 234s 65 0.154218 1.050861 1.1309885 234s 66 -1.197852 -1.044147 -0.2265269 234s 67 -0.114174 -0.094763 -0.5168926 234s 68 -2.201115 0.032271 0.8573493 234s 69 -1.307843 1.104815 -0.7741270 234s 70 0.691449 -0.676665 1.0004603 234s 71 1.150975 0.050861 -0.0717068 234s 72 -0.457293 -0.861871 0.1026350 234s 73 -0.392258 -0.897451 0.9178065 234s 74 -0.584658 -1.450471 0.3201857 234s 75 -0.972517 -0.063777 1.8223995 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 1.1985 1.0871 1.0086 234s ---------------------------------------------------------- 234s milk 86 8 8 5.758630 2.224809 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 PC6 234s 1 5.7090867 1.388263 0.0055924 0.3510505 -0.7335114 -1.41950731 234s 2 6.5825186 0.480410 -1.1356236 -0.3250838 -0.7343177 -1.71595400 234s 3 0.7433619 -1.749281 0.2510521 0.3450575 0.2996413 -0.34585702 234s 4 5.5733255 -1.588521 0.8934908 -0.3412408 0.0087626 0.07235942 234s 5 -1.3030839 0.142394 0.8487785 -0.5847851 0.0588053 -0.08968553 234s 6 1.7708705 0.674240 -0.4153759 -0.1915734 0.1382138 0.12454293 234s 7 2.3570866 0.381017 -0.8771357 -0.3739365 0.2918453 0.13437364 234s 8 2.5700714 0.695006 0.0061108 -0.4323695 0.1643797 -0.00469369 234s 9 -1.1725766 -2.713291 1.0677483 -0.0647875 0.1183120 -0.10762785 234s 10 -3.1357225 -1.255175 0.0666017 0.5083690 -0.1096080 -0.00647493 234s 11 -9.5333894 -1.608943 2.7307809 0.1690156 -0.1682415 -0.06597478 234s 12 -13.6028505 0.941083 2.0136258 -0.1076520 -0.0475905 -0.15295614 234s 13 -10.9497471 0.048776 -0.8765307 0.1518572 0.1428294 -0.00064406 234s 14 -12.6558378 -0.219444 1.1396273 -0.3734679 0.2875578 -0.23870524 234s 15 -10.6924790 -1.818075 3.4560731 -0.1177943 0.1101199 -0.19708172 234s 16 -3.0258070 -0.203186 1.2835368 0.5799363 0.3237454 0.23168871 234s 17 -0.7498665 -2.977505 1.6310512 0.6305329 0.3994006 0.06594881 234s 18 -2.5093526 0.924459 0.0899818 -0.4026675 0.2963072 0.11324019 234s 19 -1.9689970 -1.051282 1.4659908 0.3870104 -0.0708083 -0.02148354 234s 20 0.2695886 -1.646440 0.7597630 0.1750131 -0.3418142 0.21515143 234s 21 -3.3470252 1.989939 0.2887021 -0.3599779 0.0771965 0.16867095 234s 22 -1.4659204 0.777242 0.4090149 -0.1248050 0.1916768 -0.23160291 234s 23 -0.4944476 1.634130 0.8915509 0.1222296 -0.1231015 -0.08351169 234s 24 -0.8945477 1.239223 1.1117165 0.6018455 0.0912200 -0.01204668 234s 25 -4.1499992 1.860190 1.6062973 -0.2139736 -0.1140169 0.16632426 234s 26 -1.2647012 1.188058 1.1893430 -0.2740862 -0.0971504 -0.09851714 234s 27 -3.4280131 -0.267150 1.1969552 0.0354366 0.8482718 -0.18977667 234s 28 1.6896630 3.793723 0.7706325 0.1007287 0.0317704 -0.11269816 234s 29 3.9258127 1.691428 0.1850999 0.4485202 -0.2969916 0.16594044 234s 30 0.3178322 1.577233 0.4455231 -0.1687197 -0.1587136 -0.00823174 234s 31 0.9562350 2.258138 -1.4672169 0.2675668 0.1910110 0.03177387 234s 32 0.6738452 0.470764 -1.3496896 0.3524049 0.2008218 -0.36957179 234s 33 1.5980690 0.413899 0.1999664 0.4232293 0.0768479 -0.04627841 234s 34 0.4365091 -0.626490 0.4718364 0.3392252 0.2554060 -0.19018602 234s 35 -1.1184804 2.124234 0.2650931 0.4791171 0.2927791 -0.01579964 234s 36 3.6673986 1.659798 0.6138972 -0.1092158 -0.2705583 -0.16494176 234s 37 0.0867143 2.541765 -0.4572593 0.0024263 0.2163300 -0.20116352 234s 38 1.4191839 2.315690 0.1365887 0.1028375 0.1595780 -0.02049460 234s 39 -1.8062960 0.845438 1.1469588 0.5022406 0.1603011 -0.08751261 234s 40 3.4380914 -1.358545 0.1956896 0.6314649 0.0716078 -0.21591535 234s 41 3.4608782 1.828575 0.2012565 0.1064437 -0.7454169 -1.64629924 234s 42 6.4162310 -0.402642 0.8070441 0.5146855 0.0331594 0.04373032 234s 43 2.5906567 0.897993 -1.2612252 -0.2620162 -0.1432569 -0.10279385 234s 44 5.0299750 0.203721 0.0439110 0.8775684 -0.9536011 0.15153452 234s 45 -0.3555392 0.454930 0.1173992 0.4688991 0.1137820 0.18752442 234s 46 -0.4155426 1.892410 0.8649578 0.1827426 -0.0186113 -0.04029205 234s 47 1.9328817 0.121936 -3.9578157 -0.1135807 0.2971001 0.18733657 234s 48 -0.3947656 1.028405 -1.0370498 0.4467257 -0.1445498 0.16878692 234s 49 -2.8829860 0.279064 -1.4443310 0.5889970 -0.1883118 0.16947945 234s 50 3.2797246 -2.443968 0.4100655 0.4278962 -0.4414712 0.08598366 234s 51 -1.9272930 -0.622137 -1.5136862 -0.0483369 -0.0272502 0.16006066 234s 52 -5.7161590 -0.298434 -0.5216578 0.1385780 -0.2435931 0.10628617 234s 53 -1.1933277 -0.125878 -0.7556261 -0.3129372 -0.3166453 0.03078643 234s 54 -0.5994394 -0.031069 -0.1296378 0.0061490 -0.1869578 0.09839221 234s 55 0.4104586 0.733465 -0.2088065 -0.3645266 -0.1830137 0.04705775 234s 56 -0.2227671 -0.724741 0.1007592 -0.0838897 -0.1939960 -0.04223579 234s 57 -1.5706297 -0.292436 1.0849660 -0.2559591 -0.0917278 -0.27423151 234s 58 -0.4102168 1.263831 0.9082556 -0.4592777 -0.0676902 0.11089798 234s 59 -1.9640736 -1.340173 -0.3652736 -0.1267573 0.0775692 -0.07977644 234s 60 -1.7490968 -0.941370 -0.0849901 -0.3453455 0.2858594 0.06413468 234s 61 -0.1583416 -1.699326 0.2385988 -0.2231496 -0.0513883 -0.12227279 234s 62 2.2124878 -1.942366 0.0743514 0.2627321 -0.2844018 -0.15848039 234s 63 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 234s 64 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 234s 65 -0.3779208 -2.987354 0.6819006 0.1942611 0.0529259 0.01315140 234s 66 -2.6385498 -1.331204 -0.0367809 -0.2327572 0.1845076 -0.08521680 234s 67 0.0526645 -1.301299 0.0912198 0.1634869 -0.0068236 0.24131589 234s 68 -1.1013065 -2.004809 -1.9168056 0.0260663 -0.2029903 -0.12625268 234s 69 -0.9495853 -0.831697 0.0389476 -0.2123483 -0.0202267 0.38463410 234s 70 2.6935893 5.369312 0.6987368 -4.5754846 -9.6833013 -2.32910628 234s 71 -2.4037611 -1.983509 0.3109848 -0.1015686 -0.0071432 0.06410351 234s 72 2.0795505 -0.392730 -0.4534128 -0.4054224 -0.0312781 0.25408988 234s 73 -2.0038405 -2.874605 -0.6269939 0.2408421 0.5184666 0.11140104 234s 74 -11.2683996 -0.361851 3.9219448 0.4045689 -0.2203308 0.05930132 234s 75 -0.1028287 -2.295813 -0.7769187 0.3071821 0.4537196 0.00522380 234s 76 -1.8466137 -0.425825 -1.1261209 -0.1760585 0.0165729 -0.10698465 234s 77 -8.4124493 -1.174820 2.2700712 0.4213953 0.3446597 -0.20636892 234s 78 1.1103236 -1.299480 -0.5787732 -0.1455945 0.0732148 -0.01806218 234s 79 -0.5451834 -0.620170 -0.7830595 -0.1746479 0.0723052 -0.26017118 234s 80 -3.8647223 1.126328 1.3299567 0.2645241 -0.1881443 0.00485531 234s 81 0.7690939 0.887363 0.0513096 -0.2730980 0.0076447 -0.07590882 234s 82 2.7287618 -1.435327 0.1602865 0.4465859 0.2129425 0.16104418 234s 83 2.2241485 -0.042822 -0.8316486 -0.1230697 -0.1193057 -0.35207561 234s 84 0.2452905 0.491732 -2.0050683 0.0286567 -0.1159415 -0.24887542 234s 85 1.0655845 -2.360746 2.2456131 -0.1479972 -0.1186670 -0.14020891 234s 86 -0.0091659 0.952208 -1.3429189 -0.2944676 -0.2433277 0.15354490 234s PC7 PC8 234s 1 -0.09778744 2.3157e-03 234s 2 0.05189698 1.8077e-03 234s 3 0.70506895 1.2838e-03 234s 4 -0.08541140 3.2781e-04 234s 5 0.11768945 8.3496e-04 234s 6 -0.17886391 1.5222e-03 234s 7 0.14143613 1.3261e-04 234s 8 -0.07724578 7.1241e-04 234s 9 -0.12298048 -7.0110e-04 234s 10 0.07569878 2.3093e-05 234s 11 0.29299858 -3.4542e-04 234s 12 0.07764899 -2.1390e-03 234s 13 -0.08945524 -2.2633e-03 234s 14 0.03597787 -1.8891e-03 234s 15 0.11780498 -2.0279e-03 234s 16 0.46501534 -2.3266e-03 234s 17 0.08603290 -2.4073e-03 234s 18 0.52605757 -9.8822e-04 234s 19 0.31007227 -1.3919e-03 234s 20 0.61582059 -2.3549e-05 234s 21 0.01199350 -6.1649e-05 234s 22 0.03654587 1.3302e-05 234s 23 0.27549986 -3.6759e-04 234s 24 -0.04155354 -2.9882e-04 234s 25 0.11473708 -7.9629e-04 234s 26 0.06673183 -8.3728e-04 234s 27 0.16937729 -9.5775e-04 234s 28 -0.41753592 -7.5544e-05 234s 29 -0.03693100 -2.2481e-04 234s 30 0.08461537 -1.3611e-04 234s 31 0.02476253 -1.4319e-04 234s 32 -0.09756048 -1.2234e-04 234s 33 0.06442434 -2.4915e-04 234s 34 -0.17828409 -9.5882e-05 234s 35 0.00881239 -7.1427e-05 234s 36 -0.01041003 -2.8489e-04 234s 37 0.15994729 -3.1472e-04 234s 38 -0.22386895 6.1384e-04 234s 39 0.03666242 2.8506e-04 234s 40 0.35883231 -8.3062e-05 234s 41 0.18521851 8.5509e-04 234s 42 0.00733985 -6.4477e-04 234s 43 0.35466617 3.2923e-04 234s 44 -0.74952524 -7.6869e-05 234s 45 0.09907237 7.9128e-04 234s 46 0.05119980 1.0606e-03 234s 47 -0.48571583 -9.3780e-04 234s 48 -0.27463442 -2.7037e-04 234s 49 0.06787536 -3.0554e-05 234s 50 0.08499400 3.1181e-04 234s 51 -0.09197457 1.1213e-04 234s 52 -0.24513244 3.9100e-04 234s 53 0.24012780 3.2068e-04 234s 54 0.07999888 3.5689e-04 234s 55 -0.09825475 6.6675e-04 234s 56 0.05133674 -7.2984e-05 234s 57 -0.10302363 -2.0693e-04 234s 58 -0.12323360 -1.6620e-04 234s 59 -0.05119989 -1.1016e-03 234s 60 0.00082131 -3.2951e-04 234s 61 0.08128272 -1.1550e-04 234s 62 -0.01789040 -1.1579e-04 234s 63 -0.07188070 -7.8367e-04 234s 64 -0.07188070 -7.8367e-04 234s 65 0.00917085 -2.6800e-05 234s 66 0.03121573 -5.3492e-05 234s 67 0.12202335 -3.0466e-04 234s 68 -0.04764366 -2.6126e-04 234s 69 0.13828337 -3.9331e-04 234s 70 0.10401069 4.2870e-03 234s 71 -0.14369640 3.7669e-05 234s 72 -0.10334451 -2.6456e-04 234s 73 0.17655402 1.0917e-04 234s 74 0.26779696 1.8685e-03 234s 75 -0.75016549 2.1079e-05 234s 76 0.01802016 7.7555e-04 234s 77 0.13081368 6.4286e-04 234s 78 0.01409131 4.9476e-04 234s 79 0.06643384 2.6590e-04 234s 80 -0.12624376 5.9801e-04 234s 81 -0.14074469 -3.2172e-04 234s 82 0.09228230 4.4064e-04 234s 83 -0.06352151 -3.6274e-04 234s 84 -0.02642452 -3.9742e-04 234s 85 -0.03502188 6.9814e-04 234s 86 -0.11749109 -5.1283e-04 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 2.39971451 1.49157920 0.93184037 0.33183258 0.19628996 0.16485446 0.12784351 234s [8] 0.00052622 234s ---------------------------------------------------------- 234s bushfire 38 5 5 11393.979994 197.523453 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 234s 1 -91.383 -16.17804 0.56195 -0.252428 1.261840 234s 2 -93.033 -13.93251 -0.67212 0.042287 0.470924 234s 3 -85.400 -10.72512 -3.09832 -1.224797 -0.504718 234s 4 -68.381 -12.12202 -3.31950 -0.676880 -0.228383 234s 5 -36.742 -21.04171 -1.98872 0.397655 -0.932613 234s 6 -12.095 -30.21719 0.59595 2.100702 0.384714 234s 7 -227.949 -71.40450 35.57308 -7.880296 -2.710415 234s 8 -262.815 -111.81228 -11.04574 2.397832 -13.646407 234s 9 -263.767 -114.13702 -13.71407 3.131736 -13.825200 234s 10 -264.312 -90.69643 9.72320 0.967173 -8.800150 234s 11 -266.681 -72.85993 16.55010 0.291092 -8.373583 234s 12 -274.050 -18.41395 20.74273 -2.464589 -1.505967 234s 13 -218.299 19.16040 7.69765 0.069012 0.054846 234s 14 29.646 10.52526 -7.50754 0.855493 1.966680 234s 15 159.575 3.86633 -6.95837 -2.753953 0.616068 234s 16 114.286 2.47164 0.62690 -3.146317 -0.501623 234s 17 111.289 3.45086 1.97182 -0.303064 -0.094416 234s 18 99.626 -1.80416 4.88197 -0.013096 -1.438397 234s 19 103.353 -3.50426 3.58993 1.578169 -1.317194 234s 20 113.769 0.84544 3.28254 2.204926 0.131167 234s 21 95.186 3.50703 4.97153 0.916181 0.351658 234s 22 86.996 4.00938 2.95209 1.281788 1.920404 234s 23 -44.232 8.50898 6.30689 -1.038871 0.400078 234s 24 -99.527 13.81377 1.75130 -0.260669 0.394804 234s 25 -34.855 5.99709 -0.57224 -1.660513 -0.620158 234s 26 -41.265 2.94659 -1.04825 -2.243950 -0.440017 234s 27 -56.148 10.14428 -5.41858 0.321752 -0.608412 234s 28 -32.366 20.27795 -8.60687 3.806572 -1.267249 234s 29 -22.438 34.73585 -11.19123 8.296154 -0.511610 234s 30 -79.035 37.05713 -1.51591 9.892959 -1.618635 234s 31 49.465 39.37414 5.95714 22.874813 -1.883481 234s 32 304.825 30.19205 37.68900 45.175923 -1.293939 234s 33 341.237 7.04985 65.43451 44.553009 -3.148116 234s 34 337.467 6.16879 66.48222 43.278480 -3.688631 234s 35 342.929 7.38548 66.91291 43.941556 -1.937887 234s 36 340.143 6.70203 67.85433 42.479161 -3.873639 234s 37 337.931 7.43184 70.50828 42.333220 -2.645830 234s 38 339.281 8.07267 71.34405 42.400459 -2.392774 234s ------------- 234s Call: 234s PcaCov(x = x) 234s 234s Standard deviations: 234s [1] 106.7426 14.0543 4.9184 1.8263 1.0193 234s ---------------------------------------------------------- 234s ========================================================== 234s > dodata(method="grid") 234s 234s Call: dodata(method = "grid") 234s Data Set n p k e1 e2 234s ========================================================== 234s heart 12 2 2 516.143549 23.932102 234s Scores: 234s PC1 PC2 234s [1,] 6.4694 3.8179 234s [2,] 61.7387 19.1814 234s [3,] 1.4722 -1.0161 234s [4,] -3.8056 1.5127 234s [5,] 18.6760 5.3303 234s [6,] -16.8411 1.7900 234s [7,] 4.9962 4.1638 234s [8,] -26.8665 -13.3010 234s [9,] -1.0648 -1.2690 234s [10,] -25.7734 -12.4037 234s [11,] -13.3987 -4.0751 234s [12,] 46.7700 15.1272 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 22.719 4.892 234s ---------------------------------------------------------- 234s starsCYG 47 2 2 0.473800 0.026486 234s Scores: 234s PC1 PC2 234s [1,] 0.181489 -0.0300854 234s [2,] 0.695337 0.1492475 234s [3,] -0.120738 -0.1338110 234s [4,] 0.695337 0.1492475 234s [5,] 0.140039 -0.0992368 234s [6,] 0.413314 0.0551030 234s [7,] -0.409428 -0.5478860 234s [8,] 0.225647 0.1690378 234s [9,] 0.519123 -0.1471454 234s [10,] 0.071513 -0.0277935 234s [11,] 0.663045 -0.9203119 234s [12,] 0.402691 0.0253179 234s [13,] 0.373739 0.0759321 234s [14,] -1.005756 -0.3654219 234s [15,] -0.789968 -0.0898580 234s [16,] -0.467328 0.0334465 234s [17,] -1.111148 -0.1431778 234s [18,] -0.867242 0.0417806 234s [19,] -0.871200 -0.1481782 234s [20,] 0.823011 -0.9236455 234s [21,] -0.669994 -0.0923582 234s [22,] -0.829959 -0.0890246 234s [23,] -0.627294 0.0367802 234s [24,] -0.195929 0.0978059 234s [25,] -0.028257 -0.0157122 234s [26,] -0.387346 0.0317797 234s [27,] -0.390054 -0.0981920 234s [28,] -0.148231 -0.0132120 234s [29,] -0.661454 -0.1625514 234s [30,] 0.982767 -0.9369769 234s [31,] -0.628127 -0.0032112 234s [32,] 0.055476 0.1625819 234s [33,] 0.173158 0.0501056 234s [34,] 1.222924 -0.9319795 234s [35,] -0.711235 -0.1515118 234s [36,] 0.576613 0.2117347 234s [37,] 0.054851 0.1325884 234s [38,] 0.173158 0.0501056 234s [39,] 0.134833 0.1309216 234s [40,] 0.522665 0.0228177 234s [41,] -0.428171 -0.0073782 234s [42,] 0.013192 0.0534392 234s [43,] 0.294173 0.0975945 234s [44,] 0.293132 0.0476054 234s [45,] 0.495172 0.1434167 234s [46,] -0.066790 0.0551060 234s [47,] -0.547311 0.0351134 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 0.68833 0.16275 234s ---------------------------------------------------------- 234s phosphor 18 2 2 392.155327 50.657228 234s Scores: 234s PC1 PC2 234s 1 5.6537 -15.2305 234s 2 -21.2150 -1.8862 234s 3 -23.5966 2.3112 234s 4 -11.2742 -6.6000 234s 5 -18.4067 1.5202 234s 6 16.9795 -19.4039 234s 7 1.5964 -3.1666 234s 8 -9.7354 3.2429 234s 9 -10.8594 5.4759 234s 10 15.5585 -6.5279 234s 11 -4.0058 1.2905 234s 12 9.4815 8.2139 234s 13 13.0640 6.4346 234s 14 7.0230 7.7600 234s 15 18.4378 3.7658 234s 16 -8.9047 -6.3253 234s 17 21.8748 6.1900 234s 18 16.9843 12.0801 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 19.8029 7.1174 234s ---------------------------------------------------------- 234s stackloss 21 3 3 109.445054 16.741203 234s Scores: 234s PC1 PC2 PC3 234s [1,] 15.136434 14.82909 -2.0387704 234s [2,] 14.393636 15.46816 -1.8391595 234s [3,] 12.351209 10.12290 -2.3458098 234s [4,] 2.510036 2.07589 1.8251581 234s [5,] 1.767140 1.78527 -0.0088651 234s [6,] 2.138588 1.93058 0.9081465 234s [7,] 6.966825 -1.75851 0.6274924 234s [8,] 6.966825 -1.75851 0.6274924 234s [9,] -0.089513 -1.09062 2.2894224 234s [10,] -7.146340 2.65628 -0.8983590 234s [11,] -0.461157 -3.09532 -2.6948576 234s [12,] -1.575403 -2.60157 -3.4122582 234s [13,] -5.660744 1.37815 -1.2975809 234s [14,] 2.881484 -5.50628 -2.5762898 234s [15,] -4.917360 -9.13772 0.0676942 234s [16,] -7.145755 -7.22052 0.6665270 234s [17,] -17.173481 1.87173 4.3780920 234s [18,] -11.973894 -2.60174 2.9808153 234s [19,] -10.859648 -3.09549 3.6982160 234s [20,] -6.031899 0.15817 1.2270803 234s [21,] 8.451640 4.98077 -5.4038839 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 10.4616 4.0916 2.8271 234s ---------------------------------------------------------- 234s salinity 28 3 3 14.911546 8.034974 234s Scores: 234s PC1 PC2 PC3 234s 1 -2.72400 0.79288 0.688038 234s 2 -3.45684 0.86162 1.941690 234s 3 -5.73471 -4.79507 0.129202 234s 4 -6.17045 -3.04372 -0.352797 234s 5 -4.72453 -5.59543 4.144851 234s 6 -5.75447 -1.07062 0.579975 234s 7 -4.40759 0.47731 0.680203 234s 8 -2.76360 2.30716 0.540271 234s 9 -0.28782 -1.40644 -2.373399 234s 10 2.64361 -1.43362 -0.266957 234s 11 1.91078 -1.66975 1.312215 234s 12 -0.40661 0.68573 -0.200135 234s 13 -0.14911 1.88993 0.044001 234s 14 1.99005 2.43874 1.373229 234s 15 2.88128 -2.21263 -0.863674 234s 16 -0.12935 -8.28831 6.483875 234s 17 -0.16895 -1.68742 0.905190 234s 18 -3.08054 0.23753 -0.269165 234s 19 -0.38685 -1.08501 -2.736860 234s 20 1.45520 -0.33209 -1.686406 234s 21 1.13834 2.53553 -0.381657 234s 22 2.48522 3.42927 0.417050 234s 23 4.56487 -3.36542 0.711908 234s 24 2.94072 -3.08490 1.556939 234s 25 0.82140 -0.26895 -0.406490 234s 26 1.17794 1.61119 -0.863764 234s 27 2.02965 2.80707 -0.489050 234s 28 2.98039 3.21462 0.747622 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 3.86155 2.83460 0.95394 234s ---------------------------------------------------------- 234s hbk 75 3 3 3.714805 3.187126 234s Scores: 234s PC1 PC2 PC3 234s 1 8.423138 24.765818 19.413334 234s 2 7.823138 25.295092 20.356662 234s 3 9.023138 27.411905 20.218454 234s 4 8.223138 28.010236 21.568269 234s 5 8.623138 27.442650 21.123471 234s 6 9.123138 25.601873 20.279943 234s 7 8.823138 25.463855 20.770811 234s 8 8.223138 25.264348 19.451646 234s 9 8.023138 27.373593 20.716984 234s 10 7.623138 26.752275 19.666288 234s 11 9.323138 31.108975 24.313778 234s 12 10.323138 33.179719 23.469966 234s 13 10.323138 29.958667 26.231274 234s 14 9.323138 29.345676 34.207755 234s 15 1.723138 -0.077538 0.754886 234s 16 1.423138 -1.818609 -0.080979 234s 17 -1.676862 -1.872341 -0.686878 234s 18 0.623138 -0.077633 -0.548955 234s 19 -0.876862 -0.576068 0.716574 234s 20 1.423138 -0.016144 1.261078 234s 21 0.923138 -0.223313 0.041619 234s 22 -1.276862 -0.299937 1.038679 234s 23 0.323138 -1.327742 0.057038 234s 24 -0.376862 -1.626860 0.034051 234s 25 -0.676862 -1.550331 -2.266849 234s 26 -0.776862 0.290637 1.184359 234s 27 1.623138 0.750760 0.417361 234s 28 0.123138 -0.016334 -1.346603 234s 29 -0.476862 -1.220468 -1.338846 234s 30 -0.476862 1.387213 -1.339036 234s 31 1.423138 -1.059368 -0.824991 234s 32 -1.176862 -1.833934 0.118433 234s 33 -0.176862 -0.691099 0.908323 234s 34 -1.276862 -1.251213 -2.243862 234s 35 1.423138 0.858128 0.325317 234s 36 -0.576862 0.574335 0.102918 234s 37 -1.576862 0.413330 0.892903 234s 38 -0.176862 -1.841691 -1.085702 234s 39 0.423138 -0.752683 -2.205550 234s 40 -1.176862 -0.905930 -0.211430 234s 41 1.723138 0.819721 -0.479993 234s 42 -1.376862 0.666284 -1.093554 234s 43 -1.576862 -1.304659 1.061761 234s 44 0.123138 1.203126 -1.553772 234s 45 0.223138 -1.358581 -2.151818 234s 46 0.123138 1.003714 -1.569097 234s 47 1.323138 -1.159169 -2.136494 234s 48 1.423138 0.919427 -0.472331 234s 49 1.423138 -0.246300 0.340737 234s 50 0.423138 0.727773 0.716479 234s 51 0.623138 -1.665267 -0.771259 234s 52 1.623138 -0.798657 -1.607314 234s 53 -1.376862 1.310494 -1.645816 234s 54 -0.576862 -1.879908 0.716669 234s 55 -1.176862 -1.235698 0.164407 234s 56 0.123138 -1.296997 0.962055 234s 57 0.123138 -1.304849 -1.545920 234s 58 0.723138 -0.714086 1.207441 234s 59 -0.076862 0.881115 0.026199 234s 60 -1.376862 1.226208 -0.549050 234s 61 -1.276862 0.781504 1.322377 234s 62 -0.776862 -1.657699 -2.174806 234s 63 -0.576862 -1.956627 0.409888 234s 64 1.123138 0.712448 0.915891 234s 65 0.323138 0.689271 -1.392672 234s 66 -1.476862 -1.289430 -0.441492 234s 67 -0.076862 -0.905930 -0.211430 234s 68 -1.576862 -0.852389 -2.213213 234s 69 0.323138 -1.696011 -1.676276 234s 70 -0.676862 0.773747 0.118243 234s 71 0.523138 0.152524 0.371386 234s 72 -1.076862 -0.606812 -0.188443 234s 73 -1.376862 0.114117 -0.433924 234s 74 -1.676862 -0.522431 0.018632 234s 75 -1.376862 0.612552 -1.699453 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 1.9274 1.7853 1.6714 234s ---------------------------------------------------------- 234s milk 86 8 8 9.206694 2.910585 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 PC6 234s [1,] 6.090978 0.590424 1.1644466 -0.3835606 1.0342867 -0.4752288 234s [2,] 6.903009 -0.575027 0.8613622 -1.1221795 0.7221616 -1.3097951 234s [3,] 0.622903 -1.594239 1.2122863 -0.0555128 0.3252629 -0.2799581 234s [4,] 5.282665 -1.815742 2.2543268 0.9824543 -0.5345577 -0.7331037 234s [5,] -1.039753 0.663906 0.3353811 0.3070599 -0.3224317 -0.4056666 234s [6,] 2.247786 0.218255 -0.3382923 0.1270005 -0.0271307 -0.2035021 234s [7,] 2.784293 -0.291678 -0.4897587 0.0198481 0.0752345 -0.5986846 234s [8,] 2.942266 0.315608 0.1603961 0.3568462 -0.0647311 -0.5316127 234s [9,] -1.420086 -1.751212 1.7027572 0.0708340 -0.9226517 0.0738411 234s [10,] -2.921113 -0.727554 0.0113966 -0.3915037 -0.0772913 0.6062573 234s [11,] -9.568075 0.792291 1.0217507 0.2554182 -0.6254883 0.8899897 234s [12,] -12.885166 3.423607 -1.2579351 -0.4300397 -0.4094558 1.1727128 234s [13,] -10.038470 1.274931 -2.6913262 -1.6219658 -0.3284974 1.1228303 234s [14,] -12.044003 2.096254 -1.2859668 -0.9602250 -0.7937418 0.8264019 234s [15,] -10.798341 1.159257 1.4870766 0.3248231 -1.0787537 0.8723637 234s [16,] -2.841629 0.500846 0.4771762 0.5975365 0.3197882 0.5804087 234s [17,] -1.150691 -1.978038 2.3229313 0.5275273 -0.5339514 0.5421631 234s [18,] -1.992369 1.131288 -0.8385615 0.1156462 0.2253010 -0.3393814 234s [19,] -1.999699 -0.252876 1.2229972 0.5081648 0.0082612 0.3373454 234s [20,] 0.091385 -1.439422 1.1836134 0.6297789 0.0961407 -0.2126653 234s [21,] -2.571346 2.280701 -1.2845660 0.1463583 0.0949331 0.0902039 234s [22,] -0.990078 1.087033 -0.1638640 -0.0351472 0.0743205 -0.0040605 234s [23,] -0.010631 1.704171 0.0038808 0.5765418 0.6086460 0.0329995 234s [24,] -0.440350 1.500798 0.2769870 0.5556999 0.4751445 0.6516120 234s [25,] -3.578249 2.672783 -0.3534268 0.7398104 0.1108289 0.2704730 234s [26,] -0.854914 1.626684 0.2301131 0.5530224 0.0662862 -0.0999969 234s [27,] -3.175381 0.762609 0.5101987 0.0849002 -0.2137237 0.2729808 234s [28,] 2.599844 3.370137 -0.5174736 0.7409946 0.6853156 0.2430943 234s [29,] 4.395534 0.823611 0.1610152 0.8184845 0.7665555 0.0779724 234s [30,] 0.843794 1.438263 -0.2366601 0.4600650 0.3424806 -0.1768083 234s [31,] 1.890815 1.266935 -1.8218143 -0.3909337 0.8390127 0.1026821 234s [32,] 1.300145 -0.085976 -0.8965312 -0.8855787 0.4156780 0.1478055 234s [33,] 1.923087 0.137638 0.3487435 0.2958367 0.4245932 0.1566678 234s [34,] 0.615762 -0.390711 0.8107376 0.0295536 -0.1169590 0.2940241 234s [35,] -0.372946 2.037079 -0.7663299 0.1907237 0.6959350 0.5366205 234s [36,] 4.068134 1.129044 0.5492962 0.7640964 0.4799859 -0.4080205 234s [37,] 0.937617 2.048258 -1.2326566 -0.0942856 0.7885267 -0.1004018 234s [38,] 2.141223 1.877022 -0.5178216 0.3750868 0.4767003 0.1240656 234s [39,] -1.403505 1.327163 0.3165610 0.3989824 0.3505825 0.5915956 234s [40,] 3.337528 -1.689495 1.4737175 0.2584843 0.4308444 -0.0810597 234s [41,] 3.938506 1.384908 0.8103687 -0.5875595 1.1616535 -0.6492603 234s [42,] 6.327471 -1.061362 1.9861187 1.1016484 0.3512405 -0.1540592 234s [43,] 3.120160 -0.064108 -0.8370717 -0.2229341 0.5623447 -0.7152184 234s [44,] 5.290520 -0.669008 0.8597130 0.5518503 0.2470856 0.6454703 234s [45,] 0.058291 0.356399 -0.1896007 0.2427518 0.3705541 0.3975085 234s [46,] 0.150881 1.942057 -0.1140726 0.5656469 0.5227623 0.2151825 234s [47,] 2.870881 -1.446283 -2.8450062 -1.7292144 -0.0888429 -0.1347003 234s [48,] 0.335593 0.500884 -1.3154520 -0.3874864 0.3449038 0.5387692 234s [49,] -2.179494 -0.021237 -1.7792344 -0.8445930 0.4435338 0.6547961 234s [50,] 2.968304 -2.588546 1.8552104 0.4590101 -0.1755089 -0.0550378 234s [51,] -1.399208 -0.820296 -1.3660014 -0.8890243 -0.2344105 0.1236943 234s [52,] -5.112989 0.318983 -1.3852993 -0.8461529 -0.3467685 0.7349666 234s [53,] -0.773103 -0.267333 -0.8154896 -0.3783062 0.0113880 -0.3304648 234s [54,] -0.244565 -0.066211 -0.2541557 0.0043037 0.0390890 0.0074067 234s [55,] 0.894921 0.516411 -0.4443369 0.0708354 -0.0637890 -0.2799646 234s [56,] -0.038706 -0.588256 0.3166588 -0.0196663 -0.1793472 -0.1179341 234s [57,] -1.377469 0.428939 0.7502430 0.1458375 -0.3818977 -0.0380258 234s [58,] 0.042787 1.488605 0.0252606 0.6377516 -0.1524172 -0.1898723 234s [59,] -1.734357 -0.966494 -0.1026850 -0.5656888 -0.4831402 0.0308069 234s [60,] -1.501991 -0.544918 -0.0837127 -0.2362486 -0.5382026 -0.1351338 234s [61,] -0.175102 -1.339436 0.8403933 -0.0907428 -0.4846145 -0.2795153 234s [62,] 2.100915 -2.004702 1.3031556 -0.0041957 -0.2067776 -0.0793613 234s [63,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 234s [64,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 234s [65,] -0.665219 -2.325594 1.6287363 0.0607163 -0.6996720 0.1353325 234s [66,] -2.439244 -0.737375 0.0187770 -0.4561269 -0.5425315 -0.0208332 234s [67,] 0.121564 -1.214385 0.4877707 0.1809998 -0.1943262 0.0662506 234s [68,] -0.804267 -2.238327 -0.8547917 -1.3449926 -0.3577254 -0.0293779 234s [69,] -0.761319 -0.676391 -0.0245494 0.2262894 -0.3396872 -0.1166505 234s [70,] 3.385399 4.360467 -0.7946150 -0.0417895 0.4474362 -4.6626174 234s [71,] -2.364955 -1.257673 0.5226907 -0.2346145 -0.7838777 0.1815821 234s [72,] 2.334511 -0.794530 0.0175620 0.1848925 -0.3437761 -0.4522442 234s [73,] -2.023440 -2.449907 0.2525041 -0.6657474 -0.5509480 0.2118442 234s [74,] -11.180192 2.456516 1.1036540 0.8711496 -0.3833194 1.3548314 234s [75,] 0.058297 -2.094811 0.3075211 -0.8052760 -0.9527729 0.5850255 234s [76,] -1.355742 -0.464355 -1.0183333 -0.8525619 -0.1577144 -0.0767323 234s [77,] -8.296881 0.945092 0.8088967 -0.0071463 -0.4527530 1.0614233 234s [78,] 1.251696 -1.460466 0.2511701 -0.2717606 -0.3158308 -0.2964813 234s [79,] -0.192380 -0.662365 -0.3671703 -0.6722658 -0.1243452 -0.2388225 234s [80,] -3.355201 1.915096 -0.1086672 0.3560062 0.0956865 0.6974817 234s [81,] 1.245305 0.736787 -0.1662155 0.1309822 -0.0122872 -0.2182528 234s [82,] 2.679561 -1.666401 1.1576691 0.3960280 -0.0059146 0.0584136 234s [83,] 2.596651 -0.556654 -0.0807307 -0.4468501 0.0964927 -0.3922894 234s [84,] 0.959377 -0.272038 -1.5879803 -1.1153057 0.3412508 -0.1281556 234s [85,] 0.602737 -1.384591 2.8844745 0.9479144 -0.7946454 -0.2014038 234s [86,] 0.698125 0.335743 -1.5248055 -0.4443037 0.0768256 -0.1999790 234s PC7 PC8 234s [1,] 0.9281777 -0.05158594 234s [2,] 0.8397946 -0.04276628 234s [3,] -0.5189230 0.04913688 234s [4,] -0.0178377 0.01578074 234s [5,] -0.0129237 0.01056305 234s [6,] -0.0764270 0.01469518 234s [7,] -0.3059779 0.04237267 234s [8,] -0.0684673 0.02289928 234s [9,] -0.2549733 -0.00832119 234s [10,] -0.0578118 -0.01894694 234s [11,] 0.0415545 -0.03474479 234s [12,] 0.0869267 -0.04485633 234s [13,] -0.2843977 -0.03100709 234s [14,] -0.3375083 -0.02155574 234s [15,] -0.1718828 -0.02996980 234s [16,] -0.4176728 0.03232381 234s [17,] -0.5923252 0.01765700 234s [18,] -0.3190679 0.04476532 234s [19,] -0.0279426 -0.00236626 234s [20,] 0.1299811 0.00586022 234s [21,] 0.0474059 0.00563264 234s [22,] -0.1240299 0.01123557 234s [23,] 0.2232631 0.00551065 234s [24,] 0.0122404 0.00060079 234s [25,] 0.2627442 -0.00824800 234s [26,] 0.2257329 -0.00440907 234s [27,] -0.8496967 0.05266701 234s [28,] 0.3473502 -0.00500580 234s [29,] 0.4172329 -0.00542705 234s [30,] 0.2773880 -0.00014648 234s [31,] -0.1224270 0.02372808 234s [32,] -0.2224748 0.00757892 234s [33,] -0.0633903 0.01236118 234s [34,] -0.2616599 0.00561781 234s [35,] -0.1671986 0.01988458 234s [36,] 0.4502086 -0.00418541 234s [37,] -0.0773232 0.02768282 234s [38,] 0.0464683 0.01134849 234s [39,] -0.0927182 0.00555823 234s [40,] -0.2162796 0.02467605 234s [41,] 0.9440753 -0.04806541 234s [42,] -0.0078920 0.02022925 234s [43,] 0.1152244 0.02074199 234s [44,] 1.0406693 -0.08815111 234s [45,] -0.1376804 0.01424369 234s [46,] 0.1673461 0.00442877 234s [47,] -0.4125225 0.01038694 234s [48,] 0.1556289 -0.02103354 234s [49,] 0.0434415 -0.01782739 234s [50,] 0.2518610 -0.02154540 234s [51,] -0.1186185 -0.00881133 234s [52,] 0.1507435 -0.04523343 234s [53,] 0.2161208 -0.00967982 234s [54,] 0.1374909 -0.00783970 234s [55,] 0.2417108 -0.00895268 234s [56,] 0.1253846 -0.01188643 234s [57,] 0.1390898 -0.01831232 234s [58,] 0.2219634 -0.00364174 234s [59,] -0.2045636 -0.00589047 234s [60,] -0.3679942 0.01673699 234s [61,] -0.0705611 -0.00273407 234s [62,] 0.1447701 -0.02026768 234s [63,] -0.1854788 0.02686899 234s [64,] -0.1854788 0.02686899 234s [65,] -0.2626650 -0.00376657 234s [66,] -0.3044266 0.00484197 234s [67,] -0.1358811 0.00605789 234s [68,] -0.0551482 -0.02379410 234s [69,] -0.0914891 0.00812122 234s [70,] 10.2524854 -0.64367029 234s [71,] -0.1326972 -0.01666774 234s [72,] 0.0051905 0.00656777 234s [73,] -0.8236843 0.03367265 234s [74,] 0.2140104 -0.04092219 234s [75,] -0.5684260 -0.00987116 234s [76,] -0.1225779 -0.00204629 234s [77,] -0.4235612 -0.00450631 234s [78,] -0.1935155 0.00973901 234s [79,] -0.1615883 0.00518643 234s [80,] 0.2915052 -0.02960159 234s [81,] 0.0908823 0.00038216 234s [82,] -0.3392789 0.02605374 234s [83,] 0.1112141 -0.00629308 234s [84,] 0.0510771 -0.00845572 234s [85,] 0.0748700 -0.01174487 234s [86,] 0.2488127 -0.01446339 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 3.034253 1.706044 1.167717 0.670864 0.536071 0.396285 0.266625 0.020768 234s ---------------------------------------------------------- 234s bushfire 38 5 5 38232.614428 1580.825276 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 234s [1,] -67.120 -23.70481 -1.06551 1.129721 1.311630 234s [2,] -69.058 -21.42113 -1.54798 0.983735 0.430774 234s [3,] -61.939 -17.23665 -3.81386 -0.635074 -0.600149 234s [4,] -44.952 -16.53458 -5.16114 0.411753 -0.390518 234s [5,] -12.644 -21.62271 -7.14146 3.519877 -1.211923 234s [6,] 12.820 -27.86930 -7.66114 7.230422 0.040330 234s [7,] -194.634 -100.67730 27.43084 -0.026242 -0.134248 234s [8,] -229.349 -129.75912 -19.46346 25.591651 -18.592601 234s [9,] -230.306 -131.28743 -22.22175 27.251157 -19.214683 234s [10,] -231.118 -115.10815 3.70208 16.303210 -10.573515 234s [11,] -234.540 -100.24984 13.67112 10.325539 -8.727961 234s [12,] -246.507 -51.03515 27.61698 -5.352226 0.514087 234s [13,] -195.712 -5.81324 20.04485 -9.226807 1.721886 234s [14,] 49.881 16.90911 -9.97400 -1.900739 2.190429 234s [15,] 179.545 23.96999 -18.71166 -2.987136 1.332713 234s [16,] 135.356 15.81282 -9.24353 -4.703584 0.971669 234s [17,] 132.350 16.65014 -7.01838 -2.428578 1.346198 234s [18,] 121.499 9.75832 -4.45699 -1.587450 0.131923 234s [19,] 125.222 9.17601 -5.88919 0.582516 -0.061642 234s [20,] 135.112 14.63812 -5.90351 0.411704 1.460488 234s [21,] 116.581 14.47390 -3.04021 -1.842579 2.005998 234s [22,] 108.223 14.62103 -4.47428 -1.196993 3.288463 234s [23,] -22.095 3.26439 6.58391 -6.164581 2.125258 234s [24,] -77.831 3.46616 6.59280 -6.373595 1.545789 234s [25,] -13.092 3.41344 -0.99296 -5.076733 0.299636 234s [26,] -19.206 -0.17007 -1.84209 -4.858675 0.347945 234s [27,] -35.022 6.54155 -3.12767 -3.556587 -0.327873 234s [28,] -12.651 20.14894 -4.61607 -2.025539 -1.214190 234s [29,] -4.404 36.39823 -3.81590 -0.633155 -0.602027 234s [30,] -60.018 30.40980 9.44610 -1.763156 -0.765133 234s [31,] 67.689 47.40087 12.70229 9.791794 -0.671751 234s [32,] 324.134 63.46147 31.52512 30.099817 2.406344 234s [33,] 364.639 38.84260 51.20467 30.648590 3.218678 234s [34,] 361.089 37.09494 52.00522 29.394356 2.861158 234s [35,] 366.403 38.88889 52.31879 29.878844 4.650618 234s [36,] 363.821 37.40859 53.10394 28.286557 2.922632 234s [37,] 361.761 37.21276 55.73012 27.648760 4.477279 234s [38,] 363.106 37.78395 56.56345 27.460078 4.845396 234s ------------- 234s Call: 234s PcaGrid(x = x) 234s 234s Standard deviations: 234s [1] 195.5316 39.7596 11.7329 7.3743 1.7656 234s ---------------------------------------------------------- 234s ========================================================== 234s > 234s > ## IGNORE_RDIFF_BEGIN 234s > dodata(method="proj") 234s 234s Call: dodata(method = "proj") 234s Data Set n p k e1 e2 234s ========================================================== 234s heart 12 2 2 512.772467 29.052346 234s Scores: 234s PC1 PC2 234s [1,] 6.7568 3.2826 234s [2,] 63.0869 14.1293 234s [3,] 1.3852 -1.1318 234s [4,] -3.6709 1.8153 234s [5,] 19.0457 3.8035 234s [6,] -16.6413 3.1452 234s [7,] 5.3163 3.7464 234s [8,] -27.8536 -11.0863 234s [9,] -1.1638 -1.1788 234s [10,] -26.6915 -10.2803 234s [11,] -13.6842 -2.9790 234s [12,] 47.8395 11.2980 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 22.644 5.390 234s ---------------------------------------------------------- 234s starsCYG 47 2 2 0.470874 0.024681 234s Scores: 234s PC1 PC2 234s [1,] 0.181333 -3.1013e-02 234s [2,] 0.696091 1.4569e-01 234s [3,] -0.121421 -1.3319e-01 234s [4,] 0.696091 1.4569e-01 234s [5,] 0.139530 -9.9951e-02 234s [6,] 0.413590 5.2989e-02 234s [7,] -0.412224 -5.4579e-01 234s [8,] 0.226508 1.6788e-01 234s [9,] 0.518364 -1.4980e-01 234s [10,] 0.071370 -2.8159e-02 234s [11,] 0.658332 -9.2369e-01 234s [12,] 0.402815 2.3259e-02 234s [13,] 0.374123 7.4020e-02 234s [14,] -1.007611 -3.6028e-01 234s [15,] -0.790417 -8.5818e-02 234s [16,] -0.467151 3.5835e-02 234s [17,] -1.111866 -1.3750e-01 234s [18,] -0.867017 4.6214e-02 234s [19,] -0.871946 -1.4372e-01 234s [20,] 0.818278 -9.2784e-01 234s [21,] -0.670457 -8.8932e-02 234s [22,] -0.830403 -8.4781e-02 234s [23,] -0.627097 3.9987e-02 234s [24,] -0.195426 9.8806e-02 234s [25,] -0.028337 -1.5568e-02 234s [26,] -0.387178 3.3760e-02 234s [27,] -0.390551 -9.6197e-02 234s [28,] -0.148297 -1.2454e-02 234s [29,] -0.662277 -1.5917e-01 234s [30,] 0.977965 -9.4199e-01 234s [31,] -0.628135 -7.3179e-16 234s [32,] 0.056306 1.6230e-01 234s [33,] 0.173412 4.9220e-02 234s [34,] 1.218143 -9.3822e-01 234s [35,] -0.712000 -1.4787e-01 234s [36,] 0.577688 2.0878e-01 234s [37,] 0.055528 1.3231e-01 234s [38,] 0.173412 4.9220e-02 234s [39,] 0.135501 1.3023e-01 234s [40,] 0.522775 2.0145e-02 234s [41,] -0.428203 -5.1892e-03 234s [42,] 0.013465 5.3371e-02 234s [43,] 0.294668 9.6089e-02 234s [44,] 0.293371 4.6106e-02 234s [45,] 0.495898 1.4088e-01 234s [46,] -0.066508 5.5447e-02 234s [47,] -0.547124 3.7911e-02 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 0.6862 0.1571 234s ---------------------------------------------------------- 234s phosphor 18 2 2 388.639033 51.954664 234s Scores: 234s PC1 PC2 234s 1 5.8164 -15.1691 234s 2 -21.1936 -2.1132 234s 3 -23.6199 2.0585 234s 4 -11.2029 -6.7203 234s 5 -18.4220 1.3231 234s 6 17.1862 -19.2211 234s 7 1.6302 -3.1493 234s 8 -9.7695 3.1385 234s 9 -10.9174 5.3594 234s 10 15.6275 -6.3610 234s 11 -4.0194 1.2476 234s 12 9.3931 8.3149 234s 13 12.9944 6.5741 234s 14 6.9396 7.8348 234s 15 18.3964 3.9629 234s 16 -8.8365 -6.4202 234s 17 21.8073 6.4237 234s 18 16.8541 12.2611 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 19.714 7.208 234s ---------------------------------------------------------- 234s stackloss 21 3 3 97.347030 38.052774 234s Scores: 234s PC1 PC2 PC3 234s [1,] 19.08066 -9.06092 -2.64544 234s [2,] 18.55152 -9.90152 -2.76118 234s [3,] 15.04269 -5.37517 -2.31373 234s [4,] 2.79667 -1.78925 1.70823 234s [5,] 2.21768 -1.17513 -0.10495 234s [6,] 2.50717 -1.48219 0.80164 234s [7,] 5.97151 3.25438 2.40268 234s [8,] 5.97151 3.25438 2.40268 234s [9,] -0.68332 0.30263 2.42495 234s [10,] -5.83478 -4.04630 -2.91819 234s [11,] -1.07253 3.51914 -1.87651 234s [12,] -1.89116 2.98559 -2.89885 234s [13,] -4.77650 -2.36509 -2.68671 234s [14,] 1.33353 6.57450 -0.50696 234s [15,] -7.45351 7.08878 1.37012 234s [16,] -9.04093 4.56697 1.02289 234s [17,] -16.15938 -7.50855 0.30909 234s [18,] -12.45541 -1.62432 1.11929 234s [19,] -11.63677 -1.09077 2.14162 234s [20,] -5.79275 -2.08680 -0.06187 234s [21,] 10.13623 -0.76824 -4.70180 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 9.8665 6.1687 3.2669 234s ---------------------------------------------------------- 234s salinity 28 3 3 12.120566 8.431549 234s Scores: 234s PC1 PC2 PC3 234s 1 -2.52547 1.45945 -1.1943e-01 234s 2 -3.32298 2.15704 8.7594e-01 234s 3 -6.64947 -3.26398 1.0135e+00 234s 4 -6.64427 -1.81382 -1.6392e-01 234s 5 -6.16898 -2.52222 5.1373e+00 234s 6 -5.87594 0.26440 -3.1956e-15 234s 7 -4.23084 1.46250 -2.8008e-01 234s 8 -2.21502 2.76478 -8.3789e-01 234s 9 -0.40186 -2.17785 -1.6702e+00 234s 10 2.27089 -1.84923 7.3391e-01 234s 11 1.37935 -1.29276 2.1418e+00 234s 12 -0.22635 0.60372 -5.0980e-01 234s 13 0.27224 1.73920 -7.0505e-01 234s 14 2.36592 2.40462 6.4320e-01 234s 15 2.37640 -2.83174 5.2669e-01 234s 16 -2.49175 -4.77664 9.0404e+00 234s 17 -0.61250 -1.11672 1.4398e+00 234s 18 -2.91853 0.63310 -8.3666e-01 234s 19 -0.39732 -2.02029 -2.1396e+00 234s 20 1.47554 -1.23407 -1.1712e+00 234s 21 1.70104 1.92401 -1.1292e+00 234s 22 3.14437 2.81928 -5.2415e-01 234s 23 3.62890 -3.51450 2.6740e+00 234s 24 2.04538 -2.63992 3.0718e+00 234s 25 0.77088 -0.54783 -1.3370e-01 234s 26 1.57254 0.89176 -1.2089e+00 234s 27 2.63610 1.97075 -1.1855e+00 234s 28 3.55112 2.67606 -6.0915e-02 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 3.4815 2.9037 1.3810 234s ---------------------------------------------------------- 234s hbk 75 3 3 3.801978 3.574192 234s Scores: 234s PC1 PC2 PC3 234s 1 28.747049 15.134042 2.3959241 234s 2 29.021724 16.318941 2.6207988 234s 3 31.271908 15.869319 3.4420860 234s 4 31.586189 17.508798 3.6246706 234s 5 31.299168 16.838093 3.2402573 234s 6 30.037754 15.591930 2.1421166 234s 7 29.888160 16.139376 1.9750096 234s 8 28.994463 15.350167 2.8226275 234s 9 30.758047 16.820526 3.7269602 234s 10 29.759314 16.079531 4.0486097 234s 11 35.301371 19.637962 3.7433562 234s 12 37.193371 18.709303 4.9915250 234s 13 35.634808 20.497713 1.4740727 234s 14 36.816439 27.523024 -2.3006796 234s 15 1.237203 -0.331072 -1.3801401 234s 16 -0.451166 -1.118847 -1.9707479 234s 17 -2.604733 0.067276 0.0130015 234s 18 0.179177 -0.804398 -0.1285240 234s 19 -0.765512 0.982349 -0.2513990 234s 20 1.236727 0.259123 -1.4210070 234s 21 0.428326 -0.503724 -0.6830690 234s 22 -0.724774 1.507943 -0.0022175 234s 23 -0.745349 -0.330094 -1.0982084 234s 24 -1.407850 -0.011831 -0.8987075 234s 25 -2.190427 -1.732051 0.4497793 234s 26 0.058631 1.444044 0.0446166 234s 27 1.680557 -0.429402 -0.6031146 234s 28 -0.315122 -1.179169 0.5822607 234s 29 -1.563355 -1.026914 0.1040012 234s 30 0.329957 -0.633156 1.8533795 234s 31 -0.110108 -1.617131 -1.0958807 234s 32 -2.035875 0.463421 -0.6346632 234s 33 -0.356033 0.740564 -0.8116369 234s 34 -2.342887 -1.340168 0.9724491 234s 35 1.607131 -0.379763 -0.3747630 234s 36 0.084455 0.486671 0.6551654 234s 37 -0.436144 1.659467 0.7145344 234s 38 -1.754819 -1.076076 -0.6037590 234s 39 -0.904375 -2.161949 0.3436723 234s 40 -1.455274 0.331839 0.1499308 234s 41 1.539788 -1.212921 -0.1715110 234s 42 -0.688338 -0.048173 1.7491184 234s 43 -1.635822 1.539067 -0.5208916 234s 44 0.511762 -1.165641 1.5020865 234s 45 -1.454500 -2.099954 0.0219268 234s 46 0.362645 -1.208389 1.3758464 234s 47 -0.615800 -2.658098 -0.4629006 234s 48 1.426278 -1.027667 0.0582638 234s 49 0.809592 -0.533893 -1.1232120 234s 50 0.996105 0.469082 -0.0988805 234s 51 -1.036368 -1.227376 -1.0843166 234s 52 -0.016464 -2.331540 -0.6477169 234s 53 -0.376625 -0.405855 2.4526088 234s 54 -1.524100 0.621590 -1.2927429 234s 55 -1.588523 0.591668 -0.2559428 234s 56 -0.592710 0.529426 -1.4111404 234s 57 -1.306991 -1.538024 -0.1841717 234s 58 0.275991 0.491888 -1.4739863 234s 59 0.598971 0.196673 0.6208960 234s 60 -0.127953 0.485014 1.8571970 234s 61 0.140584 1.905037 0.5838465 234s 62 -2.305069 -1.617811 0.3880825 234s 63 -1.666479 0.357251 -1.1934779 234s 64 1.480143 0.248671 -0.5959984 234s 65 0.309561 -1.219790 0.9671263 234s 66 -1.986789 0.248245 0.1723620 234s 67 -0.765691 -0.269054 -0.4611368 234s 68 -2.232721 -1.090790 1.3915841 234s 69 -1.502453 -1.813763 -0.4936268 234s 70 0.170883 0.584046 0.8369571 234s 71 0.543623 0.043244 -0.3707674 234s 72 -1.168908 0.341335 0.2837393 234s 73 -0.902885 0.411872 1.0546196 234s 74 -1.425273 0.852445 0.5719123 234s 75 -0.898536 -0.555475 2.0107684 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 1.9499 1.8906 1.2797 234s ---------------------------------------------------------- 234s milk 86 8 8 8.369408 3.530461 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 PC6 234s [1,] 6.337004 -0.245000 0.7704092 -4.9848e-01 -1.6599e-01 1.1763e-01 234s [2,] 7.021899 1.030349 0.2832977 -1.2673e+00 -8.7296e-01 2.0547e-01 234s [3,] 0.600831 1.686247 0.9682032 -3.2663e-02 7.4112e-02 4.7412e-01 234s [4,] 5.206465 2.665956 1.5942253 9.8285e-01 -5.4159e-01 -2.0155e-01 234s [5,] -0.955757 -0.579889 0.3206393 5.1174e-01 -6.1684e-01 -3.8990e-02 234s [6,] 2.198695 0.073770 -0.5712493 1.9440e-01 -1.0237e-01 4.1825e-02 234s [7,] 2.695361 0.644049 -0.8645373 8.1894e-02 -2.6953e-01 1.6884e-01 234s [8,] 2.945361 0.137227 -0.2071463 5.0841e-01 -4.2075e-01 5.8589e-02 234s [9,] -1.539013 1.879894 1.6952390 1.6792e-01 -2.8195e-01 5.0563e-02 234s [10,] -2.977110 0.319666 0.3515636 -5.2496e-01 4.6898e-01 8.5978e-03 234s [11,] -9.375355 -1.638105 1.9026171 4.1237e-01 1.8768e-02 -1.8546e-01 234s [12,] -12.602600 -4.715888 0.0273004 -4.7798e-02 -1.2246e-02 9.6858e-03 234s [13,] -10.114331 -2.487462 -1.6331544 -1.5139e+00 4.1903e-01 2.8313e-01 234s [14,] -11.949336 -3.190157 -0.2146943 -5.0060e-01 -2.9537e-01 3.2160e-01 234s [15,] -10.595396 -1.905517 2.3716887 7.6651e-01 -3.3531e-01 1.9933e-02 234s [16,] -2.735720 -0.748282 0.6750464 7.2415e-01 5.5304e-01 2.2283e-01 234s [17,] -1.248116 2.131195 2.2596886 6.4958e-01 3.5634e-01 2.9021e-01 234s [18,] -1.904210 -1.285804 -0.7746460 3.0198e-01 -2.7407e-01 1.7500e-01 234s [19,] -1.902313 0.095461 1.3824711 5.0369e-01 2.2193e-01 -5.5628e-02 234s [20,] 0.123220 1.399444 1.1517634 3.2546e-01 7.8261e-02 -4.0733e-01 234s [21,] -2.436023 -2.524827 -1.0197416 3.4819e-01 -1.4914e-01 -4.3669e-02 234s [22,] -0.904931 -1.114894 -0.1235807 2.0285e-01 -1.6200e-01 2.5681e-01 234s [23,] 0.220231 -1.767325 0.0482262 6.4418e-01 9.8618e-02 -5.7683e-02 234s [24,] -0.274403 -1.561826 0.3820323 7.0016e-01 5.5220e-01 1.4376e-01 234s [25,] -3.306400 -2.980247 0.0252488 9.4001e-01 -1.0841e-01 -2.5303e-01 234s [26,] -0.658015 -1.625199 0.3021005 7.2702e-01 -3.0299e-01 -1.2339e-01 234s [27,] -3.137066 -0.774218 0.5577497 6.4188e-01 -8.0125e-02 7.7819e-01 234s [28,] 2.867950 -3.099435 -0.6435415 1.0366e+00 1.5908e-01 7.6524e-02 234s [29,] 4.523097 -0.527338 -0.1032516 6.4537e-01 4.7286e-01 -2.7166e-01 234s [30,] 1.002381 -1.376693 -0.2735956 5.0522e-01 -1.2750e-01 -1.6178e-01 234s [31,] 1.894615 -1.296202 -1.9117282 -3.8032e-01 4.6473e-01 3.1085e-01 234s [32,] 1.210291 0.067230 -0.9832930 -8.5379e-01 3.2823e-01 4.9994e-01 234s [33,] 1.964118 0.022175 0.1818518 3.0464e-01 3.5596e-01 1.4985e-01 234s [34,] 0.576738 0.567851 0.6982155 1.8415e-01 1.8695e-01 3.2706e-01 234s [35,] -0.231793 -2.143909 -0.6825523 4.0681e-01 5.4492e-01 3.6259e-01 234s [36,] 4.250883 -0.719760 0.2157706 7.7167e-01 -1.9064e-01 -2.0611e-01 234s [37,] 1.077364 -2.054664 -1.3064867 1.0043e-01 8.6092e-02 3.5416e-01 234s [38,] 2.259260 -1.653588 -0.6730692 5.7300e-01 1.6930e-01 1.6986e-01 234s [39,] -1.251576 -1.451593 0.4671580 5.8957e-01 4.2672e-01 2.2495e-01 234s [40,] 3.304245 1.998193 1.0941231 1.3734e-01 3.7012e-01 2.4142e-01 234s [41,] 4.286315 -1.280951 0.5856744 -6.0980e-01 -4.3090e-01 1.9801e-01 234s [42,] 6.343820 1.801880 1.3481119 1.0355e+00 2.9802e-01 -8.4501e-04 234s [43,] 3.119491 0.214077 -1.1216236 -3.8134e-01 -1.9523e-01 -2.6706e-02 234s [44,] 5.285254 0.938072 0.7440487 1.1539e-02 8.1629e-01 -7.9286e-01 234s [45,] 0.082429 -0.416631 -0.1588203 2.3098e-01 5.1867e-01 9.4503e-02 234s [46,] 0.357862 -1.951997 -0.0731829 7.0393e-01 1.8828e-01 1.5707e-02 234s [47,] 2.428744 1.522538 -3.0467213 -1.9114e+00 2.4638e-01 3.5871e-01 234s [48,] 0.282348 -0.697287 -1.1592508 -5.4929e-01 6.2199e-01 -5.4596e-02 234s [49,] -2.266009 -0.559548 -1.3794914 -1.1300e+00 7.8872e-01 -2.0411e-02 234s [50,] 2.868649 2.860857 1.6128307 6.7382e-02 2.2344e-01 -4.1484e-01 234s [51,] -1.596061 0.546812 -1.1779327 -1.0512e+00 1.3522e-01 -9.4865e-03 234s [52,] -5.186121 -1.000829 -0.7440599 -9.6302e-01 3.0732e-01 -1.7009e-01 234s [53,] -0.800232 0.049087 -0.6946842 -5.8284e-01 -2.1277e-01 -2.7004e-01 234s [54,] -0.246388 -0.030606 -0.1814302 -1.1632e-01 5.7767e-02 -1.8637e-01 234s [55,] 0.914315 -0.428594 -0.4919557 4.5039e-02 -2.7868e-01 -2.2140e-01 234s [56,] -0.061827 0.583572 0.3263056 -1.1589e-01 -1.2973e-01 -1.6518e-01 234s [57,] -1.295979 -0.421943 0.8410805 3.0441e-01 -3.9478e-01 -4.5233e-02 234s [58,] 0.174908 -1.343854 0.0115086 8.0227e-01 -3.9364e-01 -2.2918e-01 234s [59,] -1.869684 0.840823 0.0109543 -5.5536e-01 -1.4155e-01 1.0613e-01 234s [60,] -1.614271 0.557309 -0.0690787 -9.1753e-02 -3.0975e-01 1.6192e-01 234s [61,] -0.258192 1.434984 0.7684636 -1.1998e-01 -3.4662e-01 -4.8808e-02 234s [62,] 2.000275 2.204730 1.1194067 -2.3783e-01 5.9953e-02 -1.5836e-01 234s [63,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 234s [64,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 234s [65,] -0.822201 2.427550 1.5859438 -3.5437e-16 2.2436e-15 -4.7251e-15 234s [66,] -2.545586 0.605953 0.1469837 -3.5318e-01 -2.5871e-01 1.6901e-01 234s [67,] 0.028900 1.253717 0.4474540 5.3595e-02 1.6063e-01 -1.0980e-01 234s [68,] -1.086135 1.968868 -0.7220293 -1.6576e+00 6.2061e-02 -7.0998e-04 234s [69,] -0.836638 0.660453 0.0049966 1.3663e-01 -1.0131e-01 -2.4008e-01 234s [70,] 4.843092 -6.035092 0.8250084 -3.4481e+00 -4.8538e+00 -7.8407e+00 234s [71,] -2.500038 1.146245 0.6967314 -2.4611e-01 -1.4266e-01 -8.2996e-02 234s [72,] 2.220676 1.122951 -0.2444075 1.1066e-01 -3.1540e-01 -2.1344e-01 234s [73,] -2.310518 2.354552 0.2706503 -6.4192e-01 2.0566e-01 4.5520e-01 234s [74,] -10.802799 -3.462655 2.2031446 1.1326e+00 2.8049e-01 -2.9749e-01 234s [75,] -0.301038 2.284366 0.2440764 -6.9450e-01 2.6435e-01 4.3129e-01 234s [76,] -1.477936 0.245154 -0.8869850 -8.9900e-01 -9.8013e-02 1.1983e-01 234s [77,] -8.169236 -1.599780 1.4987144 3.7767e-01 2.4726e-01 3.8246e-01 234s [78,] 1.096654 1.646072 0.0591327 -3.3138e-01 -1.7936e-01 6.2716e-02 234s [79,] -0.289199 0.625796 -0.3974294 -6.6099e-01 -2.0857e-01 2.1190e-01 234s [80,] -3.160557 -2.282579 0.3255355 4.6181e-01 2.7753e-01 -1.5673e-01 234s [81,] 1.284356 -0.548854 -0.2907281 2.4017e-01 -2.5254e-01 -1.4289e-03 234s [82,] 2.562817 2.019485 0.8249162 3.2973e-01 3.3866e-01 1.3889e-01 234s [83,] 2.538825 0.759863 -0.3142506 -5.1028e-01 -2.0539e-01 8.8979e-02 234s [84,] 0.841123 0.110035 -1.5793120 -1.2807e+00 1.2332e-01 1.6224e-01 234s [85,] 0.636271 1.793014 2.6824860 1.0329e+00 -4.8850e-01 -2.3012e-01 234s [86,] 0.633183 -0.426511 -1.4791366 -6.1314e-01 -7.0534e-02 -2.3778e-01 234s PC7 PC8 234s [1,] 1.0196e-01 -1.7180e-03 234s [2,] 2.6131e-01 -8.5191e-03 234s [3,] 6.9637e-01 -8.0573e-03 234s [4,] -1.3548e-01 -1.4969e-03 234s [5,] 3.1443e-02 -2.7307e-03 234s [6,] -2.5079e-01 3.6450e-03 234s [7,] 4.5377e-02 -2.6071e-03 234s [8,] -1.6060e-01 -2.3761e-04 234s [9,] -1.5152e-01 -4.3079e-04 234s [10,] 9.1089e-02 1.9536e-03 234s [11,] 2.5654e-01 -1.4875e-03 234s [12,] -2.3798e-03 -1.0954e-04 234s [13,] -1.3687e-01 2.8402e-03 234s [14,] -6.5248e-02 -1.5114e-03 234s [15,] 3.7695e-02 -2.7827e-03 234s [16,] 3.8131e-01 -3.7990e-03 234s [17,] 4.5661e-02 -1.4965e-03 234s [18,] 3.9910e-01 -7.2703e-03 234s [19,] 2.9353e-01 -3.3342e-03 234s [20,] 6.0915e-01 -6.0837e-03 234s [21,] -1.0079e-01 1.0179e-03 234s [22,] -2.2945e-02 -1.0515e-03 234s [23,] 2.3631e-01 -2.5558e-03 234s [24,] -7.7207e-02 3.4800e-03 234s [25,] 1.4903e-02 -3.2430e-04 234s [26,] 3.8032e-03 -2.1705e-03 234s [27,] 3.7208e-02 -3.0631e-03 234s [28,] -4.8147e-01 6.1089e-03 234s [29,] -4.0388e-02 2.8549e-03 234s [30,] 3.4318e-02 -1.0014e-03 234s [31,] -2.2872e-02 1.8706e-03 234s [32,] -8.4542e-02 1.3368e-03 234s [33,] 4.5274e-02 5.3383e-04 234s [34,] -2.0048e-01 2.4727e-03 234s [35,] -5.6482e-02 2.9923e-03 234s [36,] -2.6046e-02 -1.2910e-03 234s [37,] 9.6038e-02 -1.8897e-03 234s [38,] -2.9035e-01 4.4317e-03 234s [39,] -4.6322e-03 2.4336e-03 234s [40,] 3.8686e-01 -3.9300e-03 234s [41,] 3.7834e-01 -7.8976e-03 234s [42,] -8.2037e-04 -4.3106e-05 234s [43,] 3.3467e-01 -5.2401e-03 234s [44,] -6.2170e-01 1.2840e-02 234s [45,] 5.3557e-02 2.9156e-03 234s [46,] 5.1785e-04 2.0738e-03 234s [47,] -5.2141e-01 5.7206e-03 234s [48,] -2.7669e-01 6.7329e-03 234s [49,] 8.4319e-02 3.8528e-03 234s [50,] 1.4210e-01 1.6961e-04 234s [51,] -1.1871e-01 2.6676e-03 234s [52,] -2.5036e-01 6.4121e-03 234s [53,] 2.2399e-01 -2.8200e-03 234s [54,] 5.6532e-02 4.9304e-04 234s [55,] -1.4343e-01 1.2558e-03 234s [56,] 4.1682e-02 -9.6490e-04 234s [57,] -1.3014e-01 -6.2709e-04 234s [58,] -2.1428e-01 8.2594e-04 234s [59,] -7.9775e-02 -8.9776e-04 234s [60,] -8.6835e-02 -1.0498e-03 234s [61,] 6.2470e-02 -2.7499e-03 234s [62,] 3.3052e-02 -3.2369e-04 234s [63,] -1.7137e-01 -3.1087e-04 234s [64,] -1.7137e-01 -3.1087e-04 234s [65,] -1.4435e-14 -1.8299e-12 234s [66,] -2.2016e-02 -1.2206e-03 234s [67,] 8.5160e-02 -1.4837e-04 234s [68,] -2.2535e-03 1.9054e-04 234s [69,] 5.9976e-02 -8.6961e-04 234s [70,] 1.0448e+00 -2.0167e-02 234s [71,] -1.7609e-01 1.9378e-03 234s [72,] -1.7047e-01 2.6076e-04 234s [73,] 1.1885e-01 -8.1624e-04 234s [74,] 2.0942e-01 3.3164e-03 234s [75,] -7.7528e-01 9.9316e-03 234s [76,] -4.6285e-03 2.5153e-04 234s [77,] 7.0218e-02 1.5708e-03 234s [78,] -1.4859e-02 -6.7049e-04 234s [79,] 5.1054e-02 -2.0198e-03 234s [80,] -1.5770e-01 4.9579e-03 234s [81,] -1.9411e-01 4.4401e-04 234s [82,] 6.0634e-02 8.7960e-04 234s [83,] -4.4635e-02 -1.7048e-03 234s [84,] -2.3612e-03 -2.2242e-04 234s [85,] -5.5171e-02 -1.1222e-03 234s [86,] -1.4972e-01 1.4543e-03 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 2.8929930 1.8789522 0.9946460 0.7479403 0.3744197 0.2596328 0.1421387 234s [8] 0.0025753 234s ---------------------------------------------------------- 234s bushfire 38 5 5 37473.439646 1742.633018 234s Scores: 234s PC1 PC2 PC3 PC4 PC5 234s [1,] -67.2152 -2.3010e+01 4.4179e+00 1.0892e+00 1.7536e+00 234s [2,] -69.0225 -2.1417e+01 2.5382e+00 1.1092e+00 9.3919e-01 234s [3,] -61.6651 -1.8580e+01 -6.1022e-01 -8.1124e-01 -1.6462e-01 234s [4,] -44.5883 -1.8234e+01 -3.9899e-01 -5.2145e-01 2.0050e-01 234s [5,] -12.2941 -2.2954e+01 3.5970e+00 1.1037e+00 -2.4384e-01 234s [6,] 13.0282 -2.8133e+01 8.7670e+00 3.4751e+00 1.3728e+00 234s [7,] -199.0774 -7.7956e+01 5.4935e+01 6.3134e+00 -1.9919e+00 234s [8,] -228.2849 -1.3258e+02 2.2340e+01 2.1656e+01 -1.2594e+01 234s [9,] -228.9164 -1.3560e+02 2.0463e+01 2.2625e+01 -1.2743e+01 234s [10,] -232.4703 -1.0661e+02 3.5597e+01 1.7915e+01 -7.7659e+00 234s [11,] -236.7410 -8.8072e+01 3.6632e+01 1.5095e+01 -7.4695e+00 234s [12,] -249.4091 -3.6830e+01 2.4010e+01 4.7317e+00 -1.2986e+00 234s [13,] -197.0450 2.3179e-14 2.4481e-14 -1.1772e-13 -5.9580e-13 234s [14,] 50.9487 1.1397e+01 -1.1247e+01 -4.8733e+00 2.4511e+00 234s [15,] 180.7896 1.7571e+01 -8.0454e+00 -1.0582e+01 1.2714e+00 234s [16,] 135.6178 1.4189e+01 -4.9116e-01 -9.2701e+00 1.4021e-01 234s [17,] 132.5344 1.5577e+01 2.2990e-01 -6.4963e+00 7.3370e-01 234s [18,] 121.3422 1.0471e+01 4.5656e+00 -4.9831e+00 -5.2314e-01 234s [19,] 125.2722 9.0272e+00 3.7365e+00 -3.3313e+00 -2.9097e-01 234s [20,] 135.2370 1.4091e+01 2.0639e+00 -3.6800e+00 1.1733e+00 234s [21,] 116.4250 1.5147e+01 2.9085e+00 -4.8084e+00 1.2603e+00 234s [22,] 108.2925 1.4223e+01 7.7165e-01 -4.5065e+00 2.7943e+00 234s [23,] -22.8258 6.4234e+00 2.4654e+00 -3.9627e+00 7.9847e-01 234s [24,] -78.1850 4.6631e+00 -3.6818e+00 -2.7688e+00 5.8508e-01 234s [25,] -13.0417 2.7521e+00 -3.1955e+00 -4.6824e+00 -3.1085e-01 234s [26,] -19.1244 -9.5045e-01 -2.6771e+00 -4.7104e+00 -1.6172e-01 234s [27,] -34.4379 3.2761e+00 -9.2826e+00 -2.9861e+00 -3.3561e-01 234s [28,] -11.5852 1.4506e+01 -1.5649e+01 -1.6260e+00 -8.5347e-01 234s [29,] -2.9366 2.8741e+01 -2.2907e+01 3.9749e-01 3.5861e-02 234s [30,] -59.7518 2.8633e+01 -1.4710e+01 3.5226e+00 -9.9066e-01 234s [31,] 67.8017 4.7241e+01 -9.1255e+00 1.3201e+01 6.9227e-14 234s [32,] 321.9941 7.6188e+01 2.2491e+01 3.1537e+01 3.2368e+00 234s [33,] 359.5155 6.6710e+01 5.6061e+01 3.4541e+01 2.0718e+00 234s [34,] 355.8007 6.5695e+01 5.7430e+01 3.3578e+01 1.4640e+00 234s [35,] 361.1076 6.7577e+01 5.7402e+01 3.3832e+01 3.2618e+00 234s [36,] 358.3592 6.6791e+01 5.8643e+01 3.2720e+01 1.2487e+00 234s [37,] 355.9974 6.8071e+01 6.0927e+01 3.2560e+01 2.4898e+00 234s [38,] 357.2530 6.9073e+01 6.1517e+01 3.2523e+01 2.7558e+00 234s ------------- 234s Call: 234s PcaProj(x = x) 234s 234s Standard deviations: 234s [1] 193.5806 41.7449 16.7665 8.1585 1.6074 234s ---------------------------------------------------------- 234s ========================================================== 234s > ## IGNORE_RDIFF_END 234s > 234s > ## VT::14.11.2018 - commented out - on some platforms PcaHubert will choose only 1 PC 234s > ## and will show difference 234s > ## test.case.1() 234s > 234s > test.case.2() 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s [1] TRUE 234s > 234s BEGIN TEST tlda.R 234s 234s R version 4.4.3 (2025-02-28) -- "Trophy Case" 234s Copyright (C) 2025 The R Foundation for Statistical Computing 234s Platform: s390x-ibm-linux-gnu 234s 234s R is free software and comes with ABSOLUTELY NO WARRANTY. 234s You are welcome to redistribute it under certain conditions. 234s Type 'license()' or 'licence()' for distribution details. 234s 234s R is a collaborative project with many contributors. 234s Type 'contributors()' for more information and 234s 'citation()' on how to cite R or R packages in publications. 234s 234s Type 'demo()' for some demos, 'help()' for on-line help, or 234s 'help.start()' for an HTML browser interface to help. 234s Type 'q()' to quit R. 234s 234s > ## VT::15.09.2013 - this will render the output independent 234s > ## from the version of the package 234s > suppressPackageStartupMessages(library(rrcov)) 234s > library(MASS) 234s > 234s > ## VT::14.01.2020 234s > ## On some platforms minor differences are shown - use 234s > ## IGNORE_RDIFF_BEGIN 234s > ## IGNORE_RDIFF_END 234s > 234s > dodata <- function(method) { 234s + 234s + options(digits = 5) 234s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 234s + 234s + tmp <- sys.call() 234s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 234s + cat("===================================================\n") 234s + 234s + cat("\nData: ", "hemophilia\n") 234s + data(hemophilia) 234s + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "anorexia\n") 234s + data(anorexia) 234s + show(rlda <- Linda(Treat~., data=anorexia, method=method)) 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "Pima\n") 234s + data(Pima.tr) 234s + show(rlda <- Linda(type~., data=Pima.tr, method=method)) 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "Forest soils\n") 234s + data(soil) 234s + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) 234s + 234s + ## This will not work within the function, of course 234s + ## - comment it out 234s + ## IGNORE_RDIFF_BEGIN 234s + rlda <- Linda(F~., data=soil1983, method=method) 234s + ## show(rlda) 234s + ## IGNORE_RDIFF_END 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "Raven and Miller diabetes data\n") 234s + data(diabetes) 234s + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "iris\n") 234s + data(iris) 234s + if(method != "mcdA") 234s + { 234s + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) 234s + show(predict(rlda)) 234s + } 234s + 234s + cat("\nData: ", "crabs\n") 234s + data(crabs) 234s + show(rlda <- Linda(sp~., data=crabs, method=method)) 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "fish\n") 234s + data(fish) 234s + fish <- fish[-14,] # remove observation #14 containing missing value 234s + 234s + # The height and width are calculated as percentages 234s + # of the third length variable 234s + fish[,5] <- fish[,5]*fish[,4]/100 234s + fish[,6] <- fish[,6]*fish[,4]/100 234s + 234s + ## There is one class with only 6 observations (p=6). Normally 234s + ## Linda will fail, therefore use l1med=TRUE. 234s + ## This works only for methods mcdB and mcdC 234s + 234s + table(fish$Species) 234s + if(method != "mcdA") 234s + { 234s + ## IGNORE_RDIFF_BEGIN 234s + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) 234s + ## show(rlda) 234s + ## IGNORE_RDIFF_END 234s + show(predict(rlda)) 234s + } 234s + 234s + cat("\nData: ", "pottery\n") 234s + data(pottery) 234s + show(rlda <- Linda(origin~., data=pottery, method=method)) 234s + show(predict(rlda)) 234s + 234s + cat("\nData: ", "olitos\n") 234s + data(olitos) 234s + if(method != "mcdA") 234s + { 234s + ## IGNORE_RDIFF_BEGIN 234s + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) 234s + ## show(rlda) 234s + ## IGNORE_RDIFF_END 234s + show(predict(rlda)) 234s + } 234s + 234s + cat("===================================================\n") 234s + } 234s > 234s > 234s > ## -- now do it: 234s > dodata(method="mcdA") 234s 234s Call: dodata(method = "mcdA") 234s =================================================== 234s 234s Data: hemophilia 234s Call: 234s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 234s 234s Prior Probabilities of Groups: 234s carrier normal 234s 0.6 0.4 234s 234s Group means: 234s AHFactivity AHFantigen 234s carrier -0.30795 -0.0059911 234s normal -0.12920 -0.0603000 234s 234s Within-groups Covariance Matrix: 234s AHFactivity AHFantigen 234s AHFactivity 0.018036 0.011853 234s AHFantigen 0.011853 0.019185 234s 234s Linear Coeficients: 234s AHFactivity AHFantigen 234s carrier -28.4029 17.2368 234s normal -8.5834 2.1602 234s 234s Constants: 234s carrier normal 234s -4.8325 -1.4056 234s 234s Apparent error rate 0.1333 234s 234s Classification table 234s Predicted 234s Actual carrier normal 234s carrier 39 6 234s normal 4 26 234s 234s Confusion matrix 234s Predicted 234s Actual carrier normal 234s carrier 0.867 0.133 234s normal 0.133 0.867 234s 234s Data: anorexia 234s Call: 234s Linda(Treat ~ ., data = anorexia, method = method) 234s 234s Prior Probabilities of Groups: 234s CBT Cont FT 234s 0.40278 0.36111 0.23611 234s 234s Group means: 234s Prewt Postwt 234s CBT 82.633 82.950 234s Cont 81.558 81.108 234s FT 84.331 94.762 234s 234s Within-groups Covariance Matrix: 234s Prewt Postwt 234s Prewt 26.9291 3.3862 234s Postwt 3.3862 18.2368 234s 234s Linear Coeficients: 234s Prewt Postwt 234s CBT 2.5563 4.0738 234s Cont 2.5284 3.9780 234s FT 2.5374 4.7250 234s 234s Constants: 234s CBT Cont FT 234s -275.49 -265.45 -332.31 234s 234s Apparent error rate 0.3889 234s 234s Classification table 234s Predicted 234s Actual CBT Cont FT 234s CBT 16 5 8 234s Cont 11 15 0 234s FT 0 4 13 234s 234s Confusion matrix 234s Predicted 234s Actual CBT Cont FT 234s CBT 0.552 0.172 0.276 234s Cont 0.423 0.577 0.000 234s FT 0.000 0.235 0.765 234s 234s Data: Pima 234s Call: 234s Linda(type ~ ., data = Pima.tr, method = method) 234s 234s Prior Probabilities of Groups: 234s No Yes 234s 0.66 0.34 234s 234s Group means: 234s npreg glu bp skin bmi ped age 234s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 234s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 234s 234s Within-groups Covariance Matrix: 234s npreg glu bp skin bmi ped age 234s npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 234s glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 234s bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 234s skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 234s bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 234s ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 234s age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 234s 234s Linear Coeficients: 234s npreg glu bp skin bmi ped age 234s No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 234s Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 234s 234s Constants: 234s No Yes 234s -37.050 -51.586 234s 234s Apparent error rate 0.22 234s 234s Classification table 234s Predicted 234s Actual No Yes 234s No 107 25 234s Yes 19 49 234s 234s Confusion matrix 234s Predicted 234s Actual No Yes 234s No 0.811 0.189 234s Yes 0.279 0.721 234s 234s Data: Forest soils 234s 234s Apparent error rate 0.3103 234s 234s Classification table 234s Predicted 234s Actual 1 2 3 234s 1 7 2 2 234s 2 3 13 7 234s 3 1 3 20 234s 234s Confusion matrix 234s Predicted 234s Actual 1 2 3 234s 1 0.636 0.182 0.182 234s 2 0.130 0.565 0.304 234s 3 0.042 0.125 0.833 234s 234s Data: Raven and Miller diabetes data 234s Call: 234s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 234s 234s Prior Probabilities of Groups: 234s normal chemical overt 234s 0.52414 0.24828 0.22759 234s 234s Group means: 234s insulin glucose sspg 234s normal 163.939 345.8 99.076 234s chemical 299.448 476.9 223.621 234s overt 95.958 1026.4 343.000 234s 234s Within-groups Covariance Matrix: 234s insulin glucose sspg 234s insulin 7582.0 -1263.1 1095.8 234s glucose -1263.1 18952.4 4919.3 234s sspg 1095.8 4919.3 3351.2 234s 234s Linear Coeficients: 234s insulin glucose sspg 234s normal 0.027694 0.023859 -0.014514 234s chemical 0.040288 0.022532 0.020479 234s overt 0.017144 0.048768 0.025158 234s 234s Constants: 234s normal chemical overt 234s -6.3223 -15.0879 -31.6445 234s 234s Apparent error rate 0.1862 234s 234s Classification table 234s Predicted 234s Actual normal chemical overt 234s normal 69 7 0 234s chemical 13 23 0 234s overt 2 5 26 234s 234s Confusion matrix 234s Predicted 234s Actual normal chemical overt 234s normal 0.908 0.092 0.000 234s chemical 0.361 0.639 0.000 234s overt 0.061 0.152 0.788 234s 234s Data: iris 234s 234s Data: crabs 235s Call: 235s Linda(sp ~ ., data = crabs, method = method) 235s 235s Prior Probabilities of Groups: 235s B O 235s 0.5 0.5 235s 235s Group means: 235s sexM index FL RW CL CW BD 235s B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 235s O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 235s 235s Within-groups Covariance Matrix: 235s sexM index FL RW CL CW BD 235s sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 235s index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 235s FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 235s RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 235s CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 235s CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 235s BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 235s 235s Linear Coeficients: 235s sexM index FL RW CL CW BD 235s B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 235s O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 235s 235s Constants: 235s B O 235s -78.317 -159.259 235s 235s Apparent error rate 0 235s 235s Classification table 235s Predicted 235s Actual B O 235s B 100 0 235s O 0 100 235s 235s Confusion matrix 235s Predicted 235s Actual B O 235s B 1 0 235s O 0 1 235s 235s Data: fish 235s 235s Data: pottery 235s Call: 235s Linda(origin ~ ., data = pottery, method = method) 235s 235s Prior Probabilities of Groups: 235s Attic Eritrean 235s 0.48148 0.51852 235s 235s Group means: 235s SI AL FE MG CA TI 235s Attic 55.36 13.73 9.82 5.45 6.03 0.863 235s Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 235s 235s Within-groups Covariance Matrix: 235s SI AL FE MG CA TI 235s SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 235s AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 235s FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 235s MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 235s CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 235s TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 235s 235s Linear Coeficients: 235s SI AL FE MG CA TI 235s Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 235s Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 235s 235s Constants: 235s Attic Eritrean 235s -1578.14 -901.13 235s 235s Apparent error rate 0.1111 235s 235s Classification table 235s Predicted 235s Actual Attic Eritrean 235s Attic 12 1 235s Eritrean 2 12 235s 235s Confusion matrix 235s Predicted 235s Actual Attic Eritrean 235s Attic 0.923 0.077 235s Eritrean 0.143 0.857 235s 235s Data: olitos 235s =================================================== 235s > dodata(method="mcdB") 235s 235s Call: dodata(method = "mcdB") 235s =================================================== 235s 235s Data: hemophilia 235s Call: 235s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 235s 235s Prior Probabilities of Groups: 235s carrier normal 235s 0.6 0.4 235s 235s Group means: 235s AHFactivity AHFantigen 235s carrier -0.31456 -0.014775 235s normal -0.13582 -0.069084 235s 235s Within-groups Covariance Matrix: 235s AHFactivity AHFantigen 235s AHFactivity 0.0125319 0.0086509 235s AHFantigen 0.0086509 0.0182424 235s 235s Linear Coeficients: 235s AHFactivity AHFantigen 235s carrier -36.486 16.4923 235s normal -12.226 2.0107 235s 235s Constants: 235s carrier normal 235s -6.1276 -1.6771 235s 235s Apparent error rate 0.16 235s 235s Classification table 235s Predicted 235s Actual carrier normal 235s carrier 38 7 235s normal 5 25 235s 235s Confusion matrix 235s Predicted 235s Actual carrier normal 235s carrier 0.844 0.156 235s normal 0.167 0.833 235s 235s Data: anorexia 235s Call: 235s Linda(Treat ~ ., data = anorexia, method = method) 235s 235s Prior Probabilities of Groups: 235s CBT Cont FT 235s 0.40278 0.36111 0.23611 235s 235s Group means: 235s Prewt Postwt 235s CBT 83.254 82.381 235s Cont 82.178 80.539 235s FT 84.951 94.193 235s 235s Within-groups Covariance Matrix: 235s Prewt Postwt 235s Prewt 19.1751 8.8546 235s Postwt 8.8546 25.2326 235s 235s Linear Coeficients: 235s Prewt Postwt 235s CBT 3.3822 2.0780 235s Cont 3.3555 2.0144 235s FT 3.2299 2.5996 235s 235s Constants: 235s CBT Cont FT 235s -227.29 -220.01 -261.06 235s 235s Apparent error rate 0.4444 235s 235s Classification table 235s Predicted 235s Actual CBT Cont FT 235s CBT 16 5 8 235s Cont 12 11 3 235s FT 0 4 13 235s 235s Confusion matrix 235s Predicted 235s Actual CBT Cont FT 235s CBT 0.552 0.172 0.276 235s Cont 0.462 0.423 0.115 235s FT 0.000 0.235 0.765 235s 235s Data: Pima 235s Call: 235s Linda(type ~ ., data = Pima.tr, method = method) 235s 235s Prior Probabilities of Groups: 235s No Yes 235s 0.66 0.34 235s 235s Group means: 235s npreg glu bp skin bmi ped age 235s No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 235s Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 235s 235s Within-groups Covariance Matrix: 235s npreg glu bp skin bmi ped age 235s npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 235s glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 235s bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 235s skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 235s bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 235s ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 235s age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 235s 235s Linear Coeficients: 235s npreg glu bp skin bmi ped age 235s No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 235s Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 235s 235s Constants: 235s No Yes 235s -38.774 -53.654 235s 235s Apparent error rate 0.25 235s 235s Classification table 235s Predicted 235s Actual No Yes 235s No 104 28 235s Yes 22 46 235s 235s Confusion matrix 235s Predicted 235s Actual No Yes 235s No 0.788 0.212 235s Yes 0.324 0.676 235s 235s Data: Forest soils 235s 235s Apparent error rate 0.3448 235s 235s Classification table 235s Predicted 235s Actual 1 2 3 235s 1 4 3 4 235s 2 2 14 7 235s 3 2 2 20 235s 235s Confusion matrix 235s Predicted 235s Actual 1 2 3 235s 1 0.364 0.273 0.364 235s 2 0.087 0.609 0.304 235s 3 0.083 0.083 0.833 235s 235s Data: Raven and Miller diabetes data 235s Call: 235s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 235s 235s Prior Probabilities of Groups: 235s normal chemical overt 235s 0.52414 0.24828 0.22759 235s 235s Group means: 235s insulin glucose sspg 235s normal 152.405 346.55 99.387 235s chemical 288.244 478.80 226.226 235s overt 84.754 1028.28 345.605 235s 235s Within-groups Covariance Matrix: 235s insulin glucose sspg 235s insulin 5061.46 289.69 2071.71 235s glucose 289.69 1983.07 385.31 235s sspg 2071.71 385.31 3000.17 235s 235s Linear Coeficients: 235s insulin glucose sspg 235s normal 0.021952 0.17236 -0.0041671 235s chemical 0.034852 0.23217 0.0215200 235s overt -0.045700 0.50940 0.0813292 235s 235s Constants: 235s normal chemical overt 235s -31.976 -64.433 -275.502 235s 235s Apparent error rate 0.0966 235s 235s Classification table 235s Predicted 235s Actual normal chemical overt 235s normal 73 3 0 235s chemical 4 32 0 235s overt 0 7 26 235s 235s Confusion matrix 235s Predicted 235s Actual normal chemical overt 235s normal 0.961 0.039 0.000 235s chemical 0.111 0.889 0.000 235s overt 0.000 0.212 0.788 235s 235s Data: iris 235s Call: 235s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 235s 235s Prior Probabilities of Groups: 235s setosa versicolor virginica 235s 0.33333 0.33333 0.33333 235s 235s Group means: 235s Sepal.Length Sepal.Width Petal.Length Petal.Width 235s setosa 4.9834 3.4153 1.4532 0.22474 235s versicolor 5.8947 2.8149 4.2263 1.35024 235s virginica 6.5255 3.0017 5.4485 2.06756 235s 235s Within-groups Covariance Matrix: 235s Sepal.Length Sepal.Width Petal.Length Petal.Width 235s Sepal.Length 0.201176 0.084299 0.102984 0.037019 235s Sepal.Width 0.084299 0.108394 0.050253 0.031757 235s Petal.Length 0.102984 0.050253 0.120215 0.045016 235s Petal.Width 0.037019 0.031757 0.045016 0.032825 235s 235s Linear Coeficients: 235s Sepal.Length Sepal.Width Petal.Length Petal.Width 235s setosa 22.536 27.422168 -3.6855 -40.0445 235s versicolor 17.559 6.374082 24.1965 -18.0178 235s virginica 16.488 0.015576 29.9586 3.2926 235s 235s Constants: 235s setosa versicolor virginica 235s -96.901 -100.790 -139.937 235s 235s Apparent error rate 0.0267 235s 235s Classification table 235s Predicted 235s Actual setosa versicolor virginica 235s setosa 50 0 0 235s versicolor 0 48 2 235s virginica 0 2 48 235s 235s Confusion matrix 235s Predicted 235s Actual setosa versicolor virginica 235s setosa 1 0.00 0.00 235s versicolor 0 0.96 0.04 235s virginica 0 0.04 0.96 235s 235s Data: crabs 235s Call: 235s Linda(sp ~ ., data = crabs, method = method) 235s 235s Prior Probabilities of Groups: 235s B O 235s 0.5 0.5 235s 235s Group means: 235s sexM index FL RW CL CW BD 235s B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 235s O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 235s 235s Within-groups Covariance Matrix: 235s sexM index FL RW CL CW BD 235s sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 235s index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 235s FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 235s RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 235s CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 235s CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 235s BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 235s 235s Linear Coeficients: 235s sexM index FL RW CL CW BD 235s B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 235s O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 235s 235s Constants: 235s B O 235s -66.688 -134.375 235s 235s Apparent error rate 0 235s 235s Classification table 235s Predicted 235s Actual B O 235s B 100 0 235s O 0 100 235s 235s Confusion matrix 235s Predicted 235s Actual B O 235s B 1 0 235s O 0 1 235s 235s Data: fish 235s 235s Apparent error rate 0.0949 235s 235s Classification table 235s Predicted 235s Actual 1 2 3 4 5 6 7 235s 1 34 0 0 0 0 0 0 235s 2 0 6 0 0 0 0 0 235s 3 0 0 20 0 0 0 0 235s 4 0 0 0 11 0 0 0 235s 5 0 0 0 0 13 0 1 235s 6 0 0 0 0 0 17 0 235s 7 0 13 0 0 1 0 42 235s 235s Confusion matrix 235s Predicted 235s Actual 1 2 3 4 5 6 7 235s 1 1 0.000 0 0 0.000 0 0.000 235s 2 0 1.000 0 0 0.000 0 0.000 235s 3 0 0.000 1 0 0.000 0 0.000 235s 4 0 0.000 0 1 0.000 0 0.000 235s 5 0 0.000 0 0 0.929 0 0.071 235s 6 0 0.000 0 0 0.000 1 0.000 235s 7 0 0.232 0 0 0.018 0 0.750 235s 235s Data: pottery 235s Call: 235s Linda(origin ~ ., data = pottery, method = method) 235s 235s Prior Probabilities of Groups: 235s Attic Eritrean 235s 0.48148 0.51852 235s 235s Group means: 235s SI AL FE MG CA TI 235s Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 235s Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 235s 235s Within-groups Covariance Matrix: 235s SI AL FE MG CA TI 235s SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 235s AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 235s FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 235s MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 235s CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 235s TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 235s 235s Linear Coeficients: 235s SI AL FE MG CA TI 235s Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 235s Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 235s 235s Constants: 235s Attic Eritrean 235s -3252.6 -1961.9 235s 235s Apparent error rate 0.1111 235s 235s Classification table 235s Predicted 235s Actual Attic Eritrean 235s Attic 12 1 235s Eritrean 2 12 235s 235s Confusion matrix 235s Predicted 235s Actual Attic Eritrean 235s Attic 0.923 0.077 235s Eritrean 0.143 0.857 235s 235s Data: olitos 235s 235s Apparent error rate 0.15 235s 235s Classification table 235s Predicted 235s Actual 1 2 3 4 235s 1 44 1 4 1 235s 2 2 23 0 0 235s 3 6 1 26 1 235s 4 1 1 0 9 235s 235s Confusion matrix 235s Predicted 235s Actual 1 2 3 4 235s 1 0.880 0.020 0.080 0.020 235s 2 0.080 0.920 0.000 0.000 235s 3 0.176 0.029 0.765 0.029 235s 4 0.091 0.091 0.000 0.818 235s =================================================== 235s > dodata(method="mcdC") 235s 235s Call: dodata(method = "mcdC") 235s =================================================== 235s 235s Data: hemophilia 235s Call: 235s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 235s 235s Prior Probabilities of Groups: 235s carrier normal 235s 0.6 0.4 235s 235s Group means: 235s AHFactivity AHFantigen 235s carrier -0.32583 -0.011545 235s normal -0.12783 -0.071377 235s 235s Within-groups Covariance Matrix: 235s AHFactivity AHFantigen 235s AHFactivity 0.0120964 0.0075536 235s AHFantigen 0.0075536 0.0164883 235s 235s Linear Coeficients: 235s AHFactivity AHFantigen 235s carrier -37.117 16.30377 235s normal -11.015 0.71742 235s 235s Constants: 235s carrier normal 235s -6.4636 -1.5947 235s 235s Apparent error rate 0.16 235s 235s Classification table 235s Predicted 235s Actual carrier normal 235s carrier 38 7 235s normal 5 25 235s 235s Confusion matrix 235s Predicted 235s Actual carrier normal 235s carrier 0.844 0.156 235s normal 0.167 0.833 235s 235s Data: anorexia 235s Call: 235s Linda(Treat ~ ., data = anorexia, method = method) 235s 235s Prior Probabilities of Groups: 235s CBT Cont FT 235s 0.40278 0.36111 0.23611 235s 235s Group means: 235s Prewt Postwt 235s CBT 82.477 82.073 235s Cont 82.039 80.835 235s FT 85.242 94.750 235s 235s Within-groups Covariance Matrix: 235s Prewt Postwt 235s Prewt 19.6589 8.3891 235s Postwt 8.3891 22.8805 235s 235s Linear Coeficients: 235s Prewt Postwt 235s CBT 3.1590 2.4288 235s Cont 3.1599 2.3743 235s FT 3.0454 3.0245 235s 235s Constants: 235s CBT Cont FT 235s -230.85 -226.60 -274.53 235s 235s Apparent error rate 0.4583 235s 235s Classification table 235s Predicted 235s Actual CBT Cont FT 235s CBT 16 5 8 235s Cont 14 10 2 235s FT 0 4 13 235s 235s Confusion matrix 235s Predicted 235s Actual CBT Cont FT 235s CBT 0.552 0.172 0.276 235s Cont 0.538 0.385 0.077 235s FT 0.000 0.235 0.765 235s 235s Data: Pima 235s Call: 235s Linda(type ~ ., data = Pima.tr, method = method) 235s 235s Prior Probabilities of Groups: 235s No Yes 235s 0.66 0.34 235s 235s Group means: 235s npreg glu bp skin bmi ped age 235s No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 235s Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 235s 235s Within-groups Covariance Matrix: 235s npreg glu bp skin bmi ped age 235s npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 235s glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 235s bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 235s skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 235s bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 235s ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 235s age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 235s 235s Linear Coeficients: 235s npreg glu bp skin bmi ped age 235s No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 235s Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 235s 235s Constants: 235s No Yes 235s -41.716 -55.056 235s 235s Apparent error rate 0.235 235s 235s Classification table 235s Predicted 235s Actual No Yes 235s No 107 25 235s Yes 22 46 235s 235s Confusion matrix 235s Predicted 235s Actual No Yes 235s No 0.811 0.189 235s Yes 0.324 0.676 235s 235s Data: Forest soils 235s 235s Apparent error rate 0.3276 235s 235s Classification table 235s Predicted 235s Actual 1 2 3 235s 1 5 2 4 235s 2 2 13 8 235s 3 1 2 21 235s 235s Confusion matrix 235s Predicted 235s Actual 1 2 3 235s 1 0.455 0.182 0.364 235s 2 0.087 0.565 0.348 235s 3 0.042 0.083 0.875 235s 235s Data: Raven and Miller diabetes data 235s Call: 235s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 235s 235s Prior Probabilities of Groups: 235s normal chemical overt 235s 0.52414 0.24828 0.22759 235s 235s Group means: 235s insulin glucose sspg 235s normal 167.31 348.69 106.44 235s chemical 247.18 478.18 213.36 235s overt 101.83 932.92 322.42 235s 235s Within-groups Covariance Matrix: 235s insulin glucose sspg 235s insulin 4070.84 118.89 1701.54 235s glucose 118.89 2195.95 426.95 235s sspg 1701.54 426.95 2664.49 235s 235s Linear Coeficients: 235s insulin glucose sspg 235s normal 0.041471 0.15888 -0.011992 235s chemical 0.048103 0.21216 0.015359 235s overt -0.013579 0.41323 0.063462 235s 235s Constants: 235s normal chemical overt 235s -31.177 -59.703 -203.775 235s 235s Apparent error rate 0.0828 235s 235s Classification table 235s Predicted 235s Actual normal chemical overt 235s normal 72 4 0 235s chemical 2 34 0 235s overt 0 6 27 235s 235s Confusion matrix 235s Predicted 235s Actual normal chemical overt 235s normal 0.947 0.053 0.000 235s chemical 0.056 0.944 0.000 235s overt 0.000 0.182 0.818 235s 235s Data: iris 235s Call: 235s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 235s 235s Prior Probabilities of Groups: 235s setosa versicolor virginica 235s 0.33333 0.33333 0.33333 235s 235s Group means: 235s Sepal.Length Sepal.Width Petal.Length Petal.Width 235s setosa 5.0163 3.4510 1.4653 0.2449 235s versicolor 5.9435 2.7891 4.2543 1.3239 235s virginica 6.3867 3.0033 5.3767 2.0700 235s 235s Within-groups Covariance Matrix: 235s Sepal.Length Sepal.Width Petal.Length Petal.Width 235s Sepal.Length 0.186186 0.082478 0.094998 0.035445 235s Sepal.Width 0.082478 0.100137 0.049723 0.030678 235s Petal.Length 0.094998 0.049723 0.113105 0.043078 235s Petal.Width 0.035445 0.030678 0.043078 0.030885 235s 235s Linear Coeficients: 235s Sepal.Length Sepal.Width Petal.Length Petal.Width 235s setosa 23.678 30.2896 -3.1124 -44.9900 235s versicolor 20.342 4.6372 27.3265 -23.2006 235s virginica 18.377 -2.0004 31.4235 4.0906 235s 235s Constants: 235s setosa versicolor virginica 235s -104.96 -110.79 -145.49 235s 235s Apparent error rate 0.0333 235s 235s Classification table 235s Predicted 235s Actual setosa versicolor virginica 235s setosa 50 0 0 235s versicolor 0 48 2 235s virginica 0 3 47 235s 235s Confusion matrix 235s Predicted 235s Actual setosa versicolor virginica 235s setosa 1 0.00 0.00 235s versicolor 0 0.96 0.04 235s virginica 0 0.06 0.94 235s 235s Data: crabs 235s Call: 235s Linda(sp ~ ., data = crabs, method = method) 235s 235s Prior Probabilities of Groups: 235s B O 235s 0.5 0.5 235s 235s Group means: 235s sexM index FL RW CL CW BD 235s B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 235s O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 235s 235s Within-groups Covariance Matrix: 235s sexM index FL RW CL CW BD 235s sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 235s index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 235s FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 235s RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 235s CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 235s CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 235s BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 235s 235s Linear Coeficients: 235s sexM index FL RW CL CW BD 235s B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 235s O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 235s 235s Constants: 235s B O 235s -72.687 -142.365 235s 235s Apparent error rate 0 235s 235s Classification table 235s Predicted 235s Actual B O 235s B 100 0 235s O 0 100 235s 235s Confusion matrix 235s Predicted 235s Actual B O 235s B 1 0 235s O 0 1 235s 235s Data: fish 236s 236s Apparent error rate 0.0316 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 4 5 6 7 236s 1 34 0 0 0 0 0 0 236s 2 0 5 0 0 1 0 0 236s 3 0 0 20 0 0 0 0 236s 4 0 0 0 11 0 0 0 236s 5 0 0 0 0 13 0 1 236s 6 0 0 0 0 0 17 0 236s 7 0 0 0 0 3 0 53 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 4 5 6 7 236s 1 1 0.000 0 0 0.000 0 0.000 236s 2 0 0.833 0 0 0.167 0 0.000 236s 3 0 0.000 1 0 0.000 0 0.000 236s 4 0 0.000 0 1 0.000 0 0.000 236s 5 0 0.000 0 0 0.929 0 0.071 236s 6 0 0.000 0 0 0.000 1 0.000 236s 7 0 0.000 0 0 0.054 0 0.946 236s 236s Data: pottery 236s Call: 236s Linda(origin ~ ., data = pottery, method = method) 236s 236s Prior Probabilities of Groups: 236s Attic Eritrean 236s 0.48148 0.51852 236s 236s Group means: 236s SI AL FE MG CA TI 236s Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 236s Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 236s 236s Within-groups Covariance Matrix: 236s SI AL FE MG CA TI 236s SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 236s AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 236s FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 236s MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 236s CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 236s TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 236s 236s Linear Coeficients: 236s SI AL FE MG CA TI 236s Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 236s Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 236s 236s Constants: 236s Attic Eritrean 236s -5924.2 -3802.9 236s 236s Apparent error rate 0.1111 236s 236s Classification table 236s Predicted 236s Actual Attic Eritrean 236s Attic 12 1 236s Eritrean 2 12 236s 236s Confusion matrix 236s Predicted 236s Actual Attic Eritrean 236s Attic 0.923 0.077 236s Eritrean 0.143 0.857 236s 236s Data: olitos 236s 236s Apparent error rate 0.1667 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 4 236s 1 44 1 2 3 236s 2 2 22 0 1 236s 3 5 2 25 2 236s 4 1 1 0 9 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 4 236s 1 0.880 0.020 0.040 0.060 236s 2 0.080 0.880 0.000 0.040 236s 3 0.147 0.059 0.735 0.059 236s 4 0.091 0.091 0.000 0.818 236s =================================================== 236s > dodata(method="mrcd") 236s 236s Call: dodata(method = "mrcd") 236s =================================================== 236s 236s Data: hemophilia 236s Call: 236s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 236s 236s Prior Probabilities of Groups: 236s carrier normal 236s 0.6 0.4 236s 236s Group means: 236s AHFactivity AHFantigen 236s carrier -0.34048 -0.055943 236s normal -0.13566 -0.081191 236s 236s Within-groups Covariance Matrix: 236s AHFactivity AHFantigen 236s AHFactivity 0.0133676 0.0088055 236s AHFantigen 0.0088055 0.0221225 236s 236s Linear Coeficients: 236s AHFactivity AHFantigen 236s carrier -32.264 10.31334 236s normal -10.478 0.50044 236s 236s Constants: 236s carrier normal 236s -5.7149 -1.6067 236s 236s Apparent error rate 0.16 236s 236s Classification table 236s Predicted 236s Actual carrier normal 236s carrier 38 7 236s normal 5 25 236s 236s Confusion matrix 236s Predicted 236s Actual carrier normal 236s carrier 0.844 0.156 236s normal 0.167 0.833 236s 236s Data: anorexia 236s Call: 236s Linda(Treat ~ ., data = anorexia, method = method) 236s 236s Prior Probabilities of Groups: 236s CBT Cont FT 236s 0.40278 0.36111 0.23611 236s 236s Group means: 236s Prewt Postwt 236s CBT 83.114 84.009 236s Cont 80.327 80.125 236s FT 85.161 94.371 236s 236s Within-groups Covariance Matrix: 236s Prewt Postwt 236s Prewt 22.498 11.860 236s Postwt 11.860 20.426 236s 236s Linear Coeficients: 236s Prewt Postwt 236s CBT 2.1994 2.8357 236s Cont 2.1653 2.6654 236s FT 1.9451 3.4907 236s 236s Constants: 236s CBT Cont FT 236s -211.42 -194.77 -248.97 236s 236s Apparent error rate 0.3889 236s 236s Classification table 236s Predicted 236s Actual CBT Cont FT 236s CBT 15 6 8 236s Cont 6 16 4 236s FT 0 4 13 236s 236s Confusion matrix 236s Predicted 236s Actual CBT Cont FT 236s CBT 0.517 0.207 0.276 236s Cont 0.231 0.615 0.154 236s FT 0.000 0.235 0.765 236s 236s Data: Pima 236s Call: 236s Linda(type ~ ., data = Pima.tr, method = method) 236s 236s Prior Probabilities of Groups: 236s No Yes 236s 0.66 0.34 236s 236s Group means: 236s npreg glu bp skin bmi ped age 236s No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 236s Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 236s 236s Within-groups Covariance Matrix: 236s npreg glu bp skin bmi ped age 236s npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 236s glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 236s bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 236s skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 236s bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 236s ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 236s age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 236s 236s Linear Coeficients: 236s npreg glu bp skin bmi ped age 236s No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 236s Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 236s 236s Constants: 236s No Yes 236s -33.429 -45.434 236s 236s Apparent error rate 0.28 236s 236s Classification table 236s Predicted 236s Actual No Yes 236s No 105 27 236s Yes 29 39 236s 236s Confusion matrix 236s Predicted 236s Actual No Yes 236s No 0.795 0.205 236s Yes 0.426 0.574 236s 236s Data: Forest soils 236s 236s Apparent error rate 0.3448 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 236s 1 7 2 2 236s 2 4 14 5 236s 3 3 4 17 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 236s 1 0.636 0.182 0.182 236s 2 0.174 0.609 0.217 236s 3 0.125 0.167 0.708 236s 236s Data: Raven and Miller diabetes data 236s Call: 236s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 236s 236s Prior Probabilities of Groups: 236s normal chemical overt 236s 0.52414 0.24828 0.22759 236s 236s Group means: 236s insulin glucose sspg 236s normal 154.014 346.07 91.606 236s chemical 248.841 451.10 221.936 236s overt 89.766 1064.16 335.100 236s 236s Within-groups Covariance Matrix: 236s insulin glucose sspg 236s insulin 4948.1 1007.61 1471.12 236s glucose 1007.6 2597.38 358.57 236s sspg 1471.1 358.57 3180.04 236s 236s Linear Coeficients: 236s insulin glucose sspg 236s normal 0.00027839 0.13121 0.013882 236s chemical 0.00148074 0.16615 0.050371 236s overt -0.10102404 0.43466 0.103100 236s 236s Constants: 236s normal chemical overt 236s -24.008 -44.642 -245.497 236s 236s Apparent error rate 0.0966 236s 236s Classification table 236s Predicted 236s Actual normal chemical overt 236s normal 71 5 0 236s chemical 2 34 0 236s overt 0 7 26 236s 236s Confusion matrix 236s Predicted 236s Actual normal chemical overt 236s normal 0.934 0.066 0.000 236s chemical 0.056 0.944 0.000 236s overt 0.000 0.212 0.788 236s 236s Data: iris 236s Call: 236s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 236s 236s Prior Probabilities of Groups: 236s setosa versicolor virginica 236s 0.33333 0.33333 0.33333 236s 236s Group means: 236s Sepal.Length Sepal.Width Petal.Length Petal.Width 236s setosa 4.9755 3.3826 1.4608 0.22053 236s versicolor 5.8868 2.7823 4.2339 1.34603 236s virginica 6.5176 2.9691 5.4560 2.06335 236s 236s Within-groups Covariance Matrix: 