0s autopkgtest [22:50:48]: starting date and time: 2026-02-09 22:50:48+0000 0s autopkgtest [22:50:48]: git checkout: 4b346b80 nova: make wait_reboot return success even when a no-op 0s autopkgtest [22:50:48]: host juju-7f2275-prod-proposed-migration-environment-15; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.emtsvxi_/out --timeout-copy=6000 --needs-internet=try --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:lattice --apt-upgrade r-cran-rrcov --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=lattice/0.22-9-1 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest-cpu2-ram4-disk20-ppc64el --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-15@sto01-ppc64el-14.secgroup --name adt-resolute-ppc64el-r-cran-rrcov-20260209-225048-juju-7f2275-prod-proposed-migration-environment-15-03a822bb-50cb-4c36-8464-fd6dcfe3d4ee --image adt/ubuntu-resolute-ppc64el-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-15 --net-id=net_prod-autopkgtest-workers-ppc64el -e TERM=linux --mirror=http://ftpmaster.internal/ubuntu/ 3s Creating nova instance adt-resolute-ppc64el-r-cran-rrcov-20260209-225048-juju-7f2275-prod-proposed-migration-environment-15-03a822bb-50cb-4c36-8464-fd6dcfe3d4ee from image adt/ubuntu-resolute-ppc64el-server-20260209.img (UUID f7f31435-4cd1-4090-aa55-59cfefa097ca)... 138s autopkgtest [22:53:06]: testbed dpkg architecture: ppc64el 138s autopkgtest [22:53:06]: testbed apt version: 3.1.15 138s autopkgtest [22:53:06]: @@@@@@@@@@@@@@@@@@@@ test bed setup 138s autopkgtest [22:53:06]: testbed release detected to be: None 140s autopkgtest [22:53:07]: updating testbed package index (apt update) 140s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [124 kB] 140s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 140s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 140s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 140s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [176 kB] 140s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [1645 kB] 142s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [29.4 kB] 142s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/main ppc64el Packages [246 kB] 142s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/universe ppc64el Packages [1534 kB] 145s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse ppc64el Packages [19.4 kB] 145s Fetched 3774 kB in 6s (621 kB/s) 146s Reading package lists... 147s Hit:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease 147s Hit:2 http://ftpmaster.internal/ubuntu resolute InRelease 147s Hit:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease 147s Hit:4 http://ftpmaster.internal/ubuntu resolute-security InRelease 148s Reading package lists... 148s Reading package lists... 148s Building dependency tree... 148s Reading state information... 148s Calculating upgrade... 148s The following packages will be upgraded: 148s cryptsetup-bin dracut-install iproute2 iptables libcryptsetup12 libip4tc2 148s libip6tc2 libxtables12 wget 148s 9 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 148s Need to get 3125 kB of archives. 148s After this operation, 78.8 kB of additional disk space will be used. 148s Get:1 http://ftpmaster.internal/ubuntu resolute/main ppc64el iptables ppc64el 1.8.11-2ubuntu3 [464 kB] 149s Get:2 http://ftpmaster.internal/ubuntu resolute/main ppc64el libip4tc2 ppc64el 1.8.11-2ubuntu3 [27.8 kB] 149s Get:3 http://ftpmaster.internal/ubuntu resolute/main ppc64el libip6tc2 ppc64el 1.8.11-2ubuntu3 [28.2 kB] 149s Get:4 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxtables12 ppc64el 1.8.11-2ubuntu3 [41.2 kB] 149s Get:5 http://ftpmaster.internal/ubuntu resolute/main ppc64el iproute2 ppc64el 6.18.0-1ubuntu1 [1458 kB] 153s Get:6 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcryptsetup12 ppc64el 2:2.8.0-1ubuntu3 [404 kB] 153s Get:7 http://ftpmaster.internal/ubuntu resolute/main ppc64el wget ppc64el 1.25.0-2ubuntu4 [401 kB] 153s Get:8 http://ftpmaster.internal/ubuntu resolute/main ppc64el cryptsetup-bin ppc64el 2:2.8.0-1ubuntu3 [250 kB] 153s Get:9 http://ftpmaster.internal/ubuntu resolute/main ppc64el dracut-install ppc64el 109-11ubuntu1 [51.3 kB] 153s dpkg-preconfigure: unable to re-open stdin: No such file or directory 153s Fetched 3125 kB in 5s (647 kB/s) 154s (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 ... 122003 files and directories currently installed.) 154s Preparing to unpack .../0-iptables_1.8.11-2ubuntu3_ppc64el.deb ... 154s Unpacking iptables (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 154s Preparing to unpack .../1-libip4tc2_1.8.11-2ubuntu3_ppc64el.deb ... 154s Unpacking libip4tc2:ppc64el (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 154s Preparing to unpack .../2-libip6tc2_1.8.11-2ubuntu3_ppc64el.deb ... 154s Unpacking libip6tc2:ppc64el (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 154s Preparing to unpack .../3-libxtables12_1.8.11-2ubuntu3_ppc64el.deb ... 154s Unpacking libxtables12:ppc64el (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 154s Preparing to unpack .../4-iproute2_6.18.0-1ubuntu1_ppc64el.deb ... 154s Unpacking iproute2 (6.18.0-1ubuntu1) over (6.16.0-1ubuntu3) ... 155s Preparing to unpack .../5-libcryptsetup12_2%3a2.8.0-1ubuntu3_ppc64el.deb ... 155s Unpacking libcryptsetup12:ppc64el (2:2.8.0-1ubuntu3) over (2:2.8.0-1ubuntu2) ... 155s Preparing to unpack .../6-wget_1.25.0-2ubuntu4_ppc64el.deb ... 155s Unpacking wget (1.25.0-2ubuntu4) over (1.25.0-2ubuntu3) ... 155s Preparing to unpack .../7-cryptsetup-bin_2%3a2.8.0-1ubuntu3_ppc64el.deb ... 155s Unpacking cryptsetup-bin (2:2.8.0-1ubuntu3) over (2:2.8.0-1ubuntu2) ... 155s Preparing to unpack .../8-dracut-install_109-11ubuntu1_ppc64el.deb ... 155s Unpacking dracut-install (109-11ubuntu1) over (109-9ubuntu1) ... 155s Setting up libip4tc2:ppc64el (1.8.11-2ubuntu3) ... 155s Setting up wget (1.25.0-2ubuntu4) ... 155s Setting up libip6tc2:ppc64el (1.8.11-2ubuntu3) ... 155s Setting up libxtables12:ppc64el (1.8.11-2ubuntu3) ... 155s Setting up dracut-install (109-11ubuntu1) ... 155s Setting up libcryptsetup12:ppc64el (2:2.8.0-1ubuntu3) ... 155s Setting up cryptsetup-bin (2:2.8.0-1ubuntu3) ... 155s Setting up iptables (1.8.11-2ubuntu3) ... 155s Setting up iproute2 (6.18.0-1ubuntu1) ... 155s Processing triggers for man-db (2.13.1-1build1) ... 157s Processing triggers for install-info (7.2-5) ... 157s Processing triggers for libc-bin (2.42-2ubuntu4) ... 157s autopkgtest [22:53:25]: upgrading testbed (apt dist-upgrade and autopurge) 158s Reading package lists... 158s Building dependency tree... 158s Reading state information... 158s Calculating upgrade... 158s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 158s Reading package lists... 158s Building dependency tree... 158s Reading state information... 158s Solving dependencies... 158s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 161s autopkgtest [22:53:29]: testbed running kernel: Linux 6.19.0-3-generic #3-Ubuntu SMP PREEMPT_DYNAMIC Fri Jan 23 20:13:51 UTC 2026 161s autopkgtest [22:53:29]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-rrcov 164s Get:1 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (dsc) [2146 B] 164s Get:2 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (tar) [1542 kB] 164s Get:3 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (diff) [3160 B] 165s gpgv: Signature made Fri Sep 6 03:10:50 2024 UTC 165s gpgv: using RSA key 73471499CC60ED9EEE805946C5BD6C8F2295D502 165s gpgv: issuer "plessy@debian.org" 165s gpgv: Can't check signature: No public key 165s dpkg-source: warning: cannot verify inline signature for ./r-cran-rrcov_1.7-6-1.dsc: no acceptable signature found 165s autopkgtest [22:53:33]: testing package r-cran-rrcov version 1.7-6-1 165s autopkgtest [22:53:33]: build not needed 166s autopkgtest [22:53:34]: test run-unit-test: preparing testbed 167s Reading package lists... 167s Building dependency tree... 167s Reading state information... 167s Solving dependencies... 167s The following NEW packages will be installed: 167s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono libblas3 167s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libgfortran5 libgomp1 167s libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 libjpeg8 167s liblapack3 liblerc4 libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 167s libpaper-utils libpaper2 libpixman-1-0 libsharpyuv0 libsm6 libtcl8.6 167s libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 libxcb-shm0 167s libxft2 libxrender1 libxss1 libxt6t64 r-base-core r-cran-deoptimr 167s r-cran-lattice r-cran-mass r-cran-mvtnorm r-cran-pcapp r-cran-robustbase 167s r-cran-rrcov unzip x11-common xdg-utils zip 167s 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. 167s Need to get 51.4 MB of archives. 167s After this operation, 103 MB of additional disk space will be used. 167s Get:1 http://ftpmaster.internal/ubuntu resolute/main ppc64el fonts-dejavu-mono all 2.37-8build1 [502 kB] 167s Get:2 http://ftpmaster.internal/ubuntu resolute/main ppc64el fonts-dejavu-core all 2.37-8build1 [834 kB] 168s Get:3 http://ftpmaster.internal/ubuntu resolute/main ppc64el fontconfig-config ppc64el 2.17.1-3ubuntu1 [38.5 kB] 168s Get:4 http://ftpmaster.internal/ubuntu resolute/main ppc64el libfontconfig1 ppc64el 2.17.1-3ubuntu1 [193 kB] 168s Get:5 http://ftpmaster.internal/ubuntu resolute/main ppc64el fontconfig ppc64el 2.17.1-3ubuntu1 [182 kB] 168s Get:6 http://ftpmaster.internal/ubuntu resolute/main ppc64el libblas3 ppc64el 3.12.1-7ubuntu1 [291 kB] 168s Get:7 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpixman-1-0 ppc64el 0.46.4-1 [347 kB] 168s Get:8 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-render0 ppc64el 1.17.0-2ubuntu1 [17.4 kB] 168s Get:9 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxcb-shm0 ppc64el 1.17.0-2ubuntu1 [6072 B] 168s Get:10 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxrender1 ppc64el 1:0.9.12-1 [23.0 kB] 168s Get:11 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcairo2 ppc64el 1.18.4-3 [759 kB] 169s Get:12 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdatrie1 ppc64el 0.2.14-1 [22.7 kB] 169s Get:13 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdeflate0 ppc64el 1.23-2build1 [64.1 kB] 169s Get:14 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgfortran5 ppc64el 15.2.0-12ubuntu1 [620 kB] 170s Get:15 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgomp1 ppc64el 15.2.0-12ubuntu1 [169 kB] 170s Get:16 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgraphite2-3 ppc64el 1.3.14-11ubuntu1 [85.3 kB] 170s Get:17 http://ftpmaster.internal/ubuntu resolute/main ppc64el libharfbuzz0b ppc64el 12.3.2-1 [663 kB] 171s Get:18 http://ftpmaster.internal/ubuntu resolute/main ppc64el x11-common all 1:7.7+24ubuntu1 [22.4 kB] 171s Get:19 http://ftpmaster.internal/ubuntu resolute/main ppc64el libice6 ppc64el 2:1.1.1-1build1 [51.9 kB] 171s Get:20 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg-turbo8 ppc64el 2.1.5-4ubuntu3 [214 kB] 171s Get:21 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg8 ppc64el 8c-2ubuntu11 [2148 B] 171s Get:22 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblapack3 ppc64el 3.12.1-7ubuntu1 [2960 kB] 176s Get:23 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblerc4 ppc64el 4.0.0+ds-5ubuntu2 [315 kB] 176s Get:24 http://ftpmaster.internal/ubuntu resolute/main ppc64el libthai-data all 0.1.30-1 [155 kB] 176s Get:25 http://ftpmaster.internal/ubuntu resolute/main ppc64el libthai0 ppc64el 0.1.30-1 [22.5 kB] 176s Get:26 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpango-1.0-0 ppc64el 1.57.0-1 [283 kB] 176s Get:27 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpangoft2-1.0-0 ppc64el 1.57.0-1 [61.2 kB] 176s Get:28 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpangocairo-1.0-0 ppc64el 1.57.0-1 [31.0 kB] 176s Get:29 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpaper2 ppc64el 2.2.5-0.3build1 [18.1 kB] 176s Get:30 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpaper-utils ppc64el 2.2.5-0.3build1 [15.7 kB] 176s Get:31 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsharpyuv0 ppc64el 1.5.0-0.1build1 [24.7 kB] 176s Get:32 http://ftpmaster.internal/ubuntu resolute/main ppc64el libsm6 ppc64el 2:1.2.6-1build1 [18.6 kB] 176s Get:33 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtcl8.6 ppc64el 8.6.17+dfsg-1build1 [1239 kB] 178s Get:34 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjbig0 ppc64el 2.1-6.1ubuntu3 [37.1 kB] 178s Get:35 http://ftpmaster.internal/ubuntu resolute/main ppc64el libwebp7 ppc64el 1.5.0-0.1build1 [330 kB] 178s Get:36 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtiff6 ppc64el 4.7.0-3ubuntu3 [307 kB] 178s Get:37 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxft2 ppc64el 2.3.6-1build2 [61.6 kB] 178s Get:38 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxss1 ppc64el 1:1.2.3-1build4 [7470 B] 178s Get:39 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtk8.6 ppc64el 8.6.17-1 [968 kB] 180s Get:40 http://ftpmaster.internal/ubuntu resolute/main ppc64el libxt6t64 ppc64el 1:1.2.1-1.3 [203 kB] 180s Get:41 http://ftpmaster.internal/ubuntu resolute/main ppc64el zip ppc64el 3.0-15ubuntu3 [198 kB] 180s Get:42 http://ftpmaster.internal/ubuntu resolute/main ppc64el unzip ppc64el 6.0-29ubuntu1 [200 kB] 180s Get:43 http://ftpmaster.internal/ubuntu resolute/main ppc64el xdg-utils all 1.2.1-2ubuntu2 [66.1 kB] 180s Get:44 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-base-core ppc64el 4.5.2-1ubuntu2 [29.3 MB] 244s Get:45 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-cran-deoptimr all 1.1-4-1 [76.7 kB] 244s Get:46 http://ftpmaster.internal/ubuntu resolute-proposed/universe ppc64el r-cran-lattice ppc64el 0.22-9-1 [1399 kB] 247s Get:47 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-cran-mass ppc64el 7.3-65-1 [1116 kB] 248s Get:48 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-cran-mvtnorm ppc64el 1.3-3-1build1 [928 kB] 250s Get:49 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-cran-pcapp ppc64el 2.0-5-1 [385 kB] 251s Get:50 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-cran-robustbase ppc64el 0.99-7-1 [3103 kB] 255s Get:51 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-cran-rrcov ppc64el 1.7-6-1 [2419 kB] 259s Preconfiguring packages ... 259s Fetched 51.4 MB in 1min 32s (558 kB/s) 259s Selecting previously unselected package fonts-dejavu-mono. 259s (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 ... 122006 files and directories currently installed.) 259s Preparing to unpack .../00-fonts-dejavu-mono_2.37-8build1_all.deb ... 259s Unpacking fonts-dejavu-mono (2.37-8build1) ... 259s Selecting previously unselected package fonts-dejavu-core. 259s Preparing to unpack .../01-fonts-dejavu-core_2.37-8build1_all.deb ... 259s Unpacking fonts-dejavu-core (2.37-8build1) ... 259s Selecting previously unselected package fontconfig-config. 259s Preparing to unpack .../02-fontconfig-config_2.17.1-3ubuntu1_ppc64el.deb ... 260s Unpacking fontconfig-config (2.17.1-3ubuntu1) ... 260s Selecting previously unselected package libfontconfig1:ppc64el. 260s Preparing to unpack .../03-libfontconfig1_2.17.1-3ubuntu1_ppc64el.deb ... 260s Unpacking libfontconfig1:ppc64el (2.17.1-3ubuntu1) ... 260s Selecting previously unselected package fontconfig. 260s Preparing to unpack .../04-fontconfig_2.17.1-3ubuntu1_ppc64el.deb ... 260s Unpacking fontconfig (2.17.1-3ubuntu1) ... 260s Selecting previously unselected package libblas3:ppc64el. 260s Preparing to unpack .../05-libblas3_3.12.1-7ubuntu1_ppc64el.deb ... 260s Unpacking libblas3:ppc64el (3.12.1-7ubuntu1) ... 260s Selecting previously unselected package libpixman-1-0:ppc64el. 260s Preparing to unpack .../06-libpixman-1-0_0.46.4-1_ppc64el.deb ... 260s Unpacking libpixman-1-0:ppc64el (0.46.4-1) ... 260s Selecting previously unselected package libxcb-render0:ppc64el. 260s Preparing to unpack .../07-libxcb-render0_1.17.0-2ubuntu1_ppc64el.deb ... 260s Unpacking libxcb-render0:ppc64el (1.17.0-2ubuntu1) ... 260s Selecting previously unselected package libxcb-shm0:ppc64el. 260s Preparing to unpack .../08-libxcb-shm0_1.17.0-2ubuntu1_ppc64el.deb ... 260s Unpacking libxcb-shm0:ppc64el (1.17.0-2ubuntu1) ... 260s Selecting previously unselected package libxrender1:ppc64el. 260s Preparing to unpack .../09-libxrender1_1%3a0.9.12-1_ppc64el.deb ... 260s Unpacking libxrender1:ppc64el (1:0.9.12-1) ... 260s Selecting previously unselected package libcairo2:ppc64el. 260s Preparing to unpack .../10-libcairo2_1.18.4-3_ppc64el.deb ... 260s Unpacking libcairo2:ppc64el (1.18.4-3) ... 260s Selecting previously unselected package libdatrie1:ppc64el. 260s Preparing to unpack .../11-libdatrie1_0.2.14-1_ppc64el.deb ... 260s Unpacking libdatrie1:ppc64el (0.2.14-1) ... 260s Selecting previously unselected package libdeflate0:ppc64el. 260s Preparing to unpack .../12-libdeflate0_1.23-2build1_ppc64el.deb ... 260s Unpacking libdeflate0:ppc64el (1.23-2build1) ... 260s Selecting previously unselected package libgfortran5:ppc64el. 260s Preparing to unpack .../13-libgfortran5_15.2.0-12ubuntu1_ppc64el.deb ... 260s Unpacking libgfortran5:ppc64el (15.2.0-12ubuntu1) ... 260s Selecting previously unselected package libgomp1:ppc64el. 260s Preparing to unpack .../14-libgomp1_15.2.0-12ubuntu1_ppc64el.deb ... 260s Unpacking libgomp1:ppc64el (15.2.0-12ubuntu1) ... 260s Selecting previously unselected package libgraphite2-3:ppc64el. 260s Preparing to unpack .../15-libgraphite2-3_1.3.14-11ubuntu1_ppc64el.deb ... 260s Unpacking libgraphite2-3:ppc64el (1.3.14-11ubuntu1) ... 260s Selecting previously unselected package libharfbuzz0b:ppc64el. 260s Preparing to unpack .../16-libharfbuzz0b_12.3.2-1_ppc64el.deb ... 260s Unpacking libharfbuzz0b:ppc64el (12.3.2-1) ... 260s Selecting previously unselected package x11-common. 260s Preparing to unpack .../17-x11-common_1%3a7.7+24ubuntu1_all.deb ... 260s Unpacking x11-common (1:7.7+24ubuntu1) ... 260s Selecting previously unselected package libice6:ppc64el. 260s Preparing to unpack .../18-libice6_2%3a1.1.1-1build1_ppc64el.deb ... 260s Unpacking libice6:ppc64el (2:1.1.1-1build1) ... 260s Selecting previously unselected package libjpeg-turbo8:ppc64el. 260s Preparing to unpack .../19-libjpeg-turbo8_2.1.5-4ubuntu3_ppc64el.deb ... 260s Unpacking libjpeg-turbo8:ppc64el (2.1.5-4ubuntu3) ... 260s Selecting previously unselected package libjpeg8:ppc64el. 260s Preparing to unpack .../20-libjpeg8_8c-2ubuntu11_ppc64el.deb ... 260s Unpacking libjpeg8:ppc64el (8c-2ubuntu11) ... 260s Selecting previously unselected package liblapack3:ppc64el. 260s Preparing to unpack .../21-liblapack3_3.12.1-7ubuntu1_ppc64el.deb ... 260s Unpacking liblapack3:ppc64el (3.12.1-7ubuntu1) ... 260s Selecting previously unselected package liblerc4:ppc64el. 260s Preparing to unpack .../22-liblerc4_4.0.0+ds-5ubuntu2_ppc64el.deb ... 260s Unpacking liblerc4:ppc64el (4.0.0+ds-5ubuntu2) 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264s Setting up r-base-core (4.5.2-1ubuntu2) ... 264s Creating config file /etc/R/Renviron with new version 264s Setting up r-cran-lattice (0.22-9-1) ... 264s Setting up r-cran-deoptimr (1.1-4-1) ... 264s Setting up r-cran-mass (7.3-65-1) ... 264s Setting up r-cran-mvtnorm (1.3-3-1build1) ... 264s Setting up r-cran-robustbase (0.99-7-1) ... 264s Setting up r-cran-pcapp (2.0-5-1) ... 264s Setting up r-cran-rrcov (1.7-6-1) ... 264s Processing triggers for libc-bin (2.42-2ubuntu4) ... 264s Processing triggers for man-db (2.13.1-1build1) ... 265s Processing triggers for install-info (7.2-5) ... 266s autopkgtest [22:55:14]: test run-unit-test: [----------------------- 266s BEGIN TEST thubert.R 266s 266s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 266s Copyright (C) 2025 The R Foundation for Statistical Computing 266s Platform: powerpc64le-unknown-linux-gnu 266s 266s R is free software and comes with ABSOLUTELY NO WARRANTY. 266s You are welcome to redistribute it under certain conditions. 266s Type 'license()' or 'licence()' for distribution details. 266s 266s R is a collaborative project with many contributors. 266s Type 'contributors()' for more information and 266s 'citation()' on how to cite R or R packages in publications. 266s 266s Type 'demo()' for some demos, 'help()' for on-line help, or 266s 'help.start()' for an HTML browser interface to help. 266s Type 'q()' to quit R. 266s 266s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, 266s + method=c("hubert", "hubert.mcd", "locantore", "cov", "classic", 266s + "grid", "proj")) 266s + { 266s + ## Test the PcaXxx() functions on the literature datasets: 266s + ## 266s + ## Call PcaHubert() and the other functions for all regression 266s + ## data sets available in robustbase/rrcov and print: 266s + ## - execution time (if time == TRUE) 266s + ## - loadings 266s + ## - eigenvalues 266s + ## - scores 266s + ## 266s + 266s + dopca <- function(x, xname, nrep=1){ 266s + 266s + n <- dim(x)[1] 266s + p <- dim(x)[2] 266s + if(method == "hubert.mcd") 266s + pca <- PcaHubert(x, k=p) 266s + else if(method == "hubert") 266s + pca <- PcaHubert(x, mcd=FALSE) 266s + else if(method == "locantore") 266s + pca <- PcaLocantore(x) 266s + else if(method == "cov") 266s + pca <- PcaCov(x) 266s + else if(method == "classic") 266s + pca <- PcaClassic(x) 266s + else if(method == "grid") 266s + pca <- PcaGrid(x) 266s + else if(method == "proj") 266s + pca <- PcaProj(x) 266s + else 266s + stop("Undefined PCA method: ", method) 266s + 266s + 266s + e1 <- getEigenvalues(pca)[1] 266s + e2 <- getEigenvalues(pca)[2] 266s + k <- pca@k 266s + 266s + if(time){ 266s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 266s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f %10.3f\n", dim(x)[1], dim(x)[2], k, e1, e2, xtime) 266s + } 266s + else{ 266s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f\n", dim(x)[1], dim(x)[2], k, e1, e2) 266s + } 266s + lpad<-lname-nchar(xname) 266s + cat(pad.right(xname, lpad), xres) 266s + 266s + if(!short){ 266s + cat("Scores: \n") 266s + print(getScores(pca)) 266s + 266s + if(full){ 266s + cat("-------------\n") 266s + show(pca) 266s + } 266s + cat("----------------------------------------------------------\n") 266s + } 266s + } 266s + 266s + stopifnot(length(nrep) == 1, nrep >= 1) 266s + method <- match.arg(method) 266s + 266s + options(digits = 5) 266s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 266s + 266s + lname <- 20 266s + 266s + ## VT::15.09.2013 - this will render the output independent 266s + ## from the version of the package 266s + suppressPackageStartupMessages(library(rrcov)) 266s + 266s + data(Animals, package = "MASS") 266s + brain <- Animals[c(1:24, 26:25, 27:28),] 266s + 266s + tmp <- sys.call() 266s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 266s + 266s + cat("Data Set n p k e1 e2\n") 266s + cat("==========================================================\n") 266s + dopca(heart[, 1:2], data(heart), nrep) 266s + dopca(starsCYG, data(starsCYG), nrep) 266s + dopca(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 266s + dopca(stack.x, data(stackloss), nrep) 266s + ## dopca(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) # differences between the architectures 266s + dopca(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 266s + ## dopca(data.matrix(subset(wood, select = -y)), data(wood), nrep) # differences between the architectures 266s + dopca(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 266s + 266s + ## dopca(brain, "Animals", nrep) 266s + dopca(milk, data(milk), nrep) 266s + dopca(bushfire, data(bushfire), nrep) 266s + cat("==========================================================\n") 266s + } 266s > 266s > dogen <- function(nrep=1, eps=0.49, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 266s + 266s + dopca <- function(x, nrep=1){ 266s + gc() 266s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 266s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 266s + xtime 266s + } 266s + 266s + set.seed(1234) 266s + 266s + ## VT::15.09.2013 - this will render the output independent 266s + ## from the version of the package 266s + suppressPackageStartupMessages(library(rrcov)) 266s + library(MASS) 266s + 266s + method <- match.arg(method) 266s + 266s + ap <- c(2, 5, 10, 20, 30) 266s + an <- c(100, 500, 1000, 10000, 50000) 266s + 266s + tottime <- 0 266s + cat(" n p Time\n") 266s + cat("=====================\n") 266s + for(i in 1:length(an)) { 266s + for(j in 1:length(ap)) { 266s + n <- an[i] 266s + p <- ap[j] 266s + if(5*p <= n){ 266s + xx <- gendata(n, p, eps) 266s + X <- xx$X 266s + ## print(dimnames(X)) 266s + tottime <- tottime + dopca(X, nrep) 266s + } 266s + } 266s + } 266s + 266s + cat("=====================\n") 266s + cat("Total time: ", tottime*nrep, "\n") 266s + } 266s > 266s > dorep <- function(x, nrep=1, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 266s + 266s + method <- match.arg(method) 266s + for(i in 1:nrep) 266s + if(method == "hubert.mcd") 266s + PcaHubert(x) 266s + else if(method == "hubert") 266s + PcaHubert(x, mcd=FALSE) 266s + else if(method == "locantore") 266s + PcaLocantore(x) 266s + else if(method == "cov") 266s + PcaCov(x) 266s + else 266s + stop("Undefined PCA method: ", method) 266s + } 266s > 266s > #### gendata() #### 266s > # Generates a location contaminated multivariate 266s > # normal sample of n observations in p dimensions 266s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 266s > # where 266s > # m = (b,b,...,b) 266s > # Defaults: eps=0 and b=10 266s > # 266s > gendata <- function(n,p,eps=0,b=10){ 266s + 266s + if(missing(n) || missing(p)) 266s + stop("Please specify (n,p)") 266s + if(eps < 0 || eps >= 0.5) 266s + stop(message="eps must be in [0,0.5)") 266s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 266s + nbad <- as.integer(eps * n) 266s + xind <- vector("numeric") 266s + if(nbad > 0){ 266s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 266s + xind <- sample(n,nbad) 266s + X[xind,] <- Xbad 266s + } 266s + list(X=X, xind=xind) 266s + } 266s > 266s > pad.right <- function(z, pads) 266s + { 266s + ### Pads spaces to right of text 266s + padding <- paste(rep(" ", pads), collapse = "") 266s + paste(z, padding, sep = "") 266s + } 266s > 266s > whatis <- function(x){ 266s + if(is.data.frame(x)) 266s + cat("Type: data.frame\n") 266s + else if(is.matrix(x)) 266s + cat("Type: matrix\n") 266s + else if(is.vector(x)) 266s + cat("Type: vector\n") 266s + else 266s + cat("Type: don't know\n") 266s + } 266s > 266s > ################################################################# 266s > ## VT::27.08.2010 266s > ## bug report from Stephen Milborrow 266s > ## 266s > test.case.1 <- function() 266s + { 266s + X <- matrix(c( 266s + -0.79984, -1.00103, 0.899794, 0.00000, 266s + 0.34279, 0.52832, -1.303783, -1.17670, 266s + -0.79984, -1.00103, 0.899794, 0.00000, 266s + 0.34279, 0.52832, -1.303783, -1.17670, 266s + 0.34279, 0.52832, -1.303783, -1.17670, 266s + 1.48542, 0.66735, 0.716162, 1.17670, 266s + -0.79984, -1.00103, 0.899794, 0.00000, 266s + 1.69317, 1.91864, -0.018363, 1.76505, 266s + -1.00759, -0.16684, -0.385626, 0.58835, 266s + -0.79984, -1.00103, 0.899794, 0.00000), ncol=4, byrow=TRUE) 266s + 266s + cc1 <- PcaHubert(X, k=3) 266s + 266s + cc2 <- PcaLocantore(X, k=3) 266s + cc3 <- PcaCov(X, k=3, cov.control=CovControlSest()) 266s + 266s + cc4 <- PcaProj(X, k=2) # with k=3 will produce warnings in .distances - too small eignevalues 266s + cc5 <- PcaGrid(X, k=2) # dito 266s + 266s + list(cc1, cc2, cc3, cc4, cc5) 266s + } 266s > 266s > ################################################################# 266s > ## VT::05.08.2016 266s > ## bug report from Matthieu Lesnoff 266s > ## 266s > test.case.2 <- function() 266s + { 266s + do.test.case.2 <- function(z) 266s + { 266s + if(missing(z)) 266s + { 266s + set.seed(12345678) 266s + n <- 5 266s + z <- data.frame(v1 = rnorm(n), v2 = rnorm(n), v3 = rnorm(n)) 266s + z 266s + } 266s + 266s + fm <- PcaLocantore(z, k = 2, scale = TRUE) 266s + fm@scale 266s + apply(z, MARGIN = 2, FUN = mad) 266s + scale(z, center = fm@center, scale = fm@scale) 266s + 266s + T <- fm@scores 266s + P <- fm@loadings 266s + E <- scale(z, center = fm@center, scale = fm@scale) - T %*% t(P) 266s + d2 <- apply(E^2, MARGIN = 1, FUN = sum) 266s + ## print(sqrt(d2)); print(fm@od) 266s + print(ret <- all.equal(sqrt(d2), fm@od)) 266s + 266s + ret 266s + } 266s + do.test.case.2() 266s + do.test.case.2(phosphor) 266s + do.test.case.2(stackloss) 266s + do.test.case.2(salinity) 266s + do.test.case.2(hbk) 266s + do.test.case.2(milk) 266s + do.test.case.2(bushfire) 266s + data(rice); do.test.case.2(rice) 266s + data(un86); do.test.case.2(un86) 266s + } 266s > 266s > ## VT::15.09.2013 - this will render the output independent 266s > ## from the version of the package 266s > suppressPackageStartupMessages(library(rrcov)) 267s > 267s > dodata(method="classic") 267s 267s Call: dodata(method = "classic") 267s Data Set n p k e1 e2 267s ========================================================== 267s heart 12 2 2 812.379735 9.084962 267s Scores: 267s PC1 PC2 267s 1 2.7072 1.46576 267s 2 59.9990 -1.43041 267s 3 -3.5619 -1.54067 267s 4 -7.7696 2.52687 267s 5 14.7660 -0.95822 267s 6 -20.0489 6.91079 267s 7 1.4189 2.25961 267s 8 -34.3308 -4.23717 267s 9 -6.0487 -0.97859 267s 10 -33.0102 -3.73143 267s 11 -18.6372 0.25821 267s 12 44.5163 -0.54476 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 28.5023 3.0141 267s ---------------------------------------------------------- 267s starsCYG 47 2 2 0.331279 0.079625 267s Scores: 267s PC1 PC2 267s 1 0.2072999 0.089973 267s 2 0.6855999 0.349644 267s 3 -0.0743007 -0.061028 267s 4 0.6855999 0.349644 267s 5 0.1775161 0.015053 267s 6 0.4223986 0.211351 267s 7 -0.2926077 -0.516156 267s 8 0.2188453 0.293607 267s 9 0.5593696 0.028761 267s 10 0.0983878 0.074540 267s 11 0.8258140 -0.711176 267s 12 0.4167063 0.180244 267s 13 0.3799883 0.225541 267s 14 -0.9105236 -0.432014 267s 15 -0.7418831 -0.125322 267s 16 -0.4432862 0.048287 267s 17 -1.0503005 -0.229623 267s 18 -0.8393302 -0.007831 267s 19 -0.8126742 -0.195952 267s 20 0.9842316 -0.688729 267s 21 -0.6230699 -0.108486 267s 22 -0.7814875 -0.130933 267s 23 -0.6017038 0.025840 267s 24 -0.1857772 0.155474 267s 25 -0.0020261 0.070412 267s 26 -0.3640775 0.059510 267s 27 -0.3458392 -0.069204 267s 28 -0.1208393 0.053577 267s 29 -0.6033482 -0.176391 267s 30 1.1440521 -0.676183 267s 31 -0.5960920 -0.013765 267s 32 0.0519296 0.259855 267s 33 0.1861752 0.167779 267s 34 1.3802755 -0.632611 267s 35 -0.6542566 -0.173505 267s 36 0.5583690 0.392215 267s 37 0.0561384 0.230152 267s 38 0.1861752 0.167779 267s 39 0.1353472 0.241376 267s 40 0.5355195 0.197080 267s 41 -0.3980701 0.014294 267s 42 0.0277576 0.145332 267s 43 0.2979736 0.234120 267s 44 0.3049884 0.184614 267s 45 0.4889809 0.311684 267s 46 -0.0514512 0.134108 267s 47 -0.5224950 0.037063 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 0.57557 0.28218 267s ---------------------------------------------------------- 267s phosphor 18 2 2 220.403422 68.346121 267s Scores: 267s PC1 PC2 267s 1 4.04290 -15.3459 267s 2 -22.30489 -1.0004 267s 3 -24.52683 3.2836 267s 4 -12.54839 -6.0848 267s 5 -19.37044 2.2979 267s 6 15.20366 -19.9424 267s 7 0.44222 -3.1379 267s 8 -10.64042 3.6933 267s 9 -11.67967 5.9670 267s 10 14.26805 -7.0221 267s 11 -4.98832 1.5268 267s 12 8.74986 7.9379 267s 13 12.26290 6.0251 267s 14 6.27607 7.5768 267s 15 17.53246 3.1560 267s 16 -10.17024 -5.8994 267s 17 21.05826 5.4492 267s 18 16.39281 11.5191 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 14.8460 8.2672 267s ---------------------------------------------------------- 267s stackloss 21 3 3 99.576089 19.581136 267s Scores: 267s PC1 PC2 PC3 267s 1 20.15352 -4.359452 0.324585 267s 2 19.81554 -5.300468 0.308294 267s 3 15.45222 -1.599136 -0.203125 267s 4 2.40370 -0.145282 2.370302 267s 5 1.89538 0.070566 0.448061 267s 6 2.14954 -0.037358 1.409182 267s 7 4.43153 5.500810 2.468051 267s 8 4.43153 5.500810 2.468051 267s 9 -1.47521 1.245404 2.511773 267s 10 -5.11183 -4.802083 -2.407870 267s 11 -2.07009 3.667055 -2.261247 267s 12 -2.66223 2.833964 -3.238659 267s 13 -4.43589 -2.920053 -2.375287 267s 14 -0.46404 7.323193 -1.234961 267s 15 -9.31959 6.232579 -0.056064 267s 16 -10.33350 3.409533 -0.104938 267s 17 -14.81094 -9.872607 0.628103 267s 18 -12.44514 -3.285499 0.742143 267s 19 -11.85300 -2.452408 1.719555 267s 20 -5.73994 -2.494520 0.098250 267s 21 9.98843 1.484952 -3.614198 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 9.9788 4.4251 1.8986 267s ---------------------------------------------------------- 267s salinity 28 3 3 11.410736 7.075409 267s Scores: 267s PC1 PC2 PC3 267s 1 -0.937789 -2.40535 0.812909 267s 2 -1.752631 -2.57774 2.004437 267s 3 -6.509364 -0.78762 -1.821906 267s 4 -5.619847 -2.41333 -1.586891 267s 5 -7.268242 1.61012 1.563568 267s 6 -4.316558 -3.20411 0.029376 267s 7 -2.379545 -3.32371 0.703101 267s 8 0.013514 -3.50586 1.260502 267s 9 0.265262 -0.16736 -2.886883 267s 10 1.890755 2.43623 -0.986832 267s 11 0.804196 2.56656 0.387577 267s 12 0.935082 -1.03559 -0.074081 267s 13 1.814839 -1.61087 0.612290 267s 14 3.407535 -0.15880 2.026088 267s 15 1.731273 2.95159 -1.840286 267s 16 -6.129708 7.21368 2.632273 267s 17 -0.645124 1.06260 0.028697 267s 18 -1.307532 -2.54679 -0.280273 267s 19 0.483455 -0.55896 -3.097281 267s 20 2.053267 0.47308 -1.858703 267s 21 3.277664 -1.31002 0.453753 267s 22 4.631644 -0.78005 1.519894 267s 23 1.864403 5.32790 -0.849694 267s 24 0.623899 4.29317 0.056461 267s 25 1.301696 0.37871 -0.646220 267s 26 2.852126 -0.79527 -0.347711 267s 27 4.134051 -0.92756 0.449222 267s 28 4.781679 -0.20467 1.736616 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 3.3780 2.6600 1.4836 267s ---------------------------------------------------------- 267s hbk 75 3 3 216.162129 1.981077 267s Scores: 267s PC1 PC2 PC3 267s 1 26.2072 -0.660756 0.503340 267s 2 27.0406 -0.108506 -0.225059 267s 3 28.8351 -1.683721 0.263078 267s 4 29.9221 -0.812174 -0.674480 267s 5 29.3181 -0.909915 -0.121600 267s 6 27.5360 -0.599697 0.916574 267s 7 27.6617 -0.073753 0.676620 267s 8 26.5576 -0.882312 0.159620 267s 9 28.8726 -1.074223 -0.673462 267s 10 27.6643 -1.463829 -0.868593 267s 11 34.2019 -0.664473 -0.567265 267s 12 35.4805 -2.730949 -0.259320 267s 13 34.7544 1.325449 0.749884 267s 14 38.9522 8.171389 0.034382 267s 15 -5.5375 0.390704 1.679172 267s 16 -7.4319 0.803850 1.925633 267s 17 -8.5880 0.957577 -1.010312 267s 18 -6.6022 -0.425109 0.625148 267s 19 -6.5596 1.154721 -0.640680 267s 20 -5.2525 0.812527 1.377832 267s 21 -6.2771 0.067747 0.958907 267s 22 -6.2501 1.325491 -1.104428 267s 23 -7.2419 0.839808 0.728712 267s 24 -7.6489 1.131606 0.154897 267s 25 -9.0763 -0.670721 -0.167577 267s 26 -5.5967 0.999411 -0.810000 267s 27 -5.1460 -0.339018 1.326712 267s 28 -7.1659 -0.993461 0.125933 267s 29 -8.2104 -0.169338 -0.073569 267s 30 -6.2499 -1.689222 -0.877481 267s 31 -7.3180 -0.225795 1.687204 267s 32 -7.9446 1.473868 -0.541790 267s 33 -6.3604 1.237472 0.061800 267s 34 -8.9812 -0.710662 -0.830422 267s 35 -5.1698 -0.435484 1.102817 267s 36 -5.9995 -0.058135 -0.713550 267s 37 -5.8753 0.852882 -1.610556 267s 38 -8.4501 0.334363 0.404813 267s 39 -8.1751 -1.300317 0.633282 267s 40 -7.4495 0.672712 -0.829815 267s 41 -5.6213 -1.106765 1.395315 267s 42 -6.8571 -0.900977 -1.509937 267s 43 -7.0633 1.987372 -1.079934 267s 44 -6.3763 -1.867647 -0.251224 267s 45 -8.6456 -0.866053 0.630132 267s 46 -6.5356 -1.763526 -0.189838 267s 47 -8.2224 -1.183284 1.615150 267s 48 -5.6136 -1.100704 1.079239 267s 49 -5.9907 0.220336 1.443387 267s 50 -5.2675 0.142923 0.194023 267s 51 -7.9324 0.324710 1.113289 267s 52 -7.5544 -1.033884 1.792496 267s 53 -6.7119 -1.712257 -1.711778 267s 54 -7.4679 1.856542 0.046658 267s 55 -7.4666 1.161504 -0.725948 267s 56 -6.7110 1.574868 0.534288 267s 57 -8.2571 -0.399824 0.521995 267s 58 -5.9781 1.312567 0.926790 267s 59 -5.6960 -0.394338 -0.332938 267s 60 -6.1017 -0.797579 -1.679359 267s 61 -5.2628 0.919128 -1.436156 267s 62 -9.1245 -0.516135 -0.229065 267s 63 -7.7140 1.659145 0.068510 267s 64 -4.9886 0.173613 0.865810 267s 65 -6.6157 -1.479786 0.098390 267s 66 -7.9511 0.772770 -0.998321 267s 67 -7.1856 0.459602 0.216588 267s 68 -8.7345 -0.860784 -1.238576 267s 69 -8.5833 -0.313481 0.832074 267s 70 -5.8642 -0.142883 -0.870064 267s 71 -5.8879 0.186456 0.464467 267s 72 -7.1865 0.497156 -0.826767 267s 73 -6.8671 -0.058606 -1.335842 267s 74 -7.1398 0.727642 -1.422331 267s 75 -7.2696 -1.347832 -1.496927 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 14.70245 1.40751 0.95725 267s ---------------------------------------------------------- 267s milk 86 8 8 15.940298 2.771345 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 PC6 PC7 267s 1 6.471620 1.031110 0.469432 0.5736412 1.0294362 -0.6054039 -0.2005117 267s 2 7.439545 0.320597 0.081922 -0.6305898 0.7128977 -1.1601053 -0.1170582 267s 3 1.240654 -1.840458 0.520870 -0.1717469 0.2752079 -0.3815506 0.6004089 267s 4 5.952685 -1.856375 1.638710 0.3358626 -0.5834205 -0.0665348 -0.1580799 267s 5 -0.706973 0.261795 0.423736 0.2916399 -0.5307716 -0.3325563 -0.0062349 267s 6 2.524050 0.293380 -0.572997 0.2466367 -0.3497882 0.0386014 -0.1418131 267s 7 3.136085 -0.050202 -0.818165 -0.0451560 -0.5226337 -0.1597194 0.1669050 267s 8 3.260390 0.312365 -0.110776 0.4908006 -0.5225353 -0.1972222 -0.1068433 267s 9 -0.808914 -2.355785 1.344204 -0.4743284 -0.1394914 -0.1390080 -0.2620731 267s 10 -2.511226 -0.995321 -0.087218 -0.5950040 0.4268321 0.2561918 0.0891170 267s 11 -9.204096 -0.598364 1.587275 0.0833647 0.1865626 0.0358228 0.0920394 267s 12 -12.946774 1.951332 -0.179186 0.2560603 0.1300954 -0.1179820 -0.0999494 267s 13 -10.011603 0.726323 -2.102423 -1.3105560 0.3291550 0.0660007 -0.0794410 267s 14 -11.983644 0.768224 -0.532227 -0.5161201 -0.0817164 -0.4358934 -0.1734612 267s 15 -10.465714 -0.704271 2.035437 0.3713778 -0.0564830 -0.2696432 -0.1940091 267s 16 -2.527619 -0.286939 0.354497 0.8571223 0.1585009 0.2272835 0.4386955 267s 17 -0.514527 -2.895087 1.657181 0.2208239 0.1961109 0.1280496 -0.0182491 267s 18 -1.763931 0.854269 -0.686282 0.2848209 -0.4813608 -0.2623962 0.4757030 267s 19 -1.538419 -0.866477 1.103818 0.3874507 0.2086661 0.1267277 0.2354264 267s 20 0.732842 -1.455594 1.097358 -0.2530588 -0.0302385 0.2654274 0.6093330 267s 21 -2.530155 1.932885 -0.873095 0.6202295 -0.4153607 0.0048383 0.0067484 267s 22 -0.772646 0.675846 -0.259539 0.4844670 -0.0893266 -0.2785557 -0.0424662 267s 23 0.185417 1.413719 0.066135 1.1014470 0.0468093 0.0288637 0.2539994 267s 24 -0.280536 0.908864 0.113221 1.3370381 0.3289929 0.2588134 -0.0356289 267s 25 -3.503626 1.971233 0.203620 1.1975494 -0.3175317 0.1149685 0.0584396 267s 26 -0.639313 1.175503 0.403906 0.9082134 -0.2648165 -0.1238813 -0.0174853 267s 27 -2.923327 -0.365168 0.149478 0.8201430 -0.1544609 -0.4856934 -0.0058424 267s 28 2.505633 3.050292 -0.554424 2.1416405 -0.0378764 0.1002280 -0.3888580 267s 29 4.649504 1.054863 -0.081018 1.1454466 0.1502080 0.4967323 0.0879775 267s 30 1.049282 1.355215 -0.142701 0.7805566 -0.2059790 0.0193142 0.0815524 267s 31 1.962583 1.595396 -2.050642 0.3556747 0.1384801 0.1197984 0.1608247 267s 32 1.554846 0.095644 -1.423054 -0.3175620 0.4260008 -0.1612463 -0.0567196 267s 33 2.248977 0.010348 -0.062469 0.6388269 0.2098648 0.1330250 0.0906704 267s 34 0.993109 -0.828812 0.284059 0.3446686 0.1899096 -0.0515571 -0.2281197 267s 35 -0.335103 1.614093 -0.920661 1.2502617 0.2435013 0.1264875 0.0469238 267s 36 4.346795 1.208134 0.368889 1.1429977 -0.1362052 -0.0158169 -0.0183852 267s 37 0.992634 2.013738 -1.350619 0.8714694 0.0057776 -0.2122691 0.1760918 267s 38 2.213341 1.706516 -0.705418 1.2670281 -0.0707149 0.0670467 -0.1863588 267s 39 -1.213255 0.644062 0.163988 1.1213961 0.2945355 0.1093574 0.0019574 267s 40 3.942604 -1.704266 0.660327 0.1618506 0.4259076 0.0070193 0.3462765 267s 41 4.262054 1.687193 0.351875 0.5396477 1.0052810 -0.9331689 0.0056063 267s 42 6.865198 -1.091248 1.153585 1.1248797 0.0873276 0.2565221 0.0333265 267s 43 3.476720 0.555449 -1.030771 -0.3015720 -0.1748109 -0.1584968 0.4079902 267s 44 5.691730 -0.141240 0.565189 0.3174238 0.6478440 1.0579977 -0.5387916 267s 45 0.327134 0.152011 -0.394798 0.4998430 0.1599781 0.3159518 0.1623656 267s 46 0.280225 1.569387 -0.100397 1.2800976 0.0446645 0.0946513 0.0461599 267s 47 3.119928 -0.384834 -3.325600 -1.8865310 -0.1334744 0.1249987 -0.2561273 267s 48 0.501542 0.739816 -1.384556 -0.1244721 0.2948958 0.4836170 -0.1182802 267s 49 -1.953218 0.269986 -1.726474 -0.8510637 0.5047958 0.4860651 0.2318735 267s 50 3.706878 -2.400570 1.361047 -0.4949076 0.2180352 0.4080879 0.1156540 267s 51 -1.060358 -0.521609 -1.387412 -1.2767491 -0.0521356 0.1665452 -0.0044412 267s 52 -4.900528 0.157011 -1.015880 -0.9941168 0.2069608 0.3239762 -0.1921715 267s 53 -0.388496 0.062051 -0.643721 -0.8544141 -0.1857141 0.0063293 0.2664606 267s 54 0.109234 -0.018709 -0.242825 -0.2064701 -0.0585165 0.1720867 0.1117397 267s 55 1.176175 0.644539 -0.373694 0.0038605 -0.3436524 0.0194450 -0.0838883 267s 56 0.407259 -0.606637 0.222915 -0.3622451 -0.0737834 0.0228104 0.0297333 267s 57 -1.022756 -0.071860 0.741957 0.2273628 -0.1388444 -0.2396467 -0.2327738 267s 58 0.245419 1.167059 0.225934 0.8318795 -0.5365166 -0.0090816 -0.1680757 267s 59 -1.300617 -1.110325 -0.262740 -0.8857801 -0.0816954 -0.1186886 -0.0928322 267s 60 -1.110561 -0.832357 -0.212713 -0.4754481 -0.4105982 -0.1886992 -0.0602872 267s 61 0.381831 -1.475116 0.601047 -0.6260156 -0.1854501 -0.1749306 -0.0013904 267s 62 2.734462 -1.887861 0.813453 -0.5856987 0.2310656 0.1117041 -0.0293373 267s 63 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 267s 64 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 267s 65 0.004744 -2.712679 1.178987 -0.6677199 0.0208119 0.0621903 -0.0655693 267s 66 -2.014851 -1.060090 -0.099959 -0.7225044 -0.1947648 -0.2282902 -0.0505015 267s 67 0.621739 -1.296106 0.255632 -0.3309504 -0.0880200 0.2524306 0.1465779 267s 68 -0.271385 -1.709161 -1.100349 -2.0937671 0.2166264 0.0191278 0.0114174 267s 69 -0.326350 -0.737232 0.021639 -0.3850383 -0.4338287 0.2156624 0.1597594 267s 70 4.187093 9.708082 4.632803 -4.9751240 -0.0881576 0.2392433 0.0568049 267s 71 -1.868507 -1.600166 0.436353 -0.8078214 -0.1530893 0.0479471 -0.1999893 267s 72 2.768081 -0.556824 -0.148923 -0.3197853 -0.5524427 0.0907804 -0.0694488 267s 73 -1.441846 -2.735114 -0.294134 -1.2172969 0.0109453 -0.0562910 0.1505788 267s 74 -10.995490 0.615992 1.950966 1.1687190 0.2798335 0.2713257 0.0652135 267s 75 0.508992 -2.363945 -0.407064 -0.9522316 0.1040307 0.1088110 -0.7368484 267s 76 -1.015714 -0.307662 -1.088162 -1.0181862 -0.0440888 -0.1362208 0.0271200 267s 77 -8.028891 -0.580763 0.933638 0.4619362 0.3379832 -0.1368644 -0.0669441 267s 78 1.763308 -1.336175 -0.127809 -0.7161775 -0.1904861 -0.0900461 0.0037539 267s 79 0.208944 -0.580698 -0.626297 -0.7620610 -0.0262368 -0.2928202 0.0285908 267s 80 -3.230608 1.251352 0.195280 0.8687004 0.1812011 0.2600692 -0.1516375 267s 81 1.498160 0.669731 -0.266114 0.3772866 -0.2769688 -0.1066593 -0.1608395 267s 82 3.232051 -1.776018 0.485524 0.1170945 0.0557260 0.2219872 0.1187681 267s 83 2.999977 -0.228275 -0.467724 -0.4287672 0.0494902 -0.2337809 -0.0718159 267s 84 1.238083 0.320956 -1.806006 -1.0142266 0.2359630 -0.0857149 0.0593938 267s 85 1.276376 -2.081214 2.540850 0.3745805 -0.2596482 -0.1228412 -0.2199985 267s 86 0.930715 0.836457 -1.385153 -0.6074929 -0.2476354 0.1680713 -0.0117324 267s PC8 267s 1 9.0765e-04 267s 2 2.1811e-04 267s 3 1.1834e-03 267s 4 8.4077e-05 267s 5 9.9209e-04 267s 6 1.6277e-03 267s 7 2.4907e-04 267s 8 6.8383e-04 267s 9 -5.0924e-04 267s 10 3.1215e-04 267s 11 3.0654e-04 267s 12 -1.1951e-03 267s 13 -1.2849e-03 267s 14 -9.0801e-04 267s 15 -1.2686e-03 267s 16 -1.8441e-03 267s 17 -2.1068e-03 267s 18 -5.7816e-04 267s 19 -1.2330e-03 267s 20 3.3857e-05 267s 21 3.8623e-04 267s 22 1.3035e-04 267s 23 -3.8648e-04 267s 24 -1.7400e-04 267s 25 -3.9196e-04 267s 26 -7.6996e-04 267s 27 -4.8042e-04 267s 28 -2.0628e-04 267s 29 -4.5672e-04 267s 30 -1.4716e-04 267s 31 -4.6385e-05 267s 32 -2.0481e-04 267s 33 -3.0020e-04 267s 34 -5.8179e-05 267s 35 1.3870e-04 267s 36 -6.7177e-04 267s 37 -3.0799e-04 267s 38 6.2140e-04 267s 39 4.5912e-04 267s 40 -3.7165e-04 267s 41 -5.4362e-04 267s 42 -1.0155e-03 267s 43 1.3449e-04 267s 44 -5.4761e-04 267s 45 1.0300e-03 267s 46 1.1039e-03 267s 47 -6.4858e-04 267s 48 -7.6886e-05 267s 49 3.2590e-04 267s 50 8.6845e-05 267s 51 4.9423e-04 267s 52 9.2973e-04 267s 53 4.4342e-04 267s 54 4.9888e-04 267s 55 7.2171e-04 267s 56 -3.2133e-05 267s 57 -1.8101e-04 267s 58 -5.4969e-06 267s 59 -8.3841e-04 267s 60 5.9446e-05 267s 61 -6.5683e-05 267s 62 -3.4073e-04 267s 63 -6.5145e-04 267s 64 -6.5145e-04 267s 65 1.4986e-04 267s 66 2.8096e-04 267s 67 -6.5170e-05 267s 68 -1.3775e-04 267s 69 6.8225e-06 267s 70 -1.6290e-04 267s 71 3.9009e-04 267s 72 -1.3981e-04 267s 73 6.2613e-04 267s 74 2.6513e-03 267s 75 3.7088e-04 267s 76 9.9539e-04 267s 77 1.2979e-03 267s 78 5.6500e-04 267s 79 3.0940e-04 267s 80 8.7993e-04 267s 81 -3.1353e-04 267s 82 4.9625e-04 267s 83 -6.3951e-04 267s 84 -4.5582e-04 267s 85 5.9440e-04 267s 86 -3.6234e-04 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 3.99253025 1.66473582 1.10660264 0.96987790 0.33004256 0.29263512 0.20843280 267s [8] 0.00074024 267s ---------------------------------------------------------- 267s bushfire 38 5 5 38435.075910 1035.305774 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 267s 1 -111.9345 4.9970 -1.00881 -1.224361 3.180569 267s 2 -113.4128 7.4784 -0.79170 -0.235184 2.385812 267s 3 -105.8364 10.9615 -3.15662 -0.251662 1.017328 267s 4 -89.1684 8.7232 -6.15080 -0.075611 1.431111 267s 5 -58.7216 -1.9543 -12.70661 -0.151328 1.425570 267s 6 -35.0370 -12.8434 -17.06841 -0.525664 3.499743 267s 7 -250.2123 -49.4348 23.31261 -19.070238 0.647348 267s 8 -292.6877 -69.7708 -21.30815 13.093808 -1.288764 267s 9 -294.0765 -70.9903 -23.96326 14.940985 -0.939076 267s 10 -290.0193 -57.3747 3.51346 1.858995 0.083107 267s 11 -289.8168 -43.3207 16.08046 -1.745099 -1.506042 267s 12 -290.8645 6.2503 40.52173 -7.496479 -0.033767 267s 13 -232.6865 41.8090 37.19429 -1.280348 -0.470837 267s 14 9.8483 25.1954 -14.56970 0.538484 1.772046 267s 15 137.1924 11.8521 -37.12452 -5.130459 -0.586695 267s 16 92.9804 10.3923 -24.97267 -7.551314 -1.867125 267s 17 90.4493 10.5630 -21.92735 -5.669651 -1.001362 267s 18 78.6325 5.2211 -19.74718 -6.107880 -1.939986 267s 19 82.1178 3.6913 -21.37810 -4.259855 -1.278838 267s 20 92.9044 7.1961 -21.22900 -4.125571 -0.127089 267s 21 74.9157 10.2991 -16.60924 -5.660751 -0.406343 267s 22 66.7350 12.0460 -16.73298 -4.669080 1.333436 267s 23 -62.1981 22.7394 6.03613 -5.182356 -0.453624 267s 24 -116.5696 32.3182 12.74846 -1.465657 -0.097851 267s 25 -53.8907 22.4278 -2.18861 -2.742014 -0.990071 267s 26 -60.6384 20.2952 -3.05206 -2.953685 -0.629061 267s 27 -74.7621 28.9067 -0.65817 1.473357 -0.443957 267s 28 -50.2202 37.3457 -1.44989 5.530426 -1.073521 267s 29 -38.7483 50.2749 2.34469 10.156457 -0.416262 267s 30 -93.3887 51.7884 20.08872 8.798781 -1.620216 267s 31 35.3096 41.7158 13.46272 14.464358 -0.475973 267s 32 290.8493 3.5924 7.41501 15.244293 2.141354 267s 33 326.7236 -29.8194 15.64898 2.612061 0.064931 267s 34 322.9095 -30.6372 16.21520 1.248005 -0.711322 267s 35 328.5307 -29.9533 16.49656 1.138916 0.974792 267s 36 325.6791 -30.6990 16.83840 -0.050949 -1.211360 267s 37 323.8136 -30.7474 19.55764 -1.545150 -0.267580 267s 38 325.2991 -30.5350 20.31878 -1.928580 -0.120425 267s ------------- 267s Call: 267s PcaClassic(x = x) 267s 267s Standard deviations: 267s [1] 196.0487 32.1762 18.4819 6.9412 1.3510 267s ---------------------------------------------------------- 267s ========================================================== 267s > dodata(method="hubert.mcd") 267s 267s Call: dodata(method = "hubert.mcd") 267s Data Set n p k e1 e2 267s ========================================================== 267s heart 12 2 2 358.175786 4.590630 267s Scores: 267s PC1 PC2 267s 1 -12.2285 0.86283 267s 2 -68.9906 -7.43256 267s 3 -5.7035 -1.53793 267s 4 -1.8988 2.90891 267s 5 -24.0044 -2.68946 267s 6 9.9115 8.43321 267s 7 -11.0210 1.77484 267s 8 25.1826 -1.31573 267s 9 -3.2809 -0.74345 267s 10 23.8200 -0.93701 267s 11 9.1344 1.67701 267s 12 -53.6607 -5.08826 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 18.9255 2.1426 267s ---------------------------------------------------------- 267s starsCYG 47 2 2 0.280653 0.005921 267s Scores: 267s PC1 PC2 267s 1 -0.285731 -0.0899858 267s 2 -0.819689 0.0153191 267s 3 0.028077 -0.1501882 267s 4 -0.819689 0.0153191 267s 5 -0.234971 -0.1526225 267s 6 -0.527231 -0.0382380 267s 7 0.372118 -0.5195605 267s 8 -0.357448 0.1009508 267s 9 -0.603553 -0.2533541 267s 10 -0.177170 -0.0722541 267s 11 -0.637339 -1.0390758 267s 12 -0.512526 -0.0662337 267s 13 -0.490978 -0.0120517 267s 14 0.936868 -0.2550656 267s 15 0.684479 -0.0125787 267s 16 0.347708 0.0641382 267s 17 1.009966 -0.0202111 267s 18 0.742477 0.1286170 267s 19 0.773105 -0.0588983 267s 20 -0.795247 -1.0648673 267s 21 0.566048 -0.0319223 267s 22 0.723956 -0.0061308 267s 23 0.505616 0.0899297 267s 24 0.069956 0.0896997 267s 25 -0.080090 -0.0462652 267s 26 0.268755 0.0512425 267s 27 0.289710 -0.0770574 267s 28 0.038341 -0.0269216 267s 29 0.567463 -0.1026188 267s 30 -0.951542 -1.1005280 267s 31 0.512064 0.0504528 267s 32 -0.188059 0.1184850 267s 33 -0.288758 -0.0094200 267s 34 -1.190016 -1.1293460 267s 35 0.615197 -0.0846898 267s 36 -0.710930 