236s Sepal.Length Sepal.Width Petal.Length Petal.Width 236s Sepal.Length 0.238417 0.136325 0.086377 0.036955 236s Sepal.Width 0.136325 0.148452 0.067500 0.034804 236s Petal.Length 0.086377 0.067500 0.100934 0.035968 236s Petal.Width 0.036955 0.034804 0.035968 0.023856 236s 236s Linear Coeficients: 236s Sepal.Length Sepal.Width Petal.Length Petal.Width 236s setosa 17.106 15.693 7.8806 -52.031 236s versicolor 21.744 -15.813 38.0139 -11.505 236s virginica 23.032 -26.567 43.6391 23.777 236s 236s Constants: 236s setosa versicolor virginica 236s -70.214 -115.832 -180.294 236s 236s Apparent error rate 0.02 236s 236s Classification table 236s Predicted 236s Actual setosa versicolor virginica 236s setosa 50 0 0 236s versicolor 0 49 1 236s virginica 0 2 48 236s 236s Confusion matrix 236s Predicted 236s Actual setosa versicolor virginica 236s setosa 1 0.00 0.00 236s versicolor 0 0.98 0.02 236s virginica 0 0.04 0.96 236s 236s Data: crabs 236s Call: 236s Linda(sp ~ ., data = crabs, method = method) 236s 236s Prior Probabilities of Groups: 236s B O 236s 0.5 0.5 236s 236s Group means: 236s sexM index FL RW CL CW BD 236s B 0 25.5 13.270 12.138 28.102 32.624 11.816 236s O 1 25.5 16.626 12.262 33.688 37.188 15.324 236s 236s Within-groups Covariance Matrix: 236s sexM index FL RW CL CW BD 236s sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 236s index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 236s FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 236s RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 236s CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 236s CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 236s BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 236s 236s Linear Coeficients: 236s sexM index FL RW CL CW BD 236s B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 236s O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 236s 236s Constants: 236s B O 236s -2.1491e+01 -3.2776e+06 236s 236s Apparent error rate 0.5 236s 236s Classification table 236s Predicted 236s Actual B O 236s B 50 50 236s O 50 50 236s 236s Confusion matrix 236s Predicted 236s Actual B O 236s B 0.5 0.5 236s O 0.5 0.5 236s 236s Data: fish 236s 236s Apparent error rate 0.2532 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 4 5 6 7 236s 1 33 0 0 1 0 0 0 236s 2 0 3 0 0 0 0 3 236s 3 0 2 5 0 0 0 13 236s 4 0 0 0 11 0 0 0 236s 5 0 0 0 0 14 0 0 236s 6 0 0 0 0 0 17 0 236s 7 0 19 0 0 2 0 35 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 4 5 6 7 236s 1 0.971 0.000 0.00 0.029 0.000 0 0.000 236s 2 0.000 0.500 0.00 0.000 0.000 0 0.500 236s 3 0.000 0.100 0.25 0.000 0.000 0 0.650 236s 4 0.000 0.000 0.00 1.000 0.000 0 0.000 236s 5 0.000 0.000 0.00 0.000 1.000 0 0.000 236s 6 0.000 0.000 0.00 0.000 0.000 1 0.000 236s 7 0.000 0.339 0.00 0.000 0.036 0 0.625 236s 236s Data: pottery 236s Call: 236s Linda(origin ~ ., data = pottery, method = method) 236s 236s Prior Probabilities of Groups: 236s Attic Eritrean 236s 0.48148 0.51852 236s 236s Group means: 236s SI AL FE MG CA TI 236s Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 236s Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 236s 236s Within-groups Covariance Matrix: 236s SI AL FE MG CA TI 236s SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 236s AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 236s FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 236s MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 236s CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 236s TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 236s 236s Linear Coeficients: 236s SI AL FE MG CA TI 236s Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 236s Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 236s 236s Constants: 236s Attic Eritrean 236s -4000.1 -3776.1 236s 236s Apparent error rate 0.1111 236s 236s Classification table 236s Predicted 236s Actual Attic Eritrean 236s Attic 12 1 236s Eritrean 2 12 236s 236s Confusion matrix 236s Predicted 236s Actual Attic Eritrean 236s Attic 0.923 0.077 236s Eritrean 0.143 0.857 236s 236s Data: olitos 236s 236s Apparent error rate 0.125 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 4 236s 1 44 2 3 1 236s 2 1 23 1 0 236s 3 4 1 27 2 236s 4 0 0 0 11 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 4 236s 1 0.880 0.040 0.060 0.020 236s 2 0.040 0.920 0.040 0.000 236s 3 0.118 0.029 0.794 0.059 236s 4 0.000 0.000 0.000 1.000 236s =================================================== 236s > dodata(method="ogk") 236s 236s Call: dodata(method = "ogk") 236s =================================================== 236s 236s Data: hemophilia 236s Call: 236s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 236s 236s Prior Probabilities of Groups: 236s carrier normal 236s 0.6 0.4 236s 236s Group means: 236s AHFactivity AHFantigen 236s carrier -0.29043 -0.00052902 236s normal -0.12463 -0.06715037 236s 236s Within-groups Covariance Matrix: 236s AHFactivity AHFantigen 236s AHFactivity 0.015688 0.010544 236s AHFantigen 0.010544 0.016633 236s 236s Linear Coeficients: 236s AHFactivity AHFantigen 236s carrier -32.2203 20.3935 236s normal -9.1149 1.7409 236s 236s Constants: 236s carrier normal 236s -5.1843 -1.4259 236s 236s Apparent error rate 0.1467 236s 236s Classification table 236s Predicted 236s Actual carrier normal 236s carrier 38 7 236s normal 4 26 236s 236s Confusion matrix 236s Predicted 236s Actual carrier normal 236s carrier 0.844 0.156 236s normal 0.133 0.867 236s 236s Data: anorexia 236s Call: 236s Linda(Treat ~ ., data = anorexia, method = method) 236s 236s Prior Probabilities of Groups: 236s CBT Cont FT 236s 0.40278 0.36111 0.23611 236s 236s Group means: 236s Prewt Postwt 236s CBT 82.634 82.060 236s Cont 81.605 80.459 236s FT 85.157 93.822 236s 236s Within-groups Covariance Matrix: 236s Prewt Postwt 236s Prewt 15.8294 4.4663 236s Postwt 4.4663 19.6356 236s 236s Linear Coeficients: 236s Prewt Postwt 236s CBT 4.3183 3.1970 236s Cont 4.2734 3.1256 236s FT 4.3080 3.7983 236s 236s Constants: 236s CBT Cont FT 236s -310.50 -301.12 -363.05 236s 236s Apparent error rate 0.4583 236s 236s Classification table 236s Predicted 236s Actual CBT Cont FT 236s CBT 15 5 9 236s Cont 14 11 1 236s FT 0 4 13 236s 236s Confusion matrix 236s Predicted 236s Actual CBT Cont FT 236s CBT 0.517 0.172 0.310 236s Cont 0.538 0.423 0.038 236s FT 0.000 0.235 0.765 236s 236s Data: Pima 236s Call: 236s Linda(type ~ ., data = Pima.tr, method = method) 236s 236s Prior Probabilities of Groups: 236s No Yes 236s 0.66 0.34 236s 236s Group means: 236s npreg glu bp skin bmi ped age 236s No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 236s Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 236s 236s Within-groups Covariance Matrix: 236s npreg glu bp skin bmi ped age 236s npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 236s glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 236s bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 236s skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 236s bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 236s ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 236s age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 236s 236s Linear Coeficients: 236s npreg glu bp skin bmi ped age 236s No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 236s Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 236s 236s Constants: 236s No Yes 236s -43.449 -60.176 236s 236s Apparent error rate 0.23 236s 236s Classification table 236s Predicted 236s Actual No Yes 236s No 108 24 236s Yes 22 46 236s 236s Confusion matrix 236s Predicted 236s Actual No Yes 236s No 0.818 0.182 236s Yes 0.324 0.676 236s 236s Data: Forest soils 236s 236s Apparent error rate 0.3621 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 236s 1 7 3 1 236s 2 4 13 6 236s 3 3 4 17 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 236s 1 0.636 0.273 0.091 236s 2 0.174 0.565 0.261 236s 3 0.125 0.167 0.708 236s 236s Data: Raven and Miller diabetes data 236s Call: 236s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 236s 236s Prior Probabilities of Groups: 236s normal chemical overt 236s 0.52414 0.24828 0.22759 236s 236s Group means: 236s insulin glucose sspg 236s normal 159.540 344.06 99.486 236s chemical 252.992 478.16 219.442 236s overt 79.635 1094.96 338.517 236s 236s Within-groups Covariance Matrix: 236s insulin glucose sspg 236s insulin 3844.877 67.238 1456.55 236s glucose 67.238 2228.396 324.21 236s sspg 1456.548 324.205 2181.73 236s 236s Linear Coeficients: 236s insulin glucose sspg 236s normal 0.040407 0.15379 -0.0042303 236s chemical 0.047858 0.20764 0.0377766 236s overt -0.026158 0.47736 0.1016873 236s 236s Constants: 236s normal chemical overt 236s -30.115 -61.233 -278.996 236s 236s Apparent error rate 0.0966 236s 236s Classification table 236s Predicted 236s Actual normal chemical overt 236s normal 71 5 0 236s chemical 2 34 0 236s overt 0 7 26 236s 236s Confusion matrix 236s Predicted 236s Actual normal chemical overt 236s normal 0.934 0.066 0.000 236s chemical 0.056 0.944 0.000 236s overt 0.000 0.212 0.788 236s 236s Data: iris 236s Call: 236s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 236s 236s Prior Probabilities of Groups: 236s setosa versicolor virginica 236s 0.33333 0.33333 0.33333 236s 236s Group means: 236s Sepal.Length Sepal.Width Petal.Length Petal.Width 236s setosa 4.9654 3.3913 1.4507 0.21639 236s versicolor 5.8767 2.7909 4.2238 1.34189 236s virginica 6.5075 2.9777 5.4459 2.05921 236s 236s Within-groups Covariance Matrix: 236s Sepal.Length Sepal.Width Petal.Length Petal.Width 236s Sepal.Length 0.180280 0.068876 0.101512 0.036096 236s Sepal.Width 0.068876 0.079556 0.047722 0.029409 236s Petal.Length 0.101512 0.047722 0.111492 0.038658 236s Petal.Width 0.036096 0.029409 0.038658 0.029965 236s 236s Linear Coeficients: 236s Sepal.Length Sepal.Width Petal.Length Petal.Width 236s setosa 28.582 46.5236 -13.859 -54.9877 236s versicolor 19.837 11.9601 20.865 -17.7704 236s virginica 16.999 1.9046 29.808 7.9189 236s 236s Constants: 236s setosa versicolor virginica 236s -134.94 -108.22 -148.56 236s 236s Apparent error rate 0.0133 236s 236s Classification table 236s Predicted 236s Actual setosa versicolor virginica 236s setosa 50 0 0 236s versicolor 0 49 1 236s virginica 0 1 49 236s 236s Confusion matrix 236s Predicted 236s Actual setosa versicolor virginica 236s setosa 1 0.00 0.00 236s versicolor 0 0.98 0.02 236s virginica 0 0.02 0.98 236s 236s Data: crabs 236s Call: 236s Linda(sp ~ ., data = crabs, method = method) 236s 236s Prior Probabilities of Groups: 236s B O 236s 0.5 0.5 236s 236s Group means: 236s sexM index FL RW CL CW BD 236s B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 236s O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 236s 236s Within-groups Covariance Matrix: 236s sexM index FL RW CL CW BD 236s sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 236s index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 236s FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 236s RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 236s CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 236s CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 236s BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 236s 236s Linear Coeficients: 236s sexM index FL RW CL CW BD 236s B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 236s O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 236s 236s Constants: 236s B O 236s -77.695 -147.287 236s 236s Apparent error rate 0 236s 236s Classification table 236s Predicted 236s Actual B O 236s B 100 0 236s O 0 100 236s 236s Confusion matrix 236s Predicted 236s Actual B O 236s B 1 0 236s O 0 1 236s 236s Data: fish 236s 236s Apparent error rate 0.0063 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 4 5 6 7 236s 1 34 0 0 0 0 0 0 236s 2 0 6 0 0 0 0 0 236s 3 0 0 20 0 0 0 0 236s 4 0 0 0 11 0 0 0 236s 5 0 0 0 0 14 0 0 236s 6 0 0 0 0 0 17 0 236s 7 0 0 0 0 1 0 55 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 4 5 6 7 236s 1 1 0 0 0 0.000 0 0.000 236s 2 0 1 0 0 0.000 0 0.000 236s 3 0 0 1 0 0.000 0 0.000 236s 4 0 0 0 1 0.000 0 0.000 236s 5 0 0 0 0 1.000 0 0.000 236s 6 0 0 0 0 0.000 1 0.000 236s 7 0 0 0 0 0.018 0 0.982 236s 236s Data: pottery 236s Call: 236s Linda(origin ~ ., data = pottery, method = method) 236s 236s Prior Probabilities of Groups: 236s Attic Eritrean 236s 0.48148 0.51852 236s 236s Group means: 236s SI AL FE MG CA TI 236s Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 236s Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 236s 236s Within-groups Covariance Matrix: 236s SI AL FE MG CA TI 236s SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 236s AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 236s FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 236s MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 236s CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 236s TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 236s 236s Linear Coeficients: 236s SI AL FE MG CA TI 236s Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 236s Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 236s 236s Constants: 236s Attic Eritrean 236s -4346 -4139 236s 236s Apparent error rate 0.1111 236s 236s Classification table 236s Predicted 236s Actual Attic Eritrean 236s Attic 12 1 236s Eritrean 2 12 236s 236s Confusion matrix 236s Predicted 236s Actual Attic Eritrean 236s Attic 0.923 0.077 236s Eritrean 0.143 0.857 236s 236s Data: olitos 236s 236s Apparent error rate 0.1 236s 236s Classification table 236s Predicted 236s Actual 1 2 3 4 236s 1 45 2 2 1 236s 2 0 25 0 0 236s 3 4 1 27 2 236s 4 0 0 0 11 236s 236s Confusion matrix 236s Predicted 236s Actual 1 2 3 4 236s 1 0.900 0.040 0.040 0.020 236s 2 0.000 1.000 0.000 0.000 236s 3 0.118 0.029 0.794 0.059 236s 4 0.000 0.000 0.000 1.000 236s =================================================== 236s > #dodata(method="fsa") 236s > 236s BEGIN TEST tldapp.R 236s 236s R version 4.4.3 (2025-02-28) -- "Trophy Case" 236s Copyright (C) 2025 The R Foundation for Statistical Computing 236s Platform: s390x-ibm-linux-gnu 236s 236s R is free software and comes with ABSOLUTELY NO WARRANTY. 236s You are welcome to redistribute it under certain conditions. 236s Type 'license()' or 'licence()' for distribution details. 236s 236s R is a collaborative project with many contributors. 236s Type 'contributors()' for more information and 236s 'citation()' on how to cite R or R packages in publications. 236s 236s Type 'demo()' for some demos, 'help()' for on-line help, or 236s 'help.start()' for an HTML browser interface to help. 236s Type 'q()' to quit R. 236s 237s > ## VT::15.09.2013 - this will render the output independent 237s > ## from the version of the package 237s > suppressPackageStartupMessages(library(rrcov)) 237s > library(MASS) 237s > 237s > dodata <- function(method) { 237s + 237s + options(digits = 5) 237s + set.seed(101) 237s + 237s + tmp <- sys.call() 237s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 237s + cat("===================================================\n") 237s + 237s + data(pottery); show(lda <- LdaPP(origin~., data=pottery, method=method)); show(predict(lda)) 237s + data(hemophilia); show(lda <- LdaPP(as.factor(gr)~., data=hemophilia, method=method)); show(predict(lda)) 237s + data(anorexia); show(lda <- LdaPP(Treat~., data=anorexia, method=method)); show(predict(lda)) 237s + data(Pima.tr); show(lda <- LdaPP(type~., data=Pima.tr, method=method)); show(predict(lda)) 237s + data(crabs); show(lda <- LdaPP(sp~., data=crabs, method=method)); show(predict(lda)) 237s + 237s + cat("===================================================\n") 237s + } 237s > 237s > 237s > ## -- now do it: 237s > 237s > ## Commented out - still to slow 237s > ##dodata(method="huber") 237s > ##dodata(method="sest") 237s > 237s > ## VT::14.11.2018 - Commented out: too slow 237s > ## dodata(method="mad") 237s > ## dodata(method="class") 237s > 237s BEGIN TEST tmcd4.R 237s 237s R version 4.4.3 (2025-02-28) -- "Trophy Case" 237s Copyright (C) 2025 The R Foundation for Statistical Computing 237s Platform: s390x-ibm-linux-gnu 237s 237s R is free software and comes with ABSOLUTELY NO WARRANTY. 237s You are welcome to redistribute it under certain conditions. 237s Type 'license()' or 'licence()' for distribution details. 237s 237s R is a collaborative project with many contributors. 237s Type 'contributors()' for more information and 237s 'citation()' on how to cite R or R packages in publications. 237s 237s Type 'demo()' for some demos, 'help()' for on-line help, or 237s 'help.start()' for an HTML browser interface to help. 237s Type 'q()' to quit R. 237s 237s > ## Test the exact fit property of CovMcd 237s > doexactfit <- function(){ 237s + exact <-function(seed=1234){ 237s + 237s + set.seed(seed) 237s + 237s + n1 <- 45 237s + p <- 2 237s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 237s + x1[,p] <- x1[,p] + 3 237s + n2 <- 55 237s + m1 <- 0 237s + m2 <- 3 237s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 237s + x<-rbind(x1,x2) 237s + colnames(x) <- c("X1","X2") 237s + x 237s + } 237s + print(CovMcd(exact())) 237s + } 237s > 237s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 237s + ##@bdescr 237s + ## Test the function covMcd() on the literature datasets: 237s + ## 237s + ## Call CovMcd() for all regression datasets available in rrcov and print: 237s + ## - execution time (if time == TRUE) 237s + ## - objective fucntion 237s + ## - best subsample found (if short == false) 237s + ## - outliers identified (with cutoff 0.975) (if short == false) 237s + ## - estimated center and covarinance matrix if full == TRUE) 237s + ## 237s + ##@edescr 237s + ## 237s + ##@in nrep : [integer] number of repetitions to use for estimating the 237s + ## (average) execution time 237s + ##@in time : [boolean] whether to evaluate the execution time 237s + ##@in short : [boolean] whether to do short output (i.e. only the 237s + ## objective function value). If short == FALSE, 237s + ## the best subsample and the identified outliers are 237s + ## printed. See also the parameter full below 237s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 237s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 237s + 237s + doest <- function(x, xname, nrep=1){ 237s + n <- dim(x)[1] 237s + p <- dim(x)[2] 237s + if(method == "MASS"){ 237s + mcd<-cov.mcd(x) 237s + quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2) 237s + } 237s + else{ 237s + mcd <- if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 237s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 237s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 237s + else CovMcd(x, trace=FALSE) 237s + quan <- as.integer(mcd@quan) 237s + } 237s + 237s + crit <- mcd@crit 237s + 237s + if(time){ 237s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 237s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 237s + } 237s + else{ 237s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 237s + } 237s + lpad<-lname-nchar(xname) 237s + cat(pad.right(xname,lpad), xres) 237s + 237s + if(!short){ 237s + cat("Best subsample: \n") 237s + if(length(mcd@best) > 150) 237s + cat("Too long... \n") 237s + else 237s + print(mcd@best) 237s + 237s + ibad <- which(mcd@wt==0) 237s + names(ibad) <- NULL 237s + nbad <- length(ibad) 237s + cat("Outliers: ",nbad,"\n") 237s + if(nbad > 0 & nbad < 150) 237s + print(ibad) 237s + else 237s + cat("Too many to print ... \n") 237s + if(full){ 237s + cat("-------------\n") 237s + show(mcd) 237s + } 237s + cat("--------------------------------------------------------\n") 237s + } 237s + } 237s + 237s + options(digits = 5) 237s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 237s + 237s + lname <- 20 237s + 237s + ## VT::15.09.2013 - this will render the output independent 237s + ## from the version of the package 237s + suppressPackageStartupMessages(library(rrcov)) 237s + 237s + method <- match.arg(method) 237s + if(method == "MASS") 237s + library(MASS) 237s + 237s + data(Animals, package = "MASS") 237s + brain <- Animals[c(1:24, 26:25, 27:28),] 237s + 237s + data(fish) 237s + data(pottery) 237s + data(rice) 237s + data(un86) 237s + data(wages) 237s + 237s + tmp <- sys.call() 237s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 237s + 237s + cat("Data Set n p Half LOG(obj) Time\n") 237s + cat("========================================================\n") 237s + 237s + if(method=="exact") 237s + { 237s + ## only small data sets 237s + doest(heart[, 1:2], data(heart), nrep) 237s + doest(starsCYG, data(starsCYG), nrep) 237s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 237s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 237s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 237s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 237s + doest(brain, "Animals", nrep) 237s + doest(lactic, data(lactic), nrep) 237s + doest(pension, data(pension), nrep) 237s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 237s + doest(stack.x, data(stackloss), nrep) 237s + doest(pilot, data(pilot), nrep) 237s + } else 237s + { 237s + doest(heart[, 1:2], data(heart), nrep) 237s + doest(starsCYG, data(starsCYG), nrep) 237s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 237s + doest(stack.x, data(stackloss), nrep) 237s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 237s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 237s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 237s + doest(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 237s + 237s + doest(brain, "Animals", nrep) 237s + ## doest(milk, data(milk), nrep) # difference between 386 and x64 237s + doest(bushfire, data(bushfire), nrep) 237s + 237s + doest(lactic, data(lactic), nrep) 237s + doest(pension, data(pension), nrep) 237s + ## doest(pilot, data(pilot), nrep) # difference between 386 and x64 237s + 237s + if(method != "MRCD") # these two are quite slow for MRCD, especially the second one 237s + { 237s + doest(radarImage, data(radarImage), nrep) 237s + doest(NOxEmissions, data(NOxEmissions), nrep) 237s + } 237s + 237s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 237s + doest(data.matrix(subset(wagnerGrowth, select = -Period)), data(wagnerGrowth), nrep) 237s + 237s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep) 237s + doest(data.matrix(subset(pottery, select = -origin)), data(pottery), nrep) 237s + doest(rice, data(rice), nrep) 237s + doest(un86, data(un86), nrep) 237s + 237s + doest(wages, data(wages), nrep) 237s + 237s + ## from package 'datasets' 237s + doest(airquality[,1:4], data(airquality), nrep) 237s + doest(attitude, data(attitude), nrep) 237s + doest(attenu, data(attenu), nrep) 237s + doest(USJudgeRatings, data(USJudgeRatings), nrep) 237s + doest(USArrests, data(USArrests), nrep) 237s + doest(longley, data(longley), nrep) 237s + doest(Loblolly, data(Loblolly), nrep) 237s + doest(quakes[,1:4], data(quakes), nrep) 237s + } 237s + cat("========================================================\n") 237s + } 237s > 237s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMCD", "MASS")){ 237s + 237s + doest <- function(x, nrep=1){ 237s + gc() 237s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 237s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 237s + xtime 237s + } 237s + 237s + set.seed(1234) 237s + 237s + ## VT::15.09.2013 - this will render the output independent 237s + ## from the version of the package 237s + suppressPackageStartupMessages(library(rrcov)) 237s + 237s + library(MASS) 237s + method <- match.arg(method) 237s + 237s + ap <- c(2, 5, 10, 20, 30) 237s + an <- c(100, 500, 1000, 10000, 50000) 237s + 237s + tottime <- 0 237s + cat(" n p Time\n") 237s + cat("=====================\n") 237s + for(i in 1:length(an)) { 237s + for(j in 1:length(ap)) { 237s + n <- an[i] 237s + p <- ap[j] 237s + if(5*p <= n){ 237s + xx <- gendata(n, p, eps) 237s + X <- xx$X 237s + tottime <- tottime + doest(X, nrep) 237s + } 237s + } 237s + } 237s + 237s + cat("=====================\n") 237s + cat("Total time: ", tottime*nrep, "\n") 237s + } 237s > 237s > docheck <- function(n, p, eps){ 237s + xx <- gendata(n,p,eps) 237s + mcd <- CovMcd(xx$X) 237s + check(mcd, xx$xind) 237s + } 237s > 237s > check <- function(mcd, xind){ 237s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 237s + ## did not get zero weight 237s + mymatch <- xind %in% which(mcd@wt == 0) 237s + length(xind) - length(which(mymatch)) 237s + } 237s > 237s > dorep <- function(x, nrep=1, method=c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 237s + 237s + method <- match.arg(method) 237s + for(i in 1:nrep) 237s + if(method == "MASS") 237s + cov.mcd(x) 237s + else 237s + { 237s + if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 237s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 237s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 237s + else CovMcd(x, trace=FALSE) 237s + } 237s + } 237s > 237s > #### gendata() #### 237s > # Generates a location contaminated multivariate 237s > # normal sample of n observations in p dimensions 237s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 237s > # where 237s > # m = (b,b,...,b) 237s > # Defaults: eps=0 and b=10 237s > # 237s > gendata <- function(n,p,eps=0,b=10){ 237s + 237s + if(missing(n) || missing(p)) 237s + stop("Please specify (n,p)") 237s + if(eps < 0 || eps >= 0.5) 237s + stop(message="eps must be in [0,0.5)") 237s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 237s + nbad <- as.integer(eps * n) 237s + if(nbad > 0){ 237s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 237s + xind <- sample(n,nbad) 237s + X[xind,] <- Xbad 237s + } 237s + list(X=X, xind=xind) 237s + } 237s > 237s > pad.right <- function(z, pads) 237s + { 237s + ### Pads spaces to right of text 237s + padding <- paste(rep(" ", pads), collapse = "") 237s + paste(z, padding, sep = "") 237s + } 237s > 237s > whatis<-function(x){ 237s + if(is.data.frame(x)) 237s + cat("Type: data.frame\n") 237s + else if(is.matrix(x)) 237s + cat("Type: matrix\n") 237s + else if(is.vector(x)) 237s + cat("Type: vector\n") 237s + else 237s + cat("Type: don't know\n") 237s + } 237s > 237s > ## VT::15.09.2013 - this will render the output independent 237s > ## from the version of the package 237s > suppressPackageStartupMessages(library(rrcov)) 237s > 237s > dodata() 237s 237s Call: dodata() 237s Data Set n p Half LOG(obj) Time 237s ======================================================== 237s heart 12 2 7 5.678742 237s Best subsample: 237s [1] 1 3 4 5 7 9 11 237s Outliers: 0 237s Too many to print ... 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s height weight 237s 38.3 33.1 237s 237s Robust Estimate of Covariance: 237s height weight 237s height 135 259 237s weight 259 564 237s -------------------------------------------------------- 237s starsCYG 47 2 25 -8.031215 237s Best subsample: 237s [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 237s Outliers: 7 237s [1] 7 9 11 14 20 30 34 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s log.Te log.light 237s 4.41 4.95 237s 237s Robust Estimate of Covariance: 237s log.Te log.light 237s log.Te 0.0132 0.0394 237s log.light 0.0394 0.2743 237s -------------------------------------------------------- 237s phosphor 18 2 10 6.878847 237s Best subsample: 237s [1] 3 5 8 9 11 12 13 14 15 17 237s Outliers: 3 237s [1] 1 6 10 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s inorg organic 237s 13.4 38.8 237s 237s Robust Estimate of Covariance: 237s inorg organic 237s inorg 129 130 237s organic 130 182 237s -------------------------------------------------------- 237s stackloss 21 3 12 5.472581 237s Best subsample: 237s [1] 4 5 6 7 8 9 10 11 12 13 14 20 237s Outliers: 9 237s [1] 1 2 3 15 16 17 18 19 21 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s Air.Flow Water.Temp Acid.Conc. 237s 59.5 20.8 87.3 237s 237s Robust Estimate of Covariance: 237s Air.Flow Water.Temp Acid.Conc. 237s Air.Flow 6.29 5.85 5.74 237s Water.Temp 5.85 9.23 6.14 237s Acid.Conc. 5.74 6.14 23.25 237s -------------------------------------------------------- 237s coleman 20 5 13 1.286808 237s Best subsample: 237s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 237s Outliers: 7 237s [1] 1 6 9 10 11 15 18 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s salaryP fatherWc sstatus teacherSc motherLev 237s 2.76 48.38 6.12 25.00 6.40 237s 237s Robust Estimate of Covariance: 237s salaryP fatherWc sstatus teacherSc motherLev 237s salaryP 0.253 1.786 -0.266 0.151 0.075 237s fatherWc 1.786 1303.382 330.496 12.604 34.503 237s sstatus -0.266 330.496 119.888 3.833 10.131 237s teacherSc 0.151 12.604 3.833 0.785 0.555 237s motherLev 0.075 34.503 10.131 0.555 1.043 237s -------------------------------------------------------- 237s salinity 28 3 16 1.326364 237s Best subsample: 237s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 237s Outliers: 4 237s [1] 5 16 23 24 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s X1 X2 X3 237s 10.08 2.78 22.78 237s 237s Robust Estimate of Covariance: 237s X1 X2 X3 237s X1 10.44 1.01 -3.19 237s X2 1.01 3.83 -1.44 237s X3 -3.19 -1.44 2.39 237s -------------------------------------------------------- 237s wood 20 5 13 -36.270094 237s Best subsample: 237s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 237s Outliers: 7 237s [1] 4 6 7 8 11 16 19 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s x1 x2 x3 x4 x5 237s 0.587 0.122 0.531 0.538 0.892 237s 237s Robust Estimate of Covariance: 237s x1 x2 x3 x4 x5 237s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 237s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 237s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 237s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 237s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 237s -------------------------------------------------------- 237s hbk 75 3 39 -1.047858 237s Best subsample: 237s [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 237s [26] 55 56 58 59 61 63 64 66 67 70 71 72 73 74 237s Outliers: 14 237s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=39); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s X1 X2 X3 237s 1.54 1.78 1.69 237s 237s Robust Estimate of Covariance: 237s X1 X2 X3 237s X1 1.227 0.055 0.127 237s X2 0.055 1.249 0.153 237s X3 0.127 0.153 1.160 237s -------------------------------------------------------- 237s Animals 28 2 15 14.555543 237s Best subsample: 237s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 237s Outliers: 14 237s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s body brain 237s 18.7 64.9 237s 237s Robust Estimate of Covariance: 237s body brain 237s body 929 1576 237s brain 1576 5646 237s -------------------------------------------------------- 237s bushfire 38 5 22 18.135810 237s Best subsample: 237s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 237s Outliers: 16 237s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=22); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s V1 V2 V3 V4 V5 237s 105 147 274 218 279 237s 237s Robust Estimate of Covariance: 237s V1 V2 V3 V4 V5 237s V1 346 268 -1692 -381 -311 237s V2 268 236 -1125 -230 -194 237s V3 -1692 -1125 9993 2455 1951 237s V4 -381 -230 2455 647 505 237s V5 -311 -194 1951 505 398 237s -------------------------------------------------------- 237s lactic 20 2 11 0.359580 237s Best subsample: 237s [1] 1 2 3 4 5 7 8 9 10 11 12 237s Outliers: 4 237s [1] 17 18 19 20 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s X Y 237s 3.86 5.01 237s 237s Robust Estimate of Covariance: 237s X Y 237s X 10.6 14.6 237s Y 14.6 21.3 237s -------------------------------------------------------- 237s pension 18 2 10 16.675508 237s Best subsample: 237s [1] 1 2 3 4 5 6 8 9 11 12 237s Outliers: 5 237s [1] 14 15 16 17 18 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s Income Reserves 237s 52.3 560.9 237s 237s Robust Estimate of Covariance: 237s Income Reserves 237s Income 1420 11932 237s Reserves 11932 208643 237s -------------------------------------------------------- 237s radarImage 1573 5 789 36.694425 237s Best subsample: 237s Too long... 237s Outliers: 117 237s [1] 164 237 238 242 261 262 351 450 451 462 480 481 509 516 535 237s [16] 542 572 597 620 643 654 669 697 737 802 803 804 818 832 833 237s [31] 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 1202 1223 237s [46] 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 1376 1393 237s [61] 1394 1402 1403 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1479 1487 237s [76] 1492 1504 1510 1511 1512 1517 1518 1519 1520 1521 1522 1525 1526 1527 1528 237s [91] 1530 1532 1534 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 237s [106] 1557 1558 1561 1562 1564 1565 1566 1567 1569 1570 1571 1573 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=789); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s X.coord Y.coord Band.1 Band.2 Band.3 237s 52.80 35.12 6.77 18.44 8.90 237s 237s Robust Estimate of Covariance: 237s X.coord Y.coord Band.1 Band.2 Band.3 237s X.coord 123.6 23.0 -361.9 -197.1 -22.5 237s Y.coord 23.0 400.6 34.3 -191.1 -39.1 237s Band.1 -361.9 34.3 27167.9 8178.8 473.7 237s Band.2 -197.1 -191.1 8178.8 26021.8 952.4 237s Band.3 -22.5 -39.1 473.7 952.4 4458.4 237s -------------------------------------------------------- 237s NOxEmissions 8088 4 4046 2.474539 237s Best subsample: 237s Too long... 237s Outliers: 2156 237s Too many to print ... 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=4046); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s julday LNOx LNOxEm sqrtWS 237s 168.19 4.73 7.91 1.37 237s 237s Robust Estimate of Covariance: 237s julday LNOx LNOxEm sqrtWS 237s julday 9180.6297 12.0306 0.7219 -10.1273 237s LNOx 12.0306 0.4721 0.1418 -0.1526 237s LNOxEm 0.7219 0.1418 0.2516 0.0438 237s sqrtWS -10.1273 -0.1526 0.0438 0.2073 237s -------------------------------------------------------- 237s vaso 39 2 21 -3.972244 237s Best subsample: 237s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 237s Outliers: 4 237s [1] 1 2 17 31 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s Volume Rate 237s 1.16 1.72 237s 237s Robust Estimate of Covariance: 237s Volume Rate 237s Volume 0.313 -0.167 237s Rate -0.167 0.728 237s -------------------------------------------------------- 237s wagnerGrowth 63 6 35 6.572208 237s Best subsample: 237s [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 237s [26] 48 51 52 53 54 55 56 57 60 62 237s Outliers: 13 237s [1] 1 8 15 21 22 28 29 33 42 43 46 50 63 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=35); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s Region PA GPA HS GHS y 237s 11.00 33.66 -2.00 2.48 0.31 7.48 237s 237s Robust Estimate of Covariance: 237s Region PA GPA HS GHS y 237s Region 35.5615 17.9337 -0.5337 -0.9545 -0.3093 -14.0090 237s PA 17.9337 27.7333 -4.9017 -1.4174 0.0343 -28.7040 237s GPA -0.5337 -4.9017 5.3410 0.2690 -0.1484 4.0006 237s HS -0.9545 -1.4174 0.2690 0.8662 -0.0454 2.9024 237s GHS -0.3093 0.0343 -0.1484 -0.0454 0.1772 0.7457 237s y -14.0090 -28.7040 4.0006 2.9024 0.7457 82.6877 237s -------------------------------------------------------- 237s fish 159 6 82 8.879005 237s Best subsample: 237s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 237s [20] 20 21 22 23 24 25 26 27 28 30 32 35 36 37 42 43 44 45 46 237s [39] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 107 109 110 111 113 237s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 237s [77] 134 135 136 137 138 139 237s Outliers: 63 237s [1] 30 39 40 41 42 62 63 64 65 66 68 69 70 73 74 75 76 77 78 237s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 237s [39] 98 99 100 101 102 103 104 105 141 143 144 145 147 148 149 150 151 152 153 237s [58] 154 155 156 157 158 159 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=82); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s Weight Length1 Length2 Length3 Height Width 237s 329.9 24.5 26.6 29.7 31.1 14.7 237s 237s Robust Estimate of Covariance: 237s Weight Length1 Length2 Length3 Height Width 237s Weight 69082.99 1477.81 1613.64 1992.62 1439.32 -62.12 237s Length1 1477.81 34.68 37.61 45.51 28.82 -1.31 237s Length2 1613.64 37.61 40.88 49.52 31.81 -1.40 237s Length3 1992.62 45.51 49.52 61.16 42.65 -2.25 237s Height 1439.32 28.82 31.81 42.65 46.74 -2.82 237s Width -62.12 -1.31 -1.40 -2.25 -2.82 1.01 237s -------------------------------------------------------- 237s pottery 27 6 17 -10.586933 237s Best subsample: 237s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 237s Outliers: 9 237s [1] 3 8 12 16 17 18 23 24 25 237s ------------- 237s 237s Call: 237s CovMcd(x = x, trace = FALSE) 237s -> Method: Fast MCD(alpha=0.5 ==> h=17); nsamp = 500; (n,k)mini = (300,5) 237s 237s Robust Estimate of Location: 237s SI AL FE MG CA TI 237s 54.983 15.206 9.700 3.817 5.211 0.859 237s 237s Robust Estimate of Covariance: 237s SI AL FE MG CA TI 237s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 237s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 237s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 237s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 237s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 237s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 237s -------------------------------------------------------- 238s rice 105 6 56 -14.463986 238s Best subsample: 238s [1] 2 4 6 8 10 12 15 18 21 22 24 29 30 31 32 33 34 36 37 238s [20] 38 41 44 45 47 51 52 53 54 55 59 61 65 67 68 69 70 72 76 238s [39] 78 79 80 81 82 83 84 85 86 92 93 94 95 97 98 99 102 105 238s Outliers: 13 238s [1] 9 14 19 28 40 42 49 58 62 71 75 77 89 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=56); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s Favor Appearance Taste Stickiness 238s -0.2731 0.0600 -0.1468 0.0646 238s Toughness Overall_evaluation 238s 0.0894 -0.2192 238s 238s Robust Estimate of Covariance: 238s Favor Appearance Taste Stickiness Toughness 238s Favor 0.388 0.323 0.393 0.389 -0.195 238s Appearance 0.323 0.503 0.494 0.494 -0.270 238s Taste 0.393 0.494 0.640 0.629 -0.361 238s Stickiness 0.389 0.494 0.629 0.815 -0.486 238s Toughness -0.195 -0.270 -0.361 -0.486 0.451 238s Overall_evaluation 0.471 0.575 0.723 0.772 -0.457 238s Overall_evaluation 238s Favor 0.471 238s Appearance 0.575 238s Taste 0.723 238s Stickiness 0.772 238s Toughness -0.457 238s Overall_evaluation 0.882 238s -------------------------------------------------------- 238s un86 73 7 40 17.009322 238s Best subsample: 238s [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 238s [26] 51 52 55 56 60 61 62 63 64 65 67 70 71 72 73 238s Outliers: 30 238s [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 238s [26] 58 59 66 68 69 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s POP MOR CAR DR GNP DEN TB 238s 20.740 71.023 6.435 0.817 1.146 56.754 0.441 238s 238s Robust Estimate of Covariance: 238s POP MOR CAR DR GNP DEN 238s POP 582.4034 224.9343 -12.6722 -1.6729 -3.3664 226.1952 238s MOR 224.9343 2351.3907 -286.9504 -32.0743 -35.5649 -527.4684 238s CAR -12.6722 -286.9504 58.1190 5.7393 6.6365 83.6180 238s DR -1.6729 -32.0743 5.7393 0.8339 0.5977 12.1938 238s GNP -3.3664 -35.5649 6.6365 0.5977 1.4175 13.0709 238s DEN 226.1952 -527.4684 83.6180 12.1938 13.0709 2041.5809 238s TB 0.4002 -1.1807 0.2701 0.0191 0.0058 -0.9346 238s TB 238s POP 0.4002 238s MOR -1.1807 238s CAR 0.2701 238s DR 0.0191 238s GNP 0.0058 238s DEN -0.9346 238s TB 0.0184 238s -------------------------------------------------------- 238s wages 39 10 19 22.994272 238s Best subsample: 238s [1] 1 2 6 7 8 9 10 11 12 13 14 15 17 18 19 25 26 27 28 238s Outliers: 9 238s [1] 4 5 6 24 28 30 32 33 34 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 238s 2153.37 2.87 1129.16 297.53 360.58 6876.58 39.48 2.36 238s RACE SCHOOL 238s 38.88 10.17 238s 238s Robust Estimate of Covariance: 238s HRS RATE ERSP ERNO NEIN ASSET 238s HRS 6.12e+03 1.73e+01 -1.67e+03 -2.06e+03 9.10e+03 2.02e+05 238s RATE 1.73e+01 2.52e-01 2.14e+01 -3.54e+00 5.85e+01 1.37e+03 238s ERSP -1.67e+03 2.14e+01 1.97e+04 7.76e+01 -1.71e+03 -1.41e+04 238s ERNO -2.06e+03 -3.54e+00 7.76e+01 2.06e+03 -2.02e+03 -4.83e+04 238s NEIN 9.10e+03 5.85e+01 -1.71e+03 -2.02e+03 2.02e+04 4.54e+05 238s ASSET 2.02e+05 1.37e+03 -1.41e+04 -4.83e+04 4.54e+05 1.03e+07 238s AGE -6.29e+01 -2.61e-01 4.83e+00 2.44e+01 -1.08e+02 -2.46e+03 238s DEP -6.17e+00 -7.05e-02 -2.13e+01 2.29e+00 -1.30e+01 -3.16e+02 238s RACE -2.17e+03 -9.46e+00 7.19e+02 5.59e+02 -3.95e+03 -8.77e+04 238s SCHOOL 7.12e+01 5.87e-01 5.39e+01 -2.14e+01 1.63e+02 3.79e+03 238s AGE DEP RACE SCHOOL 238s HRS -6.29e+01 -6.17e+00 -2.17e+03 7.12e+01 238s RATE -2.61e-01 -7.05e-02 -9.46e+00 5.87e-01 238s ERSP 4.83e+00 -2.13e+01 7.19e+02 5.39e+01 238s ERNO 2.44e+01 2.29e+00 5.59e+02 -2.14e+01 238s NEIN -1.08e+02 -1.30e+01 -3.95e+03 1.63e+02 238s ASSET -2.46e+03 -3.16e+02 -8.77e+04 3.79e+03 238s AGE 1.01e+00 7.03e-02 2.39e+01 -9.52e-01 238s DEP 7.03e-02 4.62e-02 2.72e+00 -1.94e-01 238s RACE 2.39e+01 2.72e+00 8.74e+02 -3.09e+01 238s SCHOOL -9.52e-01 -1.94e-01 -3.09e+01 1.62e+00 238s -------------------------------------------------------- 238s airquality 153 4 58 18.213499 238s Best subsample: 238s [1] 3 22 24 25 28 29 32 33 35 36 37 38 39 40 41 42 43 44 46 238s [20] 47 48 49 50 52 56 57 58 59 60 64 66 67 68 69 71 72 73 74 238s [39] 76 78 80 82 83 84 86 87 89 90 91 92 93 94 95 97 98 105 109 238s [58] 110 238s Outliers: 14 238s [1] 8 9 15 18 20 21 23 24 28 30 48 62 117 148 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=58); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s Ozone Solar.R Wind Temp 238s 43.2 192.9 9.6 80.5 238s 238s Robust Estimate of Covariance: 238s Ozone Solar.R Wind Temp 238s Ozone 959.69 771.68 -60.92 198.38 238s Solar.R 771.68 7089.72 -1.72 95.75 238s Wind -60.92 -1.72 10.71 -11.96 238s Temp 198.38 95.75 -11.96 62.78 238s -------------------------------------------------------- 238s attitude 30 7 19 24.442803 238s Best subsample: 238s [1] 2 3 4 5 7 8 10 12 15 17 19 20 22 23 25 27 28 29 30 238s Outliers: 10 238s [1] 1 6 9 13 14 16 18 21 24 26 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s rating complaints privileges learning raises critical 238s 67.1 68.0 52.4 57.6 67.2 77.4 238s advance 238s 43.4 238s 238s Robust Estimate of Covariance: 238s rating complaints privileges learning raises critical advance 238s rating 169.34 127.83 40.48 110.26 91.71 -3.59 53.84 238s complaints 127.83 156.80 52.65 110.97 96.56 7.27 76.03 238s privileges 40.48 52.65 136.91 92.38 69.00 9.53 87.98 238s learning 110.26 110.97 92.38 157.77 112.92 6.74 75.51 238s raises 91.71 96.56 69.00 112.92 112.79 4.91 70.22 238s critical -3.59 7.27 9.53 6.74 4.91 52.25 15.00 238s advance 53.84 76.03 87.98 75.51 70.22 15.00 93.11 238s -------------------------------------------------------- 238s attenu 182 5 86 6.440834 238s Best subsample: 238s [1] 68 69 70 71 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 238s [20] 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 115 116 117 118 238s [39] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 238s [58] 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 238s [77] 157 158 159 160 161 162 163 164 165 166 238s Outliers: 61 238s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 238s [20] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 238s [39] 40 45 46 47 54 55 56 57 58 59 60 61 64 65 82 97 98 100 101 238s [58] 102 103 104 105 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=86); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s event mag station dist accel 238s 18.624 5.752 67.861 22.770 0.141 238s 238s Robust Estimate of Covariance: 238s event mag station dist accel 238s event 1.64e+01 -1.22e+00 5.59e+01 9.98e+00 -8.37e-02 238s mag -1.22e+00 4.13e-01 -3.19e+00 1.35e+00 1.22e-02 238s station 5.59e+01 -3.19e+00 1.03e+03 7.00e+01 5.56e-01 238s dist 9.98e+00 1.35e+00 7.00e+01 2.21e+02 -9.24e-01 238s accel -8.37e-02 1.22e-02 5.56e-01 -9.24e-01 9.62e-03 238s -------------------------------------------------------- 238s USJudgeRatings 43 12 28 -47.889993 238s Best subsample: 238s [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 238s [26] 38 41 43 238s Outliers: 14 238s [1] 5 7 8 12 13 14 20 21 23 30 31 35 40 42 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=28); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 238s 7.40 8.19 7.80 7.96 7.74 7.82 7.74 7.73 7.57 7.63 8.25 7.94 238s 238s Robust Estimate of Covariance: 238s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL 238s CONT 0.852 -0.266 -0.422 -0.155 -0.049 -0.074 -0.117 -0.119 -0.177 238s INTG -0.266 0.397 0.537 0.406 0.340 0.325 0.404 0.409 0.430 238s DMNR -0.422 0.537 0.824 0.524 0.458 0.437 0.520 0.504 0.569 238s DILG -0.155 0.406 0.524 0.486 0.426 0.409 0.506 0.515 0.511 238s CFMG -0.049 0.340 0.458 0.426 0.427 0.403 0.466 0.476 0.478 238s DECI -0.074 0.325 0.437 0.409 0.403 0.396 0.449 0.462 0.460 238s PREP -0.117 0.404 0.520 0.506 0.466 0.449 0.552 0.565 0.551 238s FAMI -0.119 0.409 0.504 0.515 0.476 0.462 0.565 0.594 0.571 238s ORAL -0.177 0.430 0.569 0.511 0.478 0.460 0.551 0.571 0.575 238s WRIT -0.159 0.427 0.549 0.515 0.480 0.461 0.556 0.580 0.574 238s PHYS -0.184 0.269 0.362 0.308 0.298 0.307 0.335 0.358 0.369 238s RTEN -0.260 0.472 0.642 0.519 0.467 0.455 0.539 0.554 0.573 238s WRIT PHYS RTEN 238s CONT -0.159 -0.184 -0.260 238s INTG 0.427 0.269 0.472 238s DMNR 0.549 0.362 0.642 238s DILG 0.515 0.308 0.519 238s CFMG 0.480 0.298 0.467 238s DECI 0.461 0.307 0.455 238s PREP 0.556 0.335 0.539 238s FAMI 0.580 0.358 0.554 238s ORAL 0.574 0.369 0.573 238s WRIT 0.580 0.365 0.567 238s PHYS 0.365 0.300 0.378 238s RTEN 0.567 0.378 0.615 238s -------------------------------------------------------- 238s USArrests 50 4 27 15.391648 238s Best subsample: 238s [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 238s [26] 49 50 238s Outliers: 11 238s [1] 2 3 5 6 10 18 24 28 33 37 47 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s Murder Assault UrbanPop Rape 238s 6.71 145.42 65.06 17.88 238s 238s Robust Estimate of Covariance: 238s Murder Assault UrbanPop Rape 238s Murder 16.1 269.3 20.3 25.2 238s Assault 269.3 6613.0 567.8 453.7 238s UrbanPop 20.3 567.8 225.4 47.7 238s Rape 25.2 453.7 47.7 50.9 238s -------------------------------------------------------- 238s longley 16 7 12 12.747678 238s Best subsample: 238s [1] 5 6 7 8 9 10 11 12 13 14 15 16 238s Outliers: 4 238s [1] 1 2 3 4 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s GNP.deflator GNP Unemployed Armed.Forces Population 238s 106.5 430.6 328.2 295.0 120.2 238s Year Employed 238s 1956.5 66.9 238s 238s Robust Estimate of Covariance: 238s GNP.deflator GNP Unemployed Armed.Forces Population 238s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 238s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 238s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 238s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 238s Population 81.4 803.4 929.1 -327.4 63.5 238s Year 51.6 504.3 595.6 -216.7 39.7 238s Employed 34.2 344.1 323.6 -149.5 26.2 238s Year Employed 238s GNP.deflator 51.6 34.2 238s GNP 504.3 344.1 238s Unemployed 595.6 323.6 238s Armed.Forces -216.7 -149.5 238s Population 39.7 26.2 238s Year 25.1 16.7 238s Employed 16.7 12.4 238s -------------------------------------------------------- 238s Loblolly 84 3 44 4.898174 238s Best subsample: 238s [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 238s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 238s Outliers: 31 238s [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 238s [26] 72 76 77 78 83 84 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=44); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s height age Seed 238s 20.44 8.19 7.72 238s 238s Robust Estimate of Covariance: 238s height age Seed 238s height 247.8 79.5 11.9 238s age 79.5 25.7 3.0 238s Seed 11.9 3.0 17.1 238s -------------------------------------------------------- 238s quakes 1000 4 502 8.274369 238s Best subsample: 238s Too long... 238s Outliers: 265 238s Too many to print ... 238s ------------- 238s 238s Call: 238s CovMcd(x = x, trace = FALSE) 238s -> Method: Fast MCD(alpha=0.5 ==> h=502); nsamp = 500; (n,k)mini = (300,5) 238s 238s Robust Estimate of Location: 238s lat long depth mag 238s -21.31 182.48 361.35 4.54 238s 238s Robust Estimate of Covariance: 238s lat long depth mag 238s lat 1.47e+01 3.53e+00 1.34e+02 -2.52e-01 238s long 3.53e+00 4.55e+00 -3.63e+02 4.36e-02 238s depth 1.34e+02 -3.63e+02 4.84e+04 -1.29e+01 238s mag -2.52e-01 4.36e-02 -1.29e+01 1.38e-01 238s -------------------------------------------------------- 238s ======================================================== 238s > dodata(method="deterministic") 238s 238s Call: dodata(method = "deterministic") 238s Data Set n p Half LOG(obj) Time 238s ======================================================== 238s heart 12 2 7 5.678742 238s Best subsample: 238s [1] 1 3 4 5 7 9 11 238s Outliers: 0 238s Too many to print ... 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=7) 238s 238s Robust Estimate of Location: 238s height weight 238s 38.3 33.1 238s 238s Robust Estimate of Covariance: 238s height weight 238s height 135 259 238s weight 259 564 238s -------------------------------------------------------- 238s starsCYG 47 2 25 -8.028718 238s Best subsample: 238s [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 238s Outliers: 7 238s [1] 7 9 11 14 20 30 34 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=25) 238s 238s Robust Estimate of Location: 238s log.Te log.light 238s 4.41 4.95 238s 238s Robust Estimate of Covariance: 238s log.Te log.light 238s log.Te 0.0132 0.0394 238s log.light 0.0394 0.2743 238s -------------------------------------------------------- 238s phosphor 18 2 10 7.732906 238s Best subsample: 238s [1] 2 4 5 7 8 9 11 12 14 16 238s Outliers: 1 238s [1] 6 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 238s 238s Robust Estimate of Location: 238s inorg organic 238s 12.5 40.8 238s 238s Robust Estimate of Covariance: 238s inorg organic 238s inorg 124 101 238s organic 101 197 238s -------------------------------------------------------- 238s stackloss 21 3 12 6.577286 238s Best subsample: 238s [1] 4 5 6 7 8 9 11 13 16 18 19 20 238s Outliers: 2 238s [1] 1 2 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 238s 238s Robust Estimate of Location: 238s Air.Flow Water.Temp Acid.Conc. 238s 58.4 20.5 86.1 238s 238s Robust Estimate of Covariance: 238s Air.Flow Water.Temp Acid.Conc. 238s Air.Flow 56.28 13.33 26.68 238s Water.Temp 13.33 8.28 6.98 238s Acid.Conc. 26.68 6.98 37.97 238s -------------------------------------------------------- 238s coleman 20 5 13 2.149184 238s Best subsample: 238s [1] 3 4 5 7 8 12 13 14 16 17 18 19 20 238s Outliers: 2 238s [1] 6 10 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 238s 238s Robust Estimate of Location: 238s salaryP fatherWc sstatus teacherSc motherLev 238s 2.76 41.08 2.76 25.01 6.27 238s 238s Robust Estimate of Covariance: 238s salaryP fatherWc sstatus teacherSc motherLev 238s salaryP 0.391 2.956 2.146 0.447 0.110 238s fatherWc 2.956 1358.640 442.724 12.235 32.842 238s sstatus 2.146 442.724 205.590 6.464 11.382 238s teacherSc 0.447 12.235 6.464 1.179 0.510 238s motherLev 0.110 32.842 11.382 0.510 0.919 238s -------------------------------------------------------- 238s salinity 28 3 16 1.940763 238s Best subsample: 238s [1] 1 8 10 12 13 14 15 17 18 20 21 22 25 26 27 28 238s Outliers: 2 238s [1] 5 16 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=16) 238s 238s Robust Estimate of Location: 238s X1 X2 X3 238s 10.50 2.58 23.12 238s 238s Robust Estimate of Covariance: 238s X1 X2 X3 238s X1 10.90243 -0.00457 -1.46156 238s X2 -0.00457 3.85051 -1.94604 238s X3 -1.46156 -1.94604 3.21424 238s -------------------------------------------------------- 238s wood 20 5 13 -35.240819 238s Best subsample: 238s [1] 1 2 3 5 9 11 12 13 14 15 17 18 20 238s Outliers: 4 238s [1] 4 6 8 19 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 238s 238s Robust Estimate of Location: 238s x1 x2 x3 x4 x5 238s 0.582 0.125 0.530 0.534 0.888 238s 238s Robust Estimate of Covariance: 238s x1 x2 x3 x4 x5 238s x1 1.05e-02 1.81e-03 2.08e-03 -6.41e-04 -9.61e-04 238s x2 1.81e-03 5.55e-04 8.76e-04 -2.03e-04 -4.70e-05 238s x3 2.08e-03 8.76e-04 5.60e-03 -1.11e-03 -1.26e-05 238s x4 -6.41e-04 -2.03e-04 -1.11e-03 4.27e-03 2.60e-03 238s x5 -9.61e-04 -4.70e-05 -1.26e-05 2.60e-03 2.95e-03 238s -------------------------------------------------------- 238s hbk 75 3 39 -1.045501 238s Best subsample: 238s [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 238s [26] 54 55 56 58 59 63 64 66 67 70 71 72 73 74 238s Outliers: 14 238s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=39) 238s 238s Robust Estimate of Location: 238s X1 X2 X3 238s 1.54 1.78 1.69 238s 238s Robust Estimate of Covariance: 238s X1 X2 X3 238s X1 1.227 0.055 0.127 238s X2 0.055 1.249 0.153 238s X3 0.127 0.153 1.160 238s -------------------------------------------------------- 238s Animals 28 2 15 14.555543 238s Best subsample: 238s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 238s Outliers: 14 238s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=15) 238s 238s Robust Estimate of Location: 238s body brain 238s 18.7 64.9 238s 238s Robust Estimate of Covariance: 238s body brain 238s body 929 1576 238s brain 1576 5646 238s -------------------------------------------------------- 238s bushfire 38 5 22 18.135810 238s Best subsample: 238s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 238s Outliers: 16 238s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=22) 238s 238s Robust Estimate of Location: 238s V1 V2 V3 V4 V5 238s 105 147 274 218 279 238s 238s Robust Estimate of Covariance: 238s V1 V2 V3 V4 V5 238s V1 346 268 -1692 -381 -311 238s V2 268 236 -1125 -230 -194 238s V3 -1692 -1125 9993 2455 1951 238s V4 -381 -230 2455 647 505 238s V5 -311 -194 1951 505 398 238s -------------------------------------------------------- 238s lactic 20 2 11 0.359580 238s Best subsample: 238s [1] 1 2 3 4 5 7 8 9 10 11 12 238s Outliers: 4 238s [1] 17 18 19 20 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=11) 238s 238s Robust Estimate of Location: 238s X Y 238s 3.86 5.01 238s 238s Robust Estimate of Covariance: 238s X Y 238s X 10.6 14.6 238s Y 14.6 21.3 238s -------------------------------------------------------- 238s pension 18 2 10 16.675508 238s Best subsample: 238s [1] 1 2 3 4 5 6 8 9 11 12 238s Outliers: 5 238s [1] 14 15 16 17 18 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 238s 238s Robust Estimate of Location: 238s Income Reserves 238s 52.3 560.9 238s 238s Robust Estimate of Covariance: 238s Income Reserves 238s Income 1420 11932 238s Reserves 11932 208643 238s -------------------------------------------------------- 238s radarImage 1573 5 789 36.694865 238s Best subsample: 238s Too long... 238s Outliers: 114 238s [1] 164 237 238 242 261 262 351 450 451 462 463 480 481 509 516 238s [16] 535 542 572 597 620 643 654 669 679 697 737 802 803 804 818 238s [31] 832 833 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 238s [46] 1202 1223 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 238s [61] 1376 1393 1394 1402 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1504 238s [76] 1510 1511 1512 1518 1519 1520 1521 1522 1525 1526 1527 1528 1530 1532 1534 238s [91] 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1557 1558 1561 238s [106] 1562 1564 1565 1566 1567 1569 1570 1571 1573 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=789) 238s 238s Robust Estimate of Location: 238s X.coord Y.coord Band.1 Band.2 Band.3 238s 52.78 35.37 7.12 18.81 9.09 238s 238s Robust Estimate of Covariance: 238s X.coord Y.coord Band.1 Band.2 Band.3 238s X.coord 123.2 21.5 -363.9 -200.1 -24.3 238s Y.coord 21.5 410.7 46.5 -177.3 -33.4 238s Band.1 -363.9 46.5 27051.1 8138.9 469.3 238s Band.2 -200.1 -177.3 8138.9 25938.0 946.2 238s Band.3 -24.3 -33.4 469.3 946.2 4470.1 238s -------------------------------------------------------- 238s NOxEmissions 8088 4 4046 2.474536 238s Best subsample: 238s Too long... 238s Outliers: 2152 238s Too many to print ... 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=4046) 238s 238s Robust Estimate of Location: 238s julday LNOx LNOxEm sqrtWS 238s 168.20 4.73 7.91 1.37 238s 238s Robust Estimate of Covariance: 238s julday LNOx LNOxEm sqrtWS 238s julday 9176.2934 12.0355 0.7022 -10.1387 238s LNOx 12.0355 0.4736 0.1430 -0.1528 238s LNOxEm 0.7022 0.1430 0.2527 0.0436 238s sqrtWS -10.1387 -0.1528 0.0436 0.2074 238s -------------------------------------------------------- 238s vaso 39 2 21 -3.972244 238s Best subsample: 238s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 238s Outliers: 4 238s [1] 1 2 17 31 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=21) 238s 238s Robust Estimate of Location: 238s Volume Rate 238s 1.16 1.72 238s 238s Robust Estimate of Covariance: 238s Volume Rate 238s Volume 0.313 -0.167 238s Rate -0.167 0.728 238s -------------------------------------------------------- 238s wagnerGrowth 63 6 35 6.511864 238s Best subsample: 238s [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 238s [26] 48 51 52 53 54 55 56 57 60 62 238s Outliers: 15 238s [1] 1 8 15 21 22 28 29 33 39 42 43 46 49 50 63 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=35) 238s 238s Robust Estimate of Location: 238s Region PA GPA HS GHS y 238s 10.91 33.65 -2.05 2.43 0.31 6.98 238s 238s Robust Estimate of Covariance: 238s Region PA GPA HS GHS y 238s Region 35.1365 17.7291 -1.4003 -0.6554 -0.4728 -14.9305 238s PA 17.7291 28.4297 -5.5245 -1.2444 -0.0452 -29.6181 238s GPA -1.4003 -5.5245 5.2170 0.3954 -0.2152 3.8252 238s HS -0.6554 -1.2444 0.3954 0.7273 -0.0107 2.1514 238s GHS -0.4728 -0.0452 -0.2152 -0.0107 0.1728 0.8440 238s y -14.9305 -29.6181 3.8252 2.1514 0.8440 79.0511 238s -------------------------------------------------------- 238s fish 159 6 82 8.880459 238s Best subsample: 238s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 238s [20] 20 21 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 238s [39] 50 51 52 53 54 55 56 57 58 59 60 106 107 108 109 110 111 112 113 238s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 238s [77] 134 135 136 137 138 139 238s Outliers: 64 238s [1] 30 39 40 41 62 63 64 65 66 68 69 70 73 74 75 76 77 78 79 238s [20] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 238s [39] 99 100 101 102 103 104 105 141 142 143 144 145 146 147 148 149 150 151 152 238s [58] 153 154 155 156 157 158 159 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=82) 238s 238s Robust Estimate of Location: 238s Weight Length1 Length2 Length3 Height Width 238s 316.3 24.1 26.3 29.3 31.0 14.7 238s 238s Robust Estimate of Covariance: 238s Weight Length1 Length2 Length3 Height Width 238s Weight 64662.19 1412.34 1541.95 1917.21 1420.83 -61.15 238s Length1 1412.34 34.14 37.04 45.07 29.25 -1.26 238s Length2 1541.95 37.04 40.26 49.04 32.21 -1.34 238s Length3 1917.21 45.07 49.04 60.82 43.03 -2.15 238s Height 1420.83 29.25 32.21 43.03 46.50 -2.66 238s Width -61.15 -1.26 -1.34 -2.15 -2.66 1.02 238s -------------------------------------------------------- 238s pottery 27 6 17 -10.586933 238s Best subsample: 238s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 238s Outliers: 9 238s [1] 3 8 12 16 17 18 23 24 25 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=17) 238s 238s Robust Estimate of Location: 238s SI AL FE MG CA TI 238s 54.983 15.206 9.700 3.817 5.211 0.859 238s 238s Robust Estimate of Covariance: 238s SI AL FE MG CA TI 238s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 238s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 238s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 238s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 238s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 238s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 238s -------------------------------------------------------- 238s rice 105 6 56 -14.423048 238s Best subsample: 238s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 238s [20] 38 44 45 47 51 52 53 55 59 60 65 66 67 70 72 74 76 78 79 238s [39] 80 81 82 83 84 85 86 90 92 93 94 95 97 98 99 100 101 105 238s Outliers: 13 238s [1] 9 19 28 40 42 43 49 58 62 64 71 75 77 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=56) 238s 238s Robust Estimate of Location: 238s Favor Appearance Taste Stickiness 238s -0.2950 0.0799 -0.1555 0.0363 238s Toughness Overall_evaluation 238s 0.0530 -0.2284 238s 238s Robust Estimate of Covariance: 238s Favor Appearance Taste Stickiness Toughness 238s Favor 0.466 0.389 0.471 0.447 -0.198 238s Appearance 0.389 0.610 0.592 0.570 -0.293 238s Taste 0.471 0.592 0.760 0.718 -0.356 238s Stickiness 0.447 0.570 0.718 0.820 -0.419 238s Toughness -0.198 -0.293 -0.356 -0.419 0.400 238s Overall_evaluation 0.557 0.669 0.838 0.846 -0.425 238s Overall_evaluation 238s Favor 0.557 238s Appearance 0.669 238s Taste 0.838 238s Stickiness 0.846 238s Toughness -0.425 238s Overall_evaluation 0.987 238s -------------------------------------------------------- 238s un86 73 7 40 17.117142 238s Best subsample: 238s [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 238s [26] 52 55 56 57 60 61 62 63 64 65 67 70 71 72 73 238s Outliers: 30 238s [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 238s [26] 58 59 66 68 69 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=40) 238s 238s Robust Estimate of Location: 238s POP MOR CAR DR GNP DEN TB 238s 17.036 68.512 6.444 0.877 1.134 64.140 0.433 238s 238s Robust Estimate of Covariance: 238s POP MOR CAR DR GNP DEN 238s POP 3.61e+02 1.95e+02 -6.28e+00 -1.91e-02 -2.07e+00 5.79e+01 238s MOR 1.95e+02 2.39e+03 -2.79e+02 -3.37e+01 -3.39e+01 -9.21e+02 238s CAR -6.28e+00 -2.79e+02 5.76e+01 5.77e+00 6.59e+00 7.81e+01 238s DR -1.91e-02 -3.37e+01 5.77e+00 9.07e-01 5.66e-01 1.69e+01 238s GNP -2.07e+00 -3.39e+01 6.59e+00 5.66e-01 1.42e+00 9.28e+00 238s DEN 5.79e+01 -9.21e+02 7.81e+01 1.69e+01 9.28e+00 3.53e+03 238s TB -6.09e-02 -9.93e-01 2.50e-01 1.98e-02 6.82e-03 -9.75e-01 238s TB 238s POP -6.09e-02 238s MOR -9.93e-01 238s CAR 2.50e-01 238s DR 1.98e-02 238s GNP 6.82e-03 238s DEN -9.75e-01 238s TB 1.64e-02 238s -------------------------------------------------------- 238s wages 39 10 19 23.119456 238s Best subsample: 238s [1] 1 2 5 6 7 9 10 11 12 13 14 15 19 21 23 25 26 27 28 238s Outliers: 9 238s [1] 4 5 9 24 25 26 28 32 34 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 238s 238s Robust Estimate of Location: 238s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 238s 2161.89 2.95 1114.21 297.68 374.00 7269.37 39.13 2.43 238s RACE SCHOOL 238s 36.13 10.39 238s 238s Robust Estimate of Covariance: 238s HRS RATE ERSP ERNO NEIN ASSET 238s HRS 3.53e+03 8.31e+00 -5.96e+03 -6.43e+02 5.15e+03 1.12e+05 238s RATE 8.31e+00 1.78e-01 8.19e+00 2.70e+00 3.90e+01 8.94e+02 238s ERSP -5.96e+03 8.19e+00 1.90e+04 1.13e+03 -4.73e+03 -9.49e+04 238s ERNO -6.43e+02 2.70e+00 1.13e+03 1.80e+03 -3.56e+02 -7.33e+03 238s NEIN 5.15e+03 3.90e+01 -4.73e+03 -3.56e+02 1.38e+04 3.00e+05 238s ASSET 1.12e+05 8.94e+02 -9.49e+04 -7.33e+03 3.00e+05 6.62e+06 238s AGE -3.33e+01 -6.55e-02 8.33e+01 1.50e+00 -3.28e+01 -7.55e+02 238s DEP 4.50e+00 -4.01e-02 -2.77e+01 1.31e+00 -8.09e+00 -1.61e+02 238s RACE -1.30e+03 -6.06e+00 1.80e+03 1.48e+02 -2.58e+03 -5.59e+04 238s SCHOOL 3.01e+01 3.58e-01 -5.57e+00 2.84e+00 9.26e+01 2.10e+03 238s AGE DEP RACE SCHOOL 238s HRS -3.33e+01 4.50e+00 -1.30e+03 3.01e+01 238s RATE -6.55e-02 -4.01e-02 -6.06e+00 3.58e-01 238s ERSP 8.33e+01 -2.77e+01 1.80e+03 -5.57e+00 238s ERNO 1.50e+00 1.31e+00 1.48e+02 2.84e+00 238s NEIN -3.28e+01 -8.09e+00 -2.58e+03 9.26e+01 238s ASSET -7.55e+02 -1.61e+02 -5.59e+04 2.10e+03 238s AGE 6.57e-01 -1.64e-01 1.13e+01 -2.67e-01 238s DEP -1.64e-01 9.20e-02 2.38e-01 -6.01e-02 238s RACE 1.13e+01 2.38e-01 5.73e+02 -1.67e+01 238s SCHOOL -2.67e-01 -6.01e-02 -1.67e+01 7.95e-01 238s -------------------------------------------------------- 238s airquality 153 4 58 18.316848 238s Best subsample: 238s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 238s [20] 47 48 49 50 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 238s [39] 81 82 84 86 87 89 90 91 92 95 97 98 100 101 105 106 108 109 110 238s [58] 111 238s Outliers: 10 238s [1] 8 9 15 18 24 30 48 62 117 148 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=58) 238s 238s Robust Estimate of Location: 238s Ozone Solar.R Wind Temp 238s 40.80 189.37 9.66 78.81 238s 238s Robust Estimate of Covariance: 238s Ozone Solar.R Wind Temp 238s Ozone 935.54 857.76 -56.30 220.48 238s Solar.R 857.76 8507.83 1.36 155.13 238s Wind -56.30 1.36 9.90 -11.61 238s Temp 220.48 155.13 -11.61 84.00 238s -------------------------------------------------------- 238s attitude 30 7 19 24.464288 238s Best subsample: 238s [1] 2 3 4 5 7 8 10 11 12 15 17 19 21 22 23 25 27 28 29 238s Outliers: 8 238s [1] 6 9 13 14 16 18 24 26 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 238s 238s Robust Estimate of Location: 238s rating complaints privileges learning raises critical 238s 64.4 65.2 51.0 55.5 65.9 77.4 238s advance 238s 43.2 238s 238s Robust Estimate of Covariance: 238s rating complaints privileges learning raises critical advance 238s rating 199.95 162.36 115.83 160.44 128.87 -13.55 66.20 238s complaints 162.36 204.84 130.33 170.66 150.19 16.28 96.66 238s privileges 115.83 130.33 181.31 152.63 106.56 4.52 91.44 238s learning 160.44 170.66 152.63 213.06 156.57 9.92 88.31 238s raises 128.87 150.19 106.56 156.57 152.05 23.10 84.00 238s critical -13.55 16.28 4.52 9.92 23.10 80.22 27.15 238s advance 66.20 96.66 91.44 88.31 84.00 27.15 95.51 238s -------------------------------------------------------- 238s attenu 182 5 86 6.593068 238s Best subsample: 238s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 238s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 238s [39] 115 116 117 119 120 121 122 124 125 126 127 128 129 130 131 132 133 134 135 238s [58] 136 137 138 139 140 141 144 145 146 147 148 149 150 151 152 153 154 155 156 238s [77] 157 158 159 160 161 162 163 164 165 166 238s Outliers: 49 238s [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 238s [20] 23 24 25 27 28 29 30 31 32 33 40 45 47 59 60 61 64 65 78 238s [39] 82 83 97 98 100 101 102 103 104 105 117 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=86) 238s 238s Robust Estimate of Location: 238s event mag station dist accel 238s 17.122 5.798 63.461 25.015 0.131 238s 238s Robust Estimate of Covariance: 238s event mag station dist accel 238s event 2.98e+01 -1.58e+00 9.49e+01 -8.36e+00 -3.59e-02 238s mag -1.58e+00 4.26e-01 -3.88e+00 3.13e+00 5.30e-03 238s station 9.49e+01 -3.88e+00 1.10e+03 2.60e+01 5.38e-01 238s dist -8.36e+00 3.13e+00 2.60e+01 2.66e+02 -9.23e-01 238s accel -3.59e-02 5.30e-03 5.38e-01 -9.23e-01 7.78e-03 238s -------------------------------------------------------- 238s USJudgeRatings 43 12 28 -47.886937 238s Best subsample: 238s [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 238s [26] 40 41 43 238s Outliers: 14 238s [1] 1 5 7 8 12 13 14 17 20 21 23 31 35 42 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=28) 238s 238s Robust Estimate of Location: 238s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 238s 7.46 8.26 7.88 8.06 7.85 7.92 7.84 7.83 7.67 7.74 8.31 8.03 238s 238s Robust Estimate of Covariance: 238s CONT INTG DMNR DILG CFMG DECI PREP FAMI 238s CONT 0.7363 -0.2916 -0.4193 -0.1943 -0.0555 -0.0690 -0.1703 -0.1727 238s INTG -0.2916 0.4179 0.5511 0.4167 0.3176 0.3102 0.4247 0.4279 238s DMNR -0.4193 0.5511 0.8141 0.5256 0.4092 0.3934 0.5294 0.5094 238s DILG -0.1943 0.4167 0.5256 0.4820 0.3904 0.3819 0.5054 0.5104 238s CFMG -0.0555 0.3176 0.4092 0.3904 0.3595 0.3368 0.4180 0.4206 238s DECI -0.0690 0.3102 0.3934 0.3819 0.3368 0.3310 0.4135 0.4194 238s PREP -0.1703 0.4247 0.5294 0.5054 0.4180 0.4135 0.5647 0.5752 238s FAMI -0.1727 0.4279 0.5094 0.5104 0.4206 0.4194 0.5752 0.6019 238s ORAL -0.2109 0.4453 0.5646 0.5054 0.4200 0.4121 0.5575 0.5735 238s WRIT -0.2033 0.4411 0.5466 0.5087 0.4222 0.4147 0.5592 0.5787 238s PHYS -0.1624 0.2578 0.3163 0.2833 0.2268 0.2362 0.3108 0.3284 238s RTEN -0.2622 0.4872 0.6324 0.5203 0.4145 0.4081 0.5488 0.5595 238s ORAL WRIT PHYS RTEN 238s CONT -0.2109 -0.2033 -0.1624 -0.2622 238s INTG 0.4453 0.4411 0.2578 0.4872 238s DMNR 0.5646 0.5466 0.3163 0.6324 238s DILG 0.5054 0.5087 0.2833 0.5203 238s CFMG 0.4200 0.4222 0.2268 0.4145 238s DECI 0.4121 0.4147 0.2362 0.4081 238s PREP 0.5575 0.5592 0.3108 0.5488 238s FAMI 0.5735 0.5787 0.3284 0.5595 238s ORAL 0.5701 0.5677 0.3283 0.5688 238s WRIT 0.5677 0.5715 0.3268 0.5645 238s PHYS 0.3283 0.3268 0.2302 0.3308 238s RTEN 0.5688 0.5645 0.3308 0.6057 238s -------------------------------------------------------- 238s USArrests 50 4 27 15.438912 238s Best subsample: 238s [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 238s [26] 49 50 238s Outliers: 7 238s [1] 2 5 6 10 24 28 33 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=27) 238s 238s Robust Estimate of Location: 238s Murder Assault UrbanPop Rape 238s 6.91 150.10 65.88 18.75 238s 238s Robust Estimate of Covariance: 238s Murder Assault UrbanPop Rape 238s Murder 17.9 285.4 17.6 25.0 238s Assault 285.4 6572.8 524.9 465.0 238s UrbanPop 17.6 524.9 211.9 50.5 238s Rape 25.0 465.0 50.5 56.4 238s -------------------------------------------------------- 238s longley 16 7 12 12.747678 238s Best subsample: 238s [1] 5 6 7 8 9 10 11 12 13 14 15 16 238s Outliers: 4 238s [1] 1 2 3 4 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 238s 238s Robust Estimate of Location: 238s GNP.deflator GNP Unemployed Armed.Forces Population 238s 106.5 430.6 328.2 295.0 120.2 238s Year Employed 238s 1956.5 66.9 238s 238s Robust Estimate of Covariance: 238s GNP.deflator GNP Unemployed Armed.Forces Population 238s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 238s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 238s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 238s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 238s Population 81.4 803.4 929.1 -327.4 63.5 238s Year 51.6 504.3 595.6 -216.7 39.7 238s Employed 34.2 344.1 323.6 -149.5 26.2 238s Year Employed 238s GNP.deflator 51.6 34.2 238s GNP 504.3 344.1 238s Unemployed 595.6 323.6 238s Armed.Forces -216.7 -149.5 238s Population 39.7 26.2 238s Year 25.1 16.7 238s Employed 16.7 12.4 238s -------------------------------------------------------- 238s Loblolly 84 3 44 4.898174 238s Best subsample: 238s [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 238s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 238s Outliers: 31 238s [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 238s [26] 72 76 77 78 83 84 238s ------------- 238s 238s Call: 238s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 238s -> Method: Deterministic MCD(alpha=0.5 ==> h=44) 238s 238s Robust Estimate of Location: 238s height age Seed 238s 20.44 8.19 7.72 238s 238s Robust Estimate of Covariance: 238s height age Seed 238s height 247.8 79.5 11.9 238s age 79.5 25.7 3.0 238s Seed 11.9 3.0 17.1 238s -------------------------------------------------------- 239s quakes 1000 4 502 8.274209 239s Best subsample: 239s Too long... 239s Outliers: 266 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 239s -> Method: Deterministic MCD(alpha=0.5 ==> h=502) 239s 239s Robust Estimate of Location: 239s lat long depth mag 239s -21.34 182.47 360.58 4.54 239s 239s Robust Estimate of Covariance: 239s lat long depth mag 239s lat 1.50e+01 3.58e+00 1.37e+02 -2.66e-01 239s long 3.58e+00 4.55e+00 -3.61e+02 4.64e-02 239s depth 1.37e+02 -3.61e+02 4.84e+04 -1.36e+01 239s mag -2.66e-01 4.64e-02 -1.36e+01 1.34e-01 239s -------------------------------------------------------- 239s ======================================================== 239s > dodata(method="exact") 239s 239s Call: dodata(method = "exact") 239s Data Set n p Half LOG(obj) Time 239s ======================================================== 239s heart 12 2 7 5.678742 239s Best subsample: 239s [1] 1 3 4 5 7 9 11 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s height weight 239s 38.3 33.1 239s 239s Robust Estimate of Covariance: 239s height weight 239s height 135 259 239s weight 259 564 239s -------------------------------------------------------- 239s starsCYG 47 2 25 -8.031215 239s Best subsample: 239s [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 239s Outliers: 7 239s [1] 7 9 11 14 20 30 34 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s log.Te log.light 239s 4.41 4.95 239s 239s Robust Estimate of Covariance: 239s log.Te log.light 239s log.Te 0.0132 0.0394 239s log.light 0.0394 0.2743 239s -------------------------------------------------------- 239s phosphor 18 2 10 6.878847 239s Best subsample: 239s [1] 3 5 8 9 11 12 13 14 15 17 239s Outliers: 3 239s [1] 1 6 10 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s inorg organic 239s 13.4 38.8 239s 239s Robust Estimate of Covariance: 239s inorg organic 239s inorg 129 130 239s organic 130 182 239s -------------------------------------------------------- 239s coleman 20 5 13 1.286808 239s Best subsample: 239s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 239s Outliers: 7 239s [1] 1 6 9 10 11 15 18 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s salaryP fatherWc sstatus teacherSc motherLev 239s 2.76 48.38 6.12 25.00 6.40 239s 239s Robust Estimate of Covariance: 239s salaryP fatherWc sstatus teacherSc motherLev 239s salaryP 0.253 1.786 -0.266 0.151 0.075 239s fatherWc 1.786 1303.382 330.496 12.604 34.503 239s sstatus -0.266 330.496 119.888 3.833 10.131 239s teacherSc 0.151 12.604 3.833 0.785 0.555 239s motherLev 0.075 34.503 10.131 0.555 1.043 239s -------------------------------------------------------- 239s salinity 28 3 16 1.326364 239s Best subsample: 239s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 239s Outliers: 4 239s [1] 5 16 23 24 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s X1 X2 X3 239s 10.08 2.78 22.78 239s 239s Robust Estimate of Covariance: 239s X1 X2 X3 239s X1 10.44 1.01 -3.19 239s X2 1.01 3.83 -1.44 239s X3 -3.19 -1.44 2.39 239s -------------------------------------------------------- 239s wood 20 5 13 -36.270094 239s Best subsample: 239s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 239s Outliers: 7 239s [1] 4 6 7 8 11 16 19 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s x1 x2 x3 x4 x5 239s 0.587 0.122 0.531 0.538 0.892 239s 239s Robust Estimate of Covariance: 239s x1 x2 x3 x4 x5 239s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 239s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 239s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 239s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 239s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 239s -------------------------------------------------------- 239s Animals 28 2 15 14.555543 239s Best subsample: 239s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 239s Outliers: 14 239s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s body brain 239s 18.7 64.9 239s 239s Robust Estimate of Covariance: 239s body brain 239s body 929 1576 239s brain 1576 5646 239s -------------------------------------------------------- 239s lactic 20 2 11 0.359580 239s Best subsample: 239s [1] 1 2 3 4 5 7 8 9 10 11 12 239s Outliers: 4 239s [1] 17 18 19 20 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s X Y 239s 3.86 5.01 239s 239s Robust Estimate of Covariance: 239s X Y 239s X 10.6 14.6 239s Y 14.6 21.3 239s -------------------------------------------------------- 239s pension 18 2 10 16.675508 239s Best subsample: 239s [1] 1 2 3 4 5 6 8 9 11 12 239s Outliers: 5 239s [1] 14 15 16 17 18 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s Income Reserves 239s 52.3 560.9 239s 239s Robust Estimate of Covariance: 239s Income Reserves 239s Income 1420 11932 239s Reserves 11932 208643 239s -------------------------------------------------------- 239s vaso 39 2 21 -3.972244 239s Best subsample: 239s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 239s Outliers: 4 239s [1] 1 2 17 31 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s Volume Rate 239s 1.16 1.72 239s 239s Robust Estimate of Covariance: 239s Volume Rate 239s Volume 0.313 -0.167 239s Rate -0.167 0.728 239s -------------------------------------------------------- 239s stackloss 21 3 12 5.472581 239s Best subsample: 239s [1] 4 5 6 7 8 9 10 11 12 13 14 20 239s Outliers: 9 239s [1] 1 2 3 15 16 17 18 19 21 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s Air.Flow Water.Temp Acid.Conc. 239s 59.5 20.8 87.3 239s 239s Robust Estimate of Covariance: 239s Air.Flow Water.Temp Acid.Conc. 239s Air.Flow 6.29 5.85 5.74 239s Water.Temp 5.85 9.23 6.14 239s Acid.Conc. 5.74 6.14 23.25 239s -------------------------------------------------------- 239s pilot 20 2 11 6.487287 239s Best subsample: 239s [1] 2 3 6 7 9 12 15 16 17 18 20 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMcd(x = x, nsamp = "exact", trace = FALSE) 239s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 239s 239s Robust Estimate of Location: 239s X Y 239s 101.1 67.7 239s 239s Robust Estimate of Covariance: 239s X Y 239s X 3344 1070 239s Y 1070 343 239s -------------------------------------------------------- 239s ======================================================== 239s > dodata(method="MRCD") 239s 239s Call: dodata(method = "MRCD") 239s Data Set n p Half LOG(obj) Time 239s ======================================================== 239s heart 12 2 6 7.446266 239s Best subsample: 239s [1] 1 3 4 7 9 11 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=6) 239s 239s Robust Estimate of Location: 239s height weight 239s 38.8 33.0 239s 239s Robust Estimate of Covariance: 239s height weight 239s height 47.4 75.2 239s weight 75.2 155.4 239s -------------------------------------------------------- 239s starsCYG 47 2 24 -5.862050 239s Best subsample: 239s [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 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=24) 239s 239s Robust Estimate of Location: 239s log.Te log.light 239s 4.44 5.05 239s 239s Robust Estimate of Covariance: 239s log.Te log.light 239s log.Te 0.00867 0.02686 239s log.light 0.02686 0.41127 239s -------------------------------------------------------- 239s phosphor 18 2 9 9.954788 239s Best subsample: 239s [1] 4 7 8 9 11 12 13 14 16 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 239s 239s Robust Estimate of Location: 239s inorg organic 239s 12.5 39.0 239s 239s Robust Estimate of Covariance: 239s inorg organic 239s inorg 236 140 239s organic 140 172 239s -------------------------------------------------------- 239s stackloss 21 3 11 7.991165 239s Best subsample: 239s [1] 4 5 6 7 8 9 10 13 18 19 20 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=11) 239s 239s Robust Estimate of Location: 239s Air.Flow Water.Temp Acid.Conc. 239s 58.2 21.4 85.2 239s 239s Robust Estimate of Covariance: 239s Air.Flow Water.Temp Acid.Conc. 239s Air.Flow 49.8 17.2 42.7 239s Water.Temp 17.2 13.8 25.2 239s Acid.Conc. 42.7 25.2 58.2 239s -------------------------------------------------------- 239s coleman 20 5 10 5.212156 239s Best subsample: 239s [1] 3 4 5 7 8 9 14 16 19 20 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 239s 239s Robust Estimate of Location: 239s salaryP fatherWc sstatus teacherSc motherLev 239s 2.78 59.44 9.28 25.41 6.70 239s 239s Robust Estimate of Covariance: 239s salaryP fatherWc sstatus teacherSc motherLev 239s salaryP 0.1582 -0.2826 0.4112 0.1754 0.0153 239s fatherWc -0.2826 902.9210 201.5815 -2.1236 18.8736 239s sstatus 0.4112 201.5815 65.4580 -0.3876 4.7794 239s teacherSc 0.1754 -2.1236 -0.3876 0.7233 -0.0322 239s motherLev 0.0153 18.8736 4.7794 -0.0322 0.5417 239s -------------------------------------------------------- 239s salinity 28 3 14 3.586919 239s Best subsample: 239s [1] 1 7 8 12 13 14 18 20 21 22 25 26 27 28 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 239s 239s Robust Estimate of Location: 239s X1 X2 X3 239s 10.95 3.71 21.99 239s 239s Robust Estimate of Covariance: 239s X1 X2 X3 239s X1 14.153 0.718 -3.359 239s X2 0.718 3.565 -0.722 239s X3 -3.359 -0.722 1.607 239s -------------------------------------------------------- 239s wood 20 5 10 -33.100492 239s Best subsample: 239s [1] 1 2 3 5 11 14 15 17 18 20 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 239s 239s Robust Estimate of Location: 239s x1 x2 x3 x4 x5 239s 0.572 0.120 0.504 0.545 0.899 239s 239s Robust Estimate of Covariance: 239s x1 x2 x3 x4 x5 239s x1 0.007543 0.001720 0.000412 -0.001230 -0.001222 239s x2 0.001720 0.000568 0.000355 -0.000533 -0.000132 239s x3 0.000412 0.000355 0.002478 0.000190 0.000811 239s x4 -0.001230 -0.000533 0.000190 0.002327 0.000967 239s x5 -0.001222 -0.000132 0.000811 0.000967 0.001894 239s -------------------------------------------------------- 239s hbk 75 3 38 1.539545 239s Best subsample: 239s [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 239s [26] 55 56 58 59 63 64 66 67 70 71 72 73 74 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=38) 239s 239s Robust Estimate of Location: 239s X1 X2 X3 239s 1.60 2.37 1.64 239s 239s Robust Estimate of Covariance: 239s X1 X2 X3 239s X1 2.810 0.124 1.248 239s X2 0.124 1.017 0.208 239s X3 1.248 0.208 2.218 239s -------------------------------------------------------- 239s Animals 28 2 14 16.278395 239s Best subsample: 239s [1] 1 3 4 5 10 11 18 19 20 21 22 23 26 27 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 239s 239s Robust Estimate of Location: 239s body brain 239s 19.5 56.8 239s 239s Robust Estimate of Covariance: 239s body brain 239s body 2802 5179 239s brain 5179 13761 239s -------------------------------------------------------- 239s bushfire 38 5 19 28.483413 239s Best subsample: 239s [1] 1 2 3 4 5 14 15 16 17 18 19 20 21 22 23 24 25 26 27 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=19) 239s 239s Robust Estimate of Location: 239s V1 V2 V3 V4 V5 239s 103 145 287 221 281 239s 239s Robust Estimate of Covariance: 239s V1 V2 V3 V4 V5 239s V1 366 249 -1993 -503 -396 239s V2 249 252 -1223 -291 -233 239s V3 -1993 -1223 14246 3479 2718 239s V4 -503 -291 3479 1083 748 239s V5 -396 -233 2718 748 660 239s -------------------------------------------------------- 239s lactic 20 2 10 2.593141 239s Best subsample: 239s [1] 1 2 3 4 5 7 8 9 10 11 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 239s 239s Robust Estimate of Location: 239s X Y 239s 2.60 3.63 239s 239s Robust Estimate of Covariance: 239s X Y 239s X 8.13 13.54 239s Y 13.54 24.17 239s -------------------------------------------------------- 239s pension 18 2 9 18.931204 239s Best subsample: 239s [1] 2 3 4 5 6 8 9 11 12 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 239s 239s Robust Estimate of Location: 239s Income Reserves 239s 45.7 466.9 239s 239s Robust Estimate of Covariance: 239s Income Reserves 239s Income 2127 23960 239s Reserves 23960 348275 239s -------------------------------------------------------- 239s vaso 39 2 20 -1.864710 239s Best subsample: 239s [1] 3 4 8 14 18 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=20) 239s 239s Robust Estimate of Location: 239s Volume Rate 239s 1.14 1.77 239s 239s Robust Estimate of Covariance: 239s Volume Rate 239s Volume 0.44943 -0.00465 239s Rate -0.00465 0.34480 239s -------------------------------------------------------- 239s wagnerGrowth 63 6 32 9.287760 239s Best subsample: 239s [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 239s [26] 53 54 55 56 57 60 62 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=32) 239s 239s Robust Estimate of Location: 239s Region PA GPA HS GHS y 239s 10.719 33.816 -2.144 2.487 0.293 4.918 239s 239s Robust Estimate of Covariance: 239s Region PA GPA HS GHS y 239s Region 56.7128 17.4919 -2.9710 -0.6491 -0.4545 -10.4287 239s PA 17.4919 29.9968 -7.6846 -1.3141 0.5418 -35.6434 239s GPA -2.9710 -7.6846 6.3238 1.1257 -0.4757 12.4707 239s HS -0.6491 -1.3141 1.1257 1.1330 -0.0915 3.3617 239s GHS -0.4545 0.5418 -0.4757 -0.0915 0.1468 -1.1228 239s y -10.4287 -35.6434 12.4707 3.3617 -1.1228 67.4215 239s -------------------------------------------------------- 239s fish 159 6 79 22.142828 239s Best subsample: 239s [1] 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 239s [20] 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 50 51 239s [39] 52 53 54 55 56 57 58 59 60 71 105 106 107 109 110 111 113 114 115 239s [58] 116 117 118 119 120 122 123 124 125 126 127 128 129 130 131 132 134 135 136 239s [77] 137 138 139 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=79) 239s 239s Robust Estimate of Location: 239s Weight Length1 Length2 Length3 Height Width 239s 291.7 23.8 25.9 28.9 30.4 14.7 239s 239s Robust Estimate of Covariance: 239s Weight Length1 Length2 Length3 Height Width 239s Weight 77155.07 1567.55 1713.74 2213.16 1912.62 -103.97 239s Length1 1567.55 45.66 41.57 52.14 38.66 -2.39 239s Length2 1713.74 41.57 54.26 56.77 42.72 -2.55 239s Length3 2213.16 52.14 56.77 82.57 58.84 -3.65 239s Height 1912.62 38.66 42.72 58.84 70.51 -3.80 239s Width -103.97 -2.39 -2.55 -3.65 -3.80 1.19 239s -------------------------------------------------------- 239s pottery 27 6 14 -6.897459 239s Best subsample: 239s [1] 1 2 4 5 6 10 11 13 14 15 19 21 22 26 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 239s 239s Robust Estimate of Location: 239s SI AL FE MG CA TI 239s 54.39 14.93 9.78 3.82 5.11 0.86 239s 239s Robust Estimate of Covariance: 239s SI AL FE MG CA TI 239s SI 17.47469 -0.16656 0.39943 4.48192 -0.71153 0.06515 239s AL -0.16656 3.93154 -0.35738 -2.29899 0.14770 -0.02050 239s FE 0.39943 -0.35738 0.20434 0.37562 -0.22460 0.00943 239s MG 4.48192 -2.29899 0.37562 2.82339 -0.16027 0.02943 239s CA -0.71153 0.14770 -0.22460 -0.16027 0.88443 -0.01711 239s TI 0.06515 -0.02050 0.00943 0.02943 -0.01711 0.00114 239s -------------------------------------------------------- 239s rice 105 6 53 -8.916472 239s Best subsample: 239s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 239s [20] 38 44 45 47 51 52 53 54 55 59 60 65 67 70 72 76 79 80 81 239s [39] 82 83 84 85 86 90 92 93 94 95 97 98 99 101 105 239s Outliers: 0 239s Too many to print ... 