0.0938781 267s 37 -0.183223 0.0888774 267s 38 -0.288758 -0.0094200 267s 39 -0.262177 0.0759816 267s 40 -0.630957 -0.0855773 267s 41 0.314679 0.0182135 267s 42 -0.130850 0.0163715 267s 43 -0.415248 0.0205825 267s 44 -0.407188 -0.0287636 267s 45 -0.620693 0.0376892 267s 46 -0.051896 0.0292672 267s 47 0.426662 0.0770340 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 0.529767 0.076946 267s ---------------------------------------------------------- 267s phosphor 18 2 2 285.985489 32.152099 267s Scores: 267s PC1 PC2 267s 1 -2.89681 -18.08811 267s 2 21.34021 -0.40854 267s 3 22.98065 4.13006 267s 4 12.33544 -6.72947 267s 5 17.99823 2.47611 267s 6 -13.35773 -24.10967 267s 7 -0.92957 -5.51314 267s 8 9.16061 2.71354 267s 9 9.89243 5.10403 267s 10 -14.12600 -11.17832 267s 11 3.84175 -0.17605 267s 12 -10.61905 4.37646 267s 13 -13.85065 2.01919 267s 14 -8.11927 4.34325 267s 15 -18.69805 -1.51673 267s 16 9.95352 -6.85784 267s 17 -22.49433 0.29387 267s 18 -18.66592 6.92359 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 16.9111 5.6703 267s ---------------------------------------------------------- 267s stackloss 21 3 3 78.703690 19.249085 267s Scores: 267s PC1 PC2 PC3 267s 1 -20.323997 10.26124 0.92041 267s 2 -19.761418 11.08797 0.92383 267s 3 -16.469919 6.43190 0.22593 267s 4 -4.171902 1.68262 2.50695 267s 5 -3.756174 1.40774 0.57004 267s 6 -3.964038 1.54518 1.53850 267s 7 -7.547376 -3.27780 2.48643 267s 8 -7.547376 -3.27780 2.48643 267s 9 -0.763294 -0.63699 2.53518 267s 10 4.214079 4.46296 -2.28315 267s 11 -0.849132 -2.97767 -2.31393 267s 12 -0.078689 -2.28838 -3.27896 267s 13 3.088921 2.80948 -2.28999 267s 14 -3.307313 -6.14718 -1.35916 267s 15 5.552354 -7.34201 -0.32057 267s 16 7.240091 -4.86180 -0.31031 267s 17 14.908334 6.84995 0.70603 267s 18 10.970281 1.06279 0.68209 267s 19 10.199838 0.37350 1.64712 267s 20 4.273564 1.99328 0.14526 267s 21 -11.992249 2.19025 -3.37391 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 8.8715 4.3874 2.1990 267s ---------------------------------------------------------- 267s salinity 28 3 3 11.651966 4.107426 267s Scores: 267s PC1 PC2 PC3 267s 1 1.68712 1.62591 0.19812128 267s 2 2.35772 2.37290 1.24965734 267s 3 6.80132 -2.14412 0.68142276 267s 4 6.41982 -0.61348 -0.31907921 267s 5 6.36697 -1.98030 4.87319903 267s 6 5.22050 1.20864 0.10252555 267s 7 3.34007 2.02950 0.00064329 267s 8 1.06220 2.89801 -0.35658064 267s 9 0.34692 -2.20572 -1.71677710 267s 10 -2.21421 -2.74842 0.76862599 267s 11 -1.40111 -2.16163 2.21124383 267s 12 -0.38242 0.32284 -0.23732191 267s 13 -1.12809 1.33152 -0.28800043 267s 14 -3.24998 1.35943 1.17514969 267s 15 -2.11006 -3.70114 0.45102357 267s 16 3.46920 -5.41242 8.56937909 267s 17 0.46682 -1.46753 1.48992481 267s 18 2.21807 0.99168 -0.61894625 267s 19 0.28525 -2.00333 -2.16450483 267s 20 -1.66639 -1.76768 -1.06946404 267s 21 -2.58106 1.23534 -0.65557612 267s 22 -4.15573 1.71244 0.08170141 267s 23 -3.07670 -4.87628 2.53200755 267s 24 -1.70808 -3.71657 2.99305849 267s 25 -1.08172 -1.05713 0.02468813 267s 26 -2.23187 0.27323 -0.85760867 267s 27 -3.50498 1.07657 -0.68503455 267s 28 -4.49819 1.43219 0.53416609 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 3.4135 2.0267 1.0764 267s ---------------------------------------------------------- 267s hbk 75 3 3 1.459908 1.201048 267s Scores: 267s PC1 PC2 PC3 267s 1 -31.105415 4.714217 10.4566165 267s 2 -31.707650 5.748724 10.7682402 267s 3 -33.366131 4.625897 12.1570167 267s 4 -34.173377 6.069657 12.4466895 267s 5 -33.780418 5.508823 11.9872893 267s 6 -32.493478 4.684595 10.5679819 267s 7 -32.592637 5.235522 10.3765493 267s 8 -31.293363 4.865797 10.9379676 267s 9 -33.160964 5.714260 12.3098920 267s 10 -31.919786 5.384537 12.3374332 267s 11 -38.231962 6.810641 13.5994385 267s 12 -39.290479 5.393906 15.2942554 267s 13 -39.418445 7.326461 11.5194898 267s 14 -43.906584 13.214819 8.3282743 267s 15 -1.906326 -0.716061 -0.8635112 267s 16 -0.263255 -0.926016 -1.9009292 267s 17 1.776489 1.072332 -0.5496140 267s 18 -0.464648 -0.702441 0.0482897 267s 19 -0.267826 1.283779 -0.2925812 267s 20 -2.122108 -0.165970 -0.8924686 267s 21 -0.937217 -0.548532 -0.4132196 267s 22 -0.423273 1.781869 -0.0323061 267s 23 -0.047532 -0.018909 -1.1259327 267s 24 0.490041 0.520202 -1.1065753 267s 25 2.143049 -0.720869 -0.0495474 267s 26 -1.094748 1.459175 0.2226246 267s 27 -2.070705 -0.898573 0.0023229 267s 28 0.294998 -0.830258 0.5929001 267s 29 1.242995 -0.300216 -0.2010507 267s 30 -0.147958 -0.439099 2.0003038 267s 31 -0.170818 -1.440946 -0.9755627 267s 32 0.958531 1.199730 -1.0129867 267s 33 -0.697307 0.874343 -0.7260649 267s 34 2.278946 -0.261106 0.4196544 267s 35 -1.962829 -0.809318 0.2033113 267s 36 -0.626631 0.600666 0.8004036 267s 37 -0.550885 1.881448 0.7382776 267s 38 1.249717 -0.336214 -0.9349845 267s 39 1.106696 -1.569418 0.1869576 267s 40 0.684034 0.939963 -0.1034965 267s 41 -1.559314 -1.551408 0.3660323 267s 42 0.538741 0.447358 1.6361099 267s 43 0.252685 2.080564 -0.7765259 267s 44 -0.217012 -1.027281 1.7015154 267s 45 1.497600 -1.349234 -0.2698932 267s 46 -0.100388 -1.026443 1.5390401 267s 47 0.811117 -2.195271 -0.5208141 267s 48 -1.462210 -1.321318 0.5600144 267s 49 -1.383976 -0.740714 -0.7348906 267s 50 -1.636773 0.215464 0.3195369 267s 51 0.530918 -0.759743 -1.2069247 267s 52 0.109566 -2.107455 -0.5315473 267s 53 0.564334 0.060847 2.3910630 267s 54 0.272234 1.122711 -1.5060028 267s 55 0.608660 1.197219 -0.5255609 267s 56 -0.565430 0.710345 -1.3708230 267s 57 1.115629 -0.888816 -0.4186014 267s 58 -1.351288 0.374815 -1.1980618 267s 59 -0.998016 0.151228 0.9007970 267s 60 -0.124017 0.764846 1.9005963 267s 61 -1.189858 1.905264 0.7721322 267s 62 2.190589 -0.579614 -0.1377914 267s 63 0.518278 0.931130 -1.4534768 267s 64 -2.124566 -0.194391 -0.0327092 267s 65 -0.154218 -1.050861 1.1309885 267s 66 1.197852 1.044147 -0.2265269 267s 67 0.114174 0.094763 -0.5168926 267s 68 2.201115 -0.032271 0.8573493 267s 69 1.307843 -1.104815 -0.7741270 267s 70 -0.691449 0.676665 1.0004603 267s 71 -1.150975 -0.050861 -0.0717068 267s 72 0.457293 0.861871 0.1026350 267s 73 0.392258 0.897451 0.9178065 267s 74 0.584658 1.450471 0.3201857 267s 75 0.972517 0.063777 1.8223995 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 1.2083 1.0959 1.0168 267s ---------------------------------------------------------- 267s milk 86 8 8 5.739740 2.405262 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 PC6 PC7 267s 1 -5.710924 -1.346213 0.01332091 -0.3709242 -0.566813 0.7529298 -1.2525433 267s 2 -6.578612 -0.440749 1.16354746 0.2870685 -0.573207 0.7368064 -1.6101427 267s 3 -0.720902 1.777381 -0.21532020 -0.3213950 0.287603 -0.4764464 -0.5638337 267s 4 -5.545889 1.621147 -0.85212883 0.4380154 0.022241 0.0718035 0.1176140 267s 5 1.323210 -0.143897 -0.78611461 0.5966857 0.043139 -0.0512545 -0.1419726 267s 6 -1.760792 -0.662792 0.46402240 0.2149752 0.130000 0.0797221 0.1916948 267s 7 -2.344198 -0.363657 0.92442296 0.3921371 0.241463 -0.2370967 0.0636268 267s 8 -2.556824 -0.680132 0.04339934 0.4635077 0.154136 0.0371259 0.0260340 267s 9 1.203234 2.712342 -1.00693092 0.1251739 0.170679 0.2231851 -0.0118196 267s 10 3.151858 1.255826 -0.01678562 -0.5087398 -0.087933 0.0115055 -0.0097828 267s 11 9.562891 1.580419 -2.65612113 -0.1748178 -0.153031 -0.0880112 -0.1648752 267s 12 13.617821 -0.999033 -1.92168237 0.0326918 -0.038488 0.0870082 -0.1809687 267s 13 10.958032 -0.097916 0.95915085 -0.2348663 0.147875 0.1219202 0.0419067 267s 14 12.675941 0.158747 -1.04153243 0.3117402 0.302036 0.1187749 -0.2310830 267s 15 10.726828 1.775339 -3.36786799 0.1285422 0.151594 0.0998947 -0.2028458 267s 16 3.042705 0.212589 -1.23921907 -0.5596596 0.277061 -0.5037073 0.0612182 267s 17 0.780071 2.990008 -1.58490147 -0.5441119 0.436485 -0.0603833 0.1016610 267s 18 2.523916 -0.923373 -0.03221722 0.3830822 0.208008 -0.5505270 -0.1252648 267s 19 1.990563 1.062648 -1.42038451 -0.3602257 -0.068006 -0.1932744 -0.1197842 267s 20 -0.243938 1.674555 -0.72225359 -0.1475652 -0.397855 -0.5385123 -0.0559660 267s 21 3.354424 -2.001060 -0.22542149 0.3346180 0.032502 -0.0953825 0.1293148 267s 22 1.477177 -0.777534 -0.35362339 0.1224412 0.203208 0.0514382 -0.2166274 267s 23 0.502055 -1.618511 -0.85013853 -0.1298862 -0.144328 -0.1941806 -0.1923681 267s 24 0.900504 -1.227820 -1.07180474 -0.5851197 0.112657 0.0467164 0.0405544 267s 25 4.161393 -1.869015 -1.54507759 0.2003123 -0.152582 -0.1382908 0.0864320 267s 26 1.277795 -1.185179 -1.13445511 0.2771556 -0.101901 0.0070037 -0.1279016 267s 27 3.447256 0.257652 -1.13407954 -0.0077859 0.853002 -0.1376443 -0.1897380 267s 28 -1.695730 -3.781876 -0.72940594 -0.0956421 0.064475 0.3665470 0.0726448 267s 29 -3.923610 -1.654818 -0.16117226 -0.4242302 -0.303749 -0.0209844 0.1723890 267s 30 -0.309616 -1.564739 -0.39909943 0.1657509 -0.178739 -0.0600221 -0.0571706 267s 31 -0.960838 -2.242733 1.50477679 -0.2957897 0.163758 -0.1034399 0.0257903 267s 32 -0.671285 -0.459839 1.39124475 -0.3669914 0.246127 0.2094780 -0.2681284 267s 33 -1.589089 -0.390812 -0.16505762 -0.3992573 0.086870 -0.0402114 -0.0399923 267s 34 -0.421868 0.636139 -0.42563447 -0.2985726 0.311365 0.2398515 -0.0540852 267s 35 1.118429 -2.116328 -0.22329747 -0.4864401 0.289927 -0.0503006 0.0101706 267s 36 -3.660291 -1.630831 -0.57876280 0.1294792 -0.260224 0.0912904 -0.1565668 267s 37 -0.087686 -2.530609 0.50076931 -0.0319873 0.194898 -0.1233526 -0.2494283 267s 38 -1.418620 -2.303011 -0.09405565 -0.0931745 0.169466 0.1581787 0.0850095 267s 39 1.815225 -0.838968 -1.10222194 -0.4897630 0.180933 0.0096330 -0.0600652 267s 40 -3.420975 1.398516 -0.17143314 -0.5852146 0.090464 -0.2066323 -0.2974177 267s 41 -3.462295 -1.795174 -0.17500650 -0.1610267 -0.595086 0.5981680 -1.5930268 267s 42 -6.401429 0.451242 -0.78723149 -0.4285618 0.055395 -0.0212476 0.0808936 267s 43 -2.583017 -0.871790 1.29937081 0.2422349 -0.190002 -0.2822972 -0.2625721 267s 44 -5.027244 -0.167503 -0.02382957 -0.8288929 -0.852207 0.7399343 0.4606076 267s 45 0.364494 -0.440380 -0.07746564 -0.4552133 0.095711 -0.1662998 0.1566706 267s 46 0.420706 -1.880819 -0.82180986 -0.1823454 -0.022661 -0.0304227 -0.0516440 267s 47 -1.932985 -0.120002 4.00934170 0.0930728 0.295428 0.2787446 0.3766231 267s 48 0.395402 -1.021393 1.07953292 -0.4599764 -0.132386 0.1895780 0.2771755 267s 49 2.886100 -0.276587 1.48851137 -0.6314648 -0.203963 -0.0891955 0.1347804 267s 50 -3.255379 2.479232 -0.37933775 -0.3651497 -0.415000 0.0045750 0.0671055 267s 51 1.939333 0.617579 1.57113225 0.0310866 -0.039226 0.0409183 0.1830694 267s 52 5.727154 0.275898 0.58814711 -0.1739820 -0.222791 0.2553797 0.1959402 267s 53 1.207873 0.131451 0.80899235 0.2872465 -0.353544 -0.1697200 -0.0987230 267s 54 0.612921 0.040062 0.17807459 -0.0053074 -0.202244 -0.0671788 0.0530276 267s 55 -0.399075 -0.727144 0.26196635 0.3657576 -0.192705 0.0903564 0.0641289 267s 56 0.240719 0.733792 -0.05030509 0.0967214 -0.186906 0.0310231 -0.0594812 267s 57 1.589641 0.289427 -1.02478822 0.2723190 -0.048378 0.2599262 -0.2040853 267s 58 0.423483 -1.262515 -0.85026016 0.4749963 -0.082647 0.0752412 0.1352259 267s 59 1.983684 1.335122 0.42593757 0.1345894 0.096456 0.1153107 -0.0385994 267s 60 1.770171 0.935428 0.14901569 0.3641973 0.274015 -0.0280119 0.0690244 267s 61 0.182845 1.706453 -0.18364654 0.2517421 -0.035773 0.0357087 -0.1363470 267s 62 -2.191617 1.966324 -0.03573689 -0.2203900 -0.235704 0.1682332 -0.1145174 267s 63 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 267s 64 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 267s 65 0.407575 2.996346 -0.63021113 -0.1335795 0.087668 0.0627032 0.0486166 267s 66 2.660379 1.322824 0.10122110 0.2420451 0.192938 0.0344019 -0.0771918 267s 67 -0.032273 1.315299 -0.04511689 -0.1293380 -0.025923 -0.1655965 0.1887534 267s 68 1.117637 2.005809 1.97078787 -0.0429209 -0.176568 0.1634287 -0.0916254 267s 69 0.970730 0.837158 0.01621375 0.2347502 -0.071757 -0.2464626 0.2907551 267s 70 -2.688271 -5.335891 -0.64225481 4.1819517 -9.523550 2.0943027 -2.8098426 267s 71 2.428718 1.976051 -0.24749122 0.1308738 0.018276 0.1711292 0.1346284 267s 72 -2.061944 0.405943 0.50472914 0.4393739 -0.056420 -0.0031558 0.2663880 267s 73 2.029606 2.874991 0.68310320 -0.2067254 0.511537 -0.2010371 0.0805608 267s 74 11.293757 0.328931 -3.84783031 -0.4130266 -0.210499 -0.1103148 -0.0381326 267s 75 0.120896 2.287914 0.83639076 -0.2462845 0.551353 0.6629701 0.3789055 267s 76 1.859499 0.422019 1.18435547 0.1546108 0.017266 0.0470615 -0.1071011 267s 77 8.435857 1.147499 -2.19924186 -0.4156770 0.386548 0.0294075 -0.1911399 267s 78 -1.090858 1.311287 0.62897430 0.1727009 0.077341 0.0135972 -0.0096934 267s 79 0.560012 0.623617 0.83727267 0.1680787 0.087477 0.0611949 -0.2588084 267s 80 3.873817 -1.133641 -1.27469019 -0.2717298 -0.165066 0.1696232 0.0635047 267s 81 -0.758664 -0.880260 0.00057124 0.2838720 0.016243 0.1527299 -0.0150514 267s 82 -2.709588 1.464049 -0.12598126 -0.3828567 0.213647 -0.1425385 0.1552827 267s 83 -2.213670 0.059563 0.87565603 0.1255703 -0.082005 0.2189829 -0.2938264 267s 84 -0.242242 -0.483552 2.05089334 -0.0681005 -0.101578 0.1304632 -0.2218093 267s 85 -1.032129 2.375018 -2.19321259 0.2332079 -0.066379 0.1854598 -0.0873859 267s 86 0.015327 -0.948155 1.39530555 0.2701225 -0.268889 0.0578145 0.1608678 267s PC8 267s 1 2.1835e-03 267s 2 1.6801e-03 267s 3 1.6623e-03 267s 4 2.6286e-04 267s 5 9.5884e-04 267s 6 1.4430e-03 267s 7 1.8784e-04 267s 8 6.8473e-04 267s 9 -6.8490e-04 267s 10 1.1565e-04 267s 11 5.6907e-06 267s 12 -1.8395e-03 267s 13 -2.1582e-03 267s 14 -1.6294e-03 267s 15 -1.6964e-03 267s 16 -1.9664e-03 267s 17 -2.2448e-03 267s 18 -6.5884e-04 267s 19 -1.1536e-03 267s 20 2.6887e-04 267s 21 3.3199e-05 267s 22 1.1170e-04 267s 23 -1.7617e-04 267s 24 -2.1577e-04 267s 25 -6.1495e-04 267s 26 -7.2903e-04 267s 27 -6.8773e-04 267s 28 -2.0742e-04 267s 29 -2.6937e-04 267s 30 -6.7472e-05 267s 31 -1.3222e-04 267s 32 -1.6516e-04 267s 33 -1.8836e-04 267s 34 -1.1273e-04 267s 35 3.0703e-05 267s 36 -3.0311e-04 267s 37 -1.9380e-04 267s 38 5.5526e-04 267s 39 4.1987e-04 267s 40 8.4807e-05 267s 41 8.8725e-04 267s 42 -6.5647e-04 267s 43 4.3202e-04 267s 44 -5.3330e-04 267s 45 8.9161e-04 267s 46 1.1588e-03 267s 47 -1.2714e-03 267s 48 -4.0376e-04 267s 49 4.1280e-06 267s 50 3.0116e-04 267s 51 5.8510e-05 267s 52 3.3236e-04 267s 53 4.0982e-04 267s 54 4.0428e-04 267s 55 6.1600e-04 267s 56 -4.0496e-05 267s 57 -1.8342e-04 267s 58 -1.6748e-04 267s 59 -1.0894e-03 267s 60 -2.6876e-04 267s 61 -5.8951e-05 267s 62 -1.5517e-04 267s 63 -7.9933e-04 267s 64 -7.9933e-04 267s 65 2.2592e-05 267s 66 2.4984e-05 267s 67 -2.2714e-04 267s 68 -3.3991e-04 267s 69 -3.0375e-04 267s 70 3.4033e-03 267s 71 2.3288e-05 267s 72 -3.4126e-04 267s 73 2.5528e-04 267s 74 2.2760e-03 267s 75 -2.8985e-04 267s 76 7.9077e-04 267s 77 9.4636e-04 267s 78 4.9099e-04 267s 79 3.0501e-04 267s 80 6.5280e-04 267s 81 -3.6570e-04 267s 82 4.9966e-04 267s 83 -4.3245e-04 267s 84 -4.6152e-04 267s 85 7.4691e-04 267s 86 -6.1103e-04 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 2.39577535 1.55089079 0.92557331 0.33680677 0.19792033 0.17855133 0.16041702 267s [8] 0.00054179 267s ---------------------------------------------------------- 267s bushfire 38 5 5 31248.552973 358.974577 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 267s 1 155.972 1.08098 -23.31135 -1.93015 1.218941 267s 2 157.738 0.35648 -20.95658 -2.42375 0.466415 267s 3 150.667 2.12545 -16.20395 -2.00140 -0.582924 267s 4 133.892 5.25124 -15.88873 -2.78469 -0.275261 267s 5 102.462 13.00611 -21.54096 -4.69409 -0.944176 267s 6 77.694 18.75377 -28.71865 -6.44244 0.446350 267s 7 286.266 -11.36184 -98.67134 10.95233 -3.625338 267s 8 326.627 29.92767 -112.60824 -29.26330 -13.710094 267s 9 327.898 32.39553 -113.34314 -31.65905 -13.830781 267s 10 325.131 5.81628 -105.58927 -13.45695 -8.987971 267s 11 326.458 -7.84562 -94.25242 -6.11547 -8.572845 267s 12 333.171 -37.69907 -50.89207 8.98187 -1.742979 267s 13 279.789 -40.78415 -8.06209 7.65884 0.181748 267s 14 37.714 10.54231 13.46530 -1.55051 2.102662 267s 15 -90.034 34.68964 18.98186 0.69260 0.417573 267s 16 -46.492 23.65086 10.07282 4.36090 -0.748517 267s 17 -43.990 20.36443 9.61049 2.83084 -0.127983 267s 18 -32.938 19.11199 2.64850 2.92879 -1.473988 267s 19 -36.555 20.60142 2.01879 0.63832 -1.235075 267s 20 -46.837 19.89630 6.65142 0.89120 0.271108 267s 21 -28.670 15.29534 6.59311 3.29638 0.402194 267s 22 -20.331 15.06559 7.33721 2.16591 2.006327 267s 23 108.644 -7.92707 -1.45130 6.27388 0.356715 267s 24 163.697 -16.15568 0.61663 4.24231 0.464415 267s 25 100.471 -0.30739 0.87762 2.86452 -0.692735 267s 26 106.922 0.90864 -1.91436 2.54557 -0.565023 267s 27 121.966 -3.29641 4.85626 -0.47676 -0.490047 267s 28 98.650 -4.51455 16.64160 -3.08996 -0.839397 267s 29 88.795 -10.85457 30.46708 -5.37360 0.315657 267s 30 142.981 -27.89100 22.40713 -1.67126 -0.680158 267s 31 14.125 -21.60028 29.80480 -8.25272 -0.019693 267s 32 -244.044 -11.76430 24.53390 -12.52294 2.022312 267s 33 -283.842 -13.21931 -6.23565 -2.63367 -0.080728 267s 34 -280.168 -13.41903 -7.69318 -1.24571 -0.722513 267s 35 -285.666 -13.78452 -6.50318 -1.23756 1.074669 267s 36 -282.938 -13.82281 -7.63902 0.20435 -0.971673 267s 37 -281.129 -16.20408 -8.57154 1.85797 0.234486 267s 38 -282.589 -16.91969 -8.36010 2.35589 0.490630 267s ------------- 267s Call: 267s PcaHubert(x = x, k = p) 267s 267s Standard deviations: 267s [1] 176.77260 18.94662 16.21701 3.95755 0.92761 267s ---------------------------------------------------------- 267s ========================================================== 267s > dodata(method="hubert") 267s 267s Call: dodata(method = "hubert") 267s Data Set n p k e1 e2 267s ========================================================== 267s heart 12 2 1 315.227002 NA 267s Scores: 267s PC1 267s 1 13.2197 267s 2 69.9817 267s 3 6.6946 267s 4 2.8899 267s 5 24.9956 267s 6 -8.9203 267s 7 12.0121 267s 8 -24.1915 267s 9 4.2721 267s 10 -22.8289 267s 11 -8.1433 267s 12 54.6519 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 17.755 267s ---------------------------------------------------------- 267s starsCYG 47 2 1 0.308922 NA 267s Scores: 267s PC1 267s 1 0.224695 267s 2 0.758653 267s 3 -0.089113 267s 4 0.758653 267s 5 0.173934 267s 6 0.466195 267s 7 -0.433154 267s 8 0.296411 267s 9 0.542517 267s 10 0.116133 267s 11 0.576303 267s 12 0.451490 267s 13 0.429942 267s 14 -0.997904 267s 15 -0.745515 267s 16 -0.408745 267s 17 -1.071002 267s 18 -0.803514 267s 19 -0.834141 267s 20 0.734210 267s 21 -0.627085 267s 22 -0.784992 267s 23 -0.566652 267s 24 -0.130992 267s 25 0.019053 267s 26 -0.329791 267s 27 -0.350747 267s 28 -0.099378 267s 29 -0.628499 267s 30 0.890506 267s 31 -0.573100 267s 32 0.127022 267s 33 0.227721 267s 34 1.128979 267s 35 -0.676234 267s 36 0.649894 267s 37 0.122186 267s 38 0.227721 267s 39 0.201140 267s 40 0.569920 267s 41 -0.375716 267s 42 0.069814 267s 43 0.354212 267s 44 0.346152 267s 45 0.559656 267s 46 -0.009140 267s 47 -0.487699 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 0.55581 267s ---------------------------------------------------------- 267s phosphor 18 2 1 215.172048 NA 267s Scores: 267s PC1 267s 1 1.12634 267s 2 -22.10340 267s 3 -23.49216 267s 4 -13.45927 267s 5 -18.60808 267s 6 11.24086 267s 7 -0.14748 267s 8 -9.77075 267s 9 -10.37022 267s 10 12.71798 267s 11 -4.61857 267s 12 10.07037 267s 13 13.16767 267s 14 7.57254 267s 15 17.81362 267s 16 -11.08799 267s 17 21.70358 267s 18 18.24496 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 14.669 267s ---------------------------------------------------------- 267s stackloss 21 3 2 77.038636 18.859777 267s Scores: 267s PC1 PC2 267s 1 -20.334936 10.28081 267s 2 -19.772121 11.10736 267s 3 -16.461573 6.43794 267s 4 -4.258672 1.73213 267s 5 -3.773146 1.41928 267s 6 -4.015909 1.57571 267s 7 -7.635560 -3.22715 267s 8 -7.635560 -3.22715 267s 9 -0.855388 -0.58707 267s 10 4.298129 4.41664 267s 11 -0.767202 -3.02229 267s 12 0.038375 -2.35217 267s 13 3.172500 2.76354 267s 14 -3.261224 -6.17206 267s 15 5.553840 -7.34784 267s 16 7.242284 -4.86820 267s 17 14.878925 6.85989 267s 18 10.939223 1.07406 267s 19 10.133645 0.40394 267s 20 4.267234 1.99501 267s 21 -11.859921 2.12579 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 8.7772 4.3428 267s ---------------------------------------------------------- 267s salinity 28 3 2 8.001175 5.858089 267s Scores: 267s PC1 PC2 267s 1 2.858444 1.04359 267s 2 3.807704 1.55974 267s 3 6.220733 -4.32114 267s 4 6.388841 -2.83649 267s 5 6.077450 -3.70092 267s 6 5.974494 -0.67230 267s 7 4.531584 0.78322 267s 8 2.725849 2.41297 267s 9 0.100501 -2.13615 267s 10 -2.358003 -1.49718 267s 11 -1.317688 -1.15391 267s 12 0.434635 0.58230 267s 13 0.116019 1.79022 267s 14 -1.771501 2.71749 267s 15 -2.630757 -2.44003 267s 16 2.289743 -5.51829 267s 17 0.637985 -1.26452 267s 18 3.076147 0.19883 267s 19 0.097381 -1.95868 267s 20 -1.572471 -0.93003 267s 21 -1.284185 2.21858 267s 22 -2.531713 3.30313 267s 23 -3.865359 -3.01230 267s 24 -2.143461 -2.41918 267s 25 -0.714414 -0.41227 267s 26 -1.327781 1.18373 267s 27 -2.201166 2.41566 267s 28 -2.931988 3.20536 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 2.8286 2.4203 267s ---------------------------------------------------------- 267s hbk 75 3 3 1.459908 1.201048 267s Scores: 267s PC1 PC2 PC3 267s 1 31.105415 -4.714217 -10.4566165 267s 2 31.707650 -5.748724 -10.7682402 267s 3 33.366131 -4.625897 -12.1570167 267s 4 34.173377 -6.069657 -12.4466895 267s 5 33.780418 -5.508823 -11.9872893 267s 6 32.493478 -4.684595 -10.5679819 267s 7 32.592637 -5.235522 -10.3765493 267s 8 31.293363 -4.865797 -10.9379676 267s 9 33.160964 -5.714260 -12.3098920 267s 10 31.919786 -5.384537 -12.3374332 267s 11 38.231962 -6.810641 -13.5994385 267s 12 39.290479 -5.393906 -15.2942554 267s 13 39.418445 -7.326461 -11.5194898 267s 14 43.906584 -13.214819 -8.3282743 267s 15 1.906326 0.716061 0.8635112 267s 16 0.263255 0.926016 1.9009292 267s 17 -1.776489 -1.072332 0.5496140 267s 18 0.464648 0.702441 -0.0482897 267s 19 0.267826 -1.283779 0.2925812 267s 20 2.122108 0.165970 0.8924686 267s 21 0.937217 0.548532 0.4132196 267s 22 0.423273 -1.781869 0.0323061 267s 23 0.047532 0.018909 1.1259327 267s 24 -0.490041 -0.520202 1.1065753 267s 25 -2.143049 0.720869 0.0495474 267s 26 1.094748 -1.459175 -0.2226246 267s 27 2.070705 0.898573 -0.0023229 267s 28 -0.294998 0.830258 -0.5929001 267s 29 -1.242995 0.300216 0.2010507 267s 30 0.147958 0.439099 -2.0003038 267s 31 0.170818 1.440946 0.9755627 267s 32 -0.958531 -1.199730 1.0129867 267s 33 0.697307 -0.874343 0.7260649 267s 34 -2.278946 0.261106 -0.4196544 267s 35 1.962829 0.809318 -0.2033113 267s 36 0.626631 -0.600666 -0.8004036 267s 37 0.550885 -1.881448 -0.7382776 267s 38 -1.249717 0.336214 0.9349845 267s 39 -1.106696 1.569418 -0.1869576 267s 40 -0.684034 -0.939963 0.1034965 267s 41 1.559314 1.551408 -0.3660323 267s 42 -0.538741 -0.447358 -1.6361099 267s 43 -0.252685 -2.080564 0.7765259 267s 44 0.217012 1.027281 -1.7015154 267s 45 -1.497600 1.349234 0.2698932 267s 46 0.100388 1.026443 -1.5390401 267s 47 -0.811117 2.195271 0.5208141 267s 48 1.462210 1.321318 -0.5600144 267s 49 1.383976 0.740714 0.7348906 267s 50 1.636773 -0.215464 -0.3195369 267s 51 -0.530918 0.759743 1.2069247 267s 52 -0.109566 2.107455 0.5315473 267s 53 -0.564334 -0.060847 -2.3910630 267s 54 -0.272234 -1.122711 1.5060028 267s 55 -0.608660 -1.197219 0.5255609 267s 56 0.565430 -0.710345 1.3708230 267s 57 -1.115629 0.888816 0.4186014 267s 58 1.351288 -0.374815 1.1980618 267s 59 0.998016 -0.151228 -0.9007970 267s 60 0.124017 -0.764846 -1.9005963 267s 61 1.189858 -1.905264 -0.7721322 267s 62 -2.190589 0.579614 0.1377914 267s 63 -0.518278 -0.931130 1.4534768 267s 64 2.124566 0.194391 0.0327092 267s 65 0.154218 1.050861 -1.1309885 267s 66 -1.197852 -1.044147 0.2265269 267s 67 -0.114174 -0.094763 0.5168926 267s 68 -2.201115 0.032271 -0.8573493 267s 69 -1.307843 1.104815 0.7741270 267s 70 0.691449 -0.676665 -1.0004603 267s 71 1.150975 0.050861 0.0717068 267s 72 -0.457293 -0.861871 -0.1026350 267s 73 -0.392258 -0.897451 -0.9178065 267s 74 -0.584658 -1.450471 -0.3201857 267s 75 -0.972517 -0.063777 -1.8223995 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 1.2083 1.0959 1.0168 267s ---------------------------------------------------------- 267s milk 86 8 2 6.040806 2.473780 267s Scores: 267s PC1 PC2 267s 1 -5.768003 -0.9174359 267s 2 -6.664422 0.0280812 267s 3 -0.484521 1.7923710 267s 4 -5.211590 2.0747301 267s 5 1.422641 -0.3268437 267s 6 -1.810360 -0.5469828 267s 7 -2.402924 -0.1987041 267s 8 -2.553389 -0.4963662 267s 9 1.583399 2.5410448 267s 10 3.267946 0.9141367 267s 11 9.924771 0.6501301 267s 12 13.628569 -2.3009846 267s 13 10.774550 -1.1628697 267s 14 12.716376 -1.0670330 267s 15 11.176408 0.7403371 267s 16 3.209269 -0.0804317 267s 17 1.256577 2.8931153 267s 18 2.468720 -1.2008647 267s 19 2.253229 0.8379608 267s 20 0.021073 1.6394221 267s 21 3.205298 -2.3518286 267s 22 1.470733 -0.9618655 267s 23 0.475732 -1.7044535 267s 24 0.930144 -1.3288398 267s 25 4.151553 -2.2882554 267s 26 1.314488 -1.3527439 267s 27 3.613405 -0.0813605 267s 28 -1.909178 -3.6473200 267s 29 -3.987263 -1.3255834 267s 30 -0.370601 -1.5855086 267s 31 -1.273254 -2.1892809 267s 32 -0.816634 -0.4514478 267s 33 -1.553394 -0.2792004 267s 34 -0.275027 0.6359374 267s 35 0.980782 -2.2353223 267s 36 -3.678470 -1.3459182 267s 37 -0.327102 -2.5615283 267s 38 -1.563492 -2.2008288 267s 39 1.876146 -1.0292641 267s 40 -3.204182 1.6694332 267s 41 -3.561892 -1.5844770 267s 42 -6.175135 1.0123714 267s 43 -2.736601 -0.7040261 267s 44 -4.981783 0.2434304 267s 45 0.368802 -0.5011413 267s 46 0.369508 -1.9511091 267s 47 -2.306673 -0.0089446 267s 48 0.215195 -1.1000357 267s 49 2.704678 -0.5919929 267s 50 -2.930879 2.7161936 267s 51 1.846250 0.3732500 267s 52 5.661288 -0.3139157 267s 53 1.154929 -0.0575094 267s 54 0.625715 -0.0733934 267s 55 -0.453714 -0.7535924 267s 56 0.343722 0.6460318 267s 57 1.743002 0.0794685 267s 58 0.433705 -1.3500731 267s 59 2.078550 1.0860506 267s 60 1.867913 0.7162287 267s 61 0.392645 1.6184583 267s 62 -1.958732 2.0993596 267s 63 -2.383251 -0.0253919 267s 64 -2.383251 -0.0253919 267s 65 0.780239 2.9018927 267s 66 2.785329 1.0142893 267s 67 0.131210 1.2703167 267s 68 1.110073 1.8140467 267s 69 1.076878 0.6954148 267s 70 -3.260160 -5.6233069 267s 71 2.647036 1.6892084 267s 72 -2.017340 0.5353349 267s 73 2.247524 2.6406249 267s 74 11.649291 -0.7374197 267s 75 0.280544 2.2306959 267s 76 1.791213 0.1796005 267s 77 8.730344 0.3412271 267s 78 -0.987405 1.3467910 267s 79 0.560808 0.5006661 267s 80 3.897879 -1.5270179 267s 81 -0.792759 -0.8649399 267s 82 -2.493611 1.6796838 267s 83 -2.245966 0.1889555 267s 84 -0.468812 -0.5359088 267s 85 -0.538372 2.4105954 267s 86 -0.185347 -1.0176989 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 2.4578 1.5728 267s ---------------------------------------------------------- 267s bushfire 38 5 1 38435.075910 NA 267s Scores: 267s PC1 267s 1 -111.9345 267s 2 -113.4128 267s 3 -105.8364 267s 4 -89.1684 267s 5 -58.7216 267s 6 -35.0370 267s 7 -250.2123 267s 8 -292.6877 267s 9 -294.0765 267s 10 -290.0193 267s 11 -289.8168 267s 12 -290.8645 267s 13 -232.6865 267s 14 9.8483 267s 15 137.1924 267s 16 92.9804 267s 17 90.4493 267s 18 78.6325 267s 19 82.1178 267s 20 92.9044 267s 21 74.9157 267s 22 66.7350 267s 23 -62.1981 267s 24 -116.5696 267s 25 -53.8907 267s 26 -60.6384 267s 27 -74.7621 267s 28 -50.2202 267s 29 -38.7483 267s 30 -93.3887 267s 31 35.3096 267s 32 290.8493 267s 33 326.7236 267s 34 322.9095 267s 35 328.5307 267s 36 325.6791 267s 37 323.8136 267s 38 325.2991 267s ------------- 267s Call: 267s PcaHubert(x = x, mcd = FALSE) 267s 267s Standard deviations: 267s [1] 196.05 267s ---------------------------------------------------------- 267s ========================================================== 267s > 267s > dodata(method="locantore") 267s 267s Call: dodata(method = "locantore") 267s Data Set n p k e1 e2 267s ========================================================== 267s heart 12 2 2 1.835912 0.084745 267s Scores: 267s PC1 PC2 267s [1,] 7.3042 1.745289 267s [2,] 64.6474 0.164425 267s [3,] 1.1057 -1.404189 267s [4,] -3.1943 2.565728 267s [5,] 19.4154 -0.401369 267s [6,] -15.5709 6.666752 267s [7,] 5.9980 2.509372 267s [8,] -29.5933 -4.805972 267s [9,] -1.3933 -0.899323 267s [10,] -28.2845 -4.270057 267s [11,] -14.0069 0.048311 267s [12,] 49.1484 0.694598 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 1.35496 0.29111 267s ---------------------------------------------------------- 267s starsCYG 47 2 2 0.779919 0.050341 267s Scores: 267s PC1 PC2 267s [1,] 0.174291 -0.0489127 267s [2,] 0.703776 0.0769650 267s [3,] -0.136954 -0.1212071 267s [4,] 0.703776 0.0769650 267s [5,] 0.125991 -0.1134658 267s [6,] 0.413609 0.0121367 267s [7,] -0.466451 -0.5036094 267s [8,] 0.238569 0.1446547 267s [9,] 0.498194 -0.1998666 267s [10,] 0.065125 -0.0353931 267s [11,] 0.562344 -0.9836936 267s [12,] 0.399997 -0.0164068 267s [13,] 0.376370 0.0369013 267s [14,] -1.041009 -0.2611550 267s [15,] -0.798187 -0.0090880 267s [16,] -0.464636 0.0805967 267s [17,] -1.123135 -0.0293034 267s [18,] -0.861603 0.1297588 267s [19,] -0.884955 -0.0588007 267s [20,] 0.721130 -1.0033585 267s [21,] -0.679097 -0.0238366 267s [22,] -0.837884 -0.0041718 267s [23,] -0.623423 0.1002615 267s [24,] -0.188079 0.1168815 267s [25,] -0.032888 -0.0131784 267s [26,] -0.385242 0.0707643 267s [27,] -0.401220 -0.0582501 267s [28,] -0.151978 0.0015702 267s [29,] -0.677776 -0.0945350 267s [30,] 0.878688 -1.0329475 267s [31,] -0.628339 0.0605648 267s [32,] 0.068629 0.1556245 267s [33,] 0.174199 0.0317098 267s [34,] 1.118098 -1.0525206 267s [35,] -0.726168 -0.0784655 267s [36,] 0.592061 0.1512588 267s [37,] 0.064942 0.1258519 267s [38,] 0.174199 0.0317098 267s [39,] 0.144335 0.1160195 267s [40,] 0.519088 -0.0311555 267s [41,] -0.429855 0.0359837 267s [42,] 0.015412 0.0513747 267s [43,] 0.299435 0.0665821 267s [44,] 0.293289 0.0169612 267s [45,] 0.504064 0.0916219 267s [46,] -0.063981 0.0612071 267s [47,] -0.544029 0.0904291 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 0.88313 0.22437 267s ---------------------------------------------------------- 267s phosphor 18 2 2 0.933905 0.279651 267s Scores: 267s PC1 PC2 267s 1 4.5660 -15.58981 267s 2 -21.2978 -0.38905 267s 3 -23.3783 3.96546 267s 4 -11.7131 -5.79023 267s 5 -18.2569 2.81141 267s 6 15.5702 -20.54935 267s 7 1.3671 -3.27043 267s 8 -9.4859 3.92005 267s 9 -10.4501 6.22662 267s 10 15.0583 -7.60532 267s 11 -3.9078 1.56960 267s 12 10.0330 7.52732 267s 13 13.4815 5.50056 267s 14 7.5487 7.24752 267s 15 18.6543 2.46040 267s 16 -9.3301 -5.68285 267s 17 22.2533 4.63689 267s 18 17.7892 10.85633 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 0.96639 0.52882 267s ---------------------------------------------------------- 267s stackloss 21 3 3 1.137747 0.196704 267s Scores: 267s PC1 PC2 PC3 267s [1,] 19.98046 -6.20875 -3.93576 267s [2,] 19.57014 -7.11509 -4.03666 267s [3,] 15.48729 -3.14247 -3.29600 267s [4,] 3.12341 -1.38969 1.50633 267s [5,] 2.35380 -0.84492 -0.25745 267s [6,] 2.73860 -1.11731 0.62444 267s [7,] 5.58533 4.04837 2.11170 267s [8,] 5.58533 4.04837 2.11170 267s [9,] -0.56851 0.17483 2.46656 267s [10,] -5.36478 -4.80766 -2.64915 267s [11,] -1.67190 3.34943 -1.74110 267s [12,] -2.46702 2.71547 -2.72389 267s [13,] -4.54414 -2.99497 -2.44736 267s [14,] 0.35419 6.70241 -0.45563 267s [15,] -8.28612 5.93369 1.94314 267s [16,] -9.51708 3.21466 1.64046 267s [17,] -14.87676 -9.74652 1.10983 267s [18,] -12.00452 -3.40212 1.81609 267s [19,] -11.20939 -2.76816 2.79887 267s [20,] -5.42808 -2.89367 0.23748 267s [21,] 9.83969 0.74095 -5.30190 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 1.06665 0.44351 0.33935 267s ---------------------------------------------------------- 267s salinity 28 3 3 1.038873 0.621380 267s Scores: 267s PC1 PC2 PC3 267s 1 -2.7215590 -0.98924 0.3594538 267s 2 -3.6251829 -1.03361 1.4973993 267s 3 -6.0588883 4.23861 -1.1012038 267s 4 -6.2741857 2.42372 -1.4875092 267s 5 -5.7274076 5.42190 2.9332011 267s 6 -5.8431892 0.57161 -0.3385363 267s 7 -4.4051377 -0.83292 0.0851817 267s 8 -2.6155827 -2.50739 0.3386166 267s 9 -0.0426575 1.19631 -2.5025726 267s 10 2.5297488 1.65029 -0.0110335 267s 11 1.5528097 1.93255 1.4216262 267s 12 -0.3140451 -0.73269 -0.1961364 267s 13 0.0010783 -1.88658 0.1849912 267s 14 1.9554303 -2.13519 1.8471356 267s 15 2.7897250 2.40211 -0.6327944 267s 16 -1.7665706 8.69449 5.6608836 267s 17 -0.4374125 1.72696 0.7230753 267s 18 -2.9752196 -0.54118 -0.6829760 267s 19 -0.0599346 0.84127 -2.8473543 267s 20 1.6597909 0.34191 -1.4847516 267s 21 1.3857395 -2.43924 0.0039271 267s 22 2.6664754 -3.14291 1.0600254 267s 23 4.1202067 3.81886 1.0608640 267s 24 2.4163743 3.45141 1.6874099 267s 25 0.8493897 0.31424 -0.3073115 267s 26 1.4216265 -1.55310 -0.5455012 267s 27 2.3021676 -2.63392 0.0481451 267s 28 3.0877115 -2.85951 1.4378956 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 1.01925 0.78828 0.36470 267s ---------------------------------------------------------- 267s hbk 75 3 3 1.038833 0.363386 267s Scores: 267s PC1 PC2 PC3 267s 1 32.393698 -3.4318297 0.051248 267s 2 33.103072 -4.4154651 0.294662 267s 3 35.038965 -3.5996035 -0.940929 267s 4 35.955809 -4.9285404 -0.479059 267s 5 35.424918 -4.3076292 -0.366699 267s 6 33.753497 -3.2463136 0.289013 267s 7 33.817375 -3.6819421 0.684167 267s 8 32.717119 -3.7074394 -0.279567 267s 9 34.932190 -4.6939061 -0.738196 267s 10 33.737339 -4.5702346 -1.193206 267s 11 40.202273 -5.4336890 -0.229323 267s 12 41.638189 -4.5304173 -1.996311 267s 13 40.768565 -5.0531048 2.123222 267s 14 44.408749 -8.8448536 8.236462 267s 15 0.977343 1.3057899 0.938694 267s 16 -0.900390 1.6169842 1.382855 267s 17 -2.384467 -0.9835430 0.375495 267s 18 -0.143306 0.7859701 -0.237712 267s 19 -0.344479 -0.9791245 0.733869 267s 20 1.199115 0.8330752 1.216827 267s 21 0.184475 0.8630593 0.351029 267s 22 -0.100389 -1.5084406 0.718236 267s 23 -0.847925 0.4823829 0.958677 267s 24 -1.334366 -0.1021190 1.000300 267s 25 -2.669352 0.4692990 -0.811134 267s 26 0.601538 -1.1984283 0.541627 267s 27 1.373423 1.2098621 0.136249 267s 28 -0.721268 0.6164612 -0.963817 267s 29 -1.832615 0.2543279 -0.297658 267s 30 0.120086 -0.1558590 -1.976558 267s 31 -0.747437 1.7749106 0.342824 267s 32 -1.727558 -0.8325772 1.043088 267s 33 -0.073907 -0.3923823 1.083904 267s 34 -2.646454 -0.1350138 -1.101448 267s 35 1.331096 1.0443905 -0.039328 267s 36 0.281192 -0.6569943 -0.404009 267s 37 0.245349 -1.8406517 0.093656 267s 38 -2.049446 0.5320301 0.347219 267s 39 -1.645547 1.3268749 -1.068792 267s 40 -1.216874 -0.8556007 0.201262 267s 41 0.959445 1.6250030 -0.553881 267s 42 -0.603579 -0.9569812 -1.502730 267s 43 -0.946870 -1.6333180 1.324763 267s 44 0.076217 0.5018427 -1.902369 267s 45 -2.140584 1.2192726 -0.677180 267s 46 -0.081677 0.5389288 -1.785347 267s 47 -1.590461 2.1881067 -0.583771 267s 48 0.931421 1.3321181 -0.669782 267s 49 0.512639 1.2123979 0.683099 267s 50 1.095415 0.0045968 0.143109 267s 51 -1.456417 1.1186245 0.619657 267s 52 -0.917904 2.2084467 -0.366392 267s 53 -0.429654 -0.8524437 -2.326637 267s 54 -1.213858 -0.4996891 1.630709 267s 55 -1.253877 -0.9438354 0.692022 267s 56 -0.390657 -0.0427482 1.571167 267s 57 -1.797537 0.8934866 -0.281980 267s 58 0.396886 0.3227454 1.492494 267s 59 0.646360 -0.2194210 -0.562699 267s 60 0.119900 -1.2480691 -1.459763 267s 61 0.867946 -1.7843458 0.232229 267s 62 -2.733997 0.3604288 -0.692947 267s 63 -1.442683 -0.3732483 1.452800 267s 64 1.444934 0.5727959 0.434633 267s 65 -0.147284 0.7055205 -1.413940 267s 66 -1.739552 -0.9838385 0.220303 267s 67 -0.824644 0.1503195 0.411693 267s 68 -2.437638 -0.4835278 -1.392882 267s 69 -2.091970 1.1865192 -0.088483 267s 70 0.403429 -0.7855276 -0.540161 267s 71 0.507512 0.3152001 0.276885 267s 72 -0.944376 -0.8197825 0.044859 267s 73 -0.648597 -1.1160277 -0.658528 267s 74 -0.979453 -1.4589411 0.029182 267s 75 -0.982282 -0.7226425 -1.917060 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 1.01923 0.60282 0.46137 267s ---------------------------------------------------------- 267s milk 86 8 8 1.175171 0.426506 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 PC6 267s [1,] 6.1907998 0.58762698 0.686510 -0.209679 0.3321757 -1.3424985 267s [2,] 7.0503894 -0.49576086 -0.322697 -0.767415 -0.0165833 -1.4596064 267s [3,] 0.7670594 -1.83556812 0.468814 0.346810 -0.0204610 -0.2115383 267s [4,] 5.4656748 -2.29797862 1.612819 -0.378295 -0.2050232 0.3486957 267s [5,] -1.0291160 0.37303007 0.634604 -0.521527 -0.3299543 0.0859469 267s [6,] 2.2186300 0.39396818 -0.236987 -0.033975 -0.2549238 0.2541221 267s [7,] 2.7938591 -0.01152811 -0.600546 -0.098564 -0.3906602 0.3798516 267s [8,] 2.9544176 0.32646226 0.273051 -0.275073 -0.3982959 0.2377581 267s [9,] -1.3344639 -2.45440308 1.001792 -0.104783 -0.1744718 -0.0887272 267s [10,] -2.9294174 -0.79860558 -0.260533 0.375330 0.3425169 -0.2056682 267s [11,] -9.5810648 -0.09577968 1.565111 -0.112002 0.3143032 -0.3190238 267s [12,] -13.1147240 2.95665890 0.228086 -0.180867 0.0136463 -0.4604390 267s [13,] -10.2989319 1.53220781 -2.244629 0.323950 -0.0398642 -0.3463501 267s [14,] -12.2553418 1.62281167 -0.472862 -0.212983 -0.4124280 -0.4253719 267s [15,] -10.8346894 -0.09781844 2.134079 -0.272304 -0.1090226 -0.3725738 267s [16,] -2.8358474 0.28109809 0.945309 0.603249 0.1615955 0.1762086 267s [17,] -1.0353408 -2.75475311 1.677879 0.598578 0.0078965 0.0228522 267s [18,] -2.0271810 1.25894451 -0.266038 -0.168565 -0.3000200 0.2891774 267s [19,] -1.9279394 -0.68339726 1.264416 0.186749 0.3018226 -0.0869321 267s [20,] 0.2568334 -1.62632029 0.854279 -0.088175 0.5458645 0.2217019 267s [21,] -2.7017404 2.45223507 -0.243639 -0.211402 -0.2102323 0.2140100 267s [22,] -1.0386097 0.99459030 0.188462 -0.033434 -0.2857078 -0.1438517 267s [23,] -0.0198126 1.73285416 0.761979 0.005501 0.1671992 -0.0375468 267s [24,] -0.4909448 1.40982693 0.967440 0.521275 0.1625359 -0.0892501 267s [25,] -3.6632699 2.51414455 0.966410 -0.272694 0.0467958 0.1572715 267s [26,] -0.8733564 1.42247465 0.946038 -0.338985 -0.0804141 -0.0080759 267s [27,] -3.2254798 0.26912538 0.799468 0.372442 -0.6886191 -0.0553515 267s [28,] 2.4675785 3.56128696 0.813964 0.118354 -0.1677073 -0.0303774 267s [29,] 4.4177264 1.13316321 0.613509 0.261488 0.4229929 0.1780620 267s [30,] 0.8240097 1.54163297 0.398148 -0.221825 0.0309586 0.0830110 267s [31,] 1.7735990 2.00615332 -1.399933 0.469158 -0.0740282 0.0692312 267s [32,] 1.2348922 0.28918604 -1.239899 0.470999 -0.1511519 -0.3692504 267s [33,] 1.9407276 0.19123540 0.406623 0.389965 0.0994854 -0.0204286 267s [34,] 0.6225565 -0.65636700 0.565253 0.369897 -0.1612501 -0.1774611 267s [35,] -0.4869219 2.26301333 0.071825 0.588101 -0.0579092 -0.0362009 267s [36,] 4.1117242 1.16638974 0.982790 -0.266009 0.0728797 -0.0018914 267s [37,] 0.8415225 2.46677043 -0.526780 0.167456 -0.2370116 -0.0731483 267s [38,] 2.0528334 2.09648023 0.220912 0.206722 -0.1924842 0.0676382 267s [39,] -1.4493644 1.14916103 0.904194 0.455498 0.0678893 -0.1476540 267s [40,] 3.4867792 -1.82367389 0.730183 0.499859 0.2327704 -0.1518819 267s [41,] 4.0222120 1.34765470 0.580852 -0.453301 0.2482908 -1.5306566 267s [42,] 6.4789035 -1.25599522 1.644194 0.381331 0.1699942 0.1847594 267s [43,] 3.1529354 0.44884526 -0.967114 -0.220364 0.0037036 0.0802727 267s [44,] 5.3344976 -0.47975673 0.642789 0.298705 0.9983145 -0.1310548 267s [45,] 0.0325597 0.49900084 0.076948 0.486521 0.1642679 0.1392696 267s [46,] 0.1014401 1.97657735 0.733879 0.127235 0.0650844 -0.0144271 267s [47,] 2.7217685 -0.37859042 -3.696163 0.355401 -0.4123714 0.2114024 267s [48,] 0.2292225 1.01473918 -1.115726 0.434557 0.2668316 0.0103147 267s [49,] -2.2803784 0.59474034 -1.783003 0.549252 0.4660435 -0.0802352 267s [50,] 3.1560404 -2.84820361 0.913015 0.077151 0.5803961 0.0350246 267s [51,] -1.4680905 -0.43078891 -1.733657 0.074684 0.0026718 0.0819023 267s [52,] -5.2469034 0.48385240 -1.246027 0.081379 0.2380924 -0.1663831 267s [53,] -0.7670982 0.00234561 -0.923030 -0.366820 0.1582141 0.0508747 267s [54,] -0.2428655 0.04714401 -0.217187 -0.059549 0.1762969 0.0806339 267s [55,] 0.8723441 0.66109329 -0.224917 -0.360607 -0.0638127 0.1310131 267s [56,] 0.0019700 -0.67624071 0.081304 -0.182908 0.1045597 -0.0281936 267s [57,] -1.3684663 -0.00045069 0.860560 -0.350684 -0.1443970 -0.2270651 267s [58,] 0.0079047 1.36376727 0.750919 -0.437914 -0.1894910 0.2345556 267s [59,] -1.7430794 -1.06973583 -0.569381 -0.055139 -0.1582790 -0.0873605 267s [60,] -1.5171606 -0.69340281 -0.287048 -0.136559 -0.3871182 0.1606979 267s [61,] -0.0955085 -1.64221260 0.263650 -0.265665 -0.0808644 -0.0476862 267s [62,] 2.2259171 -2.22161516 0.426279 0.027834 0.2924338 -0.1784242 267s [63,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 267s [64,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 267s [65,] -0.5520071 -2.86186682 0.746248 0.109945 0.0556927 -0.0135739 267s [66,] -2.4472964 -0.94969715 -0.329042 -0.113895 -0.2728443 -0.0523337 267s [67,] 0.1790969 -1.29190443 0.146657 0.140234 0.1534048 0.2318353 267s [68,] -0.8017055 -1.93331421 -1.968273 0.017854 0.1287513 -0.2306786 267s [69,] -0.7356418 -0.68868398 -0.075215 -0.156944 0.0302876 0.4232626 267s [70,] 3.8821693 5.16959880 0.215490 -8.985938 5.2189361 -2.8089276 267s [71,] -2.3478937 -1.60220695 0.058822 -0.111845 -0.0539018 0.0087982 267s [72,] 2.3676739 -0.70331436 -0.214457 -0.307311 -0.1582719 0.3995413 267s [73,] -1.9906385 -2.60946629 -0.730312 0.485522 -0.2391998 0.1009341 267s [74,] -11.2435515 1.44868683 2.482678 0.026711 0.4922865 -0.2822136 267s [75,] 0.0044207 -2.29768358 -0.692425 0.538923 -0.4110598 -0.0824903 267s [76,] -1.4045239 -0.22649785 -1.343257 -0.067382 -0.1322233 -0.1072330 267s [77,] -8.3637576 0.14167751 1.267616 0.384528 -0.0728561 -0.4017300 267s [78,] 1.3022939 -1.47457541 -0.394623 -0.068014 -0.1502832 0.0757414 267s [79,] -0.1950676 -0.58254701 -0.824931 -0.088174 -0.2071634 -0.1896613 267s [80,] -3.4432989 1.73593273 0.777996 0.094211 0.2377017 -0.1520088 267s [81,] 1.2167258 0.77512068 0.085803 -0.214850 -0.2201173 0.0432435 267s [82,] 2.7778798 -1.80071342 0.583878 0.465898 0.0648352 0.2148470 267s [83,] 2.6218578 -0.39825539 -0.553372 -0.145721 -0.0977092 -0.2485337 267s [84,] 0.8946018 0.33790104 -1.974267 0.091828 0.0051986 -0.2606274 267s [85,] 0.7759316 -2.34860124 2.423325 -0.384149 -0.0167182 -0.0353374 267s [86,] 0.6266756 0.87099609 -1.407948 -0.237762 0.0361644 0.1675792 267s PC7 PC8 267s [1,] -0.1014312 1.5884e-03 267s [2,] -0.3831443 1.0212e-03 267s [3,] -0.7164683 1.2035e-03 267s [4,] 0.0892864 3.5409e-04 267s [5,] -0.0943992 1.0547e-03 267s [6,] 0.1184847 1.5031e-03 267s [7,] -0.2509793 1.6850e-05 267s [8,] -0.0136880 7.0308e-04 267s [9,] 0.2238736 -1.9164e-04 267s [10,] 0.0754413 1.3614e-04 267s [11,] 0.0784380 3.5175e-04 267s [12,] 0.2033489 -1.3174e-03 267s [13,] 0.2139525 -1.7101e-03 267s [14,] 0.1209735 -9.1070e-04 267s [15,] 0.2119647 -9.2843e-04 267s [16,] -0.3011483 -2.1474e-03 267s [17,] 0.0660858 -1.9036e-03 267s [18,] -0.5199396 -9.4385e-04 267s [19,] -0.1232622 -1.2649e-03 267s [20,] -0.3900208 -2.6927e-04 267s [21,] 0.0264834 7.6074e-05 267s [22,] -0.0736288 1.7240e-04 267s [23,] -0.2156005 -5.5661e-04 267s [24,] 0.1143327 -2.5248e-04 267s [25,] 0.0481580 -6.1531e-04 267s [26,] -0.0084802 -7.5928e-04 267s [27,] -0.2173883 -3.0971e-04 267s [28,] 0.3288873 -1.8975e-04 267s [29,] 0.0788974 -7.2436e-04 267s [30,] -0.0598663 -3.0463e-04 267s [31,] -0.1511658 -4.8751e-04 267s [32,] -0.0532375 -2.5207e-04 267s [33,] -0.0635290 -3.9270e-04 267s [34,] 0.1598240 1.3024e-04 267s [35,] -0.0355175 -8.5374e-05 267s [36,] -0.0174096 -6.3294e-04 267s [37,] -0.2883141 -5.2809e-04 267s [38,] 0.1426412 5.3331e-04 267s [39,] 0.0313308 4.2738e-04 267s [40,] -0.3536195 -3.4170e-04 267s [41,] -0.3925168 1.4588e-04 267s [42,] -0.0056267 -9.1925e-04 267s [43,] -0.4447402 -1.8415e-04 267s [44,] 0.9184385 -5.9685e-04 267s [45,] -0.0340987 7.2924e-04 267s [46,] -0.0162866 9.7800e-04 267s [47,] 0.2428769 -1.1208e-03 267s [48,] 0.3026758 -4.5769e-04 267s [49,] 0.0246345 -2.6207e-04 267s [50,] 0.0857698 7.6439e-05 267s [51,] 0.1136658 1.3013e-04 267s [52,] 0.3993357 6.2796e-04 267s [53,] -0.1765161 1.1329e-04 267s [54,] 0.0016144 2.5870e-04 267s [55,] 0.1064371 5.8188e-04 267s [56,] 0.0207478 -8.7595e-05 267s [57,] 0.1560065 6.3987e-05 267s [58,] 0.1684561 -5.0193e-05 267s [59,] 0.0778732 -8.5458e-04 267s [60,] 0.0037585 1.0429e-05 267s [61,] -0.0296083 3.1526e-05 267s [62,] 0.0913974 -2.2794e-04 267s [63,] 0.0358917 -7.3721e-04 267s [64,] 0.0358917 -7.3721e-04 267s [65,] 0.1209159 2.9398e-04 267s [66,] -0.0027574 2.9380e-04 267s [67,] -0.0091059 -2.7494e-04 267s [68,] 0.0555970 -3.3016e-04 267s [69,] -0.0149255 -3.1228e-04 267s [70,] 0.9282997 4.7859e-05 267s [71,] 0.2630142 4.2617e-04 267s [72,] 0.1063248 -3.0070e-04 267s [73,] -0.1462452 4.9607e-04 267s [74,] 0.2027591 2.6399e-03 267s [75,] 0.6934350 6.0284e-04 267s [76,] -0.0430524 8.1271e-04 267s [77,] 0.0789302 1.4655e-03 267s [78,] -0.0318359 5.2799e-04 267s [79,] -0.1269568 2.9497e-04 267s [80,] 0.2903958 7.8932e-04 267s [81,] 0.0979443 -3.1531e-04 267s [82,] -0.0548155 4.2140e-04 267s [83,] -0.0371550 -5.6653e-04 267s [84,] -0.0835149 -7.0682e-04 267s [85,] 0.1864954 1.0604e-03 267s [86,] 0.1074252 -7.4859e-04 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 1.08405293 0.65307452 0.28970076 0.11162824 0.09072195 0.06659711 0.05888048 267s [8] 0.00022877 267s ---------------------------------------------------------- 267s bushfire 38 5 5 1.464779 0.043290 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 267s [1,] -69.9562 -13.0364 0.98678 1.054123 2.411188 267s [2,] -71.5209 -10.5459 0.31081 1.631208 1.663470 267s [3,] -63.9308 -7.4622 -2.43241 0.671038 0.465836 267s [4,] -47.0413 -9.6343 -3.83609 0.758349 0.683983 267s [5,] -15.9088 -20.1737 -5.55893 1.181744 -0.053563 267s [6,] 8.3484 -30.7646 -5.51541 1.877227 1.338037 267s [7,] -207.7458 -66.2492 34.48519 -5.894885 -1.051729 267s [8,] -246.4327 -97.0433 -9.57057 22.286225 -9.234869 267s [9,] -247.5984 -98.8613 -12.13406 23.948770 -9.250401 267s [10,] -245.8121 -79.2634 12.47990 13.046128 -5.125478 267s [11,] -246.8887 -62.5899 21.21764 9.111011 -5.080985 267s [12,] -251.1354 -9.2115 31.77448 0.236379 0.707528 267s [13,] -194.0239 27.1288 21.05023 0.940913 1.781359 267s [14,] 51.7182 8.5038 -11.22109 -2.132458 1.984807 267s [15,] 180.5597 -4.8151 -21.36630 -9.390663 -0.817036 267s [16,] 135.7246 -5.0756 -11.33517 -10.015567 -1.670831 267s [17,] 133.0151 -4.0344 -8.95540 -7.702087 -0.923277 267s [18,] 121.2619 -9.0627 -5.96042 -7.210971 -2.092872 267s [19,] 124.9038 -10.6649 -7.22555 -5.349553 -1.771009 267s [20,] 135.5410 -6.8146 -7.52834 -5.562769 -0.396924 267s [21,] 117.1950 -3.5643 -4.67473 -6.862117 -0.234551 267s [22,] 108.9944 -2.3344 -5.90349 -5.928299 1.455538 267s [23,] -21.4031 8.0668 6.19525 -4.784890 0.671394 267s [24,] -76.3499 16.7804 6.52545 -1.391250 1.219282 267s [25,] -12.5732 6.1109 -1.45259 -3.512072 -0.375837 267s [26,] -19.1800 3.4685 -2.02243 -3.490028 -0.169127 267s [27,] -33.6733 12.0757 -3.53322 0.048666 0.067468 267s [28,] -9.3966 21.5055 -5.91671 2.650895 -0.449672 267s [29,] 1.4123 35.8559 -5.98222 5.982362 0.613667 267s [30,] -54.2683 39.6029 7.82694 6.759994 0.035048 267s [31,] 74.8866 34.9048 10.03986 12.592158 0.149308 267s [32,] 331.4144 9.3079 27.73391 17.334531 1.015536 267s [33,] 367.6915 -19.5135 48.52753 10.213314 -1.268047 267s [34,] 363.8686 -20.4079 49.32855 8.986581 -1.930673 267s [35,] 369.4371 -19.5074 49.66761 9.001542 -0.179566 267s [36,] 366.5850 -20.2555 50.30290 7.745330 -2.259131 267s [37,] 364.5463 -19.8198 53.00407 6.757796 -1.083372 267s [38,] 365.9709 -19.3753 53.80168 6.467284 -0.854384 267s ------------- 267s Call: 267s PcaLocantore(x = x) 267s 267s Standard deviations: 267s [1] 1.210280 0.208063 0.177790 0.062694 0.014423 267s ---------------------------------------------------------- 267s ========================================================== 267s > dodata(method="cov") 267s 267s Call: dodata(method = "cov") 267s Data Set n p k e1 e2 267s ========================================================== 267s heart 12 2 2 685.776266 13.127306 267s Scores: 267s PC1 PC2 267s 1 8.18562 1.17998 267s 2 65.41185 -2.80723 267s 3 1.86039 -1.70646 267s 4 -2.26910 2.44051 267s 5 20.19603 -1.47331 267s 6 -14.46264 7.05759 267s 7 6.91264 1.99823 267s 8 -28.95436 -3.81624 267s 9 -0.61523 -1.09711 267s 10 -27.62427 -3.33575 267s 11 -13.17788 0.37931 267s 12 49.94879 -1.62675 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 26.1873 3.6232 267s ---------------------------------------------------------- 267s starsCYG 47 2 2 0.280150 0.007389 267s Scores: 267s PC1 PC2 267s 1 0.272263 -0.07964458 267s 2 0.804544 0.03382837 267s 3 -0.040587 -0.14464760 267s 4 0.804544 0.03382837 267s 5 0.222468 -0.14305159 267s 6 0.512941 -0.02420304 267s 7 -0.378928 -0.51924735 267s 8 0.341045 0.11236831 267s 9 0.592550 -0.23812462 267s 10 0.163442 -0.06357822 267s 11 0.638370 -1.02323643 267s 12 0.498667 -0.05242075 267s 13 0.476291 0.00142479 267s 14 -0.947664 -0.26343572 267s 15 -0.699020 -0.01711057 267s 16 -0.363464 0.06475681 267s 17 -1.024352 -0.02972862 267s 18 -0.759174 0.12317995 267s 19 -0.786925 -0.06478250 267s 20 0.796654 -1.04660568 267s 21 -0.580307 -0.03463751 267s 22 -0.738591 -0.01126825 267s 23 -0.521748 0.08812607 267s 24 -0.086135 0.09457052 267s 25 0.065975 -0.03907968 267s 26 -0.284322 0.05307219 267s 27 -0.303309 -0.07553370 267s 28 -0.052738 -0.02155274 267s 29 -0.580638 -0.10534741 267s 30 0.953478 -1.07986770 267s 31 -0.527590 