239s ------------- 239s 239s Call: 239s CovMrcd(x = x, trace = FALSE) 239s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=53) 239s 239s Robust Estimate of Location: 239s Favor Appearance Taste Stickiness 239s -0.1741 0.0774 -0.0472 0.1868 239s Toughness Overall_evaluation 239s -0.0346 -0.0683 239s 239s Robust Estimate of Covariance: 239s Favor Appearance Taste Stickiness Toughness 239s Favor 0.402 0.306 0.378 0.364 -0.134 239s Appearance 0.306 0.508 0.474 0.407 -0.146 239s Taste 0.378 0.474 0.708 0.611 -0.258 239s Stickiness 0.364 0.407 0.611 0.795 -0.320 239s Toughness -0.134 -0.146 -0.258 -0.320 0.302 239s Overall_evaluation 0.453 0.536 0.746 0.745 -0.327 239s Overall_evaluation 239s Favor 0.453 239s Appearance 0.536 239s Taste 0.746 239s Stickiness 0.745 239s Toughness -0.327 239s Overall_evaluation 0.963 239s -------------------------------------------------------- 240s un86 73 7 37 19.832993 240s Best subsample: 240s [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 240s [26] 56 57 60 62 63 64 65 67 70 71 72 73 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=37) 240s 240s Robust Estimate of Location: 240s POP MOR CAR DR GNP DEN TB 240s 14.462 66.892 6.670 0.858 1.251 55.518 0.429 240s 240s Robust Estimate of Covariance: 240s POP MOR CAR DR GNP DEN 240s POP 3.00e+02 1.58e+02 9.83e+00 2.74e+00 5.51e-01 6.87e+01 240s MOR 1.58e+02 2.96e+03 -4.24e+02 -4.72e+01 -5.40e+01 -1.01e+03 240s CAR 9.83e+00 -4.24e+02 9.12e+01 8.71e+00 1.13e+01 1.96e+02 240s DR 2.74e+00 -4.72e+01 8.71e+00 1.25e+00 1.03e+00 2.74e+01 240s GNP 5.51e-01 -5.40e+01 1.13e+01 1.03e+00 2.31e+00 2.36e+01 240s DEN 6.87e+01 -1.01e+03 1.96e+02 2.74e+01 2.36e+01 3.12e+03 240s TB 2.04e-02 -1.81e+00 3.42e-01 2.57e-02 2.09e-02 -6.88e-01 240s TB 240s POP 2.04e-02 240s MOR -1.81e+00 240s CAR 3.42e-01 240s DR 2.57e-02 240s GNP 2.09e-02 240s DEN -6.88e-01 240s TB 2.59e-02 240s -------------------------------------------------------- 240s wages 39 10 14 35.698016 240s Best subsample: 240s [1] 1 2 5 6 9 10 11 13 15 19 23 25 26 28 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 240s 240s Robust Estimate of Location: 240s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 240s 2167.71 2.96 1113.50 300.43 382.29 7438.00 39.06 2.41 240s RACE SCHOOL 240s 33.00 10.45 240s 240s Robust Estimate of Covariance: 240s HRS RATE ERSP ERNO NEIN ASSET 240s HRS 1.97e+03 -4.14e-01 -4.71e+03 -6.58e+02 1.81e+03 3.84e+04 240s RATE -4.14e-01 1.14e-01 1.79e+01 3.08e+00 1.40e+01 3.57e+02 240s ERSP -4.71e+03 1.79e+01 1.87e+04 2.33e+03 -2.06e+03 -3.57e+04 240s ERNO -6.58e+02 3.08e+00 2.33e+03 5.36e+02 -3.42e+02 -5.56e+03 240s NEIN 1.81e+03 1.40e+01 -2.06e+03 -3.42e+02 5.77e+03 1.10e+05 240s ASSET 3.84e+04 3.57e+02 -3.57e+04 -5.56e+03 1.10e+05 2.86e+06 240s AGE -1.83e+01 1.09e-02 6.69e+01 8.78e+00 -5.07e+00 -1.51e+02 240s DEP 4.82e+00 -3.14e-02 -2.52e+01 -2.96e+00 -5.33e+00 -1.03e+02 240s RACE -5.67e+02 -1.33e+00 1.21e+03 1.81e+02 -9.13e+02 -1.96e+04 240s SCHOOL 5.33e+00 1.87e-01 1.86e+01 3.12e+00 3.20e+01 7.89e+02 240s AGE DEP RACE SCHOOL 240s HRS -1.83e+01 4.82e+00 -5.67e+02 5.33e+00 240s RATE 1.09e-02 -3.14e-02 -1.33e+00 1.87e-01 240s ERSP 6.69e+01 -2.52e+01 1.21e+03 1.86e+01 240s ERNO 8.78e+00 -2.96e+00 1.81e+02 3.12e+00 240s NEIN -5.07e+00 -5.33e+00 -9.13e+02 3.20e+01 240s ASSET -1.51e+02 -1.03e+02 -1.96e+04 7.89e+02 240s AGE 5.71e-01 -1.56e-01 4.58e+00 -5.00e-02 240s DEP -1.56e-01 8.08e-02 -3.02e-01 -4.47e-02 240s RACE 4.58e+00 -3.02e-01 2.36e+02 -4.54e+00 240s SCHOOL -5.00e-02 -4.47e-02 -4.54e+00 4.23e-01 240s -------------------------------------------------------- 240s airquality 153 4 56 21.136376 240s Best subsample: 240s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 240s [20] 47 48 49 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 81 240s [39] 82 84 86 87 89 90 91 92 96 97 98 100 101 105 106 109 110 111 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=56) 240s 240s Robust Estimate of Location: 240s Ozone Solar.R Wind Temp 240s 41.84 197.21 8.93 80.39 240s 240s Robust Estimate of Covariance: 240s Ozone Solar.R Wind Temp 240s Ozone 1480.7 1562.8 -99.9 347.3 240s Solar.R 1562.8 11401.2 -35.2 276.8 240s Wind -99.9 -35.2 11.4 -23.5 240s Temp 347.3 276.8 -23.5 107.7 240s -------------------------------------------------------- 240s attitude 30 7 15 27.040805 240s Best subsample: 240s [1] 2 3 4 5 7 8 10 12 15 19 22 23 25 27 28 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=15) 240s 240s Robust Estimate of Location: 240s rating complaints privileges learning raises critical 240s 65.8 66.5 50.1 56.1 66.7 78.1 240s advance 240s 41.7 240s 240s Robust Estimate of Covariance: 240s rating complaints privileges learning raises critical advance 240s rating 138.77 80.02 59.22 107.33 95.83 -1.24 54.36 240s complaints 80.02 97.23 50.59 99.50 79.15 -2.71 42.81 240s privileges 59.22 50.59 84.92 90.03 60.88 22.39 44.93 240s learning 107.33 99.50 90.03 187.67 128.71 15.48 63.67 240s raises 95.83 79.15 60.88 128.71 123.94 -1.46 49.98 240s critical -1.24 -2.71 22.39 15.48 -1.46 61.23 12.88 240s advance 54.36 42.81 44.93 63.67 49.98 12.88 48.61 240s -------------------------------------------------------- 240s attenu 182 5 83 9.710111 240s Best subsample: 240s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 240s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 240s [39] 115 116 117 121 122 124 125 126 127 128 129 130 131 132 133 134 135 136 137 240s [58] 138 139 140 141 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 240s [77] 160 161 162 163 164 165 166 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=83) 240s 240s Robust Estimate of Location: 240s event mag station dist accel 240s 18.940 5.741 67.988 23.365 0.124 240s 240s Robust Estimate of Covariance: 240s event mag station dist accel 240s event 2.86e+01 -2.31e+00 1.02e+02 2.68e+01 -1.99e-01 240s mag -2.31e+00 6.17e-01 -7.03e+00 4.67e-01 2.59e-02 240s station 1.02e+02 -7.03e+00 1.66e+03 1.62e+02 7.96e-02 240s dist 2.68e+01 4.67e-01 1.62e+02 3.61e+02 -1.23e+00 240s accel -1.99e-01 2.59e-02 7.96e-02 -1.23e+00 9.42e-03 240s -------------------------------------------------------- 240s USJudgeRatings 43 12 22 -23.463708 240s Best subsample: 240s [1] 2 3 4 6 9 11 15 16 18 19 24 25 26 27 28 29 32 33 34 36 37 38 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=22) 240s 240s Robust Estimate of Location: 240s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 240s 7.24 8.42 8.10 8.19 7.95 8.00 7.96 7.96 7.81 7.89 8.40 8.20 240s 240s Robust Estimate of Covariance: 240s CONT INTG DMNR DILG CFMG DECI PREP 240s CONT 0.61805 -0.05601 -0.09540 0.00694 0.09853 0.06261 0.03939 240s INTG -0.05601 0.23560 0.27537 0.20758 0.16603 0.17281 0.21128 240s DMNR -0.09540 0.27537 0.55349 0.28872 0.24014 0.24293 0.28886 240s DILG 0.00694 0.20758 0.28872 0.34099 0.23502 0.23917 0.29672 240s CFMG 0.09853 0.16603 0.24014 0.23502 0.31649 0.23291 0.27651 240s DECI 0.06261 0.17281 0.24293 0.23917 0.23291 0.30681 0.27737 240s PREP 0.03939 0.21128 0.28886 0.29672 0.27651 0.27737 0.42020 240s FAMI 0.04588 0.20388 0.26072 0.29037 0.27179 0.27737 0.34857 240s ORAL 0.03000 0.21379 0.29606 0.28764 0.27338 0.27424 0.33503 240s WRIT 0.03261 0.20258 0.26931 0.27962 0.26382 0.26610 0.32677 240s PHYS -0.04485 0.13598 0.17659 0.16834 0.14554 0.16467 0.18948 240s RTEN 0.01543 0.22654 0.32117 0.27307 0.23826 0.24669 0.29450 240s FAMI ORAL WRIT PHYS RTEN 240s CONT 0.04588 0.03000 0.03261 -0.04485 0.01543 240s INTG 0.20388 0.21379 0.20258 0.13598 0.22654 240s DMNR 0.26072 0.29606 0.26931 0.17659 0.32117 240s DILG 0.29037 0.28764 0.27962 0.16834 0.27307 240s CFMG 0.27179 0.27338 0.26382 0.14554 0.23826 240s DECI 0.27737 0.27424 0.26610 0.16467 0.24669 240s PREP 0.34857 0.33503 0.32677 0.18948 0.29450 240s FAMI 0.47232 0.33762 0.33420 0.19759 0.29015 240s ORAL 0.33762 0.40361 0.32208 0.19794 0.29544 240s WRIT 0.33420 0.32208 0.38733 0.19276 0.28184 240s PHYS 0.19759 0.19794 0.19276 0.20284 0.18097 240s RTEN 0.29015 0.29544 0.28184 0.18097 0.36877 240s -------------------------------------------------------- 240s USArrests 50 4 25 17.834643 240s Best subsample: 240s [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 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=25) 240s 240s Robust Estimate of Location: 240s Murder Assault UrbanPop Rape 240s 5.38 121.68 63.80 16.33 240s 240s Robust Estimate of Covariance: 240s Murder Assault UrbanPop Rape 240s Murder 17.8 316.3 48.5 31.1 240s Assault 316.3 6863.0 1040.0 548.9 240s UrbanPop 48.5 1040.0 424.8 93.6 240s Rape 31.1 548.9 93.6 63.8 240s -------------------------------------------------------- 240s longley 16 7 8 31.147844 240s Best subsample: 240s [1] 5 6 7 9 10 11 13 14 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=8) 240s 240s Robust Estimate of Location: 240s GNP.deflator GNP Unemployed Armed.Forces Population 240s 104.3 410.8 278.8 300.1 118.2 240s Year Employed 240s 1955.4 66.5 240s 240s Robust Estimate of Covariance: 240s GNP.deflator GNP Unemployed Armed.Forces Population 240s GNP.deflator 85.0 652.3 784.4 -370.7 48.7 240s GNP 652.3 7502.9 7328.6 -3414.2 453.9 240s Unemployed 784.4 7328.6 10760.3 -4646.7 548.1 240s Armed.Forces -370.7 -3414.2 -4646.7 2824.3 -253.9 240s Population 48.7 453.9 548.1 -253.9 40.2 240s Year 33.5 312.7 378.8 -176.1 23.4 240s Employed 23.9 224.8 263.6 -128.3 16.8 240s Year Employed 240s GNP.deflator 33.5 23.9 240s GNP 312.7 224.8 240s Unemployed 378.8 263.6 240s Armed.Forces -176.1 -128.3 240s Population 23.4 16.8 240s Year 18.9 11.7 240s Employed 11.7 10.3 240s -------------------------------------------------------- 240s Loblolly 84 3 42 11.163448 240s Best subsample: 240s [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 240s [26] 53 54 57 58 59 63 64 65 66 70 71 76 77 81 82 83 84 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=42) 240s 240s Robust Estimate of Location: 240s height age Seed 240s 44.20 17.26 6.76 240s 240s Robust Estimate of Covariance: 240s height age Seed 240s height 326.74 139.18 3.50 240s age 139.18 68.48 -2.72 240s Seed 3.50 -2.72 25.43 240s -------------------------------------------------------- 240s quakes 1000 4 500 11.802478 240s Best subsample: 240s Too long... 240s Outliers: 0 240s Too many to print ... 240s ------------- 240s 240s Call: 240s CovMrcd(x = x, trace = FALSE) 240s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=500) 240s 240s Robust Estimate of Location: 240s lat long depth mag 240s -20.59 182.13 432.46 4.42 240s 240s Robust Estimate of Covariance: 240s lat long depth mag 240s lat 15.841 5.702 -106.720 -0.441 240s long 5.702 7.426 -577.189 -0.136 240s depth -106.720 -577.189 66701.479 3.992 240s mag -0.441 -0.136 3.992 0.144 240s -------------------------------------------------------- 240s ======================================================== 240s > ##doexactfit() 240s > 240s BEGIN TEST tmest4.R 240s 240s R version 4.4.3 (2025-02-28) -- "Trophy Case" 240s Copyright (C) 2025 The R Foundation for Statistical Computing 240s Platform: s390x-ibm-linux-gnu 240s 240s R is free software and comes with ABSOLUTELY NO WARRANTY. 240s You are welcome to redistribute it under certain conditions. 240s Type 'license()' or 'licence()' for distribution details. 240s 240s R is a collaborative project with many contributors. 240s Type 'contributors()' for more information and 240s 'citation()' on how to cite R or R packages in publications. 240s 240s Type 'demo()' for some demos, 'help()' for on-line help, or 240s 'help.start()' for an HTML browser interface to help. 240s Type 'q()' to quit R. 240s 240s > ## VT::15.09.2013 - this will render the output independent 240s > ## from the version of the package 240s > suppressPackageStartupMessages(library(rrcov)) 240s > 240s > library(MASS) 240s > dodata <- function(nrep = 1, time = FALSE, full = TRUE) { 240s + domest <- function(x, xname, nrep = 1) { 240s + n <- dim(x)[1] 240s + p <- dim(x)[2] 240s + mm <- CovMest(x) 240s + crit <- log(mm@crit) 240s + ## c1 <- mm@psi@c1 240s + ## M <- mm$psi@M 240s + 240s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 240s + lpad <- lname-nchar(xname) 240s + cat(pad.right(xname,lpad), xres) 240s + 240s + dist <- getDistance(mm) 240s + quantiel <- qchisq(0.975, p) 240s + ibad <- which(dist >= quantiel) 240s + names(ibad) <- NULL 240s + nbad <- length(ibad) 240s + cat("Outliers: ",nbad,"\n") 240s + if(nbad > 0) 240s + print(ibad) 240s + cat("-------------\n") 240s + show(mm) 240s + cat("--------------------------------------------------------\n") 240s + } 240s + 240s + options(digits = 5) 240s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 240s + 240s + lname <- 20 240s + 240s + data(heart) 240s + data(starsCYG) 240s + data(phosphor) 240s + data(stackloss) 240s + data(coleman) 240s + data(salinity) 240s + data(wood) 240s + data(hbk) 240s + 240s + data(Animals, package = "MASS") 240s + brain <- Animals[c(1:24, 26:25, 27:28),] 240s + data(milk) 240s + data(bushfire) 240s + 240s + tmp <- sys.call() 240s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 240s + 240s + cat("Data Set n p c1 M LOG(det) Time\n") 240s + cat("======================================================================\n") 240s + domest(heart[, 1:2], data(heart), nrep) 240s + domest(starsCYG, data(starsCYG), nrep) 240s + domest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 240s + domest(stack.x, data(stackloss), nrep) 240s + domest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 240s + domest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 240s + domest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 240s + domest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 240s + 240s + 240s + domest(brain, "Animals", nrep) 240s + domest(milk, data(milk), nrep) 240s + domest(bushfire, data(bushfire), nrep) 240s + cat("======================================================================\n") 240s + } 240s > 240s > # generate contaminated data using the function gendata with different 240s > # number of outliers and check if the M-estimate breaks - i.e. the 240s > # largest eigenvalue is larger than e.g. 5. 240s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 240s > # outliers grater than 20. 240s > dogen <- function(){ 240s + eig <- vector("numeric",26) 240s + for(i in 0:25) { 240s + gg <- gendata(eps=i) 240s + mm <- CovMest(gg$x, t0=gg$tgood, S0=gg$sgood, arp=0.001) 240s + eig[i+1] <- ev <- getEvals(mm)[1] 240s + # cat(i, ev, "\n") 240s + 240s + stopifnot(ev < 5 || i > 20) 240s + } 240s + # plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 240s + } 240s > 240s > # 240s > # generate data 50x10 as multivariate normal N(0,I) and add 240s > # eps % outliers by adding d=5.0 to each component. 240s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 240s > # - if eps >= 1, it is the number of outliers 240s > # - use the center and cov of the good data as good start 240s > # - use the center and the cov of all data as a bad start 240s > # If using a good start, the M-estimate must iterate to 240s > # the good solution: the largest eigenvalue is less then e.g. 5 240s > # 240s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 240s + 240s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 240s + stop("eps is out of range") 240s + 240s + library(MASS) 240s + 240s + x <- mvrnorm(n, rep(0,p), diag(p)) 240s + bad <- vector("numeric") 240s + nbad = if(eps < 1) eps*n else eps 240s + if(nbad > 0){ 240s + bad <- sample(n, nbad) 240s + x[bad,] <- x[bad,] + d 240s + } 240s + cov1 <- cov.wt(x) 240s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 240s + 240s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 240s + } 240s > 240s > pad.right <- function(z, pads) 240s + { 240s + ## Pads spaces to right of text 240s + padding <- paste(rep(" ", pads), collapse = "") 240s + paste(z, padding, sep = "") 240s + } 240s > 240s > 240s > ## -- now do it: 240s > dodata() 240s 240s Call: dodata() 240s Data Set n p c1 M LOG(det) Time 240s ====================================================================== 240s heart 12 2 7.160341 240s Outliers: 3 240s [1] 2 6 12 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s height weight 240s 34.9 27.0 240s 240s Robust Estimate of Covariance: 240s height weight 240s height 102 155 240s weight 155 250 240s -------------------------------------------------------- 240s starsCYG 47 2 -5.994588 240s Outliers: 7 240s [1] 7 9 11 14 20 30 34 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s log.Te log.light 240s 4.42 4.95 240s 240s Robust Estimate of Covariance: 240s log.Te log.light 240s log.Te 0.0169 0.0587 240s log.light 0.0587 0.3523 240s -------------------------------------------------------- 240s phosphor 18 2 8.867522 240s Outliers: 3 240s [1] 1 6 10 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s inorg organic 240s 15.4 39.1 240s 240s Robust Estimate of Covariance: 240s inorg organic 240s inorg 169 213 240s organic 213 308 240s -------------------------------------------------------- 240s stackloss 21 3 7.241400 240s Outliers: 9 240s [1] 1 2 3 15 16 17 18 19 21 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s Air.Flow Water.Temp Acid.Conc. 240s 59.5 20.8 87.3 240s 240s Robust Estimate of Covariance: 240s Air.Flow Water.Temp Acid.Conc. 240s Air.Flow 9.34 8.69 8.52 240s Water.Temp 8.69 13.72 9.13 240s Acid.Conc. 8.52 9.13 34.54 240s -------------------------------------------------------- 240s coleman 20 5 2.574752 240s Outliers: 7 240s [1] 2 6 9 10 12 13 15 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s salaryP fatherWc sstatus teacherSc motherLev 240s 2.82 48.44 5.30 25.19 6.51 240s 240s Robust Estimate of Covariance: 240s salaryP fatherWc sstatus teacherSc motherLev 240s salaryP 0.2850 0.1045 1.7585 0.3074 0.0355 240s fatherWc 0.1045 824.8305 260.7062 3.7507 17.7959 240s sstatus 1.7585 260.7062 105.6135 4.1140 5.7714 240s teacherSc 0.3074 3.7507 4.1140 0.6753 0.1563 240s motherLev 0.0355 17.7959 5.7714 0.1563 0.4147 240s -------------------------------------------------------- 240s salinity 28 3 3.875096 240s Outliers: 9 240s [1] 3 5 10 11 15 16 17 23 24 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s X1 X2 X3 240s 10.02 3.21 22.36 240s 240s Robust Estimate of Covariance: 240s X1 X2 X3 240s X1 15.353 1.990 -5.075 240s X2 1.990 5.210 -0.769 240s X3 -5.075 -0.769 2.314 240s -------------------------------------------------------- 240s wood 20 5 -35.156305 240s Outliers: 7 240s [1] 4 6 7 8 11 16 19 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s x1 x2 x3 x4 x5 240s 0.587 0.122 0.531 0.538 0.892 240s 240s Robust Estimate of Covariance: 240s x1 x2 x3 x4 x5 240s x1 6.45e-03 1.21e-03 2.03e-03 -3.77e-04 -1.05e-03 240s x2 1.21e-03 3.12e-04 8.16e-04 -3.34e-05 1.52e-05 240s x3 2.03e-03 8.16e-04 4.27e-03 -5.60e-04 2.27e-04 240s x4 -3.77e-04 -3.34e-05 -5.60e-04 1.83e-03 1.18e-03 240s x5 -1.05e-03 1.52e-05 2.27e-04 1.18e-03 1.78e-03 240s -------------------------------------------------------- 240s hbk 75 3 1.432485 240s Outliers: 14 240s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s X1 X2 X3 240s 1.54 1.78 1.69 240s 240s Robust Estimate of Covariance: 240s X1 X2 X3 240s X1 1.6485 0.0739 0.1709 240s X2 0.0739 1.6780 0.2049 240s X3 0.1709 0.2049 1.5584 240s -------------------------------------------------------- 240s Animals 28 2 18.194822 240s Outliers: 10 240s [1] 2 6 7 9 12 14 15 16 25 28 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s body brain 240s 18.7 64.9 240s 240s Robust Estimate of Covariance: 240s body brain 240s body 4993 8466 240s brain 8466 30335 240s -------------------------------------------------------- 240s milk 86 8 -25.041802 240s Outliers: 20 240s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s X1 X2 X3 X4 X5 X6 X7 X8 240s 1.03 35.88 33.04 26.11 25.09 25.02 123.12 14.39 240s 240s Robust Estimate of Covariance: 240s X1 X2 X3 X4 X5 X6 X7 240s X1 4.89e-07 9.64e-05 1.83e-04 1.76e-04 1.57e-04 1.48e-04 6.53e-04 240s X2 9.64e-05 2.05e+00 3.38e-01 2.37e-01 1.70e-01 2.71e-01 1.91e+00 240s X3 1.83e-04 3.38e-01 1.16e+00 8.56e-01 8.48e-01 8.31e-01 8.85e-01 240s X4 1.76e-04 2.37e-01 8.56e-01 6.83e-01 6.55e-01 6.40e-01 6.91e-01 240s X5 1.57e-04 1.70e-01 8.48e-01 6.55e-01 6.93e-01 6.52e-01 6.90e-01 240s X6 1.48e-04 2.71e-01 8.31e-01 6.40e-01 6.52e-01 6.61e-01 6.95e-01 240s X7 6.53e-04 1.91e+00 8.85e-01 6.91e-01 6.90e-01 6.95e-01 4.40e+00 240s X8 5.56e-06 2.60e-01 1.98e-01 1.29e-01 1.12e-01 1.19e-01 4.12e-01 240s X8 240s X1 5.56e-06 240s X2 2.60e-01 240s X3 1.98e-01 240s X4 1.29e-01 240s X5 1.12e-01 240s X6 1.19e-01 240s X7 4.12e-01 240s X8 1.65e-01 240s -------------------------------------------------------- 240s bushfire 38 5 23.457490 240s Outliers: 15 240s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 240s ------------- 240s 240s Call: 240s CovMest(x = x) 240s -> Method: M-Estimates 240s 240s Robust Estimate of Location: 240s V1 V2 V3 V4 V5 240s 107 147 263 215 277 240s 240s Robust Estimate of Covariance: 240s V1 V2 V3 V4 V5 240s V1 775 560 -4179 -925 -759 240s V2 560 478 -2494 -510 -431 240s V3 -4179 -2494 27433 6441 5196 240s V4 -925 -510 6441 1607 1276 240s V5 -759 -431 5196 1276 1020 240s -------------------------------------------------------- 240s ====================================================================== 240s > dogen() 241s > #cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 241s > 241s BEGIN TEST tmve4.R 241s 241s R version 4.4.3 (2025-02-28) -- "Trophy Case" 241s Copyright (C) 2025 The R Foundation for Statistical Computing 241s Platform: s390x-ibm-linux-gnu 241s 241s R is free software and comes with ABSOLUTELY NO WARRANTY. 241s You are welcome to redistribute it under certain conditions. 241s Type 'license()' or 'licence()' for distribution details. 241s 241s R is a collaborative project with many contributors. 241s Type 'contributors()' for more information and 241s 'citation()' on how to cite R or R packages in publications. 241s 241s Type 'demo()' for some demos, 'help()' for on-line help, or 241s 'help.start()' for an HTML browser interface to help. 241s Type 'q()' to quit R. 241s 241s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMVE","MASS")){ 241s + ##@bdescr 241s + ## Test the function covMve() on the literature datasets: 241s + ## 241s + ## Call covMve() for all regression datasets available in rrco/robustbasev and print: 241s + ## - execution time (if time == TRUE) 241s + ## - objective fucntion 241s + ## - best subsample found (if short == false) 241s + ## - outliers identified (with cutoff 0.975) (if short == false) 241s + ## - estimated center and covarinance matrix if full == TRUE) 241s + ## 241s + ##@edescr 241s + ## 241s + ##@in nrep : [integer] number of repetitions to use for estimating the 241s + ## (average) execution time 241s + ##@in time : [boolean] whether to evaluate the execution time 241s + ##@in short : [boolean] whether to do short output (i.e. only the 241s + ## objective function value). If short == FALSE, 241s + ## the best subsample and the identified outliers are 241s + ## printed. See also the parameter full below 241s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 241s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 241s + 241s + domve <- function(x, xname, nrep=1){ 241s + n <- dim(x)[1] 241s + p <- dim(x)[2] 241s + alpha <- 0.5 241s + h <- h.alpha.n(alpha, n, p) 241s + if(method == "MASS"){ 241s + mve <- cov.mve(x, quantile.used=h) 241s + quan <- h #default: floor((n+p+1)/2) 241s + crit <- mve$crit 241s + best <- mve$best 241s + mah <- mahalanobis(x, mve$center, mve$cov) 241s + quantiel <- qchisq(0.975, p) 241s + wt <- as.numeric(mah < quantiel) 241s + } 241s + else{ 241s + mve <- CovMve(x, trace=FALSE) 241s + quan <- as.integer(mve@quan) 241s + crit <- log(mve@crit) 241s + best <- mve@best 241s + wt <- mve@wt 241s + } 241s + 241s + 241s + if(time){ 241s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 241s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 241s + } 241s + else{ 241s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 241s + } 241s + 241s + lpad<-lname-nchar(xname) 241s + cat(pad.right(xname,lpad), xres) 241s + 241s + if(!short){ 241s + cat("Best subsample: \n") 241s + print(best) 241s + 241s + ibad <- which(wt == 0) 241s + names(ibad) <- NULL 241s + nbad <- length(ibad) 241s + cat("Outliers: ", nbad, "\n") 241s + if(nbad > 0) 241s + print(ibad) 241s + if(full){ 241s + cat("-------------\n") 241s + show(mve) 241s + } 241s + cat("--------------------------------------------------------\n") 241s + } 241s + } 241s + 241s + options(digits = 5) 241s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 241s + 241s + lname <- 20 241s + 241s + ## VT::15.09.2013 - this will render the output independent 241s + ## from the version of the package 241s + suppressPackageStartupMessages(library(rrcov)) 241s + 241s + method <- match.arg(method) 241s + if(method == "MASS") 241s + library(MASS) 241s + 241s + 241s + data(heart) 241s + data(starsCYG) 241s + data(phosphor) 241s + data(stackloss) 241s + data(coleman) 241s + data(salinity) 241s + data(wood) 241s + 241s + data(hbk) 241s + 241s + data(Animals, package = "MASS") 241s + brain <- Animals[c(1:24, 26:25, 27:28),] 241s + data(milk) 241s + data(bushfire) 241s + 241s + tmp <- sys.call() 241s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 241s + 241s + cat("Data Set n p Half LOG(obj) Time\n") 241s + cat("========================================================\n") 241s + domve(heart[, 1:2], data(heart), nrep) 241s + domve(starsCYG, data(starsCYG), nrep) 241s + domve(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 241s + domve(stack.x, data(stackloss), nrep) 241s + domve(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 241s + domve(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 241s + domve(data.matrix(subset(wood, select = -y)), data(wood), nrep) 241s + domve(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 241s + 241s + domve(brain, "Animals", nrep) 241s + domve(milk, data(milk), nrep) 241s + domve(bushfire, data(bushfire), nrep) 241s + cat("========================================================\n") 241s + } 241s > 241s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMVE", "MASS")){ 241s + 241s + domve <- function(x, nrep=1){ 241s + gc() 241s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 241s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 241s + xtime 241s + } 241s + 241s + set.seed(1234) 241s + 241s + ## VT::15.09.2013 - this will render the output independent 241s + ## from the version of the package 241s + suppressPackageStartupMessages(library(rrcov)) 241s + library(MASS) 241s + 241s + method <- match.arg(method) 241s + 241s + ap <- c(2, 5, 10, 20, 30) 241s + an <- c(100, 500, 1000, 10000, 50000) 241s + 241s + tottime <- 0 241s + cat(" n p Time\n") 241s + cat("=====================\n") 241s + for(i in 1:length(an)) { 241s + for(j in 1:length(ap)) { 241s + n <- an[i] 241s + p <- ap[j] 241s + if(5*p <= n){ 241s + xx <- gendata(n, p, eps) 241s + X <- xx$X 241s + tottime <- tottime + domve(X, nrep) 241s + } 241s + } 241s + } 241s + 241s + cat("=====================\n") 241s + cat("Total time: ", tottime*nrep, "\n") 241s + } 241s > 241s > docheck <- function(n, p, eps){ 241s + xx <- gendata(n,p,eps) 241s + mve <- CovMve(xx$X) 241s + check(mve, xx$xind) 241s + } 241s > 241s > check <- function(mcd, xind){ 241s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 241s + ## did not get zero weight 241s + mymatch <- xind %in% which(mcd@wt == 0) 241s + length(xind) - length(which(mymatch)) 241s + } 241s > 241s > dorep <- function(x, nrep=1, method=c("FASTMVE","MASS")){ 241s + 241s + method <- match.arg(method) 241s + for(i in 1:nrep) 241s + if(method == "MASS") 241s + cov.mve(x) 241s + else 241s + CovMve(x) 241s + } 241s > 241s > #### gendata() #### 241s > # Generates a location contaminated multivariate 241s > # normal sample of n observations in p dimensions 241s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 241s > # where 241s > # m = (b,b,...,b) 241s > # Defaults: eps=0 and b=10 241s > # 241s > gendata <- function(n,p,eps=0,b=10){ 241s + 241s + if(missing(n) || missing(p)) 241s + stop("Please specify (n,p)") 241s + if(eps < 0 || eps >= 0.5) 241s + stop(message="eps must be in [0,0.5)") 241s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 241s + nbad <- as.integer(eps * n) 241s + if(nbad > 0){ 241s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 241s + xind <- sample(n,nbad) 241s + X[xind,] <- Xbad 241s + } 241s + list(X=X, xind=xind) 241s + } 241s > 241s > pad.right <- function(z, pads) 241s + { 241s + ### Pads spaces to right of text 241s + padding <- paste(rep(" ", pads), collapse = "") 241s + paste(z, padding, sep = "") 241s + } 241s > 241s > whatis<-function(x){ 241s + if(is.data.frame(x)) 241s + cat("Type: data.frame\n") 241s + else if(is.matrix(x)) 241s + cat("Type: matrix\n") 241s + else if(is.vector(x)) 241s + cat("Type: vector\n") 241s + else 241s + cat("Type: don't know\n") 241s + } 241s > 241s > ## VT::15.09.2013 - this will render the output independent 241s > ## from the version of the package 241s > suppressPackageStartupMessages(library(rrcov)) 241s > 241s > dodata() 241s 241s Call: dodata() 241s Data Set n p Half LOG(obj) Time 241s ======================================================== 241s heart 12 2 7 3.827606 241s Best subsample: 241s [1] 1 4 7 8 9 10 11 241s Outliers: 3 241s [1] 2 6 12 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s height weight 241s 34.9 27.0 241s 241s Robust Estimate of Covariance: 241s height weight 241s height 142 217 241s weight 217 350 241s -------------------------------------------------------- 241s starsCYG 47 2 25 -2.742997 241s Best subsample: 241s [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 241s Outliers: 7 241s [1] 7 9 11 14 20 30 34 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s log.Te log.light 241s 4.41 4.93 241s 241s Robust Estimate of Covariance: 241s log.Te log.light 241s log.Te 0.0173 0.0578 241s log.light 0.0578 0.3615 241s -------------------------------------------------------- 241s phosphor 18 2 10 4.443101 241s Best subsample: 241s [1] 3 5 8 9 11 12 13 14 15 17 241s Outliers: 3 241s [1] 1 6 10 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s inorg organic 241s 15.2 39.4 241s 241s Robust Estimate of Covariance: 241s inorg organic 241s inorg 188 230 241s organic 230 339 241s -------------------------------------------------------- 241s stackloss 21 3 12 3.327582 241s Best subsample: 241s [1] 4 5 6 7 8 9 10 11 12 13 14 20 241s Outliers: 3 241s [1] 1 2 3 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s Air.Flow Water.Temp Acid.Conc. 241s 56.7 20.2 85.5 241s 241s Robust Estimate of Covariance: 241s Air.Flow Water.Temp Acid.Conc. 241s Air.Flow 34.31 11.07 23.54 241s Water.Temp 11.07 9.23 7.85 241s Acid.Conc. 23.54 7.85 47.35 241s -------------------------------------------------------- 241s coleman 20 5 13 2.065143 241s Best subsample: 241s [1] 1 3 4 5 7 8 11 14 16 17 18 19 20 241s Outliers: 5 241s [1] 2 6 9 10 13 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s salaryP fatherWc sstatus teacherSc motherLev 241s 2.79 44.26 3.59 25.08 6.38 241s 241s Robust Estimate of Covariance: 241s salaryP fatherWc sstatus teacherSc motherLev 241s salaryP 0.2920 1.1188 2.0421 0.3487 0.0748 241s fatherWc 1.1188 996.7540 338.6587 7.1673 23.1783 241s sstatus 2.0421 338.6587 148.2501 4.4894 7.8135 241s teacherSc 0.3487 7.1673 4.4894 0.9082 0.3204 241s motherLev 0.0748 23.1783 7.8135 0.3204 0.6024 241s -------------------------------------------------------- 241s salinity 28 3 16 2.002555 241s Best subsample: 241s [1] 1 7 8 9 12 13 14 18 19 20 21 22 25 26 27 28 241s Outliers: 5 241s [1] 5 11 16 23 24 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s X1 X2 X3 241s 10.2 3.1 22.4 241s 241s Robust Estimate of Covariance: 241s X1 X2 X3 241s X1 14.387 1.153 -4.072 241s X2 1.153 5.005 -0.954 241s X3 -4.072 -0.954 2.222 241s -------------------------------------------------------- 241s wood 20 5 13 -5.471407 241s Best subsample: 241s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 241s Outliers: 5 241s [1] 4 6 8 11 19 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s x1 x2 x3 x4 x5 241s 0.576 0.123 0.531 0.538 0.889 241s 241s Robust Estimate of Covariance: 241s x1 x2 x3 x4 x5 241s x1 7.45e-03 1.11e-03 1.83e-03 -2.90e-05 -5.65e-04 241s x2 1.11e-03 3.11e-04 7.68e-04 3.37e-05 3.85e-05 241s x3 1.83e-03 7.68e-04 4.30e-03 -9.96e-04 -6.27e-05 241s x4 -2.90e-05 3.37e-05 -9.96e-04 3.02e-03 1.91e-03 241s x5 -5.65e-04 3.85e-05 -6.27e-05 1.91e-03 2.25e-03 241s -------------------------------------------------------- 241s hbk 75 3 39 1.096831 241s Best subsample: 241s [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 241s [26] 55 56 58 59 64 65 66 67 70 71 72 73 74 75 241s Outliers: 14 241s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s X1 X2 X3 241s 1.48 1.86 1.73 241s 241s Robust Estimate of Covariance: 241s X1 X2 X3 241s X1 1.695 0.230 0.265 241s X2 0.230 1.679 0.119 241s X3 0.265 0.119 1.683 241s -------------------------------------------------------- 241s Animals 28 2 15 8.945423 241s Best subsample: 241s [1] 1 3 4 5 10 11 17 18 21 22 23 24 26 27 28 241s Outliers: 9 241s [1] 2 6 7 9 12 14 15 16 25 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s body brain 241s 48.3 127.3 241s 241s Robust Estimate of Covariance: 241s body brain 241s body 10767 16872 241s brain 16872 46918 241s -------------------------------------------------------- 241s milk 86 8 47 -1.160085 241s Best subsample: 241s [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 241s [26] 46 54 56 57 59 60 61 62 63 64 65 66 67 69 72 76 78 79 81 82 83 85 241s Outliers: 18 241s [1] 1 2 3 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s X1 X2 X3 X4 X5 X6 X7 X8 241s 1.03 35.91 33.02 26.08 25.06 24.99 122.93 14.38 241s 241s Robust Estimate of Covariance: 241s X1 X2 X3 X4 X5 X6 X7 241s X1 6.00e-07 1.51e-04 3.34e-04 3.09e-04 2.82e-04 2.77e-04 1.09e-03 241s X2 1.51e-04 2.03e+00 3.83e-01 3.04e-01 2.20e-01 3.51e-01 2.18e+00 241s X3 3.34e-04 3.83e-01 1.58e+00 1.21e+00 1.18e+00 1.20e+00 1.60e+00 241s X4 3.09e-04 3.04e-01 1.21e+00 9.82e-01 9.39e-01 9.53e-01 1.36e+00 241s X5 2.82e-04 2.20e-01 1.18e+00 9.39e-01 9.67e-01 9.52e-01 1.34e+00 241s X6 2.77e-04 3.51e-01 1.20e+00 9.53e-01 9.52e-01 9.92e-01 1.38e+00 241s X7 1.09e-03 2.18e+00 1.60e+00 1.36e+00 1.34e+00 1.38e+00 6.73e+00 241s X8 3.33e-05 2.92e-01 2.65e-01 1.83e-01 1.65e-01 1.76e-01 5.64e-01 241s X8 241s X1 3.33e-05 241s X2 2.92e-01 241s X3 2.65e-01 241s X4 1.83e-01 241s X5 1.65e-01 241s X6 1.76e-01 241s X7 5.64e-01 241s X8 1.80e-01 241s -------------------------------------------------------- 241s bushfire 38 5 22 5.644315 241s Best subsample: 241s [1] 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 241s Outliers: 15 241s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 241s ------------- 241s 241s Call: 241s CovMve(x = x, trace = FALSE) 241s -> Method: Minimum volume ellipsoid estimator 241s 241s Robust Estimate of Location: 241s V1 V2 V3 V4 V5 241s 107 147 263 215 277 241s 241s Robust Estimate of Covariance: 241s V1 V2 V3 V4 V5 241s V1 519 375 -2799 -619 -509 241s V2 375 320 -1671 -342 -289 241s V3 -2799 -1671 18373 4314 3480 241s V4 -619 -342 4314 1076 854 241s V5 -509 -289 3480 854 683 241s -------------------------------------------------------- 241s ======================================================== 241s > 241s BEGIN TEST togk4.R 241s 241s R version 4.4.3 (2025-02-28) -- "Trophy Case" 241s Copyright (C) 2025 The R Foundation for Statistical Computing 241s Platform: s390x-ibm-linux-gnu 241s 241s R is free software and comes with ABSOLUTELY NO WARRANTY. 241s You are welcome to redistribute it under certain conditions. 241s Type 'license()' or 'licence()' for distribution details. 241s 241s R is a collaborative project with many contributors. 241s Type 'contributors()' for more information and 241s 'citation()' on how to cite R or R packages in publications. 241s 241s Type 'demo()' for some demos, 'help()' for on-line help, or 241s 'help.start()' for an HTML browser interface to help. 241s Type 'q()' to quit R. 241s 241s > ## VT::15.09.2013 - this will render the output independent 241s > ## from the version of the package 241s > suppressPackageStartupMessages(library(rrcov)) 241s > 241s > ## VT::14.01.2020 241s > ## On some platforms minor differences are shown - use 241s > ## IGNORE_RDIFF_BEGIN 241s > ## IGNORE_RDIFF_END 241s > 241s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS")){ 241s + domcd <- function(x, xname, nrep=1){ 241s + n <- dim(x)[1] 241s + p <- dim(x)[2] 241s + 241s + mcd<-CovOgk(x) 241s + 241s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 241s + 241s + lpad<-lname-nchar(xname) 241s + cat(pad.right(xname,lpad), xres) 241s + 241s + dist <- getDistance(mcd) 241s + quantiel <- qchisq(0.975, p) 241s + ibad <- which(dist >= quantiel) 241s + names(ibad) <- NULL 241s + nbad <- length(ibad) 241s + cat("Outliers: ",nbad,"\n") 241s + if(nbad > 0) 241s + print(ibad) 241s + cat("-------------\n") 241s + show(mcd) 241s + cat("--------------------------------------------------------\n") 241s + } 241s + 241s + lname <- 20 241s + 241s + ## VT::15.09.2013 - this will render the output independent 241s + ## from the version of the package 241s + suppressPackageStartupMessages(library(rrcov)) 241s + 241s + method <- match.arg(method) 241s + 241s + data(heart) 241s + data(starsCYG) 241s + data(phosphor) 241s + data(stackloss) 241s + data(coleman) 241s + data(salinity) 241s + data(wood) 241s + 241s + data(hbk) 241s + 241s + data(Animals, package = "MASS") 241s + brain <- Animals[c(1:24, 26:25, 27:28),] 241s + data(milk) 241s + data(bushfire) 241s + 241s + tmp <- sys.call() 241s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 241s + 241s + cat("Data Set n p Half LOG(obj) Time\n") 241s + cat("========================================================\n") 241s + domcd(heart[, 1:2], data(heart), nrep) 241s + ## This will not work within the function, of course 241s + ## - comment it out 241s + ## IGNORE_RDIFF_BEGIN 241s + ## domcd(starsCYG,data(starsCYG), nrep) 241s + ## IGNORE_RDIFF_END 241s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 241s + domcd(stack.x,data(stackloss), nrep) 241s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 241s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 241s + ## IGNORE_RDIFF_BEGIN 241s + ## domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 241s + ## IGNORE_RDIFF_END 241s + domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 241s + 241s + domcd(brain, "Animals", nrep) 241s + domcd(milk, data(milk), nrep) 241s + domcd(bushfire, data(bushfire), nrep) 241s + cat("========================================================\n") 241s + } 241s > 241s > pad.right <- function(z, pads) 241s + { 241s + ### Pads spaces to right of text 241s + padding <- paste(rep(" ", pads), collapse = "") 241s + paste(z, padding, sep = "") 241s + } 241s > 241s > dodata() 241s 241s Call: dodata() 241s Data Set n p Half LOG(obj) Time 241s ======================================================== 241s heart 12 2 241s Outliers: 5 241s [1] 2 6 8 10 12 241s ------------- 241s 241s Call: 241s CovOgk(x = x) 241s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 241s 241s Robust Estimate of Location: 241s height weight 241s 39.76 35.71 241s 241s Robust Estimate of Covariance: 241s height weight 241s height 15.88 32.07 241s weight 32.07 78.28 241s -------------------------------------------------------- 242s phosphor 18 2 242s Outliers: 2 242s [1] 1 6 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s inorg organic 242s 13.31 40.00 242s 242s Robust Estimate of Covariance: 242s inorg organic 242s inorg 92.82 93.24 242s organic 93.24 152.62 242s -------------------------------------------------------- 242s stackloss 21 3 242s Outliers: 2 242s [1] 1 2 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s Air.Flow Water.Temp Acid.Conc. 242s 57.72 20.50 85.78 242s 242s Robust Estimate of Covariance: 242s Air.Flow Water.Temp Acid.Conc. 242s Air.Flow 38.423 11.306 18.605 242s Water.Temp 11.306 6.806 5.889 242s Acid.Conc. 18.605 5.889 29.840 242s -------------------------------------------------------- 242s coleman 20 5 242s Outliers: 3 242s [1] 1 6 10 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s salaryP fatherWc sstatus teacherSc motherLev 242s 2.723 43.202 2.912 25.010 6.290 242s 242s Robust Estimate of Covariance: 242s salaryP fatherWc sstatus teacherSc motherLev 242s salaryP 0.12867 2.80048 0.92026 0.15118 0.06413 242s fatherWc 2.80048 678.72549 227.36415 9.30826 16.15102 242s sstatus 0.92026 227.36415 101.39094 3.38013 5.63283 242s teacherSc 0.15118 9.30826 3.38013 0.57112 0.27701 242s motherLev 0.06413 16.15102 5.63283 0.27701 0.44801 242s -------------------------------------------------------- 242s salinity 28 3 242s Outliers: 3 242s [1] 3 5 16 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s X1 X2 X3 242s 10.74 2.68 22.99 242s 242s Robust Estimate of Covariance: 242s X1 X2 X3 242s X1 8.1047 -0.6365 -0.4720 242s X2 -0.6365 3.0976 -1.3520 242s X3 -0.4720 -1.3520 2.3648 242s -------------------------------------------------------- 242s hbk 75 3 242s Outliers: 14 242s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s X1 X2 X3 242s 1.538 1.780 1.687 242s 242s Robust Estimate of Covariance: 242s X1 X2 X3 242s X1 1.11350 0.04992 0.11541 242s X2 0.04992 1.13338 0.13843 242s X3 0.11541 0.13843 1.05261 242s -------------------------------------------------------- 242s Animals 28 2 242s Outliers: 12 242s [1] 2 6 7 9 12 14 15 16 17 24 25 28 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s body brain 242s 39.65 105.83 242s 242s Robust Estimate of Covariance: 242s body brain 242s body 3981 7558 242s brain 7558 16594 242s -------------------------------------------------------- 242s milk 86 8 242s Outliers: 22 242s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 50 70 74 75 77 85 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s X1 X2 X3 X4 X5 X6 X7 X8 242s 1.03 35.80 33.10 26.15 25.13 25.06 123.06 14.39 242s 242s Robust Estimate of Covariance: 242s X1 X2 X3 X4 X5 X6 X7 242s X1 4.074e-07 5.255e-05 1.564e-04 1.506e-04 1.340e-04 1.234e-04 5.308e-04 242s X2 5.255e-05 1.464e+00 3.425e-01 2.465e-01 1.847e-01 2.484e-01 1.459e+00 242s X3 1.564e-04 3.425e-01 1.070e+00 7.834e-01 7.665e-01 7.808e-01 7.632e-01 242s X4 1.506e-04 2.465e-01 7.834e-01 6.178e-01 5.868e-01 5.959e-01 5.923e-01 242s X5 1.340e-04 1.847e-01 7.665e-01 5.868e-01 6.124e-01 5.967e-01 5.868e-01 242s X6 1.234e-04 2.484e-01 7.808e-01 5.959e-01 5.967e-01 6.253e-01 5.819e-01 242s X7 5.308e-04 1.459e+00 7.632e-01 5.923e-01 5.868e-01 5.819e-01 3.535e+00 242s X8 1.990e-07 1.851e-01 1.861e-01 1.210e-01 1.041e-01 1.116e-01 3.046e-01 242s X8 242s X1 1.990e-07 242s X2 1.851e-01 242s X3 1.861e-01 242s X4 1.210e-01 242s X5 1.041e-01 242s X6 1.116e-01 242s X7 3.046e-01 242s X8 1.292e-01 242s -------------------------------------------------------- 242s bushfire 38 5 242s Outliers: 17 242s [1] 7 8 9 10 11 12 28 29 30 31 32 33 34 35 36 37 38 242s ------------- 242s 242s Call: 242s CovOgk(x = x) 242s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 242s 242s Robust Estimate of Location: 242s V1 V2 V3 V4 V5 242s 104.5 146.0 275.6 217.8 279.3 242s 242s Robust Estimate of Covariance: 242s V1 V2 V3 V4 V5 242s V1 266.8 203.2 -1380.7 -311.1 -252.2 242s V2 203.2 178.4 -910.9 -185.9 -155.9 242s V3 -1380.7 -910.9 8279.7 2035.5 1615.4 242s V4 -311.1 -185.9 2035.5 536.5 418.6 242s V5 -252.2 -155.9 1615.4 418.6 329.2 242s -------------------------------------------------------- 242s ======================================================== 242s > 242s BEGIN TEST tqda.R 242s 242s R version 4.4.3 (2025-02-28) -- "Trophy Case" 242s Copyright (C) 2025 The R Foundation for Statistical Computing 242s Platform: s390x-ibm-linux-gnu 242s 242s R is free software and comes with ABSOLUTELY NO WARRANTY. 242s You are welcome to redistribute it under certain conditions. 242s Type 'license()' or 'licence()' for distribution details. 242s 242s R is a collaborative project with many contributors. 242s Type 'contributors()' for more information and 242s 'citation()' on how to cite R or R packages in publications. 242s 242s Type 'demo()' for some demos, 'help()' for on-line help, or 242s 'help.start()' for an HTML browser interface to help. 242s Type 'q()' to quit R. 242s 242s > ## VT::15.09.2013 - this will render the output independent 242s > ## from the version of the package 242s > suppressPackageStartupMessages(library(rrcov)) 242s > 242s > dodata <- function(method) { 242s + 242s + options(digits = 5) 242s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 242s + 242s + tmp <- sys.call() 242s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 242s + cat("===================================================\n") 242s + 242s + data(hemophilia); show(QdaCov(as.factor(gr)~., data=hemophilia, method=method)) 242s + data(anorexia, package="MASS"); show(QdaCov(Treat~., data=anorexia, method=method)) 242s + data(Pima.tr, package="MASS"); show(QdaCov(type~., data=Pima.tr, method=method)) 242s + data(iris); # show(QdaCov(Species~., data=iris, method=method)) 242s + data(crabs, package="MASS"); # show(QdaCov(sp~., data=crabs, method=method)) 242s + 242s + show(QdaClassic(as.factor(gr)~., data=hemophilia)) 242s + show(QdaClassic(Treat~., data=anorexia)) 242s + show(QdaClassic(type~., data=Pima.tr)) 242s + show(QdaClassic(Species~., data=iris)) 242s + ## show(QdaClassic(sp~., data=crabs)) 242s + cat("===================================================\n") 242s + } 242s > 242s > 242s > ## -- now do it: 242s > dodata(method="mcd") 242s 242s Call: dodata(method = "mcd") 242s =================================================== 242s Call: 242s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.30795 -0.0059911 242s normal -0.12920 -0.0603000 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.023784 0.015376 242s AHFantigen 0.015376 0.024035 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.0057546 0.0042606 242s AHFantigen 0.0042606 0.0084914 242s Call: 242s QdaCov(Treat ~ ., data = anorexia, method = method) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.633 82.950 242s Cont 81.558 81.108 242s FT 84.331 94.762 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 9.8671 8.6611 242s Postwt 8.6611 11.8966 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 32.5705 -4.3705 242s Postwt -4.3705 22.5079 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 33.056 10.814 242s Postwt 10.814 14.265 242s Call: 242s QdaCov(type ~ ., data = Pima.tr, method = method) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 242s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 2.221983 -0.18658 1.86507 -0.44427 0.1725348 -0.0683616 2.63439 242s glu -0.186582 471.88789 45.28021 8.95404 30.6551510 -0.6359899 3.50218 242s bp 1.865066 45.28021 110.09787 26.11192 14.4739180 -0.2104074 13.23392 242s skin -0.444272 8.95404 26.11192 118.30521 52.3115719 -0.2995751 8.65861 242s bmi 0.172535 30.65515 14.47392 52.31157 43.3140415 0.0079866 6.75720 242s ped -0.068362 -0.63599 -0.21041 -0.29958 0.0079866 0.0587710 -0.18683 242s age 2.634387 3.50218 13.23392 8.65861 6.7572019 -0.1868284 12.09493 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 17.875215 -13.740021 9.03580 4.498580 1.787458 0.079504 26.92283 242s glu -13.740021 917.719003 55.30399 27.976265 10.755113 0.092673 38.94970 242s bp 9.035798 55.303991 129.97953 34.130200 10.104275 0.198342 32.95351 242s skin 4.498580 27.976265 34.13020 101.842647 30.297210 0.064739 3.59427 242s bmi 1.787458 10.755113 10.10428 30.297210 22.529467 0.084369 -6.64317 242s ped 0.079504 0.092673 0.19834 0.064739 0.084369 0.066667 0.11199 242s age 26.922828 38.949697 32.95351 3.594266 -6.643165 0.111992 143.69752 242s Call: 242s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.30795 -0.0059911 242s normal -0.13487 -0.0778567 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.023784 0.015376 242s AHFantigen 0.015376 0.024035 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.020897 0.015515 242s AHFantigen 0.015515 0.017920 242s Call: 242s QdaClassic(Treat ~ ., data = anorexia) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.690 85.697 242s Cont 81.558 81.108 242s FT 83.229 90.494 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 23.479 19.910 242s Postwt 19.910 69.755 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 32.5705 -4.3705 242s Postwt -4.3705 22.5079 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 25.167 22.883 242s Postwt 22.883 71.827 242s Call: 242s QdaClassic(type ~ ., data = Pima.tr) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 242s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 242s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 242s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 242s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 242s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 242s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 242s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 242s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 242s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 242s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 242s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 242s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 242s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 242s Call: 242s QdaClassic(Species ~ ., data = iris) 242s 242s Prior Probabilities of Groups: 242s setosa versicolor virginica 242s 0.33333 0.33333 0.33333 242s 242s Group means: 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s setosa 5.006 3.428 1.462 0.246 242s versicolor 5.936 2.770 4.260 1.326 242s virginica 6.588 2.974 5.552 2.026 242s 242s Group: setosa 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 242s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 242s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 242s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 242s 242s Group: versicolor 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.266433 0.085184 0.182898 0.055780 242s Sepal.Width 0.085184 0.098469 0.082653 0.041204 242s Petal.Length 0.182898 0.082653 0.220816 0.073102 242s Petal.Width 0.055780 0.041204 0.073102 0.039106 242s 242s Group: virginica 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.404343 0.093763 0.303290 0.049094 242s Sepal.Width 0.093763 0.104004 0.071380 0.047629 242s Petal.Length 0.303290 0.071380 0.304588 0.048824 242s Petal.Width 0.049094 0.047629 0.048824 0.075433 242s =================================================== 242s > dodata(method="m") 242s 242s Call: dodata(method = "m") 242s =================================================== 242s Call: 242s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.29810 -0.0028222 242s normal -0.13081 -0.0675283 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.026018 0.017653 242s AHFantigen 0.017653 0.030128 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.0081933 0.0065737 242s AHFantigen 0.0065737 0.0118565 242s Call: 242s QdaCov(Treat ~ ., data = anorexia, method = method) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.436 82.631 242s Cont 81.559 80.272 242s FT 85.120 94.657 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 23.630 25.128 242s Postwt 25.128 38.142 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 35.8824 -8.2405 242s Postwt -8.2405 23.7416 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 33.805 18.206 242s Postwt 18.206 24.639 242s Call: 242s QdaCov(type ~ ., data = Pima.tr, method = method) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.5225 111.26 68.081 26.640 30.801 0.40452 26.306 242s Yes 5.0702 144.32 75.088 31.982 34.267 0.47004 37.140 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 5.74219 14.47051 6.63766 4.98559 0.826570 -0.128106 10.71303 242s glu 14.47051 591.08717 68.81219 44.73311 40.658393 -0.545716 38.01918 242s bp 6.63766 68.81219 121.02716 30.46466 16.789801 -0.320065 25.29371 242s skin 4.98559 44.73311 30.46466 136.52176 56.604475 -0.250711 19.73259 242s bmi 0.82657 40.65839 16.78980 56.60447 47.859747 0.046358 6.94523 242s ped -0.12811 -0.54572 -0.32006 -0.25071 0.046358 0.061485 -0.34653 242s age 10.71303 38.01918 25.29371 19.73259 6.945227 -0.346527 35.66101 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.98861 -1.2430 10.48556 9.05947 2.425316 0.162453 30.149872 242s glu -1.24304 867.1076 46.43838 25.92297 5.517382 1.044360 31.152650 242s bp 10.48556 46.4384 130.12536 17.21407 6.343942 -0.235121 41.091494 242s skin 9.05947 25.9230 17.21407 85.96314 26.089017 0.170061 14.562516 242s bmi 2.42532 5.5174 6.34394 26.08902 22.051976 0.097786 -5.297971 242s ped 0.16245 1.0444 -0.23512 0.17006 0.097786 0.057112 0.055286 242s age 30.14987 31.1527 41.09149 14.56252 -5.297971 0.055286 137.440921 242s Call: 242s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.30795 -0.0059911 242s normal -0.13487 -0.0778567 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.023784 0.015376 242s AHFantigen 0.015376 0.024035 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.020897 0.015515 242s AHFantigen 0.015515 0.017920 242s Call: 242s QdaClassic(Treat ~ ., data = anorexia) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.690 85.697 242s Cont 81.558 81.108 242s FT 83.229 90.494 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 23.479 19.910 242s Postwt 19.910 69.755 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 32.5705 -4.3705 242s Postwt -4.3705 22.5079 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 25.167 22.883 242s Postwt 22.883 71.827 242s Call: 242s QdaClassic(type ~ ., data = Pima.tr) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 242s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 242s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 242s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 242s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 242s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 242s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 242s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 242s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 242s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 242s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 242s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 242s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 242s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 242s Call: 242s QdaClassic(Species ~ ., data = iris) 242s 242s Prior Probabilities of Groups: 242s setosa versicolor virginica 242s 0.33333 0.33333 0.33333 242s 242s Group means: 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s setosa 5.006 3.428 1.462 0.246 242s versicolor 5.936 2.770 4.260 1.326 242s virginica 6.588 2.974 5.552 2.026 242s 242s Group: setosa 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 242s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 242s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 242s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 242s 242s Group: versicolor 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.266433 0.085184 0.182898 0.055780 242s Sepal.Width 0.085184 0.098469 0.082653 0.041204 242s Petal.Length 0.182898 0.082653 0.220816 0.073102 242s Petal.Width 0.055780 0.041204 0.073102 0.039106 242s 242s Group: virginica 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.404343 0.093763 0.303290 0.049094 242s Sepal.Width 0.093763 0.104004 0.071380 0.047629 242s Petal.Length 0.303290 0.071380 0.304588 0.048824 242s Petal.Width 0.049094 0.047629 0.048824 0.075433 242s =================================================== 242s > dodata(method="ogk") 242s 242s Call: dodata(method = "ogk") 242s =================================================== 242s Call: 242s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.29324 0.00033953 242s normal -0.12744 -0.06628182 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.019260 0.013026 242s AHFantigen 0.013026 0.021889 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.0049651 0.0039707 242s AHFantigen 0.0039707 0.0066084 242s Call: 242s QdaCov(Treat ~ ., data = anorexia, method = method) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.587 82.709 242s Cont 81.558 81.108 242s FT 85.110 94.470 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 10.452 15.115 242s Postwt 15.115 37.085 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 31.3178 -4.2024 242s Postwt -4.2024 21.6422 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 5.5309 1.4813 242s Postwt 1.4813 7.5501 242s Call: 242s QdaCov(type ~ ., data = Pima.tr, method = method) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.4286 110.35 67.495 25.905 30.275 0.39587 26.248 242s Yes 5.1964 142.71 75.357 32.732 34.809 0.48823 37.607 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 3.97823 8.70612 4.58776 4.16463 0.250612 -0.117238 8.21769 242s glu 8.70612 448.91392 51.71120 38.66213 21.816345 -0.296524 24.29370 242s bp 4.58776 51.71120 99.41188 24.27574 10.491311 -0.290753 20.02975 242s skin 4.16463 38.66213 24.27574 98.61950 41.682404 -0.335213 16.60454 242s bmi 0.25061 21.81634 10.49131 41.68240 35.237101 -0.019774 5.12042 242s ped -0.11724 -0.29652 -0.29075 -0.33521 -0.019774 0.051431 -0.36275 242s age 8.21769 24.29370 20.02975 16.60454 5.120417 -0.362748 31.32916 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.26499 6.30612 3.01913 3.76690 0.94825 0.12076 22.64860 242s glu 6.30612 688.16837 22.22704 12.81633 3.55791 0.68833 32.28061 242s bp 3.01913 22.22704 103.97959 9.95281 2.09860 0.45672 31.17602 242s skin 3.76690 12.81633 9.95281 67.51754 19.51489 0.59831 -2.35523 242s bmi 0.94825 3.55791 2.09860 19.51489 17.20331 0.24347 -6.88221 242s ped 0.12076 0.68833 0.45672 0.59831 0.24347 0.05933 0.43798 242s age 22.64860 32.28061 31.17602 -2.35523 -6.88221 0.43798 111.16709 242s Call: 242s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.30795 -0.0059911 242s normal -0.13487 -0.0778567 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.023784 0.015376 242s AHFantigen 0.015376 0.024035 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.020897 0.015515 242s AHFantigen 0.015515 0.017920 242s Call: 242s QdaClassic(Treat ~ ., data = anorexia) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.690 85.697 242s Cont 81.558 81.108 242s FT 83.229 90.494 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 23.479 19.910 242s Postwt 19.910 69.755 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 32.5705 -4.3705 242s Postwt -4.3705 22.5079 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 25.167 22.883 242s Postwt 22.883 71.827 242s Call: 242s QdaClassic(type ~ ., data = Pima.tr) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 242s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 242s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 242s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 242s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 242s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 242s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 242s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 242s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 242s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 242s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 242s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 242s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 242s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 242s Call: 242s QdaClassic(Species ~ ., data = iris) 242s 242s Prior Probabilities of Groups: 242s setosa versicolor virginica 242s 0.33333 0.33333 0.33333 242s 242s Group means: 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s setosa 5.006 3.428 1.462 0.246 242s versicolor 5.936 2.770 4.260 1.326 242s virginica 6.588 2.974 5.552 2.026 242s 242s Group: setosa 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 242s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 242s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 242s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 242s 242s Group: versicolor 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.266433 0.085184 0.182898 0.055780 242s Sepal.Width 0.085184 0.098469 0.082653 0.041204 242s Petal.Length 0.182898 0.082653 0.220816 0.073102 242s Petal.Width 0.055780 0.041204 0.073102 0.039106 242s 242s Group: virginica 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.404343 0.093763 0.303290 0.049094 242s Sepal.Width 0.093763 0.104004 0.071380 0.047629 242s Petal.Length 0.303290 0.071380 0.304588 0.048824 242s Petal.Width 0.049094 0.047629 0.048824 0.075433 242s =================================================== 242s > dodata(method="sde") 242s 242s Call: dodata(method = "sde") 242s =================================================== 242s Call: 242s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.29834 -0.0032286 242s normal -0.12944 -0.0676930 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.025398 0.017810 242s AHFantigen 0.017810 0.030639 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.0083435 0.0067686 242s AHFantigen 0.0067686 0.0119565 242s Call: 242s QdaCov(Treat ~ ., data = anorexia, method = method) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.949 83.323 242s Cont 81.484 80.840 242s FT 84.596 93.835 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 22.283 17.084 242s Postwt 17.084 25.308 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 37.1864 -8.8896 242s Postwt -8.8896 31.1930 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 20.7108 3.1531 242s Postwt 3.1531 25.7046 242s Call: 242s QdaCov(type ~ ., data = Pima.tr, method = method) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.2567 109.91 67.538 25.484 30.355 0.38618 25.628 242s Yes 5.2216 141.64 75.048 32.349 34.387 0.47742 37.634 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 4.396832 10.20629 5.43346 4.38313 7.9891e-01 -0.09389257 7.45638 242s glu 10.206286 601.12211 56.62047 49.67071 3.3829e+01 -0.31896741 31.64508 242s bp 5.433462 56.62047 120.38052 34.38984 1.4817e+01 -0.21784446 26.44853 242s skin 4.383134 49.67071 34.38984 136.94931 6.1576e+01 -0.47532490 17.74141 242s bmi 0.798908 33.82928 14.81668 61.57578 5.1441e+01 0.00061983 8.56856 242s ped -0.093893 -0.31897 -0.21784 -0.47532 6.1983e-04 0.06012655 -0.26872 242s age 7.456376 31.64508 26.44853 17.74141 8.5686e+00 -0.26872005 29.93856 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.91978 7.7491 7.24229 10.46802 5.40627 0.320434 25.88314 242s glu 7.74907 856.4955 58.59554 29.65331 11.44745 1.388745 38.24430 242s bp 7.24229 58.5955 89.66288 21.36597 6.46859 0.764486 36.30462 242s skin 10.46802 29.6533 21.36597 86.79253 26.22071 0.620654 5.28665 242s bmi 5.40627 11.4475 6.46859 26.22071 20.12351 0.211701 0.71583 242s ped 0.32043 1.3887 0.76449 0.62065 0.21170 0.062727 0.93743 242s age 25.88314 38.2443 36.30462 5.28665 0.71583 0.937430 136.24335 242s Call: 242s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 242s 242s Prior Probabilities of Groups: 242s carrier normal 242s 0.6 0.4 242s 242s Group means: 242s AHFactivity AHFantigen 242s carrier -0.30795 -0.0059911 242s normal -0.13487 -0.0778567 242s 242s Group: carrier 242s AHFactivity AHFantigen 242s AHFactivity 0.023784 0.015376 242s AHFantigen 0.015376 0.024035 242s 242s Group: normal 242s AHFactivity AHFantigen 242s AHFactivity 0.020897 0.015515 242s AHFantigen 0.015515 0.017920 242s Call: 242s QdaClassic(Treat ~ ., data = anorexia) 242s 242s Prior Probabilities of Groups: 242s CBT Cont FT 242s 0.40278 0.36111 0.23611 242s 242s Group means: 242s Prewt Postwt 242s CBT 82.690 85.697 242s Cont 81.558 81.108 242s FT 83.229 90.494 242s 242s Group: CBT 242s Prewt Postwt 242s Prewt 23.479 19.910 242s Postwt 19.910 69.755 242s 242s Group: Cont 242s Prewt Postwt 242s Prewt 32.5705 -4.3705 242s Postwt -4.3705 22.5079 242s 242s Group: FT 242s Prewt Postwt 242s Prewt 25.167 22.883 242s Postwt 22.883 71.827 242s Call: 242s QdaClassic(type ~ ., data = Pima.tr) 242s 242s Prior Probabilities of Groups: 242s No Yes 242s 0.66 0.34 242s 242s Group means: 242s npreg glu bp skin bmi ped age 242s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 242s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 242s 242s Group: No 242s npreg glu bp skin bmi ped age 242s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 242s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 242s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 242s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 242s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 242s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 242s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 242s 242s Group: Yes 242s npreg glu bp skin bmi ped age 242s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 242s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 242s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 242s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 242s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 242s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 242s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 242s Call: 242s QdaClassic(Species ~ ., data = iris) 242s 242s Prior Probabilities of Groups: 242s setosa versicolor virginica 242s 0.33333 0.33333 0.33333 242s 242s Group means: 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s setosa 5.006 3.428 1.462 0.246 242s versicolor 5.936 2.770 4.260 1.326 242s virginica 6.588 2.974 5.552 2.026 242s 242s Group: setosa 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 242s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 242s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 242s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 242s 242s Group: versicolor 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.266433 0.085184 0.182898 0.055780 242s Sepal.Width 0.085184 0.098469 0.082653 0.041204 242s Petal.Length 0.182898 0.082653 0.220816 0.073102 242s Petal.Width 0.055780 0.041204 0.073102 0.039106 242s 242s Group: virginica 242s Sepal.Length Sepal.Width Petal.Length Petal.Width 242s Sepal.Length 0.404343 0.093763 0.303290 0.049094 242s Sepal.Width 0.093763 0.104004 0.071380 0.047629 242s Petal.Length 0.303290 0.071380 0.304588 0.048824 242s Petal.Width 0.049094 0.047629 0.048824 0.075433 242s =================================================== 242s > 242s BEGIN TEST tsde.R 242s 242s R version 4.4.3 (2025-02-28) -- "Trophy Case" 242s Copyright (C) 2025 The R Foundation for Statistical Computing 242s Platform: s390x-ibm-linux-gnu 242s 242s R is free software and comes with ABSOLUTELY NO WARRANTY. 242s You are welcome to redistribute it under certain conditions. 242s Type 'license()' or 'licence()' for distribution details. 242s 242s R is a collaborative project with many contributors. 242s Type 'contributors()' for more information and 242s 'citation()' on how to cite R or R packages in publications. 242s 242s Type 'demo()' for some demos, 'help()' for on-line help, or 242s 'help.start()' for an HTML browser interface to help. 242s Type 'q()' to quit R. 242s 242s > ## Test for singularity 242s > doexact <- function(){ 242s + exact <-function(){ 242s + n1 <- 45 242s + p <- 2 242s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 242s + x1[,p] <- x1[,p] + 3 242s + ## library(MASS) 242s + ## x1 <- mvrnorm(n=n1, mu=c(0,3), Sigma=diag(1,nrow=p)) 242s + 242s + n2 <- 55 242s + m1 <- 0 242s + m2 <- 3 242s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 242s + x<-rbind(x1,x2) 242s + colnames(x) <- c("X1","X2") 242s + x 242s + } 242s + print(CovSde(exact())) 242s + } 242s > 242s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE){ 242s + 242s + domcd <- function(x, xname, nrep=1){ 242s + n <- dim(x)[1] 242s + p <- dim(x)[2] 242s + 242s + mcd<-CovSde(x) 242s + 242s + if(time){ 242s + xtime <- system.time(dorep(x, nrep))[1]/nrep 242s + xres <- sprintf("%3d %3d %3d\n", dim(x)[1], dim(x)[2], xtime) 242s + } 242s + else{ 242s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 242s + } 242s + lpad<-lname-nchar(xname) 242s + cat(pad.right(xname,lpad), xres) 242s + 242s + if(!short){ 242s + 242s + ibad <- which(mcd@wt==0) 242s + names(ibad) <- NULL 242s + nbad <- length(ibad) 242s + cat("Outliers: ",nbad,"\n") 242s + if(nbad > 0) 242s + print(ibad) 242s + if(full){ 242s + cat("-------------\n") 242s + show(mcd) 242s + } 242s + cat("--------------------------------------------------------\n") 242s + } 242s + } 242s + 242s + options(digits = 5) 242s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 242s + 242s + lname <- 20 242s + 242s + ## VT::15.09.2013 - this will render the output independent 242s + ## from the version of the package 242s + suppressPackageStartupMessages(library(rrcov)) 242s + 242s + data(heart) 242s + data(starsCYG) 242s + data(phosphor) 242s + data(stackloss) 242s + data(coleman) 242s + data(salinity) 242s + data(wood) 242s + 242s + data(hbk) 242s + 242s + data(Animals, package = "MASS") 242s + brain <- Animals[c(1:24, 26:25, 27:28),] 242s + data(milk) 242s + data(bushfire) 242s + 242s + tmp <- sys.call() 242s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 242s + 242s + cat("Data Set n p Half LOG(obj) Time\n") 242s + cat("========================================================\n") 242s + domcd(heart[, 1:2], data(heart), nrep) 242s + domcd(starsCYG, data(starsCYG), nrep) 242s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 242s + domcd(stack.x, data(stackloss), nrep) 242s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 242s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 242s + domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 242s + domcd(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 242s + 242s + domcd(brain, "Animals", nrep) 242s + domcd(milk, data(milk), nrep) 242s + domcd(bushfire, data(bushfire), nrep) 242s + ## VT::19.07.2010: test the univariate SDE 242s + for(i in 1:ncol(bushfire)) 242s + domcd(bushfire[i], data(bushfire), nrep) 242s + cat("========================================================\n") 242s + } 242s > 242s > dogen <- function(nrep=1, eps=0.49){ 242s + 242s + library(MASS) 242s + domcd <- function(x, nrep=1){ 242s + gc() 242s + xtime <- system.time(dorep(x, nrep))[1]/nrep 242s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 242s + xtime 242s + } 242s + 242s + set.seed(1234) 242s + 242s + ## VT::15.09.2013 - this will render the output independent 242s + ## from the version of the package 242s + suppressPackageStartupMessages(library(rrcov)) 242s + 242s + ap <- c(2, 5, 10, 20, 30) 242s + an <- c(100, 500, 1000, 10000, 50000) 242s + 242s + tottime <- 0 242s + cat(" n p Time\n") 242s + cat("=====================\n") 242s + for(i in 1:length(an)) { 242s + for(j in 1:length(ap)) { 242s + n <- an[i] 242s + p <- ap[j] 242s + if(5*p <= n){ 242s + xx <- gendata(n, p, eps) 242s + X <- xx$X 242s + tottime <- tottime + domcd(X, nrep) 242s + } 242s + } 242s + } 242s + 242s + cat("=====================\n") 242s + cat("Total time: ", tottime*nrep, "\n") 242s + } 242s > 242s > docheck <- function(n, p, eps){ 242s + xx <- gendata(n,p,eps) 242s + mcd <- CovSde(xx$X) 242s + check(mcd, xx$xind) 242s + } 242s > 242s > check <- function(mcd, xind){ 242s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 242s + ## did not get zero weight 242s + mymatch <- xind %in% which(mcd@wt == 0) 242s + length(xind) - length(which(mymatch)) 242s + } 242s > 242s > dorep <- function(x, nrep=1){ 242s + 242s + for(i in 1:nrep) 242s + CovSde(x) 242s + } 242s > 242s > #### gendata() #### 242s > # Generates a location contaminated multivariate 242s > # normal sample of n observations in p dimensions 242s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 242s > # where 242s > # m = (b,b,...,b) 242s > # Defaults: eps=0 and b=10 242s > # 242s > gendata <- function(n,p,eps=0,b=10){ 242s + 242s + if(missing(n) || missing(p)) 242s + stop("Please specify (n,p)") 242s + if(eps < 0 || eps >= 0.5) 242s + stop(message="eps must be in [0,0.5)") 242s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 242s + nbad <- as.integer(eps * n) 242s + if(nbad > 0){ 242s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 242s + xind <- sample(n,nbad) 242s + X[xind,] <- Xbad 242s + } 242s + list(X=X, xind=xind) 242s + } 242s > 242s > pad.right <- function(z, pads) 242s + { 242s + ### Pads spaces to right of text 242s + padding <- paste(rep(" ", pads), collapse = "") 242s + paste(z, padding, sep = "") 242s + } 242s > 242s > whatis<-function(x){ 242s + if(is.data.frame(x)) 242s + cat("Type: data.frame\n") 242s + else if(is.matrix(x)) 242s + cat("Type: matrix\n") 242s + else if(is.vector(x)) 242s + cat("Type: vector\n") 242s + else 242s + cat("Type: don't know\n") 242s + } 242s > 242s > ## VT::15.09.2013 - this will render the output independent 242s > ## from the version of the package 242s > suppressPackageStartupMessages(library(rrcov)) 243s > 243s > dodata() 243s 243s Call: dodata() 243s Data Set n p Half LOG(obj) Time 243s ======================================================== 243s heart 12 2 243s Outliers: 5 243s [1] 2 6 8 10 12 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s height weight 243s 39.8 35.7 243s 243s Robust Estimate of Covariance: 243s height weight 243s height 38.2 77.1 243s weight 77.1 188.1 243s -------------------------------------------------------- 243s starsCYG 47 2 243s Outliers: 7 243s [1] 7 9 11 14 20 30 34 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s log.Te log.light 243s 4.42 4.96 243s 243s Robust Estimate of Covariance: 243s log.Te log.light 243s log.Te 0.0163 0.0522 243s log.light 0.0522 0.3243 243s -------------------------------------------------------- 243s phosphor 18 2 243s Outliers: 2 243s [1] 1 6 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s inorg organic 243s 13.3 39.7 243s 243s Robust Estimate of Covariance: 243s inorg organic 243s inorg 133 134 243s organic 134 204 243s -------------------------------------------------------- 243s stackloss 21 3 243s Outliers: 6 243s [1] 1 2 3 15 17 21 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s Air.Flow Water.Temp Acid.Conc. 243s 57.8 20.7 86.4 243s 243s Robust Estimate of Covariance: 243s Air.Flow Water.Temp Acid.Conc. 243s Air.Flow 39.7 15.6 25.0 243s Water.Temp 15.6 13.0 11.9 243s Acid.Conc. 25.0 11.9 40.3 243s -------------------------------------------------------- 243s coleman 20 5 243s Outliers: 8 243s [1] 1 2 6 10 11 12 15 18 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s salaryP fatherWc sstatus teacherSc motherLev 243s 2.78 58.64 9.09 25.37 6.69 243s 243s Robust Estimate of Covariance: 243s salaryP fatherWc sstatus teacherSc motherLev 243s salaryP 0.2556 -1.0144 0.6599 0.2673 0.0339 243s fatherWc -1.0144 1615.9192 382.7846 -4.8287 36.0227 243s sstatus 0.6599 382.7846 108.1781 -0.7904 9.1027 243s teacherSc 0.2673 -4.8287 -0.7904 0.9346 -0.0239 243s motherLev 0.0339 36.0227 9.1027 -0.0239 0.9155 243s -------------------------------------------------------- 243s salinity 28 3 243s Outliers: 9 243s [1] 3 4 5 9 11 16 19 23 24 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s X1 X2 X3 243s 10.84 3.35 22.48 243s 243s Robust Estimate of Covariance: 243s X1 X2 X3 243s X1 10.75 -1.62 -2.05 243s X2 -1.62 4.21 -1.43 243s X3 -2.05 -1.43 2.63 243s -------------------------------------------------------- 243s wood 20 5 243s Outliers: 11 243s [1] 4 6 7 8 9 10 12 13 16 19 20 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s x1 x2 x3 x4 x5 243s 0.573 0.119 0.517 0.549 0.904 243s 243s Robust Estimate of Covariance: 243s x1 x2 x3 x4 x5 243s x1 0.025185 0.004279 -0.001262 -0.000382 -0.001907 243s x2 0.004279 0.001011 0.000197 -0.000117 0.000247 243s x3 -0.001262 0.000197 0.003042 0.002034 0.001773 243s x4 -0.000382 -0.000117 0.002034 0.007943 0.001137 243s x5 -0.001907 0.000247 0.001773 0.001137 0.005392 243s -------------------------------------------------------- 243s hbk 75 3 243s Outliers: 15 243s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 53 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s X1 X2 X3 243s 1.59 1.79 1.67 243s 243s Robust Estimate of Covariance: 243s X1 X2 X3 243s X1 1.6354 0.0793 0.2284 243s X2 0.0793 1.6461 0.3186 243s X3 0.2284 0.3186 1.5673 243s -------------------------------------------------------- 243s Animals 28 2 243s Outliers: 13 243s [1] 2 6 7 8 9 12 13 14 15 16 24 25 28 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s body brain 243s 18.7 64.9 243s 243s Robust Estimate of Covariance: 243s body brain 243s body 4702 7973 243s brain 7973 28571 243s -------------------------------------------------------- 243s milk 86 8 243s Outliers: 21 243s [1] 1 2 3 6 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s X1 X2 X3 X4 X5 X6 X7 X8 243s 1.03 35.90 33.04 26.11 25.10 25.02 123.06 14.37 243s 243s Robust Estimate of Covariance: 243s X1 X2 X3 X4 X5 X6 X7 243s X1 4.73e-07 6.57e-05 1.79e-04 1.71e-04 1.62e-04 1.42e-04 6.85e-04 243s X2 6.57e-05 1.57e+00 1.36e-01 9.28e-02 4.18e-02 1.30e-01 1.58e+00 243s X3 1.79e-04 1.36e-01 1.12e+00 8.20e-01 8.27e-01 8.00e-01 6.66e-01 243s X4 1.71e-04 9.28e-02 8.20e-01 6.57e-01 6.41e-01 6.18e-01 5.47e-01 243s X5 1.62e-04 4.18e-02 8.27e-01 6.41e-01 6.93e-01 6.44e-01 5.71e-01 243s X6 1.42e-04 1.30e-01 8.00e-01 6.18e-01 6.44e-01 6.44e-01 5.55e-01 243s X7 6.85e-04 1.58e+00 6.66e-01 5.47e-01 5.71e-01 5.55e-01 4.17e+00 243s X8 1.40e-05 2.33e-01 1.74e-01 1.06e-01 9.44e-02 9.86e-02 3.54e-01 243s X8 243s X1 1.40e-05 243s X2 2.33e-01 243s X3 1.74e-01 243s X4 1.06e-01 243s X5 9.44e-02 243s X6 9.86e-02 243s X7 3.54e-01 243s X8 1.57e-01 243s -------------------------------------------------------- 243s bushfire 38 5 243s Outliers: 23 243s [1] 1 5 6 7 8 9 10 11 12 13 15 16 28 29 30 31 32 33 34 35 36 37 38 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s V1 V2 V3 V4 V5 243s 105 148 287 223 283 243s 243s Robust Estimate of Covariance: 243s V1 V2 V3 V4 V5 243s V1 1964 1712 -10230 -2504 -2066 243s V2 1712 1526 -8732 -2145 -1763 243s V3 -10230 -8732 56327 13803 11472 243s V4 -2504 -2145 13803 3509 2894 243s V5 -2066 -1763 11472 2894 2404 243s -------------------------------------------------------- 243s bushfire 38 1 243s Outliers: 2 243s [1] 13 30 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s V1 243s 98.5 243s 243s Robust Estimate of Covariance: 243s V1 243s V1 431 243s -------------------------------------------------------- 243s bushfire 38 1 243s Outliers: 6 243s [1] 33 34 35 36 37 38 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s V2 243s 141 243s 243s Robust Estimate of Covariance: 243s V2 243s V2 528 243s -------------------------------------------------------- 243s bushfire 38 1 243s Outliers: 0 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s V3 243s 238 243s 243s Robust Estimate of Covariance: 243s V3 243s V3 37148 243s -------------------------------------------------------- 243s bushfire 38 1 243s Outliers: 9 243s [1] 8 9 32 33 34 35 36 37 38 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s V4 243s 210 243s 243s Robust Estimate of Covariance: 243s V4 243s V4 2543 243s -------------------------------------------------------- 243s bushfire 38 1 243s Outliers: 9 243s [1] 8 9 32 33 34 35 36 37 38 243s ------------- 243s 243s Call: 243s CovSde(x = x) 243s -> Method: Stahel-Donoho estimator 243s 243s Robust Estimate of Location: 243s V5 243s 273 243s 243s Robust Estimate of Covariance: 243s V5 243s V5 1575 243s -------------------------------------------------------- 243s ======================================================== 243s > ##doexact() 243s > 243s BEGIN TEST tsest.R 243s 243s R version 4.4.3 (2025-02-28) -- "Trophy Case" 243s Copyright (C) 2025 The R Foundation for Statistical Computing 243s Platform: s390x-ibm-linux-gnu 243s 243s R is free software and comes with ABSOLUTELY NO WARRANTY. 243s You are welcome to redistribute it under certain conditions. 243s Type 'license()' or 'licence()' for distribution details. 243s 243s R is a collaborative project with many contributors. 243s Type 'contributors()' for more information and 243s 'citation()' on how to cite R or R packages in publications. 243s 243s Type 'demo()' for some demos, 'help()' for on-line help, or 243s 'help.start()' for an HTML browser interface to help. 243s Type 'q()' to quit R. 243s 243s > ## VT::15.09.2013 - this will render the output independent 243s > ## from the version of the package 243s > suppressPackageStartupMessages(library(rrcov)) 243s > 243s > library(MASS) 243s > 243s > dodata <- function(nrep = 1, time = FALSE, full = TRUE, method) { 243s + doest <- function(x, xname, nrep = 1, method=c("sfast", "surreal", "bisquare", "rocke", "suser", "MM", "sdet")) { 243s + 243s + method <- match.arg(method) 243s + 243s + lname <- 20 243s + n <- dim(x)[1] 243s + p <- dim(x)[2] 243s + 243s + mm <- if(method == "MM") CovMMest(x) else CovSest(x, method=method) 243s + 243s + crit <- log(mm@crit) 243s + 243s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 243s + lpad <- lname-nchar(xname) 243s + cat(pad.right(xname,lpad), xres) 243s + 243s + dist <- getDistance(mm) 243s + quantiel <- qchisq(0.975, p) 243s + ibad <- which(dist >= quantiel) 243s + names(ibad) <- NULL 243s + nbad <- length(ibad) 243s + cat("Outliers: ",nbad,"\n") 243s + if(nbad > 0) 243s + print(ibad) 243s + cat("-------------\n") 243s + show(mm) 243s + cat("--------------------------------------------------------\n") 243s + } 243s + 243s + options(digits = 5) 243s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 243s + 243s + data(heart) 243s + data(starsCYG) 243s + data(phosphor) 243s + data(stackloss) 243s + data(coleman) 243s + data(salinity) 243s + data(wood) 243s + data(hbk) 243s + 243s + data(Animals, package = "MASS") 243s + brain <- Animals[c(1:24, 26:25, 27:28),] 243s + data(milk) 243s + data(bushfire) 243s + ### 243s + data(rice) 243s + data(hemophilia) 243s + data(fish) 243s + 243s + tmp <- sys.call() 243s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 243s + 243s + cat("Data Set n p LOG(det) Time\n") 243s + cat("===================================================\n") 243s + doest(heart[, 1:2], data(heart), nrep, method=method) 243s + doest(starsCYG, data(starsCYG), nrep, method=method) 243s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep, method=method) 243s + doest(stack.x, data(stackloss), nrep, method=method) 243s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep, method=method) 243s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep, method=method) 243s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep, method=method) 243s + doest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep, method=method) 243s + 243s + 243s + doest(brain, "Animals", nrep, method=method) 243s + doest(milk, data(milk), nrep, method=method) 243s + doest(bushfire, data(bushfire), nrep, method=method) 243s + 243s + doest(data.matrix(subset(rice, select = -Overall_evaluation)), data(rice), nrep, method=method) 243s + doest(data.matrix(subset(hemophilia, select = -gr)), data(hemophilia), nrep, method=method) 243s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep, method=method) 243s + 243s + ## from package 'datasets' 243s + doest(airquality[,1:4], data(airquality), nrep, method=method) 243s + doest(attitude, data(attitude), nrep, method=method) 243s + doest(attenu, data(attenu), nrep, method=method) 243s + doest(USJudgeRatings, data(USJudgeRatings), nrep, method=method) 243s + doest(USArrests, data(USArrests), nrep, method=method) 243s + doest(longley, data(longley), nrep, method=method) 243s + doest(Loblolly, data(Loblolly), nrep, method=method) 243s + doest(quakes[,1:4], data(quakes), nrep, method=method) 243s + 243s + cat("===================================================\n") 243s + } 243s > 243s > # generate contaminated data using the function gendata with different 243s > # number of outliers and check if the M-estimate breaks - i.e. the 243s > # largest eigenvalue is larger than e.g. 5. 243s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 243s > # outliers grater than 20. 243s > dogen <- function(){ 243s + eig <- vector("numeric",26) 243s + for(i in 0:25) { 243s + gg <- gendata(eps=i) 243s + mm <- CovSRocke(gg$x, t0=gg$tgood, S0=gg$sgood) 243s + eig[i+1] <- ev <- getEvals(mm)[1] 243s + cat(i, ev, "\n") 243s + 243s + ## stopifnot(ev < 5 || i > 20) 243s + } 243s + plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 243s + } 243s > 243s > # 243s > # generate data 50x10 as multivariate normal N(0,I) and add 243s > # eps % outliers by adding d=5.0 to each component. 