0.04855502 267s 32 0.171408 0.12730538 267s 33 0.274054 0.00095808 267s 34 1.192364 -1.10502882 267s 35 -0.628641 -0.08815176 267s 36 0.694595 0.11071187 267s 37 0.167026 0.09762710 267s 38 0.274054 0.00095808 267s 39 0.246168 0.08594248 267s 40 0.617380 -0.06994769 267s 41 -0.329735 0.01934346 267s 42 0.115770 0.02432733 267s 43 0.400071 0.03289494 267s 44 0.392768 -0.01656886 267s 45 0.605229 0.05314718 267s 46 0.036628 0.03601196 267s 47 -0.442606 0.07644144 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 0.529292 0.085957 267s ---------------------------------------------------------- 267s phosphor 18 2 2 288.018150 22.020514 267s Scores: 267s PC1 PC2 267s 1 2.7987 -19.015683 267s 2 -20.4311 -0.032022 267s 3 -21.8198 4.589809 267s 4 -11.7869 -6.837833 267s 5 -16.9357 2.664785 267s 6 12.9132 -25.602526 267s 7 1.5249 -6.351664 267s 8 -8.0984 2.416616 267s 9 -8.6979 4.843680 267s 10 14.3903 -12.732868 267s 11 -2.9462 -0.760656 267s 12 11.7427 2.991004 267s 13 14.8400 0.459849 267s 14 9.2449 3.095095 267s 15 19.4860 -3.336883 267s 16 -9.4156 -7.096788 267s 17 23.3759 -1.737460 267s 18 19.9173 5.092467 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 16.9711 4.6926 267s ---------------------------------------------------------- 267s stackloss 21 3 3 28.153060 8.925048 267s Scores: 267s PC1 PC2 PC3 267s [1,] 10.538448 13.596944 12.84989 267s [2,] 9.674846 14.098881 12.89733 267s [3,] 8.993255 9.221043 9.94062 267s [4,] 1.744427 3.649104 0.17292 267s [5,] 0.980215 2.223126 1.34874 267s [6,] 1.362321 2.936115 0.76083 267s [7,] 6.926040 0.637480 -0.11170 267s [8,] 6.926040 0.637480 -0.11170 267s [9,] 0.046655 0.977727 -2.46930 267s [10,] -7.909092 0.926343 0.80232 267s [11,] -0.136672 -3.591094 0.37539 267s [12,] -1.382381 -3.802146 1.01074 267s [13,] -6.181887 -0.077532 0.70744 267s [14,] 3.699843 -4.885854 -0.40226 267s [15,] -2.768005 -7.507870 -6.08487 267s [16,] -5.358811 -6.002058 -5.94256 267s [17,] -17.067135 1.738055 -5.86637 267s [18,] -11.021920 -1.775507 -6.19842 267s [19,] -9.776212 -1.564455 -6.83377 267s [20,] -6.075508 0.369252 -2.08345 267s [21,] 6.301743 2.706174 8.79509 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 5.3059 2.9875 1.3020 267s ---------------------------------------------------------- 267s salinity 28 3 3 11.801732 3.961826 267s Scores: 267s PC1 PC2 PC3 267s 1 -1.59888 1.582157 0.135248 267s 2 -2.26975 2.429177 1.107832 267s 3 -6.79543 -2.034636 0.853876 267s 4 -6.36795 -0.602960 -0.267268 267s 5 -6.42044 -1.520259 5.022962 267s 6 -5.13821 1.225470 0.016977 267s 7 -3.24014 1.998671 -0.123418 267s 8 -0.93998 2.789889 -0.515656 267s 9 -0.30856 -2.424345 -1.422752 267s 10 2.20362 -2.800513 1.142127 267s 11 1.38120 -2.076832 2.515630 267s 12 0.44997 0.207439 -0.152835 267s 13 1.21669 1.193701 -0.277116 267s 14 3.31664 1.306627 1.213342 267s 15 2.08484 -3.774814 0.905400 267s 16 -3.64862 -4.677257 9.046484 267s 17 -0.46124 -1.411762 1.706719 267s 18 -2.13038 0.890401 -0.633349 267s 19 -0.23610 -2.262304 -1.885048 267s 20 1.70337 -1.970773 -0.781880 267s 21 2.67273 1.038742 -0.610945 267s 22 4.24561 1.547290 0.108927 267s 23 2.99619 -4.785343 3.094945 267s 24 1.64474 -3.564562 3.432429 267s 25 1.11703 -1.158030 0.237700 267s 26 2.30707 0.069668 -0.735809 267s 27 3.59356 0.860498 -0.611380 267s 28 4.57550 1.300407 0.589307 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 3.43536 1.99043 0.94546 267s ---------------------------------------------------------- 267s hbk 75 3 3 1.436470 1.181766 267s Scores: 267s PC1 PC2 PC3 267s 1 31.105415 -4.714217 10.4566165 267s 2 31.707650 -5.748724 10.7682402 267s 3 33.366131 -4.625897 12.1570167 267s 4 34.173377 -6.069657 12.4466895 267s 5 33.780418 -5.508823 11.9872893 267s 6 32.493478 -4.684595 10.5679819 267s 7 32.592637 -5.235522 10.3765493 267s 8 31.293363 -4.865797 10.9379676 267s 9 33.160964 -5.714260 12.3098920 267s 10 31.919786 -5.384537 12.3374332 267s 11 38.231962 -6.810641 13.5994385 267s 12 39.290479 -5.393906 15.2942554 267s 13 39.418445 -7.326461 11.5194898 267s 14 43.906584 -13.214819 8.3282743 267s 15 1.906326 0.716061 -0.8635112 267s 16 0.263255 0.926016 -1.9009292 267s 17 -1.776489 -1.072332 -0.5496140 267s 18 0.464648 0.702441 0.0482897 267s 19 0.267826 -1.283779 -0.2925812 267s 20 2.122108 0.165970 -0.8924686 267s 21 0.937217 0.548532 -0.4132196 267s 22 0.423273 -1.781869 -0.0323061 267s 23 0.047532 0.018909 -1.1259327 267s 24 -0.490041 -0.520202 -1.1065753 267s 25 -2.143049 0.720869 -0.0495474 267s 26 1.094748 -1.459175 0.2226246 267s 27 2.070705 0.898573 0.0023229 267s 28 -0.294998 0.830258 0.5929001 267s 29 -1.242995 0.300216 -0.2010507 267s 30 0.147958 0.439099 2.0003038 267s 31 0.170818 1.440946 -0.9755627 267s 32 -0.958531 -1.199730 -1.0129867 267s 33 0.697307 -0.874343 -0.7260649 267s 34 -2.278946 0.261106 0.4196544 267s 35 1.962829 0.809318 0.2033113 267s 36 0.626631 -0.600666 0.8004036 267s 37 0.550885 -1.881448 0.7382776 267s 38 -1.249717 0.336214 -0.9349845 267s 39 -1.106696 1.569418 0.1869576 267s 40 -0.684034 -0.939963 -0.1034965 267s 41 1.559314 1.551408 0.3660323 267s 42 -0.538741 -0.447358 1.6361099 267s 43 -0.252685 -2.080564 -0.7765259 267s 44 0.217012 1.027281 1.7015154 267s 45 -1.497600 1.349234 -0.2698932 267s 46 0.100388 1.026443 1.5390401 267s 47 -0.811117 2.195271 -0.5208141 267s 48 1.462210 1.321318 0.5600144 267s 49 1.383976 0.740714 -0.7348906 267s 50 1.636773 -0.215464 0.3195369 267s 51 -0.530918 0.759743 -1.2069247 267s 52 -0.109566 2.107455 -0.5315473 267s 53 -0.564334 -0.060847 2.3910630 267s 54 -0.272234 -1.122711 -1.5060028 267s 55 -0.608660 -1.197219 -0.5255609 267s 56 0.565430 -0.710345 -1.3708230 267s 57 -1.115629 0.888816 -0.4186014 267s 58 1.351288 -0.374815 -1.1980618 267s 59 0.998016 -0.151228 0.9007970 267s 60 0.124017 -0.764846 1.9005963 267s 61 1.189858 -1.905264 0.7721322 267s 62 -2.190589 0.579614 -0.1377914 267s 63 -0.518278 -0.931130 -1.4534768 267s 64 2.124566 0.194391 -0.0327092 267s 65 0.154218 1.050861 1.1309885 267s 66 -1.197852 -1.044147 -0.2265269 267s 67 -0.114174 -0.094763 -0.5168926 267s 68 -2.201115 0.032271 0.8573493 267s 69 -1.307843 1.104815 -0.7741270 267s 70 0.691449 -0.676665 1.0004603 267s 71 1.150975 0.050861 -0.0717068 267s 72 -0.457293 -0.861871 0.1026350 267s 73 -0.392258 -0.897451 0.9178065 267s 74 -0.584658 -1.450471 0.3201857 267s 75 -0.972517 -0.063777 1.8223995 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 1.1985 1.0871 1.0086 267s ---------------------------------------------------------- 267s milk 86 8 8 5.758630 2.224809 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 PC6 267s 1 5.7090867 1.388263 0.0055924 0.3510505 -0.7335114 -1.41950731 267s 2 6.5825186 0.480410 -1.1356236 -0.3250838 -0.7343177 -1.71595400 267s 3 0.7433619 -1.749281 0.2510521 0.3450575 0.2996413 -0.34585702 267s 4 5.5733255 -1.588521 0.8934908 -0.3412408 0.0087626 0.07235942 267s 5 -1.3030839 0.142394 0.8487785 -0.5847851 0.0588053 -0.08968553 267s 6 1.7708705 0.674240 -0.4153759 -0.1915734 0.1382138 0.12454293 267s 7 2.3570866 0.381017 -0.8771357 -0.3739365 0.2918453 0.13437364 267s 8 2.5700714 0.695006 0.0061108 -0.4323695 0.1643797 -0.00469369 267s 9 -1.1725766 -2.713291 1.0677483 -0.0647875 0.1183120 -0.10762785 267s 10 -3.1357225 -1.255175 0.0666017 0.5083690 -0.1096080 -0.00647493 267s 11 -9.5333894 -1.608943 2.7307809 0.1690156 -0.1682415 -0.06597478 267s 12 -13.6028505 0.941083 2.0136258 -0.1076520 -0.0475905 -0.15295614 267s 13 -10.9497471 0.048776 -0.8765307 0.1518572 0.1428294 -0.00064406 267s 14 -12.6558378 -0.219444 1.1396273 -0.3734679 0.2875578 -0.23870524 267s 15 -10.6924790 -1.818075 3.4560731 -0.1177943 0.1101199 -0.19708172 267s 16 -3.0258070 -0.203186 1.2835368 0.5799363 0.3237454 0.23168871 267s 17 -0.7498665 -2.977505 1.6310512 0.6305329 0.3994006 0.06594881 267s 18 -2.5093526 0.924459 0.0899818 -0.4026675 0.2963072 0.11324019 267s 19 -1.9689970 -1.051282 1.4659908 0.3870104 -0.0708083 -0.02148354 267s 20 0.2695886 -1.646440 0.7597630 0.1750131 -0.3418142 0.21515143 267s 21 -3.3470252 1.989939 0.2887021 -0.3599779 0.0771965 0.16867095 267s 22 -1.4659204 0.777242 0.4090149 -0.1248050 0.1916768 -0.23160291 267s 23 -0.4944476 1.634130 0.8915509 0.1222296 -0.1231015 -0.08351169 267s 24 -0.8945477 1.239223 1.1117165 0.6018455 0.0912200 -0.01204668 267s 25 -4.1499992 1.860190 1.6062973 -0.2139736 -0.1140169 0.16632426 267s 26 -1.2647012 1.188058 1.1893430 -0.2740862 -0.0971504 -0.09851714 267s 27 -3.4280131 -0.267150 1.1969552 0.0354366 0.8482718 -0.18977667 267s 28 1.6896630 3.793723 0.7706325 0.1007287 0.0317704 -0.11269816 267s 29 3.9258127 1.691428 0.1850999 0.4485202 -0.2969916 0.16594044 267s 30 0.3178322 1.577233 0.4455231 -0.1687197 -0.1587136 -0.00823174 267s 31 0.9562350 2.258138 -1.4672169 0.2675668 0.1910110 0.03177387 267s 32 0.6738452 0.470764 -1.3496896 0.3524049 0.2008218 -0.36957179 267s 33 1.5980690 0.413899 0.1999664 0.4232293 0.0768479 -0.04627841 267s 34 0.4365091 -0.626490 0.4718364 0.3392252 0.2554060 -0.19018602 267s 35 -1.1184804 2.124234 0.2650931 0.4791171 0.2927791 -0.01579964 267s 36 3.6673986 1.659798 0.6138972 -0.1092158 -0.2705583 -0.16494176 267s 37 0.0867143 2.541765 -0.4572593 0.0024263 0.2163300 -0.20116352 267s 38 1.4191839 2.315690 0.1365887 0.1028375 0.1595780 -0.02049460 267s 39 -1.8062960 0.845438 1.1469588 0.5022406 0.1603011 -0.08751261 267s 40 3.4380914 -1.358545 0.1956896 0.6314649 0.0716078 -0.21591535 267s 41 3.4608782 1.828575 0.2012565 0.1064437 -0.7454169 -1.64629924 267s 42 6.4162310 -0.402642 0.8070441 0.5146855 0.0331594 0.04373032 267s 43 2.5906567 0.897993 -1.2612252 -0.2620162 -0.1432569 -0.10279385 267s 44 5.0299750 0.203721 0.0439110 0.8775684 -0.9536011 0.15153452 267s 45 -0.3555392 0.454930 0.1173992 0.4688991 0.1137820 0.18752442 267s 46 -0.4155426 1.892410 0.8649578 0.1827426 -0.0186113 -0.04029205 267s 47 1.9328817 0.121936 -3.9578157 -0.1135807 0.2971001 0.18733657 267s 48 -0.3947656 1.028405 -1.0370498 0.4467257 -0.1445498 0.16878692 267s 49 -2.8829860 0.279064 -1.4443310 0.5889970 -0.1883118 0.16947945 267s 50 3.2797246 -2.443968 0.4100655 0.4278962 -0.4414712 0.08598366 267s 51 -1.9272930 -0.622137 -1.5136862 -0.0483369 -0.0272502 0.16006066 267s 52 -5.7161590 -0.298434 -0.5216578 0.1385780 -0.2435931 0.10628617 267s 53 -1.1933277 -0.125878 -0.7556261 -0.3129372 -0.3166453 0.03078643 267s 54 -0.5994394 -0.031069 -0.1296378 0.0061490 -0.1869578 0.09839221 267s 55 0.4104586 0.733465 -0.2088065 -0.3645266 -0.1830137 0.04705775 267s 56 -0.2227671 -0.724741 0.1007592 -0.0838897 -0.1939960 -0.04223579 267s 57 -1.5706297 -0.292436 1.0849660 -0.2559591 -0.0917278 -0.27423151 267s 58 -0.4102168 1.263831 0.9082556 -0.4592777 -0.0676902 0.11089798 267s 59 -1.9640736 -1.340173 -0.3652736 -0.1267573 0.0775692 -0.07977644 267s 60 -1.7490968 -0.941370 -0.0849901 -0.3453455 0.2858594 0.06413468 267s 61 -0.1583416 -1.699326 0.2385988 -0.2231496 -0.0513883 -0.12227279 267s 62 2.2124878 -1.942366 0.0743514 0.2627321 -0.2844018 -0.15848039 267s 63 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 267s 64 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 267s 65 -0.3779208 -2.987354 0.6819006 0.1942611 0.0529259 0.01315140 267s 66 -2.6385498 -1.331204 -0.0367809 -0.2327572 0.1845076 -0.08521680 267s 67 0.0526645 -1.301299 0.0912198 0.1634869 -0.0068236 0.24131589 267s 68 -1.1013065 -2.004809 -1.9168056 0.0260663 -0.2029903 -0.12625268 267s 69 -0.9495853 -0.831697 0.0389476 -0.2123483 -0.0202267 0.38463410 267s 70 2.6935893 5.369312 0.6987368 -4.5754846 -9.6833013 -2.32910628 267s 71 -2.4037611 -1.983509 0.3109848 -0.1015686 -0.0071432 0.06410351 267s 72 2.0795505 -0.392730 -0.4534128 -0.4054224 -0.0312781 0.25408988 267s 73 -2.0038405 -2.874605 -0.6269939 0.2408421 0.5184666 0.11140104 267s 74 -11.2683996 -0.361851 3.9219448 0.4045689 -0.2203308 0.05930132 267s 75 -0.1028287 -2.295813 -0.7769187 0.3071821 0.4537196 0.00522380 267s 76 -1.8466137 -0.425825 -1.1261209 -0.1760585 0.0165729 -0.10698465 267s 77 -8.4124493 -1.174820 2.2700712 0.4213953 0.3446597 -0.20636892 267s 78 1.1103236 -1.299480 -0.5787732 -0.1455945 0.0732148 -0.01806218 267s 79 -0.5451834 -0.620170 -0.7830595 -0.1746479 0.0723052 -0.26017118 267s 80 -3.8647223 1.126328 1.3299567 0.2645241 -0.1881443 0.00485531 267s 81 0.7690939 0.887363 0.0513096 -0.2730980 0.0076447 -0.07590882 267s 82 2.7287618 -1.435327 0.1602865 0.4465859 0.2129425 0.16104418 267s 83 2.2241485 -0.042822 -0.8316486 -0.1230697 -0.1193057 -0.35207561 267s 84 0.2452905 0.491732 -2.0050683 0.0286567 -0.1159415 -0.24887542 267s 85 1.0655845 -2.360746 2.2456131 -0.1479972 -0.1186670 -0.14020891 267s 86 -0.0091659 0.952208 -1.3429189 -0.2944676 -0.2433277 0.15354490 267s PC7 PC8 267s 1 -0.09778744 2.3157e-03 267s 2 0.05189698 1.8077e-03 267s 3 0.70506895 1.2838e-03 267s 4 -0.08541140 3.2781e-04 267s 5 0.11768945 8.3496e-04 267s 6 -0.17886391 1.5222e-03 267s 7 0.14143613 1.3261e-04 267s 8 -0.07724578 7.1241e-04 267s 9 -0.12298048 -7.0110e-04 267s 10 0.07569878 2.3093e-05 267s 11 0.29299858 -3.4542e-04 267s 12 0.07764899 -2.1390e-03 267s 13 -0.08945524 -2.2633e-03 267s 14 0.03597787 -1.8891e-03 267s 15 0.11780498 -2.0279e-03 267s 16 0.46501534 -2.3266e-03 267s 17 0.08603290 -2.4073e-03 267s 18 0.52605757 -9.8822e-04 267s 19 0.31007227 -1.3919e-03 267s 20 0.61582059 -2.3549e-05 267s 21 0.01199350 -6.1649e-05 267s 22 0.03654587 1.3302e-05 267s 23 0.27549986 -3.6759e-04 267s 24 -0.04155354 -2.9882e-04 267s 25 0.11473708 -7.9629e-04 267s 26 0.06673183 -8.3728e-04 267s 27 0.16937729 -9.5775e-04 267s 28 -0.41753592 -7.5544e-05 267s 29 -0.03693100 -2.2481e-04 267s 30 0.08461537 -1.3611e-04 267s 31 0.02476253 -1.4319e-04 267s 32 -0.09756048 -1.2234e-04 267s 33 0.06442434 -2.4915e-04 267s 34 -0.17828409 -9.5882e-05 267s 35 0.00881239 -7.1427e-05 267s 36 -0.01041003 -2.8489e-04 267s 37 0.15994729 -3.1472e-04 267s 38 -0.22386895 6.1384e-04 267s 39 0.03666242 2.8506e-04 267s 40 0.35883231 -8.3062e-05 267s 41 0.18521851 8.5509e-04 267s 42 0.00733985 -6.4477e-04 267s 43 0.35466617 3.2923e-04 267s 44 -0.74952524 -7.6869e-05 267s 45 0.09907237 7.9128e-04 267s 46 0.05119980 1.0606e-03 267s 47 -0.48571583 -9.3780e-04 267s 48 -0.27463442 -2.7037e-04 267s 49 0.06787536 -3.0554e-05 267s 50 0.08499400 3.1181e-04 267s 51 -0.09197457 1.1213e-04 267s 52 -0.24513244 3.9100e-04 267s 53 0.24012780 3.2068e-04 267s 54 0.07999888 3.5689e-04 267s 55 -0.09825475 6.6675e-04 267s 56 0.05133674 -7.2984e-05 267s 57 -0.10302363 -2.0693e-04 267s 58 -0.12323360 -1.6620e-04 267s 59 -0.05119989 -1.1016e-03 267s 60 0.00082131 -3.2951e-04 267s 61 0.08128272 -1.1550e-04 267s 62 -0.01789040 -1.1579e-04 267s 63 -0.07188070 -7.8367e-04 267s 64 -0.07188070 -7.8367e-04 267s 65 0.00917085 -2.6800e-05 267s 66 0.03121573 -5.3492e-05 267s 67 0.12202335 -3.0466e-04 267s 68 -0.04764366 -2.6126e-04 267s 69 0.13828337 -3.9331e-04 267s 70 0.10401069 4.2870e-03 267s 71 -0.14369640 3.7669e-05 267s 72 -0.10334451 -2.6456e-04 267s 73 0.17655402 1.0917e-04 267s 74 0.26779696 1.8685e-03 267s 75 -0.75016549 2.1079e-05 267s 76 0.01802016 7.7555e-04 267s 77 0.13081368 6.4286e-04 267s 78 0.01409131 4.9476e-04 267s 79 0.06643384 2.6590e-04 267s 80 -0.12624376 5.9801e-04 267s 81 -0.14074469 -3.2172e-04 267s 82 0.09228230 4.4064e-04 267s 83 -0.06352151 -3.6274e-04 267s 84 -0.02642452 -3.9742e-04 267s 85 -0.03502188 6.9814e-04 267s 86 -0.11749109 -5.1283e-04 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 2.39971451 1.49157920 0.93184037 0.33183258 0.19628996 0.16485446 0.12784351 267s [8] 0.00052622 267s ---------------------------------------------------------- 267s bushfire 38 5 5 11393.979994 197.523453 267s Scores: 267s PC1 PC2 PC3 PC4 PC5 267s 1 -91.383 -16.17804 0.56195 -0.252428 1.261840 267s 2 -93.033 -13.93251 -0.67212 0.042287 0.470924 267s 3 -85.400 -10.72512 -3.09832 -1.224797 -0.504718 267s 4 -68.381 -12.12202 -3.31950 -0.676880 -0.228383 267s 5 -36.742 -21.04171 -1.98872 0.397655 -0.932613 267s 6 -12.095 -30.21719 0.59595 2.100702 0.384714 267s 7 -227.949 -71.40450 35.57308 -7.880296 -2.710415 267s 8 -262.815 -111.81228 -11.04574 2.397832 -13.646407 267s 9 -263.767 -114.13702 -13.71407 3.131736 -13.825200 267s 10 -264.312 -90.69643 9.72320 0.967173 -8.800150 267s 11 -266.681 -72.85993 16.55010 0.291092 -8.373583 267s 12 -274.050 -18.41395 20.74273 -2.464589 -1.505967 267s 13 -218.299 19.16040 7.69765 0.069012 0.054846 267s 14 29.646 10.52526 -7.50754 0.855493 1.966680 267s 15 159.575 3.86633 -6.95837 -2.753953 0.616068 267s 16 114.286 2.47164 0.62690 -3.146317 -0.501623 267s 17 111.289 3.45086 1.97182 -0.303064 -0.094416 267s 18 99.626 -1.80416 4.88197 -0.013096 -1.438397 267s 19 103.353 -3.50426 3.58993 1.578169 -1.317194 267s 20 113.769 0.84544 3.28254 2.204926 0.131167 267s 21 95.186 3.50703 4.97153 0.916181 0.351658 267s 22 86.996 4.00938 2.95209 1.281788 1.920404 267s 23 -44.232 8.50898 6.30689 -1.038871 0.400078 267s 24 -99.527 13.81377 1.75130 -0.260669 0.394804 267s 25 -34.855 5.99709 -0.57224 -1.660513 -0.620158 267s 26 -41.265 2.94659 -1.04825 -2.243950 -0.440017 267s 27 -56.148 10.14428 -5.41858 0.321752 -0.608412 267s 28 -32.366 20.27795 -8.60687 3.806572 -1.267249 267s 29 -22.438 34.73585 -11.19123 8.296154 -0.511610 267s 30 -79.035 37.05713 -1.51591 9.892959 -1.618635 267s 31 49.465 39.37414 5.95714 22.874813 -1.883481 267s 32 304.825 30.19205 37.68900 45.175923 -1.293939 267s 33 341.237 7.04985 65.43451 44.553009 -3.148116 267s 34 337.467 6.16879 66.48222 43.278480 -3.688631 267s 35 342.929 7.38548 66.91291 43.941556 -1.937887 267s 36 340.143 6.70203 67.85433 42.479161 -3.873639 267s 37 337.931 7.43184 70.50828 42.333220 -2.645830 267s 38 339.281 8.07267 71.34405 42.400459 -2.392774 267s ------------- 267s Call: 267s PcaCov(x = x) 267s 267s Standard deviations: 267s [1] 106.7426 14.0543 4.9184 1.8263 1.0193 267s ---------------------------------------------------------- 267s ========================================================== 267s > dodata(method="grid") 267s 267s Call: dodata(method = "grid") 267s Data Set n p k e1 e2 267s ========================================================== 268s heart 12 2 2 516.143549 23.932102 268s Scores: 268s PC1 PC2 268s [1,] 6.4694 3.8179 268s [2,] 61.7387 19.1814 268s [3,] 1.4722 -1.0161 268s [4,] -3.8056 1.5127 268s [5,] 18.6760 5.3303 268s [6,] -16.8411 1.7900 268s [7,] 4.9962 4.1638 268s [8,] -26.8665 -13.3010 268s [9,] -1.0648 -1.2690 268s [10,] -25.7734 -12.4037 268s [11,] -13.3987 -4.0751 268s [12,] 46.7700 15.1272 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 22.719 4.892 268s ---------------------------------------------------------- 268s starsCYG 47 2 2 0.473800 0.026486 268s Scores: 268s PC1 PC2 268s [1,] 0.181489 -0.0300854 268s [2,] 0.695337 0.1492475 268s [3,] -0.120738 -0.1338110 268s [4,] 0.695337 0.1492475 268s [5,] 0.140039 -0.0992368 268s [6,] 0.413314 0.0551030 268s [7,] -0.409428 -0.5478860 268s [8,] 0.225647 0.1690378 268s [9,] 0.519123 -0.1471454 268s [10,] 0.071513 -0.0277935 268s [11,] 0.663045 -0.9203119 268s [12,] 0.402691 0.0253179 268s [13,] 0.373739 0.0759321 268s [14,] -1.005756 -0.3654219 268s [15,] -0.789968 -0.0898580 268s [16,] -0.467328 0.0334465 268s [17,] -1.111148 -0.1431778 268s [18,] -0.867242 0.0417806 268s [19,] -0.871200 -0.1481782 268s [20,] 0.823011 -0.9236455 268s [21,] -0.669994 -0.0923582 268s [22,] -0.829959 -0.0890246 268s [23,] -0.627294 0.0367802 268s [24,] -0.195929 0.0978059 268s [25,] -0.028257 -0.0157122 268s [26,] -0.387346 0.0317797 268s [27,] -0.390054 -0.0981920 268s [28,] -0.148231 -0.0132120 268s [29,] -0.661454 -0.1625514 268s [30,] 0.982767 -0.9369769 268s [31,] -0.628127 -0.0032112 268s [32,] 0.055476 0.1625819 268s [33,] 0.173158 0.0501056 268s [34,] 1.222924 -0.9319795 268s [35,] -0.711235 -0.1515118 268s [36,] 0.576613 0.2117347 268s [37,] 0.054851 0.1325884 268s [38,] 0.173158 0.0501056 268s [39,] 0.134833 0.1309216 268s [40,] 0.522665 0.0228177 268s [41,] -0.428171 -0.0073782 268s [42,] 0.013192 0.0534392 268s [43,] 0.294173 0.0975945 268s [44,] 0.293132 0.0476054 268s [45,] 0.495172 0.1434167 268s [46,] -0.066790 0.0551060 268s [47,] -0.547311 0.0351134 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 0.68833 0.16275 268s ---------------------------------------------------------- 268s phosphor 18 2 2 392.155327 50.657228 268s Scores: 268s PC1 PC2 268s 1 5.6537 -15.2305 268s 2 -21.2150 -1.8862 268s 3 -23.5966 2.3112 268s 4 -11.2742 -6.6000 268s 5 -18.4067 1.5202 268s 6 16.9795 -19.4039 268s 7 1.5964 -3.1666 268s 8 -9.7354 3.2429 268s 9 -10.8594 5.4759 268s 10 15.5585 -6.5279 268s 11 -4.0058 1.2905 268s 12 9.4815 8.2139 268s 13 13.0640 6.4346 268s 14 7.0230 7.7600 268s 15 18.4378 3.7658 268s 16 -8.9047 -6.3253 268s 17 21.8748 6.1900 268s 18 16.9843 12.0801 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 19.8029 7.1174 268s ---------------------------------------------------------- 268s stackloss 21 3 3 109.445054 16.741203 268s Scores: 268s PC1 PC2 PC3 268s [1,] 15.136434 14.82909 -2.0387704 268s [2,] 14.393636 15.46816 -1.8391595 268s [3,] 12.351209 10.12290 -2.3458098 268s [4,] 2.510036 2.07589 1.8251581 268s [5,] 1.767140 1.78527 -0.0088651 268s [6,] 2.138588 1.93058 0.9081465 268s [7,] 6.966825 -1.75851 0.6274924 268s [8,] 6.966825 -1.75851 0.6274924 268s [9,] -0.089513 -1.09062 2.2894224 268s [10,] -7.146340 2.65628 -0.8983590 268s [11,] -0.461157 -3.09532 -2.6948576 268s [12,] -1.575403 -2.60157 -3.4122582 268s [13,] -5.660744 1.37815 -1.2975809 268s [14,] 2.881484 -5.50628 -2.5762898 268s [15,] -4.917360 -9.13772 0.0676942 268s [16,] -7.145755 -7.22052 0.6665270 268s [17,] -17.173481 1.87173 4.3780920 268s [18,] -11.973894 -2.60174 2.9808153 268s [19,] -10.859648 -3.09549 3.6982160 268s [20,] -6.031899 0.15817 1.2270803 268s [21,] 8.451640 4.98077 -5.4038839 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 10.4616 4.0916 2.8271 268s ---------------------------------------------------------- 268s salinity 28 3 3 14.911546 8.034974 268s Scores: 268s PC1 PC2 PC3 268s 1 -2.72400 0.79288 0.688038 268s 2 -3.45684 0.86162 1.941690 268s 3 -5.73471 -4.79507 0.129202 268s 4 -6.17045 -3.04372 -0.352797 268s 5 -4.72453 -5.59543 4.144851 268s 6 -5.75447 -1.07062 0.579975 268s 7 -4.40759 0.47731 0.680203 268s 8 -2.76360 2.30716 0.540271 268s 9 -0.28782 -1.40644 -2.373399 268s 10 2.64361 -1.43362 -0.266957 268s 11 1.91078 -1.66975 1.312215 268s 12 -0.40661 0.68573 -0.200135 268s 13 -0.14911 1.88993 0.044001 268s 14 1.99005 2.43874 1.373229 268s 15 2.88128 -2.21263 -0.863674 268s 16 -0.12935 -8.28831 6.483875 268s 17 -0.16895 -1.68742 0.905190 268s 18 -3.08054 0.23753 -0.269165 268s 19 -0.38685 -1.08501 -2.736860 268s 20 1.45520 -0.33209 -1.686406 268s 21 1.13834 2.53553 -0.381657 268s 22 2.48522 3.42927 0.417050 268s 23 4.56487 -3.36542 0.711908 268s 24 2.94072 -3.08490 1.556939 268s 25 0.82140 -0.26895 -0.406490 268s 26 1.17794 1.61119 -0.863764 268s 27 2.02965 2.80707 -0.489050 268s 28 2.98039 3.21462 0.747622 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 3.86155 2.83460 0.95394 268s ---------------------------------------------------------- 268s hbk 75 3 3 3.714805 3.187126 268s Scores: 268s PC1 PC2 PC3 268s 1 8.423138 24.765818 19.413334 268s 2 7.823138 25.295092 20.356662 268s 3 9.023138 27.411905 20.218454 268s 4 8.223138 28.010236 21.568269 268s 5 8.623138 27.442650 21.123471 268s 6 9.123138 25.601873 20.279943 268s 7 8.823138 25.463855 20.770811 268s 8 8.223138 25.264348 19.451646 268s 9 8.023138 27.373593 20.716984 268s 10 7.623138 26.752275 19.666288 268s 11 9.323138 31.108975 24.313778 268s 12 10.323138 33.179719 23.469966 268s 13 10.323138 29.958667 26.231274 268s 14 9.323138 29.345676 34.207755 268s 15 1.723138 -0.077538 0.754886 268s 16 1.423138 -1.818609 -0.080979 268s 17 -1.676862 -1.872341 -0.686878 268s 18 0.623138 -0.077633 -0.548955 268s 19 -0.876862 -0.576068 0.716574 268s 20 1.423138 -0.016144 1.261078 268s 21 0.923138 -0.223313 0.041619 268s 22 -1.276862 -0.299937 1.038679 268s 23 0.323138 -1.327742 0.057038 268s 24 -0.376862 -1.626860 0.034051 268s 25 -0.676862 -1.550331 -2.266849 268s 26 -0.776862 0.290637 1.184359 268s 27 1.623138 0.750760 0.417361 268s 28 0.123138 -0.016334 -1.346603 268s 29 -0.476862 -1.220468 -1.338846 268s 30 -0.476862 1.387213 -1.339036 268s 31 1.423138 -1.059368 -0.824991 268s 32 -1.176862 -1.833934 0.118433 268s 33 -0.176862 -0.691099 0.908323 268s 34 -1.276862 -1.251213 -2.243862 268s 35 1.423138 0.858128 0.325317 268s 36 -0.576862 0.574335 0.102918 268s 37 -1.576862 0.413330 0.892903 268s 38 -0.176862 -1.841691 -1.085702 268s 39 0.423138 -0.752683 -2.205550 268s 40 -1.176862 -0.905930 -0.211430 268s 41 1.723138 0.819721 -0.479993 268s 42 -1.376862 0.666284 -1.093554 268s 43 -1.576862 -1.304659 1.061761 268s 44 0.123138 1.203126 -1.553772 268s 45 0.223138 -1.358581 -2.151818 268s 46 0.123138 1.003714 -1.569097 268s 47 1.323138 -1.159169 -2.136494 268s 48 1.423138 0.919427 -0.472331 268s 49 1.423138 -0.246300 0.340737 268s 50 0.423138 0.727773 0.716479 268s 51 0.623138 -1.665267 -0.771259 268s 52 1.623138 -0.798657 -1.607314 268s 53 -1.376862 1.310494 -1.645816 268s 54 -0.576862 -1.879908 0.716669 268s 55 -1.176862 -1.235698 0.164407 268s 56 0.123138 -1.296997 0.962055 268s 57 0.123138 -1.304849 -1.545920 268s 58 0.723138 -0.714086 1.207441 268s 59 -0.076862 0.881115 0.026199 268s 60 -1.376862 1.226208 -0.549050 268s 61 -1.276862 0.781504 1.322377 268s 62 -0.776862 -1.657699 -2.174806 268s 63 -0.576862 -1.956627 0.409888 268s 64 1.123138 0.712448 0.915891 268s 65 0.323138 0.689271 -1.392672 268s 66 -1.476862 -1.289430 -0.441492 268s 67 -0.076862 -0.905930 -0.211430 268s 68 -1.576862 -0.852389 -2.213213 268s 69 0.323138 -1.696011 -1.676276 268s 70 -0.676862 0.773747 0.118243 268s 71 0.523138 0.152524 0.371386 268s 72 -1.076862 -0.606812 -0.188443 268s 73 -1.376862 0.114117 -0.433924 268s 74 -1.676862 -0.522431 0.018632 268s 75 -1.376862 0.612552 -1.699453 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 1.9274 1.7853 1.6714 268s ---------------------------------------------------------- 268s milk 86 8 8 9.206694 2.910585 268s Scores: 268s PC1 PC2 PC3 PC4 PC5 PC6 268s [1,] 6.090978 0.590424 1.1644466 -0.3835606 1.0342867 -0.4752288 268s [2,] 6.903009 -0.575027 0.8613622 -1.1221795 0.7221616 -1.3097951 268s [3,] 0.622903 -1.594239 1.2122863 -0.0555128 0.3252629 -0.2799581 268s [4,] 5.282665 -1.815742 2.2543268 0.9824543 -0.5345577 -0.7331037 268s [5,] -1.039753 0.663906 0.3353811 0.3070599 -0.3224317 -0.4056666 268s [6,] 2.247786 0.218255 -0.3382923 0.1270005 -0.0271307 -0.2035021 268s [7,] 2.784293 -0.291678 -0.4897587 0.0198481 0.0752345 -0.5986846 268s [8,] 2.942266 0.315608 0.1603961 0.3568462 -0.0647311 -0.5316127 268s [9,] -1.420086 -1.751212 1.7027572 0.0708340 -0.9226517 0.0738411 268s [10,] -2.921113 -0.727554 0.0113966 -0.3915037 -0.0772913 0.6062573 268s [11,] -9.568075 0.792291 1.0217507 0.2554182 -0.6254883 0.8899897 268s [12,] -12.885166 3.423607 -1.2579351 -0.4300397 -0.4094558 1.1727128 268s [13,] -10.038470 1.274931 -2.6913262 -1.6219658 -0.3284974 1.1228303 268s [14,] -12.044003 2.096254 -1.2859668 -0.9602250 -0.7937418 0.8264019 268s [15,] -10.798341 1.159257 1.4870766 0.3248231 -1.0787537 0.8723637 268s [16,] -2.841629 0.500846 0.4771762 0.5975365 0.3197882 0.5804087 268s [17,] -1.150691 -1.978038 2.3229313 0.5275273 -0.5339514 0.5421631 268s [18,] -1.992369 1.131288 -0.8385615 0.1156462 0.2253010 -0.3393814 268s [19,] -1.999699 -0.252876 1.2229972 0.5081648 0.0082612 0.3373454 268s [20,] 0.091385 -1.439422 1.1836134 0.6297789 0.0961407 -0.2126653 268s [21,] -2.571346 2.280701 -1.2845660 0.1463583 0.0949331 0.0902039 268s [22,] -0.990078 1.087033 -0.1638640 -0.0351472 0.0743205 -0.0040605 268s [23,] -0.010631 1.704171 0.0038808 0.5765418 0.6086460 0.0329995 268s [24,] -0.440350 1.500798 0.2769870 0.5556999 0.4751445 0.6516120 268s [25,] -3.578249 2.672783 -0.3534268 0.7398104 0.1108289 0.2704730 268s [26,] -0.854914 1.626684 0.2301131 0.5530224 0.0662862 -0.0999969 268s [27,] -3.175381 0.762609 0.5101987 0.0849002 -0.2137237 0.2729808 268s [28,] 2.599844 3.370137 -0.5174736 0.7409946 0.6853156 0.2430943 268s [29,] 4.395534 0.823611 0.1610152 0.8184845 0.7665555 0.0779724 268s [30,] 0.843794 1.438263 -0.2366601 0.4600650 0.3424806 -0.1768083 268s [31,] 1.890815 1.266935 -1.8218143 -0.3909337 0.8390127 0.1026821 268s [32,] 1.300145 -0.085976 -0.8965312 -0.8855787 0.4156780 0.1478055 268s [33,] 1.923087 0.137638 0.3487435 0.2958367 0.4245932 0.1566678 268s [34,] 0.615762 -0.390711 0.8107376 0.0295536 -0.1169590 0.2940241 268s [35,] -0.372946 2.037079 -0.7663299 0.1907237 0.6959350 0.5366205 268s [36,] 4.068134 1.129044 0.5492962 0.7640964 0.4799859 -0.4080205 268s [37,] 0.937617 2.048258 -1.2326566 -0.0942856 0.7885267 -0.1004018 268s [38,] 2.141223 1.877022 -0.5178216 0.3750868 0.4767003 0.1240656 268s [39,] -1.403505 1.327163 0.3165610 0.3989824 0.3505825 0.5915956 268s [40,] 3.337528 -1.689495 1.4737175 0.2584843 0.4308444 -0.0810597 268s [41,] 3.938506 1.384908 0.8103687 -0.5875595 1.1616535 -0.6492603 268s [42,] 6.327471 -1.061362 1.9861187 1.1016484 0.3512405 -0.1540592 268s [43,] 3.120160 -0.064108 -0.8370717 -0.2229341 0.5623447 -0.7152184 268s [44,] 5.290520 -0.669008 0.8597130 0.5518503 0.2470856 0.6454703 268s [45,] 0.058291 0.356399 -0.1896007 0.2427518 0.3705541 0.3975085 268s [46,] 0.150881 1.942057 -0.1140726 0.5656469 0.5227623 0.2151825 268s [47,] 2.870881 -1.446283 -2.8450062 -1.7292144 -0.0888429 -0.1347003 268s [48,] 0.335593 0.500884 -1.3154520 -0.3874864 0.3449038 0.5387692 268s [49,] -2.179494 -0.021237 -1.7792344 -0.8445930 0.4435338 0.6547961 268s [50,] 2.968304 -2.588546 1.8552104 0.4590101 -0.1755089 -0.0550378 268s [51,] -1.399208 -0.820296 -1.3660014 -0.8890243 -0.2344105 0.1236943 268s [52,] -5.112989 0.318983 -1.3852993 -0.8461529 -0.3467685 0.7349666 268s [53,] -0.773103 -0.267333 -0.8154896 -0.3783062 0.0113880 -0.3304648 268s [54,] -0.244565 -0.066211 -0.2541557 0.0043037 0.0390890 0.0074067 268s [55,] 0.894921 0.516411 -0.4443369 0.0708354 -0.0637890 -0.2799646 268s [56,] -0.038706 -0.588256 0.3166588 -0.0196663 -0.1793472 -0.1179341 268s [57,] -1.377469 0.428939 0.7502430 0.1458375 -0.3818977 -0.0380258 268s [58,] 0.042787 1.488605 0.0252606 0.6377516 -0.1524172 -0.1898723 268s [59,] -1.734357 -0.966494 -0.1026850 -0.5656888 -0.4831402 0.0308069 268s [60,] -1.501991 -0.544918 -0.0837127 -0.2362486 -0.5382026 -0.1351338 268s [61,] -0.175102 -1.339436 0.8403933 -0.0907428 -0.4846145 -0.2795153 268s [62,] 2.100915 -2.004702 1.3031556 -0.0041957 -0.2067776 -0.0793613 268s [63,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 268s [64,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 268s [65,] -0.665219 -2.325594 1.6287363 0.0607163 -0.6996720 0.1353325 268s [66,] -2.439244 -0.737375 0.0187770 -0.4561269 -0.5425315 -0.0208332 268s [67,] 0.121564 -1.214385 0.4877707 0.1809998 -0.1943262 0.0662506 268s [68,] -0.804267 -2.238327 -0.8547917 -1.3449926 -0.3577254 -0.0293779 268s [69,] -0.761319 -0.676391 -0.0245494 0.2262894 -0.3396872 -0.1166505 268s [70,] 3.385399 4.360467 -0.7946150 -0.0417895 0.4474362 -4.6626174 268s [71,] -2.364955 -1.257673 0.5226907 -0.2346145 -0.7838777 0.1815821 268s [72,] 2.334511 -0.794530 0.0175620 0.1848925 -0.3437761 -0.4522442 268s [73,] -2.023440 -2.449907 0.2525041 -0.6657474 -0.5509480 0.2118442 268s [74,] -11.180192 2.456516 1.1036540 0.8711496 -0.3833194 1.3548314 268s [75,] 0.058297 -2.094811 0.3075211 -0.8052760 -0.9527729 0.5850255 268s [76,] -1.355742 -0.464355 -1.0183333 -0.8525619 -0.1577144 -0.0767323 268s [77,] -8.296881 0.945092 0.8088967 -0.0071463 -0.4527530 1.0614233 268s [78,] 1.251696 -1.460466 0.2511701 -0.2717606 -0.3158308 -0.2964813 268s [79,] -0.192380 -0.662365 -0.3671703 -0.6722658 -0.1243452 -0.2388225 268s [80,] -3.355201 1.915096 -0.1086672 0.3560062 0.0956865 0.6974817 268s [81,] 1.245305 0.736787 -0.1662155 0.1309822 -0.0122872 -0.2182528 268s [82,] 2.679561 -1.666401 1.1576691 0.3960280 -0.0059146 0.0584136 268s [83,] 2.596651 -0.556654 -0.0807307 -0.4468501 0.0964927 -0.3922894 268s [84,] 0.959377 -0.272038 -1.5879803 -1.1153057 0.3412508 -0.1281556 268s [85,] 0.602737 -1.384591 2.8844745 0.9479144 -0.7946454 -0.2014038 268s [86,] 0.698125 0.335743 -1.5248055 -0.4443037 0.0768256 -0.1999790 268s PC7 PC8 268s [1,] 0.9281777 -0.05158594 268s [2,] 0.8397946 -0.04276628 268s [3,] -0.5189230 0.04913688 268s [4,] -0.0178377 0.01578074 268s [5,] -0.0129237 0.01056305 268s [6,] -0.0764270 0.01469518 268s [7,] -0.3059779 0.04237267 268s [8,] -0.0684673 0.02289928 268s [9,] -0.2549733 -0.00832119 268s [10,] -0.0578118 -0.01894694 268s [11,] 0.0415545 -0.03474479 268s [12,] 0.0869267 -0.04485633 268s [13,] -0.2843977 -0.03100709 268s [14,] -0.3375083 -0.02155574 268s [15,] -0.1718828 -0.02996980 268s [16,] -0.4176728 0.03232381 268s [17,] -0.5923252 0.01765700 268s [18,] -0.3190679 0.04476532 268s [19,] -0.0279426 -0.00236626 268s [20,] 0.1299811 0.00586022 268s [21,] 0.0474059 0.00563264 268s [22,] -0.1240299 0.01123557 268s [23,] 0.2232631 0.00551065 268s [24,] 0.0122404 0.00060079 268s [25,] 0.2627442 -0.00824800 268s [26,] 0.2257329 -0.00440907 268s [27,] -0.8496967 0.05266701 268s [28,] 0.3473502 -0.00500580 268s [29,] 0.4172329 -0.00542705 268s [30,] 0.2773880 -0.00014648 268s [31,] -0.1224270 0.02372808 268s [32,] -0.2224748 0.00757892 268s [33,] -0.0633903 0.01236118 268s [34,] -0.2616599 0.00561781 268s [35,] -0.1671986 0.01988458 268s [36,] 0.4502086 -0.00418541 268s [37,] -0.0773232 0.02768282 268s [38,] 0.0464683 0.01134849 268s [39,] -0.0927182 0.00555823 268s [40,] -0.2162796 0.02467605 268s [41,] 0.9440753 -0.04806541 268s [42,] -0.0078920 0.02022925 268s [43,] 0.1152244 0.02074199 268s [44,] 1.0406693 -0.08815111 268s [45,] -0.1376804 0.01424369 268s [46,] 0.1673461 0.00442877 268s [47,] -0.4125225 0.01038694 268s [48,] 0.1556289 -0.02103354 268s [49,] 0.0434415 -0.01782739 268s [50,] 0.2518610 -0.02154540 268s [51,] -0.1186185 -0.00881133 268s [52,] 0.1507435 -0.04523343 268s [53,] 0.2161208 -0.00967982 268s [54,] 0.1374909 -0.00783970 268s [55,] 0.2417108 -0.00895268 268s [56,] 0.1253846 -0.01188643 268s [57,] 0.1390898 -0.01831232 268s [58,] 0.2219634 -0.00364174 268s [59,] -0.2045636 -0.00589047 268s [60,] -0.3679942 0.01673699 268s [61,] -0.0705611 -0.00273407 268s [62,] 0.1447701 -0.02026768 268s [63,] -0.1854788 0.02686899 268s [64,] -0.1854788 0.02686899 268s [65,] -0.2626650 -0.00376657 268s [66,] -0.3044266 0.00484197 268s [67,] -0.1358811 0.00605789 268s [68,] -0.0551482 -0.02379410 268s [69,] -0.0914891 0.00812122 268s [70,] 10.2524854 -0.64367029 268s [71,] -0.1326972 -0.01666774 268s [72,] 0.0051905 0.00656777 268s [73,] -0.8236843 0.03367265 268s [74,] 0.2140104 -0.04092219 268s [75,] -0.5684260 -0.00987116 268s [76,] -0.1225779 -0.00204629 268s [77,] -0.4235612 -0.00450631 268s [78,] -0.1935155 0.00973901 268s [79,] -0.1615883 0.00518643 268s [80,] 0.2915052 -0.02960159 268s [81,] 0.0908823 0.00038216 268s [82,] -0.3392789 0.02605374 268s [83,] 0.1112141 -0.00629308 268s [84,] 0.0510771 -0.00845572 268s [85,] 0.0748700 -0.01174487 268s [86,] 0.2488127 -0.01446339 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 3.034253 1.706044 1.167717 0.670864 0.536071 0.396285 0.266625 0.020768 268s ---------------------------------------------------------- 268s bushfire 38 5 5 38232.614428 1580.825276 268s Scores: 268s PC1 PC2 PC3 PC4 PC5 268s [1,] -67.120 -23.70481 -1.06551 1.129721 1.311630 268s [2,] -69.058 -21.42113 -1.54798 0.983735 0.430774 268s [3,] -61.939 -17.23665 -3.81386 -0.635074 -0.600149 268s [4,] -44.952 -16.53458 -5.16114 0.411753 -0.390518 268s [5,] -12.644 -21.62271 -7.14146 3.519877 -1.211923 268s [6,] 12.820 -27.86930 -7.66114 7.230422 0.040330 268s [7,] -194.634 -100.67730 27.43084 -0.026242 -0.134248 268s [8,] -229.349 -129.75912 -19.46346 25.591651 -18.592601 268s [9,] -230.306 -131.28743 -22.22175 27.251157 -19.214683 268s [10,] -231.118 -115.10815 3.70208 16.303210 -10.573515 268s [11,] -234.540 -100.24984 13.67112 10.325539 -8.727961 268s [12,] -246.507 -51.03515 27.61698 -5.352226 0.514087 268s [13,] -195.712 -5.81324 20.04485 -9.226807 1.721886 268s [14,] 49.881 16.90911 -9.97400 -1.900739 2.190429 268s [15,] 179.545 23.96999 -18.71166 -2.987136 1.332713 268s [16,] 135.356 15.81282 -9.24353 -4.703584 0.971669 268s [17,] 132.350 16.65014 -7.01838 -2.428578 1.346198 268s [18,] 121.499 9.75832 -4.45699 -1.587450 0.131923 268s [19,] 125.222 9.17601 -5.88919 0.582516 -0.061642 268s [20,] 135.112 14.63812 -5.90351 0.411704 1.460488 268s [21,] 116.581 14.47390 -3.04021 -1.842579 2.005998 268s [22,] 108.223 14.62103 -4.47428 -1.196993 3.288463 268s [23,] -22.095 3.26439 6.58391 -6.164581 2.125258 268s [24,] -77.831 3.46616 6.59280 -6.373595 1.545789 268s [25,] -13.092 3.41344 -0.99296 -5.076733 0.299636 268s [26,] -19.206 -0.17007 -1.84209 -4.858675 0.347945 268s [27,] -35.022 6.54155 -3.12767 -3.556587 -0.327873 268s [28,] -12.651 20.14894 -4.61607 -2.025539 -1.214190 268s [29,] -4.404 36.39823 -3.81590 -0.633155 -0.602027 268s [30,] -60.018 30.40980 9.44610 -1.763156 -0.765133 268s [31,] 67.689 47.40087 12.70229 9.791794 -0.671751 268s [32,] 324.134 63.46147 31.52512 30.099817 2.406344 268s [33,] 364.639 38.84260 51.20467 30.648590 3.218678 268s [34,] 361.089 37.09494 52.00522 29.394356 2.861158 268s [35,] 366.403 38.88889 52.31879 29.878844 4.650618 268s [36,] 363.821 37.40859 53.10394 28.286557 2.922632 268s [37,] 361.761 37.21276 55.73012 27.648760 4.477279 268s [38,] 363.106 37.78395 56.56345 27.460078 4.845396 268s ------------- 268s Call: 268s PcaGrid(x = x) 268s 268s Standard deviations: 268s [1] 195.5316 39.7596 11.7329 7.3743 1.7656 268s ---------------------------------------------------------- 268s ========================================================== 268s > 268s > ## IGNORE_RDIFF_BEGIN 268s > dodata(method="proj") 268s 268s Call: dodata(method = "proj") 268s Data Set n p k e1 e2 268s ========================================================== 268s heart 12 2 2 512.772467 29.052346 268s Scores: 268s PC1 PC2 268s [1,] 6.7568 3.2826 268s [2,] 63.0869 14.1293 268s [3,] 1.3852 -1.1318 268s [4,] -3.6709 1.8153 268s [5,] 19.0457 3.8035 268s [6,] -16.6413 3.1452 268s [7,] 5.3163 3.7464 268s [8,] -27.8536 -11.0863 268s [9,] -1.1638 -1.1788 268s [10,] -26.6915 -10.2803 268s [11,] -13.6842 -2.9790 268s [12,] 47.8395 11.2980 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 22.644 5.390 268s ---------------------------------------------------------- 268s starsCYG 47 2 2 0.470874 0.024681 268s Scores: 268s PC1 PC2 268s [1,] 0.181333 -3.1013e-02 268s [2,] 0.696091 1.4569e-01 268s [3,] -0.121421 -1.3319e-01 268s [4,] 0.696091 1.4569e-01 268s [5,] 0.139530 -9.9951e-02 268s [6,] 0.413590 5.2989e-02 268s [7,] -0.412224 -5.4579e-01 268s [8,] 0.226508 1.6788e-01 268s [9,] 0.518364 -1.4980e-01 268s [10,] 0.071370 -2.8159e-02 268s [11,] 0.658332 -9.2369e-01 268s [12,] 0.402815 2.3259e-02 268s [13,] 0.374123 7.4020e-02 268s [14,] -1.007611 -3.6028e-01 268s [15,] -0.790417 -8.5818e-02 268s [16,] -0.467151 3.5835e-02 268s [17,] -1.111866 -1.3750e-01 268s [18,] -0.867017 4.6214e-02 268s [19,] -0.871946 -1.4372e-01 268s [20,] 0.818278 -9.2784e-01 268s [21,] -0.670457 -8.8932e-02 268s [22,] -0.830403 -8.4781e-02 268s [23,] -0.627097 3.9987e-02 268s [24,] -0.195426 9.8806e-02 268s [25,] -0.028337 -1.5568e-02 268s [26,] -0.387178 3.3760e-02 268s [27,] -0.390551 -9.6197e-02 268s [28,] -0.148297 -1.2454e-02 268s [29,] -0.662277 -1.5917e-01 268s [30,] 0.977965 -9.4199e-01 268s [31,] -0.628135 -7.3179e-16 268s [32,] 0.056306 1.6230e-01 268s [33,] 0.173412 4.9220e-02 268s [34,] 1.218143 -9.3822e-01 268s [35,] -0.712000 -1.4787e-01 268s [36,] 0.577688 2.0878e-01 268s [37,] 0.055528 1.3231e-01 268s [38,] 0.173412 4.9220e-02 268s [39,] 0.135501 1.3023e-01 268s [40,] 0.522775 2.0145e-02 268s [41,] -0.428203 -5.1892e-03 268s [42,] 0.013465 5.3371e-02 268s [43,] 0.294668 9.6089e-02 268s [44,] 0.293371 4.6106e-02 268s [45,] 0.495898 1.4088e-01 268s [46,] -0.066508 5.5447e-02 268s [47,] -0.547124 3.7911e-02 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 0.6862 0.1571 268s ---------------------------------------------------------- 268s phosphor 18 2 2 388.639033 51.954664 268s Scores: 268s PC1 PC2 268s 1 5.8164 -15.1691 268s 2 -21.1936 -2.1132 268s 3 -23.6199 2.0585 268s 4 -11.2029 -6.7203 268s 5 -18.4220 1.3231 268s 6 17.1862 -19.2211 268s 7 1.6302 -3.1493 268s 8 -9.7695 3.1385 268s 9 -10.9174 5.3594 268s 10 15.6275 -6.3610 268s 11 -4.0194 1.2476 268s 12 9.3931 8.3149 268s 13 12.9944 6.5741 268s 14 6.9396 7.8348 268s 15 18.3964 3.9629 268s 16 -8.8365 -6.4202 268s 17 21.8073 6.4237 268s 18 16.8541 12.2611 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 19.714 7.208 268s ---------------------------------------------------------- 268s stackloss 21 3 3 97.347030 38.052774 268s Scores: 268s PC1 PC2 PC3 268s [1,] 19.08066 -9.06092 -2.64544 268s [2,] 18.55152 -9.90152 -2.76118 268s [3,] 15.04269 -5.37517 -2.31373 268s [4,] 2.79667 -1.78925 1.70823 268s [5,] 2.21768 -1.17513 -0.10495 268s [6,] 2.50717 -1.48219 0.80164 268s [7,] 5.97151 3.25438 2.40268 268s [8,] 5.97151 3.25438 2.40268 268s [9,] -0.68332 0.30263 2.42495 268s [10,] -5.83478 -4.04630 -2.91819 268s [11,] -1.07253 3.51914 -1.87651 268s [12,] -1.89116 2.98559 -2.89885 268s [13,] -4.77650 -2.36509 -2.68671 268s [14,] 1.33353 6.57450 -0.50696 268s [15,] -7.45351 7.08878 1.37012 268s [16,] -9.04093 4.56697 1.02289 268s [17,] -16.15938 -7.50855 0.30909 268s [18,] -12.45541 -1.62432 1.11929 268s [19,] -11.63677 -1.09077 2.14162 268s [20,] -5.79275 -2.08680 -0.06187 268s [21,] 10.13623 -0.76824 -4.70180 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 9.8665 6.1687 3.2669 268s ---------------------------------------------------------- 268s salinity 28 3 3 12.120566 8.431549 268s Scores: 268s PC1 PC2 PC3 268s 1 -2.52547 1.45945 -1.1943e-01 268s 2 -3.32298 2.15704 8.7594e-01 268s 3 -6.64947 -3.26398 1.0135e+00 268s 4 -6.64427 -1.81382 -1.6392e-01 268s 5 -6.16898 -2.52222 5.1373e+00 268s 6 -5.87594 0.26440 -3.1956e-15 268s 7 -4.23084 1.46250 -2.8008e-01 268s 8 -2.21502 2.76478 -8.3789e-01 268s 9 -0.40186 -2.17785 -1.6702e+00 268s 10 2.27089 -1.84923 7.3391e-01 268s 11 1.37935 -1.29276 2.1418e+00 268s 12 -0.22635 0.60372 -5.0980e-01 268s 13 0.27224 1.73920 -7.0505e-01 268s 14 2.36592 2.40462 6.4320e-01 268s 15 2.37640 -2.83174 5.2669e-01 268s 16 -2.49175 -4.77664 9.0404e+00 268s 17 -0.61250 -1.11672 1.4398e+00 268s 18 -2.91853 0.63310 -8.3666e-01 268s 19 -0.39732 -2.02029 -2.1396e+00 268s 20 1.47554 -1.23407 -1.1712e+00 268s 21 1.70104 1.92401 -1.1292e+00 268s 22 3.14437 2.81928 -5.2415e-01 268s 23 3.62890 -3.51450 2.6740e+00 268s 24 2.04538 -2.63992 3.0718e+00 268s 25 0.77088 -0.54783 -1.3370e-01 268s 26 1.57254 0.89176 -1.2089e+00 268s 27 2.63610 1.97075 -1.1855e+00 268s 28 3.55112 2.67606 -6.0915e-02 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 3.4815 2.9037 1.3810 268s ---------------------------------------------------------- 268s hbk 75 3 3 3.801978 3.574192 268s Scores: 268s PC1 PC2 PC3 268s 1 28.747049 15.134042 2.3959241 268s 2 29.021724 16.318941 2.6207988 268s 3 31.271908 15.869319 3.4420860 268s 4 31.586189 17.508798 3.6246706 268s 5 31.299168 16.838093 3.2402573 268s 6 30.037754 15.591930 2.1421166 268s 7 29.888160 16.139376 1.9750096 268s 8 28.994463 15.350167 2.8226275 268s 9 30.758047 16.820526 3.7269602 268s 10 29.759314 16.079531 4.0486097 268s 11 35.301371 19.637962 3.7433562 268s 12 37.193371 18.709303 4.9915250 268s 13 35.634808 20.497713 1.4740727 268s 14 36.816439 27.523024 -2.3006796 268s 15 1.237203 -0.331072 -1.3801401 268s 16 -0.451166 -1.118847 -1.9707479 268s 17 -2.604733 0.067276 0.0130015 268s 18 0.179177 -0.804398 -0.1285240 268s 19 -0.765512 0.982349 -0.2513990 268s 20 1.236727 0.259123 -1.4210070 268s 21 0.428326 -0.503724 -0.6830690 268s 22 -0.724774 1.507943 -0.0022175 268s 23 -0.745349 -0.330094 -1.0982084 268s 24 -1.407850 -0.011831 -0.8987075 268s 25 -2.190427 -1.732051 0.4497793 268s 26 0.058631 1.444044 0.0446166 268s 27 1.680557 -0.429402 -0.6031146 268s 28 -0.315122 -1.179169 0.5822607 268s 29 -1.563355 -1.026914 0.1040012 268s 30 0.329957 -0.633156 1.8533795 268s 31 -0.110108 -1.617131 -1.0958807 268s 32 -2.035875 0.463421 -0.6346632 268s 33 -0.356033 0.740564 -0.8116369 268s 34 -2.342887 -1.340168 0.9724491 268s 35 1.607131 -0.379763 -0.3747630 268s 36 0.084455 0.486671 0.6551654 268s 37 -0.436144 1.659467 0.7145344 268s 38 -1.754819 -1.076076 -0.6037590 268s 39 -0.904375 -2.161949 0.3436723 268s 40 -1.455274 0.331839 0.1499308 268s 41 1.539788 -1.212921 -0.1715110 268s 42 -0.688338 -0.048173 1.7491184 268s 43 -1.635822 1.539067 -0.5208916 268s 44 0.511762 -1.165641 1.5020865 268s 45 -1.454500 -2.099954 0.0219268 268s 46 0.362645 -1.208389 1.3758464 268s 47 -0.615800 -2.658098 -0.4629006 268s 48 1.426278 -1.027667 0.0582638 268s 49 0.809592 -0.533893 -1.1232120 268s 50 0.996105 0.469082 -0.0988805 268s 51 -1.036368 -1.227376 -1.0843166 268s 52 -0.016464 -2.331540 -0.6477169 268s 53 -0.376625 -0.405855 2.4526088 268s 54 -1.524100 0.621590 -1.2927429 268s 55 -1.588523 0.591668 -0.2559428 268s 56 -0.592710 0.529426 -1.4111404 268s 57 -1.306991 -1.538024 -0.1841717 268s 58 0.275991 0.491888 -1.4739863 268s 59 0.598971 0.196673 0.6208960 268s 60 -0.127953 0.485014 1.8571970 268s 61 0.140584 1.905037 0.5838465 268s 62 -2.305069 -1.617811 0.3880825 268s 63 -1.666479 0.357251 -1.1934779 268s 64 1.480143 0.248671 -0.5959984 268s 65 0.309561 -1.219790 0.9671263 268s 66 -1.986789 0.248245 0.1723620 268s 67 -0.765691 -0.269054 -0.4611368 268s 68 -2.232721 -1.090790 1.3915841 268s 69 -1.502453 -1.813763 -0.4936268 268s 70 0.170883 0.584046 0.8369571 268s 71 0.543623 0.043244 -0.3707674 268s 72 -1.168908 0.341335 0.2837393 268s 73 -0.902885 0.411872 1.0546196 268s 74 -1.425273 0.852445 0.5719123 268s 75 -0.898536 -0.555475 2.0107684 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 1.9499 1.8906 1.2797 268s ---------------------------------------------------------- 268s milk 86 8 8 8.369408 3.530461 268s Scores: 268s PC1 PC2 PC3 PC4 PC5 PC6 268s [1,] 6.337004 -0.245000 0.7704092 -4.9848e-01 -1.6599e-01 1.1763e-01 268s [2,] 7.021899 1.030349 0.2832977 -1.2673e+00 -8.7296e-01 2.0547e-01 268s [3,] 0.600831 1.686247 0.9682032 -3.2663e-02 7.4112e-02 4.7412e-01 268s [4,] 5.206465 2.665956 1.5942253 9.8285e-01 -5.4159e-01 -2.0155e-01 268s [5,] -0.955757 -0.579889 0.3206393 5.1174e-01 -6.1684e-01 -3.8990e-02 268s [6,] 2.198695 0.073770 -0.5712493 1.9440e-01 -1.0237e-01 4.1825e-02 268s [7,] 2.695361 0.644049 -0.8645373 8.1894e-02 -2.6953e-01 1.6884e-01 268s [8,] 2.945361 0.137227 -0.2071463 5.0841e-01 -4.2075e-01 5.8589e-02 268s [9,] -1.539013 1.879894 1.6952390 1.6792e-01 -2.8195e-01 5.0563e-02 268s [10,] -2.977110 0.319666 0.3515636 -5.2496e-01 4.6898e-01 8.5978e-03 268s [11,] -9.375355 -1.638105 1.9026171 4.1237e-01 1.8768e-02 -1.8546e-01 268s [12,] -12.602600 -4.715888 0.0273004 -4.7798e-02 -1.2246e-02 9.6858e-03 268s [13,] -10.114331 -2.487462 -1.6331544 -1.5139e+00 4.1903e-01 2.8313e-01 268s [14,] -11.949336 -3.190157 -0.2146943 -5.0060e-01 -2.9537e-01 3.2160e-01 268s [15,] -10.595396 -1.905517 2.3716887 7.6651e-01 -3.3531e-01 1.9933e-02 268s [16,] -2.735720 -0.748282 0.6750464 7.2415e-01 5.5304e-01 2.2283e-01 268s [17,] -1.248116 2.131195 2.2596886 6.4958e-01 3.5634e-01 2.9021e-01 268s [18,] -1.904210 -1.285804 -0.7746460 3.0198e-01 -2.7407e-01 1.7500e-01 268s [19,] -1.902313 0.095461 1.3824711 5.0369e-01 2.2193e-01 -5.5628e-02 268s [20,] 0.123220 1.399444 1.1517634 3.2546e-01 7.8261e-02 -4.0733e-01 268s [21,] -2.436023 -2.524827 -1.0197416 3.4819e-01 -1.4914e-01 -4.3669e-02 268s [22,] -0.904931 -1.114894 -0.1235807 2.0285e-01 -1.6200e-01 2.5681e-01 268s [23,] 0.220231 -1.767325 0.0482262 6.4418e-01 9.8618e-02 -5.7683e-02 268s [24,] -0.274403 -1.561826 0.3820323 7.0016e-01 5.5220e-01 1.4376e-01 268s [25,] -3.306400 -2.980247 0.0252488 9.4001e-01 -1.0841e-01 -2.5303e-01 268s [26,] -0.658015 -1.625199 0.3021005 7.2702e-01 -3.0299e-01 -1.2339e-01 268s [27,] -3.137066 -0.774218 0.5577497 6.4188e-01 -8.0125e-02 7.7819e-01 268s [28,] 2.867950 -3.099435 -0.6435415 1.0366e+00 1.5908e-01 7.6524e-02 268s [29,] 4.523097 -0.527338 -0.1032516 6.4537e-01 4.7286e-01 -2.7166e-01 268s [30,] 1.002381 -1.376693 -0.2735956 5.0522e-01 -1.2750e-01 -1.6178e-01 268s [31,] 1.894615 -1.296202 -1.9117282 -3.8032e-01 4.6473e-01 3.1085e-01 268s [32,] 1.210291 0.067230 -0.9832930 -8.5379e-01 3.2823e-01 4.9994e-01 268s [33,] 1.964118 0.022175 0.1818518 3.0464e-01 3.5596e-01 1.4985e-01 268s [34,] 0.576738 0.567851 0.6982155 1.8415e-01 1.8695e-01 3.2706e-01 268s [35,] -0.231793 -2.143909 -0.6825523 4.0681e-01 5.4492e-01 3.6259e-01 268s [36,] 4.250883 -0.719760 0.2157706 7.7167e-01 -1.9064e-01 -2.0611e-01 268s [37,] 1.077364 -2.054664 -1.3064867 1.0043e-01 8.6092e-02 3.5416e-01 268s [38,] 2.259260 -1.653588 -0.6730692 5.7300e-01 1.6930e-01 1.6986e-01 268s [39,] -1.251576 -1.451593 0.4671580 5.8957e-01 4.2672e-01 2.2495e-01 268s [40,] 3.304245 1.998193 1.0941231 1.3734e-01 3.7012e-01 2.4142e-01 268s [41,] 4.286315 -1.280951 0.5856744 -6.0980e-01 -4.3090e-01 1.9801e-01 268s [42,] 6.343820 1.801880 1.3481119 1.0355e+00 2.9802e-01 -8.4501e-04 268s [43,] 3.119491 0.214077 -1.1216236 -3.8134e-01 -1.9523e-01 -2.6706e-02 268s [44,] 5.285254 0.938072 0.7440487 1.1539e-02 8.1629e-01 -7.9286e-01 268s [45,] 0.082429 -0.416631 -0.1588203 2.3098e-01 5.1867e-01 9.4503e-02 268s [46,] 0.357862 -1.951997 -0.0731829 7.0393e-01 1.8828e-01 1.5707e-02 268s [47,] 2.428744 1.522538 -3.0467213 -1.9114e+00 2.4638e-01 3.5871e-01 268s [48,] 0.282348 -0.697287 -1.1592508 -5.4929e-01 6.2199e-01 -5.4596e-02 268s [49,] -2.266009 -0.559548 -1.3794914 -1.1300e+00 7.8872e-01 -2.0411e-02 268s [50,] 2.868649 2.860857 1.6128307 6.7382e-02 2.2344e-01 -4.1484e-01 268s [51,] -1.596061 0.546812 -1.1779327 -1.0512e+00 1.3522e-01 -9.4865e-03 268s [52,] -5.186121 -1.000829 -0.7440599 -9.6302e-01 3.0732e-01 -1.7009e-01 268s [53,] -0.800232 0.049087 -0.6946842 -5.8284e-01 -2.1277e-01 -2.7004e-01 268s [54,] -0.246388 -0.030606 -0.1814302 -1.1632e-01 5.7767e-02 -1.8637e-01 268s [55,] 0.914315 -0.428594 -0.4919557 4.5039e-02 -2.7868e-01 -2.2140e-01 268s [56,] -0.061827 0.583572 0.3263056 -1.1589e-01 -1.2973e-01 -1.6518e-01 268s [57,] -1.295979 -0.421943 0.8410805 3.0441e-01 -3.9478e-01 -4.5233e-02 268s [58,] 0.174908 -1.343854 0.0115086 8.0227e-01 -3.9364e-01 -2.2918e-01 268s [59,] -1.869684 0.840823 0.0109543 -5.5536e-01 -1.4155e-01 1.0613e-01 268s [60,] -1.614271 0.557309 -0.0690787 -9.1753e-02 -3.0975e-01 1.6192e-01 268s [61,] -0.258192 1.434984 0.7684636 -1.1998e-01 -3.4662e-01 -4.8808e-02 268s [62,] 2.000275 2.204730 1.1194067 -2.3783e-01 5.9953e-02 -1.5836e-01 268s [63,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 268s [64,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 268s [65,] -0.822201 2.427550 1.5859438 7.2736e-17 -1.1950e-15 -4.2685e-16 268s [66,] -2.545586 0.605953 0.1469837 -3.5318e-01 -2.5871e-01 1.6901e-01 268s [67,] 0.028900 1.253717 0.4474540 5.3595e-02 1.6063e-01 -1.0980e-01 268s [68,] -1.086135 1.968868 -0.7220293 -1.6576e+00 6.2061e-02 -7.0998e-04 268s [69,] -0.836638 0.660453 0.0049966 1.3663e-01 -1.0131e-01 -2.4008e-01 268s [70,] 4.843092 -6.035092 0.8250084 -3.4481e+00 -4.8538e+00 -7.8407e+00 268s [71,] -2.500038 1.146245 0.6967314 -2.4611e-01 -1.4266e-01 -8.2996e-02 268s [72,] 2.220676 1.122951 -0.2444075 1.1066e-01 -3.1540e-01 -2.1344e-01 268s [73,] -2.310518 2.354552 0.2706503 -6.4192e-01 2.0566e-01 4.5520e-01 268s [74,] -10.802799 -3.462655 2.2031446 1.1326e+00 2.8049e-01 -2.9749e-01 268s [75,] -0.301038 2.284366 0.2440764 -6.9450e-01 2.6435e-01 4.3129e-01 268s [76,] -1.477936 0.245154 -0.8869850 -8.9900e-01 -9.8013e-02 1.1983e-01 268s [77,] -8.169236 -1.599780 1.4987144 3.7767e-01 2.4726e-01 3.8246e-01 268s [78,] 1.096654 1.646072 0.0591327 -3.3138e-01 -1.7936e-01 6.2716e-02 268s [79,] -0.289199 0.625796 -0.3974294 -6.6099e-01 -2.0857e-01 2.1190e-01 268s [80,] -3.160557 -2.282579 0.3255355 4.6181e-01 2.7753e-01 -1.5673e-01 268s [81,] 1.284356 -0.548854 -0.2907281 2.4017e-01 -2.5254e-01 -1.4289e-03 268s [82,] 2.562817 2.019485 0.8249162 3.2973e-01 3.3866e-01 1.3889e-01 268s [83,] 2.538825 0.759863 -0.3142506 -5.1028e-01 -2.0539e-01 8.8979e-02 268s [84,] 0.841123 0.110035 -1.5793120 -1.2807e+00 1.2332e-01 1.6224e-01 268s [85,] 0.636271 1.793014 2.6824860 1.0329e+00 -4.8850e-01 -2.3012e-01 268s [86,] 0.633183 -0.426511 -1.4791366 -6.1314e-01 -7.0534e-02 -2.3778e-01 268s PC7 PC8 268s [1,] 1.0196e-01 -1.7180e-03 268s [2,] 2.6131e-01 -8.5191e-03 268s [3,] 6.9637e-01 -8.0573e-03 268s [4,] -1.3548e-01 -1.4969e-03 268s [5,] 3.1443e-02 -2.7307e-03 268s [6,] -2.5079e-01 3.6450e-03 268s [7,] 4.5377e-02 -2.6071e-03 268s [8,] -1.6060e-01 -2.3761e-04 268s [9,] -1.5152e-01 -4.3079e-04 268s [10,] 9.1089e-02 1.9536e-03 268s [11,] 2.5654e-01 -1.4875e-03 268s [12,] -2.3798e-03 -1.0954e-04 268s [13,] -1.3687e-01 2.8402e-03 268s [14,] -6.5248e-02 -1.5114e-03 268s [15,] 3.7695e-02 -2.7827e-03 268s [16,] 3.8131e-01 -3.7990e-03 268s [17,] 4.5661e-02 -1.4965e-03 268s [18,] 3.9910e-01 -7.2703e-03 268s [19,] 2.9353e-01 -3.3342e-03 268s [20,] 6.0915e-01 -6.0837e-03 268s [21,] -1.0079e-01 1.0179e-03 268s [22,] -2.2945e-02 -1.0515e-03 268s [23,] 2.3631e-01 -2.5558e-03 268s [24,] -7.7207e-02 3.4800e-03 268s [25,] 1.4903e-02 -3.2430e-04 268s [26,] 3.8032e-03 -2.1705e-03 268s [27,] 3.7208e-02 -3.0631e-03 268s [28,] -4.8147e-01 6.1089e-03 268s [29,] -4.0388e-02 2.8549e-03 268s [30,] 3.4318e-02 -1.0014e-03 268s [31,] -2.2872e-02 1.8706e-03 268s [32,] -8.4542e-02 1.3368e-03 268s [33,] 4.5274e-02 5.3383e-04 268s [34,] -2.0048e-01 2.4727e-03 268s [35,] -5.6482e-02 2.9923e-03 268s [36,] -2.6046e-02 -1.2910e-03 268s [37,] 9.6038e-02 -1.8897e-03 268s [38,] -2.9035e-01 4.4317e-03 268s [39,] -4.6322e-03 2.4336e-03 268s [40,] 3.8686e-01 -3.9300e-03 268s [41,] 3.7834e-01 -7.8976e-03 268s [42,] -8.2037e-04 -4.3106e-05 268s [43,] 3.3467e-01 -5.2401e-03 268s [44,] -6.2170e-01 1.2840e-02 268s [45,] 5.3557e-02 2.9156e-03 268s [46,] 5.1785e-04 2.0738e-03 268s [47,] -5.2141e-01 5.7206e-03 268s [48,] -2.7669e-01 6.7329e-03 268s [49,] 8.4319e-02 3.8528e-03 268s [50,] 1.4210e-01 1.6961e-04 268s [51,] -1.1871e-01 2.6676e-03 268s [52,] -2.5036e-01 6.4121e-03 268s [53,] 2.2399e-01 -2.8200e-03 268s [54,] 5.6532e-02 4.9304e-04 268s [55,] -1.4343e-01 1.2558e-03 268s [56,] 4.1682e-02 -9.6490e-04 268s [57,] -1.3014e-01 -6.2709e-04 268s [58,] -2.1428e-01 8.2594e-04 268s [59,] -7.9775e-02 -8.9776e-04 268s [60,] -8.6835e-02 -1.0498e-03 268s [61,] 6.2470e-02 -2.7499e-03 268s [62,] 3.3052e-02 -3.2369e-04 268s [63,] -1.7137e-01 -3.1087e-04 268s [64,] -1.7137e-01 -3.1087e-04 268s [65,] -2.8125e-15 -4.2917e-13 268s [66,] -2.2016e-02 -1.2206e-03 268s [67,] 8.5160e-02 -1.4837e-04 268s [68,] -2.2535e-03 1.9054e-04 268s [69,] 5.9976e-02 -8.6961e-04 268s [70,] 1.0448e+00 -2.0167e-02 268s [71,] -1.7609e-01 1.9378e-03 268s [72,] -1.7047e-01 2.6076e-04 268s [73,] 1.1885e-01 -8.1624e-04 268s [74,] 2.0942e-01 3.3164e-03 268s [75,] -7.7528e-01 9.9316e-03 268s [76,] -4.6285e-03 2.5153e-04 268s [77,] 7.0218e-02 1.5708e-03 268s [78,] -1.4859e-02 -6.7049e-04 268s [79,] 5.1054e-02 -2.0198e-03 268s [80,] -1.5770e-01 4.9579e-03 268s [81,] -1.9411e-01 4.4401e-04 268s [82,] 6.0634e-02 8.7960e-04 268s [83,] -4.4635e-02 -1.7048e-03 268s [84,] -2.3612e-03 -2.2242e-04 268s [85,] -5.5171e-02 -1.1222e-03 268s [86,] -1.4972e-01 1.4543e-03 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 2.8929930 1.8789522 0.9946460 0.7479403 0.3744197 0.2596328 0.1421387 268s [8] 0.0025753 268s ---------------------------------------------------------- 268s bushfire 38 5 5 37473.439646 1742.633018 268s Scores: 268s PC1 PC2 PC3 PC4 PC5 268s [1,] -67.2152 -2.3010e+01 4.4179e+00 1.0892e+00 1.7536e+00 268s [2,] -69.0225 -2.1417e+01 2.5382e+00 1.1092e+00 9.3919e-01 268s [3,] -61.6651 -1.8580e+01 -6.1022e-01 -8.1124e-01 -1.6462e-01 268s [4,] -44.5883 -1.8234e+01 -3.9899e-01 -5.2145e-01 2.0050e-01 268s [5,] -12.2941 -2.2954e+01 3.5970e+00 1.1037e+00 -2.4384e-01 268s [6,] 13.0282 -2.8133e+01 8.7670e+00 3.4751e+00 1.3728e+00 268s [7,] -199.0774 -7.7956e+01 5.4935e+01 6.3134e+00 -1.9919e+00 268s [8,] -228.2849 -1.3258e+02 2.2340e+01 2.1656e+01 -1.2594e+01 268s [9,] -228.9164 -1.3560e+02 2.0463e+01 2.2625e+01 -1.2743e+01 268s [10,] -232.4703 -1.0661e+02 3.5597e+01 1.7915e+01 -7.7659e+00 268s [11,] -236.7410 -8.8072e+01 3.6632e+01 1.5095e+01 -7.4695e+00 268s [12,] -249.4091 -3.6830e+01 2.4010e+01 4.7317e+00 -1.2986e+00 268s [13,] -197.0450 2.3179e-14 2.8034e-14 -1.1323e-13 -5.3540e-13 268s [14,] 50.9487 1.1397e+01 -1.1247e+01 -4.8733e+00 2.4511e+00 268s [15,] 180.7896 1.7571e+01 -8.0454e+00 -1.0582e+01 1.2714e+00 268s [16,] 135.6178 1.4189e+01 -4.9116e-01 -9.2701e+00 1.4021e-01 268s [17,] 132.5344 1.5577e+01 2.2990e-01 -6.4963e+00 7.3370e-01 268s [18,] 121.3422 1.0471e+01 4.5656e+00 -4.9831e+00 -5.2314e-01 268s [19,] 125.2722 9.0272e+00 3.7365e+00 -3.3313e+00 -2.9097e-01 268s [20,] 135.2370 1.4091e+01 2.0639e+00 -3.6800e+00 1.1733e+00 268s [21,] 116.4250 1.5147e+01 2.9085e+00 -4.8084e+00 1.2603e+00 268s [22,] 108.2925 1.4223e+01 7.7165e-01 -4.5065e+00 2.7943e+00 268s [23,] -22.8258 6.4234e+00 2.4654e+00 -3.9627e+00 7.9847e-01 268s [24,] -78.1850 4.6631e+00 -3.6818e+00 -2.7688e+00 5.8508e-01 268s [25,] -13.0417 2.7521e+00 -3.1955e+00 -4.6824e+00 -3.1085e-01 268s [26,] -19.1244 -9.5045e-01 -2.6771e+00 -4.7104e+00 -1.6172e-01 268s [27,] -34.4379 3.2761e+00 -9.2826e+00 -2.9861e+00 -3.3561e-01 268s [28,] -11.5852 1.4506e+01 -1.5649e+01 -1.6260e+00 -8.5347e-01 268s [29,] -2.9366 2.8741e+01 -2.2907e+01 3.9749e-01 3.5861e-02 268s [30,] -59.7518 2.8633e+01 -1.4710e+01 3.5226e+00 -9.9066e-01 268s [31,] 67.8017 4.7241e+01 -9.1255e+00 1.3201e+01 9.7648e-14 268s [32,] 321.9941 7.6188e+01 2.2491e+01 3.1537e+01 3.2368e+00 268s [33,] 359.5155 6.6710e+01 5.6061e+01 3.4541e+01 2.0718e+00 268s [34,] 355.8007 6.5695e+01 5.7430e+01 3.3578e+01 1.4640e+00 268s [35,] 361.1076 6.7577e+01 5.7402e+01 3.3832e+01 3.2618e+00 268s [36,] 358.3592 6.6791e+01 5.8643e+01 3.2720e+01 1.2487e+00 268s [37,] 355.9974 6.8071e+01 6.0927e+01 3.2560e+01 2.4898e+00 268s [38,] 357.2530 6.9073e+01 6.1517e+01 3.2523e+01 2.7558e+00 268s ------------- 268s Call: 268s PcaProj(x = x) 268s 268s Standard deviations: 268s [1] 193.5806 41.7449 16.7665 8.1585 1.6074 268s ---------------------------------------------------------- 268s ========================================================== 268s > ## IGNORE_RDIFF_END 268s > 268s > ## VT::14.11.2018 - commented out - on some platforms PcaHubert will choose only 1 PC 268s > ## and will show difference 268s > ## test.case.1() 268s > 268s > test.case.2() 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s [1] TRUE 268s > 268s BEGIN TEST tlda.R 268s 268s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 268s Copyright (C) 2025 The R Foundation for Statistical Computing 268s Platform: powerpc64le-unknown-linux-gnu 268s 268s R is free software and comes with ABSOLUTELY NO WARRANTY. 268s You are welcome to redistribute it under certain conditions. 268s Type 'license()' or 'licence()' for distribution details. 268s 268s R is a collaborative project with many contributors. 268s Type 'contributors()' for more information and 268s 'citation()' on how to cite R or R packages in publications. 268s 268s Type 'demo()' for some demos, 'help()' for on-line help, or 268s 'help.start()' for an HTML browser interface to help. 268s Type 'q()' to quit R. 268s 268s > ## VT::15.09.2013 - this will render the output independent 268s > ## from the version of the package 268s > suppressPackageStartupMessages(library(rrcov)) 268s > library(MASS) 268s > 268s > ## VT::14.01.2020 268s > ## On some platforms minor differences are shown - use 268s > ## IGNORE_RDIFF_BEGIN 268s > ## IGNORE_RDIFF_END 268s > 268s > dodata <- function(method) { 268s + 268s + options(digits = 5) 268s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 268s + 268s + tmp <- sys.call() 268s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 268s + cat("===================================================\n") 268s + 268s + cat("\nData: ", "hemophilia\n") 268s + data(hemophilia) 268s + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "anorexia\n") 268s + data(anorexia) 268s + show(rlda <- Linda(Treat~., data=anorexia, method=method)) 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "Pima\n") 268s + data(Pima.tr) 268s + show(rlda <- Linda(type~., data=Pima.tr, method=method)) 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "Forest soils\n") 268s + data(soil) 268s + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) 268s + 268s + ## This will not work within the function, of course 268s + ## - comment it out 268s + ## IGNORE_RDIFF_BEGIN 268s + rlda <- Linda(F~., data=soil1983, method=method) 268s + ## show(rlda) 268s + ## IGNORE_RDIFF_END 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "Raven and Miller diabetes data\n") 268s + data(diabetes) 268s + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "iris\n") 268s + data(iris) 268s + if(method != "mcdA") 268s + { 268s + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) 268s + show(predict(rlda)) 268s + } 268s + 268s + cat("\nData: ", "crabs\n") 268s + data(crabs) 268s + show(rlda <- Linda(sp~., data=crabs, method=method)) 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "fish\n") 268s + data(fish) 268s + fish <- fish[-14,] # remove observation #14 containing missing value 268s + 268s + # The height and width are calculated as percentages 268s + # of the third length variable 268s + fish[,5] <- fish[,5]*fish[,4]/100 268s + fish[,6] <- fish[,6]*fish[,4]/100 268s + 268s + ## There is one class with only 6 observations (p=6). Normally 268s + ## Linda will fail, therefore use l1med=TRUE. 268s + ## This works only for methods mcdB and mcdC 268s + 268s + table(fish$Species) 268s + if(method != "mcdA") 268s + { 268s + ## IGNORE_RDIFF_BEGIN 268s + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) 268s + ## show(rlda) 268s + ## IGNORE_RDIFF_END 268s + show(predict(rlda)) 268s + } 268s + 268s + cat("\nData: ", "pottery\n") 268s + data(pottery) 268s + show(rlda <- Linda(origin~., data=pottery, method=method)) 268s + show(predict(rlda)) 268s + 268s + cat("\nData: ", "olitos\n") 268s + data(olitos) 268s + if(method != "mcdA") 268s + { 268s + ## IGNORE_RDIFF_BEGIN 268s + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) 268s + ## show(rlda) 268s + ## IGNORE_RDIFF_END 268s + show(predict(rlda)) 268s + } 268s + 268s + cat("===================================================\n") 268s + } 268s > 268s > 268s > ## -- now do it: 268s > dodata(method="mcdA") 268s 268s Call: dodata(method = "mcdA") 268s =================================================== 268s 268s Data: hemophilia 268s Call: 268s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 268s 268s Prior Probabilities of Groups: 268s carrier normal 268s 0.6 0.4 268s 268s Group means: 268s AHFactivity AHFantigen 268s carrier -0.30795 -0.0059911 268s normal -0.12920 -0.0603000 268s 268s Within-groups Covariance Matrix: 268s AHFactivity AHFantigen 268s AHFactivity 0.018036 0.011853 268s AHFantigen 0.011853 0.019185 268s 268s Linear Coeficients: 268s AHFactivity AHFantigen 268s carrier -28.4029 17.2368 268s normal -8.5834 2.1602 268s 268s Constants: 268s carrier normal 268s -4.8325 -1.4056 268s 268s Apparent error rate 0.1333 268s 268s Classification table 268s Predicted 268s Actual carrier normal 268s carrier 39 6 268s normal 4 26 268s 268s Confusion matrix 268s Predicted 268s Actual carrier normal 268s carrier 0.867 0.133 268s normal 0.133 0.867 268s 268s Data: anorexia 268s Call: 268s Linda(Treat ~ ., data = anorexia, method = method) 268s 268s Prior Probabilities of Groups: 268s CBT Cont FT 268s 0.40278 0.36111 0.23611 268s 268s Group means: 268s Prewt Postwt 268s CBT 82.633 82.950 268s Cont 81.558 81.108 268s FT 84.331 94.762 268s 268s Within-groups Covariance Matrix: 268s Prewt Postwt 268s Prewt 26.9291 3.3862 268s Postwt 3.3862 18.2368 268s 268s Linear Coeficients: 268s Prewt Postwt 268s CBT 2.5563 4.0738 268s Cont 2.5284 3.9780 268s FT 2.5374 4.7250 268s 268s Constants: 268s CBT Cont FT 268s -275.49 -265.45 -332.31 268s 268s Apparent error rate 0.3889 268s 268s Classification table 268s Predicted 268s Actual CBT Cont FT 268s CBT 16 5 8 268s Cont 11 15 0 268s FT 0 4 13 268s 268s Confusion matrix 268s Predicted 268s Actual CBT Cont FT 268s CBT 0.552 0.172 0.276 268s Cont 0.423 0.577 0.000 268s FT 0.000 0.235 0.765 268s 268s Data: Pima 268s Call: 268s Linda(type ~ ., data = Pima.tr, method = method) 268s 268s Prior Probabilities of Groups: 268s No Yes 268s 0.66 0.34 268s 268s Group means: 268s npreg glu bp skin bmi ped age 268s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 268s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 268s 268s Within-groups Covariance Matrix: 268s npreg glu bp skin bmi ped age 268s npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 268s glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 268s bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 268s skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 268s bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 268s ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 268s age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 268s 268s Linear Coeficients: 268s npreg glu bp skin bmi ped age 268s No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 268s Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 268s 268s Constants: 268s No Yes 268s -37.050 -51.586 268s 268s Apparent error rate 0.22 268s 268s Classification table 268s Predicted 268s Actual No Yes 268s No 107 25 268s Yes 19 49 268s 268s Confusion matrix 268s Predicted 268s Actual No Yes 268s No 0.811 0.189 268s Yes 0.279 0.721 268s 268s Data: Forest soils 268s 268s Apparent error rate 0.3103 268s 268s Classification table 268s Predicted 268s Actual 1 2 3 268s 1 7 2 2 268s 2 3 13 7 268s 3 1 3 20 268s 268s Confusion matrix 268s Predicted 268s Actual 1 2 3 268s 1 0.636 0.182 0.182 268s 2 0.130 0.565 0.304 268s 3 0.042 0.125 0.833 268s 268s Data: Raven and Miller diabetes data 268s Call: 268s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 268s 268s Prior Probabilities of Groups: 268s normal chemical overt 268s 0.52414 0.24828 0.22759 268s 268s Group means: 268s insulin glucose sspg 268s normal 163.939 345.8 99.076 268s chemical 299.448 476.9 223.621 268s overt 95.958 1026.4 343.000 268s 268s Within-groups Covariance Matrix: 268s insulin glucose sspg 268s insulin 7582.0 -1263.1 1095.8 268s glucose -1263.1 18952.4 4919.3 268s sspg 1095.8 4919.3 3351.2 268s 268s Linear Coeficients: 268s insulin glucose sspg 268s normal 0.027694 0.023859 -0.014514 268s chemical 0.040288 0.022532 0.020479 268s overt 0.017144 0.048768 0.025158 268s 268s Constants: 268s normal chemical overt 268s -6.3223 -15.0879 -31.6445 268s 268s Apparent error rate 0.1862 268s 268s Classification table 268s Predicted 268s Actual normal chemical overt 268s normal 69 7 0 268s chemical 13 23 0 268s overt 2 5 26 268s 268s Confusion matrix 268s Predicted 268s Actual normal chemical overt 268s normal 0.908 0.092 0.000 268s chemical 0.361 0.639 0.000 268s overt 0.061 0.152 0.788 268s 268s Data: iris 268s 268s Data: crabs 268s Call: 268s Linda(sp ~ ., data = crabs, method = method) 268s 268s Prior Probabilities of Groups: 268s B O 268s 0.5 0.5 268s 268s Group means: 268s sexM index FL RW CL CW BD 268s B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 268s O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 268s 268s Within-groups Covariance Matrix: 268s sexM index FL RW CL CW BD 268s sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 268s index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 268s FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 268s RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 268s CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 268s CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 268s BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 268s 268s Linear Coeficients: 268s sexM index FL RW CL CW BD 268s B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 268s O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 268s 268s Constants: 268s B O 268s -78.317 -159.259 268s 268s Apparent error rate 0 268s 268s Classification table 268s Predicted 268s Actual B O 268s B 100 0 268s O 0 100 268s 268s Confusion matrix 268s Predicted 268s Actual B O 268s B 1 0 268s O 0 1 268s 268s Data: fish 268s 268s Data: pottery 268s Call: 268s Linda(origin ~ ., data = pottery, method = method) 268s 268s Prior Probabilities of Groups: 268s Attic Eritrean 268s 0.48148 0.51852 268s 268s Group means: 268s SI AL FE MG CA TI 268s Attic 55.36 13.73 9.82 5.45 6.03 0.863 268s Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 268s 268s Within-groups Covariance Matrix: 268s SI AL FE MG CA TI 268s SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 268s AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 268s FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 268s MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 268s CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 268s TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 268s 268s Linear Coeficients: 268s SI AL FE MG CA TI 268s Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 268s Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 268s 268s Constants: 268s Attic Eritrean 268s -1578.14 -901.13 268s 268s Apparent error rate 0.1111 268s 268s Classification table 268s Predicted 268s Actual Attic Eritrean 268s Attic 12 1 268s Eritrean 2 12 268s 268s Confusion matrix 268s Predicted 268s Actual Attic Eritrean 268s Attic 0.923 0.077 268s Eritrean 0.143 0.857 268s 268s Data: olitos 268s =================================================== 268s > dodata(method="mcdB") 268s 268s Call: dodata(method = "mcdB") 268s =================================================== 268s 268s Data: hemophilia 268s Call: 268s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 268s 268s Prior Probabilities of Groups: 268s carrier normal 268s 0.6 0.4 268s 268s Group means: 268s AHFactivity AHFantigen 268s carrier -0.31456 -0.014775 268s normal -0.13582 -0.069084 268s 268s Within-groups Covariance Matrix: 268s AHFactivity AHFantigen 268s AHFactivity 0.0125319 0.0086509 268s AHFantigen 0.0086509 0.0182424 268s 268s Linear Coeficients: 268s AHFactivity AHFantigen 268s carrier -36.486 16.4923 268s normal -12.226 2.0107 268s 268s Constants: 268s carrier normal 268s -6.1276 -1.6771 268s 268s Apparent error rate 0.16 268s 268s Classification table 268s Predicted 268s Actual carrier normal 268s carrier 38 7 268s normal 5 25 268s 268s Confusion matrix 268s Predicted 268s Actual carrier normal 268s carrier 0.844 0.156 268s normal 0.167 0.833 268s 268s Data: anorexia 268s Call: 268s Linda(Treat ~ ., data = anorexia, method = method) 268s 268s Prior Probabilities of Groups: 268s CBT Cont FT 268s 0.40278 0.36111 0.23611 268s 268s Group means: 268s Prewt Postwt 268s CBT 83.254 82.381 268s Cont 82.178 80.539 268s FT 84.951 94.193 268s 268s Within-groups Covariance Matrix: 268s Prewt Postwt 268s Prewt 19.1751 8.8546 268s Postwt 8.8546 25.2326 268s 268s Linear Coeficients: 268s Prewt Postwt 268s CBT 3.3822 2.0780 268s Cont 3.3555 2.0144 268s FT 3.2299 2.5996 268s 268s Constants: 268s CBT Cont FT 268s -227.29 -220.01 -261.06 268s 268s Apparent error rate 0.4444 268s 268s Classification table 268s Predicted 268s Actual CBT Cont FT 268s CBT 16 5 8 268s Cont 12 11 3 268s FT 0 4 13 268s 268s Confusion matrix 268s Predicted 268s Actual CBT Cont FT 268s CBT 0.552 0.172 0.276 268s Cont 0.462 0.423 0.115 268s FT 0.000 0.235 0.765 268s 268s Data: Pima 269s Call: 269s Linda(type ~ ., data = Pima.tr, method = method) 269s 269s Prior Probabilities of Groups: 269s No Yes 269s 0.66 0.34 269s 269s Group means: 269s npreg glu bp skin bmi ped age 269s No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 269s Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 269s 269s Within-groups Covariance Matrix: 269s npreg glu bp skin bmi ped age 269s npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 269s glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 269s bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 269s skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 269s bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 269s ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 269s age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 269s 269s Linear Coeficients: 269s npreg glu bp skin bmi ped age 269s No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 269s Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 269s 269s Constants: 269s No Yes 269s -38.774 -53.654 269s 269s Apparent error rate 0.25 269s 269s Classification table 269s Predicted 269s Actual No Yes 269s No 104 28 269s Yes 22 46 269s 269s Confusion matrix 269s Predicted 269s Actual No Yes 269s No 0.788 0.212 269s Yes 0.324 0.676 269s 269s Data: Forest soils 269s 269s Apparent error rate 0.3448 269s 269s Classification table 269s Predicted 269s Actual 1 2 3 269s 1 4 3 4 269s 2 2 14 7 269s 3 2 2 20 269s 269s Confusion matrix 269s Predicted 269s Actual 1 2 3 269s 1 0.364 0.273 0.364 269s 2 0.087 0.609 0.304 269s 3 0.083 0.083 0.833 269s 269s Data: Raven and Miller diabetes data 269s Call: 269s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 269s 269s Prior Probabilities of Groups: 269s normal chemical overt 269s 0.52414 0.24828 0.22759 269s 269s Group means: 269s insulin glucose sspg 269s normal 152.405 346.55 99.387 269s chemical 288.244 478.80 226.226 269s overt 84.754 1028.28 345.605 269s 269s Within-groups Covariance Matrix: 269s insulin glucose sspg 269s insulin 5061.46 289.69 2071.71 269s glucose 289.69 1983.07 385.31 269s sspg 2071.71 385.31 3000.17 269s 269s Linear Coeficients: 269s insulin glucose sspg 269s normal 0.021952 0.17236 -0.0041671 269s chemical 0.034852 0.23217 0.0215200 269s overt -0.045700 0.50940 0.0813292 269s 269s Constants: 269s normal chemical overt 269s -31.976 -64.433 -275.502 269s 269s Apparent error rate 0.0966 269s 269s Classification table 269s Predicted 269s Actual normal chemical overt 269s normal 73 3 0 269s chemical 4 32 0 269s overt 0 7 26 269s 269s Confusion matrix 269s Predicted 269s Actual normal chemical overt 269s normal 0.961 0.039 0.000 269s chemical 0.111 0.889 0.000 269s overt 0.000 0.212 0.788 269s 269s Data: iris 269s Call: 269s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 269s 269s Prior Probabilities of Groups: 269s setosa versicolor virginica 269s 0.33333 0.33333 0.33333 269s 269s Group means: 269s Sepal.Length Sepal.Width Petal.Length Petal.Width 269s setosa 4.9834 3.4153 1.4532 0.22474 269s versicolor 5.8947 2.8149 4.2263 1.35024 269s virginica 6.5255 3.0017 5.4485 2.06756 269s 269s Within-groups Covariance Matrix: 269s Sepal.Length Sepal.Width Petal.Length Petal.Width 269s Sepal.Length 0.201176 0.084299 0.102984 0.037019 269s Sepal.Width 0.084299 0.108394 0.050253 0.031757 269s Petal.Length 0.102984 0.050253 0.120215 0.045016 269s Petal.Width 0.037019 0.031757 0.045016 0.032825 269s 269s Linear Coeficients: 269s Sepal.Length Sepal.Width Petal.Length Petal.Width 269s setosa 22.536 27.422168 -3.6855 -40.0445 269s versicolor 17.559 6.374082 24.1965 -18.0178 269s virginica 16.488 0.015576 29.9586 3.2926 269s 269s Constants: 269s setosa versicolor virginica 269s -96.901 -100.790 -139.937 269s 269s Apparent error rate 0.0267 269s 269s Classification table 269s Predicted 269s Actual setosa versicolor virginica 269s setosa 50 0 0 269s versicolor 0 48 2 269s virginica 0 2 48 269s 269s Confusion matrix 269s Predicted 269s Actual setosa versicolor virginica 269s setosa 1 0.00 0.00 269s versicolor 0 0.96 0.04 269s virginica 0 0.04 0.96 269s 269s Data: crabs 269s Call: 269s Linda(sp ~ ., data = crabs, method = method) 269s 269s Prior Probabilities of Groups: 269s B O 269s 0.5 0.5 269s 269s Group means: 269s sexM index FL RW CL CW BD 269s B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 269s O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 269s 269s Within-groups Covariance Matrix: 269s sexM index FL RW CL CW BD 269s sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 269s index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 269s FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 269s RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 269s CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 269s CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 269s BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 269s 269s Linear Coeficients: 269s sexM index FL RW CL CW BD 269s B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 269s O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 269s 269s Constants: 269s B O 269s -66.688 -134.375 269s 269s Apparent error rate 0 269s 269s Classification table 269s Predicted 269s Actual B O 269s B 100 0 269s O 0 100 269s 269s Confusion matrix 269s Predicted 269s Actual B O 269s B 1 0 269s O 0 1 269s 269s Data: fish 269s 269s Apparent error rate 0.0949 269s 269s Classification table 269s Predicted 269s Actual 1 2 3 4 5 6 7 269s 1 34 0 0 0 0 0 0 269s 2 0 6 0 0 0 0 0 269s 3 0 0 20 0 0 0 0 269s 4 0 0 0 11 0 0 0 269s 5 0 0 0 0 13 0 1 269s 6 0 0 0 0 0 17 0 269s 7 0 13 0 0 1 0 42 269s 269s Confusion matrix 269s Predicted 269s Actual 1 2 3 4 5 6 7 269s 1 1 0.000 0 0 0.000 0 0.000 269s 2 0 1.000 0 0 0.000 0 0.000 269s 3 0 0.000 1 0 0.000 0 0.000 269s 4 0 0.000 0 1 0.000 0 0.000 269s 5 0 0.000 0 0 0.929 0 0.071 269s 6 0 0.000 0 0 0.000 1 0.000 269s 7 0 0.232 0 0 0.018 0 0.750 269s 269s Data: pottery 269s Call: 269s Linda(origin ~ ., data = pottery, method = method) 269s 269s Prior Probabilities of Groups: 269s Attic Eritrean 269s 0.48148 0.51852 269s 269s Group means: 269s SI AL FE MG CA TI 269s Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 269s Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 269s 269s Within-groups Covariance Matrix: 269s SI AL FE MG CA TI 269s SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 269s AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 269s FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 269s MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 269s CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 269s TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 269s 269s Linear Coeficients: 269s SI AL FE MG CA TI 269s Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 269s Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 269s 269s Constants: 269s Attic Eritrean 269s -3252.6 -1961.9 269s 269s Apparent error rate 0.1111 269s 269s Classification table 269s Predicted 269s Actual Attic Eritrean 269s Attic 12 1 269s Eritrean 2 12 269s 269s Confusion matrix 269s Predicted 269s Actual Attic Eritrean 269s Attic 0.923 0.077 269s Eritrean 0.143 0.857 269s 269s Data: olitos 269s 269s Apparent error rate 0.15 269s 269s Classification table 269s Predicted 269s Actual 1 2 3 4 269s 1 44 1 4 1 269s 2 2 23 0 0 269s 3 6 1 26 1 269s 4 1 1 0 9 269s 269s Confusion matrix 269s Predicted 269s Actual 1 2 3 4 269s 1 0.880 0.020 0.080 0.020 269s 2 0.080 0.920 0.000 0.000 269s 3 0.176 0.029 0.765 0.029 269s 4 0.091 0.091 0.000 0.818 269s =================================================== 269s > dodata(method="mcdC") 269s 269s Call: dodata(method = "mcdC") 269s =================================================== 269s 269s Data: hemophilia 269s Call: 269s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 269s 269s Prior Probabilities of Groups: 269s carrier normal 269s 0.6 0.4 269s 269s Group means: 269s AHFactivity AHFantigen 269s carrier -0.32583 -0.011545 269s normal -0.12783 -0.071377 269s 269s Within-groups Covariance Matrix: 269s AHFactivity AHFantigen 269s AHFactivity 0.0120964 0.0075536 269s AHFantigen 0.0075536 0.0164883 269s 269s Linear Coeficients: 269s AHFactivity AHFantigen 269s carrier -37.117 16.30377 269s normal -11.015 0.71742 269s 269s Constants: 269s carrier normal 269s -6.4636 -1.5947 269s 269s Apparent error rate 0.16 269s 269s Classification table 269s Predicted 269s Actual carrier normal 269s carrier 38 7 269s normal 5 25 269s 269s Confusion matrix 269s Predicted 269s Actual carrier normal 269s carrier 0.844 0.156 269s normal 0.167 0.833 269s 269s Data: anorexia 269s Call: 269s Linda(Treat ~ ., data = anorexia, method = method) 269s 269s Prior Probabilities of Groups: 269s CBT Cont FT 269s 0.40278 0.36111 0.23611 269s 269s Group means: 269s Prewt Postwt 269s CBT 82.477 82.073 269s Cont 82.039 80.835 269s FT 85.242 94.750 269s 269s Within-groups Covariance Matrix: 269s Prewt Postwt 269s Prewt 19.6589 8.3891 269s Postwt 8.3891 22.8805 269s 269s Linear Coeficients: 269s Prewt Postwt 269s CBT 3.1590 2.4288 269s Cont 3.1599 2.3743 269s FT 3.0454 3.0245 269s 269s Constants: 269s CBT Cont FT 269s -230.85 -226.60 -274.53 269s 269s Apparent error rate 0.4583 269s 269s Classification table 269s Predicted 269s Actual CBT Cont FT 269s CBT 16 5 8 269s Cont 14 10 2 269s FT 0 4 13 269s 269s Confusion matrix 269s Predicted 269s Actual CBT Cont FT 269s CBT 0.552 0.172 0.276 269s Cont 0.538 0.385 0.077 269s FT 0.000 0.235 0.765 269s 269s Data: Pima 269s Call: 269s Linda(type ~ ., data = Pima.tr, method = method) 269s 269s Prior Probabilities of Groups: 269s No Yes 269s 0.66 0.34 269s 269s Group means: 269s npreg glu bp skin bmi ped age 269s No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 269s Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 269s 269s Within-groups Covariance Matrix: 269s npreg glu bp skin bmi ped age 269s npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 269s glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 269s bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 269s skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 269s bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 269s ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 269s age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 269s 269s Linear Coeficients: 269s npreg glu bp skin bmi ped age 269s No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 269s Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 269s 269s Constants: 269s No Yes 269s -41.716 -55.056 269s 269s Apparent error rate 0.235 269s 269s Classification table 269s Predicted 269s Actual No Yes 269s No 107 25 269s Yes 22 46 269s 269s Confusion matrix 269s Predicted 269s Actual No Yes 269s No 0.811 0.189 269s Yes 0.324 0.676 269s 269s Data: Forest soils 269s 269s Apparent error rate 0.3276 269s 269s Classification table 269s Predicted 269s Actual 1 2 3 269s 1 5 2 4 269s 2 2 13 8 269s 3 1 2 21 269s 269s Confusion matrix 269s Predicted 269s Actual 1 2 3 269s 1 0.455 0.182 0.364 269s 2 0.087 0.565 0.348 269s 3 0.042 0.083 0.875 269s 269s Data: Raven and Miller diabetes data 269s Call: 269s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 269s 269s Prior Probabilities of Groups: 269s normal chemical overt 269s 0.52414 0.24828 0.22759 269s 269s Group means: 269s insulin glucose sspg 269s normal 167.31 348.69 106.44 269s chemical 247.18 478.18 213.36 269s overt 101.83 932.92 322.42 269s 269s Within-groups Covariance Matrix: 269s insulin glucose sspg 269s insulin 4070.84 118.89 1701.54 269s glucose 118.89 2195.95 426.95 269s sspg 1701.54 426.95 2664.49 269s 269s Linear Coeficients: 269s insulin glucose sspg 269s normal 0.041471 0.15888 -0.011992 269s chemical 0.048103 0.21216 0.015359 269s overt -0.013579 0.41323 0.063462 269s 269s Constants: 269s normal chemical overt 269s -31.177 -59.703 -203.775 269s 269s Apparent error rate 0.0828 269s 269s Classification table 269s Predicted 269s Actual normal chemical overt 269s normal 72 4 0 269s chemical 2 34 0 269s overt 0 6 27 269s 269s Confusion matrix 269s Predicted 269s Actual normal chemical overt 269s normal 0.947 0.053 0.000 269s chemical 0.056 0.944 0.000 269s overt 0.000 0.182 0.818 269s 269s Data: iris 269s Call: 269s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 269s 269s Prior Probabilities of Groups: 269s setosa versicolor virginica 269s 0.33333 0.33333 0.33333 269s 269s Group means: 269s Sepal.Length Sepal.Width Petal.Length Petal.Width 269s setosa 5.0163 3.4510 1.4653 0.2449 269s versicolor 5.9435 2.7891 4.2543 1.3239 269s virginica 6.3867 3.0033 5.3767 2.0700 269s 269s Within-groups Covariance Matrix: 269s Sepal.Length Sepal.Width Petal.Length Petal.Width 269s Sepal.Length 0.186186 0.082478 0.094998 0.035445 269s Sepal.Width 0.082478 0.100137 0.049723 0.030678 269s Petal.Length 0.094998 0.049723 0.113105 0.043078 269s Petal.Width 0.035445 0.030678 0.043078 0.030885 269s 269s Linear Coeficients: 269s Sepal.Length Sepal.Width Petal.Length Petal.Width 269s setosa 23.678 30.2896 -3.1124 -44.9900 269s versicolor 20.342 4.6372 27.3265 -23.2006 269s virginica 18.377 -2.0004 31.4235 4.0906 269s 269s Constants: 269s setosa versicolor virginica 269s -104.96 -110.79 -145.49 269s 269s Apparent error rate 0.0333 269s 269s Classification table 269s Predicted 269s Actual setosa versicolor virginica 269s setosa 50 0 0 269s versicolor 0 48 2 269s virginica 0 3 47 269s 269s Confusion matrix 269s Predicted 269s Actual setosa versicolor virginica 269s setosa 1 0.00 0.00 269s versicolor 0 0.96 0.04 269s virginica 0 0.06 0.94 269s 269s Data: crabs 270s Call: 270s Linda(sp ~ ., data = crabs, method = method) 270s 270s Prior Probabilities of Groups: 270s B O 270s 0.5 0.5 270s 270s Group means: 270s sexM index FL RW CL CW BD 270s B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 270s O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 270s 270s Within-groups Covariance Matrix: 270s sexM index FL RW CL CW BD 270s sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 270s index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 270s FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 270s RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 270s CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 270s CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 270s BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 270s 270s Linear Coeficients: 270s sexM index FL RW CL CW BD 270s B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 270s O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 270s 270s Constants: 270s B O 270s -72.687 -142.365 270s 270s Apparent error rate 0 270s 270s Classification table 270s Predicted 270s Actual B O 270s B 100 0 270s O 0 100 270s 270s Confusion matrix 270s Predicted 270s Actual B O 270s B 1 0 270s O 0 1 270s 270s Data: fish 270s 270s Apparent error rate 0.0316 270s 270s Classification table 270s Predicted 270s Actual 1 2 3 4 5 6 7 270s 1 34 0 0 0 0 0 0 270s 2 0 5 0 0 1 0 0 270s 3 0 0 20 0 0 0 0 270s 4 0 0 0 11 0 0 0 270s 5 0 0 0 0 13 0 1 270s 6 0 0 0 0 0 17 0 270s 7 0 0 0 0 3 0 53 270s 270s Confusion matrix 270s Predicted 270s Actual 1 2 3 4 5 6 7 270s 1 1 0.000 0 0 0.000 0 0.000 270s 2 0 0.833 0 0 0.167 0 0.000 270s 3 0 0.000 1 0 0.000 0 0.000 270s 4 0 0.000 0 1 0.000 0 0.000 270s 5 0 0.000 0 0 0.929 0 0.071 270s 6 0 0.000 0 0 0.000 1 0.000 270s 7 0 0.000 0 0 0.054 0 0.946 270s 270s Data: pottery 270s Call: 270s Linda(origin ~ ., data = pottery, method = method) 270s 270s Prior Probabilities of Groups: 270s Attic Eritrean 270s 0.48148 0.51852 270s 270s Group means: 270s SI AL FE MG CA TI 270s Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 270s Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 270s 270s Within-groups Covariance Matrix: 270s SI AL FE MG CA TI 270s SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 270s AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 270s FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 270s MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 270s CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 270s TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 270s 270s Linear Coeficients: 270s SI AL FE MG CA TI 270s Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 270s Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 270s 270s Constants: 270s Attic Eritrean 270s -5924.2 -3802.9 270s 270s Apparent error rate 0.1111 270s 270s Classification table 270s Predicted 270s Actual Attic Eritrean 270s Attic 12 1 270s Eritrean 2 12 270s 270s Confusion matrix 270s Predicted 270s Actual Attic Eritrean 270s Attic 0.923 0.077 270s Eritrean 0.143 0.857 270s 270s Data: olitos 270s 270s Apparent error rate 0.1667 270s 270s Classification table 270s Predicted 270s Actual 1 2 3 4 270s 1 44 1 2 3 270s 2 2 22 0 1 270s 3 5 2 25 2 270s 4 1 1 0 9 270s 270s Confusion matrix 270s Predicted 270s Actual 1 2 3 4 270s 1 0.880 0.020 0.040 0.060 270s 2 0.080 0.880 0.000 0.040 270s 3 0.147 0.059 0.735 0.059 270s 4 0.091 0.091 0.000 0.818 270s =================================================== 270s > dodata(method="mrcd") 270s 270s Call: dodata(method = "mrcd") 270s =================================================== 270s 270s Data: hemophilia 270s Call: 270s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 270s 270s Prior Probabilities of Groups: 270s carrier normal 270s 0.6 0.4 270s 270s Group means: 270s AHFactivity AHFantigen 270s carrier -0.34048 -0.055943 270s normal -0.13566 -0.081191 270s 270s Within-groups Covariance Matrix: 270s AHFactivity AHFantigen 270s AHFactivity 0.0133676 0.0088055 270s AHFantigen 0.0088055 0.0221225 270s 270s Linear Coeficients: 270s AHFactivity AHFantigen 270s carrier -32.264 10.31334 270s normal -10.478 0.50044 270s 270s Constants: 270s carrier normal 270s -5.7149 -1.6067 270s 270s Apparent error rate 0.16 270s 270s Classification table 270s Predicted 270s Actual carrier normal 270s carrier 38 7 270s normal 5 25 270s 270s Confusion matrix 270s Predicted 270s Actual carrier normal 270s carrier 0.844 0.156 270s normal 0.167 0.833 270s 270s Data: anorexia 270s Call: 270s Linda(Treat ~ ., data = anorexia, method = method) 270s 270s Prior Probabilities of Groups: 270s CBT Cont FT 270s 0.40278 0.36111 0.23611 270s 270s Group means: 270s Prewt Postwt 270s CBT 83.114 84.009 270s Cont 80.327 80.125 270s FT 85.161 94.371 270s 270s Within-groups Covariance Matrix: 270s Prewt Postwt 270s Prewt 22.498 11.860 270s Postwt 11.860 20.426 270s 270s Linear Coeficients: 270s Prewt Postwt 270s CBT 2.1994 2.8357 270s Cont 2.1653 2.6654 270s FT 1.9451 3.4907 270s 270s Constants: 270s CBT Cont FT 270s -211.42 -194.77 -248.97 270s 270s Apparent error rate 0.3889 270s 270s Classification table 270s Predicted 270s Actual CBT Cont FT 270s CBT 15 6 8 270s Cont 6 16 4 270s FT 0 4 13 270s 270s Confusion matrix 270s Predicted 270s Actual CBT Cont FT 270s CBT 0.517 0.207 0.276 270s Cont 0.231 0.615 0.154 270s FT 0.000 0.235 0.765 270s 270s Data: Pima 270s Call: 270s Linda(type ~ ., data = Pima.tr, method = method) 270s 270s Prior Probabilities of Groups: 270s No Yes 270s 0.66 0.34 270s 270s Group means: 270s npreg glu bp skin bmi ped age 270s No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 270s Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 270s 270s Within-groups Covariance Matrix: 270s npreg glu bp skin bmi ped age 270s npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 270s glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 270s bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 270s skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 270s bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 270s ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 270s age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 270s 270s Linear Coeficients: 270s npreg glu bp skin bmi ped age 270s No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 270s Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 270s 270s Constants: 270s No Yes 270s -33.429 -45.434 270s 270s Apparent error rate 0.28 270s 270s Classification table 270s Predicted 270s Actual No Yes 270s No 105 27 270s Yes 29 39 270s 270s Confusion matrix 270s Predicted 270s Actual No Yes 270s No 0.795 0.205 270s Yes 0.426 0.574 270s 270s Data: Forest soils 270s 270s Apparent error rate 0.3448 270s 270s Classification table 270s Predicted 270s Actual 1 2 3 270s 1 7 2 2 270s 2 4 14 5 270s 3 3 4 17 270s 270s Confusion matrix 270s Predicted 270s Actual 1 2 3 270s 1 0.636 0.182 0.182 270s 2 0.174 0.609 0.217 270s 3 0.125 0.167 0.708 270s 270s Data: Raven and Miller diabetes data 270s Call: 270s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 270s 270s Prior Probabilities of Groups: 270s normal chemical overt 270s 0.52414 0.24828 0.22759 270s 270s Group means: 270s insulin glucose sspg 270s normal 154.014 346.07 91.606 270s chemical 248.841 451.10 221.936 270s overt 89.766 1064.16 335.100 270s 270s Within-groups Covariance Matrix: 270s insulin glucose sspg 270s insulin 4948.1 1007.61 1471.12 270s glucose 1007.6 2597.38 358.57 270s sspg 1471.1 358.57 3180.04 270s 270s Linear Coeficients: 270s insulin glucose sspg 270s normal 0.00027839 0.13121 0.013882 270s chemical 0.00148074 0.16615 0.050371 270s overt -0.10102404 0.43466 0.103100 270s 270s Constants: 270s normal chemical overt 270s -24.008 -44.642 -245.497 270s 270s Apparent error rate 0.0966 270s 270s Classification table 270s Predicted 270s Actual normal chemical overt 270s normal 71 5 0 270s chemical 2 34 0 270s overt 0 7 26 270s 270s Confusion matrix 270s Predicted 270s Actual normal chemical overt 270s normal 0.934 0.066 0.000 270s chemical 0.056 0.944 0.000 270s overt 0.000 0.212 0.788 270s 270s Data: iris 270s Call: 270s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 270s 270s Prior Probabilities of Groups: 270s setosa versicolor virginica 270s 0.33333 0.33333 0.33333 270s 270s Group means: 270s Sepal.Length Sepal.Width Petal.Length Petal.Width 270s setosa 4.9755 3.3826 1.4608 0.22053 270s versicolor 5.8868 2.7823 4.2339 1.34603 270s virginica 6.5176 2.9691 5.4560 2.06335 270s 270s Within-groups Covariance Matrix: 270s Sepal.Length Sepal.Width Petal.Length Petal.Width 270s Sepal.Length 0.238417 0.136325 0.086377 0.036955 270s Sepal.Width 0.136325 0.148452 0.067500 0.034804 270s Petal.Length 0.086377 0.067500 0.100934 0.035968 270s Petal.Width 0.036955 0.034804 0.035968 0.023856 270s 270s Linear Coeficients: 270s Sepal.Length Sepal.Width Petal.Length Petal.Width 270s setosa 17.106 15.693 7.8806 -52.031 270s versicolor 21.744 -15.813 38.0139 -11.505 270s virginica 23.032 -26.567 43.6391 23.777 270s 270s Constants: 270s setosa versicolor virginica 270s -70.214 -115.832 -180.294 270s 270s Apparent error rate 0.02 270s 270s Classification table 270s Predicted 270s Actual setosa versicolor virginica 270s setosa 50 0 0 270s versicolor 0 49 1 270s virginica 0 2 48 270s 270s Confusion matrix 270s Predicted 270s Actual setosa versicolor virginica 270s setosa 1 0.00 0.00 270s versicolor 0 0.98 0.02 270s virginica 0 0.04 0.96 270s 270s Data: crabs 270s Call: 270s Linda(sp ~ ., data = crabs, method = method) 270s 270s Prior Probabilities of Groups: 270s B O 270s 0.5 0.5 270s 270s Group means: 270s sexM index FL RW CL CW BD 270s B 0 25.5 13.270 12.138 28.102 32.624 11.816 270s O 1 25.5 16.626 12.262 33.688 37.188 15.324 270s 270s Within-groups Covariance Matrix: 270s sexM index FL RW CL CW BD 270s sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 270s index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 270s FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 270s RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 270s CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 270s CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 270s BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 270s 270s Linear Coeficients: 270s sexM index FL RW CL CW BD 270s B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 270s O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 270s 270s Constants: 270s B O 270s -2.1491e+01 -3.2776e+06 270s 270s Apparent error rate 0.5 270s 270s Classification table 270s Predicted 270s Actual B O 270s B 50 50 270s O 50 50 270s 270s Confusion matrix 270s Predicted 270s Actual B O 270s B 0.5 0.5 270s O 0.5 0.5 270s 270s Data: fish 270s 270s Apparent error rate 0.2532 270s 270s Classification table 270s Predicted 270s Actual 1 2 3 4 5 6 7 270s 1 33 0 0 1 0 0 0 270s 2 0 3 0 0 0 0 3 270s 3 0 2 5 0 0 0 13 270s 4 0 0 0 11 0 0 0 270s 5 0 0 0 0 14 0 0 270s 6 0 0 0 0 0 17 0 270s 7 0 19 0 0 2 0 35 270s 270s Confusion matrix 270s Predicted 270s Actual 1 2 3 4 5 6 7 270s 1 0.971 0.000 0.00 0.029 0.000 0 0.000 270s 2 0.000 0.500 0.00 0.000 0.000 0 0.500 270s 3 0.000 0.100 0.25 0.000 0.000 0 0.650 270s 4 0.000 0.000 0.00 1.000 0.000 0 0.000 270s 5 0.000 0.000 0.00 0.000 1.000 0 0.000 270s 6 0.000 0.000 0.00 0.000 0.000 1 0.000 270s 7 0.000 0.339 0.00 0.000 0.036 0 0.625 270s 270s Data: pottery 270s Call: 270s Linda(origin ~ ., data = pottery, method = method) 270s 270s Prior Probabilities of Groups: 270s Attic Eritrean 270s 0.48148 0.51852 270s 270s Group means: 270s SI AL FE MG CA TI 270s Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 270s Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 270s 270s Within-groups Covariance Matrix: 270s SI AL FE MG CA TI 270s SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 270s AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 270s FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 270s MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 270s CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 270s TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 270s 270s Linear Coeficients: 270s SI AL FE MG CA TI 270s Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 270s Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 270s 270s Constants: 270s Attic Eritrean 270s -4000.1 -3776.1 270s 270s Apparent error rate 0.1111 270s 270s Classification table 270s Predicted 270s Actual Attic Eritrean 270s Attic 12 1 270s Eritrean 2 12 270s 270s Confusion matrix 270s Predicted 270s Actual Attic Eritrean 270s Attic 0.923 0.077 270s Eritrean 0.143 0.857 270s 270s Data: olitos 271s 271s Apparent error rate 0.125 271s 271s Classification table 271s Predicted 271s Actual 1 2 3 4 271s 1 44 2 3 1 271s 2 1 23 1 0 271s 3 4 1 27 2 271s 4 0 0 0 11 271s 271s Confusion matrix 271s Predicted 271s Actual 1 2 3 4 271s 1 0.880 0.040 0.060 0.020 271s 2 0.040 0.920 0.040 0.000 271s 3 0.118 0.029 0.794 0.059 271s 4 0.000 0.000 0.000 1.000 271s =================================================== 271s > dodata(method="ogk") 271s 271s Call: dodata(method = "ogk") 271s =================================================== 271s 271s Data: hemophilia 271s Call: 271s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 271s 271s Prior Probabilities of Groups: 271s carrier normal 271s 0.6 0.4 271s 271s Group means: 271s AHFactivity AHFantigen 271s carrier -0.29043 -0.00052902 271s normal -0.12463 -0.06715037 271s 271s Within-groups Covariance Matrix: 271s AHFactivity AHFantigen 271s AHFactivity 0.015688 0.010544 271s AHFantigen 0.010544 0.016633 271s 271s Linear Coeficients: 271s AHFactivity AHFantigen 271s carrier -32.2203 20.3935 271s normal -9.1149 1.7409 271s 271s Constants: 271s carrier normal 271s -5.1843 -1.4259 271s 271s Apparent error rate 0.1467 271s 271s Classification table 271s Predicted 271s Actual carrier normal 271s carrier 38 7 271s normal 4 26 271s 271s Confusion matrix 271s Predicted 271s Actual carrier normal 271s carrier 0.844 0.156 271s normal 0.133 0.867 271s 271s Data: anorexia 271s Call: 271s Linda(Treat ~ ., data = anorexia, method = method) 271s 271s Prior Probabilities of Groups: 271s CBT Cont FT 271s 0.40278 0.36111 0.23611 271s 271s Group means: 271s Prewt Postwt 271s CBT 82.634 82.060 271s Cont 81.605 80.459 271s FT 85.157 93.822 271s 271s Within-groups Covariance Matrix: 271s Prewt Postwt 271s Prewt 15.8294 4.4663 271s Postwt 4.4663 19.6356 271s 271s Linear Coeficients: 271s Prewt Postwt 271s CBT 4.3183 3.1970 271s Cont 4.2734 3.1256 271s FT 4.3080 3.7983 271s 271s Constants: 271s CBT Cont FT 271s -310.50 -301.12 -363.05 271s 271s Apparent error rate 0.4583 271s 271s Classification table 271s Predicted 271s Actual CBT Cont FT 271s CBT 15 5 9 271s Cont 14 11 1 271s FT 0 4 13 271s 271s Confusion matrix 271s Predicted 271s Actual CBT Cont FT 271s CBT 0.517 0.172 0.310 271s Cont 0.538 0.423 0.038 271s FT 0.000 0.235 0.765 271s 271s Data: Pima 271s Call: 271s Linda(type ~ ., data = Pima.tr, method = method) 271s 271s Prior Probabilities of Groups: 271s No Yes 271s 0.66 0.34 271s 271s Group means: 271s npreg glu bp skin bmi ped age 271s No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 271s Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 271s 271s Within-groups Covariance Matrix: 271s npreg glu bp skin bmi ped age 271s npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 271s glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 271s bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 271s skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 271s bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 271s ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 271s age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 271s 271s Linear Coeficients: 271s npreg glu bp skin bmi ped age 271s No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 271s Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 271s 271s Constants: 271s No Yes 271s -43.449 -60.176 271s 271s Apparent error rate 0.23 271s 271s Classification table 271s Predicted 271s Actual No Yes 271s No 108 24 271s Yes 22 46 271s 271s Confusion matrix 271s Predicted 271s Actual No Yes 271s No 0.818 0.182 271s Yes 0.324 0.676 271s 271s Data: Forest soils 271s 271s Apparent error rate 0.3621 271s 271s Classification table 271s Predicted 271s Actual 1 2 3 271s 1 7 3 1 271s 2 4 13 6 271s 3 3 4 17 271s 271s Confusion matrix 271s Predicted 271s Actual 1 2 3 271s 1 0.636 0.273 0.091 271s 2 0.174 0.565 0.261 271s 3 0.125 0.167 0.708 271s 271s Data: Raven and Miller diabetes data 271s Call: 271s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 271s 271s Prior Probabilities of Groups: 271s normal chemical overt 271s 0.52414 0.24828 0.22759 271s 271s Group means: 271s insulin glucose sspg 271s normal 159.540 344.06 99.486 271s chemical 252.992 478.16 219.442 271s overt 79.635 1094.96 338.517 271s 271s Within-groups Covariance Matrix: 271s insulin glucose sspg 271s insulin 3844.877 67.238 1456.55 271s glucose 67.238 2228.396 324.21 271s sspg 1456.548 324.205 2181.73 271s 271s Linear Coeficients: 271s insulin glucose sspg 271s normal 0.040407 0.15379 -0.0042303 271s chemical 0.047858 0.20764 0.0377766 271s overt -0.026158 0.47736 0.1016873 271s 271s Constants: 271s normal chemical overt 271s -30.115 -61.233 -278.996 271s 271s Apparent error rate 0.0966 271s 271s Classification table 271s Predicted 271s Actual normal chemical overt 271s normal 71 5 0 271s chemical 2 34 0 271s overt 0 7 26 271s 271s Confusion matrix 271s Predicted 271s Actual normal chemical overt 271s normal 0.934 0.066 0.000 271s chemical 0.056 0.944 0.000 271s overt 0.000 0.212 0.788 271s 271s Data: iris 271s Call: 271s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 271s 271s Prior Probabilities of Groups: 271s setosa versicolor virginica 271s 0.33333 0.33333 0.33333 271s 271s Group means: 271s Sepal.Length Sepal.Width Petal.Length Petal.Width 271s setosa 4.9654 3.3913 1.4507 0.21639 271s versicolor 5.8767 2.7909 4.2238 1.34189 271s virginica 6.5075 2.9777 5.4459 2.05921 271s 271s Within-groups Covariance Matrix: 271s Sepal.Length Sepal.Width Petal.Length Petal.Width 271s Sepal.Length 0.180280 0.068876 0.101512 0.036096 271s Sepal.Width 0.068876 0.079556 0.047722 0.029409 271s Petal.Length 0.101512 0.047722 0.111492 0.038658 271s Petal.Width 0.036096 0.029409 0.038658 0.029965 271s 271s Linear Coeficients: 271s Sepal.Length Sepal.Width Petal.Length Petal.Width 271s setosa 28.582 46.5236 -13.859 -54.9877 271s versicolor 19.837 11.9601 20.865 -17.7704 271s virginica 16.999 1.9046 29.808 7.9189 271s 271s Constants: 271s setosa versicolor virginica 271s -134.94 -108.22 -148.56 271s 271s Apparent error rate 0.0133 271s 271s Classification table 271s Predicted 271s Actual setosa versicolor virginica 271s setosa 50 0 0 271s versicolor 0 49 1 271s virginica 0 1 49 271s 271s Confusion matrix 271s Predicted 271s Actual setosa versicolor virginica 271s setosa 1 0.00 0.00 271s versicolor 0 0.98 0.02 271s virginica 0 0.02 0.98 271s 271s Data: crabs 271s Call: 271s Linda(sp ~ ., data = crabs, method = method) 271s 271s Prior Probabilities of Groups: 271s B O 271s 0.5 0.5 271s 271s Group means: 271s sexM index FL RW CL CW BD 271s B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 271s O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 271s 271s Within-groups Covariance Matrix: 271s sexM index FL RW CL CW BD 271s sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 271s index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 271s FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 271s RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 271s CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 271s CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 271s BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 271s 271s Linear Coeficients: 271s sexM index FL RW CL CW BD 271s B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 271s O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 271s 271s Constants: 271s B O 271s -77.695 -147.287 271s 271s Apparent error rate 0 271s 271s Classification table 271s Predicted 271s Actual B O 271s B 100 0 271s O 0 100 271s 271s Confusion matrix 271s Predicted 271s Actual B O 271s B 1 0 271s O 0 1 271s 271s Data: fish 271s 271s Apparent error rate 0.0063 271s 271s Classification table 271s Predicted 271s Actual 1 2 3 4 5 6 7 271s 1 34 0 0 0 0 0 0 271s 2 0 6 0 0 0 0 0 271s 3 0 0 20 0 0 0 0 271s 4 0 0 0 11 0 0 0 271s 5 0 0 0 0 14 0 0 271s 6 0 0 0 0 0 17 0 271s 7 0 0 0 0 1 0 55 271s 271s Confusion matrix 271s Predicted 271s Actual 1 2 3 4 5 6 7 271s 1 1 0 0 0 0.000 0 0.000 271s 2 0 1 0 0 0.000 0 0.000 271s 3 0 0 1 0 0.000 0 0.000 271s 4 0 0 0 1 0.000 0 0.000 271s 5 0 0 0 0 1.000 0 0.000 271s 6 0 0 0 0 0.000 1 0.000 271s 7 0 0 0 0 0.018 0 0.982 271s 271s Data: pottery 271s Call: 271s Linda(origin ~ ., data = pottery, method = method) 271s 271s Prior Probabilities of Groups: 271s Attic Eritrean 271s 0.48148 0.51852 271s 271s Group means: 271s SI AL FE MG CA TI 271s Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 271s Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 271s 271s Within-groups Covariance Matrix: 271s SI AL FE MG CA TI 271s SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 271s AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 271s FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 271s MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 271s CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 271s TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 271s 271s Linear Coeficients: 271s SI AL FE MG CA TI 271s Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 271s Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 271s 271s Constants: 271s Attic Eritrean 271s -4346 -4139 271s 271s Apparent error rate 0.1111 271s 271s Classification table 271s Predicted 271s Actual Attic Eritrean 271s Attic 12 1 271s Eritrean 2 12 271s 271s Confusion matrix 271s Predicted 271s Actual Attic Eritrean 271s Attic 0.923 0.077 271s Eritrean 0.143 0.857 271s 271s Data: olitos 271s 271s Apparent error rate 0.1 271s 271s Classification table 271s Predicted 271s Actual 1 2 3 4 271s 1 45 2 2 1 271s 2 0 25 0 0 271s 3 4 1 27 2 271s 4 0 0 0 11 271s 271s Confusion matrix 271s Predicted 271s Actual 1 2 3 4 271s 1 0.900 0.040 0.040 0.020 271s 2 0.000 1.000 0.000 0.000 271s 3 0.118 0.029 0.794 0.059 271s 4 0.000 0.000 0.000 1.000 271s =================================================== 271s > #dodata(method="fsa") 271s > 271s BEGIN TEST tldapp.R 271s 271s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 271s Copyright (C) 2025 The R Foundation for Statistical Computing 271s Platform: powerpc64le-unknown-linux-gnu 271s 271s R is free software and comes with ABSOLUTELY NO WARRANTY. 271s You are welcome to redistribute it under certain conditions. 271s Type 'license()' or 'licence()' for distribution details. 271s 271s R is a collaborative project with many contributors. 271s Type 'contributors()' for more information and 271s 'citation()' on how to cite R or R packages in publications. 271s 271s Type 'demo()' for some demos, 'help()' for on-line help, or 271s 'help.start()' for an HTML browser interface to help. 271s Type 'q()' to quit R. 271s 271s > ## VT::15.09.2013 - this will render the output independent 271s > ## from the version of the package 271s > suppressPackageStartupMessages(library(rrcov)) 271s > library(MASS) 271s > 271s > dodata <- function(method) { 271s + 271s + options(digits = 5) 271s + set.seed(101) 271s + 271s + tmp <- sys.call() 271s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 271s + cat("===================================================\n") 271s + 271s + data(pottery); show(lda <- LdaPP(origin~., data=pottery, method=method)); show(predict(lda)) 271s + data(hemophilia); show(lda <- LdaPP(as.factor(gr)~., data=hemophilia, method=method)); show(predict(lda)) 271s + data(anorexia); show(lda <- LdaPP(Treat~., data=anorexia, method=method)); show(predict(lda)) 271s + data(Pima.tr); show(lda <- LdaPP(type~., data=Pima.tr, method=method)); show(predict(lda)) 271s + data(crabs); show(lda <- LdaPP(sp~., data=crabs, method=method)); show(predict(lda)) 271s + 271s + cat("===================================================\n") 271s + } 271s > 271s > 271s > ## -- now do it: 271s > 271s > ## Commented out - still to slow 271s > ##dodata(method="huber") 271s > ##dodata(method="sest") 271s > 271s > ## VT::14.11.2018 - Commented out: too slow 271s > ## dodata(method="mad") 271s > ## dodata(method="class") 271s > 271s BEGIN TEST tmcd4.R 271s 271s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 271s Copyright (C) 2025 The R Foundation for Statistical Computing 271s Platform: powerpc64le-unknown-linux-gnu 271s 271s R is free software and comes with ABSOLUTELY NO WARRANTY. 271s You are welcome to redistribute it under certain conditions. 271s Type 'license()' or 'licence()' for distribution details. 271s 271s R is a collaborative project with many contributors. 271s Type 'contributors()' for more information and 271s 'citation()' on how to cite R or R packages in publications. 271s 271s Type 'demo()' for some demos, 'help()' for on-line help, or 271s 'help.start()' for an HTML browser interface to help. 271s Type 'q()' to quit R. 271s 271s > ## Test the exact fit property of CovMcd 271s > doexactfit <- function(){ 271s + exact <-function(seed=1234){ 271s + 271s + set.seed(seed) 271s + 271s + n1 <- 45 271s + p <- 2 271s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 271s + x1[,p] <- x1[,p] + 3 271s + n2 <- 55 271s + m1 <- 0 271s + m2 <- 3 271s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 271s + x<-rbind(x1,x2) 271s + colnames(x) <- c("X1","X2") 271s + x 271s + } 271s + print(CovMcd(exact())) 271s + } 271s > 271s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 271s + ##@bdescr 271s + ## Test the function covMcd() on the literature datasets: 271s + ## 271s + ## Call CovMcd() for all regression datasets available in rrcov and print: 271s + ## - execution time (if time == TRUE) 271s + ## - objective fucntion 271s + ## - best subsample found (if short == false) 271s + ## - outliers identified (with cutoff 0.975) (if short == false) 271s + ## - estimated center and covarinance matrix if full == TRUE) 271s + ## 271s + ##@edescr 271s + ## 271s + ##@in nrep : [integer] number of repetitions to use for estimating the 271s + ## (average) execution time 271s + ##@in time : [boolean] whether to evaluate the execution time 271s + ##@in short : [boolean] whether to do short output (i.e. only the 271s + ## objective function value). If short == FALSE, 271s + ## the best subsample and the identified outliers are 271s + ## printed. See also the parameter full below 271s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 271s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 271s + 271s + doest <- function(x, xname, nrep=1){ 271s + n <- dim(x)[1] 271s + p <- dim(x)[2] 271s + if(method == "MASS"){ 271s + mcd<-cov.mcd(x) 271s + quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2) 271s + } 271s + else{ 271s + mcd <- if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 271s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 271s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 271s + else CovMcd(x, trace=FALSE) 271s + quan <- as.integer(mcd@quan) 271s + } 271s + 271s + crit <- mcd@crit 271s + 271s + if(time){ 271s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 271s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 271s + } 271s + else{ 271s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 271s + } 271s + lpad<-lname-nchar(xname) 271s + cat(pad.right(xname,lpad), xres) 271s + 271s + if(!short){ 271s + cat("Best subsample: \n") 271s + if(length(mcd@best) > 150) 271s + cat("Too long... \n") 271s + else 271s + print(mcd@best) 271s + 271s + ibad <- which(mcd@wt==0) 271s + names(ibad) <- NULL 271s + nbad <- length(ibad) 271s + cat("Outliers: ",nbad,"\n") 271s + if(nbad > 0 & nbad < 150) 271s + print(ibad) 271s + else 271s + cat("Too many to print ... \n") 271s + if(full){ 271s + cat("-------------\n") 271s + show(mcd) 271s + } 271s + cat("--------------------------------------------------------\n") 271s + } 271s + } 271s + 271s + options(digits = 5) 271s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 271s + 271s + lname <- 20 271s + 271s + ## VT::15.09.2013 - this will render the output independent 271s + ## from the version of the package 271s + suppressPackageStartupMessages(library(rrcov)) 271s + 271s + method <- match.arg(method) 271s + if(method == "MASS") 271s + library(MASS) 271s + 271s + data(Animals, package = "MASS") 271s + brain <- Animals[c(1:24, 26:25, 27:28),] 271s + 271s + data(fish) 271s + data(pottery) 271s + data(rice) 271s + data(un86) 271s + data(wages) 271s + 271s + tmp <- sys.call() 271s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 271s + 271s + cat("Data Set n p Half LOG(obj) Time\n") 271s + cat("========================================================\n") 271s + 271s + if(method=="exact") 271s + { 271s + ## only small data sets 271s + doest(heart[, 1:2], data(heart), nrep) 271s + doest(starsCYG, data(starsCYG), nrep) 271s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 271s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 271s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 271s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 271s + doest(brain, "Animals", nrep) 271s + doest(lactic, data(lactic), nrep) 271s + doest(pension, data(pension), nrep) 271s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 271s + doest(stack.x, data(stackloss), nrep) 271s + doest(pilot, data(pilot), nrep) 271s + } else 271s + { 271s + doest(heart[, 1:2], data(heart), nrep) 271s + doest(starsCYG, data(starsCYG), nrep) 271s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 271s + doest(stack.x, data(stackloss), nrep) 271s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 271s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 271s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 271s + doest(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 271s + 271s + doest(brain, "Animals", nrep) 271s + ## doest(milk, data(milk), nrep) # difference between 386 and x64 271s + doest(bushfire, data(bushfire), nrep) 271s + 271s + doest(lactic, data(lactic), nrep) 271s + doest(pension, data(pension), nrep) 271s + ## doest(pilot, data(pilot), nrep) # difference between 386 and x64 271s + 271s + if(method != "MRCD") # these two are quite slow for MRCD, especially the second one 271s + { 271s + doest(radarImage, data(radarImage), nrep) 271s + doest(NOxEmissions, data(NOxEmissions), nrep) 271s + } 271s + 271s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 271s + doest(data.matrix(subset(wagnerGrowth, select = -Period)), data(wagnerGrowth), nrep) 271s + 271s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep) 271s + doest(data.matrix(subset(pottery, select = -origin)), data(pottery), nrep) 271s + doest(rice, data(rice), nrep) 271s + doest(un86, data(un86), nrep) 271s + 271s + doest(wages, data(wages), nrep) 271s + 271s + ## from package 'datasets' 271s + doest(airquality[,1:4], data(airquality), nrep) 271s + doest(attitude, data(attitude), nrep) 271s + doest(attenu, data(attenu), nrep) 271s + doest(USJudgeRatings, data(USJudgeRatings), nrep) 271s + doest(USArrests, data(USArrests), nrep) 271s + doest(longley, data(longley), nrep) 271s + doest(Loblolly, data(Loblolly), nrep) 271s + doest(quakes[,1:4], data(quakes), nrep) 271s + } 271s + cat("========================================================\n") 271s + } 271s > 271s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMCD", "MASS")){ 271s + 271s + doest <- function(x, nrep=1){ 271s + gc() 271s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 271s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 271s + xtime 271s + } 271s + 271s + set.seed(1234) 271s + 271s + ## VT::15.09.2013 - this will render the output independent 271s + ## from the version of the package 271s + suppressPackageStartupMessages(library(rrcov)) 271s + 271s + library(MASS) 271s + method <- match.arg(method) 271s + 271s + ap <- c(2, 5, 10, 20, 30) 271s + an <- c(100, 500, 1000, 10000, 50000) 271s + 271s + tottime <- 0 271s + cat(" n p Time\n") 271s + cat("=====================\n") 271s + for(i in 1:length(an)) { 271s + for(j in 1:length(ap)) { 271s + n <- an[i] 271s + p <- ap[j] 271s + if(5*p <= n){ 271s + xx <- gendata(n, p, eps) 271s + X <- xx$X 271s + tottime <- tottime + doest(X, nrep) 271s + } 271s + } 271s + } 271s + 271s + cat("=====================\n") 271s + cat("Total time: ", tottime*nrep, "\n") 271s + } 271s > 271s > docheck <- function(n, p, eps){ 271s + xx <- gendata(n,p,eps) 271s + mcd <- CovMcd(xx$X) 271s + check(mcd, xx$xind) 271s + } 271s > 271s > check <- function(mcd, xind){ 271s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 271s + ## did not get zero weight 271s + mymatch <- xind %in% which(mcd@wt == 0) 271s + length(xind) - length(which(mymatch)) 271s + } 271s > 271s > dorep <- function(x, nrep=1, method=c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 271s + 271s + method <- match.arg(method) 271s + for(i in 1:nrep) 271s + if(method == "MASS") 271s + cov.mcd(x) 271s + else 271s + { 271s + if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 271s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 271s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 271s + else CovMcd(x, trace=FALSE) 271s + } 271s + } 271s > 271s > #### gendata() #### 271s > # Generates a location contaminated multivariate 271s > # normal sample of n observations in p dimensions 271s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 271s > # where 271s > # m = (b,b,...,b) 271s > # Defaults: eps=0 and b=10 271s > # 271s > gendata <- function(n,p,eps=0,b=10){ 271s + 271s + if(missing(n) || missing(p)) 271s + stop("Please specify (n,p)") 271s + if(eps < 0 || eps >= 0.5) 271s + stop(message="eps must be in [0,0.5)") 271s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 271s + nbad <- as.integer(eps * n) 271s + if(nbad > 0){ 271s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 271s + xind <- sample(n,nbad) 271s + X[xind,] <- Xbad 271s + } 271s + list(X=X, xind=xind) 271s + } 271s > 271s > pad.right <- function(z, pads) 271s + { 271s + ### Pads spaces to right of text 271s + padding <- paste(rep(" ", pads), collapse = "") 271s + paste(z, padding, sep = "") 271s + } 271s > 271s > whatis<-function(x){ 271s + if(is.data.frame(x)) 271s + cat("Type: data.frame\n") 271s + else if(is.matrix(x)) 271s + cat("Type: matrix\n") 271s + else if(is.vector(x)) 271s + cat("Type: vector\n") 271s + else 271s + cat("Type: don't know\n") 271s + } 271s > 271s > ## VT::15.09.2013 - this will render the output independent 271s > ## from the version of the package 271s > suppressPackageStartupMessages(library(rrcov)) 271s > 271s > dodata() 272s 272s Call: dodata() 272s Data Set n p Half LOG(obj) Time 272s ======================================================== 272s heart 12 2 7 5.678742 272s Best subsample: 272s [1] 1 3 4 5 7 9 11 272s Outliers: 0 272s Too many to print ... 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s height weight 272s 38.3 33.1 272s 272s Robust Estimate of Covariance: 272s height weight 272s height 135 259 272s weight 259 564 272s -------------------------------------------------------- 272s starsCYG 47 2 25 -8.031215 272s Best subsample: 272s [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 272s Outliers: 7 272s [1] 7 9 11 14 20 30 34 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s log.Te log.light 272s 4.41 4.95 272s 272s Robust Estimate of Covariance: 272s log.Te log.light 272s log.Te 0.0132 0.0394 272s log.light 0.0394 0.2743 272s -------------------------------------------------------- 272s phosphor 18 2 10 6.878847 272s Best subsample: 272s [1] 3 5 8 9 11 12 13 14 15 17 272s Outliers: 3 272s [1] 1 6 10 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s inorg organic 272s 13.4 38.8 272s 272s Robust Estimate of Covariance: 272s inorg organic 272s inorg 129 130 272s organic 130 182 272s -------------------------------------------------------- 272s stackloss 21 3 12 5.472581 272s Best subsample: 272s [1] 4 5 6 7 8 9 10 11 12 13 14 20 272s Outliers: 9 272s [1] 1 2 3 15 16 17 18 19 21 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Air.Flow Water.Temp Acid.Conc. 272s 59.5 20.8 87.3 272s 272s Robust Estimate of Covariance: 272s Air.Flow Water.Temp Acid.Conc. 272s Air.Flow 6.29 5.85 5.74 272s Water.Temp 5.85 9.23 6.14 272s Acid.Conc. 5.74 6.14 23.25 272s -------------------------------------------------------- 272s coleman 20 5 13 1.286808 272s Best subsample: 272s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 272s Outliers: 7 272s [1] 1 6 9 10 11 15 18 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s salaryP fatherWc sstatus teacherSc motherLev 272s 2.76 48.38 6.12 25.00 6.40 272s 272s Robust Estimate of Covariance: 272s salaryP fatherWc sstatus teacherSc motherLev 272s salaryP 0.253 1.786 -0.266 0.151 0.075 272s fatherWc 1.786 1303.382 330.496 12.604 34.503 272s sstatus -0.266 330.496 119.888 3.833 10.131 272s teacherSc 0.151 12.604 3.833 0.785 0.555 272s motherLev 0.075 34.503 10.131 0.555 1.043 272s -------------------------------------------------------- 272s salinity 28 3 16 1.326364 272s Best subsample: 272s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 272s Outliers: 4 272s [1] 5 16 23 24 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s X1 X2 X3 272s 10.08 2.78 22.78 272s 272s Robust Estimate of Covariance: 272s X1 X2 X3 272s X1 10.44 1.01 -3.19 272s X2 1.01 3.83 -1.44 272s X3 -3.19 -1.44 2.39 272s -------------------------------------------------------- 272s wood 20 5 13 -36.270094 272s Best subsample: 272s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 272s Outliers: 7 272s [1] 4 6 7 8 11 16 19 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s x1 x2 x3 x4 x5 272s 0.587 0.122 0.531 0.538 0.892 272s 272s Robust Estimate of Covariance: 272s x1 x2 x3 x4 x5 272s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 272s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 272s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 272s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 272s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 272s -------------------------------------------------------- 272s hbk 75 3 39 -1.047858 272s Best subsample: 272s [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 272s [26] 55 56 58 59 61 63 64 66 67 70 71 72 73 74 272s Outliers: 14 272s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=39); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s X1 X2 X3 272s 1.54 1.78 1.69 272s 272s Robust Estimate of Covariance: 272s X1 X2 X3 272s X1 1.227 0.055 0.127 272s X2 0.055 1.249 0.153 272s X3 0.127 0.153 1.160 272s -------------------------------------------------------- 272s Animals 28 2 15 14.555543 272s Best subsample: 272s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 272s Outliers: 14 272s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s body brain 272s 18.7 64.9 272s 272s Robust Estimate of Covariance: 272s body brain 272s body 929 1576 272s brain 1576 5646 272s -------------------------------------------------------- 272s bushfire 38 5 22 18.135810 272s Best subsample: 272s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 272s Outliers: 16 272s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=22); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s V1 V2 V3 V4 V5 272s 105 147 274 218 279 272s 272s Robust Estimate of Covariance: 272s V1 V2 V3 V4 V5 272s V1 346 268 -1692 -381 -311 272s V2 268 236 -1125 -230 -194 272s V3 -1692 -1125 9993 2455 1951 272s V4 -381 -230 2455 647 505 272s V5 -311 -194 1951 505 398 272s -------------------------------------------------------- 272s lactic 20 2 11 0.359580 272s Best subsample: 272s [1] 1 2 3 4 5 7 8 9 10 11 12 272s Outliers: 4 272s [1] 17 18 19 20 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s X Y 272s 3.86 5.01 272s 272s Robust Estimate of Covariance: 272s X Y 272s X 10.6 14.6 272s Y 14.6 21.3 272s -------------------------------------------------------- 272s pension 18 2 10 16.675508 272s Best subsample: 272s [1] 1 2 3 4 5 6 8 9 11 12 272s Outliers: 5 272s [1] 14 15 16 17 18 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Income Reserves 272s 52.3 560.9 272s 272s Robust Estimate of Covariance: 272s Income Reserves 272s Income 1420 11932 272s Reserves 11932 208643 272s -------------------------------------------------------- 272s radarImage 1573 5 789 36.694425 272s Best subsample: 272s Too long... 272s Outliers: 117 272s [1] 164 237 238 242 261 262 351 450 451 462 480 481 509 516 535 272s [16] 542 572 597 620 643 654 669 697 737 802 803 804 818 832 833 272s [31] 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 1202 1223 272s [46] 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 1376 1393 272s [61] 1394 1402 1403 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1479 1487 272s [76] 1492 1504 1510 1511 1512 1517 1518 1519 1520 1521 1522 1525 1526 1527 1528 272s [91] 1530 1532 1534 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 272s [106] 1557 1558 1561 1562 1564 1565 1566 1567 1569 1570 1571 1573 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=789); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s X.coord Y.coord Band.1 Band.2 Band.3 272s 52.80 35.12 6.77 18.44 8.90 272s 272s Robust Estimate of Covariance: 272s X.coord Y.coord Band.1 Band.2 Band.3 272s X.coord 123.6 23.0 -361.9 -197.1 -22.5 272s Y.coord 23.0 400.6 34.3 -191.1 -39.1 272s Band.1 -361.9 34.3 27167.9 8178.8 473.7 272s Band.2 -197.1 -191.1 8178.8 26021.8 952.4 272s Band.3 -22.5 -39.1 473.7 952.4 4458.4 272s -------------------------------------------------------- 272s NOxEmissions 8088 4 4046 2.474539 272s Best subsample: 272s Too long... 272s Outliers: 2156 272s Too many to print ... 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=4046); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s julday LNOx LNOxEm sqrtWS 272s 168.19 4.73 7.91 1.37 272s 272s Robust Estimate of Covariance: 272s julday LNOx LNOxEm sqrtWS 272s julday 9180.6297 12.0306 0.7219 -10.1273 272s LNOx 12.0306 0.4721 0.1418 -0.1526 272s LNOxEm 0.7219 0.1418 0.2516 0.0438 272s sqrtWS -10.1273 -0.1526 0.0438 0.2073 272s -------------------------------------------------------- 272s vaso 39 2 21 -3.972244 272s Best subsample: 272s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 272s Outliers: 4 272s [1] 1 2 17 31 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Volume Rate 272s 1.16 1.72 272s 272s Robust Estimate of Covariance: 272s Volume Rate 272s Volume 0.313 -0.167 272s Rate -0.167 0.728 272s -------------------------------------------------------- 272s wagnerGrowth 63 6 35 6.572208 272s Best subsample: 272s [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 272s [26] 48 51 52 53 54 55 56 57 60 62 272s Outliers: 13 272s [1] 1 8 15 21 22 28 29 33 42 43 46 50 63 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=35); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Region PA GPA HS GHS y 272s 11.00 33.66 -2.00 2.48 0.31 7.48 272s 272s Robust Estimate of Covariance: 272s Region PA GPA HS GHS y 272s Region 35.5615 17.9337 -0.5337 -0.9545 -0.3093 -14.0090 272s PA 17.9337 27.7333 -4.9017 -1.4174 0.0343 -28.7040 272s GPA -0.5337 -4.9017 5.3410 0.2690 -0.1484 4.0006 272s HS -0.9545 -1.4174 0.2690 0.8662 -0.0454 2.9024 272s GHS -0.3093 0.0343 -0.1484 -0.0454 0.1772 0.7457 272s y -14.0090 -28.7040 4.0006 2.9024 0.7457 82.6877 272s -------------------------------------------------------- 272s fish 159 6 82 8.879005 272s Best subsample: 272s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 272s [20] 20 21 22 23 24 25 26 27 28 30 32 35 36 37 42 43 44 45 46 272s [39] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 107 109 110 111 113 272s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 272s [77] 134 135 136 137 138 139 272s Outliers: 63 272s [1] 30 39 40 41 42 62 63 64 65 66 68 69 70 73 74 75 76 77 78 272s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 272s [39] 98 99 100 101 102 103 104 105 141 143 144 145 147 148 149 150 151 152 153 272s [58] 154 155 156 157 158 159 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=82); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Weight Length1 Length2 Length3 Height Width 272s 329.9 24.5 26.6 29.7 31.1 14.7 272s 272s Robust Estimate of Covariance: 272s Weight Length1 Length2 Length3 Height Width 272s Weight 69082.99 1477.81 1613.64 1992.62 1439.32 -62.12 272s Length1 1477.81 34.68 37.61 45.51 28.82 -1.31 272s Length2 1613.64 37.61 40.88 49.52 31.81 -1.40 272s Length3 1992.62 45.51 49.52 61.16 42.65 -2.25 272s Height 1439.32 28.82 31.81 42.65 46.74 -2.82 272s Width -62.12 -1.31 -1.40 -2.25 -2.82 1.01 272s -------------------------------------------------------- 272s pottery 27 6 17 -10.586933 272s Best subsample: 272s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 272s Outliers: 9 272s [1] 3 8 12 16 17 18 23 24 25 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=17); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s SI AL FE MG CA TI 272s 54.983 15.206 9.700 3.817 5.211 0.859 272s 272s Robust Estimate of Covariance: 272s SI AL FE MG CA TI 272s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 272s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 272s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 272s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 272s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 272s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 272s -------------------------------------------------------- 272s rice 105 6 56 -14.463986 272s Best subsample: 272s [1] 2 4 6 8 10 12 15 18 21 22 24 29 30 31 32 33 34 36 37 272s [20] 38 41 44 45 47 51 52 53 54 55 59 61 65 67 68 69 70 72 76 272s [39] 78 79 80 81 82 83 84 85 86 92 93 94 95 97 98 99 102 105 272s Outliers: 13 272s [1] 9 14 19 28 40 42 49 58 62 71 75 77 89 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=56); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Favor Appearance Taste Stickiness 272s -0.2731 0.0600 -0.1468 0.0646 272s Toughness Overall_evaluation 272s 0.0894 -0.2192 272s 272s Robust Estimate of Covariance: 272s Favor Appearance Taste Stickiness Toughness 272s Favor 0.388 0.323 0.393 0.389 -0.195 272s Appearance 0.323 0.503 0.494 0.494 -0.270 272s Taste 0.393 0.494 0.640 0.629 -0.361 272s Stickiness 0.389 0.494 0.629 0.815 -0.486 272s Toughness -0.195 -0.270 -0.361 -0.486 0.451 272s Overall_evaluation 0.471 0.575 0.723 0.772 -0.457 272s Overall_evaluation 272s Favor 0.471 272s Appearance 0.575 272s Taste 0.723 272s Stickiness 0.772 272s Toughness -0.457 272s Overall_evaluation 0.882 272s -------------------------------------------------------- 272s un86 73 7 40 17.009322 272s Best subsample: 272s [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 272s [26] 51 52 55 56 60 61 62 63 64 65 67 70 71 72 73 272s Outliers: 30 272s [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 272s [26] 58 59 66 68 69 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s POP MOR CAR DR GNP DEN TB 272s 20.740 71.023 6.435 0.817 1.146 56.754 0.441 272s 272s Robust Estimate of Covariance: 272s POP MOR CAR DR GNP DEN 272s POP 582.4034 224.9343 -12.6722 -1.6729 -3.3664 226.1952 272s MOR 224.9343 2351.3907 -286.9504 -32.0743 -35.5649 -527.4684 272s CAR -12.6722 -286.9504 58.1190 5.7393 6.6365 83.6180 272s DR -1.6729 -32.0743 5.7393 0.8339 0.5977 12.1938 272s GNP -3.3664 -35.5649 6.6365 0.5977 1.4175 13.0709 272s DEN 226.1952 -527.4684 83.6180 12.1938 13.0709 2041.5809 272s TB 0.4002 -1.1807 0.2701 0.0191 0.0058 -0.9346 272s TB 272s POP 0.4002 272s MOR -1.1807 272s CAR 0.2701 272s DR 0.0191 272s GNP 0.0058 272s DEN -0.9346 272s TB 0.0184 272s -------------------------------------------------------- 272s wages 39 10 19 22.994272 272s Best subsample: 272s [1] 1 2 6 7 8 9 10 11 12 13 14 15 17 18 19 25 26 27 28 272s Outliers: 9 272s [1] 4 5 6 24 28 30 32 33 34 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 272s 2153.37 2.87 1129.16 297.53 360.58 6876.58 39.48 2.36 272s RACE SCHOOL 272s 38.88 10.17 272s 272s Robust Estimate of Covariance: 272s HRS RATE ERSP ERNO NEIN ASSET 272s HRS 6.12e+03 1.73e+01 -1.67e+03 -2.06e+03 9.10e+03 2.02e+05 272s RATE 1.73e+01 2.52e-01 2.14e+01 -3.54e+00 5.85e+01 1.37e+03 272s ERSP -1.67e+03 2.14e+01 1.97e+04 7.76e+01 -1.71e+03 -1.41e+04 272s ERNO -2.06e+03 -3.54e+00 7.76e+01 2.06e+03 -2.02e+03 -4.83e+04 272s NEIN 9.10e+03 5.85e+01 -1.71e+03 -2.02e+03 2.02e+04 4.54e+05 272s ASSET 2.02e+05 1.37e+03 -1.41e+04 -4.83e+04 4.54e+05 1.03e+07 272s AGE -6.29e+01 -2.61e-01 4.83e+00 2.44e+01 -1.08e+02 -2.46e+03 272s DEP -6.17e+00 -7.05e-02 -2.13e+01 2.29e+00 -1.30e+01 -3.16e+02 272s RACE -2.17e+03 -9.46e+00 7.19e+02 5.59e+02 -3.95e+03 -8.77e+04 272s SCHOOL 7.12e+01 5.87e-01 5.39e+01 -2.14e+01 1.63e+02 3.79e+03 272s AGE DEP RACE SCHOOL 272s HRS -6.29e+01 -6.17e+00 -2.17e+03 7.12e+01 272s RATE -2.61e-01 -7.05e-02 -9.46e+00 5.87e-01 272s ERSP 4.83e+00 -2.13e+01 7.19e+02 5.39e+01 272s ERNO 2.44e+01 2.29e+00 5.59e+02 -2.14e+01 272s NEIN -1.08e+02 -1.30e+01 -3.95e+03 1.63e+02 272s ASSET -2.46e+03 -3.16e+02 -8.77e+04 3.79e+03 272s AGE 1.01e+00 7.03e-02 2.39e+01 -9.52e-01 272s DEP 7.03e-02 4.62e-02 2.72e+00 -1.94e-01 272s RACE 2.39e+01 2.72e+00 8.74e+02 -3.09e+01 272s SCHOOL -9.52e-01 -1.94e-01 -3.09e+01 1.62e+00 272s -------------------------------------------------------- 272s airquality 153 4 58 18.213499 272s Best subsample: 272s [1] 3 22 24 25 28 29 32 33 35 36 37 38 39 40 41 42 43 44 46 272s [20] 47 48 49 50 52 56 57 58 59 60 64 66 67 68 69 71 72 73 74 272s [39] 76 78 80 82 83 84 86 87 89 90 91 92 93 94 95 97 98 105 109 272s [58] 110 272s Outliers: 14 272s [1] 8 9 15 18 20 21 23 24 28 30 48 62 117 148 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=58); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Ozone Solar.R Wind Temp 272s 43.2 192.9 9.6 80.5 272s 272s Robust Estimate of Covariance: 272s Ozone Solar.R Wind Temp 272s Ozone 959.69 771.68 -60.92 198.38 272s Solar.R 771.68 7089.72 -1.72 95.75 272s Wind -60.92 -1.72 10.71 -11.96 272s Temp 198.38 95.75 -11.96 62.78 272s -------------------------------------------------------- 272s attitude 30 7 19 24.442803 272s Best subsample: 272s [1] 2 3 4 5 7 8 10 12 15 17 19 20 22 23 25 27 28 29 30 272s Outliers: 10 272s [1] 1 6 9 13 14 16 18 21 24 26 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s rating complaints privileges learning raises critical 272s 67.1 68.0 52.4 57.6 67.2 77.4 272s advance 272s 43.4 272s 272s Robust Estimate of Covariance: 272s rating complaints privileges learning raises critical advance 272s rating 169.34 127.83 40.48 110.26 91.71 -3.59 53.84 272s complaints 127.83 156.80 52.65 110.97 96.56 7.27 76.03 272s privileges 40.48 52.65 136.91 92.38 69.00 9.53 87.98 272s learning 110.26 110.97 92.38 157.77 112.92 6.74 75.51 272s raises 91.71 96.56 69.00 112.92 112.79 4.91 70.22 272s critical -3.59 7.27 9.53 6.74 4.91 52.25 15.00 272s advance 53.84 76.03 87.98 75.51 70.22 15.00 93.11 272s -------------------------------------------------------- 272s attenu 182 5 86 6.440834 272s Best subsample: 272s [1] 68 69 70 71 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 272s [20] 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 115 116 117 118 272s [39] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 272s [58] 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 272s [77] 157 158 159 160 161 162 163 164 165 166 272s Outliers: 61 272s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 272s [20] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 272s [39] 40 45 46 47 54 55 56 57 58 59 60 61 64 65 82 97 98 100 101 272s [58] 102 103 104 105 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=86); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s event mag station dist accel 272s 18.624 5.752 67.861 22.770 0.141 272s 272s Robust Estimate of Covariance: 272s event mag station dist accel 272s event 1.64e+01 -1.22e+00 5.59e+01 9.98e+00 -8.37e-02 272s mag -1.22e+00 4.13e-01 -3.19e+00 1.35e+00 1.22e-02 272s station 5.59e+01 -3.19e+00 1.03e+03 7.00e+01 5.56e-01 272s dist 9.98e+00 1.35e+00 7.00e+01 2.21e+02 -9.24e-01 272s accel -8.37e-02 1.22e-02 5.56e-01 -9.24e-01 9.62e-03 272s -------------------------------------------------------- 272s USJudgeRatings 43 12 28 -47.889993 272s Best subsample: 272s [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 272s [26] 38 41 43 272s Outliers: 14 272s [1] 5 7 8 12 13 14 20 21 23 30 31 35 40 42 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=28); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 272s 7.40 8.19 7.80 7.96 7.74 7.82 7.74 7.73 7.57 7.63 8.25 7.94 272s 272s Robust Estimate of Covariance: 272s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL 272s CONT 0.852 -0.266 -0.422 -0.155 -0.049 -0.074 -0.117 -0.119 -0.177 272s INTG -0.266 0.397 0.537 0.406 0.340 0.325 0.404 0.409 0.430 272s DMNR -0.422 0.537 0.824 0.524 0.458 0.437 0.520 0.504 0.569 272s DILG -0.155 0.406 0.524 0.486 0.426 0.409 0.506 0.515 0.511 272s CFMG -0.049 0.340 0.458 0.426 0.427 0.403 0.466 0.476 0.478 272s DECI -0.074 0.325 0.437 0.409 0.403 0.396 0.449 0.462 0.460 272s PREP -0.117 0.404 0.520 0.506 0.466 0.449 0.552 0.565 0.551 272s FAMI -0.119 0.409 0.504 0.515 0.476 0.462 0.565 0.594 0.571 272s ORAL -0.177 0.430 0.569 0.511 0.478 0.460 0.551 0.571 0.575 272s WRIT -0.159 0.427 0.549 0.515 0.480 0.461 0.556 0.580 0.574 272s PHYS -0.184 0.269 0.362 0.308 0.298 0.307 0.335 0.358 0.369 272s RTEN -0.260 0.472 0.642 0.519 0.467 0.455 0.539 0.554 0.573 272s WRIT PHYS RTEN 272s CONT -0.159 -0.184 -0.260 272s INTG 0.427 0.269 0.472 272s DMNR 0.549 0.362 0.642 272s DILG 0.515 0.308 0.519 272s CFMG 0.480 0.298 0.467 272s DECI 0.461 0.307 0.455 272s PREP 0.556 0.335 0.539 272s FAMI 0.580 0.358 0.554 272s ORAL 0.574 0.369 0.573 272s WRIT 0.580 0.365 0.567 272s PHYS 0.365 0.300 0.378 272s RTEN 0.567 0.378 0.615 272s -------------------------------------------------------- 272s USArrests 50 4 27 15.391648 272s Best subsample: 272s [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 272s [26] 49 50 272s Outliers: 11 272s [1] 2 3 5 6 10 18 24 28 33 37 47 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s Murder Assault UrbanPop Rape 272s 6.71 145.42 65.06 17.88 272s 272s Robust Estimate of Covariance: 272s Murder Assault UrbanPop Rape 272s Murder 16.1 269.3 20.3 25.2 272s Assault 269.3 6613.0 567.8 453.7 272s UrbanPop 20.3 567.8 225.4 47.7 272s Rape 25.2 453.7 47.7 50.9 272s -------------------------------------------------------- 272s longley 16 7 12 12.747678 272s Best subsample: 272s [1] 5 6 7 8 9 10 11 12 13 14 15 16 272s Outliers: 4 272s [1] 1 2 3 4 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s GNP.deflator GNP Unemployed Armed.Forces Population 272s 106.5 430.6 328.2 295.0 120.2 272s Year Employed 272s 1956.5 66.9 272s 272s Robust Estimate of Covariance: 272s GNP.deflator GNP Unemployed Armed.Forces Population 272s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 272s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 272s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 272s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 272s Population 81.4 803.4 929.1 -327.4 63.5 272s Year 51.6 504.3 595.6 -216.7 39.7 272s Employed 34.2 344.1 323.6 -149.5 26.2 272s Year Employed 272s GNP.deflator 51.6 34.2 272s GNP 504.3 344.1 272s Unemployed 595.6 323.6 272s Armed.Forces -216.7 -149.5 272s Population 39.7 26.2 272s Year 25.1 16.7 272s Employed 16.7 12.4 272s -------------------------------------------------------- 272s Loblolly 84 3 44 4.898174 272s Best subsample: 272s [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 272s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 272s Outliers: 31 272s [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 272s [26] 72 76 77 78 83 84 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=44); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s height age Seed 272s 20.44 8.19 7.72 272s 272s Robust Estimate of Covariance: 272s height age Seed 272s height 247.8 79.5 11.9 272s age 79.5 25.7 3.0 272s Seed 11.9 3.0 17.1 272s -------------------------------------------------------- 272s quakes 1000 4 502 8.274369 272s Best subsample: 272s Too long... 272s Outliers: 265 272s Too many to print ... 272s ------------- 272s 272s Call: 272s CovMcd(x = x, trace = FALSE) 272s -> Method: Fast MCD(alpha=0.5 ==> h=502); nsamp = 500; (n,k)mini = (300,5) 272s 272s Robust Estimate of Location: 272s lat long depth mag 272s -21.31 182.48 361.35 4.54 272s 272s Robust Estimate of Covariance: 272s lat long depth mag 272s lat 1.47e+01 3.53e+00 1.34e+02 -2.52e-01 272s long 3.53e+00 4.55e+00 -3.63e+02 4.36e-02 272s depth 1.34e+02 -3.63e+02 4.84e+04 -1.29e+01 272s mag -2.52e-01 4.36e-02 -1.29e+01 1.38e-01 272s -------------------------------------------------------- 272s ======================================================== 272s > dodata(method="deterministic") 272s 272s Call: dodata(method = "deterministic") 272s Data Set n p Half LOG(obj) Time 272s ======================================================== 272s heart 12 2 7 5.678742 272s Best subsample: 272s [1] 1 3 4 5 7 9 11 272s Outliers: 0 272s Too many to print ... 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=7) 272s 272s Robust Estimate of Location: 272s height weight 272s 38.3 33.1 272s 272s Robust Estimate of Covariance: 272s height weight 272s height 135 259 272s weight 259 564 272s -------------------------------------------------------- 272s starsCYG 47 2 25 -8.028718 272s Best subsample: 272s [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 272s Outliers: 7 272s [1] 7 9 11 14 20 30 34 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=25) 272s 272s Robust Estimate of Location: 272s log.Te log.light 272s 4.41 4.95 272s 272s Robust Estimate of Covariance: 272s log.Te log.light 272s log.Te 0.0132 0.0394 272s log.light 0.0394 0.2743 272s -------------------------------------------------------- 272s phosphor 18 2 10 7.732906 272s Best subsample: 272s [1] 2 4 5 7 8 9 11 12 14 16 272s Outliers: 1 272s [1] 6 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 272s 272s Robust Estimate of Location: 272s inorg organic 272s 12.5 40.8 272s 272s Robust Estimate of Covariance: 272s inorg organic 272s inorg 124 101 272s organic 101 197 272s -------------------------------------------------------- 272s stackloss 21 3 12 6.577286 272s Best subsample: 272s [1] 4 5 6 7 8 9 11 13 16 18 19 20 272s Outliers: 2 272s [1] 1 2 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 272s 272s Robust Estimate of Location: 272s Air.Flow Water.Temp Acid.Conc. 272s 58.4 20.5 86.1 272s 272s Robust Estimate of Covariance: 272s Air.Flow Water.Temp Acid.Conc. 272s Air.Flow 56.28 13.33 26.68 272s Water.Temp 13.33 8.28 6.98 272s Acid.Conc. 26.68 6.98 37.97 272s -------------------------------------------------------- 272s coleman 20 5 13 2.149184 272s Best subsample: 272s [1] 3 4 5 7 8 12 13 14 16 17 18 19 20 272s Outliers: 2 272s [1] 6 10 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 272s 272s Robust Estimate of Location: 272s salaryP fatherWc sstatus teacherSc motherLev 272s 2.76 41.08 2.76 25.01 6.27 272s 272s Robust Estimate of Covariance: 272s salaryP fatherWc sstatus teacherSc motherLev 272s salaryP 0.391 2.956 2.146 0.447 0.110 272s fatherWc 2.956 1358.640 442.724 12.235 32.842 272s sstatus 2.146 442.724 205.590 6.464 11.382 272s teacherSc 0.447 12.235 6.464 1.179 0.510 272s motherLev 0.110 32.842 11.382 0.510 0.919 272s -------------------------------------------------------- 272s salinity 28 3 16 1.940763 272s Best subsample: 272s [1] 1 8 10 12 13 14 15 17 18 20 21 22 25 26 27 28 272s Outliers: 2 272s [1] 5 16 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=16) 272s 272s Robust Estimate of Location: 272s X1 X2 X3 272s 10.50 2.58 23.12 272s 272s Robust Estimate of Covariance: 272s X1 X2 X3 272s X1 10.90243 -0.00457 -1.46156 272s X2 -0.00457 3.85051 -1.94604 272s X3 -1.46156 -1.94604 3.21424 272s -------------------------------------------------------- 272s wood 20 5 13 -35.240819 272s Best subsample: 272s [1] 1 2 3 5 9 11 12 13 14 15 17 18 20 272s Outliers: 4 272s [1] 4 6 8 19 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 272s 272s Robust Estimate of Location: 272s x1 x2 x3 x4 x5 272s 0.582 0.125 0.530 0.534 0.888 272s 272s Robust Estimate of Covariance: 272s x1 x2 x3 x4 x5 272s x1 1.05e-02 1.81e-03 2.08e-03 -6.41e-04 -9.61e-04 272s x2 1.81e-03 5.55e-04 8.76e-04 -2.03e-04 -4.70e-05 272s x3 2.08e-03 8.76e-04 5.60e-03 -1.11e-03 -1.26e-05 272s x4 -6.41e-04 -2.03e-04 -1.11e-03 4.27e-03 2.60e-03 272s x5 -9.61e-04 -4.70e-05 -1.26e-05 2.60e-03 2.95e-03 272s -------------------------------------------------------- 272s hbk 75 3 39 -1.045501 272s Best subsample: 272s [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 272s [26] 54 55 56 58 59 63 64 66 67 70 71 72 73 74 272s Outliers: 14 272s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=39) 272s 272s Robust Estimate of Location: 272s X1 X2 X3 272s 1.54 1.78 1.69 272s 272s Robust Estimate of Covariance: 272s X1 X2 X3 272s X1 1.227 0.055 0.127 272s X2 0.055 1.249 0.153 272s X3 0.127 0.153 1.160 272s -------------------------------------------------------- 272s Animals 28 2 15 14.555543 272s Best subsample: 272s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 272s Outliers: 14 272s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=15) 272s 272s Robust Estimate of Location: 272s body brain 272s 18.7 64.9 272s 272s Robust Estimate of Covariance: 272s body brain 272s body 929 1576 272s brain 1576 5646 272s -------------------------------------------------------- 272s bushfire 38 5 22 18.135810 272s Best subsample: 272s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 272s Outliers: 16 272s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=22) 272s 272s Robust Estimate of Location: 272s V1 V2 V3 V4 V5 272s 105 147 274 218 279 272s 272s Robust Estimate of Covariance: 272s V1 V2 V3 V4 V5 272s V1 346 268 -1692 -381 -311 272s V2 268 236 -1125 -230 -194 272s V3 -1692 -1125 9993 2455 1951 272s V4 -381 -230 2455 647 505 272s V5 -311 -194 1951 505 398 272s -------------------------------------------------------- 272s lactic 20 2 11 0.359580 272s Best subsample: 272s [1] 1 2 3 4 5 7 8 9 10 11 12 272s Outliers: 4 272s [1] 17 18 19 20 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=11) 272s 272s Robust Estimate of Location: 272s X Y 272s 3.86 5.01 272s 272s Robust Estimate of Covariance: 272s X Y 272s X 10.6 14.6 272s Y 14.6 21.3 272s -------------------------------------------------------- 272s pension 18 2 10 16.675508 272s Best subsample: 272s [1] 1 2 3 4 5 6 8 9 11 12 272s Outliers: 5 272s [1] 14 15 16 17 18 272s ------------- 272s 272s Call: 272s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 272s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 272s 272s Robust Estimate of Location: 272s Income Reserves 272s 52.3 560.9 272s 272s Robust Estimate of Covariance: 272s Income Reserves 272s Income 1420 11932 272s Reserves 11932 208643 272s -------------------------------------------------------- 273s radarImage 1573 5 789 36.694865 273s Best subsample: 273s Too long... 273s Outliers: 114 273s [1] 164 237 238 242 261 262 351 450 451 462 463 480 481 509 516 273s [16] 535 542 572 597 620 643 654 669 679 697 737 802 803 804 818 273s [31] 832 833 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 273s [46] 1202 1223 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 273s [61] 1376 1393 1394 1402 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1504 273s [76] 1510 1511 1512 1518 1519 1520 1521 1522 1525 1526 1527 1528 1530 1532 1534 273s [91] 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1557 1558 1561 273s [106] 1562 1564 1565 1566 1567 1569 1570 1571 1573 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=789) 273s 273s Robust Estimate of Location: 273s X.coord Y.coord Band.1 Band.2 Band.3 273s 52.78 35.37 7.12 18.81 9.09 273s 273s Robust Estimate of Covariance: 273s X.coord Y.coord Band.1 Band.2 Band.3 273s X.coord 123.2 21.5 -363.9 -200.1 -24.3 273s Y.coord 21.5 410.7 46.5 -177.3 -33.4 273s Band.1 -363.9 46.5 27051.1 8138.9 469.3 273s Band.2 -200.1 -177.3 8138.9 25938.0 946.2 273s Band.3 -24.3 -33.4 469.3 946.2 4470.1 273s -------------------------------------------------------- 273s NOxEmissions 8088 4 4046 2.474536 273s Best subsample: 273s Too long... 273s Outliers: 2152 273s Too many to print ... 