243s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 243s > # - if eps >= 1, it is the number of outliers 243s > # - use the center and cov of the good data as good start 243s > # - use the center and the cov of all data as a bad start 243s > # If using a good start, the M-estimate must iterate to 243s > # the good solution: the largest eigenvalue is less then e.g. 5 243s > # 243s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 243s + 243s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 243s + stop("eps is out of range") 243s + 243s + library(MASS) 243s + 243s + x <- mvrnorm(n, rep(0,p), diag(p)) 243s + bad <- vector("numeric") 243s + nbad = if(eps < 1) eps*n else eps 243s + if(nbad > 0){ 243s + bad <- sample(n, nbad) 243s + x[bad,] <- x[bad,] + d 243s + } 243s + cov1 <- cov.wt(x) 243s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 243s + 243s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 243s + } 243s > 243s > pad.right <- function(z, pads) 243s + { 243s + ## Pads spaces to right of text 243s + padding <- paste(rep(" ", pads), collapse = "") 243s + paste(z, padding, sep = "") 243s + } 243s > 243s > 243s > ## -- now do it: 243s > dodata(method="sfast") 243s 243s Call: dodata(method = "sfast") 243s Data Set n p LOG(det) Time 243s =================================================== 243s heart 12 2 2.017701 243s Outliers: 3 243s [1] 2 6 12 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 36.1 29.5 243s 243s Robust Estimate of Covariance: 243s height weight 243s height 129 210 243s weight 210 365 243s -------------------------------------------------------- 243s starsCYG 47 2 -1.450032 243s Outliers: 7 243s [1] 7 9 11 14 20 30 34 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 4.42 4.97 243s 243s Robust Estimate of Covariance: 243s log.Te log.light 243s log.Te 0.0176 0.0617 243s log.light 0.0617 0.3880 243s -------------------------------------------------------- 243s phosphor 18 2 2.320721 243s Outliers: 2 243s [1] 1 6 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 14.1 38.8 243s 243s Robust Estimate of Covariance: 243s inorg organic 243s inorg 174 190 243s organic 190 268 243s -------------------------------------------------------- 243s stackloss 21 3 1.470031 243s Outliers: 3 243s [1] 1 2 3 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 57.5 20.5 86.0 243s 243s Robust Estimate of Covariance: 243s Air.Flow Water.Temp Acid.Conc. 243s Air.Flow 38.94 11.66 22.89 243s Water.Temp 11.66 9.96 7.81 243s Acid.Conc. 22.89 7.81 40.48 243s -------------------------------------------------------- 243s coleman 20 5 0.491419 243s Outliers: 2 243s [1] 6 10 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 2.77 45.58 4.13 25.13 6.39 243s 243s Robust Estimate of Covariance: 243s salaryP fatherWc sstatus teacherSc motherLev 243s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 243s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 243s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 243s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 243s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 243s -------------------------------------------------------- 243s salinity 28 3 0.734619 243s Outliers: 4 243s [1] 5 16 23 24 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 10.31 3.07 22.60 243s 243s Robust Estimate of Covariance: 243s X1 X2 X3 243s X1 13.200 0.784 -3.611 243s X2 0.784 4.441 -1.658 243s X3 -3.611 -1.658 2.877 243s -------------------------------------------------------- 243s wood 20 5 -3.202636 243s Outliers: 2 243s [1] 7 9 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 0.551 0.135 0.496 0.511 0.916 243s 243s Robust Estimate of Covariance: 243s x1 x2 x3 x4 x5 243s x1 0.011361 -0.000791 0.005473 0.004204 -0.004747 243s x2 -0.000791 0.000701 -0.000534 -0.001452 0.000864 243s x3 0.005473 -0.000534 0.004905 0.002960 -0.001914 243s x4 0.004204 -0.001452 0.002960 0.005154 -0.002187 243s x5 -0.004747 0.000864 -0.001914 -0.002187 0.003214 243s -------------------------------------------------------- 243s hbk 75 3 0.283145 243s Outliers: 14 243s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 1.53 1.83 1.66 243s 243s Robust Estimate of Covariance: 243s X1 X2 X3 243s X1 1.8091 0.0479 0.2446 243s X2 0.0479 1.8190 0.2513 243s X3 0.2446 0.2513 1.7288 243s -------------------------------------------------------- 243s Animals 28 2 4.685129 243s Outliers: 10 243s [1] 2 6 7 9 12 14 15 16 24 25 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 30.8 84.2 243s 243s Robust Estimate of Covariance: 243s body brain 243s body 14806 28767 243s brain 28767 65195 243s -------------------------------------------------------- 243s milk 86 8 -1.437863 243s Outliers: 15 243s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 243s 243s Robust Estimate of Covariance: 243s X1 X2 X3 X4 X5 X6 X7 243s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 243s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 243s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 243s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 243s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 243s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 243s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 243s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 243s X8 243s X1 7.45e-05 243s X2 3.33e-01 243s X3 2.82e-01 243s X4 2.01e-01 243s X5 1.80e-01 243s X6 1.91e-01 243s X7 6.38e-01 243s X8 2.01e-01 243s -------------------------------------------------------- 243s bushfire 38 5 2.443148 243s Outliers: 13 243s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 108 149 266 216 278 243s 243s Robust Estimate of Covariance: 243s V1 V2 V3 V4 V5 243s V1 911 688 -3961 -856 -707 243s V2 688 587 -2493 -492 -420 243s V3 -3961 -2493 24146 5765 4627 243s V4 -856 -492 5765 1477 1164 243s V5 -707 -420 4627 1164 925 243s -------------------------------------------------------- 243s rice 105 5 -0.724874 243s Outliers: 7 243s [1] 9 40 42 49 57 58 71 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 243s 243s Robust Estimate of Covariance: 243s Favor Appearance Taste Stickiness Toughness 243s Favor 0.423 0.345 0.427 0.405 -0.202 243s Appearance 0.345 0.592 0.570 0.549 -0.316 243s Taste 0.427 0.570 0.739 0.706 -0.393 243s Stickiness 0.405 0.549 0.706 0.876 -0.497 243s Toughness -0.202 -0.316 -0.393 -0.497 0.467 243s -------------------------------------------------------- 243s hemophilia 75 2 -1.868949 243s Outliers: 2 243s [1] 11 36 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] -0.2126 -0.0357 243s 243s Robust Estimate of Covariance: 243s AHFactivity AHFantigen 243s AHFactivity 0.0317 0.0112 243s AHFantigen 0.0112 0.0218 243s -------------------------------------------------------- 243s fish 159 6 1.285876 243s Outliers: 21 243s [1] 61 62 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 243s [20] 103 142 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 358.3 24.7 26.9 29.7 30.0 14.7 243s 243s Robust Estimate of Covariance: 243s Weight Length1 Length2 Length3 Height Width 243s Weight 1.33e+05 3.09e+03 3.34e+03 3.78e+03 1.72e+03 2.24e+02 243s Length1 3.09e+03 7.92e+01 8.54e+01 9.55e+01 4.04e+01 7.43e+00 243s Length2 3.34e+03 8.54e+01 9.22e+01 1.03e+02 4.49e+01 8.07e+00 243s Length3 3.78e+03 9.55e+01 1.03e+02 1.18e+02 5.92e+01 7.65e+00 243s Height 1.72e+03 4.04e+01 4.49e+01 5.92e+01 7.12e+01 8.51e-01 243s Width 2.24e+02 7.43e+00 8.07e+00 7.65e+00 8.51e-01 3.57e+00 243s -------------------------------------------------------- 243s airquality 153 4 2.684374 243s Outliers: 7 243s [1] 7 14 23 30 34 77 107 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 39.34 192.12 9.67 78.71 243s 243s Robust Estimate of Covariance: 243s Ozone Solar.R Wind Temp 243s Ozone 973.104 894.011 -61.856 243.560 243s Solar.R 894.011 9677.269 0.388 179.429 243s Wind -61.856 0.388 11.287 -14.310 243s Temp 243.560 179.429 -14.310 96.714 243s -------------------------------------------------------- 243s attitude 30 7 2.091968 243s Outliers: 4 243s [1] 14 16 18 24 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 243s 243s Robust Estimate of Covariance: 243s rating complaints privileges learning raises critical advance 243s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 243s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 243s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 243s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 243s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 243s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 243s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 243s -------------------------------------------------------- 243s attenu 182 5 1.148032 243s Outliers: 31 243s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 243s [20] 29 30 31 32 64 65 80 94 95 96 97 100 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 16.432 5.849 60.297 27.144 0.134 243s 243s Robust Estimate of Covariance: 243s event mag station dist accel 243s event 54.9236 -3.0733 181.0954 -49.4194 -0.0628 243s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 243s station 181.0954 -8.4388 1689.7161 -114.6319 0.7285 243s dist -49.4194 6.7388 -114.6319 597.3606 -1.7988 243s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 243s -------------------------------------------------------- 243s USJudgeRatings 43 12 -1.683847 243s Outliers: 7 243s [1] 5 7 12 13 14 23 31 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [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 243s 243s Robust Estimate of Covariance: 243s CONT INTG DMNR DILG CFMG DECI PREP FAMI 243s CONT 0.8710 -0.3019 -0.4682 -0.1893 -0.0569 -0.0992 -0.1771 -0.1975 243s INTG -0.3019 0.6401 0.8598 0.6955 0.5732 0.5439 0.7091 0.7084 243s DMNR -0.4682 0.8598 1.2412 0.9107 0.7668 0.7305 0.9292 0.9158 243s DILG -0.1893 0.6955 0.9107 0.8554 0.7408 0.7036 0.8865 0.8791 243s CFMG -0.0569 0.5732 0.7668 0.7408 0.6994 0.6545 0.7788 0.7721 243s DECI -0.0992 0.5439 0.7305 0.7036 0.6545 0.6342 0.7492 0.7511 243s PREP -0.1771 0.7091 0.9292 0.8865 0.7788 0.7492 0.9541 0.9556 243s FAMI -0.1975 0.7084 0.9158 0.8791 0.7721 0.7511 0.9556 0.9785 243s ORAL -0.2444 0.7453 0.9939 0.8917 0.7842 0.7551 0.9554 0.9680 243s WRIT -0.2344 0.7319 0.9649 0.8853 0.7781 0.7511 0.9498 0.9668 243s PHYS -0.1983 0.4676 0.6263 0.5629 0.5073 0.5039 0.5990 0.6140 243s RTEN -0.3152 0.8000 1.0798 0.9234 0.7952 0.7663 0.9637 0.9693 243s ORAL WRIT PHYS RTEN 243s CONT -0.2444 -0.2344 -0.1983 -0.3152 243s INTG 0.7453 0.7319 0.4676 0.8000 243s DMNR 0.9939 0.9649 0.6263 1.0798 243s DILG 0.8917 0.8853 0.5629 0.9234 243s CFMG 0.7842 0.7781 0.5073 0.7952 243s DECI 0.7551 0.7511 0.5039 0.7663 243s PREP 0.9554 0.9498 0.5990 0.9637 243s FAMI 0.9680 0.9668 0.6140 0.9693 243s ORAL 0.9853 0.9744 0.6280 1.0032 243s WRIT 0.9744 0.9711 0.6184 0.9870 243s PHYS 0.6280 0.6184 0.4716 0.6520 243s RTEN 1.0032 0.9870 0.6520 1.0622 243s -------------------------------------------------------- 243s USArrests 50 4 2.411726 243s Outliers: 4 243s [1] 2 28 33 39 243s ------------- 243s 243s Call: 243s CovSest(x = x, method = method) 243s -> Method: S-estimates: S-FAST 243s 243s Robust Estimate of Location: 243s [1] 7.05 150.66 64.66 19.37 243s 243s Robust Estimate of Covariance: 243s Murder Assault UrbanPop Rape 243s Murder 23.8 380.8 19.2 29.7 243s Assault 380.8 8436.2 605.6 645.3 243s UrbanPop 19.2 605.6 246.5 78.8 243s Rape 29.7 645.3 78.8 77.3 243s -------------------------------------------------------- 244s longley 16 7 1.038316 244s Outliers: 5 244s [1] 1 2 3 4 5 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: S-FAST 244s 244s Robust Estimate of Location: 244s [1] 107.6 440.8 339.7 292.5 121.0 1957.1 67.2 244s 244s Robust Estimate of Covariance: 244s GNP.deflator GNP Unemployed Armed.Forces Population 244s GNP.deflator 100.6 954.7 1147.1 -507.6 74.2 244s GNP 954.7 9430.9 10115.8 -4616.5 730.1 244s Unemployed 1147.1 10115.8 19838.3 -6376.9 850.6 244s Armed.Forces -507.6 -4616.5 -6376.9 3240.2 -351.3 244s Population 74.2 730.1 850.6 -351.3 57.5 244s Year 46.4 450.8 539.5 -233.0 35.3 244s Employed 30.8 310.5 274.0 -160.8 23.3 244s Year Employed 244s GNP.deflator 46.4 30.8 244s GNP 450.8 310.5 244s Unemployed 539.5 274.0 244s Armed.Forces -233.0 -160.8 244s Population 35.3 23.3 244s Year 21.9 14.6 244s Employed 14.6 11.2 244s -------------------------------------------------------- 244s Loblolly 84 3 1.481317 244s Outliers: 14 244s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: S-FAST 244s 244s Robust Estimate of Location: 244s [1] 24.22 9.65 7.50 244s 244s Robust Estimate of Covariance: 244s height age Seed 244s height 525.08 179.21 14.27 244s age 179.21 61.85 2.94 244s Seed 14.27 2.94 25.86 244s -------------------------------------------------------- 244s quakes 1000 4 1.576855 244s Outliers: 223 244s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 244s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 244s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 244s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 244s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 244s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 244s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 244s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 244s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 244s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 244s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 244s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 244s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 244s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 244s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: S-FAST 244s 244s Robust Estimate of Location: 244s [1] -21.54 182.35 369.21 4.54 244s 244s Robust Estimate of Covariance: 244s lat long depth mag 244s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 244s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 244s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 244s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 244s -------------------------------------------------------- 244s =================================================== 244s > dodata(method="sdet") 244s 244s Call: dodata(method = "sdet") 244s Data Set n p LOG(det) Time 244s =================================================== 244s heart 12 2 2.017701 244s Outliers: 3 244s [1] 2 6 12 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 36.1 29.5 244s 244s Robust Estimate of Covariance: 244s height weight 244s height 129 210 244s weight 210 365 244s -------------------------------------------------------- 244s starsCYG 47 2 -1.450032 244s Outliers: 7 244s [1] 7 9 11 14 20 30 34 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 4.42 4.97 244s 244s Robust Estimate of Covariance: 244s log.Te log.light 244s log.Te 0.0176 0.0617 244s log.light 0.0617 0.3880 244s -------------------------------------------------------- 244s phosphor 18 2 2.320721 244s Outliers: 2 244s [1] 1 6 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 14.1 38.8 244s 244s Robust Estimate of Covariance: 244s inorg organic 244s inorg 174 190 244s organic 190 268 244s -------------------------------------------------------- 244s stackloss 21 3 1.470031 244s Outliers: 3 244s [1] 1 2 3 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 57.5 20.5 86.0 244s 244s Robust Estimate of Covariance: 244s Air.Flow Water.Temp Acid.Conc. 244s Air.Flow 38.94 11.66 22.89 244s Water.Temp 11.66 9.96 7.81 244s Acid.Conc. 22.89 7.81 40.48 244s -------------------------------------------------------- 244s coleman 20 5 0.491419 244s Outliers: 2 244s [1] 6 10 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 2.77 45.58 4.13 25.13 6.39 244s 244s Robust Estimate of Covariance: 244s salaryP fatherWc sstatus teacherSc motherLev 244s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 244s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 244s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 244s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 244s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 244s -------------------------------------------------------- 244s salinity 28 3 0.734619 244s Outliers: 4 244s [1] 5 16 23 24 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 10.31 3.07 22.60 244s 244s Robust Estimate of Covariance: 244s X1 X2 X3 244s X1 13.200 0.784 -3.611 244s X2 0.784 4.441 -1.658 244s X3 -3.611 -1.658 2.877 244s -------------------------------------------------------- 244s wood 20 5 -3.220754 244s Outliers: 4 244s [1] 4 6 8 19 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 0.580 0.123 0.530 0.538 0.890 244s 244s Robust Estimate of Covariance: 244s x1 x2 x3 x4 x5 244s x1 8.16e-03 1.39e-03 1.97e-03 -2.82e-04 -7.61e-04 244s x2 1.39e-03 4.00e-04 8.14e-04 -8.51e-05 -5.07e-06 244s x3 1.97e-03 8.14e-04 4.74e-03 -9.59e-04 2.06e-05 244s x4 -2.82e-04 -8.51e-05 -9.59e-04 3.09e-03 1.87e-03 244s x5 -7.61e-04 -5.07e-06 2.06e-05 1.87e-03 2.28e-03 244s -------------------------------------------------------- 244s hbk 75 3 0.283145 244s Outliers: 14 244s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 1.53 1.83 1.66 244s 244s Robust Estimate of Covariance: 244s X1 X2 X3 244s X1 1.8091 0.0479 0.2446 244s X2 0.0479 1.8190 0.2513 244s X3 0.2446 0.2513 1.7288 244s -------------------------------------------------------- 244s Animals 28 2 4.685129 244s Outliers: 10 244s [1] 2 6 7 9 12 14 15 16 24 25 244s ------------- 244s 244s Call: 244s CovSest(x = x, method = method) 244s -> Method: S-estimates: DET-S 244s 244s Robust Estimate of Location: 244s [1] 30.8 84.2 244s 244s Robust Estimate of Covariance: 244s body brain 244s body 14806 28767 244s brain 28767 65194 244s -------------------------------------------------------- 245s milk 86 8 -1.437863 245s Outliers: 15 245s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 245s ------------- 245s 245s Call: 245s CovSest(x = x, method = method) 245s -> Method: S-estimates: DET-S 245s 245s Robust Estimate of Location: 245s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 245s 245s Robust Estimate of Covariance: 245s X1 X2 X3 X4 X5 X6 X7 245s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 245s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 245s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 245s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 245s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 245s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 245s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 245s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 245s X8 245s X1 7.45e-05 245s X2 3.33e-01 245s X3 2.82e-01 245s X4 2.01e-01 245s X5 1.80e-01 245s X6 1.91e-01 245s X7 6.38e-01 245s X8 2.01e-01 245s -------------------------------------------------------- 245s bushfire 38 5 2.443148 245s Outliers: 13 245s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 245s ------------- 245s 245s Call: 245s CovSest(x = x, method = method) 245s -> Method: S-estimates: DET-S 245s 245s Robust Estimate of Location: 245s [1] 108 149 266 216 278 245s 245s Robust Estimate of Covariance: 245s V1 V2 V3 V4 V5 245s V1 911 688 -3961 -856 -707 245s V2 688 587 -2493 -492 -420 245s V3 -3961 -2493 24146 5765 4627 245s V4 -856 -492 5765 1477 1164 245s V5 -707 -420 4627 1164 925 245s -------------------------------------------------------- 245s rice 105 5 -0.724874 245s Outliers: 7 245s [1] 9 40 42 49 57 58 71 245s ------------- 245s 245s Call: 245s CovSest(x = x, method = method) 245s -> Method: S-estimates: DET-S 245s 245s Robust Estimate of Location: 245s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 245s 245s Robust Estimate of Covariance: 245s Favor Appearance Taste Stickiness Toughness 245s Favor 0.423 0.345 0.427 0.405 -0.202 245s Appearance 0.345 0.592 0.570 0.549 -0.316 245s Taste 0.427 0.570 0.739 0.706 -0.393 245s Stickiness 0.405 0.549 0.706 0.876 -0.497 245s Toughness -0.202 -0.316 -0.393 -0.497 0.467 245s -------------------------------------------------------- 245s hemophilia 75 2 -1.868949 245s Outliers: 2 245s [1] 11 36 245s ------------- 245s 245s Call: 245s CovSest(x = x, method = method) 245s -> Method: S-estimates: DET-S 245s 245s Robust Estimate of Location: 245s [1] -0.2126 -0.0357 245s 245s Robust Estimate of Covariance: 245s AHFactivity AHFantigen 245s AHFactivity 0.0317 0.0112 245s AHFantigen 0.0112 0.0218 245s -------------------------------------------------------- 246s fish 159 6 1.267294 246s Outliers: 33 246s [1] 61 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 246s [20] 91 92 93 94 95 96 97 98 99 100 101 102 103 142 246s ------------- 246s 246s Call: 246s CovSest(x = x, method = method) 246s -> Method: S-estimates: DET-S 246s 246s Robust Estimate of Location: 246s [1] 381.2 25.6 27.8 30.8 31.0 14.9 246s 246s Robust Estimate of Covariance: 246s Weight Length1 Length2 Length3 Height Width 246s Weight 148372.04 3260.48 3508.71 3976.93 1507.43 127.94 246s Length1 3260.48 77.00 82.52 92.18 27.56 3.29 246s Length2 3508.71 82.52 88.57 99.20 30.83 3.43 246s Length3 3976.93 92.18 99.20 113.97 45.50 2.21 246s Height 1507.43 27.56 30.83 45.50 70.54 -4.95 246s Width 127.94 3.29 3.43 2.21 -4.95 2.28 246s -------------------------------------------------------- 246s airquality 153 4 2.684374 246s Outliers: 7 246s [1] 7 14 23 30 34 77 107 246s ------------- 246s 246s Call: 246s CovSest(x = x, method = method) 246s -> Method: S-estimates: DET-S 246s 246s Robust Estimate of Location: 246s [1] 39.34 192.12 9.67 78.71 246s 246s Robust Estimate of Covariance: 246s Ozone Solar.R Wind Temp 246s Ozone 973.104 894.011 -61.856 243.560 246s Solar.R 894.011 9677.269 0.388 179.429 246s Wind -61.856 0.388 11.287 -14.310 246s Temp 243.560 179.429 -14.310 96.714 246s -------------------------------------------------------- 246s attitude 30 7 2.091968 246s Outliers: 4 246s [1] 14 16 18 24 246s ------------- 246s 246s Call: 246s CovSest(x = x, method = method) 246s -> Method: S-estimates: DET-S 246s 246s Robust Estimate of Location: 246s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 246s 246s Robust Estimate of Covariance: 246s rating complaints privileges learning raises critical advance 246s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 246s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 246s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 246s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 246s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 246s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 246s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 246s -------------------------------------------------------- 246s attenu 182 5 1.148032 246s Outliers: 31 246s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 246s [20] 29 30 31 32 64 65 80 94 95 96 97 100 246s ------------- 246s 246s Call: 246s CovSest(x = x, method = method) 246s -> Method: S-estimates: DET-S 246s 246s Robust Estimate of Location: 246s [1] 16.432 5.849 60.297 27.144 0.134 246s 246s Robust Estimate of Covariance: 246s event mag station dist accel 246s event 54.9236 -3.0733 181.0954 -49.4195 -0.0628 246s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 246s station 181.0954 -8.4388 1689.7161 -114.6321 0.7285 246s dist -49.4195 6.7388 -114.6321 597.3609 -1.7988 246s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 246s -------------------------------------------------------- 246s USJudgeRatings 43 12 -1.683847 246s Outliers: 7 246s [1] 5 7 12 13 14 23 31 246s ------------- 246s 246s Call: 246s CovSest(x = x, method = method) 246s -> Method: S-estimates: DET-S 246s 246s Robust Estimate of Location: 246s [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 246s 246s Robust Estimate of Covariance: 246s CONT INTG DMNR DILG CFMG DECI PREP FAMI 246s CONT 0.8715 -0.3020 -0.4683 -0.1894 -0.0569 -0.0993 -0.1772 -0.1976 246s INTG -0.3020 0.6403 0.8600 0.6956 0.5733 0.5440 0.7093 0.7086 246s DMNR -0.4683 0.8600 1.2416 0.9109 0.7669 0.7307 0.9295 0.9161 246s DILG -0.1894 0.6956 0.9109 0.8555 0.7410 0.7037 0.8867 0.8793 246s CFMG -0.0569 0.5733 0.7669 0.7410 0.6995 0.6546 0.7789 0.7723 246s DECI -0.0993 0.5440 0.7307 0.7037 0.6546 0.6343 0.7493 0.7513 246s PREP -0.1772 0.7093 0.9295 0.8867 0.7789 0.7493 0.9543 0.9559 246s FAMI -0.1976 0.7086 0.9161 0.8793 0.7723 0.7513 0.9559 0.9788 246s ORAL -0.2445 0.7456 0.9942 0.8919 0.7844 0.7553 0.9557 0.9683 246s WRIT -0.2345 0.7321 0.9652 0.8856 0.7783 0.7513 0.9501 0.9671 246s PHYS -0.1986 0.4676 0.6264 0.5628 0.5072 0.5038 0.5990 0.6140 246s RTEN -0.3154 0.8002 1.0801 0.9236 0.7954 0.7665 0.9639 0.9695 246s ORAL WRIT PHYS RTEN 246s CONT -0.2445 -0.2345 -0.1986 -0.3154 246s INTG 0.7456 0.7321 0.4676 0.8002 246s DMNR 0.9942 0.9652 0.6264 1.0801 246s DILG 0.8919 0.8856 0.5628 0.9236 246s CFMG 0.7844 0.7783 0.5072 0.7954 246s DECI 0.7553 0.7513 0.5038 0.7665 246s PREP 0.9557 0.9501 0.5990 0.9639 246s FAMI 0.9683 0.9671 0.6140 0.9695 246s ORAL 0.9856 0.9748 0.6281 1.0035 246s WRIT 0.9748 0.9714 0.6184 0.9873 246s PHYS 0.6281 0.6184 0.4713 0.6520 246s RTEN 1.0035 0.9873 0.6520 1.0624 246s -------------------------------------------------------- 247s USArrests 50 4 2.411726 247s Outliers: 4 247s [1] 2 28 33 39 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: DET-S 247s 247s Robust Estimate of Location: 247s [1] 7.05 150.66 64.66 19.37 247s 247s Robust Estimate of Covariance: 247s Murder Assault UrbanPop Rape 247s Murder 23.8 380.8 19.2 29.7 247s Assault 380.8 8436.2 605.6 645.3 247s UrbanPop 19.2 605.6 246.5 78.8 247s Rape 29.7 645.3 78.8 77.3 247s -------------------------------------------------------- 247s longley 16 7 1.143113 247s Outliers: 4 247s [1] 1 2 3 4 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: DET-S 247s 247s Robust Estimate of Location: 247s [1] 107 435 334 293 120 1957 67 247s 247s Robust Estimate of Covariance: 247s GNP.deflator GNP Unemployed Armed.Forces Population 247s GNP.deflator 89.2 850.1 1007.4 -404.4 66.2 247s GNP 850.1 8384.4 9020.8 -3692.0 650.5 247s Unemployed 1007.4 9020.8 16585.4 -4990.7 752.5 247s Armed.Forces -404.4 -3692.0 -4990.7 2474.2 -280.9 247s Population 66.2 650.5 752.5 -280.9 51.2 247s Year 41.9 407.6 481.9 -186.4 31.9 247s Employed 27.9 279.7 255.6 -128.8 21.1 247s Year Employed 247s GNP.deflator 41.9 27.9 247s GNP 407.6 279.7 247s Unemployed 481.9 255.6 247s Armed.Forces -186.4 -128.8 247s Population 31.9 21.1 247s Year 20.2 13.4 247s Employed 13.4 10.1 247s -------------------------------------------------------- 247s Loblolly 84 3 1.481317 247s Outliers: 14 247s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: DET-S 247s 247s Robust Estimate of Location: 247s [1] 24.22 9.65 7.50 247s 247s Robust Estimate of Covariance: 247s height age Seed 247s height 525.08 179.21 14.27 247s age 179.21 61.85 2.94 247s Seed 14.27 2.94 25.86 247s -------------------------------------------------------- 247s quakes 1000 4 1.576855 247s Outliers: 223 247s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 247s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 247s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 247s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 247s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 247s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 247s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 247s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 247s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 247s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 247s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 247s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 247s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 247s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 247s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: DET-S 247s 247s Robust Estimate of Location: 247s [1] -21.54 182.35 369.21 4.54 247s 247s Robust Estimate of Covariance: 247s lat long depth mag 247s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 247s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 247s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 247s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 247s -------------------------------------------------------- 247s =================================================== 247s > ##dodata(method="suser") 247s > ##dodata(method="surreal") 247s > dodata(method="bisquare") 247s 247s Call: dodata(method = "bisquare") 247s Data Set n p LOG(det) Time 247s =================================================== 247s heart 12 2 7.721793 247s Outliers: 3 247s [1] 2 6 12 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s height weight 247s 36.1 29.4 247s 247s Robust Estimate of Covariance: 247s height weight 247s height 109 177 247s weight 177 307 247s -------------------------------------------------------- 247s starsCYG 47 2 -5.942108 247s Outliers: 7 247s [1] 7 9 11 14 20 30 34 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s log.Te log.light 247s 4.42 4.97 247s 247s Robust Estimate of Covariance: 247s log.Te log.light 247s log.Te 0.0164 0.0574 247s log.light 0.0574 0.3613 247s -------------------------------------------------------- 247s phosphor 18 2 9.269096 247s Outliers: 2 247s [1] 1 6 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s inorg organic 247s 14.1 38.7 247s 247s Robust Estimate of Covariance: 247s inorg organic 247s inorg 173 189 247s organic 189 268 247s -------------------------------------------------------- 247s stackloss 21 3 8.411100 247s Outliers: 3 247s [1] 1 2 3 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s Air.Flow Water.Temp Acid.Conc. 247s 57.5 20.5 86.0 247s 247s Robust Estimate of Covariance: 247s Air.Flow Water.Temp Acid.Conc. 247s Air.Flow 33.82 10.17 20.02 247s Water.Temp 10.17 8.70 6.84 247s Acid.Conc. 20.02 6.84 35.51 247s -------------------------------------------------------- 247s coleman 20 5 4.722046 247s Outliers: 2 247s [1] 6 10 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s salaryP fatherWc sstatus teacherSc motherLev 247s 2.77 45.59 4.14 25.13 6.39 247s 247s Robust Estimate of Covariance: 247s salaryP fatherWc sstatus teacherSc motherLev 247s salaryP 0.2135 1.8732 1.3883 0.2547 0.0648 247s fatherWc 1.8732 905.6704 296.1916 7.9820 21.0848 247s sstatus 1.3883 296.1916 128.9536 4.0196 7.1917 247s teacherSc 0.2547 7.9820 4.0196 0.7321 0.2799 247s motherLev 0.0648 21.0848 7.1917 0.2799 0.5592 247s -------------------------------------------------------- 247s salinity 28 3 4.169963 247s Outliers: 4 247s [1] 5 16 23 24 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s X1 X2 X3 247s 10.30 3.07 22.59 247s 247s Robust Estimate of Covariance: 247s X1 X2 X3 247s X1 12.234 0.748 -3.369 247s X2 0.748 4.115 -1.524 247s X3 -3.369 -1.524 2.655 247s -------------------------------------------------------- 247s wood 20 5 -33.862485 247s Outliers: 5 247s [1] 4 6 8 11 19 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s x1 x2 x3 x4 x5 247s 0.580 0.123 0.530 0.538 0.890 247s 247s Robust Estimate of Covariance: 247s x1 x2 x3 x4 x5 247s x1 5.88e-03 9.96e-04 1.43e-03 -1.96e-04 -5.46e-04 247s x2 9.96e-04 2.86e-04 5.89e-04 -5.78e-05 -2.24e-06 247s x3 1.43e-03 5.89e-04 3.42e-03 -6.95e-04 1.43e-05 247s x4 -1.96e-04 -5.78e-05 -6.95e-04 2.23e-03 1.35e-03 247s x5 -5.46e-04 -2.24e-06 1.43e-05 1.35e-03 1.65e-03 247s -------------------------------------------------------- 247s hbk 75 3 1.472421 247s Outliers: 14 247s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s X1 X2 X3 247s 1.53 1.83 1.66 247s 247s Robust Estimate of Covariance: 247s X1 X2 X3 247s X1 1.6775 0.0447 0.2268 247s X2 0.0447 1.6865 0.2325 247s X3 0.2268 0.2325 1.6032 247s -------------------------------------------------------- 247s Animals 28 2 18.528307 247s Outliers: 11 247s [1] 2 6 7 9 12 14 15 16 24 25 28 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s body brain 247s 30.7 84.1 247s 247s Robust Estimate of Covariance: 247s body brain 247s body 13278 25795 247s brain 25795 58499 247s -------------------------------------------------------- 247s milk 86 8 -24.816943 247s Outliers: 19 247s [1] 1 2 3 11 12 13 14 15 16 17 20 27 41 44 47 70 74 75 77 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s X1 X2 X3 X4 X5 X6 X7 X8 247s 1.03 35.81 32.96 26.04 25.02 24.94 122.79 14.35 247s 247s Robust Estimate of Covariance: 247s X1 X2 X3 X4 X5 X6 X7 247s X1 6.80e-07 2.20e-04 3.70e-04 3.35e-04 3.27e-04 3.30e-04 1.21e-03 247s X2 2.20e-04 1.80e+00 3.96e-01 3.03e-01 2.45e-01 3.27e-01 2.00e+00 247s X3 3.70e-04 3.96e-01 1.27e+00 9.68e-01 9.49e-01 9.56e-01 1.37e+00 247s X4 3.35e-04 3.03e-01 9.68e-01 7.86e-01 7.55e-01 7.57e-01 1.15e+00 247s X5 3.27e-04 2.45e-01 9.49e-01 7.55e-01 7.88e-01 7.61e-01 1.14e+00 247s X6 3.30e-04 3.27e-01 9.56e-01 7.57e-01 7.61e-01 7.90e-01 1.17e+00 247s X7 1.21e-03 2.00e+00 1.37e+00 1.15e+00 1.14e+00 1.17e+00 5.71e+00 247s X8 6.57e-05 2.71e-01 2.30e-01 1.64e-01 1.48e-01 1.57e-01 5.27e-01 247s X8 247s X1 6.57e-05 247s X2 2.71e-01 247s X3 2.30e-01 247s X4 1.64e-01 247s X5 1.48e-01 247s X6 1.57e-01 247s X7 5.27e-01 247s X8 1.62e-01 247s -------------------------------------------------------- 247s bushfire 38 5 21.704243 247s Outliers: 13 247s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s V1 V2 V3 V4 V5 247s 108 149 266 216 278 247s 247s Robust Estimate of Covariance: 247s V1 V2 V3 V4 V5 247s V1 528 398 -2298 -497 -410 247s V2 398 340 -1445 -285 -244 247s V3 -2298 -1445 14026 3348 2687 247s V4 -497 -285 3348 857 676 247s V5 -410 -244 2687 676 537 247s -------------------------------------------------------- 247s rice 105 5 -7.346939 247s Outliers: 8 247s [1] 9 14 40 42 49 57 58 71 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s Favor Appearance Taste Stickiness Toughness 247s -0.2480 0.1203 -0.1213 0.0710 0.0644 247s 247s Robust Estimate of Covariance: 247s Favor Appearance Taste Stickiness Toughness 247s Favor 0.415 0.338 0.419 0.398 -0.198 247s Appearance 0.338 0.580 0.559 0.539 -0.310 247s Taste 0.419 0.559 0.725 0.693 -0.386 247s Stickiness 0.398 0.539 0.693 0.859 -0.487 247s Toughness -0.198 -0.310 -0.386 -0.487 0.457 247s -------------------------------------------------------- 247s hemophilia 75 2 -7.465173 247s Outliers: 2 247s [1] 11 36 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s AHFactivity AHFantigen 247s -0.2128 -0.0366 247s 247s Robust Estimate of Covariance: 247s AHFactivity AHFantigen 247s AHFactivity 0.0321 0.0115 247s AHFantigen 0.0115 0.0220 247s -------------------------------------------------------- 247s fish 159 6 13.465134 247s Outliers: 35 247s [1] 38 61 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 247s [20] 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 142 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s Weight Length1 Length2 Length3 Height Width 247s 381.4 25.6 27.8 30.8 31.0 14.9 247s 247s Robust Estimate of Covariance: 247s Weight Length1 Length2 Length3 Height Width 247s Weight 111094.92 2440.81 2626.59 2976.92 1129.78 95.85 247s Length1 2440.81 57.63 61.75 68.98 20.67 2.46 247s Length2 2626.59 61.75 66.28 74.24 23.13 2.57 247s Length3 2976.92 68.98 74.24 85.29 34.11 1.65 247s Height 1129.78 20.67 23.13 34.11 52.75 -3.70 247s Width 95.85 2.46 2.57 1.65 -3.70 1.71 247s -------------------------------------------------------- 247s airquality 153 4 21.282926 247s Outliers: 8 247s [1] 7 11 14 23 30 34 77 107 247s ------------- 247s 247s Call: 247s CovSest(x = x, method = method) 247s -> Method: S-estimates: bisquare 247s 247s Robust Estimate of Location: 247s Ozone Solar.R Wind Temp 247s 39.40 192.29 9.66 78.74 247s 247s Robust Estimate of Covariance: 247s Ozone Solar.R Wind Temp 247s Ozone 930.566 849.644 -59.157 232.459 247s Solar.R 849.644 9207.569 0.594 168.122 247s Wind -59.157 0.594 10.783 -13.645 247s Temp 232.459 168.122 -13.645 92.048 247s -------------------------------------------------------- 248s attitude 30 7 28.084183 248s Outliers: 6 248s [1] 6 9 14 16 18 24 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s rating complaints privileges learning raises critical 248s 65.7 66.8 51.9 56.1 66.4 76.7 248s advance 248s 43.0 248s 248s Robust Estimate of Covariance: 248s rating complaints privileges learning raises critical advance 248s rating 143.88 114.95 64.97 105.69 83.95 6.96 41.78 248s complaints 114.95 143.84 79.28 115.00 101.48 19.69 66.13 248s privileges 64.97 79.28 126.38 94.70 73.87 5.37 61.07 248s learning 105.69 115.00 94.70 146.14 110.50 21.67 68.49 248s raises 83.95 101.48 73.87 110.50 115.01 24.91 77.16 248s critical 6.96 19.69 5.37 21.67 24.91 71.74 25.88 248s advance 41.78 66.13 61.07 68.49 77.16 25.88 97.71 248s -------------------------------------------------------- 248s attenu 182 5 10.109049 248s Outliers: 35 248s [1] 2 4 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 248s [20] 28 29 30 31 32 64 65 80 93 94 95 96 97 98 99 100 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s event mag station dist accel 248s 16.418 5.850 60.243 27.307 0.134 248s 248s Robust Estimate of Covariance: 248s event mag station dist accel 248s event 41.9000 -2.3543 137.8110 -39.0321 -0.0447 248s mag -2.3543 0.4978 -6.4461 5.2644 0.0118 248s station 137.8110 -6.4461 1283.9675 -90.1657 0.5554 248s dist -39.0321 5.2644 -90.1657 462.3898 -1.3672 248s accel -0.0447 0.0118 0.5554 -1.3672 0.0114 248s -------------------------------------------------------- 248s USJudgeRatings 43 12 -43.367499 248s Outliers: 10 248s [1] 5 7 8 12 13 14 20 23 31 35 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 248s 7.43 8.16 7.75 7.89 7.69 7.76 7.68 7.67 7.52 7.59 8.19 7.87 248s 248s Robust Estimate of Covariance: 248s CONT INTG DMNR DILG CFMG DECI PREP FAMI 248s CONT 0.6895 -0.2399 -0.3728 -0.1514 -0.0461 -0.0801 -0.1419 -0.1577 248s INTG -0.2399 0.5021 0.6746 0.5446 0.4479 0.4254 0.5564 0.5558 248s DMNR -0.3728 0.6746 0.9753 0.7128 0.5992 0.5715 0.7289 0.7181 248s DILG -0.1514 0.5446 0.7128 0.6691 0.5789 0.5501 0.6949 0.6892 248s CFMG -0.0461 0.4479 0.5992 0.5789 0.5468 0.5118 0.6100 0.6049 248s DECI -0.0801 0.4254 0.5715 0.5501 0.5118 0.4965 0.5872 0.5890 248s PREP -0.1419 0.5564 0.7289 0.6949 0.6100 0.5872 0.7497 0.7511 248s FAMI -0.1577 0.5558 0.7181 0.6892 0.6049 0.5890 0.7511 0.7696 248s ORAL -0.1950 0.5848 0.7798 0.6990 0.6143 0.5921 0.7508 0.7610 248s WRIT -0.1866 0.5747 0.7575 0.6946 0.6101 0.5895 0.7470 0.7607 248s PHYS -0.1620 0.3640 0.4878 0.4361 0.3927 0.3910 0.4655 0.4779 248s RTEN -0.2522 0.6268 0.8462 0.7220 0.6210 0.5991 0.7553 0.7599 248s ORAL WRIT PHYS RTEN 248s CONT -0.1950 -0.1866 -0.1620 -0.2522 248s INTG 0.5848 0.5747 0.3640 0.6268 248s DMNR 0.7798 0.7575 0.4878 0.8462 248s DILG 0.6990 0.6946 0.4361 0.7220 248s CFMG 0.6143 0.6101 0.3927 0.6210 248s DECI 0.5921 0.5895 0.3910 0.5991 248s PREP 0.7508 0.7470 0.4655 0.7553 248s FAMI 0.7610 0.7607 0.4779 0.7599 248s ORAL 0.7745 0.7665 0.4893 0.7866 248s WRIT 0.7665 0.7645 0.4823 0.7745 248s PHYS 0.4893 0.4823 0.3620 0.5062 248s RTEN 0.7866 0.7745 0.5062 0.8313 248s -------------------------------------------------------- 248s USArrests 50 4 19.266763 248s Outliers: 4 248s [1] 2 28 33 39 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s Murder Assault UrbanPop Rape 248s 7.04 150.55 64.64 19.34 248s 248s Robust Estimate of Covariance: 248s Murder Assault UrbanPop Rape 248s Murder 23.7 378.9 19.1 29.5 248s Assault 378.9 8388.2 601.3 639.7 248s UrbanPop 19.1 601.3 245.3 77.9 248s Rape 29.5 639.7 77.9 76.3 248s -------------------------------------------------------- 248s longley 16 7 13.789499 248s Outliers: 4 248s [1] 1 2 3 4 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s GNP.deflator GNP Unemployed Armed.Forces Population 248s 107 435 333 293 120 248s Year Employed 248s 1957 67 248s 248s Robust Estimate of Covariance: 248s GNP.deflator GNP Unemployed Armed.Forces Population 248s GNP.deflator 65.05 619.75 734.33 -294.02 48.27 248s GNP 619.75 6112.14 6578.12 -2684.52 474.26 248s Unemployed 734.33 6578.12 12075.90 -3627.79 548.58 248s Armed.Forces -294.02 -2684.52 -3627.79 1797.05 -204.25 248s Population 48.27 474.26 548.58 -204.25 37.36 248s Year 30.58 297.29 351.44 -135.53 23.29 248s Employed 20.36 203.96 186.62 -93.64 15.42 248s Year Employed 248s GNP.deflator 30.58 20.36 248s GNP 297.29 203.96 248s Unemployed 351.44 186.62 248s Armed.Forces -135.53 -93.64 248s Population 23.29 15.42 248s Year 14.70 9.80 248s Employed 9.80 7.36 248s -------------------------------------------------------- 248s Loblolly 84 3 8.518440 248s Outliers: 14 248s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s height age Seed 248s 24.14 9.62 7.51 248s 248s Robust Estimate of Covariance: 248s height age Seed 248s height 464.64 158.43 12.83 248s age 158.43 54.62 2.67 248s Seed 12.83 2.67 22.98 248s -------------------------------------------------------- 248s quakes 1000 4 11.611413 248s Outliers: 234 248s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 248s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 248s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 248s [46] 163 166 170 174 192 205 222 226 230 239 243 250 251 252 254 248s [61] 258 263 267 268 271 283 292 297 300 301 305 311 312 318 320 248s [76] 321 325 328 330 331 334 352 357 360 365 368 376 381 382 384 248s [91] 389 399 400 402 408 413 416 417 418 419 426 429 437 441 443 248s [106] 453 456 467 474 477 490 492 496 504 507 508 509 517 524 527 248s [121] 528 531 532 534 536 538 539 541 542 543 544 545 546 547 552 248s [136] 553 558 560 570 571 581 583 587 593 594 596 597 605 612 613 248s [151] 618 620 625 629 638 642 647 649 653 655 656 672 675 681 686 248s [166] 699 701 702 712 714 716 721 725 726 735 744 753 754 756 759 248s [181] 765 766 769 779 781 782 785 787 797 804 813 825 827 837 840 248s [196] 844 852 853 857 860 865 866 869 870 872 873 883 884 887 888 248s [211] 890 891 893 908 909 912 915 916 921 927 930 952 962 963 969 248s [226] 974 980 982 986 987 988 992 997 1000 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: bisquare 248s 248s Robust Estimate of Location: 248s lat long depth mag 248s -21.54 182.35 369.29 4.54 248s 248s Robust Estimate of Covariance: 248s lat long depth mag 248s lat 2.18e+01 4.82e+00 2.53e+02 -3.54e-01 248s long 4.82e+00 5.87e+00 -4.63e+02 7.45e-02 248s depth 2.53e+02 -4.63e+02 6.51e+04 -2.10e+01 248s mag -3.54e-01 7.45e-02 -2.10e+01 1.83e-01 248s -------------------------------------------------------- 248s =================================================== 248s > dodata(method="rocke") 248s 248s Call: dodata(method = "rocke") 248s Data Set n p LOG(det) Time 248s =================================================== 248s heart 12 2 7.285196 248s Outliers: 3 248s [1] 2 6 12 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s height weight 248s 34.3 26.1 248s 248s Robust Estimate of Covariance: 248s height weight 248s height 105 159 248s weight 159 256 248s -------------------------------------------------------- 248s starsCYG 47 2 -5.929361 248s Outliers: 7 248s [1] 7 9 11 14 20 30 34 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s log.Te log.light 248s 4.42 4.93 248s 248s Robust Estimate of Covariance: 248s log.Te log.light 248s log.Te 0.0193 0.0709 248s log.light 0.0709 0.3987 248s -------------------------------------------------------- 248s phosphor 18 2 8.907518 248s Outliers: 3 248s [1] 1 6 10 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s inorg organic 248s 15.8 39.4 248s 248s Robust Estimate of Covariance: 248s inorg organic 248s inorg 196 252 248s organic 252 360 248s -------------------------------------------------------- 248s stackloss 21 3 8.143313 248s Outliers: 4 248s [1] 1 2 3 21 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s Air.Flow Water.Temp Acid.Conc. 248s 56.8 20.2 86.4 248s 248s Robust Estimate of Covariance: 248s Air.Flow Water.Temp Acid.Conc. 248s Air.Flow 29.26 9.62 14.78 248s Water.Temp 9.62 8.54 6.25 248s Acid.Conc. 14.78 6.25 29.70 248s -------------------------------------------------------- 248s coleman 20 5 4.001659 248s Outliers: 5 248s [1] 2 6 9 10 13 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s salaryP fatherWc sstatus teacherSc motherLev 248s 2.81 40.27 2.11 25.01 6.27 248s 248s Robust Estimate of Covariance: 248s salaryP fatherWc sstatus teacherSc motherLev 248s salaryP 0.2850 1.1473 2.0254 0.3536 0.0737 248s fatherWc 1.1473 798.0714 278.0145 6.4590 18.6357 248s sstatus 2.0254 278.0145 128.7601 4.0666 6.3845 248s teacherSc 0.3536 6.4590 4.0666 0.8749 0.2980 248s motherLev 0.0737 18.6357 6.3845 0.2980 0.4948 248s -------------------------------------------------------- 248s salinity 28 3 3.455146 248s Outliers: 9 248s [1] 3 5 10 11 15 16 17 23 24 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s X1 X2 X3 248s 9.89 3.10 22.46 248s 248s Robust Estimate of Covariance: 248s X1 X2 X3 248s X1 12.710 1.868 -4.135 248s X2 1.868 4.710 -0.663 248s X3 -4.135 -0.663 1.907 248s -------------------------------------------------------- 248s wood 20 5 -35.020244 248s Outliers: 7 248s [1] 4 6 7 8 11 16 19 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s x1 x2 x3 x4 x5 248s 0.588 0.123 0.534 0.535 0.891 248s 248s Robust Estimate of Covariance: 248s x1 x2 x3 x4 x5 248s x1 6.60e-03 1.25e-03 2.16e-03 -3.73e-04 -1.10e-03 248s x2 1.25e-03 3.30e-04 8.91e-04 -1.23e-05 2.62e-05 248s x3 2.16e-03 8.91e-04 4.55e-03 -4.90e-04 1.93e-04 248s x4 -3.73e-04 -1.23e-05 -4.90e-04 2.01e-03 1.36e-03 248s x5 -1.10e-03 2.62e-05 1.93e-04 1.36e-03 1.95e-03 248s -------------------------------------------------------- 248s hbk 75 3 1.413303 248s Outliers: 14 248s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s X1 X2 X3 248s 1.56 1.77 1.68 248s 248s Robust Estimate of Covariance: 248s X1 X2 X3 248s X1 1.6483 0.0825 0.2133 248s X2 0.0825 1.6928 0.2334 248s X3 0.2133 0.2334 1.5334 248s -------------------------------------------------------- 248s Animals 28 2 17.787210 248s Outliers: 11 248s [1] 2 6 7 9 12 14 15 16 24 25 28 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s body brain 248s 60.6 150.2 248s 248s Robust Estimate of Covariance: 248s body brain 248s body 10670 19646 248s brain 19646 41147 248s -------------------------------------------------------- 248s milk 86 8 -25.169970 248s Outliers: 22 248s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 28 41 44 47 70 73 74 75 77 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s X1 X2 X3 X4 X5 X6 X7 X8 248s 1.03 35.87 33.14 26.19 25.17 25.11 123.16 14.41 248s 248s Robust Estimate of Covariance: 248s X1 X2 X3 X4 X5 X6 X7 248s X1 4.47e-07 1.77e-04 1.94e-04 1.79e-04 1.60e-04 1.45e-04 6.45e-04 248s X2 1.77e-04 2.36e+00 4.03e-01 3.08e-01 2.08e-01 3.45e-01 2.18e+00 248s X3 1.94e-04 4.03e-01 1.13e+00 8.31e-01 8.08e-01 7.79e-01 9.83e-01 248s X4 1.79e-04 3.08e-01 8.31e-01 6.62e-01 6.22e-01 5.95e-01 7.82e-01 248s X5 1.60e-04 2.08e-01 8.08e-01 6.22e-01 6.51e-01 5.93e-01 7.60e-01 248s X6 1.45e-04 3.45e-01 7.79e-01 5.95e-01 5.93e-01 5.88e-01 7.81e-01 248s X7 6.45e-04 2.18e+00 9.83e-01 7.82e-01 7.60e-01 7.81e-01 4.81e+00 248s X8 2.47e-05 2.57e-01 2.00e-01 1.37e-01 1.13e-01 1.28e-01 4.38e-01 248s X8 248s X1 2.47e-05 248s X2 2.57e-01 248s X3 2.00e-01 248s X4 1.37e-01 248s X5 1.13e-01 248s X6 1.28e-01 248s X7 4.38e-01 248s X8 1.61e-01 248s -------------------------------------------------------- 248s bushfire 38 5 21.641566 248s Outliers: 13 248s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s V1 V2 V3 V4 V5 248s 111 150 256 214 276 248s 248s Robust Estimate of Covariance: 248s V1 V2 V3 V4 V5 248s V1 554 408 -2321 -464 -393 248s V2 408 343 -1361 -244 -215 248s V3 -2321 -1361 14690 3277 2684 248s V4 -464 -244 3277 783 629 248s V5 -393 -215 2684 629 509 248s -------------------------------------------------------- 248s rice 105 5 -7.208835 248s Outliers: 8 248s [1] 9 14 40 42 49 57 58 71 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s Favor Appearance Taste Stickiness Toughness 248s -0.21721 0.20948 -0.04581 0.15355 -0.00254 248s 248s Robust Estimate of Covariance: 248s Favor Appearance Taste Stickiness Toughness 248s Favor 0.432 0.337 0.417 0.382 -0.201 248s Appearance 0.337 0.591 0.553 0.510 -0.295 248s Taste 0.417 0.553 0.735 0.683 -0.385 248s Stickiness 0.382 0.510 0.683 0.834 -0.462 248s Toughness -0.201 -0.295 -0.385 -0.462 0.408 248s -------------------------------------------------------- 248s hemophilia 75 2 -7.453807 248s Outliers: 2 248s [1] 46 53 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s AHFactivity AHFantigen 248s -0.2276 -0.0637 248s 248s Robust Estimate of Covariance: 248s AHFactivity AHFantigen 248s AHFactivity 0.0405 0.0221 248s AHFantigen 0.0221 0.0263 248s -------------------------------------------------------- 248s fish 159 6 13.110263 248s Outliers: 47 248s [1] 38 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 248s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 248s [39] 98 99 100 101 102 103 104 140 142 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s Weight Length1 Length2 Length3 Height Width 248s 452.1 27.2 29.5 32.6 30.8 15.0 248s 248s Robust Estimate of Covariance: 248s Weight Length1 