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=4046) 273s 273s Robust Estimate of Location: 273s julday LNOx LNOxEm sqrtWS 273s 168.20 4.73 7.91 1.37 273s 273s Robust Estimate of Covariance: 273s julday LNOx LNOxEm sqrtWS 273s julday 9176.2934 12.0355 0.7022 -10.1387 273s LNOx 12.0355 0.4736 0.1430 -0.1528 273s LNOxEm 0.7022 0.1430 0.2527 0.0436 273s sqrtWS -10.1387 -0.1528 0.0436 0.2074 273s -------------------------------------------------------- 273s vaso 39 2 21 -3.972244 273s Best subsample: 273s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 273s Outliers: 4 273s [1] 1 2 17 31 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=21) 273s 273s Robust Estimate of Location: 273s Volume Rate 273s 1.16 1.72 273s 273s Robust Estimate of Covariance: 273s Volume Rate 273s Volume 0.313 -0.167 273s Rate -0.167 0.728 273s -------------------------------------------------------- 273s wagnerGrowth 63 6 35 6.511864 273s Best subsample: 273s [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 273s [26] 48 51 52 53 54 55 56 57 60 62 273s Outliers: 15 273s [1] 1 8 15 21 22 28 29 33 39 42 43 46 49 50 63 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=35) 273s 273s Robust Estimate of Location: 273s Region PA GPA HS GHS y 273s 10.91 33.65 -2.05 2.43 0.31 6.98 273s 273s Robust Estimate of Covariance: 273s Region PA GPA HS GHS y 273s Region 35.1365 17.7291 -1.4003 -0.6554 -0.4728 -14.9305 273s PA 17.7291 28.4297 -5.5245 -1.2444 -0.0452 -29.6181 273s GPA -1.4003 -5.5245 5.2170 0.3954 -0.2152 3.8252 273s HS -0.6554 -1.2444 0.3954 0.7273 -0.0107 2.1514 273s GHS -0.4728 -0.0452 -0.2152 -0.0107 0.1728 0.8440 273s y -14.9305 -29.6181 3.8252 2.1514 0.8440 79.0511 273s -------------------------------------------------------- 273s fish 159 6 82 8.880459 273s Best subsample: 273s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 273s [20] 20 21 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 273s [39] 50 51 52 53 54 55 56 57 58 59 60 106 107 108 109 110 111 112 113 273s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 273s [77] 134 135 136 137 138 139 273s Outliers: 64 273s [1] 30 39 40 41 62 63 64 65 66 68 69 70 73 74 75 76 77 78 79 273s [20] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 273s [39] 99 100 101 102 103 104 105 141 142 143 144 145 146 147 148 149 150 151 152 273s [58] 153 154 155 156 157 158 159 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=82) 273s 273s Robust Estimate of Location: 273s Weight Length1 Length2 Length3 Height Width 273s 316.3 24.1 26.3 29.3 31.0 14.7 273s 273s Robust Estimate of Covariance: 273s Weight Length1 Length2 Length3 Height Width 273s Weight 64662.19 1412.34 1541.95 1917.21 1420.83 -61.15 273s Length1 1412.34 34.14 37.04 45.07 29.25 -1.26 273s Length2 1541.95 37.04 40.26 49.04 32.21 -1.34 273s Length3 1917.21 45.07 49.04 60.82 43.03 -2.15 273s Height 1420.83 29.25 32.21 43.03 46.50 -2.66 273s Width -61.15 -1.26 -1.34 -2.15 -2.66 1.02 273s -------------------------------------------------------- 273s pottery 27 6 17 -10.586933 273s Best subsample: 273s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 273s Outliers: 9 273s [1] 3 8 12 16 17 18 23 24 25 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=17) 273s 273s Robust Estimate of Location: 273s SI AL FE MG CA TI 273s 54.983 15.206 9.700 3.817 5.211 0.859 273s 273s Robust Estimate of Covariance: 273s SI AL FE MG CA TI 273s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 273s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 273s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 273s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 273s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 273s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 273s -------------------------------------------------------- 273s rice 105 6 56 -14.423048 273s Best subsample: 273s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 273s [20] 38 44 45 47 51 52 53 55 59 60 65 66 67 70 72 74 76 78 79 273s [39] 80 81 82 83 84 85 86 90 92 93 94 95 97 98 99 100 101 105 273s Outliers: 13 273s [1] 9 19 28 40 42 43 49 58 62 64 71 75 77 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=56) 273s 273s Robust Estimate of Location: 273s Favor Appearance Taste Stickiness 273s -0.2950 0.0799 -0.1555 0.0363 273s Toughness Overall_evaluation 273s 0.0530 -0.2284 273s 273s Robust Estimate of Covariance: 273s Favor Appearance Taste Stickiness Toughness 273s Favor 0.466 0.389 0.471 0.447 -0.198 273s Appearance 0.389 0.610 0.592 0.570 -0.293 273s Taste 0.471 0.592 0.760 0.718 -0.356 273s Stickiness 0.447 0.570 0.718 0.820 -0.419 273s Toughness -0.198 -0.293 -0.356 -0.419 0.400 273s Overall_evaluation 0.557 0.669 0.838 0.846 -0.425 273s Overall_evaluation 273s Favor 0.557 273s Appearance 0.669 273s Taste 0.838 273s Stickiness 0.846 273s Toughness -0.425 273s Overall_evaluation 0.987 273s -------------------------------------------------------- 273s un86 73 7 40 17.117142 273s Best subsample: 273s [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 273s [26] 52 55 56 57 60 61 62 63 64 65 67 70 71 72 73 273s Outliers: 30 273s [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 273s [26] 58 59 66 68 69 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=40) 273s 273s Robust Estimate of Location: 273s POP MOR CAR DR GNP DEN TB 273s 17.036 68.512 6.444 0.877 1.134 64.140 0.433 273s 273s Robust Estimate of Covariance: 273s POP MOR CAR DR GNP DEN 273s POP 3.61e+02 1.95e+02 -6.28e+00 -1.91e-02 -2.07e+00 5.79e+01 273s MOR 1.95e+02 2.39e+03 -2.79e+02 -3.37e+01 -3.39e+01 -9.21e+02 273s CAR -6.28e+00 -2.79e+02 5.76e+01 5.77e+00 6.59e+00 7.81e+01 273s DR -1.91e-02 -3.37e+01 5.77e+00 9.07e-01 5.66e-01 1.69e+01 273s GNP -2.07e+00 -3.39e+01 6.59e+00 5.66e-01 1.42e+00 9.28e+00 273s DEN 5.79e+01 -9.21e+02 7.81e+01 1.69e+01 9.28e+00 3.53e+03 273s TB -6.09e-02 -9.93e-01 2.50e-01 1.98e-02 6.82e-03 -9.75e-01 273s TB 273s POP -6.09e-02 273s MOR -9.93e-01 273s CAR 2.50e-01 273s DR 1.98e-02 273s GNP 6.82e-03 273s DEN -9.75e-01 273s TB 1.64e-02 273s -------------------------------------------------------- 273s wages 39 10 19 23.119456 273s Best subsample: 273s [1] 1 2 5 6 7 9 10 11 12 13 14 15 19 21 23 25 26 27 28 273s Outliers: 9 273s [1] 4 5 9 24 25 26 28 32 34 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 273s 273s Robust Estimate of Location: 273s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 273s 2161.89 2.95 1114.21 297.68 374.00 7269.37 39.13 2.43 273s RACE SCHOOL 273s 36.13 10.39 273s 273s Robust Estimate of Covariance: 273s HRS RATE ERSP ERNO NEIN ASSET 273s HRS 3.53e+03 8.31e+00 -5.96e+03 -6.43e+02 5.15e+03 1.12e+05 273s RATE 8.31e+00 1.78e-01 8.19e+00 2.70e+00 3.90e+01 8.94e+02 273s ERSP -5.96e+03 8.19e+00 1.90e+04 1.13e+03 -4.73e+03 -9.49e+04 273s ERNO -6.43e+02 2.70e+00 1.13e+03 1.80e+03 -3.56e+02 -7.33e+03 273s NEIN 5.15e+03 3.90e+01 -4.73e+03 -3.56e+02 1.38e+04 3.00e+05 273s ASSET 1.12e+05 8.94e+02 -9.49e+04 -7.33e+03 3.00e+05 6.62e+06 273s AGE -3.33e+01 -6.55e-02 8.33e+01 1.50e+00 -3.28e+01 -7.55e+02 273s DEP 4.50e+00 -4.01e-02 -2.77e+01 1.31e+00 -8.09e+00 -1.61e+02 273s RACE -1.30e+03 -6.06e+00 1.80e+03 1.48e+02 -2.58e+03 -5.59e+04 273s SCHOOL 3.01e+01 3.58e-01 -5.57e+00 2.84e+00 9.26e+01 2.10e+03 273s AGE DEP RACE SCHOOL 273s HRS -3.33e+01 4.50e+00 -1.30e+03 3.01e+01 273s RATE -6.55e-02 -4.01e-02 -6.06e+00 3.58e-01 273s ERSP 8.33e+01 -2.77e+01 1.80e+03 -5.57e+00 273s ERNO 1.50e+00 1.31e+00 1.48e+02 2.84e+00 273s NEIN -3.28e+01 -8.09e+00 -2.58e+03 9.26e+01 273s ASSET -7.55e+02 -1.61e+02 -5.59e+04 2.10e+03 273s AGE 6.57e-01 -1.64e-01 1.13e+01 -2.67e-01 273s DEP -1.64e-01 9.20e-02 2.38e-01 -6.01e-02 273s RACE 1.13e+01 2.38e-01 5.73e+02 -1.67e+01 273s SCHOOL -2.67e-01 -6.01e-02 -1.67e+01 7.95e-01 273s -------------------------------------------------------- 273s airquality 153 4 58 18.316848 273s Best subsample: 273s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 273s [20] 47 48 49 50 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 273s [39] 81 82 84 86 87 89 90 91 92 95 97 98 100 101 105 106 108 109 110 273s [58] 111 273s Outliers: 10 273s [1] 8 9 15 18 24 30 48 62 117 148 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=58) 273s 273s Robust Estimate of Location: 273s Ozone Solar.R Wind Temp 273s 40.80 189.37 9.66 78.81 273s 273s Robust Estimate of Covariance: 273s Ozone Solar.R Wind Temp 273s Ozone 935.54 857.76 -56.30 220.48 273s Solar.R 857.76 8507.83 1.36 155.13 273s Wind -56.30 1.36 9.90 -11.61 273s Temp 220.48 155.13 -11.61 84.00 273s -------------------------------------------------------- 273s attitude 30 7 19 24.464288 273s Best subsample: 273s [1] 2 3 4 5 7 8 10 11 12 15 17 19 21 22 23 25 27 28 29 273s Outliers: 8 273s [1] 6 9 13 14 16 18 24 26 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 273s 273s Robust Estimate of Location: 273s rating complaints privileges learning raises critical 273s 64.4 65.2 51.0 55.5 65.9 77.4 273s advance 273s 43.2 273s 273s Robust Estimate of Covariance: 273s rating complaints privileges learning raises critical advance 273s rating 199.95 162.36 115.83 160.44 128.87 -13.55 66.20 273s complaints 162.36 204.84 130.33 170.66 150.19 16.28 96.66 273s privileges 115.83 130.33 181.31 152.63 106.56 4.52 91.44 273s learning 160.44 170.66 152.63 213.06 156.57 9.92 88.31 273s raises 128.87 150.19 106.56 156.57 152.05 23.10 84.00 273s critical -13.55 16.28 4.52 9.92 23.10 80.22 27.15 273s advance 66.20 96.66 91.44 88.31 84.00 27.15 95.51 273s -------------------------------------------------------- 273s attenu 182 5 86 6.593068 273s Best subsample: 273s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 273s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 273s [39] 115 116 117 119 120 121 122 124 125 126 127 128 129 130 131 132 133 134 135 273s [58] 136 137 138 139 140 141 144 145 146 147 148 149 150 151 152 153 154 155 156 273s [77] 157 158 159 160 161 162 163 164 165 166 273s Outliers: 49 273s [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 273s [20] 23 24 25 27 28 29 30 31 32 33 40 45 47 59 60 61 64 65 78 273s [39] 82 83 97 98 100 101 102 103 104 105 117 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=86) 273s 273s Robust Estimate of Location: 273s event mag station dist accel 273s 17.122 5.798 63.461 25.015 0.131 273s 273s Robust Estimate of Covariance: 273s event mag station dist accel 273s event 2.98e+01 -1.58e+00 9.49e+01 -8.36e+00 -3.59e-02 273s mag -1.58e+00 4.26e-01 -3.88e+00 3.13e+00 5.30e-03 273s station 9.49e+01 -3.88e+00 1.10e+03 2.60e+01 5.38e-01 273s dist -8.36e+00 3.13e+00 2.60e+01 2.66e+02 -9.23e-01 273s accel -3.59e-02 5.30e-03 5.38e-01 -9.23e-01 7.78e-03 273s -------------------------------------------------------- 273s USJudgeRatings 43 12 28 -47.886937 273s Best subsample: 273s [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 273s [26] 40 41 43 273s Outliers: 14 273s [1] 1 5 7 8 12 13 14 17 20 21 23 31 35 42 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=28) 273s 273s Robust Estimate of Location: 273s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 273s 7.46 8.26 7.88 8.06 7.85 7.92 7.84 7.83 7.67 7.74 8.31 8.03 273s 273s Robust Estimate of Covariance: 273s CONT INTG DMNR DILG CFMG DECI PREP FAMI 273s CONT 0.7363 -0.2916 -0.4193 -0.1943 -0.0555 -0.0690 -0.1703 -0.1727 273s INTG -0.2916 0.4179 0.5511 0.4167 0.3176 0.3102 0.4247 0.4279 273s DMNR -0.4193 0.5511 0.8141 0.5256 0.4092 0.3934 0.5294 0.5094 273s DILG -0.1943 0.4167 0.5256 0.4820 0.3904 0.3819 0.5054 0.5104 273s CFMG -0.0555 0.3176 0.4092 0.3904 0.3595 0.3368 0.4180 0.4206 273s DECI -0.0690 0.3102 0.3934 0.3819 0.3368 0.3310 0.4135 0.4194 273s PREP -0.1703 0.4247 0.5294 0.5054 0.4180 0.4135 0.5647 0.5752 273s FAMI -0.1727 0.4279 0.5094 0.5104 0.4206 0.4194 0.5752 0.6019 273s ORAL -0.2109 0.4453 0.5646 0.5054 0.4200 0.4121 0.5575 0.5735 273s WRIT -0.2033 0.4411 0.5466 0.5087 0.4222 0.4147 0.5592 0.5787 273s PHYS -0.1624 0.2578 0.3163 0.2833 0.2268 0.2362 0.3108 0.3284 273s RTEN -0.2622 0.4872 0.6324 0.5203 0.4145 0.4081 0.5488 0.5595 273s ORAL WRIT PHYS RTEN 273s CONT -0.2109 -0.2033 -0.1624 -0.2622 273s INTG 0.4453 0.4411 0.2578 0.4872 273s DMNR 0.5646 0.5466 0.3163 0.6324 273s DILG 0.5054 0.5087 0.2833 0.5203 273s CFMG 0.4200 0.4222 0.2268 0.4145 273s DECI 0.4121 0.4147 0.2362 0.4081 273s PREP 0.5575 0.5592 0.3108 0.5488 273s FAMI 0.5735 0.5787 0.3284 0.5595 273s ORAL 0.5701 0.5677 0.3283 0.5688 273s WRIT 0.5677 0.5715 0.3268 0.5645 273s PHYS 0.3283 0.3268 0.2302 0.3308 273s RTEN 0.5688 0.5645 0.3308 0.6057 273s -------------------------------------------------------- 273s USArrests 50 4 27 15.438912 273s Best subsample: 273s [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 273s [26] 49 50 273s Outliers: 7 273s [1] 2 5 6 10 24 28 33 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=27) 273s 273s Robust Estimate of Location: 273s Murder Assault UrbanPop Rape 273s 6.91 150.10 65.88 18.75 273s 273s Robust Estimate of Covariance: 273s Murder Assault UrbanPop Rape 273s Murder 17.9 285.4 17.6 25.0 273s Assault 285.4 6572.8 524.9 465.0 273s UrbanPop 17.6 524.9 211.9 50.5 273s Rape 25.0 465.0 50.5 56.4 273s -------------------------------------------------------- 273s longley 16 7 12 12.747678 273s Best subsample: 273s [1] 5 6 7 8 9 10 11 12 13 14 15 16 273s Outliers: 4 273s [1] 1 2 3 4 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 273s 273s Robust Estimate of Location: 273s GNP.deflator GNP Unemployed Armed.Forces Population 273s 106.5 430.6 328.2 295.0 120.2 273s Year Employed 273s 1956.5 66.9 273s 273s Robust Estimate of Covariance: 273s GNP.deflator GNP Unemployed Armed.Forces Population 273s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 273s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 273s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 273s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 273s Population 81.4 803.4 929.1 -327.4 63.5 273s Year 51.6 504.3 595.6 -216.7 39.7 273s Employed 34.2 344.1 323.6 -149.5 26.2 273s Year Employed 273s GNP.deflator 51.6 34.2 273s GNP 504.3 344.1 273s Unemployed 595.6 323.6 273s Armed.Forces -216.7 -149.5 273s Population 39.7 26.2 273s Year 25.1 16.7 273s Employed 16.7 12.4 273s -------------------------------------------------------- 273s Loblolly 84 3 44 4.898174 273s Best subsample: 273s [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 273s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 273s Outliers: 31 273s [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 273s [26] 72 76 77 78 83 84 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=44) 273s 273s Robust Estimate of Location: 273s height age Seed 273s 20.44 8.19 7.72 273s 273s Robust Estimate of Covariance: 273s height age Seed 273s height 247.8 79.5 11.9 273s age 79.5 25.7 3.0 273s Seed 11.9 3.0 17.1 273s -------------------------------------------------------- 273s quakes 1000 4 502 8.274209 273s Best subsample: 273s Too long... 273s Outliers: 266 273s Too many to print ... 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 273s -> Method: Deterministic MCD(alpha=0.5 ==> h=502) 273s 273s Robust Estimate of Location: 273s lat long depth mag 273s -21.34 182.47 360.58 4.54 273s 273s Robust Estimate of Covariance: 273s lat long depth mag 273s lat 1.50e+01 3.58e+00 1.37e+02 -2.66e-01 273s long 3.58e+00 4.55e+00 -3.61e+02 4.64e-02 273s depth 1.37e+02 -3.61e+02 4.84e+04 -1.36e+01 273s mag -2.66e-01 4.64e-02 -1.36e+01 1.34e-01 273s -------------------------------------------------------- 273s ======================================================== 273s > dodata(method="exact") 273s 273s Call: dodata(method = "exact") 273s Data Set n p Half LOG(obj) Time 273s ======================================================== 273s heart 12 2 7 5.678742 273s Best subsample: 273s [1] 1 3 4 5 7 9 11 273s Outliers: 0 273s Too many to print ... 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "exact", trace = FALSE) 273s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = exact; (n,k)mini = (300,5) 273s 273s Robust Estimate of Location: 273s height weight 273s 38.3 33.1 273s 273s Robust Estimate of Covariance: 273s height weight 273s height 135 259 273s weight 259 564 273s -------------------------------------------------------- 273s starsCYG 47 2 25 -8.031215 273s Best subsample: 273s [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 273s Outliers: 7 273s [1] 7 9 11 14 20 30 34 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "exact", trace = FALSE) 273s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = exact; (n,k)mini = (300,5) 273s 273s Robust Estimate of Location: 273s log.Te log.light 273s 4.41 4.95 273s 273s Robust Estimate of Covariance: 273s log.Te log.light 273s log.Te 0.0132 0.0394 273s log.light 0.0394 0.2743 273s -------------------------------------------------------- 273s phosphor 18 2 10 6.878847 273s Best subsample: 273s [1] 3 5 8 9 11 12 13 14 15 17 273s Outliers: 3 273s [1] 1 6 10 273s ------------- 273s 273s Call: 273s CovMcd(x = x, nsamp = "exact", trace = FALSE) 273s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 273s 273s Robust Estimate of Location: 273s inorg organic 273s 13.4 38.8 273s 273s Robust Estimate of Covariance: 273s inorg organic 273s inorg 129 130 273s organic 130 182 273s -------------------------------------------------------- 274s coleman 20 5 13 1.286808 274s Best subsample: 274s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 274s Outliers: 7 274s [1] 1 6 9 10 11 15 18 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s salaryP fatherWc sstatus teacherSc motherLev 274s 2.76 48.38 6.12 25.00 6.40 274s 274s Robust Estimate of Covariance: 274s salaryP fatherWc sstatus teacherSc motherLev 274s salaryP 0.253 1.786 -0.266 0.151 0.075 274s fatherWc 1.786 1303.382 330.496 12.604 34.503 274s sstatus -0.266 330.496 119.888 3.833 10.131 274s teacherSc 0.151 12.604 3.833 0.785 0.555 274s motherLev 0.075 34.503 10.131 0.555 1.043 274s -------------------------------------------------------- 274s salinity 28 3 16 1.326364 274s Best subsample: 274s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 274s Outliers: 4 274s [1] 5 16 23 24 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s X1 X2 X3 274s 10.08 2.78 22.78 274s 274s Robust Estimate of Covariance: 274s X1 X2 X3 274s X1 10.44 1.01 -3.19 274s X2 1.01 3.83 -1.44 274s X3 -3.19 -1.44 2.39 274s -------------------------------------------------------- 274s wood 20 5 13 -36.270094 274s Best subsample: 274s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 274s Outliers: 7 274s [1] 4 6 7 8 11 16 19 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s x1 x2 x3 x4 x5 274s 0.587 0.122 0.531 0.538 0.892 274s 274s Robust Estimate of Covariance: 274s x1 x2 x3 x4 x5 274s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 274s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 274s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 274s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 274s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 274s -------------------------------------------------------- 274s Animals 28 2 15 14.555543 274s Best subsample: 274s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 274s Outliers: 14 274s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s body brain 274s 18.7 64.9 274s 274s Robust Estimate of Covariance: 274s body brain 274s body 929 1576 274s brain 1576 5646 274s -------------------------------------------------------- 274s lactic 20 2 11 0.359580 274s Best subsample: 274s [1] 1 2 3 4 5 7 8 9 10 11 12 274s Outliers: 4 274s [1] 17 18 19 20 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s X Y 274s 3.86 5.01 274s 274s Robust Estimate of Covariance: 274s X Y 274s X 10.6 14.6 274s Y 14.6 21.3 274s -------------------------------------------------------- 274s pension 18 2 10 16.675508 274s Best subsample: 274s [1] 1 2 3 4 5 6 8 9 11 12 274s Outliers: 5 274s [1] 14 15 16 17 18 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s Income Reserves 274s 52.3 560.9 274s 274s Robust Estimate of Covariance: 274s Income Reserves 274s Income 1420 11932 274s Reserves 11932 208643 274s -------------------------------------------------------- 274s vaso 39 2 21 -3.972244 274s Best subsample: 274s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 274s Outliers: 4 274s [1] 1 2 17 31 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s Volume Rate 274s 1.16 1.72 274s 274s Robust Estimate of Covariance: 274s Volume Rate 274s Volume 0.313 -0.167 274s Rate -0.167 0.728 274s -------------------------------------------------------- 274s stackloss 21 3 12 5.472581 274s Best subsample: 274s [1] 4 5 6 7 8 9 10 11 12 13 14 20 274s Outliers: 9 274s [1] 1 2 3 15 16 17 18 19 21 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s Air.Flow Water.Temp Acid.Conc. 274s 59.5 20.8 87.3 274s 274s Robust Estimate of Covariance: 274s Air.Flow Water.Temp Acid.Conc. 274s Air.Flow 6.29 5.85 5.74 274s Water.Temp 5.85 9.23 6.14 274s Acid.Conc. 5.74 6.14 23.25 274s -------------------------------------------------------- 274s pilot 20 2 11 6.487287 274s Best subsample: 274s [1] 2 3 6 7 9 12 15 16 17 18 20 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMcd(x = x, nsamp = "exact", trace = FALSE) 274s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 274s 274s Robust Estimate of Location: 274s X Y 274s 101.1 67.7 274s 274s Robust Estimate of Covariance: 274s X Y 274s X 3344 1070 274s Y 1070 343 274s -------------------------------------------------------- 274s ======================================================== 274s > dodata(method="MRCD") 274s 274s Call: dodata(method = "MRCD") 274s Data Set n p Half LOG(obj) Time 274s ======================================================== 274s heart 12 2 6 7.446266 274s Best subsample: 274s [1] 1 3 4 7 9 11 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=6) 274s 274s Robust Estimate of Location: 274s height weight 274s 38.8 33.0 274s 274s Robust Estimate of Covariance: 274s height weight 274s height 47.4 75.2 274s weight 75.2 155.4 274s -------------------------------------------------------- 274s starsCYG 47 2 24 -5.862050 274s Best subsample: 274s [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 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=24) 274s 274s Robust Estimate of Location: 274s log.Te log.light 274s 4.44 5.05 274s 274s Robust Estimate of Covariance: 274s log.Te log.light 274s log.Te 0.00867 0.02686 274s log.light 0.02686 0.41127 274s -------------------------------------------------------- 274s phosphor 18 2 9 9.954788 274s Best subsample: 274s [1] 4 7 8 9 11 12 13 14 16 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 274s 274s Robust Estimate of Location: 274s inorg organic 274s 12.5 39.0 274s 274s Robust Estimate of Covariance: 274s inorg organic 274s inorg 236 140 274s organic 140 172 274s -------------------------------------------------------- 274s stackloss 21 3 11 7.991165 274s Best subsample: 274s [1] 4 5 6 7 8 9 10 13 18 19 20 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=11) 274s 274s Robust Estimate of Location: 274s Air.Flow Water.Temp Acid.Conc. 274s 58.2 21.4 85.2 274s 274s Robust Estimate of Covariance: 274s Air.Flow Water.Temp Acid.Conc. 274s Air.Flow 49.8 17.2 42.7 274s Water.Temp 17.2 13.8 25.2 274s Acid.Conc. 42.7 25.2 58.2 274s -------------------------------------------------------- 274s coleman 20 5 10 5.212156 274s Best subsample: 274s [1] 3 4 5 7 8 9 14 16 19 20 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 274s 274s Robust Estimate of Location: 274s salaryP fatherWc sstatus teacherSc motherLev 274s 2.78 59.44 9.28 25.41 6.70 274s 274s Robust Estimate of Covariance: 274s salaryP fatherWc sstatus teacherSc motherLev 274s salaryP 0.1582 -0.2826 0.4112 0.1754 0.0153 274s fatherWc -0.2826 902.9210 201.5815 -2.1236 18.8736 274s sstatus 0.4112 201.5815 65.4580 -0.3876 4.7794 274s teacherSc 0.1754 -2.1236 -0.3876 0.7233 -0.0322 274s motherLev 0.0153 18.8736 4.7794 -0.0322 0.5417 274s -------------------------------------------------------- 274s salinity 28 3 14 3.586919 274s Best subsample: 274s [1] 1 7 8 12 13 14 18 20 21 22 25 26 27 28 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 274s 274s Robust Estimate of Location: 274s X1 X2 X3 274s 10.95 3.71 21.99 274s 274s Robust Estimate of Covariance: 274s X1 X2 X3 274s X1 14.153 0.718 -3.359 274s X2 0.718 3.565 -0.722 274s X3 -3.359 -0.722 1.607 274s -------------------------------------------------------- 274s wood 20 5 10 -33.100492 274s Best subsample: 274s [1] 1 2 3 5 11 14 15 17 18 20 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 274s 274s Robust Estimate of Location: 274s x1 x2 x3 x4 x5 274s 0.572 0.120 0.504 0.545 0.899 274s 274s Robust Estimate of Covariance: 274s x1 x2 x3 x4 x5 274s x1 0.007543 0.001720 0.000412 -0.001230 -0.001222 274s x2 0.001720 0.000568 0.000355 -0.000533 -0.000132 274s x3 0.000412 0.000355 0.002478 0.000190 0.000811 274s x4 -0.001230 -0.000533 0.000190 0.002327 0.000967 274s x5 -0.001222 -0.000132 0.000811 0.000967 0.001894 274s -------------------------------------------------------- 274s hbk 75 3 38 1.539545 274s Best subsample: 274s [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 274s [26] 55 56 58 59 63 64 66 67 70 71 72 73 74 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=38) 274s 274s Robust Estimate of Location: 274s X1 X2 X3 274s 1.60 2.37 1.64 274s 274s Robust Estimate of Covariance: 274s X1 X2 X3 274s X1 2.810 0.124 1.248 274s X2 0.124 1.017 0.208 274s X3 1.248 0.208 2.218 274s -------------------------------------------------------- 274s Animals 28 2 14 16.278395 274s Best subsample: 274s [1] 1 3 4 5 10 11 18 19 20 21 22 23 26 27 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 274s 274s Robust Estimate of Location: 274s body brain 274s 19.5 56.8 274s 274s Robust Estimate of Covariance: 274s body brain 274s body 2802 5179 274s brain 5179 13761 274s -------------------------------------------------------- 274s bushfire 38 5 19 28.483413 274s Best subsample: 274s [1] 1 2 3 4 5 14 15 16 17 18 19 20 21 22 23 24 25 26 27 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=19) 274s 274s Robust Estimate of Location: 274s V1 V2 V3 V4 V5 274s 103 145 287 221 281 274s 274s Robust Estimate of Covariance: 274s V1 V2 V3 V4 V5 274s V1 366 249 -1993 -503 -396 274s V2 249 252 -1223 -291 -233 274s V3 -1993 -1223 14246 3479 2718 274s V4 -503 -291 3479 1083 748 274s V5 -396 -233 2718 748 660 274s -------------------------------------------------------- 274s lactic 20 2 10 2.593141 274s Best subsample: 274s [1] 1 2 3 4 5 7 8 9 10 11 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 274s 274s Robust Estimate of Location: 274s X Y 274s 2.60 3.63 274s 274s Robust Estimate of Covariance: 274s X Y 274s X 8.13 13.54 274s Y 13.54 24.17 274s -------------------------------------------------------- 274s pension 18 2 9 18.931204 274s Best subsample: 274s [1] 2 3 4 5 6 8 9 11 12 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 274s 274s Robust Estimate of Location: 274s Income Reserves 274s 45.7 466.9 274s 274s Robust Estimate of Covariance: 274s Income Reserves 274s Income 2127 23960 274s Reserves 23960 348275 274s -------------------------------------------------------- 274s vaso 39 2 20 -1.864710 274s Best subsample: 274s [1] 3 4 8 14 18 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=20) 274s 274s Robust Estimate of Location: 274s Volume Rate 274s 1.14 1.77 274s 274s Robust Estimate of Covariance: 274s Volume Rate 274s Volume 0.44943 -0.00465 274s Rate -0.00465 0.34480 274s -------------------------------------------------------- 274s wagnerGrowth 63 6 32 9.287760 274s Best subsample: 274s [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 274s [26] 53 54 55 56 57 60 62 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=32) 274s 274s Robust Estimate of Location: 274s Region PA GPA HS GHS y 274s 10.719 33.816 -2.144 2.487 0.293 4.918 274s 274s Robust Estimate of Covariance: 274s Region PA GPA HS GHS y 274s Region 56.7128 17.4919 -2.9710 -0.6491 -0.4545 -10.4287 274s PA 17.4919 29.9968 -7.6846 -1.3141 0.5418 -35.6434 274s GPA -2.9710 -7.6846 6.3238 1.1257 -0.4757 12.4707 274s HS -0.6491 -1.3141 1.1257 1.1330 -0.0915 3.3617 274s GHS -0.4545 0.5418 -0.4757 -0.0915 0.1468 -1.1228 274s y -10.4287 -35.6434 12.4707 3.3617 -1.1228 67.4215 274s -------------------------------------------------------- 274s fish 159 6 79 22.142828 274s Best subsample: 274s [1] 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 274s [20] 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 50 51 274s [39] 52 53 54 55 56 57 58 59 60 71 105 106 107 109 110 111 113 114 115 274s [58] 116 117 118 119 120 122 123 124 125 126 127 128 129 130 131 132 134 135 136 274s [77] 137 138 139 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=79) 274s 274s Robust Estimate of Location: 274s Weight Length1 Length2 Length3 Height Width 274s 291.7 23.8 25.9 28.9 30.4 14.7 274s 274s Robust Estimate of Covariance: 274s Weight Length1 Length2 Length3 Height Width 274s Weight 77155.07 1567.55 1713.74 2213.16 1912.62 -103.97 274s Length1 1567.55 45.66 41.57 52.14 38.66 -2.39 274s Length2 1713.74 41.57 54.26 56.77 42.72 -2.55 274s Length3 2213.16 52.14 56.77 82.57 58.84 -3.65 274s Height 1912.62 38.66 42.72 58.84 70.51 -3.80 274s Width -103.97 -2.39 -2.55 -3.65 -3.80 1.19 274s -------------------------------------------------------- 274s pottery 27 6 14 -6.897459 274s Best subsample: 274s [1] 1 2 4 5 6 10 11 13 14 15 19 21 22 26 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 274s 274s Robust Estimate of Location: 274s SI AL FE MG CA TI 274s 54.39 14.93 9.78 3.82 5.11 0.86 274s 274s Robust Estimate of Covariance: 274s SI AL FE MG CA TI 274s SI 17.47469 -0.16656 0.39943 4.48192 -0.71153 0.06515 274s AL -0.16656 3.93154 -0.35738 -2.29899 0.14770 -0.02050 274s FE 0.39943 -0.35738 0.20434 0.37562 -0.22460 0.00943 274s MG 4.48192 -2.29899 0.37562 2.82339 -0.16027 0.02943 274s CA -0.71153 0.14770 -0.22460 -0.16027 0.88443 -0.01711 274s TI 0.06515 -0.02050 0.00943 0.02943 -0.01711 0.00114 274s -------------------------------------------------------- 274s rice 105 6 53 -8.916472 274s Best subsample: 274s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 274s [20] 38 44 45 47 51 52 53 54 55 59 60 65 67 70 72 76 79 80 81 274s [39] 82 83 84 85 86 90 92 93 94 95 97 98 99 101 105 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=53) 274s 274s Robust Estimate of Location: 274s Favor Appearance Taste Stickiness 274s -0.1741 0.0774 -0.0472 0.1868 274s Toughness Overall_evaluation 274s -0.0346 -0.0683 274s 274s Robust Estimate of Covariance: 274s Favor Appearance Taste Stickiness Toughness 274s Favor 0.402 0.306 0.378 0.364 -0.134 274s Appearance 0.306 0.508 0.474 0.407 -0.146 274s Taste 0.378 0.474 0.708 0.611 -0.258 274s Stickiness 0.364 0.407 0.611 0.795 -0.320 274s Toughness -0.134 -0.146 -0.258 -0.320 0.302 274s Overall_evaluation 0.453 0.536 0.746 0.745 -0.327 274s Overall_evaluation 274s Favor 0.453 274s Appearance 0.536 274s Taste 0.746 274s Stickiness 0.745 274s Toughness -0.327 274s Overall_evaluation 0.963 274s -------------------------------------------------------- 274s un86 73 7 37 19.832993 274s Best subsample: 274s [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 274s [26] 56 57 60 62 63 64 65 67 70 71 72 73 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=37) 274s 274s Robust Estimate of Location: 274s POP MOR CAR DR GNP DEN TB 274s 14.462 66.892 6.670 0.858 1.251 55.518 0.429 274s 274s Robust Estimate of Covariance: 274s POP MOR CAR DR GNP DEN 274s POP 3.00e+02 1.58e+02 9.83e+00 2.74e+00 5.51e-01 6.87e+01 274s MOR 1.58e+02 2.96e+03 -4.24e+02 -4.72e+01 -5.40e+01 -1.01e+03 274s CAR 9.83e+00 -4.24e+02 9.12e+01 8.71e+00 1.13e+01 1.96e+02 274s DR 2.74e+00 -4.72e+01 8.71e+00 1.25e+00 1.03e+00 2.74e+01 274s GNP 5.51e-01 -5.40e+01 1.13e+01 1.03e+00 2.31e+00 2.36e+01 274s DEN 6.87e+01 -1.01e+03 1.96e+02 2.74e+01 2.36e+01 3.12e+03 274s TB 2.04e-02 -1.81e+00 3.42e-01 2.57e-02 2.09e-02 -6.88e-01 274s TB 274s POP 2.04e-02 274s MOR -1.81e+00 274s CAR 3.42e-01 274s DR 2.57e-02 274s GNP 2.09e-02 274s DEN -6.88e-01 274s TB 2.59e-02 274s -------------------------------------------------------- 274s wages 39 10 14 35.698016 274s Best subsample: 274s [1] 1 2 5 6 9 10 11 13 15 19 23 25 26 28 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 274s 274s Robust Estimate of Location: 274s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 274s 2167.71 2.96 1113.50 300.43 382.29 7438.00 39.06 2.41 274s RACE SCHOOL 274s 33.00 10.45 274s 274s Robust Estimate of Covariance: 274s HRS RATE ERSP ERNO NEIN ASSET 274s HRS 1.97e+03 -4.14e-01 -4.71e+03 -6.58e+02 1.81e+03 3.84e+04 274s RATE -4.14e-01 1.14e-01 1.79e+01 3.08e+00 1.40e+01 3.57e+02 274s ERSP -4.71e+03 1.79e+01 1.87e+04 2.33e+03 -2.06e+03 -3.57e+04 274s ERNO -6.58e+02 3.08e+00 2.33e+03 5.36e+02 -3.42e+02 -5.56e+03 274s NEIN 1.81e+03 1.40e+01 -2.06e+03 -3.42e+02 5.77e+03 1.10e+05 274s ASSET 3.84e+04 3.57e+02 -3.57e+04 -5.56e+03 1.10e+05 2.86e+06 274s AGE -1.83e+01 1.09e-02 6.69e+01 8.78e+00 -5.07e+00 -1.51e+02 274s DEP 4.82e+00 -3.14e-02 -2.52e+01 -2.96e+00 -5.33e+00 -1.03e+02 274s RACE -5.67e+02 -1.33e+00 1.21e+03 1.81e+02 -9.13e+02 -1.96e+04 274s SCHOOL 5.33e+00 1.87e-01 1.86e+01 3.12e+00 3.20e+01 7.89e+02 274s AGE DEP RACE SCHOOL 274s HRS -1.83e+01 4.82e+00 -5.67e+02 5.33e+00 274s RATE 1.09e-02 -3.14e-02 -1.33e+00 1.87e-01 274s ERSP 6.69e+01 -2.52e+01 1.21e+03 1.86e+01 274s ERNO 8.78e+00 -2.96e+00 1.81e+02 3.12e+00 274s NEIN -5.07e+00 -5.33e+00 -9.13e+02 3.20e+01 274s ASSET -1.51e+02 -1.03e+02 -1.96e+04 7.89e+02 274s AGE 5.71e-01 -1.56e-01 4.58e+00 -5.00e-02 274s DEP -1.56e-01 8.08e-02 -3.02e-01 -4.47e-02 274s RACE 4.58e+00 -3.02e-01 2.36e+02 -4.54e+00 274s SCHOOL -5.00e-02 -4.47e-02 -4.54e+00 4.23e-01 274s -------------------------------------------------------- 274s airquality 153 4 56 21.136376 274s Best subsample: 274s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 274s [20] 47 48 49 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 81 274s [39] 82 84 86 87 89 90 91 92 96 97 98 100 101 105 106 109 110 111 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=56) 274s 274s Robust Estimate of Location: 274s Ozone Solar.R Wind Temp 274s 41.84 197.21 8.93 80.39 274s 274s Robust Estimate of Covariance: 274s Ozone Solar.R Wind Temp 274s Ozone 1480.7 1562.8 -99.9 347.3 274s Solar.R 1562.8 11401.2 -35.2 276.8 274s Wind -99.9 -35.2 11.4 -23.5 274s Temp 347.3 276.8 -23.5 107.7 274s -------------------------------------------------------- 274s attitude 30 7 15 27.040805 274s Best subsample: 274s [1] 2 3 4 5 7 8 10 12 15 19 22 23 25 27 28 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=15) 274s 274s Robust Estimate of Location: 274s rating complaints privileges learning raises critical 274s 65.8 66.5 50.1 56.1 66.7 78.1 274s advance 274s 41.7 274s 274s Robust Estimate of Covariance: 274s rating complaints privileges learning raises critical advance 274s rating 138.77 80.02 59.22 107.33 95.83 -1.24 54.36 274s complaints 80.02 97.23 50.59 99.50 79.15 -2.71 42.81 274s privileges 59.22 50.59 84.92 90.03 60.88 22.39 44.93 274s learning 107.33 99.50 90.03 187.67 128.71 15.48 63.67 274s raises 95.83 79.15 60.88 128.71 123.94 -1.46 49.98 274s critical -1.24 -2.71 22.39 15.48 -1.46 61.23 12.88 274s advance 54.36 42.81 44.93 63.67 49.98 12.88 48.61 274s -------------------------------------------------------- 274s attenu 182 5 83 9.710111 274s Best subsample: 274s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 274s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 274s [39] 115 116 117 121 122 124 125 126 127 128 129 130 131 132 133 134 135 136 137 274s [58] 138 139 140 141 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 274s [77] 160 161 162 163 164 165 166 274s Outliers: 0 274s Too many to print ... 274s ------------- 274s 274s Call: 274s CovMrcd(x = x, trace = FALSE) 274s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=83) 275s 275s Robust Estimate of Location: 275s event mag station dist accel 275s 18.940 5.741 67.988 23.365 0.124 275s 275s Robust Estimate of Covariance: 275s event mag station dist accel 275s event 2.86e+01 -2.31e+00 1.02e+02 2.68e+01 -1.99e-01 275s mag -2.31e+00 6.17e-01 -7.03e+00 4.67e-01 2.59e-02 275s station 1.02e+02 -7.03e+00 1.66e+03 1.62e+02 7.96e-02 275s dist 2.68e+01 4.67e-01 1.62e+02 3.61e+02 -1.23e+00 275s accel -1.99e-01 2.59e-02 7.96e-02 -1.23e+00 9.42e-03 275s -------------------------------------------------------- 275s USJudgeRatings 43 12 22 -23.463708 275s Best subsample: 275s [1] 2 3 4 6 9 11 15 16 18 19 24 25 26 27 28 29 32 33 34 36 37 38 275s Outliers: 0 275s Too many to print ... 275s ------------- 275s 275s Call: 275s CovMrcd(x = x, trace = FALSE) 275s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=22) 275s 275s Robust Estimate of Location: 275s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 275s 7.24 8.42 8.10 8.19 7.95 8.00 7.96 7.96 7.81 7.89 8.40 8.20 275s 275s Robust Estimate of Covariance: 275s CONT INTG DMNR DILG CFMG DECI PREP 275s CONT 0.61805 -0.05601 -0.09540 0.00694 0.09853 0.06261 0.03939 275s INTG -0.05601 0.23560 0.27537 0.20758 0.16603 0.17281 0.21128 275s DMNR -0.09540 0.27537 0.55349 0.28872 0.24014 0.24293 0.28886 275s DILG 0.00694 0.20758 0.28872 0.34099 0.23502 0.23917 0.29672 275s CFMG 0.09853 0.16603 0.24014 0.23502 0.31649 0.23291 0.27651 275s DECI 0.06261 0.17281 0.24293 0.23917 0.23291 0.30681 0.27737 275s PREP 0.03939 0.21128 0.28886 0.29672 0.27651 0.27737 0.42020 275s FAMI 0.04588 0.20388 0.26072 0.29037 0.27179 0.27737 0.34857 275s ORAL 0.03000 0.21379 0.29606 0.28764 0.27338 0.27424 0.33503 275s WRIT 0.03261 0.20258 0.26931 0.27962 0.26382 0.26610 0.32677 275s PHYS -0.04485 0.13598 0.17659 0.16834 0.14554 0.16467 0.18948 275s RTEN 0.01543 0.22654 0.32117 0.27307 0.23826 0.24669 0.29450 275s FAMI ORAL WRIT PHYS RTEN 275s CONT 0.04588 0.03000 0.03261 -0.04485 0.01543 275s INTG 0.20388 0.21379 0.20258 0.13598 0.22654 275s DMNR 0.26072 0.29606 0.26931 0.17659 0.32117 275s DILG 0.29037 0.28764 0.27962 0.16834 0.27307 275s CFMG 0.27179 0.27338 0.26382 0.14554 0.23826 275s DECI 0.27737 0.27424 0.26610 0.16467 0.24669 275s PREP 0.34857 0.33503 0.32677 0.18948 0.29450 275s FAMI 0.47232 0.33762 0.33420 0.19759 0.29015 275s ORAL 0.33762 0.40361 0.32208 0.19794 0.29544 275s WRIT 0.33420 0.32208 0.38733 0.19276 0.28184 275s PHYS 0.19759 0.19794 0.19276 0.20284 0.18097 275s RTEN 0.29015 0.29544 0.28184 0.18097 0.36877 275s -------------------------------------------------------- 275s USArrests 50 4 25 17.834643 275s Best subsample: 275s [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 275s Outliers: 0 275s Too many to print ... 275s ------------- 275s 275s Call: 275s CovMrcd(x = x, trace = FALSE) 275s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=25) 275s 275s Robust Estimate of Location: 275s Murder Assault UrbanPop Rape 275s 5.38 121.68 63.80 16.33 275s 275s Robust Estimate of Covariance: 275s Murder Assault UrbanPop Rape 275s Murder 17.8 316.3 48.5 31.1 275s Assault 316.3 6863.0 1040.0 548.9 275s UrbanPop 48.5 1040.0 424.8 93.6 275s Rape 31.1 548.9 93.6 63.8 275s -------------------------------------------------------- 275s longley 16 7 8 31.147844 275s Best subsample: 275s [1] 5 6 7 9 10 11 13 14 275s Outliers: 0 275s Too many to print ... 275s ------------- 275s 275s Call: 275s CovMrcd(x = x, trace = FALSE) 275s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=8) 275s 275s Robust Estimate of Location: 275s GNP.deflator GNP Unemployed Armed.Forces Population 275s 104.3 410.8 278.8 300.1 118.2 275s Year Employed 275s 1955.4 66.5 275s 275s Robust Estimate of Covariance: 275s GNP.deflator GNP Unemployed Armed.Forces Population 275s GNP.deflator 85.0 652.3 784.4 -370.7 48.7 275s GNP 652.3 7502.9 7328.6 -3414.2 453.9 275s Unemployed 784.4 7328.6 10760.3 -4646.7 548.1 275s Armed.Forces -370.7 -3414.2 -4646.7 2824.3 -253.9 275s Population 48.7 453.9 548.1 -253.9 40.2 275s Year 33.5 312.7 378.8 -176.1 23.4 275s Employed 23.9 224.8 263.6 -128.3 16.8 275s Year Employed 275s GNP.deflator 33.5 23.9 275s GNP 312.7 224.8 275s Unemployed 378.8 263.6 275s Armed.Forces -176.1 -128.3 275s Population 23.4 16.8 275s Year 18.9 11.7 275s Employed 11.7 10.3 275s -------------------------------------------------------- 275s Loblolly 84 3 42 11.163448 275s Best subsample: 275s [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 275s [26] 53 54 57 58 59 63 64 65 66 70 71 76 77 81 82 83 84 275s Outliers: 0 275s Too many to print ... 275s ------------- 275s 275s Call: 275s CovMrcd(x = x, trace = FALSE) 275s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=42) 275s 275s Robust Estimate of Location: 275s height age Seed 275s 44.20 17.26 6.76 275s 275s Robust Estimate of Covariance: 275s height age Seed 275s height 326.74 139.18 3.50 275s age 139.18 68.48 -2.72 275s Seed 3.50 -2.72 25.43 275s -------------------------------------------------------- 275s quakes 1000 4 500 11.802478 275s Best subsample: 275s Too long... 275s Outliers: 0 275s Too many to print ... 275s ------------- 275s 275s Call: 275s CovMrcd(x = x, trace = FALSE) 275s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=500) 275s 275s Robust Estimate of Location: 275s lat long depth mag 275s -20.59 182.13 432.46 4.42 275s 275s Robust Estimate of Covariance: 275s lat long depth mag 275s lat 15.841 5.702 -106.720 -0.441 275s long 5.702 7.426 -577.189 -0.136 275s depth -106.720 -577.189 66701.479 3.992 275s mag -0.441 -0.136 3.992 0.144 275s -------------------------------------------------------- 275s ======================================================== 275s > ##doexactfit() 275s > 275s BEGIN TEST tmest4.R 275s 275s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 275s Copyright (C) 2025 The R Foundation for Statistical Computing 275s Platform: powerpc64le-unknown-linux-gnu 275s 275s R is free software and comes with ABSOLUTELY NO WARRANTY. 275s You are welcome to redistribute it under certain conditions. 275s Type 'license()' or 'licence()' for distribution details. 275s 275s R is a collaborative project with many contributors. 275s Type 'contributors()' for more information and 275s 'citation()' on how to cite R or R packages in publications. 275s 275s Type 'demo()' for some demos, 'help()' for on-line help, or 275s 'help.start()' for an HTML browser interface to help. 275s Type 'q()' to quit R. 275s 275s > ## VT::15.09.2013 - this will render the output independent 275s > ## from the version of the package 275s > suppressPackageStartupMessages(library(rrcov)) 275s > 275s > library(MASS) 275s > dodata <- function(nrep = 1, time = FALSE, full = TRUE) { 275s + domest <- function(x, xname, nrep = 1) { 275s + n <- dim(x)[1] 275s + p <- dim(x)[2] 275s + mm <- CovMest(x) 275s + crit <- log(mm@crit) 275s + ## c1 <- mm@psi@c1 275s + ## M <- mm$psi@M 275s + 275s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 275s + lpad <- lname-nchar(xname) 275s + cat(pad.right(xname,lpad), xres) 275s + 275s + dist <- getDistance(mm) 275s + quantiel <- qchisq(0.975, p) 275s + ibad <- which(dist >= quantiel) 275s + names(ibad) <- NULL 275s + nbad <- length(ibad) 275s + cat("Outliers: ",nbad,"\n") 275s + if(nbad > 0) 275s + print(ibad) 275s + cat("-------------\n") 275s + show(mm) 275s + cat("--------------------------------------------------------\n") 275s + } 275s + 275s + options(digits = 5) 275s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 275s + 275s + lname <- 20 275s + 275s + data(heart) 275s + data(starsCYG) 275s + data(phosphor) 275s + data(stackloss) 275s + data(coleman) 275s + data(salinity) 275s + data(wood) 275s + data(hbk) 275s + 275s + data(Animals, package = "MASS") 275s + brain <- Animals[c(1:24, 26:25, 27:28),] 275s + data(milk) 275s + data(bushfire) 275s + 275s + tmp <- sys.call() 275s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 275s + 275s + cat("Data Set n p c1 M LOG(det) Time\n") 275s + cat("======================================================================\n") 275s + domest(heart[, 1:2], data(heart), nrep) 275s + domest(starsCYG, data(starsCYG), nrep) 275s + domest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 275s + domest(stack.x, data(stackloss), nrep) 275s + domest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 275s + domest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 275s + domest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 275s + domest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 275s + 275s + 275s + domest(brain, "Animals", nrep) 275s + domest(milk, data(milk), nrep) 275s + domest(bushfire, data(bushfire), nrep) 275s + cat("======================================================================\n") 275s + } 275s > 275s > # generate contaminated data using the function gendata with different 275s > # number of outliers and check if the M-estimate breaks - i.e. the 275s > # largest eigenvalue is larger than e.g. 5. 275s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 275s > # outliers grater than 20. 275s > dogen <- function(){ 275s + eig <- vector("numeric",26) 275s + for(i in 0:25) { 275s + gg <- gendata(eps=i) 275s + mm <- CovMest(gg$x, t0=gg$tgood, S0=gg$sgood, arp=0.001) 275s + eig[i+1] <- ev <- getEvals(mm)[1] 275s + # cat(i, ev, "\n") 275s + 275s + stopifnot(ev < 5 || i > 20) 275s + } 275s + # plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 275s + } 275s > 275s > # 275s > # generate data 50x10 as multivariate normal N(0,I) and add 275s > # eps % outliers by adding d=5.0 to each component. 275s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 275s > # - if eps >= 1, it is the number of outliers 275s > # - use the center and cov of the good data as good start 275s > # - use the center and the cov of all data as a bad start 275s > # If using a good start, the M-estimate must iterate to 275s > # the good solution: the largest eigenvalue is less then e.g. 5 275s > # 275s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 275s + 275s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 275s + stop("eps is out of range") 275s + 275s + library(MASS) 275s + 275s + x <- mvrnorm(n, rep(0,p), diag(p)) 275s + bad <- vector("numeric") 275s + nbad = if(eps < 1) eps*n else eps 275s + if(nbad > 0){ 275s + bad <- sample(n, nbad) 275s + x[bad,] <- x[bad,] + d 275s + } 275s + cov1 <- cov.wt(x) 275s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 275s + 275s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 275s + } 275s > 275s > pad.right <- function(z, pads) 275s + { 275s + ## Pads spaces to right of text 275s + padding <- paste(rep(" ", pads), collapse = "") 275s + paste(z, padding, sep = "") 275s + } 275s > 275s > 275s > ## -- now do it: 275s > dodata() 275s 275s Call: dodata() 275s Data Set n p c1 M LOG(det) Time 275s ====================================================================== 275s heart 12 2 7.160341 275s Outliers: 3 275s [1] 2 6 12 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s height weight 275s 34.9 27.0 275s 275s Robust Estimate of Covariance: 275s height weight 275s height 102 155 275s weight 155 250 275s -------------------------------------------------------- 275s starsCYG 47 2 -5.994588 275s Outliers: 7 275s [1] 7 9 11 14 20 30 34 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s log.Te log.light 275s 4.42 4.95 275s 275s Robust Estimate of Covariance: 275s log.Te log.light 275s log.Te 0.0169 0.0587 275s log.light 0.0587 0.3523 275s -------------------------------------------------------- 275s phosphor 18 2 8.867522 275s Outliers: 3 275s [1] 1 6 10 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s inorg organic 275s 15.4 39.1 275s 275s Robust Estimate of Covariance: 275s inorg organic 275s inorg 169 213 275s organic 213 308 275s -------------------------------------------------------- 275s stackloss 21 3 7.241400 275s Outliers: 9 275s [1] 1 2 3 15 16 17 18 19 21 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s Air.Flow Water.Temp Acid.Conc. 275s 59.5 20.8 87.3 275s 275s Robust Estimate of Covariance: 275s Air.Flow Water.Temp Acid.Conc. 275s Air.Flow 9.34 8.69 8.52 275s Water.Temp 8.69 13.72 9.13 275s Acid.Conc. 8.52 9.13 34.54 275s -------------------------------------------------------- 275s coleman 20 5 2.574752 275s Outliers: 7 275s [1] 2 6 9 10 12 13 15 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s salaryP fatherWc sstatus teacherSc motherLev 275s 2.82 48.44 5.30 25.19 6.51 275s 275s Robust Estimate of Covariance: 275s salaryP fatherWc sstatus teacherSc motherLev 275s salaryP 0.2850 0.1045 1.7585 0.3074 0.0355 275s fatherWc 0.1045 824.8305 260.7062 3.7507 17.7959 275s sstatus 1.7585 260.7062 105.6135 4.1140 5.7714 275s teacherSc 0.3074 3.7507 4.1140 0.6753 0.1563 275s motherLev 0.0355 17.7959 5.7714 0.1563 0.4147 275s -------------------------------------------------------- 275s salinity 28 3 3.875096 275s Outliers: 9 275s [1] 3 5 10 11 15 16 17 23 24 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s X1 X2 X3 275s 10.02 3.21 22.36 275s 275s Robust Estimate of Covariance: 275s X1 X2 X3 275s X1 15.353 1.990 -5.075 275s X2 1.990 5.210 -0.769 275s X3 -5.075 -0.769 2.314 275s -------------------------------------------------------- 275s wood 20 5 -35.156305 275s Outliers: 7 275s [1] 4 6 7 8 11 16 19 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s x1 x2 x3 x4 x5 275s 0.587 0.122 0.531 0.538 0.892 275s 275s Robust Estimate of Covariance: 275s x1 x2 x3 x4 x5 275s x1 6.45e-03 1.21e-03 2.03e-03 -3.77e-04 -1.05e-03 275s x2 1.21e-03 3.12e-04 8.16e-04 -3.34e-05 1.52e-05 275s x3 2.03e-03 8.16e-04 4.27e-03 -5.60e-04 2.27e-04 275s x4 -3.77e-04 -3.34e-05 -5.60e-04 1.83e-03 1.18e-03 275s x5 -1.05e-03 1.52e-05 2.27e-04 1.18e-03 1.78e-03 275s -------------------------------------------------------- 275s hbk 75 3 1.432485 275s Outliers: 14 275s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s X1 X2 X3 275s 1.54 1.78 1.69 275s 275s Robust Estimate of Covariance: 275s X1 X2 X3 275s X1 1.6485 0.0739 0.1709 275s X2 0.0739 1.6780 0.2049 275s X3 0.1709 0.2049 1.5584 275s -------------------------------------------------------- 275s Animals 28 2 18.194822 275s Outliers: 10 275s [1] 2 6 7 9 12 14 15 16 25 28 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s body brain 275s 18.7 64.9 275s 275s Robust Estimate of Covariance: 275s body brain 275s body 4993 8466 275s brain 8466 30335 275s -------------------------------------------------------- 275s milk 86 8 -25.041802 275s Outliers: 20 275s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s X1 X2 X3 X4 X5 X6 X7 X8 275s 1.03 35.88 33.04 26.11 25.09 25.02 123.12 14.39 275s 275s Robust Estimate of Covariance: 275s X1 X2 X3 X4 X5 X6 X7 275s X1 4.89e-07 9.64e-05 1.83e-04 1.76e-04 1.57e-04 1.48e-04 6.53e-04 275s X2 9.64e-05 2.05e+00 3.38e-01 2.37e-01 1.70e-01 2.71e-01 1.91e+00 275s X3 1.83e-04 3.38e-01 1.16e+00 8.56e-01 8.48e-01 8.31e-01 8.85e-01 275s X4 1.76e-04 2.37e-01 8.56e-01 6.83e-01 6.55e-01 6.40e-01 6.91e-01 275s X5 1.57e-04 1.70e-01 8.48e-01 6.55e-01 6.93e-01 6.52e-01 6.90e-01 275s X6 1.48e-04 2.71e-01 8.31e-01 6.40e-01 6.52e-01 6.61e-01 6.95e-01 275s X7 6.53e-04 1.91e+00 8.85e-01 6.91e-01 6.90e-01 6.95e-01 4.40e+00 275s X8 5.56e-06 2.60e-01 1.98e-01 1.29e-01 1.12e-01 1.19e-01 4.12e-01 275s X8 275s X1 5.56e-06 275s X2 2.60e-01 275s X3 1.98e-01 275s X4 1.29e-01 275s X5 1.12e-01 275s X6 1.19e-01 275s X7 4.12e-01 275s X8 1.65e-01 275s -------------------------------------------------------- 275s bushfire 38 5 23.457490 275s Outliers: 15 275s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 275s ------------- 275s 275s Call: 275s CovMest(x = x) 275s -> Method: M-Estimates 275s 275s Robust Estimate of Location: 275s V1 V2 V3 V4 V5 275s 107 147 263 215 277 275s 275s Robust Estimate of Covariance: 275s V1 V2 V3 V4 V5 275s V1 775 560 -4179 -925 -759 275s V2 560 478 -2494 -510 -431 275s V3 -4179 -2494 27433 6441 5196 275s V4 -925 -510 6441 1607 1276 275s V5 -759 -431 5196 1276 1020 275s -------------------------------------------------------- 275s ====================================================================== 275s > dogen() 276s > #cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 276s > 276s BEGIN TEST tmve4.R 276s 276s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 276s Copyright (C) 2025 The R Foundation for Statistical Computing 276s Platform: powerpc64le-unknown-linux-gnu 276s 276s R is free software and comes with ABSOLUTELY NO WARRANTY. 276s You are welcome to redistribute it under certain conditions. 276s Type 'license()' or 'licence()' for distribution details. 276s 276s R is a collaborative project with many contributors. 276s Type 'contributors()' for more information and 276s 'citation()' on how to cite R or R packages in publications. 276s 276s Type 'demo()' for some demos, 'help()' for on-line help, or 276s 'help.start()' for an HTML browser interface to help. 276s Type 'q()' to quit R. 276s 276s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMVE","MASS")){ 276s + ##@bdescr 276s + ## Test the function covMve() on the literature datasets: 276s + ## 276s + ## Call covMve() for all regression datasets available in rrco/robustbasev and print: 276s + ## - execution time (if time == TRUE) 276s + ## - objective fucntion 276s + ## - best subsample found (if short == false) 276s + ## - outliers identified (with cutoff 0.975) (if short == false) 276s + ## - estimated center and covarinance matrix if full == TRUE) 276s + ## 276s + ##@edescr 276s + ## 276s + ##@in nrep : [integer] number of repetitions to use for estimating the 276s + ## (average) execution time 276s + ##@in time : [boolean] whether to evaluate the execution time 276s + ##@in short : [boolean] whether to do short output (i.e. only the 276s + ## objective function value). If short == FALSE, 276s + ## the best subsample and the identified outliers are 276s + ## printed. See also the parameter full below 276s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 276s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 276s + 276s + domve <- function(x, xname, nrep=1){ 276s + n <- dim(x)[1] 276s + p <- dim(x)[2] 276s + alpha <- 0.5 276s + h <- h.alpha.n(alpha, n, p) 276s + if(method == "MASS"){ 276s + mve <- cov.mve(x, quantile.used=h) 276s + quan <- h #default: floor((n+p+1)/2) 276s + crit <- mve$crit 276s + best <- mve$best 276s + mah <- mahalanobis(x, mve$center, mve$cov) 276s + quantiel <- qchisq(0.975, p) 276s + wt <- as.numeric(mah < quantiel) 276s + } 276s + else{ 276s + mve <- CovMve(x, trace=FALSE) 276s + quan <- as.integer(mve@quan) 276s + crit <- log(mve@crit) 276s + best <- mve@best 276s + wt <- mve@wt 276s + } 276s + 276s + 276s + if(time){ 276s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 276s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 276s + } 276s + else{ 276s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 276s + } 276s + 276s + lpad<-lname-nchar(xname) 276s + cat(pad.right(xname,lpad), xres) 276s + 276s + if(!short){ 276s + cat("Best subsample: \n") 276s + print(best) 276s + 276s + ibad <- which(wt == 0) 276s + names(ibad) <- NULL 276s + nbad <- length(ibad) 276s + cat("Outliers: ", nbad, "\n") 276s + if(nbad > 0) 276s + print(ibad) 276s + if(full){ 276s + cat("-------------\n") 276s + show(mve) 276s + } 276s + cat("--------------------------------------------------------\n") 276s + } 276s + } 276s + 276s + options(digits = 5) 276s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 276s + 276s + lname <- 20 276s + 276s + ## VT::15.09.2013 - this will render the output independent 276s + ## from the version of the package 276s + suppressPackageStartupMessages(library(rrcov)) 276s + 276s + method <- match.arg(method) 276s + if(method == "MASS") 276s + library(MASS) 276s + 276s + 276s + data(heart) 276s + data(starsCYG) 276s + data(phosphor) 276s + data(stackloss) 276s + data(coleman) 276s + data(salinity) 276s + data(wood) 276s + 276s + data(hbk) 276s + 276s + data(Animals, package = "MASS") 276s + brain <- Animals[c(1:24, 26:25, 27:28),] 276s + data(milk) 276s + data(bushfire) 276s + 276s + tmp <- sys.call() 276s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 276s + 276s + cat("Data Set n p Half LOG(obj) Time\n") 276s + cat("========================================================\n") 276s + domve(heart[, 1:2], data(heart), nrep) 276s + domve(starsCYG, data(starsCYG), nrep) 276s + domve(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 276s + domve(stack.x, data(stackloss), nrep) 276s + domve(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 276s + domve(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 276s + domve(data.matrix(subset(wood, select = -y)), data(wood), nrep) 276s + domve(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 276s + 276s + domve(brain, "Animals", nrep) 276s + domve(milk, data(milk), nrep) 276s + domve(bushfire, data(bushfire), nrep) 276s + cat("========================================================\n") 276s + } 276s > 276s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMVE", "MASS")){ 276s + 276s + domve <- function(x, nrep=1){ 276s + gc() 276s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 276s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 276s + xtime 276s + } 276s + 276s + set.seed(1234) 276s + 276s + ## VT::15.09.2013 - this will render the output independent 276s + ## from the version of the package 276s + suppressPackageStartupMessages(library(rrcov)) 276s + library(MASS) 276s + 276s + method <- match.arg(method) 276s + 276s + ap <- c(2, 5, 10, 20, 30) 276s + an <- c(100, 500, 1000, 10000, 50000) 276s + 276s + tottime <- 0 276s + cat(" n p Time\n") 276s + cat("=====================\n") 276s + for(i in 1:length(an)) { 276s + for(j in 1:length(ap)) { 276s + n <- an[i] 276s + p <- ap[j] 276s + if(5*p <= n){ 276s + xx <- gendata(n, p, eps) 276s + X <- xx$X 276s + tottime <- tottime + domve(X, nrep) 276s + } 276s + } 276s + } 276s + 276s + cat("=====================\n") 276s + cat("Total time: ", tottime*nrep, "\n") 276s + } 276s > 276s > docheck <- function(n, p, eps){ 276s + xx <- gendata(n,p,eps) 276s + mve <- CovMve(xx$X) 276s + check(mve, xx$xind) 276s + } 276s > 276s > check <- function(mcd, xind){ 276s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 276s + ## did not get zero weight 276s + mymatch <- xind %in% which(mcd@wt == 0) 276s + length(xind) - length(which(mymatch)) 276s + } 276s > 276s > dorep <- function(x, nrep=1, method=c("FASTMVE","MASS")){ 276s + 276s + method <- match.arg(method) 276s + for(i in 1:nrep) 276s + if(method == "MASS") 276s + cov.mve(x) 276s + else 276s + CovMve(x) 276s + } 276s > 276s > #### gendata() #### 276s > # Generates a location contaminated multivariate 276s > # normal sample of n observations in p dimensions 276s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 276s > # where 276s > # m = (b,b,...,b) 276s > # Defaults: eps=0 and b=10 276s > # 276s > gendata <- function(n,p,eps=0,b=10){ 276s + 276s + if(missing(n) || missing(p)) 276s + stop("Please specify (n,p)") 276s + if(eps < 0 || eps >= 0.5) 276s + stop(message="eps must be in [0,0.5)") 276s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 276s + nbad <- as.integer(eps * n) 276s + if(nbad > 0){ 276s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 276s + xind <- sample(n,nbad) 276s + X[xind,] <- Xbad 276s + } 276s + list(X=X, xind=xind) 276s + } 276s > 276s > pad.right <- function(z, pads) 276s + { 276s + ### Pads spaces to right of text 276s + padding <- paste(rep(" ", pads), collapse = "") 276s + paste(z, padding, sep = "") 276s + } 276s > 276s > whatis<-function(x){ 276s + if(is.data.frame(x)) 276s + cat("Type: data.frame\n") 276s + else if(is.matrix(x)) 276s + cat("Type: matrix\n") 276s + else if(is.vector(x)) 276s + cat("Type: vector\n") 276s + else 276s + cat("Type: don't know\n") 276s + } 276s > 276s > ## VT::15.09.2013 - this will render the output independent 276s > ## from the version of the package 276s > suppressPackageStartupMessages(library(rrcov)) 276s > 276s > dodata() 276s 276s Call: dodata() 276s Data Set n p Half LOG(obj) Time 276s ======================================================== 276s heart 12 2 7 3.827606 276s Best subsample: 276s [1] 1 4 7 8 9 10 11 276s Outliers: 3 276s [1] 2 6 12 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s height weight 276s 34.9 27.0 276s 276s Robust Estimate of Covariance: 276s height weight 276s height 142 217 276s weight 217 350 276s -------------------------------------------------------- 276s starsCYG 47 2 25 -2.742997 276s Best subsample: 276s [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 276s Outliers: 7 276s [1] 7 9 11 14 20 30 34 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s log.Te log.light 276s 4.41 4.93 276s 276s Robust Estimate of Covariance: 276s log.Te log.light 276s log.Te 0.0173 0.0578 276s log.light 0.0578 0.3615 276s -------------------------------------------------------- 276s phosphor 18 2 10 4.443101 276s Best subsample: 276s [1] 3 5 8 9 11 12 13 14 15 17 276s Outliers: 3 276s [1] 1 6 10 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s inorg organic 276s 15.2 39.4 276s 276s Robust Estimate of Covariance: 276s inorg organic 276s inorg 188 230 276s organic 230 339 276s -------------------------------------------------------- 276s stackloss 21 3 12 3.327582 276s Best subsample: 276s [1] 4 5 6 7 8 9 10 11 12 13 14 20 276s Outliers: 3 276s [1] 1 2 3 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s Air.Flow Water.Temp Acid.Conc. 276s 56.7 20.2 85.5 276s 276s Robust Estimate of Covariance: 276s Air.Flow Water.Temp Acid.Conc. 276s Air.Flow 34.31 11.07 23.54 276s Water.Temp 11.07 9.23 7.85 276s Acid.Conc. 23.54 7.85 47.35 276s -------------------------------------------------------- 276s coleman 20 5 13 2.065143 276s Best subsample: 276s [1] 1 3 4 5 7 8 11 14 16 17 18 19 20 276s Outliers: 5 276s [1] 2 6 9 10 13 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s salaryP fatherWc sstatus teacherSc motherLev 276s 2.79 44.26 3.59 25.08 6.38 276s 276s Robust Estimate of Covariance: 276s salaryP fatherWc sstatus teacherSc motherLev 276s salaryP 0.2920 1.1188 2.0421 0.3487 0.0748 276s fatherWc 1.1188 996.7540 338.6587 7.1673 23.1783 276s sstatus 2.0421 338.6587 148.2501 4.4894 7.8135 276s teacherSc 0.3487 7.1673 4.4894 0.9082 0.3204 276s motherLev 0.0748 23.1783 7.8135 0.3204 0.6024 276s -------------------------------------------------------- 276s salinity 28 3 16 2.002555 276s Best subsample: 276s [1] 1 7 8 9 12 13 14 18 19 20 21 22 25 26 27 28 276s Outliers: 5 276s [1] 5 11 16 23 24 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s X1 X2 X3 276s 10.2 3.1 22.4 276s 276s Robust Estimate of Covariance: 276s X1 X2 X3 276s X1 14.387 1.153 -4.072 276s X2 1.153 5.005 -0.954 276s X3 -4.072 -0.954 2.222 276s -------------------------------------------------------- 276s wood 20 5 13 -5.471407 276s Best subsample: 276s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 276s Outliers: 5 276s [1] 4 6 8 11 19 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s x1 x2 x3 x4 x5 276s 0.576 0.123 0.531 0.538 0.889 276s 276s Robust Estimate of Covariance: 276s x1 x2 x3 x4 x5 276s x1 7.45e-03 1.11e-03 1.83e-03 -2.90e-05 -5.65e-04 276s x2 1.11e-03 3.11e-04 7.68e-04 3.37e-05 3.85e-05 276s x3 1.83e-03 7.68e-04 4.30e-03 -9.96e-04 -6.27e-05 276s x4 -2.90e-05 3.37e-05 -9.96e-04 3.02e-03 1.91e-03 276s x5 -5.65e-04 3.85e-05 -6.27e-05 1.91e-03 2.25e-03 276s -------------------------------------------------------- 276s hbk 75 3 39 1.096831 276s Best subsample: 276s [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 276s [26] 55 56 58 59 64 65 66 67 70 71 72 73 74 75 276s Outliers: 14 276s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s X1 X2 X3 276s 1.48 1.86 1.73 276s 276s Robust Estimate of Covariance: 276s X1 X2 X3 276s X1 1.695 0.230 0.265 276s X2 0.230 1.679 0.119 276s X3 0.265 0.119 1.683 276s -------------------------------------------------------- 276s Animals 28 2 15 8.945423 276s Best subsample: 276s [1] 1 3 4 5 10 11 17 18 21 22 23 24 26 27 28 276s Outliers: 9 276s [1] 2 6 7 9 12 14 15 16 25 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s body brain 276s 48.3 127.3 276s 276s Robust Estimate of Covariance: 276s body brain 276s body 10767 16872 276s brain 16872 46918 276s -------------------------------------------------------- 276s milk 86 8 47 -1.160085 276s Best subsample: 276s [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 276s [26] 46 54 56 57 59 60 61 62 63 64 65 66 67 69 72 76 78 79 81 82 83 85 276s Outliers: 18 276s [1] 1 2 3 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s X1 X2 X3 X4 X5 X6 X7 X8 276s 1.03 35.91 33.02 26.08 25.06 24.99 122.93 14.38 276s 276s Robust Estimate of Covariance: 276s X1 X2 X3 X4 X5 X6 X7 276s X1 6.00e-07 1.51e-04 3.34e-04 3.09e-04 2.82e-04 2.77e-04 1.09e-03 276s X2 1.51e-04 2.03e+00 3.83e-01 3.04e-01 2.20e-01 3.51e-01 2.18e+00 276s X3 3.34e-04 3.83e-01 1.58e+00 1.21e+00 1.18e+00 1.20e+00 1.60e+00 276s X4 3.09e-04 3.04e-01 1.21e+00 9.82e-01 9.39e-01 9.53e-01 1.36e+00 276s X5 2.82e-04 2.20e-01 1.18e+00 9.39e-01 9.67e-01 9.52e-01 1.34e+00 276s X6 2.77e-04 3.51e-01 1.20e+00 9.53e-01 9.52e-01 9.92e-01 1.38e+00 276s X7 1.09e-03 2.18e+00 1.60e+00 1.36e+00 1.34e+00 1.38e+00 6.73e+00 276s X8 3.33e-05 2.92e-01 2.65e-01 1.83e-01 1.65e-01 1.76e-01 5.64e-01 276s X8 276s X1 3.33e-05 276s X2 2.92e-01 276s X3 2.65e-01 276s X4 1.83e-01 276s X5 1.65e-01 276s X6 1.76e-01 276s X7 5.64e-01 276s X8 1.80e-01 276s -------------------------------------------------------- 276s bushfire 38 5 22 5.644315 276s Best subsample: 276s [1] 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 276s Outliers: 15 276s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 276s ------------- 276s 276s Call: 276s CovMve(x = x, trace = FALSE) 276s -> Method: Minimum volume ellipsoid estimator 276s 276s Robust Estimate of Location: 276s V1 V2 V3 V4 V5 276s 107 147 263 215 277 276s 276s Robust Estimate of Covariance: 276s V1 V2 V3 V4 V5 276s V1 519 375 -2799 -619 -509 276s V2 375 320 -1671 -342 -289 276s V3 -2799 -1671 18373 4314 3480 276s V4 -619 -342 4314 1076 854 276s V5 -509 -289 3480 854 683 276s -------------------------------------------------------- 276s ======================================================== 276s > 276s BEGIN TEST togk4.R 276s 276s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 276s Copyright (C) 2025 The R Foundation for Statistical Computing 276s Platform: powerpc64le-unknown-linux-gnu 276s 276s R is free software and comes with ABSOLUTELY NO WARRANTY. 276s You are welcome to redistribute it under certain conditions. 276s Type 'license()' or 'licence()' for distribution details. 276s 276s R is a collaborative project with many contributors. 276s Type 'contributors()' for more information and 276s 'citation()' on how to cite R or R packages in publications. 276s 276s Type 'demo()' for some demos, 'help()' for on-line help, or 276s 'help.start()' for an HTML browser interface to help. 276s Type 'q()' to quit R. 276s 276s > ## VT::15.09.2013 - this will render the output independent 276s > ## from the version of the package 276s > suppressPackageStartupMessages(library(rrcov)) 277s > 277s > ## VT::14.01.2020 277s > ## On some platforms minor differences are shown - use 277s > ## IGNORE_RDIFF_BEGIN 277s > ## IGNORE_RDIFF_END 277s > 277s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS")){ 277s + domcd <- function(x, xname, nrep=1){ 277s + n <- dim(x)[1] 277s + p <- dim(x)[2] 277s + 277s + mcd<-CovOgk(x) 277s + 277s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 277s + 277s + lpad<-lname-nchar(xname) 277s + cat(pad.right(xname,lpad), xres) 277s + 277s + dist <- getDistance(mcd) 277s + quantiel <- qchisq(0.975, p) 277s + ibad <- which(dist >= quantiel) 277s + names(ibad) <- NULL 277s + nbad <- length(ibad) 277s + cat("Outliers: ",nbad,"\n") 277s + if(nbad > 0) 277s + print(ibad) 277s + cat("-------------\n") 277s + show(mcd) 277s + cat("--------------------------------------------------------\n") 277s + } 277s + 277s + lname <- 20 277s + 277s + ## VT::15.09.2013 - this will render the output independent 277s + ## from the version of the package 277s + suppressPackageStartupMessages(library(rrcov)) 277s + 277s + method <- match.arg(method) 277s + 277s + data(heart) 277s + data(starsCYG) 277s + data(phosphor) 277s + data(stackloss) 277s + data(coleman) 277s + data(salinity) 277s + data(wood) 277s + 277s + data(hbk) 277s + 277s + data(Animals, package = "MASS") 277s + brain <- Animals[c(1:24, 26:25, 27:28),] 277s + data(milk) 277s + data(bushfire) 277s + 277s + tmp <- sys.call() 277s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 277s + 277s + cat("Data Set n p Half LOG(obj) Time\n") 277s + cat("========================================================\n") 277s + domcd(heart[, 1:2], data(heart), nrep) 277s + ## This will not work within the function, of course 277s + ## - comment it out 277s + ## IGNORE_RDIFF_BEGIN 277s + ## domcd(starsCYG,data(starsCYG), nrep) 277s + ## IGNORE_RDIFF_END 277s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 277s + domcd(stack.x,data(stackloss), nrep) 277s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 277s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 277s + ## IGNORE_RDIFF_BEGIN 277s + ## domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 277s + ## IGNORE_RDIFF_END 277s + domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 277s + 277s + domcd(brain, "Animals", nrep) 277s + domcd(milk, data(milk), nrep) 277s + domcd(bushfire, data(bushfire), nrep) 277s + cat("========================================================\n") 277s + } 277s > 277s > pad.right <- function(z, pads) 277s + { 277s + ### Pads spaces to right of text 277s + padding <- paste(rep(" ", pads), collapse = "") 277s + paste(z, padding, sep = "") 277s + } 277s > 277s > dodata() 277s 277s Call: dodata() 277s Data Set n p Half LOG(obj) Time 277s ======================================================== 277s heart 12 2 277s Outliers: 5 277s [1] 2 6 8 10 12 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s height weight 277s 39.76 35.71 277s 277s Robust Estimate of Covariance: 277s height weight 277s height 15.88 32.07 277s weight 32.07 78.28 277s -------------------------------------------------------- 277s phosphor 18 2 277s Outliers: 2 277s [1] 1 6 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s inorg organic 277s 13.31 40.00 277s 277s Robust Estimate of Covariance: 277s inorg organic 277s inorg 92.82 93.24 277s organic 93.24 152.62 277s -------------------------------------------------------- 277s stackloss 21 3 277s Outliers: 2 277s [1] 1 2 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s Air.Flow Water.Temp Acid.Conc. 277s 57.72 20.50 85.78 277s 277s Robust Estimate of Covariance: 277s Air.Flow Water.Temp Acid.Conc. 277s Air.Flow 38.423 11.306 18.605 277s Water.Temp 11.306 6.806 5.889 277s Acid.Conc. 18.605 5.889 29.840 277s -------------------------------------------------------- 277s coleman 20 5 277s Outliers: 3 277s [1] 1 6 10 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s salaryP fatherWc sstatus teacherSc motherLev 277s 2.723 43.202 2.912 25.010 6.290 277s 277s Robust Estimate of Covariance: 277s salaryP fatherWc sstatus teacherSc motherLev 277s salaryP 0.12867 2.80048 0.92026 0.15118 0.06413 277s fatherWc 2.80048 678.72549 227.36415 9.30826 16.15102 277s sstatus 0.92026 227.36415 101.39094 3.38013 5.63283 277s teacherSc 0.15118 9.30826 3.38013 0.57112 0.27701 277s motherLev 0.06413 16.15102 5.63283 0.27701 0.44801 277s -------------------------------------------------------- 277s salinity 28 3 277s Outliers: 3 277s [1] 3 5 16 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s X1 X2 X3 277s 10.74 2.68 22.99 277s 277s Robust Estimate of Covariance: 277s X1 X2 X3 277s X1 8.1047 -0.6365 -0.4720 277s X2 -0.6365 3.0976 -1.3520 277s X3 -0.4720 -1.3520 2.3648 277s -------------------------------------------------------- 277s hbk 75 3 277s Outliers: 14 277s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s X1 X2 X3 277s 1.538 1.780 1.687 277s 277s Robust Estimate of Covariance: 277s X1 X2 X3 277s X1 1.11350 0.04992 0.11541 277s X2 0.04992 1.13338 0.13843 277s X3 0.11541 0.13843 1.05261 277s -------------------------------------------------------- 277s Animals 28 2 277s Outliers: 12 277s [1] 2 6 7 9 12 14 15 16 17 24 25 28 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s body brain 277s 39.65 105.83 277s 277s Robust Estimate of Covariance: 277s body brain 277s body 3981 7558 277s brain 7558 16594 277s -------------------------------------------------------- 277s milk 86 8 277s Outliers: 22 277s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 50 70 74 75 77 85 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s X1 X2 X3 X4 X5 X6 X7 X8 277s 1.03 35.80 33.10 26.15 25.13 25.06 123.06 14.39 277s 277s Robust Estimate of Covariance: 277s X1 X2 X3 X4 X5 X6 X7 277s X1 4.074e-07 5.255e-05 1.564e-04 1.506e-04 1.340e-04 1.234e-04 5.308e-04 277s X2 5.255e-05 1.464e+00 3.425e-01 2.465e-01 1.847e-01 2.484e-01 1.459e+00 277s X3 1.564e-04 3.425e-01 1.070e+00 7.834e-01 7.665e-01 7.808e-01 7.632e-01 277s X4 1.506e-04 2.465e-01 7.834e-01 6.178e-01 5.868e-01 5.959e-01 5.923e-01 277s X5 1.340e-04 1.847e-01 7.665e-01 5.868e-01 6.124e-01 5.967e-01 5.868e-01 277s X6 1.234e-04 2.484e-01 7.808e-01 5.959e-01 5.967e-01 6.253e-01 5.819e-01 277s X7 5.308e-04 1.459e+00 7.632e-01 5.923e-01 5.868e-01 5.819e-01 3.535e+00 277s X8 1.990e-07 1.851e-01 1.861e-01 1.210e-01 1.041e-01 1.116e-01 3.046e-01 277s X8 277s X1 1.990e-07 277s X2 1.851e-01 277s X3 1.861e-01 277s X4 1.210e-01 277s X5 1.041e-01 277s X6 1.116e-01 277s X7 3.046e-01 277s X8 1.292e-01 277s -------------------------------------------------------- 277s bushfire 38 5 277s Outliers: 17 277s [1] 7 8 9 10 11 12 28 29 30 31 32 33 34 35 36 37 38 277s ------------- 277s 277s Call: 277s CovOgk(x = x) 277s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 277s 277s Robust Estimate of Location: 277s V1 V2 V3 V4 V5 277s 104.5 146.0 275.6 217.8 279.3 277s 277s Robust Estimate of Covariance: 277s V1 V2 V3 V4 V5 277s V1 266.8 203.2 -1380.7 -311.1 -252.2 277s V2 203.2 178.4 -910.9 -185.9 -155.9 277s V3 -1380.7 -910.9 8279.7 2035.5 1615.4 277s V4 -311.1 -185.9 2035.5 536.5 418.6 277s V5 -252.2 -155.9 1615.4 418.6 329.2 277s -------------------------------------------------------- 277s ======================================================== 277s > 277s BEGIN TEST tqda.R 277s 277s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 277s Copyright (C) 2025 The R Foundation for Statistical Computing 277s Platform: powerpc64le-unknown-linux-gnu 277s 277s R is free software and comes with ABSOLUTELY NO WARRANTY. 277s You are welcome to redistribute it under certain conditions. 277s Type 'license()' or 'licence()' for distribution details. 277s 277s R is a collaborative project with many contributors. 277s Type 'contributors()' for more information and 277s 'citation()' on how to cite R or R packages in publications. 277s 277s Type 'demo()' for some demos, 'help()' for on-line help, or 277s 'help.start()' for an HTML browser interface to help. 277s Type 'q()' to quit R. 277s 277s > ## VT::15.09.2013 - this will render the output independent 277s > ## from the version of the package 277s > suppressPackageStartupMessages(library(rrcov)) 277s > 277s > dodata <- function(method) { 277s + 277s + options(digits = 5) 277s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 277s + 277s + tmp <- sys.call() 277s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 277s + cat("===================================================\n") 277s + 277s + data(hemophilia); show(QdaCov(as.factor(gr)~., data=hemophilia, method=method)) 277s + data(anorexia, package="MASS"); show(QdaCov(Treat~., data=anorexia, method=method)) 277s + data(Pima.tr, package="MASS"); show(QdaCov(type~., data=Pima.tr, method=method)) 277s + data(iris); # show(QdaCov(Species~., data=iris, method=method)) 277s + data(crabs, package="MASS"); # show(QdaCov(sp~., data=crabs, method=method)) 277s + 277s + show(QdaClassic(as.factor(gr)~., data=hemophilia)) 277s + show(QdaClassic(Treat~., data=anorexia)) 277s + show(QdaClassic(type~., data=Pima.tr)) 277s + show(QdaClassic(Species~., data=iris)) 277s + ## show(QdaClassic(sp~., data=crabs)) 277s + cat("===================================================\n") 277s + } 277s > 277s > 277s > ## -- now do it: 277s > dodata(method="mcd") 277s 277s Call: dodata(method = "mcd") 277s =================================================== 277s Call: 277s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.30795 -0.0059911 277s normal -0.12920 -0.0603000 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.023784 0.015376 277s AHFantigen 0.015376 0.024035 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.0057546 0.0042606 277s AHFantigen 0.0042606 0.0084914 277s Call: 277s QdaCov(Treat ~ ., data = anorexia, method = method) 277s 277s Prior Probabilities of Groups: 277s CBT Cont FT 277s 0.40278 0.36111 0.23611 277s 277s Group means: 277s Prewt Postwt 277s CBT 82.633 82.950 277s Cont 81.558 81.108 277s FT 84.331 94.762 277s 277s Group: CBT 277s Prewt Postwt 277s Prewt 9.8671 8.6611 277s Postwt 8.6611 11.8966 277s 277s Group: Cont 277s Prewt Postwt 277s Prewt 32.5705 -4.3705 277s Postwt -4.3705 22.5079 277s 277s Group: FT 277s Prewt Postwt 277s Prewt 33.056 10.814 277s Postwt 10.814 14.265 277s Call: 277s QdaCov(type ~ ., data = Pima.tr, method = method) 277s 277s Prior Probabilities of Groups: 277s No Yes 277s 0.66 0.34 277s 277s Group means: 277s npreg glu bp skin bmi ped age 277s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 277s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 277s 277s Group: No 277s npreg glu bp skin bmi ped age 277s npreg 2.221983 -0.18658 1.86507 -0.44427 0.1725348 -0.0683616 2.63439 277s glu -0.186582 471.88789 45.28021 8.95404 30.6551510 -0.6359899 3.50218 277s bp 1.865066 45.28021 110.09787 26.11192 14.4739180 -0.2104074 13.23392 277s skin -0.444272 8.95404 26.11192 118.30521 52.3115719 -0.2995751 8.65861 277s bmi 0.172535 30.65515 14.47392 52.31157 43.3140415 0.0079866 6.75720 277s ped -0.068362 -0.63599 -0.21041 -0.29958 0.0079866 0.0587710 -0.18683 277s age 2.634387 3.50218 13.23392 8.65861 6.7572019 -0.1868284 12.09493 277s 277s Group: Yes 277s npreg glu bp skin bmi ped age 277s npreg 17.875215 -13.740021 9.03580 4.498580 1.787458 0.079504 26.92283 277s glu -13.740021 917.719003 55.30399 27.976265 10.755113 0.092673 38.94970 277s bp 9.035798 55.303991 129.97953 34.130200 10.104275 0.198342 32.95351 277s skin 4.498580 27.976265 34.13020 101.842647 30.297210 0.064739 3.59427 277s bmi 1.787458 10.755113 10.10428 30.297210 22.529467 0.084369 -6.64317 277s ped 0.079504 0.092673 0.19834 0.064739 0.084369 0.066667 0.11199 277s age 26.922828 38.949697 32.95351 3.594266 -6.643165 0.111992 143.69752 277s Call: 277s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.30795 -0.0059911 277s normal -0.13487 -0.0778567 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.023784 0.015376 277s AHFantigen 0.015376 0.024035 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.020897 0.015515 277s AHFantigen 0.015515 0.017920 277s Call: 277s QdaClassic(Treat ~ ., data = anorexia) 277s 277s Prior Probabilities of Groups: 277s CBT Cont FT 277s 0.40278 0.36111 0.23611 277s 277s Group means: 277s Prewt Postwt 277s CBT 82.690 85.697 277s Cont 81.558 81.108 277s FT 83.229 90.494 277s 277s Group: CBT 277s Prewt Postwt 277s Prewt 23.479 19.910 277s Postwt 19.910 69.755 277s 277s Group: Cont 277s Prewt Postwt 277s Prewt 32.5705 -4.3705 277s Postwt -4.3705 22.5079 277s 277s Group: FT 277s Prewt Postwt 277s Prewt 25.167 22.883 277s Postwt 22.883 71.827 277s Call: 277s QdaClassic(type ~ ., data = Pima.tr) 277s 277s Prior Probabilities of Groups: 277s No Yes 277s 0.66 0.34 277s 277s Group means: 277s npreg glu bp skin bmi ped age 277s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 277s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 277s 277s Group: No 277s npreg glu bp skin bmi ped age 277s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 277s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 277s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 277s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 277s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 277s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 277s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 277s 277s Group: Yes 277s npreg glu bp skin bmi ped age 277s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 277s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 277s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 277s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 277s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 277s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 277s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 277s Call: 277s QdaClassic(Species ~ ., data = iris) 277s 277s Prior Probabilities of Groups: 277s setosa versicolor virginica 277s 0.33333 0.33333 0.33333 277s 277s Group means: 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s setosa 5.006 3.428 1.462 0.246 277s versicolor 5.936 2.770 4.260 1.326 277s virginica 6.588 2.974 5.552 2.026 277s 277s Group: setosa 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 277s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 277s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 277s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 277s 277s Group: versicolor 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.266433 0.085184 0.182898 0.055780 277s Sepal.Width 0.085184 0.098469 0.082653 0.041204 277s Petal.Length 0.182898 0.082653 0.220816 0.073102 277s Petal.Width 0.055780 0.041204 0.073102 0.039106 277s 277s Group: virginica 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.404343 0.093763 0.303290 0.049094 277s Sepal.Width 0.093763 0.104004 0.071380 0.047629 277s Petal.Length 0.303290 0.071380 0.304588 0.048824 277s Petal.Width 0.049094 0.047629 0.048824 0.075433 277s =================================================== 277s > dodata(method="m") 277s 277s Call: dodata(method = "m") 277s =================================================== 277s Call: 277s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.29810 -0.0028222 277s normal -0.13081 -0.0675283 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.026018 0.017653 277s AHFantigen 0.017653 0.030128 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.0081933 0.0065737 277s AHFantigen 0.0065737 0.0118565 277s Call: 277s QdaCov(Treat ~ ., data = anorexia, method = method) 277s 277s Prior Probabilities of Groups: 277s CBT Cont FT 277s 0.40278 0.36111 0.23611 277s 277s Group means: 277s Prewt Postwt 277s CBT 82.436 82.631 277s Cont 81.559 80.272 277s FT 85.120 94.657 277s 277s Group: CBT 277s Prewt Postwt 277s Prewt 23.630 25.128 277s Postwt 25.128 38.142 277s 277s Group: Cont 277s Prewt Postwt 277s Prewt 35.8824 -8.2405 277s Postwt -8.2405 23.7416 277s 277s Group: FT 277s Prewt Postwt 277s Prewt 33.805 18.206 277s Postwt 18.206 24.639 277s Call: 277s QdaCov(type ~ ., data = Pima.tr, method = method) 277s 277s Prior Probabilities of Groups: 277s No Yes 277s 0.66 0.34 277s 277s Group means: 277s npreg glu bp skin bmi ped age 277s No 2.5225 111.26 68.081 26.640 30.801 0.40452 26.306 277s Yes 5.0702 144.32 75.088 31.982 34.267 0.47004 37.140 277s 277s Group: No 277s npreg glu bp skin bmi ped age 277s npreg 5.74219 14.47051 6.63766 4.98559 0.826570 -0.128106 10.71303 277s glu 14.47051 591.08717 68.81219 44.73311 40.658393 -0.545716 38.01918 277s bp 6.63766 68.81219 121.02716 30.46466 16.789801 -0.320065 25.29371 277s skin 4.98559 44.73311 30.46466 136.52176 56.604475 -0.250711 19.73259 277s bmi 0.82657 40.65839 16.78980 56.60447 47.859747 0.046358 6.94523 277s ped -0.12811 -0.54572 -0.32006 -0.25071 0.046358 0.061485 -0.34653 277s age 10.71303 38.01918 25.29371 19.73259 6.945227 -0.346527 35.66101 277s 277s Group: Yes 277s npreg glu bp skin bmi ped age 277s npreg 15.98861 -1.2430 10.48556 9.05947 2.425316 0.162453 30.149872 277s glu -1.24304 867.1076 46.43838 25.92297 5.517382 1.044360 31.152650 277s bp 10.48556 46.4384 130.12536 17.21407 6.343942 -0.235121 41.091494 277s skin 9.05947 25.9230 17.21407 85.96314 26.089017 0.170061 14.562516 277s bmi 2.42532 5.5174 6.34394 26.08902 22.051976 0.097786 -5.297971 277s ped 0.16245 1.0444 -0.23512 0.17006 0.097786 0.057112 0.055286 277s age 30.14987 31.1527 41.09149 14.56252 -5.297971 0.055286 137.440921 277s Call: 277s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.30795 -0.0059911 277s normal -0.13487 -0.0778567 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.023784 0.015376 277s AHFantigen 0.015376 0.024035 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.020897 0.015515 277s AHFantigen 0.015515 0.017920 277s Call: 277s QdaClassic(Treat ~ ., data = anorexia) 277s 277s Prior Probabilities of Groups: 277s CBT Cont FT 277s 0.40278 0.36111 0.23611 277s 277s Group means: 277s Prewt Postwt 277s CBT 82.690 85.697 277s Cont 81.558 81.108 277s FT 83.229 90.494 277s 277s Group: CBT 277s Prewt Postwt 277s Prewt 23.479 19.910 277s Postwt 19.910 69.755 277s 277s Group: Cont 277s Prewt Postwt 277s Prewt 32.5705 -4.3705 277s Postwt -4.3705 22.5079 277s 277s Group: FT 277s Prewt Postwt 277s Prewt 25.167 22.883 277s Postwt 22.883 71.827 277s Call: 277s QdaClassic(type ~ ., data = Pima.tr) 277s 277s Prior Probabilities of Groups: 277s No Yes 277s 0.66 0.34 277s 277s Group means: 277s npreg glu bp skin bmi ped age 277s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 277s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 277s 277s Group: No 277s npreg glu bp skin bmi ped age 277s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 277s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 277s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 277s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 277s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 277s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 277s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 277s 277s Group: Yes 277s npreg glu bp skin bmi ped age 277s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 277s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 277s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 277s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 277s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 277s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 277s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 277s Call: 277s QdaClassic(Species ~ ., data = iris) 277s 277s Prior Probabilities of Groups: 277s setosa versicolor virginica 277s 0.33333 0.33333 0.33333 277s 277s Group means: 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s setosa 5.006 3.428 1.462 0.246 277s versicolor 5.936 2.770 4.260 1.326 277s virginica 6.588 2.974 5.552 2.026 277s 277s Group: setosa 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 277s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 277s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 277s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 277s 277s Group: versicolor 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.266433 0.085184 0.182898 0.055780 277s Sepal.Width 0.085184 0.098469 0.082653 0.041204 277s Petal.Length 0.182898 0.082653 0.220816 0.073102 277s Petal.Width 0.055780 0.041204 0.073102 0.039106 277s 277s Group: virginica 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.404343 0.093763 0.303290 0.049094 277s Sepal.Width 0.093763 0.104004 0.071380 0.047629 277s Petal.Length 0.303290 0.071380 0.304588 0.048824 277s Petal.Width 0.049094 0.047629 0.048824 0.075433 277s =================================================== 277s > dodata(method="ogk") 277s 277s Call: dodata(method = "ogk") 277s =================================================== 277s Call: 277s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.29324 0.00033953 277s normal -0.12744 -0.06628182 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.019260 0.013026 277s AHFantigen 0.013026 0.021889 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.0049651 0.0039707 277s AHFantigen 0.0039707 0.0066084 277s Call: 277s QdaCov(Treat ~ ., data = anorexia, method = method) 277s 277s Prior Probabilities of Groups: 277s CBT Cont FT 277s 0.40278 0.36111 0.23611 277s 277s Group means: 277s Prewt Postwt 277s CBT 82.587 82.709 277s Cont 81.558 81.108 277s FT 85.110 94.470 277s 277s Group: CBT 277s Prewt Postwt 277s Prewt 10.452 15.115 277s Postwt 15.115 37.085 277s 277s Group: Cont 277s Prewt Postwt 277s Prewt 31.3178 -4.2024 277s Postwt -4.2024 21.6422 277s 277s Group: FT 277s Prewt Postwt 277s Prewt 5.5309 1.4813 277s Postwt 1.4813 7.5501 277s Call: 277s QdaCov(type ~ ., data = Pima.tr, method = method) 277s 277s Prior Probabilities of Groups: 277s No Yes 277s 0.66 0.34 277s 277s Group means: 277s npreg glu bp skin bmi ped age 277s No 2.4286 110.35 67.495 25.905 30.275 0.39587 26.248 277s Yes 5.1964 142.71 75.357 32.732 34.809 0.48823 37.607 277s 277s Group: No 277s npreg glu bp skin bmi ped age 277s npreg 3.97823 8.70612 4.58776 4.16463 0.250612 -0.117238 8.21769 277s glu 8.70612 448.91392 51.71120 38.66213 21.816345 -0.296524 24.29370 277s bp 4.58776 51.71120 99.41188 24.27574 10.491311 -0.290753 20.02975 277s skin 4.16463 38.66213 24.27574 98.61950 41.682404 -0.335213 16.60454 277s bmi 0.25061 21.81634 10.49131 41.68240 35.237101 -0.019774 5.12042 277s ped -0.11724 -0.29652 -0.29075 -0.33521 -0.019774 0.051431 -0.36275 277s age 8.21769 24.29370 20.02975 16.60454 5.120417 -0.362748 31.32916 277s 277s Group: Yes 277s npreg glu bp skin bmi ped age 277s npreg 15.26499 6.30612 3.01913 3.76690 0.94825 0.12076 22.64860 277s glu 6.30612 688.16837 22.22704 12.81633 3.55791 0.68833 32.28061 277s bp 3.01913 22.22704 103.97959 9.95281 2.09860 0.45672 31.17602 277s skin 3.76690 12.81633 9.95281 67.51754 19.51489 0.59831 -2.35523 277s bmi 0.94825 3.55791 2.09860 19.51489 17.20331 0.24347 -6.88221 277s ped 0.12076 0.68833 0.45672 0.59831 0.24347 0.05933 0.43798 277s age 22.64860 32.28061 31.17602 -2.35523 -6.88221 0.43798 111.16709 277s Call: 277s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.30795 -0.0059911 277s normal -0.13487 -0.0778567 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.023784 0.015376 277s AHFantigen 0.015376 0.024035 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.020897 0.015515 277s AHFantigen 0.015515 0.017920 277s Call: 277s QdaClassic(Treat ~ ., data = anorexia) 277s 277s Prior Probabilities of Groups: 277s CBT Cont FT 277s 0.40278 0.36111 0.23611 277s 277s Group means: 277s Prewt Postwt 277s CBT 82.690 85.697 277s Cont 81.558 81.108 277s FT 83.229 90.494 277s 277s Group: CBT 277s Prewt Postwt 277s Prewt 23.479 19.910 277s Postwt 19.910 69.755 277s 277s Group: Cont 277s Prewt Postwt 277s Prewt 32.5705 -4.3705 277s Postwt -4.3705 22.5079 277s 277s Group: FT 277s Prewt Postwt 277s Prewt 25.167 22.883 277s Postwt 22.883 71.827 277s Call: 277s QdaClassic(type ~ ., data = Pima.tr) 277s 277s Prior Probabilities of Groups: 277s No Yes 277s 0.66 0.34 277s 277s Group means: 277s npreg glu bp skin bmi ped age 277s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 277s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 277s 277s Group: No 277s npreg glu bp skin bmi ped age 277s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 277s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 277s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 277s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 277s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 277s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 277s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 277s 277s Group: Yes 277s npreg glu bp skin bmi ped age 277s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 277s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 277s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 277s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 277s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 277s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 277s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 277s Call: 277s QdaClassic(Species ~ ., data = iris) 277s 277s Prior Probabilities of Groups: 277s setosa versicolor virginica 277s 0.33333 0.33333 0.33333 277s 277s Group means: 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s setosa 5.006 3.428 1.462 0.246 277s versicolor 5.936 2.770 4.260 1.326 277s virginica 6.588 2.974 5.552 2.026 277s 277s Group: setosa 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 277s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 277s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 277s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 277s 277s Group: versicolor 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.266433 0.085184 0.182898 0.055780 277s Sepal.Width 0.085184 0.098469 0.082653 0.041204 277s Petal.Length 0.182898 0.082653 0.220816 0.073102 277s Petal.Width 0.055780 0.041204 0.073102 0.039106 277s 277s Group: virginica 277s Sepal.Length Sepal.Width Petal.Length Petal.Width 277s Sepal.Length 0.404343 0.093763 0.303290 0.049094 277s Sepal.Width 0.093763 0.104004 0.071380 0.047629 277s Petal.Length 0.303290 0.071380 0.304588 0.048824 277s Petal.Width 0.049094 0.047629 0.048824 0.075433 277s =================================================== 277s > dodata(method="sde") 277s 277s Call: dodata(method = "sde") 277s =================================================== 277s Call: 277s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 277s 277s Prior Probabilities of Groups: 277s carrier normal 277s 0.6 0.4 277s 277s Group means: 277s AHFactivity AHFantigen 277s carrier -0.29834 -0.0032286 277s normal -0.12944 -0.0676930 277s 277s Group: carrier 277s AHFactivity AHFantigen 277s AHFactivity 0.025398 0.017810 277s AHFantigen 0.017810 0.030639 277s 277s Group: normal 277s AHFactivity AHFantigen 277s AHFactivity 0.0083435 0.0067686 277s AHFantigen 0.0067686 0.0119565 278s Call: 278s QdaCov(Treat ~ ., data = anorexia, method = method) 278s 278s Prior Probabilities of Groups: 278s CBT Cont FT 278s 0.40278 0.36111 0.23611 278s 278s Group means: 278s Prewt Postwt 278s CBT 82.949 83.323 278s Cont 81.484 80.840 278s FT 84.596 93.835 278s 278s Group: CBT 278s Prewt Postwt 278s Prewt 22.283 17.084 278s Postwt 17.084 25.308 278s 278s Group: Cont 278s Prewt Postwt 278s Prewt 37.1864 -8.8896 278s Postwt -8.8896 31.1930 278s 278s Group: FT 278s Prewt Postwt 278s Prewt 20.7108 3.1531 278s Postwt 3.1531 25.7046 278s Call: 278s QdaCov(type ~ ., data = Pima.tr, method = method) 278s 278s Prior Probabilities of Groups: 278s No Yes 278s 0.66 0.34 278s 278s Group means: 278s npreg glu bp skin bmi ped age 278s No 2.2567 109.91 67.538 25.484 30.355 0.38618 25.628 278s Yes 5.2216 141.64 75.048 32.349 34.387 0.47742 37.634 278s 278s Group: No 278s npreg glu bp skin bmi ped age 278s npreg 4.396832 10.20629 5.43346 4.38313 7.9891e-01 -0.09389257 7.45638 278s glu 10.206286 601.12211 56.62047 49.67071 3.3829e+01 -0.31896741 31.64508 278s bp 5.433462 56.62047 120.38052 34.38984 1.4817e+01 -0.21784446 26.44853 278s skin 4.383134 49.67071 34.38984 136.94931 6.1576e+01 -0.47532490 17.74141 278s bmi 0.798908 33.82928 14.81668 61.57578 5.1441e+01 0.00061983 8.56856 278s ped -0.093893 -0.31897 -0.21784 -0.47532 6.1983e-04 0.06012655 -0.26872 278s age 7.456376 31.64508 26.44853 17.74141 8.5686e+00 -0.26872005 29.93856 278s 278s Group: Yes 278s npreg glu bp skin bmi ped age 278s npreg 15.91978 7.7491 7.24229 10.46802 5.40627 0.320434 25.88314 278s glu 7.74907 856.4955 58.59554 29.65331 11.44745 1.388745 38.24430 278s bp 7.24229 58.5955 89.66288 21.36597 6.46859 0.764486 36.30462 278s skin 10.46802 29.6533 21.36597 86.79253 26.22071 0.620654 5.28665 278s bmi 5.40627 11.4475 6.46859 26.22071 20.12351 0.211701 0.71583 278s ped 0.32043 1.3887 0.76449 0.62065 0.21170 0.062727 0.93743 278s age 25.88314 38.2443 36.30462 5.28665 0.71583 0.937430 136.24335 278s Call: 278s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 278s 278s Prior Probabilities of Groups: 278s carrier normal 278s 0.6 0.4 278s 278s Group means: 278s AHFactivity AHFantigen 278s carrier -0.30795 -0.0059911 278s normal -0.13487 -0.0778567 278s 278s Group: carrier 278s AHFactivity AHFantigen 278s AHFactivity 0.023784 0.015376 278s AHFantigen 0.015376 0.024035 278s 278s Group: normal 278s AHFactivity AHFantigen 278s AHFactivity 0.020897 0.015515 278s AHFantigen 0.015515 0.017920 278s Call: 278s QdaClassic(Treat ~ ., data = anorexia) 278s 278s Prior Probabilities of Groups: 278s CBT Cont FT 278s 0.40278 0.36111 0.23611 278s 278s Group means: 278s Prewt Postwt 278s CBT 82.690 85.697 278s Cont 81.558 81.108 278s FT 83.229 90.494 278s 278s Group: CBT 278s Prewt Postwt 278s Prewt 23.479 19.910 278s Postwt 19.910 69.755 278s 278s Group: Cont 278s Prewt Postwt 278s Prewt 32.5705 -4.3705 278s Postwt -4.3705 22.5079 278s 278s Group: FT 278s Prewt Postwt 278s Prewt 25.167 22.883 278s Postwt 22.883 71.827 278s Call: 278s QdaClassic(type ~ ., data = Pima.tr) 278s 278s Prior Probabilities of Groups: 278s No Yes 278s 0.66 0.34 278s 278s Group means: 278s npreg glu bp skin bmi ped age 278s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 278s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 278s 278s Group: No 278s npreg glu bp skin bmi ped age 278s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 278s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 278s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 278s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 278s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 278s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 278s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 278s 278s Group: Yes 278s npreg glu bp skin bmi ped age 278s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 278s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 278s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 278s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 278s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 278s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 278s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 278s Call: 278s QdaClassic(Species ~ ., data = iris) 278s 278s Prior Probabilities of Groups: 278s setosa versicolor virginica 278s 0.33333 0.33333 0.33333 278s 278s Group means: 278s Sepal.Length Sepal.Width Petal.Length Petal.Width 278s setosa 5.006 3.428 1.462 0.246 278s versicolor 5.936 2.770 4.260 1.326 278s virginica 6.588 2.974 5.552 2.026 278s 278s Group: setosa 278s Sepal.Length Sepal.Width Petal.Length Petal.Width 278s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 278s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 278s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 278s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 278s 278s Group: versicolor 278s Sepal.Length Sepal.Width Petal.Length Petal.Width 278s Sepal.Length 0.266433 0.085184 0.182898 0.055780 278s Sepal.Width 0.085184 0.098469 0.082653 0.041204 278s Petal.Length 0.182898 0.082653 0.220816 0.073102 278s Petal.Width 0.055780 0.041204 0.073102 0.039106 278s 278s Group: virginica 278s Sepal.Length Sepal.Width Petal.Length Petal.Width 278s Sepal.Length 0.404343 0.093763 0.303290 0.049094 278s Sepal.Width 0.093763 0.104004 0.071380 0.047629 278s Petal.Length 0.303290 0.071380 0.304588 0.048824 278s Petal.Width 0.049094 0.047629 0.048824 0.075433 278s =================================================== 278s > 278s BEGIN TEST tsde.R 278s 278s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 278s Copyright (C) 2025 The R Foundation for Statistical Computing 278s Platform: powerpc64le-unknown-linux-gnu 278s 278s R is free software and comes with ABSOLUTELY NO WARRANTY. 