Length2 Length3 Height Width 248s Weight 132559.85 2817.97 3035.69 3369.07 1231.68 112.19 248s Length1 2817.97 64.16 68.74 75.36 22.52 2.37 248s Length2 3035.69 68.74 73.77 81.12 25.57 2.47 248s Length3 3369.07 75.36 81.12 91.65 37.39 1.40 248s Height 1231.68 22.52 25.57 37.39 50.91 -3.92 248s Width 112.19 2.37 2.47 1.40 -3.92 1.87 248s -------------------------------------------------------- 248s airquality 153 4 21.181656 248s Outliers: 13 248s [1] 6 7 11 14 17 20 23 30 34 53 63 77 107 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s Ozone Solar.R Wind Temp 248s 40.21 198.33 9.76 79.35 248s 248s Robust Estimate of Covariance: 248s Ozone Solar.R Wind Temp 248s Ozone 885.7 581.1 -57.3 226.4 248s Solar.R 581.1 8870.9 26.2 -15.1 248s Wind -57.3 26.2 11.8 -13.4 248s Temp 226.4 -15.1 -13.4 89.4 248s -------------------------------------------------------- 248s attitude 30 7 27.836398 248s Outliers: 8 248s [1] 1 9 13 14 17 18 24 26 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s rating complaints privileges learning raises critical 248s 64.0 65.4 50.5 54.9 63.1 72.6 248s advance 248s 40.5 248s 248s Robust Estimate of Covariance: 248s rating complaints privileges learning raises critical advance 248s rating 180.10 153.16 42.04 128.90 90.25 18.75 39.81 248s complaints 153.16 192.38 58.32 142.48 94.29 8.13 45.33 248s privileges 42.04 58.32 113.65 82.31 69.53 23.13 61.96 248s learning 128.90 142.48 82.31 156.99 101.74 13.22 49.64 248s raises 90.25 94.29 69.53 101.74 110.85 47.84 55.76 248s critical 18.75 8.13 23.13 13.22 47.84 123.00 36.97 248s advance 39.81 45.33 61.96 49.64 55.76 36.97 53.59 248s -------------------------------------------------------- 248s attenu 182 5 9.726797 248s Outliers: 44 248s [1] 1 2 4 5 6 7 8 9 10 11 13 15 16 19 20 21 22 23 24 248s [20] 25 27 28 29 30 31 32 40 45 60 61 64 65 78 80 81 93 94 95 248s [39] 96 97 98 99 100 108 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s event mag station dist accel 248s 16.39 5.82 60.89 27.97 0.12 248s 248s Robust Estimate of Covariance: 248s event mag station dist accel 248s event 4.20e+01 -1.97e+00 1.44e+02 -3.50e+01 4.05e-02 248s mag -1.97e+00 5.05e-01 -4.78e+00 4.63e+00 4.19e-03 248s station 1.44e+02 -4.78e+00 1.47e+03 -5.74e+01 7.88e-01 248s dist -3.50e+01 4.63e+00 -5.74e+01 3.99e+02 -1.18e+00 248s accel 4.05e-02 4.19e-03 7.88e-01 -1.18e+00 7.71e-03 248s -------------------------------------------------------- 248s USJudgeRatings 43 12 -46.356873 248s Outliers: 15 248s [1] 1 5 7 8 12 13 14 17 20 21 23 30 31 35 42 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 248s 7.56 8.12 7.70 7.91 7.74 7.82 7.66 7.66 7.50 7.58 8.22 7.86 248s 248s Robust Estimate of Covariance: 248s CONT INTG DMNR DILG CFMG DECI PREP 248s CONT 0.63426 -0.20121 -0.31858 -0.09578 0.00521 -0.00436 -0.07140 248s INTG -0.20121 0.28326 0.37540 0.27103 0.20362 0.19838 0.25706 248s DMNR -0.31858 0.37540 0.58265 0.33615 0.25649 0.24804 0.31696 248s DILG -0.09578 0.27103 0.33615 0.32588 0.27022 0.26302 0.32236 248s CFMG 0.00521 0.20362 0.25649 0.27022 0.25929 0.24217 0.27784 248s DECI -0.00436 0.19838 0.24804 0.26302 0.24217 0.23830 0.27284 248s PREP -0.07140 0.25706 0.31696 0.32236 0.27784 0.27284 0.35071 248s FAMI -0.07118 0.25858 0.29511 0.32582 0.27863 0.27657 0.35941 248s ORAL -0.11149 0.27055 0.33919 0.31768 0.27339 0.26739 0.34200 248s WRIT -0.10050 0.26857 0.32570 0.32327 0.27860 0.27201 0.34399 248s PHYS -0.09693 0.15339 0.18416 0.17089 0.13837 0.14895 0.18472 248s RTEN -0.15643 0.31793 0.40884 0.33863 0.27073 0.26854 0.34049 248s FAMI ORAL WRIT PHYS RTEN 248s CONT -0.07118 -0.11149 -0.10050 -0.09693 -0.15643 248s INTG 0.25858 0.27055 0.26857 0.15339 0.31793 248s DMNR 0.29511 0.33919 0.32570 0.18416 0.40884 248s DILG 0.32582 0.31768 0.32327 0.17089 0.33863 248s CFMG 0.27863 0.27339 0.27860 0.13837 0.27073 248s DECI 0.27657 0.26739 0.27201 0.14895 0.26854 248s PREP 0.35941 0.34200 0.34399 0.18472 0.34049 248s FAMI 0.38378 0.35617 0.36094 0.19998 0.35048 248s ORAL 0.35617 0.34918 0.34808 0.19759 0.35217 248s WRIT 0.36094 0.34808 0.35242 0.19666 0.35090 248s PHYS 0.19998 0.19759 0.19666 0.14770 0.20304 248s RTEN 0.35048 0.35217 0.35090 0.20304 0.39451 248s -------------------------------------------------------- 248s USArrests 50 4 19.206310 248s Outliers: 4 248s [1] 2 28 33 39 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s Murder Assault UrbanPop Rape 248s 7.55 160.94 65.10 19.97 248s 248s Robust Estimate of Covariance: 248s Murder Assault UrbanPop Rape 248s Murder 25.6 409.5 23.4 32.1 248s Assault 409.5 8530.9 676.9 669.4 248s UrbanPop 23.4 676.9 269.9 76.6 248s Rape 32.1 669.4 76.6 76.6 248s -------------------------------------------------------- 248s longley 16 7 13.387132 248s Outliers: 4 248s [1] 1 2 3 4 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s GNP.deflator GNP Unemployed Armed.Forces Population 248s 105.5 422.4 318.3 299.7 119.5 248s Year Employed 248s 1956.1 66.5 248s 248s Robust Estimate of Covariance: 248s GNP.deflator GNP Unemployed Armed.Forces Population 248s GNP.deflator 59.97 582.66 694.99 -237.75 46.12 248s GNP 582.66 5849.82 6383.68 -2207.26 461.15 248s Unemployed 694.99 6383.68 11155.03 -3104.18 534.25 248s Armed.Forces -237.75 -2207.26 -3104.18 1429.11 -171.28 248s Population 46.12 461.15 534.25 -171.28 36.79 248s Year 29.01 287.48 340.95 -112.61 22.85 248s Employed 18.99 193.66 186.31 -76.88 14.94 248s Year Employed 248s GNP.deflator 29.01 18.99 248s GNP 287.48 193.66 248s Unemployed 340.95 186.31 248s Armed.Forces -112.61 -76.88 248s Population 22.85 14.94 248s Year 14.36 9.45 248s Employed 9.45 6.90 248s -------------------------------------------------------- 248s Loblolly 84 3 7.757906 248s Outliers: 27 248s [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 248s [26] 83 84 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s height age Seed 248s 21.72 8.60 7.58 248s 248s Robust Estimate of Covariance: 248s height age Seed 248s height 316.590 102.273 5.939 248s age 102.273 33.465 -0.121 248s Seed 5.939 -0.121 27.203 248s -------------------------------------------------------- 248s quakes 1000 4 11.473431 248s Outliers: 237 248s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 248s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 248s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 248s [46] 163 166 170 174 176 192 205 222 226 230 239 243 244 250 251 248s [61] 252 254 258 263 267 268 271 283 292 297 300 301 305 311 312 248s [76] 318 320 321 325 328 330 331 334 352 357 360 365 368 376 381 248s [91] 382 384 389 399 400 402 408 410 413 416 417 418 419 426 429 248s [106] 437 441 443 453 456 467 474 477 490 492 496 504 507 508 509 248s [121] 517 524 527 528 531 532 534 536 538 539 541 542 543 544 545 248s [136] 546 547 552 553 558 560 570 571 581 583 587 593 594 596 597 248s [151] 605 612 613 618 620 625 629 638 642 647 649 653 655 656 672 248s [166] 675 681 686 699 701 702 712 714 716 721 725 726 735 744 753 248s [181] 754 756 759 765 766 769 779 781 782 785 787 797 804 813 825 248s [196] 827 837 840 844 852 853 857 860 865 866 869 870 872 873 883 248s [211] 884 887 888 890 891 893 908 909 912 915 916 921 927 930 952 248s [226] 962 963 969 974 980 982 986 987 988 992 997 1000 248s ------------- 248s 248s Call: 248s CovSest(x = x, method = method) 248s -> Method: S-estimates: Rocke type 248s 248s Robust Estimate of Location: 248s lat long depth mag 248s -21.45 182.54 351.18 4.55 248s 248s Robust Estimate of Covariance: 248s lat long depth mag 248s lat 2.10e+01 4.66e+00 2.45e+02 -3.38e-01 248s long 4.66e+00 5.88e+00 -4.63e+02 9.36e-02 248s depth 2.45e+02 -4.63e+02 6.38e+04 -2.02e+01 248s mag -3.38e-01 9.36e-02 -2.02e+01 1.78e-01 248s -------------------------------------------------------- 248s =================================================== 248s > dodata(method="MM") 248s 248s Call: dodata(method = "MM") 248s Data Set n p LOG(det) Time 248s =================================================== 248s heart 12 2 2.017701 248s Outliers: 1 248s [1] 6 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s height weight 248s 40.0 37.7 248s 248s Robust Estimate of Covariance: 248s height weight 248s height 99.2 205.7 248s weight 205.7 458.9 248s -------------------------------------------------------- 248s starsCYG 47 2 -1.450032 248s Outliers: 7 248s [1] 7 9 11 14 20 30 34 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s log.Te log.light 248s 4.41 4.94 248s 248s Robust Estimate of Covariance: 248s log.Te log.light 248s log.Te 0.0180 0.0526 248s log.light 0.0526 0.3217 248s -------------------------------------------------------- 248s phosphor 18 2 2.320721 248s Outliers: 1 248s [1] 6 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s inorg organic 248s 12.3 41.4 248s 248s Robust Estimate of Covariance: 248s inorg organic 248s inorg 94.2 67.2 248s organic 67.2 162.1 248s -------------------------------------------------------- 248s stackloss 21 3 1.470031 248s Outliers: 0 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s Air.Flow Water.Temp Acid.Conc. 248s 60.2 21.0 86.4 248s 248s Robust Estimate of Covariance: 248s Air.Flow Water.Temp Acid.Conc. 248s Air.Flow 81.13 21.99 23.15 248s Water.Temp 21.99 10.01 6.43 248s Acid.Conc. 23.15 6.43 27.22 248s -------------------------------------------------------- 248s coleman 20 5 0.491419 248s Outliers: 1 248s [1] 10 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s salaryP fatherWc sstatus teacherSc motherLev 248s 2.74 43.14 3.65 25.07 6.32 248s 248s Robust Estimate of Covariance: 248s salaryP fatherWc sstatus teacherSc motherLev 248s salaryP 0.1878 2.0635 1.0433 0.2721 0.0582 248s fatherWc 2.0635 670.2232 211.0609 4.3625 15.6083 248s sstatus 1.0433 211.0609 92.8743 2.6532 5.1816 248s teacherSc 0.2721 4.3625 2.6532 1.2757 0.1613 248s motherLev 0.0582 15.6083 5.1816 0.1613 0.4192 248s -------------------------------------------------------- 248s salinity 28 3 0.734619 248s Outliers: 2 248s [1] 5 16 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s X1 X2 X3 248s 10.46 2.66 23.15 248s 248s Robust Estimate of Covariance: 248s X1 X2 X3 248s X1 10.079 -0.024 -1.899 248s X2 -0.024 3.466 -1.817 248s X3 -1.899 -1.817 3.665 248s -------------------------------------------------------- 248s wood 20 5 -3.202636 248s Outliers: 0 248s ------------- 248s 248s Call: 248s CovMMest(x = x) 248s -> Method: MM-estimates 248s 248s Robust Estimate of Location: 248s x1 x2 x3 x4 x5 248s 0.550 0.133 0.506 0.511 0.909 248s 248s Robust Estimate of Covariance: 248s x1 x2 x3 x4 x5 248s x1 0.008454 -0.000377 0.003720 0.002874 -0.003065 248s x2 -0.000377 0.000516 -0.000399 -0.000933 0.000645 248s x3 0.003720 -0.000399 0.004186 0.001720 -0.001714 248s x4 0.002874 -0.000933 0.001720 0.003993 -0.001028 248s x5 -0.003065 0.000645 -0.001714 -0.001028 0.002744 248s -------------------------------------------------------- 249s hbk 75 3 0.283145 249s Outliers: 14 249s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s X1 X2 X3 249s 1.54 1.79 1.68 249s 249s Robust Estimate of Covariance: 249s X1 X2 X3 249s X1 1.8016 0.0739 0.2000 249s X2 0.0739 1.8301 0.2295 249s X3 0.2000 0.2295 1.7101 249s -------------------------------------------------------- 249s Animals 28 2 4.685129 249s Outliers: 10 249s [1] 2 6 7 9 12 14 15 16 24 25 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s body brain 249s 82 148 249s 249s Robust Estimate of Covariance: 249s body brain 249s body 21050 24534 249s brain 24534 35135 249s -------------------------------------------------------- 249s milk 86 8 -1.437863 249s Outliers: 12 249s [1] 1 2 3 12 13 17 41 44 47 70 74 75 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s X1 X2 X3 X4 X5 X6 X7 X8 249s 1.03 35.73 32.87 25.96 24.94 24.85 122.55 14.33 249s 249s Robust Estimate of Covariance: 249s X1 X2 X3 X4 X5 X6 X7 249s X1 1.08e-06 5.36e-04 6.80e-04 5.96e-04 5.87e-04 5.91e-04 2.22e-03 249s X2 5.36e-04 2.42e+00 7.07e-01 5.51e-01 4.89e-01 5.70e-01 3.08e+00 249s X3 6.80e-04 7.07e-01 1.64e+00 1.28e+00 1.25e+00 1.26e+00 2.38e+00 249s X4 5.96e-04 5.51e-01 1.28e+00 1.05e+00 1.01e+00 1.02e+00 2.01e+00 249s X5 5.87e-04 4.89e-01 1.25e+00 1.01e+00 1.05e+00 1.02e+00 1.96e+00 249s X6 5.91e-04 5.70e-01 1.26e+00 1.02e+00 1.02e+00 1.05e+00 2.01e+00 249s X7 2.22e-03 3.08e+00 2.38e+00 2.01e+00 1.96e+00 2.01e+00 9.22e+00 249s X8 1.68e-04 4.13e-01 3.37e-01 2.53e-01 2.34e-01 2.43e-01 8.81e-01 249s X8 249s X1 1.68e-04 249s X2 4.13e-01 249s X3 3.37e-01 249s X4 2.53e-01 249s X5 2.34e-01 249s X6 2.43e-01 249s X7 8.81e-01 249s X8 2.11e-01 249s -------------------------------------------------------- 249s bushfire 38 5 2.443148 249s Outliers: 12 249s [1] 8 9 10 11 31 32 33 34 35 36 37 38 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s V1 V2 V3 V4 V5 249s 109 149 258 215 276 249s 249s Robust Estimate of Covariance: 249s V1 V2 V3 V4 V5 249s V1 708 538 -2705 -558 -464 249s V2 538 497 -1376 -248 -216 249s V3 -2705 -1376 20521 4833 3914 249s V4 -558 -248 4833 1217 969 249s V5 -464 -216 3914 969 778 249s -------------------------------------------------------- 249s rice 105 5 -0.724874 249s Outliers: 5 249s [1] 9 42 49 58 71 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s Favor Appearance Taste Stickiness Toughness 249s -0.2653 0.0969 -0.1371 0.0483 0.0731 249s 249s Robust Estimate of Covariance: 249s Favor Appearance Taste Stickiness Toughness 249s Favor 0.421 0.349 0.427 0.405 -0.191 249s Appearance 0.349 0.605 0.565 0.553 -0.316 249s Taste 0.427 0.565 0.725 0.701 -0.378 249s Stickiness 0.405 0.553 0.701 0.868 -0.484 249s Toughness -0.191 -0.316 -0.378 -0.484 0.464 249s -------------------------------------------------------- 249s hemophilia 75 2 -1.868949 249s Outliers: 2 249s [1] 11 36 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s AHFactivity AHFantigen 249s -0.2342 -0.0333 249s 249s Robust Estimate of Covariance: 249s AHFactivity AHFantigen 249s AHFactivity 0.0309 0.0122 249s AHFantigen 0.0122 0.0231 249s -------------------------------------------------------- 249s fish 159 6 1.285876 249s Outliers: 20 249s [1] 61 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 249s [20] 142 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s Weight Length1 Length2 Length3 Height Width 249s 352.7 24.3 26.4 29.2 29.7 14.6 249s 249s Robust Estimate of Covariance: 249s Weight Length1 Length2 Length3 Height Width 249s Weight 1.20e+05 2.89e+03 3.12e+03 3.51e+03 1.49e+03 2.83e+02 249s Length1 2.89e+03 7.73e+01 8.35e+01 9.28e+01 3.73e+01 9.26e+00 249s Length2 3.12e+03 8.35e+01 9.04e+01 1.01e+02 4.16e+01 1.01e+01 249s Length3 3.51e+03 9.28e+01 1.01e+02 1.14e+02 5.37e+01 1.01e+01 249s Height 1.49e+03 3.73e+01 4.16e+01 5.37e+01 6.75e+01 3.22e+00 249s Width 2.83e+02 9.26e+00 1.01e+01 1.01e+01 3.22e+00 4.18e+00 249s -------------------------------------------------------- 249s airquality 153 4 2.684374 249s Outliers: 6 249s [1] 7 14 23 30 34 77 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s Ozone Solar.R Wind Temp 249s 40.35 186.21 9.86 78.09 249s 249s Robust Estimate of Covariance: 249s Ozone Solar.R Wind Temp 249s Ozone 951.0 959.9 -62.5 224.6 249s Solar.R 959.9 8629.9 -28.1 244.9 249s Wind -62.5 -28.1 11.6 -15.8 249s Temp 224.6 244.9 -15.8 93.1 249s -------------------------------------------------------- 249s attitude 30 7 2.091968 249s Outliers: 4 249s [1] 14 16 18 24 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s rating complaints privileges learning raises critical 249s 65.0 66.5 52.4 56.2 65.3 75.6 249s advance 249s 42.7 249s 249s Robust Estimate of Covariance: 249s rating complaints privileges learning raises critical advance 249s rating 143.5 123.4 62.4 92.5 79.2 17.7 28.2 249s complaints 123.4 159.8 83.9 99.7 96.0 27.3 44.0 249s privileges 62.4 83.9 133.5 78.6 62.0 13.4 46.4 249s learning 92.5 99.7 78.6 136.0 90.9 18.9 62.6 249s raises 79.2 96.0 62.0 90.9 107.6 34.6 63.3 249s critical 17.7 27.3 13.4 18.9 34.6 84.9 25.9 249s advance 28.2 44.0 46.4 62.6 63.3 25.9 94.4 249s -------------------------------------------------------- 249s attenu 182 5 1.148032 249s Outliers: 21 249s [1] 2 7 8 9 10 11 15 16 24 25 28 29 30 31 32 64 65 94 95 249s [20] 96 100 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s event mag station dist accel 249s 15.36 5.95 58.11 33.56 0.14 249s 249s Robust Estimate of Covariance: 249s event mag station dist accel 249s event 4.88e+01 -2.74e+00 1.53e+02 -1.14e+02 5.95e-02 249s mag -2.74e+00 5.32e-01 -6.29e+00 1.10e+01 9.37e-03 249s station 1.53e+02 -6.29e+00 1.29e+03 -2.95e+02 1.04e+00 249s dist -1.14e+02 1.10e+01 -2.95e+02 1.13e+03 -2.41e+00 249s accel 5.95e-02 9.37e-03 1.04e+00 -2.41e+00 1.70e-02 249s -------------------------------------------------------- 249s USJudgeRatings 43 12 -1.683847 249s Outliers: 7 249s [1] 5 7 12 13 14 23 31 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 249s 7.45 8.15 7.74 7.87 7.67 7.74 7.65 7.65 7.50 7.57 8.17 7.85 249s 249s Robust Estimate of Covariance: 249s CONT INTG DMNR DILG CFMG DECI PREP FAMI 249s CONT 0.9403 -0.2500 -0.3953 -0.1418 -0.0176 -0.0620 -0.1304 -0.1517 249s INTG -0.2500 0.6314 0.8479 0.6889 0.5697 0.5386 0.7007 0.6985 249s DMNR -0.3953 0.8479 1.2186 0.9027 0.7613 0.7232 0.9191 0.9055 249s DILG -0.1418 0.6889 0.9027 0.8474 0.7344 0.6949 0.8751 0.8655 249s CFMG -0.0176 0.5697 0.7613 0.7344 0.6904 0.6442 0.7683 0.7594 249s DECI -0.0620 0.5386 0.7232 0.6949 0.6442 0.6219 0.7362 0.7360 249s PREP -0.1304 0.7007 0.9191 0.8751 0.7683 0.7362 0.9370 0.9357 249s FAMI -0.1517 0.6985 0.9055 0.8655 0.7594 0.7360 0.9357 0.9547 249s ORAL -0.1866 0.7375 0.9841 0.8816 0.7747 0.7433 0.9400 0.9496 249s WRIT -0.1881 0.7208 0.9516 0.8711 0.7646 0.7357 0.9302 0.9439 249s PHYS -0.1407 0.4673 0.6261 0.5661 0.5105 0.5039 0.5996 0.6112 249s RTEN -0.2494 0.7921 1.0688 0.9167 0.7902 0.7585 0.9533 0.9561 249s ORAL WRIT PHYS RTEN 249s CONT -0.1866 -0.1881 -0.1407 -0.2494 249s INTG 0.7375 0.7208 0.4673 0.7921 249s DMNR 0.9841 0.9516 0.6261 1.0688 249s DILG 0.8816 0.8711 0.5661 0.9167 249s CFMG 0.7747 0.7646 0.5105 0.7902 249s DECI 0.7433 0.7357 0.5039 0.7585 249s PREP 0.9400 0.9302 0.5996 0.9533 249s FAMI 0.9496 0.9439 0.6112 0.9561 249s ORAL 0.9712 0.9558 0.6271 0.9933 249s WRIT 0.9558 0.9483 0.6135 0.9725 249s PHYS 0.6271 0.6135 0.4816 0.6549 249s RTEN 0.9933 0.9725 0.6549 1.0540 249s -------------------------------------------------------- 249s USArrests 50 4 2.411726 249s Outliers: 3 249s [1] 2 33 39 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s Murder Assault UrbanPop Rape 249s 7.52 163.86 65.66 20.64 249s 249s Robust Estimate of Covariance: 249s Murder Assault UrbanPop Rape 249s Murder 19.05 295.96 8.32 23.40 249s Assault 295.96 6905.03 396.53 523.49 249s UrbanPop 8.32 396.53 202.98 62.81 249s Rape 23.40 523.49 62.81 79.10 249s -------------------------------------------------------- 249s longley 16 7 1.038316 249s Outliers: 5 249s [1] 1 2 3 4 5 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s GNP.deflator GNP Unemployed Armed.Forces Population 249s 107.5 440.4 339.4 293.0 120.9 249s Year Employed 249s 1957.0 67.2 249s 249s Robust Estimate of Covariance: 249s GNP.deflator GNP Unemployed Armed.Forces Population 249s GNP.deflator 100.4 953.8 1140.8 -501.8 74.3 249s GNP 953.8 9434.3 10084.3 -4573.8 731.3 249s Unemployed 1140.8 10084.3 19644.6 -6296.3 848.4 249s Armed.Forces -501.8 -4573.8 -6296.3 3192.3 -348.5 249s Population 74.3 731.3 848.4 -348.5 57.7 249s Year 46.3 450.7 537.0 -230.7 35.3 249s Employed 30.8 310.2 273.8 -159.4 23.3 249s Year Employed 249s GNP.deflator 46.3 30.8 249s GNP 450.7 310.2 249s Unemployed 537.0 273.8 249s Armed.Forces -230.7 -159.4 249s Population 35.3 23.3 249s Year 21.9 14.6 249s Employed 14.6 11.2 249s -------------------------------------------------------- 249s Loblolly 84 3 1.481317 249s Outliers: 0 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s height age Seed 249s 31.93 12.79 7.48 249s 249s Robust Estimate of Covariance: 249s height age Seed 249s height 440.644 165.652 6.958 249s age 165.652 63.500 0.681 249s Seed 6.958 0.681 16.564 249s -------------------------------------------------------- 249s quakes 1000 4 1.576855 249s Outliers: 218 249s [1] 7 12 15 17 22 27 32 37 40 41 45 48 53 63 64 249s [16] 73 78 87 91 92 94 99 108 110 117 118 119 120 121 122 249s [31] 126 133 136 141 143 145 148 152 154 155 157 159 160 163 170 249s [46] 192 205 222 226 230 239 243 250 251 252 254 258 263 267 268 249s [61] 271 283 292 300 301 305 311 312 318 320 321 325 328 330 334 249s [76] 352 357 360 365 381 382 384 389 400 402 408 413 416 417 419 249s [91] 429 437 441 443 453 456 467 474 477 490 492 496 504 507 508 249s [106] 509 517 524 527 528 531 532 534 536 538 539 541 542 543 544 249s [121] 545 546 547 552 553 560 571 581 583 587 593 594 596 597 605 249s [136] 612 613 618 620 625 629 638 642 647 649 653 655 656 672 675 249s [151] 681 686 699 701 702 712 714 716 721 725 726 735 744 754 756 249s [166] 759 765 766 769 779 781 782 785 787 797 804 813 825 827 837 249s [181] 840 844 852 853 857 860 865 866 869 870 872 873 883 884 887 249s [196] 888 890 891 893 908 909 912 915 916 921 927 930 962 963 969 249s [211] 974 980 982 986 987 988 997 1000 249s ------------- 249s 249s Call: 249s CovMMest(x = x) 249s -> Method: MM-estimates 249s 249s Robust Estimate of Location: 249s lat long depth mag 249s -21.74 182.37 356.37 4.56 249s 249s Robust Estimate of Covariance: 249s lat long depth mag 249s lat 2.97e+01 6.53e+00 3.46e+02 -4.66e-01 249s long 6.53e+00 6.92e+00 -5.05e+02 5.62e-02 249s depth 3.46e+02 -5.05e+02 7.39e+04 -2.51e+01 249s mag -4.66e-01 5.62e-02 -2.51e+01 2.32e-01 249s -------------------------------------------------------- 249s =================================================== 249s > ##dogen() 249s > ##cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 249s > 249s autopkgtest [18:29:01]: test run-unit-test: -----------------------] 250s autopkgtest [18:29:02]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 250s run-unit-test PASS 250s autopkgtest [18:29:02]: test pkg-r-autopkgtest: preparing testbed 250s Reading package lists... 251s Building dependency tree... 251s Reading state information... 251s Starting pkgProblemResolver with broken count: 0 251s Starting 2 pkgProblemResolver with broken count: 0 251s Done 251s The following NEW packages will be installed: 251s build-essential cpp cpp-14 cpp-14-s390x-linux-gnu cpp-s390x-linux-gnu 251s dctrl-tools g++ g++-14 g++-14-s390x-linux-gnu g++-s390x-linux-gnu gcc gcc-14 251s gcc-14-s390x-linux-gnu gcc-s390x-linux-gnu gfortran gfortran-14 251s gfortran-14-s390x-linux-gnu gfortran-s390x-linux-gnu icu-devtools libasan8 251s libblas-dev libbz2-dev libcc1-0 libdeflate-dev libgcc-14-dev 251s libgfortran-14-dev libicu-dev libisl23 libitm1 libjpeg-dev 251s libjpeg-turbo8-dev libjpeg8-dev liblapack-dev liblzma-dev libmpc3 251s libncurses-dev libpcre2-16-0 libpcre2-32-0 libpcre2-dev libpcre2-posix3 251s libpkgconf3 libpng-dev libreadline-dev libstdc++-14-dev libtirpc-dev 251s libubsan1 pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev zlib1g-dev 251s 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. 251s Need to get 82.3 MB of archives. 251s After this operation, 279 MB of additional disk space will be used. 251s Get:1 http://ftpmaster.internal/ubuntu plucky/main s390x libisl23 s390x 0.27-1 [704 kB] 252s Get:2 http://ftpmaster.internal/ubuntu plucky/main s390x libmpc3 s390x 1.3.1-1build2 [57.8 kB] 252s Get:3 http://ftpmaster.internal/ubuntu plucky/main s390x cpp-14-s390x-linux-gnu s390x 14.2.0-17ubuntu3 [9572 kB] 259s Get:4 http://ftpmaster.internal/ubuntu plucky/main s390x cpp-14 s390x 14.2.0-17ubuntu3 [1028 B] 259s Get:5 http://ftpmaster.internal/ubuntu plucky/main s390x cpp-s390x-linux-gnu s390x 4:14.2.0-1ubuntu1 [5556 B] 259s Get:6 http://ftpmaster.internal/ubuntu plucky/main s390x cpp s390x 4:14.2.0-1ubuntu1 [22.4 kB] 259s Get:7 http://ftpmaster.internal/ubuntu plucky/main s390x libcc1-0 s390x 15-20250222-0ubuntu1 [49.2 kB] 259s Get:8 http://ftpmaster.internal/ubuntu plucky/main s390x libitm1 s390x 15-20250222-0ubuntu1 [31.2 kB] 259s Get:9 http://ftpmaster.internal/ubuntu plucky/main s390x libasan8 s390x 15-20250222-0ubuntu1 [2970 kB] 262s Get:10 http://ftpmaster.internal/ubuntu plucky/main s390x libubsan1 s390x 15-20250222-0ubuntu1 [1212 kB] 262s Get:11 http://ftpmaster.internal/ubuntu plucky/main s390x libgcc-14-dev s390x 14.2.0-17ubuntu3 [1037 kB] 263s Get:12 http://ftpmaster.internal/ubuntu plucky/main s390x gcc-14-s390x-linux-gnu s390x 14.2.0-17ubuntu3 [18.7 MB] 275s Get:13 http://ftpmaster.internal/ubuntu plucky/main s390x gcc-14 s390x 14.2.0-17ubuntu3 [526 kB] 275s Get:14 http://ftpmaster.internal/ubuntu plucky/main s390x gcc-s390x-linux-gnu s390x 4:14.2.0-1ubuntu1 [1204 B] 275s Get:15 http://ftpmaster.internal/ubuntu plucky/main s390x gcc s390x 4:14.2.0-1ubuntu1 [5004 B] 275s Get:16 http://ftpmaster.internal/ubuntu plucky/main s390x libstdc++-14-dev s390x 14.2.0-17ubuntu3 [2611 kB] 277s Get:17 http://ftpmaster.internal/ubuntu plucky/main s390x g++-14-s390x-linux-gnu s390x 14.2.0-17ubuntu3 [11.0 MB] 286s Get:18 http://ftpmaster.internal/ubuntu plucky/main s390x g++-14 s390x 14.2.0-17ubuntu3 [21.8 kB] 286s Get:19 http://ftpmaster.internal/ubuntu plucky/main s390x g++-s390x-linux-gnu s390x 4:14.2.0-1ubuntu1 [956 B] 286s Get:20 http://ftpmaster.internal/ubuntu plucky/main s390x g++ s390x 4:14.2.0-1ubuntu1 [1080 B] 286s Get:21 http://ftpmaster.internal/ubuntu plucky/main s390x build-essential s390x 12.10ubuntu1 [4930 B] 286s Get:22 http://ftpmaster.internal/ubuntu plucky/main s390x dctrl-tools s390x 2.24-3build3 [106 kB] 286s Get:23 http://ftpmaster.internal/ubuntu plucky/main s390x libgfortran-14-dev s390x 14.2.0-17ubuntu3 [654 kB] 287s Get:24 http://ftpmaster.internal/ubuntu plucky/main s390x gfortran-14-s390x-linux-gnu s390x 14.2.0-17ubuntu3 [10.3 MB] 297s Get:25 http://ftpmaster.internal/ubuntu plucky/main s390x gfortran-14 s390x 14.2.0-17ubuntu3 [13.6 kB] 297s Get:26 http://ftpmaster.internal/ubuntu plucky/main s390x gfortran-s390x-linux-gnu s390x 4:14.2.0-1ubuntu1 [1012 B] 297s Get:27 http://ftpmaster.internal/ubuntu plucky/main s390x gfortran s390x 4:14.2.0-1ubuntu1 [1160 B] 297s Get:28 http://ftpmaster.internal/ubuntu plucky/main s390x icu-devtools s390x 76.1-1ubuntu2 [225 kB] 297s Get:29 http://ftpmaster.internal/ubuntu plucky/main s390x libblas-dev s390x 3.12.1-2 [254 kB] 297s Get:30 http://ftpmaster.internal/ubuntu plucky/main s390x libbz2-dev s390x 1.0.8-6 [39.1 kB] 297s Get:31 http://ftpmaster.internal/ubuntu plucky/main s390x libdeflate-dev s390x 1.23-1 [52.2 kB] 298s Get:32 http://ftpmaster.internal/ubuntu plucky/main s390x libicu-dev s390x 76.1-1ubuntu2 [12.2 MB] 311s Get:33 http://ftpmaster.internal/ubuntu plucky/main s390x libjpeg-turbo8-dev s390x 2.1.5-3ubuntu2 [281 kB] 312s Get:34 http://ftpmaster.internal/ubuntu plucky/main s390x libjpeg8-dev s390x 8c-2ubuntu11 [1484 B] 312s Get:35 http://ftpmaster.internal/ubuntu plucky/main s390x libjpeg-dev s390x 8c-2ubuntu11 [1484 B] 312s Get:36 http://ftpmaster.internal/ubuntu plucky/main s390x liblapack-dev s390x 3.12.1-2 [5967 kB] 320s Get:37 http://ftpmaster.internal/ubuntu plucky/main s390x libncurses-dev s390x 6.5+20250216-2 [407 kB] 320s Get:38 http://ftpmaster.internal/ubuntu plucky/main s390x libpcre2-16-0 s390x 10.45-1 [259 kB] 321s Get:39 http://ftpmaster.internal/ubuntu plucky/main s390x libpcre2-32-0 s390x 10.45-1 [245 kB] 321s Get:40 http://ftpmaster.internal/ubuntu plucky/main s390x libpcre2-posix3 s390x 10.45-1 [7080 B] 321s Get:41 http://ftpmaster.internal/ubuntu plucky/main s390x libpcre2-dev s390x 10.45-1 [899 kB] 323s Get:42 http://ftpmaster.internal/ubuntu plucky/main s390x libpkgconf3 s390x 1.8.1-4 [31.2 kB] 323s Get:43 http://ftpmaster.internal/ubuntu plucky/main s390x zlib1g-dev s390x 1:1.3.dfsg+really1.3.1-1ubuntu1 [898 kB] 324s Get:44 http://ftpmaster.internal/ubuntu plucky/main s390x libpng-dev s390x 1.6.47-1 [278 kB] 324s Get:45 http://ftpmaster.internal/ubuntu plucky/main s390x libreadline-dev s390x 8.2-6 [187 kB] 325s Get:46 http://ftpmaster.internal/ubuntu plucky/main s390x liblzma-dev s390x 5.6.4-1 [183 kB] 325s Get:47 http://ftpmaster.internal/ubuntu plucky/main s390x pkgconf-bin s390x 1.8.1-4 [21.5 kB] 325s Get:48 http://ftpmaster.internal/ubuntu plucky/main s390x pkgconf s390x 1.8.1-4 [16.7 kB] 325s Get:49 http://ftpmaster.internal/ubuntu plucky/main s390x libtirpc-dev s390x 1.3.4+ds-1.3 [196 kB] 325s Get:50 http://ftpmaster.internal/ubuntu plucky/universe s390x r-base-dev all 4.4.3-1 [4176 B] 325s Get:51 http://ftpmaster.internal/ubuntu plucky/universe s390x pkg-r-autopkgtest all 20231212ubuntu1 [6448 B] 326s Fetched 82.3 MB in 1min 15s (1104 kB/s) 326s Selecting previously unselected package libisl23:s390x. 326s (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 ... 58932 files and directories currently installed.) 326s Preparing to unpack .../00-libisl23_0.27-1_s390x.deb ... 326s Unpacking libisl23:s390x (0.27-1) ... 326s Selecting previously unselected package libmpc3:s390x. 326s Preparing to unpack .../01-libmpc3_1.3.1-1build2_s390x.deb ... 326s Unpacking libmpc3:s390x (1.3.1-1build2) ... 326s Selecting previously unselected package cpp-14-s390x-linux-gnu. 326s Preparing to unpack .../02-cpp-14-s390x-linux-gnu_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking cpp-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 326s Selecting previously unselected package cpp-14. 326s Preparing to unpack .../03-cpp-14_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking cpp-14 (14.2.0-17ubuntu3) ... 326s Selecting previously unselected package cpp-s390x-linux-gnu. 326s Preparing to unpack .../04-cpp-s390x-linux-gnu_4%3a14.2.0-1ubuntu1_s390x.deb ... 326s Unpacking cpp-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 326s Selecting previously unselected package cpp. 326s Preparing to unpack .../05-cpp_4%3a14.2.0-1ubuntu1_s390x.deb ... 326s Unpacking cpp (4:14.2.0-1ubuntu1) ... 326s Selecting previously unselected package libcc1-0:s390x. 326s Preparing to unpack .../06-libcc1-0_15-20250222-0ubuntu1_s390x.deb ... 326s Unpacking libcc1-0:s390x (15-20250222-0ubuntu1) ... 326s Selecting previously unselected package libitm1:s390x. 326s Preparing to unpack .../07-libitm1_15-20250222-0ubuntu1_s390x.deb ... 326s Unpacking libitm1:s390x (15-20250222-0ubuntu1) ... 326s Selecting previously unselected package libasan8:s390x. 326s Preparing to unpack .../08-libasan8_15-20250222-0ubuntu1_s390x.deb ... 326s Unpacking libasan8:s390x (15-20250222-0ubuntu1) ... 326s Selecting previously unselected package libubsan1:s390x. 326s Preparing to unpack .../09-libubsan1_15-20250222-0ubuntu1_s390x.deb ... 326s Unpacking libubsan1:s390x (15-20250222-0ubuntu1) ... 326s Selecting previously unselected package libgcc-14-dev:s390x. 326s Preparing to unpack .../10-libgcc-14-dev_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking libgcc-14-dev:s390x (14.2.0-17ubuntu3) ... 326s Selecting previously unselected package gcc-14-s390x-linux-gnu. 326s Preparing to unpack .../11-gcc-14-s390x-linux-gnu_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking gcc-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 326s Selecting previously unselected package gcc-14. 326s Preparing to unpack .../12-gcc-14_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking gcc-14 (14.2.0-17ubuntu3) ... 326s Selecting previously unselected package gcc-s390x-linux-gnu. 326s Preparing to unpack .../13-gcc-s390x-linux-gnu_4%3a14.2.0-1ubuntu1_s390x.deb ... 326s Unpacking gcc-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 326s Selecting previously unselected package gcc. 326s Preparing to unpack .../14-gcc_4%3a14.2.0-1ubuntu1_s390x.deb ... 326s Unpacking gcc (4:14.2.0-1ubuntu1) ... 326s Selecting previously unselected package libstdc++-14-dev:s390x. 326s Preparing to unpack .../15-libstdc++-14-dev_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking libstdc++-14-dev:s390x (14.2.0-17ubuntu3) ... 326s Selecting previously unselected package g++-14-s390x-linux-gnu. 326s Preparing to unpack .../16-g++-14-s390x-linux-gnu_14.2.0-17ubuntu3_s390x.deb ... 326s Unpacking g++-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 327s Selecting previously unselected package g++-14. 327s Preparing to unpack .../17-g++-14_14.2.0-17ubuntu3_s390x.deb ... 327s Unpacking g++-14 (14.2.0-17ubuntu3) ... 327s Selecting previously unselected package g++-s390x-linux-gnu. 327s Preparing to unpack .../18-g++-s390x-linux-gnu_4%3a14.2.0-1ubuntu1_s390x.deb ... 327s Unpacking g++-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 327s Selecting previously unselected package g++. 327s Preparing to unpack .../19-g++_4%3a14.2.0-1ubuntu1_s390x.deb ... 327s Unpacking g++ (4:14.2.0-1ubuntu1) ... 327s Selecting previously unselected package build-essential. 327s Preparing to unpack .../20-build-essential_12.10ubuntu1_s390x.deb ... 327s Unpacking build-essential (12.10ubuntu1) ... 327s Selecting previously unselected package dctrl-tools. 327s Preparing to unpack .../21-dctrl-tools_2.24-3build3_s390x.deb ... 327s Unpacking dctrl-tools (2.24-3build3) ... 327s Selecting previously unselected package libgfortran-14-dev:s390x. 327s Preparing to unpack .../22-libgfortran-14-dev_14.2.0-17ubuntu3_s390x.deb ... 327s Unpacking libgfortran-14-dev:s390x (14.2.0-17ubuntu3) ... 327s Selecting previously unselected package gfortran-14-s390x-linux-gnu. 327s Preparing to unpack .../23-gfortran-14-s390x-linux-gnu_14.2.0-17ubuntu3_s390x.deb ... 327s Unpacking gfortran-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 327s Selecting previously unselected package gfortran-14. 327s Preparing to unpack .../24-gfortran-14_14.2.0-17ubuntu3_s390x.deb ... 327s Unpacking gfortran-14 (14.2.0-17ubuntu3) ... 327s Selecting previously unselected package gfortran-s390x-linux-gnu. 327s Preparing to unpack .../25-gfortran-s390x-linux-gnu_4%3a14.2.0-1ubuntu1_s390x.deb ... 327s Unpacking gfortran-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 327s Selecting previously unselected package gfortran. 327s Preparing to unpack .../26-gfortran_4%3a14.2.0-1ubuntu1_s390x.deb ... 327s Unpacking gfortran (4:14.2.0-1ubuntu1) ... 327s Selecting previously unselected package icu-devtools. 327s Preparing to unpack .../27-icu-devtools_76.1-1ubuntu2_s390x.deb ... 327s Unpacking icu-devtools (76.1-1ubuntu2) ... 327s Selecting previously unselected package libblas-dev:s390x. 327s Preparing to unpack .../28-libblas-dev_3.12.1-2_s390x.deb ... 327s Unpacking libblas-dev:s390x (3.12.1-2) ... 327s Selecting previously unselected package libbz2-dev:s390x. 327s Preparing to unpack .../29-libbz2-dev_1.0.8-6_s390x.deb ... 327s Unpacking libbz2-dev:s390x (1.0.8-6) ... 327s Selecting previously unselected package libdeflate-dev:s390x. 327s Preparing to unpack .../30-libdeflate-dev_1.23-1_s390x.deb ... 327s Unpacking libdeflate-dev:s390x (1.23-1) ... 327s Selecting previously unselected package libicu-dev:s390x. 327s Preparing to unpack .../31-libicu-dev_76.1-1ubuntu2_s390x.deb ... 327s Unpacking libicu-dev:s390x (76.1-1ubuntu2) ... 327s Selecting previously unselected package libjpeg-turbo8-dev:s390x. 327s Preparing to unpack .../32-libjpeg-turbo8-dev_2.1.5-3ubuntu2_s390x.deb ... 327s Unpacking libjpeg-turbo8-dev:s390x (2.1.5-3ubuntu2) ... 327s Selecting previously unselected package libjpeg8-dev:s390x. 327s Preparing to unpack .../33-libjpeg8-dev_8c-2ubuntu11_s390x.deb ... 327s Unpacking libjpeg8-dev:s390x (8c-2ubuntu11) ... 327s Selecting previously unselected package libjpeg-dev:s390x. 327s Preparing to unpack .../34-libjpeg-dev_8c-2ubuntu11_s390x.deb ... 327s Unpacking libjpeg-dev:s390x (8c-2ubuntu11) ... 327s Selecting previously unselected package liblapack-dev:s390x. 327s Preparing to unpack .../35-liblapack-dev_3.12.1-2_s390x.deb ... 327s Unpacking liblapack-dev:s390x (3.12.1-2) ... 327s Selecting previously unselected package libncurses-dev:s390x. 327s Preparing to unpack .../36-libncurses-dev_6.5+20250216-2_s390x.deb ... 327s Unpacking libncurses-dev:s390x (6.5+20250216-2) ... 327s Selecting previously unselected package libpcre2-16-0:s390x. 327s Preparing to unpack .../37-libpcre2-16-0_10.45-1_s390x.deb ... 327s Unpacking libpcre2-16-0:s390x (10.45-1) ... 327s Selecting previously unselected package libpcre2-32-0:s390x. 327s Preparing to unpack .../38-libpcre2-32-0_10.45-1_s390x.deb ... 327s Unpacking libpcre2-32-0:s390x (10.45-1) ... 327s Selecting previously unselected package libpcre2-posix3:s390x. 327s Preparing to unpack .../39-libpcre2-posix3_10.45-1_s390x.deb ... 327s Unpacking libpcre2-posix3:s390x (10.45-1) ... 327s Selecting previously unselected package libpcre2-dev:s390x. 327s Preparing to unpack .../40-libpcre2-dev_10.45-1_s390x.deb ... 327s Unpacking libpcre2-dev:s390x (10.45-1) ... 327s Selecting previously unselected package libpkgconf3:s390x. 327s Preparing to unpack .../41-libpkgconf3_1.8.1-4_s390x.deb ... 327s Unpacking libpkgconf3:s390x (1.8.1-4) ... 327s Selecting previously unselected package zlib1g-dev:s390x. 327s Preparing to unpack .../42-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu1_s390x.deb ... 327s Unpacking zlib1g-dev:s390x (1:1.3.dfsg+really1.3.1-1ubuntu1) ... 327s Selecting previously unselected package libpng-dev:s390x. 327s Preparing to unpack .../43-libpng-dev_1.6.47-1_s390x.deb ... 327s Unpacking libpng-dev:s390x (1.6.47-1) ... 327s Selecting previously unselected package libreadline-dev:s390x. 327s Preparing to unpack .../44-libreadline-dev_8.2-6_s390x.deb ... 327s Unpacking libreadline-dev:s390x (8.2-6) ... 327s Selecting previously unselected package liblzma-dev:s390x. 327s Preparing to unpack .../45-liblzma-dev_5.6.4-1_s390x.deb ... 327s Unpacking liblzma-dev:s390x (5.6.4-1) ... 327s Selecting previously unselected package pkgconf-bin. 327s Preparing to unpack .../46-pkgconf-bin_1.8.1-4_s390x.deb ... 327s Unpacking pkgconf-bin (1.8.1-4) ... 327s Selecting previously unselected package pkgconf:s390x. 327s Preparing to unpack .../47-pkgconf_1.8.1-4_s390x.deb ... 327s Unpacking pkgconf:s390x (1.8.1-4) ... 327s Selecting previously unselected package libtirpc-dev:s390x. 327s Preparing to unpack .../48-libtirpc-dev_1.3.4+ds-1.3_s390x.deb ... 327s Unpacking libtirpc-dev:s390x (1.3.4+ds-1.3) ... 327s Selecting previously unselected package r-base-dev. 327s Preparing to unpack .../49-r-base-dev_4.4.3-1_all.deb ... 327s Unpacking r-base-dev (4.4.3-1) ... 327s Selecting previously unselected package pkg-r-autopkgtest. 327s Preparing to unpack .../50-pkg-r-autopkgtest_20231212ubuntu1_all.deb ... 327s Unpacking pkg-r-autopkgtest (20231212ubuntu1) ... 327s Setting up libjpeg-turbo8-dev:s390x (2.1.5-3ubuntu2) ... 327s Setting up libncurses-dev:s390x (6.5+20250216-2) ... 327s Setting up libreadline-dev:s390x (8.2-6) ... 327s Setting up libpcre2-16-0:s390x (10.45-1) ... 327s Setting up libpcre2-32-0:s390x (10.45-1) ... 327s Setting up libtirpc-dev:s390x (1.3.4+ds-1.3) ... 327s Setting up libpkgconf3:s390x (1.8.1-4) ... 327s Setting up libmpc3:s390x (1.3.1-1build2) ... 327s Setting up icu-devtools (76.1-1ubuntu2) ... 327s Setting up pkgconf-bin (1.8.1-4) ... 327s Setting up liblzma-dev:s390x (5.6.4-1) ... 327s Setting up libubsan1:s390x (15-20250222-0ubuntu1) ... 327s Setting up zlib1g-dev:s390x (1:1.3.dfsg+really1.3.1-1ubuntu1) ... 327s Setting up libpcre2-posix3:s390x (10.45-1) ... 327s Setting up libasan8:s390x (15-20250222-0ubuntu1) ... 327s Setting up libjpeg8-dev:s390x (8c-2ubuntu11) ... 327s Setting up libisl23:s390x (0.27-1) ... 328s Setting up libdeflate-dev:s390x (1.23-1) ... 328s Setting up libicu-dev:s390x (76.1-1ubuntu2) ... 328s Setting up libcc1-0:s390x (15-20250222-0ubuntu1) ... 328s Setting up libblas-dev:s390x (3.12.1-2) ... 328s update-alternatives: using /usr/lib/s390x-linux-gnu/blas/libblas.so to provide /usr/lib/s390x-linux-gnu/libblas.so (libblas.so-s390x-linux-gnu) in auto mode 328s Setting up dctrl-tools (2.24-3build3) ... 328s Setting up libitm1:s390x (15-20250222-0ubuntu1) ... 328s Setting up libbz2-dev:s390x (1.0.8-6) ... 328s Setting up libpcre2-dev:s390x (10.45-1) ... 328s Setting up libpng-dev:s390x (1.6.47-1) ... 328s Setting up libjpeg-dev:s390x (8c-2ubuntu11) ... 328s Setting up pkgconf:s390x (1.8.1-4) ... 328s Setting up liblapack-dev:s390x (3.12.1-2) ... 328s update-alternatives: using /usr/lib/s390x-linux-gnu/lapack/liblapack.so to provide /usr/lib/s390x-linux-gnu/liblapack.so (liblapack.so-s390x-linux-gnu) in auto mode 328s Setting up cpp-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 328s Setting up cpp-14 (14.2.0-17ubuntu3) ... 328s Setting up libgcc-14-dev:s390x (14.2.0-17ubuntu3) ... 328s Setting up libstdc++-14-dev:s390x (14.2.0-17ubuntu3) ... 328s Setting up libgfortran-14-dev:s390x (14.2.0-17ubuntu3) ... 328s Setting up cpp-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 328s Setting up gcc-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 328s Setting up gcc-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 328s Setting up g++-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 328s Setting up cpp (4:14.2.0-1ubuntu1) ... 328s Setting up gfortran-14-s390x-linux-gnu (14.2.0-17ubuntu3) ... 328s Setting up g++-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 328s Setting up gcc-14 (14.2.0-17ubuntu3) ... 328s Setting up g++-14 (14.2.0-17ubuntu3) ... 328s Setting up gfortran-14 (14.2.0-17ubuntu3) ... 328s Setting up gfortran-s390x-linux-gnu (4:14.2.0-1ubuntu1) ... 328s Setting up gcc (4:14.2.0-1ubuntu1) ... 328s Setting up g++ (4:14.2.0-1ubuntu1) ... 328s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 328s Setting up build-essential (12.10ubuntu1) ... 328s Setting up gfortran (4:14.2.0-1ubuntu1) ... 328s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 328s 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 328s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 328s 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 328s Setting up r-base-dev (4.4.3-1) ... 328s Setting up pkg-r-autopkgtest (20231212ubuntu1) ... 328s Processing triggers for libc-bin (2.41-1ubuntu2) ... 328s Processing triggers for man-db (2.13.0-1) ... 328s Processing triggers for install-info (7.1.1-1) ... 329s autopkgtest [18:30:21]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 329s autopkgtest [18:30:21]: test pkg-r-autopkgtest: [----------------------- 329s Test: Try to load the R library rrcov 330s 330s R version 4.4.3 (2025-02-28) -- "Trophy Case" 330s Copyright (C) 2025 The R Foundation for Statistical Computing 330s Platform: s390x-ibm-linux-gnu 330s 330s R is free software and comes with ABSOLUTELY NO WARRANTY. 330s You are welcome to redistribute it under certain conditions. 330s Type 'license()' or 'licence()' for distribution details. 330s 330s R is a collaborative project with many contributors. 330s Type 'contributors()' for more information and 330s 'citation()' on how to cite R or R packages in publications. 330s 330s Type 'demo()' for some demos, 'help()' for on-line help, or 330s 'help.start()' for an HTML browser interface to help. 330s Type 'q()' to quit R. 330s 330s > library('rrcov') 330s Loading required package: robustbase 330s Scalable Robust Estimators with High Breakdown Point (version 1.7-6) 330s 330s > 330s > 330s Other tests are currently unsupported! 330s They will be progressively added. 330s autopkgtest [18:30:22]: test pkg-r-autopkgtest: -----------------------] 331s pkg-r-autopkgtest PASS 331s autopkgtest [18:30:23]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 331s autopkgtest [18:30:23]: @@@@@@@@@@@@@@@@@@@@ summary 331s run-unit-test PASS 331s pkg-r-autopkgtest PASS 349s nova [W] Using flock in prodstack6-s390x 349s flock: timeout while waiting to get lock 349s Creating nova instance adt-plucky-s390x-r-cran-rrcov-20250315-182451-juju-7f2275-prod-proposed-migration-environment-15-ff3ae515-a874-4dab-b3dd-10a16d4e54c8 from image adt/ubuntu-plucky-s390x-server-20250315.img (UUID 3d3557fa-fd0f-4bba-9b89-8d5964e09f61)... 349s nova [W] Timed out waiting for a6ba5d7f-c1e7-462b-af3c-9c726f496394 to get deleted.