278s You are welcome to redistribute it under certain conditions. 278s Type 'license()' or 'licence()' for distribution details. 278s 278s R is a collaborative project with many contributors. 278s Type 'contributors()' for more information and 278s 'citation()' on how to cite R or R packages in publications. 278s 278s Type 'demo()' for some demos, 'help()' for on-line help, or 278s 'help.start()' for an HTML browser interface to help. 278s Type 'q()' to quit R. 278s 278s > ## Test for singularity 278s > doexact <- function(){ 278s + exact <-function(){ 278s + n1 <- 45 278s + p <- 2 278s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 278s + x1[,p] <- x1[,p] + 3 278s + ## library(MASS) 278s + ## x1 <- mvrnorm(n=n1, mu=c(0,3), Sigma=diag(1,nrow=p)) 278s + 278s + n2 <- 55 278s + m1 <- 0 278s + m2 <- 3 278s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 278s + x<-rbind(x1,x2) 278s + colnames(x) <- c("X1","X2") 278s + x 278s + } 278s + print(CovSde(exact())) 278s + } 278s > 278s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE){ 278s + 278s + domcd <- function(x, xname, nrep=1){ 278s + n <- dim(x)[1] 278s + p <- dim(x)[2] 278s + 278s + mcd<-CovSde(x) 278s + 278s + if(time){ 278s + xtime <- system.time(dorep(x, nrep))[1]/nrep 278s + xres <- sprintf("%3d %3d %3d\n", dim(x)[1], dim(x)[2], xtime) 278s + } 278s + else{ 278s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 278s + } 278s + lpad<-lname-nchar(xname) 278s + cat(pad.right(xname,lpad), xres) 278s + 278s + if(!short){ 278s + 278s + ibad <- which(mcd@wt==0) 278s + names(ibad) <- NULL 278s + nbad <- length(ibad) 278s + cat("Outliers: ",nbad,"\n") 278s + if(nbad > 0) 278s + print(ibad) 278s + if(full){ 278s + cat("-------------\n") 278s + show(mcd) 278s + } 278s + cat("--------------------------------------------------------\n") 278s + } 278s + } 278s + 278s + options(digits = 5) 278s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 278s + 278s + lname <- 20 278s + 278s + ## VT::15.09.2013 - this will render the output independent 278s + ## from the version of the package 278s + suppressPackageStartupMessages(library(rrcov)) 278s + 278s + data(heart) 278s + data(starsCYG) 278s + data(phosphor) 278s + data(stackloss) 278s + data(coleman) 278s + data(salinity) 278s + data(wood) 278s + 278s + data(hbk) 278s + 278s + data(Animals, package = "MASS") 278s + brain <- Animals[c(1:24, 26:25, 27:28),] 278s + data(milk) 278s + data(bushfire) 278s + 278s + tmp <- sys.call() 278s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 278s + 278s + cat("Data Set n p Half LOG(obj) Time\n") 278s + cat("========================================================\n") 278s + domcd(heart[, 1:2], data(heart), nrep) 278s + domcd(starsCYG, data(starsCYG), nrep) 278s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 278s + domcd(stack.x, data(stackloss), nrep) 278s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 278s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 278s + domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 278s + domcd(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 278s + 278s + domcd(brain, "Animals", nrep) 278s + domcd(milk, data(milk), nrep) 278s + domcd(bushfire, data(bushfire), nrep) 278s + ## VT::19.07.2010: test the univariate SDE 278s + for(i in 1:ncol(bushfire)) 278s + domcd(bushfire[i], data(bushfire), nrep) 278s + cat("========================================================\n") 278s + } 278s > 278s > dogen <- function(nrep=1, eps=0.49){ 278s + 278s + library(MASS) 278s + domcd <- function(x, nrep=1){ 278s + gc() 278s + xtime <- system.time(dorep(x, nrep))[1]/nrep 278s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 278s + xtime 278s + } 278s + 278s + set.seed(1234) 278s + 278s + ## VT::15.09.2013 - this will render the output independent 278s + ## from the version of the package 278s + suppressPackageStartupMessages(library(rrcov)) 278s + 278s + ap <- c(2, 5, 10, 20, 30) 278s + an <- c(100, 500, 1000, 10000, 50000) 278s + 278s + tottime <- 0 278s + cat(" n p Time\n") 278s + cat("=====================\n") 278s + for(i in 1:length(an)) { 278s + for(j in 1:length(ap)) { 278s + n <- an[i] 278s + p <- ap[j] 278s + if(5*p <= n){ 278s + xx <- gendata(n, p, eps) 278s + X <- xx$X 278s + tottime <- tottime + domcd(X, nrep) 278s + } 278s + } 278s + } 278s + 278s + cat("=====================\n") 278s + cat("Total time: ", tottime*nrep, "\n") 278s + } 278s > 278s > docheck <- function(n, p, eps){ 278s + xx <- gendata(n,p,eps) 278s + mcd <- CovSde(xx$X) 278s + check(mcd, xx$xind) 278s + } 278s > 278s > check <- function(mcd, xind){ 278s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 278s + ## did not get zero weight 278s + mymatch <- xind %in% which(mcd@wt == 0) 278s + length(xind) - length(which(mymatch)) 278s + } 278s > 278s > dorep <- function(x, nrep=1){ 278s + 278s + for(i in 1:nrep) 278s + CovSde(x) 278s + } 278s > 278s > #### gendata() #### 278s > # Generates a location contaminated multivariate 278s > # normal sample of n observations in p dimensions 278s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 278s > # where 278s > # m = (b,b,...,b) 278s > # Defaults: eps=0 and b=10 278s > # 278s > gendata <- function(n,p,eps=0,b=10){ 278s + 278s + if(missing(n) || missing(p)) 278s + stop("Please specify (n,p)") 278s + if(eps < 0 || eps >= 0.5) 278s + stop(message="eps must be in [0,0.5)") 278s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 278s + nbad <- as.integer(eps * n) 278s + if(nbad > 0){ 278s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 278s + xind <- sample(n,nbad) 278s + X[xind,] <- Xbad 278s + } 278s + list(X=X, xind=xind) 278s + } 278s > 278s > pad.right <- function(z, pads) 278s + { 278s + ### Pads spaces to right of text 278s + padding <- paste(rep(" ", pads), collapse = "") 278s + paste(z, padding, sep = "") 278s + } 278s > 278s > whatis<-function(x){ 278s + if(is.data.frame(x)) 278s + cat("Type: data.frame\n") 278s + else if(is.matrix(x)) 278s + cat("Type: matrix\n") 278s + else if(is.vector(x)) 278s + cat("Type: vector\n") 278s + else 278s + cat("Type: don't know\n") 278s + } 278s > 278s > ## VT::15.09.2013 - this will render the output independent 278s > ## from the version of the package 278s > suppressPackageStartupMessages(library(rrcov)) 278s > 278s > dodata() 278s 278s Call: dodata() 278s Data Set n p Half LOG(obj) Time 278s ======================================================== 278s heart 12 2 278s Outliers: 5 278s [1] 2 6 8 10 12 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s height weight 278s 39.8 35.7 278s 278s Robust Estimate of Covariance: 278s height weight 278s height 38.2 77.1 278s weight 77.1 188.1 278s -------------------------------------------------------- 278s starsCYG 47 2 278s Outliers: 7 278s [1] 7 9 11 14 20 30 34 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s log.Te log.light 278s 4.42 4.96 278s 278s Robust Estimate of Covariance: 278s log.Te log.light 278s log.Te 0.0163 0.0522 278s log.light 0.0522 0.3243 278s -------------------------------------------------------- 278s phosphor 18 2 278s Outliers: 2 278s [1] 1 6 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s inorg organic 278s 13.3 39.7 278s 278s Robust Estimate of Covariance: 278s inorg organic 278s inorg 133 134 278s organic 134 204 278s -------------------------------------------------------- 278s stackloss 21 3 278s Outliers: 6 278s [1] 1 2 3 15 17 21 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s Air.Flow Water.Temp Acid.Conc. 278s 57.8 20.7 86.4 278s 278s Robust Estimate of Covariance: 278s Air.Flow Water.Temp Acid.Conc. 278s Air.Flow 39.7 15.6 25.0 278s Water.Temp 15.6 13.0 11.9 278s Acid.Conc. 25.0 11.9 40.3 278s -------------------------------------------------------- 278s coleman 20 5 278s Outliers: 8 278s [1] 1 2 6 10 11 12 15 18 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s salaryP fatherWc sstatus teacherSc motherLev 278s 2.78 58.64 9.09 25.37 6.69 278s 278s Robust Estimate of Covariance: 278s salaryP fatherWc sstatus teacherSc motherLev 278s salaryP 0.2556 -1.0144 0.6599 0.2673 0.0339 278s fatherWc -1.0144 1615.9192 382.7846 -4.8287 36.0227 278s sstatus 0.6599 382.7846 108.1781 -0.7904 9.1027 278s teacherSc 0.2673 -4.8287 -0.7904 0.9346 -0.0239 278s motherLev 0.0339 36.0227 9.1027 -0.0239 0.9155 278s -------------------------------------------------------- 278s salinity 28 3 278s Outliers: 9 278s [1] 3 4 5 9 11 16 19 23 24 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s X1 X2 X3 278s 10.84 3.35 22.48 278s 278s Robust Estimate of Covariance: 278s X1 X2 X3 278s X1 10.75 -1.62 -2.05 278s X2 -1.62 4.21 -1.43 278s X3 -2.05 -1.43 2.63 278s -------------------------------------------------------- 278s wood 20 5 278s Outliers: 11 278s [1] 4 6 7 8 9 10 12 13 16 19 20 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s x1 x2 x3 x4 x5 278s 0.573 0.119 0.517 0.549 0.904 278s 278s Robust Estimate of Covariance: 278s x1 x2 x3 x4 x5 278s x1 0.025185 0.004279 -0.001262 -0.000382 -0.001907 278s x2 0.004279 0.001011 0.000197 -0.000117 0.000247 278s x3 -0.001262 0.000197 0.003042 0.002034 0.001773 278s x4 -0.000382 -0.000117 0.002034 0.007943 0.001137 278s x5 -0.001907 0.000247 0.001773 0.001137 0.005392 278s -------------------------------------------------------- 278s hbk 75 3 278s Outliers: 15 278s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 53 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s X1 X2 X3 278s 1.59 1.79 1.67 278s 278s Robust Estimate of Covariance: 278s X1 X2 X3 278s X1 1.6354 0.0793 0.2284 278s X2 0.0793 1.6461 0.3186 278s X3 0.2284 0.3186 1.5673 278s -------------------------------------------------------- 278s Animals 28 2 278s Outliers: 13 278s [1] 2 6 7 8 9 12 13 14 15 16 24 25 28 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s body brain 278s 18.7 64.9 278s 278s Robust Estimate of Covariance: 278s body brain 278s body 4702 7973 278s brain 7973 28571 278s -------------------------------------------------------- 278s milk 86 8 278s Outliers: 21 278s [1] 1 2 3 6 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s X1 X2 X3 X4 X5 X6 X7 X8 278s 1.03 35.90 33.04 26.11 25.10 25.02 123.06 14.37 278s 278s Robust Estimate of Covariance: 278s X1 X2 X3 X4 X5 X6 X7 278s X1 4.73e-07 6.57e-05 1.79e-04 1.71e-04 1.62e-04 1.42e-04 6.85e-04 278s X2 6.57e-05 1.57e+00 1.36e-01 9.28e-02 4.18e-02 1.30e-01 1.58e+00 278s X3 1.79e-04 1.36e-01 1.12e+00 8.20e-01 8.27e-01 8.00e-01 6.66e-01 278s X4 1.71e-04 9.28e-02 8.20e-01 6.57e-01 6.41e-01 6.18e-01 5.47e-01 278s X5 1.62e-04 4.18e-02 8.27e-01 6.41e-01 6.93e-01 6.44e-01 5.71e-01 278s X6 1.42e-04 1.30e-01 8.00e-01 6.18e-01 6.44e-01 6.44e-01 5.55e-01 278s X7 6.85e-04 1.58e+00 6.66e-01 5.47e-01 5.71e-01 5.55e-01 4.17e+00 278s X8 1.40e-05 2.33e-01 1.74e-01 1.06e-01 9.44e-02 9.86e-02 3.54e-01 278s X8 278s X1 1.40e-05 278s X2 2.33e-01 278s X3 1.74e-01 278s X4 1.06e-01 278s X5 9.44e-02 278s X6 9.86e-02 278s X7 3.54e-01 278s X8 1.57e-01 278s -------------------------------------------------------- 278s bushfire 38 5 278s Outliers: 23 278s [1] 1 5 6 7 8 9 10 11 12 13 15 16 28 29 30 31 32 33 34 35 36 37 38 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s V1 V2 V3 V4 V5 278s 105 148 287 223 283 278s 278s Robust Estimate of Covariance: 278s V1 V2 V3 V4 V5 278s V1 1964 1712 -10230 -2504 -2066 278s V2 1712 1526 -8732 -2145 -1763 278s V3 -10230 -8732 56327 13803 11472 278s V4 -2504 -2145 13803 3509 2894 278s V5 -2066 -1763 11472 2894 2404 278s -------------------------------------------------------- 278s bushfire 38 1 278s Outliers: 2 278s [1] 13 30 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s V1 278s 98.5 278s 278s Robust Estimate of Covariance: 278s V1 278s V1 431 278s -------------------------------------------------------- 278s bushfire 38 1 278s Outliers: 6 278s [1] 33 34 35 36 37 38 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s V2 278s 141 278s 278s Robust Estimate of Covariance: 278s V2 278s V2 528 278s -------------------------------------------------------- 278s bushfire 38 1 278s Outliers: 0 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s V3 278s 238 278s 278s Robust Estimate of Covariance: 278s V3 278s V3 37148 278s -------------------------------------------------------- 278s bushfire 38 1 278s Outliers: 9 278s [1] 8 9 32 33 34 35 36 37 38 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s V4 278s 210 278s 278s Robust Estimate of Covariance: 278s V4 278s V4 2543 278s -------------------------------------------------------- 278s bushfire 38 1 278s Outliers: 9 278s [1] 8 9 32 33 34 35 36 37 38 278s ------------- 278s 278s Call: 278s CovSde(x = x) 278s -> Method: Stahel-Donoho estimator 278s 278s Robust Estimate of Location: 278s V5 278s 273 278s 278s Robust Estimate of Covariance: 278s V5 278s V5 1575 278s -------------------------------------------------------- 278s ======================================================== 278s > ##doexact() 278s > 278s BEGIN TEST tsest.R 278s 278s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 278s Copyright (C) 2025 The R Foundation for Statistical Computing 278s Platform: powerpc64le-unknown-linux-gnu 278s 278s R is free software and comes with ABSOLUTELY NO WARRANTY. 278s You are welcome to redistribute it under certain conditions. 278s Type 'license()' or 'licence()' for distribution details. 278s 278s R is a collaborative project with many contributors. 278s Type 'contributors()' for more information and 278s 'citation()' on how to cite R or R packages in publications. 278s 278s Type 'demo()' for some demos, 'help()' for on-line help, or 278s 'help.start()' for an HTML browser interface to help. 278s Type 'q()' to quit R. 278s 278s > ## VT::15.09.2013 - this will render the output independent 278s > ## from the version of the package 278s > suppressPackageStartupMessages(library(rrcov)) 279s > 279s > library(MASS) 279s > 279s > dodata <- function(nrep = 1, time = FALSE, full = TRUE, method) { 279s + doest <- function(x, xname, nrep = 1, method=c("sfast", "surreal", "bisquare", "rocke", "suser", "MM", "sdet")) { 279s + 279s + method <- match.arg(method) 279s + 279s + lname <- 20 279s + n <- dim(x)[1] 279s + p <- dim(x)[2] 279s + 279s + mm <- if(method == "MM") CovMMest(x) else CovSest(x, method=method) 279s + 279s + crit <- log(mm@crit) 279s + 279s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 279s + lpad <- lname-nchar(xname) 279s + cat(pad.right(xname,lpad), xres) 279s + 279s + dist <- getDistance(mm) 279s + quantiel <- qchisq(0.975, p) 279s + ibad <- which(dist >= quantiel) 279s + names(ibad) <- NULL 279s + nbad <- length(ibad) 279s + cat("Outliers: ",nbad,"\n") 279s + if(nbad > 0) 279s + print(ibad) 279s + cat("-------------\n") 279s + show(mm) 279s + cat("--------------------------------------------------------\n") 279s + } 279s + 279s + options(digits = 5) 279s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 279s + 279s + data(heart) 279s + data(starsCYG) 279s + data(phosphor) 279s + data(stackloss) 279s + data(coleman) 279s + data(salinity) 279s + data(wood) 279s + data(hbk) 279s + 279s + data(Animals, package = "MASS") 279s + brain <- Animals[c(1:24, 26:25, 27:28),] 279s + data(milk) 279s + data(bushfire) 279s + ### 279s + data(rice) 279s + data(hemophilia) 279s + data(fish) 279s + 279s + tmp <- sys.call() 279s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 279s + 279s + cat("Data Set n p LOG(det) Time\n") 279s + cat("===================================================\n") 279s + doest(heart[, 1:2], data(heart), nrep, method=method) 279s + doest(starsCYG, data(starsCYG), nrep, method=method) 279s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep, method=method) 279s + doest(stack.x, data(stackloss), nrep, method=method) 279s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep, method=method) 279s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep, method=method) 279s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep, method=method) 279s + doest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep, method=method) 279s + 279s + 279s + doest(brain, "Animals", nrep, method=method) 279s + doest(milk, data(milk), nrep, method=method) 279s + doest(bushfire, data(bushfire), nrep, method=method) 279s + 279s + doest(data.matrix(subset(rice, select = -Overall_evaluation)), data(rice), nrep, method=method) 279s + doest(data.matrix(subset(hemophilia, select = -gr)), data(hemophilia), nrep, method=method) 279s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep, method=method) 279s + 279s + ## from package 'datasets' 279s + doest(airquality[,1:4], data(airquality), nrep, method=method) 279s + doest(attitude, data(attitude), nrep, method=method) 279s + doest(attenu, data(attenu), nrep, method=method) 279s + doest(USJudgeRatings, data(USJudgeRatings), nrep, method=method) 279s + doest(USArrests, data(USArrests), nrep, method=method) 279s + doest(longley, data(longley), nrep, method=method) 279s + doest(Loblolly, data(Loblolly), nrep, method=method) 279s + doest(quakes[,1:4], data(quakes), nrep, method=method) 279s + 279s + cat("===================================================\n") 279s + } 279s > 279s > # generate contaminated data using the function gendata with different 279s > # number of outliers and check if the M-estimate breaks - i.e. the 279s > # largest eigenvalue is larger than e.g. 5. 279s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 279s > # outliers grater than 20. 279s > dogen <- function(){ 279s + eig <- vector("numeric",26) 279s + for(i in 0:25) { 279s + gg <- gendata(eps=i) 279s + mm <- CovSRocke(gg$x, t0=gg$tgood, S0=gg$sgood) 279s + eig[i+1] <- ev <- getEvals(mm)[1] 279s + cat(i, ev, "\n") 279s + 279s + ## stopifnot(ev < 5 || i > 20) 279s + } 279s + plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 279s + } 279s > 279s > # 279s > # generate data 50x10 as multivariate normal N(0,I) and add 279s > # eps % outliers by adding d=5.0 to each component. 279s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 279s > # - if eps >= 1, it is the number of outliers 279s > # - use the center and cov of the good data as good start 279s > # - use the center and the cov of all data as a bad start 279s > # If using a good start, the M-estimate must iterate to 279s > # the good solution: the largest eigenvalue is less then e.g. 5 279s > # 279s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 279s + 279s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 279s + stop("eps is out of range") 279s + 279s + library(MASS) 279s + 279s + x <- mvrnorm(n, rep(0,p), diag(p)) 279s + bad <- vector("numeric") 279s + nbad = if(eps < 1) eps*n else eps 279s + if(nbad > 0){ 279s + bad <- sample(n, nbad) 279s + x[bad,] <- x[bad,] + d 279s + } 279s + cov1 <- cov.wt(x) 279s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 279s + 279s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 279s + } 279s > 279s > pad.right <- function(z, pads) 279s + { 279s + ## Pads spaces to right of text 279s + padding <- paste(rep(" ", pads), collapse = "") 279s + paste(z, padding, sep = "") 279s + } 279s > 279s > 279s > ## -- now do it: 279s > dodata(method="sfast") 279s 279s Call: dodata(method = "sfast") 279s Data Set n p LOG(det) Time 279s =================================================== 279s heart 12 2 2.017701 279s Outliers: 3 279s [1] 2 6 12 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 36.1 29.5 279s 279s Robust Estimate of Covariance: 279s height weight 279s height 129 210 279s weight 210 365 279s -------------------------------------------------------- 279s starsCYG 47 2 -1.450032 279s Outliers: 7 279s [1] 7 9 11 14 20 30 34 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 4.42 4.97 279s 279s Robust Estimate of Covariance: 279s log.Te log.light 279s log.Te 0.0176 0.0617 279s log.light 0.0617 0.3880 279s -------------------------------------------------------- 279s phosphor 18 2 2.320721 279s Outliers: 2 279s [1] 1 6 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 14.1 38.8 279s 279s Robust Estimate of Covariance: 279s inorg organic 279s inorg 174 190 279s organic 190 268 279s -------------------------------------------------------- 279s stackloss 21 3 1.470031 279s Outliers: 3 279s [1] 1 2 3 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 57.5 20.5 86.0 279s 279s Robust Estimate of Covariance: 279s Air.Flow Water.Temp Acid.Conc. 279s Air.Flow 38.94 11.66 22.89 279s Water.Temp 11.66 9.96 7.81 279s Acid.Conc. 22.89 7.81 40.48 279s -------------------------------------------------------- 279s coleman 20 5 0.491419 279s Outliers: 2 279s [1] 6 10 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 2.77 45.58 4.13 25.13 6.39 279s 279s Robust Estimate of Covariance: 279s salaryP fatherWc sstatus teacherSc motherLev 279s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 279s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 279s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 279s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 279s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 279s -------------------------------------------------------- 279s salinity 28 3 0.734619 279s Outliers: 4 279s [1] 5 16 23 24 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 10.31 3.07 22.60 279s 279s Robust Estimate of Covariance: 279s X1 X2 X3 279s X1 13.200 0.784 -3.611 279s X2 0.784 4.441 -1.658 279s X3 -3.611 -1.658 2.877 279s -------------------------------------------------------- 279s wood 20 5 -3.202636 279s Outliers: 2 279s [1] 7 9 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 0.551 0.135 0.496 0.511 0.916 279s 279s Robust Estimate of Covariance: 279s x1 x2 x3 x4 x5 279s x1 0.011361 -0.000791 0.005473 0.004204 -0.004747 279s x2 -0.000791 0.000701 -0.000534 -0.001452 0.000864 279s x3 0.005473 -0.000534 0.004905 0.002960 -0.001914 279s x4 0.004204 -0.001452 0.002960 0.005154 -0.002187 279s x5 -0.004747 0.000864 -0.001914 -0.002187 0.003214 279s -------------------------------------------------------- 279s hbk 75 3 0.283145 279s Outliers: 14 279s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 1.53 1.83 1.66 279s 279s Robust Estimate of Covariance: 279s X1 X2 X3 279s X1 1.8091 0.0479 0.2446 279s X2 0.0479 1.8190 0.2513 279s X3 0.2446 0.2513 1.7288 279s -------------------------------------------------------- 279s Animals 28 2 4.685129 279s Outliers: 10 279s [1] 2 6 7 9 12 14 15 16 24 25 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 30.8 84.2 279s 279s Robust Estimate of Covariance: 279s body brain 279s body 14806 28767 279s brain 28767 65195 279s -------------------------------------------------------- 279s milk 86 8 -1.437863 279s Outliers: 15 279s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 279s 279s Robust Estimate of Covariance: 279s X1 X2 X3 X4 X5 X6 X7 279s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 279s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 279s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 279s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 279s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 279s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 279s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 279s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 279s X8 279s X1 7.45e-05 279s X2 3.33e-01 279s X3 2.82e-01 279s X4 2.01e-01 279s X5 1.80e-01 279s X6 1.91e-01 279s X7 6.38e-01 279s X8 2.01e-01 279s -------------------------------------------------------- 279s bushfire 38 5 2.443148 279s Outliers: 13 279s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 108 149 266 216 278 279s 279s Robust Estimate of Covariance: 279s V1 V2 V3 V4 V5 279s V1 911 688 -3961 -856 -707 279s V2 688 587 -2493 -492 -420 279s V3 -3961 -2493 24146 5765 4627 279s V4 -856 -492 5765 1477 1164 279s V5 -707 -420 4627 1164 925 279s -------------------------------------------------------- 279s rice 105 5 -0.724874 279s Outliers: 7 279s [1] 9 40 42 49 57 58 71 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 279s 279s Robust Estimate of Covariance: 279s Favor Appearance Taste Stickiness Toughness 279s Favor 0.423 0.345 0.427 0.405 -0.202 279s Appearance 0.345 0.592 0.570 0.549 -0.316 279s Taste 0.427 0.570 0.739 0.706 -0.393 279s Stickiness 0.405 0.549 0.706 0.876 -0.497 279s Toughness -0.202 -0.316 -0.393 -0.497 0.467 279s -------------------------------------------------------- 279s hemophilia 75 2 -1.868949 279s Outliers: 2 279s [1] 11 36 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] -0.2126 -0.0357 279s 279s Robust Estimate of Covariance: 279s AHFactivity AHFantigen 279s AHFactivity 0.0317 0.0112 279s AHFantigen 0.0112 0.0218 279s -------------------------------------------------------- 279s fish 159 6 1.285876 279s Outliers: 21 279s [1] 61 62 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 279s [20] 103 142 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 358.3 24.7 26.9 29.7 30.0 14.7 279s 279s Robust Estimate of Covariance: 279s Weight Length1 Length2 Length3 Height Width 279s Weight 1.33e+05 3.09e+03 3.34e+03 3.78e+03 1.72e+03 2.24e+02 279s Length1 3.09e+03 7.92e+01 8.54e+01 9.55e+01 4.04e+01 7.43e+00 279s Length2 3.34e+03 8.54e+01 9.22e+01 1.03e+02 4.49e+01 8.07e+00 279s Length3 3.78e+03 9.55e+01 1.03e+02 1.18e+02 5.92e+01 7.65e+00 279s Height 1.72e+03 4.04e+01 4.49e+01 5.92e+01 7.12e+01 8.51e-01 279s Width 2.24e+02 7.43e+00 8.07e+00 7.65e+00 8.51e-01 3.57e+00 279s -------------------------------------------------------- 279s airquality 153 4 2.684374 279s Outliers: 7 279s [1] 7 14 23 30 34 77 107 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 39.34 192.12 9.67 78.71 279s 279s Robust Estimate of Covariance: 279s Ozone Solar.R Wind Temp 279s Ozone 973.104 894.011 -61.856 243.560 279s Solar.R 894.011 9677.269 0.388 179.429 279s Wind -61.856 0.388 11.287 -14.310 279s Temp 243.560 179.429 -14.310 96.714 279s -------------------------------------------------------- 279s attitude 30 7 2.091968 279s Outliers: 4 279s [1] 14 16 18 24 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 279s 279s Robust Estimate of Covariance: 279s rating complaints privileges learning raises critical advance 279s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 279s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 279s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 279s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 279s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 279s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 279s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 279s -------------------------------------------------------- 279s attenu 182 5 1.148032 279s Outliers: 31 279s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 279s [20] 29 30 31 32 64 65 80 94 95 96 97 100 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 16.432 5.849 60.297 27.144 0.134 279s 279s Robust Estimate of Covariance: 279s event mag station dist accel 279s event 54.9236 -3.0733 181.0954 -49.4194 -0.0628 279s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 279s station 181.0954 -8.4388 1689.7161 -114.6319 0.7285 279s dist -49.4194 6.7388 -114.6319 597.3606 -1.7988 279s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 279s -------------------------------------------------------- 279s USJudgeRatings 43 12 -1.683847 279s Outliers: 7 279s [1] 5 7 12 13 14 23 31 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [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 279s 279s Robust Estimate of Covariance: 279s CONT INTG DMNR DILG CFMG DECI PREP FAMI 279s CONT 0.8710 -0.3019 -0.4682 -0.1893 -0.0569 -0.0992 -0.1771 -0.1975 279s INTG -0.3019 0.6401 0.8598 0.6955 0.5732 0.5439 0.7091 0.7084 279s DMNR -0.4682 0.8598 1.2412 0.9107 0.7668 0.7305 0.9292 0.9158 279s DILG -0.1893 0.6955 0.9107 0.8554 0.7408 0.7036 0.8865 0.8791 279s CFMG -0.0569 0.5732 0.7668 0.7408 0.6994 0.6545 0.7788 0.7721 279s DECI -0.0992 0.5439 0.7305 0.7036 0.6545 0.6342 0.7492 0.7511 279s PREP -0.1771 0.7091 0.9292 0.8865 0.7788 0.7492 0.9541 0.9556 279s FAMI -0.1975 0.7084 0.9158 0.8791 0.7721 0.7511 0.9556 0.9785 279s ORAL -0.2444 0.7453 0.9939 0.8917 0.7842 0.7551 0.9554 0.9680 279s WRIT -0.2344 0.7319 0.9649 0.8853 0.7781 0.7511 0.9498 0.9668 279s PHYS -0.1983 0.4676 0.6263 0.5629 0.5073 0.5039 0.5990 0.6140 279s RTEN -0.3152 0.8000 1.0798 0.9234 0.7952 0.7663 0.9637 0.9693 279s ORAL WRIT PHYS RTEN 279s CONT -0.2444 -0.2344 -0.1983 -0.3152 279s INTG 0.7453 0.7319 0.4676 0.8000 279s DMNR 0.9939 0.9649 0.6263 1.0798 279s DILG 0.8917 0.8853 0.5629 0.9234 279s CFMG 0.7842 0.7781 0.5073 0.7952 279s DECI 0.7551 0.7511 0.5039 0.7663 279s PREP 0.9554 0.9498 0.5990 0.9637 279s FAMI 0.9680 0.9668 0.6140 0.9693 279s ORAL 0.9853 0.9744 0.6280 1.0032 279s WRIT 0.9744 0.9711 0.6184 0.9870 279s PHYS 0.6280 0.6184 0.4716 0.6520 279s RTEN 1.0032 0.9870 0.6520 1.0622 279s -------------------------------------------------------- 279s USArrests 50 4 2.411726 279s Outliers: 4 279s [1] 2 28 33 39 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 7.05 150.66 64.66 19.37 279s 279s Robust Estimate of Covariance: 279s Murder Assault UrbanPop Rape 279s Murder 23.8 380.8 19.2 29.7 279s Assault 380.8 8436.2 605.6 645.3 279s UrbanPop 19.2 605.6 246.5 78.8 279s Rape 29.7 645.3 78.8 77.3 279s -------------------------------------------------------- 279s longley 16 7 1.038316 279s Outliers: 5 279s [1] 1 2 3 4 5 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 107.6 440.8 339.7 292.5 121.0 1957.1 67.2 279s 279s Robust Estimate of Covariance: 279s GNP.deflator GNP Unemployed Armed.Forces Population 279s GNP.deflator 100.6 954.7 1147.1 -507.6 74.2 279s GNP 954.7 9430.9 10115.8 -4616.5 730.1 279s Unemployed 1147.1 10115.8 19838.3 -6376.9 850.6 279s Armed.Forces -507.6 -4616.5 -6376.9 3240.2 -351.3 279s Population 74.2 730.1 850.6 -351.3 57.5 279s Year 46.4 450.8 539.5 -233.0 35.3 279s Employed 30.8 310.5 274.0 -160.8 23.3 279s Year Employed 279s GNP.deflator 46.4 30.8 279s GNP 450.8 310.5 279s Unemployed 539.5 274.0 279s Armed.Forces -233.0 -160.8 279s Population 35.3 23.3 279s Year 21.9 14.6 279s Employed 14.6 11.2 279s -------------------------------------------------------- 279s Loblolly 84 3 1.481317 279s Outliers: 14 279s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] 24.22 9.65 7.50 279s 279s Robust Estimate of Covariance: 279s height age Seed 279s height 525.08 179.21 14.27 279s age 179.21 61.85 2.94 279s Seed 14.27 2.94 25.86 279s -------------------------------------------------------- 279s quakes 1000 4 1.576855 279s Outliers: 223 279s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 279s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 279s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 279s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 279s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 279s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 279s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 279s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 279s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 279s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 279s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 279s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 279s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 279s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 279s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 279s ------------- 279s 279s Call: 279s CovSest(x = x, method = method) 279s -> Method: S-estimates: S-FAST 279s 279s Robust Estimate of Location: 279s [1] -21.54 182.35 369.21 4.54 279s 279s Robust Estimate of Covariance: 279s lat long depth mag 279s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 279s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 279s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 279s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 279s -------------------------------------------------------- 279s =================================================== 279s > dodata(method="sdet") 279s 279s Call: dodata(method = "sdet") 279s Data Set n p LOG(det) Time 279s =================================================== 280s heart 12 2 2.017701 280s Outliers: 3 280s [1] 2 6 12 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 36.1 29.5 280s 280s Robust Estimate of Covariance: 280s height weight 280s height 129 210 280s weight 210 365 280s -------------------------------------------------------- 280s starsCYG 47 2 -1.450032 280s Outliers: 7 280s [1] 7 9 11 14 20 30 34 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 4.42 4.97 280s 280s Robust Estimate of Covariance: 280s log.Te log.light 280s log.Te 0.0176 0.0617 280s log.light 0.0617 0.3880 280s -------------------------------------------------------- 280s phosphor 18 2 2.320721 280s Outliers: 2 280s [1] 1 6 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 14.1 38.8 280s 280s Robust Estimate of Covariance: 280s inorg organic 280s inorg 174 190 280s organic 190 268 280s -------------------------------------------------------- 280s stackloss 21 3 1.470031 280s Outliers: 3 280s [1] 1 2 3 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 57.5 20.5 86.0 280s 280s Robust Estimate of Covariance: 280s Air.Flow Water.Temp Acid.Conc. 280s Air.Flow 38.94 11.66 22.89 280s Water.Temp 11.66 9.96 7.81 280s Acid.Conc. 22.89 7.81 40.48 280s -------------------------------------------------------- 280s coleman 20 5 0.491419 280s Outliers: 2 280s [1] 6 10 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 2.77 45.58 4.13 25.13 6.39 280s 280s Robust Estimate of Covariance: 280s salaryP fatherWc sstatus teacherSc motherLev 280s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 280s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 280s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 280s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 280s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 280s -------------------------------------------------------- 280s salinity 28 3 0.734619 280s Outliers: 4 280s [1] 5 16 23 24 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 10.31 3.07 22.60 280s 280s Robust Estimate of Covariance: 280s X1 X2 X3 280s X1 13.200 0.784 -3.611 280s X2 0.784 4.441 -1.658 280s X3 -3.611 -1.658 2.877 280s -------------------------------------------------------- 280s wood 20 5 -3.220754 280s Outliers: 4 280s [1] 4 6 8 19 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 0.580 0.123 0.530 0.538 0.890 280s 280s Robust Estimate of Covariance: 280s x1 x2 x3 x4 x5 280s x1 8.16e-03 1.39e-03 1.97e-03 -2.82e-04 -7.61e-04 280s x2 1.39e-03 4.00e-04 8.14e-04 -8.51e-05 -5.07e-06 280s x3 1.97e-03 8.14e-04 4.74e-03 -9.59e-04 2.06e-05 280s x4 -2.82e-04 -8.51e-05 -9.59e-04 3.09e-03 1.87e-03 280s x5 -7.61e-04 -5.07e-06 2.06e-05 1.87e-03 2.28e-03 280s -------------------------------------------------------- 280s hbk 75 3 0.283145 280s Outliers: 14 280s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 280s ------------- 280s 280s Call: 280s CovSest(x = x, method = method) 280s -> Method: S-estimates: DET-S 280s 280s Robust Estimate of Location: 280s [1] 1.53 1.83 1.66 280s 280s Robust Estimate of Covariance: 280s X1 X2 X3 280s X1 1.8091 0.0479 0.2446 280s X2 0.0479 1.8190 0.2513 280s X3 0.2446 0.2513 1.7288 280s -------------------------------------------------------- 281s Animals 28 2 4.685129 281s Outliers: 10 281s [1] 2 6 7 9 12 14 15 16 24 25 281s ------------- 281s 281s Call: 281s CovSest(x = x, method = method) 281s -> Method: S-estimates: DET-S 281s 281s Robust Estimate of Location: 281s [1] 30.8 84.2 281s 281s Robust Estimate of Covariance: 281s body brain 281s body 14806 28767 281s brain 28767 65194 281s -------------------------------------------------------- 281s milk 86 8 -1.437863 281s Outliers: 15 281s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 281s ------------- 281s 281s Call: 281s CovSest(x = x, method = method) 281s -> Method: S-estimates: DET-S 281s 281s Robust Estimate of Location: 281s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 281s 281s Robust Estimate of Covariance: 281s X1 X2 X3 X4 X5 X6 X7 281s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 281s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 281s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 281s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 281s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 281s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 281s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 281s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 281s X8 281s X1 7.45e-05 281s X2 3.33e-01 281s X3 2.82e-01 281s X4 2.01e-01 281s X5 1.80e-01 281s X6 1.91e-01 281s X7 6.38e-01 281s X8 2.01e-01 281s -------------------------------------------------------- 281s bushfire 38 5 2.443148 281s Outliers: 13 281s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 281s ------------- 281s 281s Call: 281s CovSest(x = x, method = method) 281s -> Method: S-estimates: DET-S 281s 281s Robust Estimate of Location: 281s [1] 108 149 266 216 278 281s 281s Robust Estimate of Covariance: 281s V1 V2 V3 V4 V5 281s V1 911 688 -3961 -856 -707 281s V2 688 587 -2493 -492 -420 281s V3 -3961 -2493 24146 5765 4627 281s V4 -856 -492 5765 1477 1164 281s V5 -707 -420 4627 1164 925 281s -------------------------------------------------------- 282s rice 105 5 -0.724874 282s Outliers: 7 282s [1] 9 40 42 49 57 58 71 282s ------------- 282s 282s Call: 282s CovSest(x = x, method = method) 282s -> Method: S-estimates: DET-S 282s 282s Robust Estimate of Location: 282s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 282s 282s Robust Estimate of Covariance: 282s Favor Appearance Taste Stickiness Toughness 282s Favor 0.423 0.345 0.427 0.405 -0.202 282s Appearance 0.345 0.592 0.570 0.549 -0.316 282s Taste 0.427 0.570 0.739 0.706 -0.393 282s Stickiness 0.405 0.549 0.706 0.876 -0.497 282s Toughness -0.202 -0.316 -0.393 -0.497 0.467 282s -------------------------------------------------------- 282s hemophilia 75 2 -1.868949 282s Outliers: 2 282s [1] 11 36 282s ------------- 282s 282s Call: 282s CovSest(x = x, method = method) 282s -> Method: S-estimates: DET-S 282s 282s Robust Estimate of Location: 282s [1] -0.2126 -0.0357 282s 282s Robust Estimate of Covariance: 282s AHFactivity AHFantigen 282s AHFactivity 0.0317 0.0112 282s AHFantigen 0.0112 0.0218 282s -------------------------------------------------------- 282s fish 159 6 1.267294 282s Outliers: 33 282s [1] 61 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 282s [20] 91 92 93 94 95 96 97 98 99 100 101 102 103 142 282s ------------- 282s 282s Call: 282s CovSest(x = x, method = method) 282s -> Method: S-estimates: DET-S 282s 282s Robust Estimate of Location: 282s [1] 381.2 25.6 27.8 30.8 31.0 14.9 282s 282s Robust Estimate of Covariance: 282s Weight Length1 Length2 Length3 Height Width 282s Weight 148372.04 3260.48 3508.71 3976.93 1507.43 127.94 282s Length1 3260.48 77.00 82.52 92.18 27.56 3.29 282s Length2 3508.71 82.52 88.57 99.20 30.83 3.43 282s Length3 3976.93 92.18 99.20 113.97 45.50 2.21 282s Height 1507.43 27.56 30.83 45.50 70.54 -4.95 282s Width 127.94 3.29 3.43 2.21 -4.95 2.28 282s -------------------------------------------------------- 282s airquality 153 4 2.684374 282s Outliers: 7 282s [1] 7 14 23 30 34 77 107 282s ------------- 282s 282s Call: 282s CovSest(x = x, method = method) 282s -> Method: S-estimates: DET-S 282s 282s Robust Estimate of Location: 282s [1] 39.34 192.12 9.67 78.71 282s 282s Robust Estimate of Covariance: 282s Ozone Solar.R Wind Temp 282s Ozone 973.104 894.011 -61.856 243.560 282s Solar.R 894.011 9677.269 0.388 179.429 282s Wind -61.856 0.388 11.287 -14.310 282s Temp 243.560 179.429 -14.310 96.714 282s -------------------------------------------------------- 282s attitude 30 7 2.091968 282s Outliers: 4 282s [1] 14 16 18 24 282s ------------- 282s 282s Call: 282s CovSest(x = x, method = method) 282s -> Method: S-estimates: DET-S 282s 282s Robust Estimate of Location: 282s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 282s 282s Robust Estimate of Covariance: 282s rating complaints privileges learning raises critical advance 282s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 282s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 282s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 282s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 282s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 282s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 282s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 282s -------------------------------------------------------- 283s attenu 182 5 1.148032 283s Outliers: 31 283s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 283s [20] 29 30 31 32 64 65 80 94 95 96 97 100 283s ------------- 283s 283s Call: 283s CovSest(x = x, method = method) 283s -> Method: S-estimates: DET-S 283s 283s Robust Estimate of Location: 283s [1] 16.432 5.849 60.297 27.144 0.134 283s 283s Robust Estimate of Covariance: 283s event mag station dist accel 283s event 54.9236 -3.0733 181.0954 -49.4195 -0.0628 283s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 283s station 181.0954 -8.4388 1689.7161 -114.6321 0.7285 283s dist -49.4195 6.7388 -114.6321 597.3609 -1.7988 283s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 283s -------------------------------------------------------- 283s USJudgeRatings 43 12 -1.683847 283s Outliers: 7 283s [1] 5 7 12 13 14 23 31 283s ------------- 283s 283s Call: 283s CovSest(x = x, method = method) 283s -> Method: S-estimates: DET-S 283s 283s Robust Estimate of Location: 283s [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 283s 283s Robust Estimate of Covariance: 283s CONT INTG DMNR DILG CFMG DECI PREP FAMI 283s CONT 0.8715 -0.3020 -0.4683 -0.1894 -0.0569 -0.0993 -0.1772 -0.1976 283s INTG -0.3020 0.6403 0.8600 0.6956 0.5733 0.5440 0.7093 0.7086 283s DMNR -0.4683 0.8600 1.2416 0.9109 0.7669 0.7307 0.9295 0.9161 283s DILG -0.1894 0.6956 0.9109 0.8555 0.7410 0.7037 0.8867 0.8793 283s CFMG -0.0569 0.5733 0.7669 0.7410 0.6995 0.6546 0.7789 0.7723 283s DECI -0.0993 0.5440 0.7307 0.7037 0.6546 0.6343 0.7493 0.7513 283s PREP -0.1772 0.7093 0.9295 0.8867 0.7789 0.7493 0.9543 0.9559 283s FAMI -0.1976 0.7086 0.9161 0.8793 0.7723 0.7513 0.9559 0.9788 283s ORAL -0.2445 0.7456 0.9942 0.8919 0.7844 0.7553 0.9557 0.9683 283s WRIT -0.2345 0.7321 0.9652 0.8856 0.7783 0.7513 0.9501 0.9671 283s PHYS -0.1986 0.4676 0.6264 0.5628 0.5072 0.5038 0.5990 0.6140 283s RTEN -0.3154 0.8002 1.0801 0.9236 0.7954 0.7665 0.9639 0.9695 283s ORAL WRIT PHYS RTEN 283s CONT -0.2445 -0.2345 -0.1986 -0.3154 283s INTG 0.7456 0.7321 0.4676 0.8002 283s DMNR 0.9942 0.9652 0.6264 1.0801 283s DILG 0.8919 0.8856 0.5628 0.9236 283s CFMG 0.7844 0.7783 0.5072 0.7954 283s DECI 0.7553 0.7513 0.5038 0.7665 283s PREP 0.9557 0.9501 0.5990 0.9639 283s FAMI 0.9683 0.9671 0.6140 0.9695 283s ORAL 0.9856 0.9748 0.6281 1.0035 283s WRIT 0.9748 0.9714 0.6184 0.9873 283s PHYS 0.6281 0.6184 0.4713 0.6520 283s RTEN 1.0035 0.9873 0.6520 1.0624 283s -------------------------------------------------------- 283s USArrests 50 4 2.411726 283s Outliers: 4 283s [1] 2 28 33 39 283s ------------- 283s 283s Call: 283s CovSest(x = x, method = method) 283s -> Method: S-estimates: DET-S 283s 283s Robust Estimate of Location: 283s [1] 7.05 150.66 64.66 19.37 283s 283s Robust Estimate of Covariance: 283s Murder Assault UrbanPop Rape 283s Murder 23.8 380.8 19.2 29.7 283s Assault 380.8 8436.2 605.6 645.3 283s UrbanPop 19.2 605.6 246.5 78.8 283s Rape 29.7 645.3 78.8 77.3 283s -------------------------------------------------------- 284s longley 16 7 1.143113 284s Outliers: 4 284s [1] 1 2 3 4 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: DET-S 284s 284s Robust Estimate of Location: 284s [1] 107 435 334 293 120 1957 67 284s 284s Robust Estimate of Covariance: 284s GNP.deflator GNP Unemployed Armed.Forces Population 284s GNP.deflator 89.2 850.1 1007.4 -404.4 66.2 284s GNP 850.1 8384.4 9020.8 -3692.0 650.5 284s Unemployed 1007.4 9020.8 16585.4 -4990.7 752.5 284s Armed.Forces -404.4 -3692.0 -4990.7 2474.2 -280.9 284s Population 66.2 650.5 752.5 -280.9 51.2 284s Year 41.9 407.6 481.9 -186.4 31.9 284s Employed 27.9 279.7 255.6 -128.8 21.1 284s Year Employed 284s GNP.deflator 41.9 27.9 284s GNP 407.6 279.7 284s Unemployed 481.9 255.6 284s Armed.Forces -186.4 -128.8 284s Population 31.9 21.1 284s Year 20.2 13.4 284s Employed 13.4 10.1 284s -------------------------------------------------------- 284s Loblolly 84 3 1.481317 284s Outliers: 14 284s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: DET-S 284s 284s Robust Estimate of Location: 284s [1] 24.22 9.65 7.50 284s 284s Robust Estimate of Covariance: 284s height age Seed 284s height 525.08 179.21 14.27 284s age 179.21 61.85 2.94 284s Seed 14.27 2.94 25.86 284s -------------------------------------------------------- 284s quakes 1000 4 1.576855 284s Outliers: 223 284s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 284s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 284s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 284s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 284s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 284s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 284s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 284s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 284s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 284s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 284s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 284s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 284s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 284s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 284s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: DET-S 284s 284s Robust Estimate of Location: 284s [1] -21.54 182.35 369.21 4.54 284s 284s Robust Estimate of Covariance: 284s lat long depth mag 284s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 284s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 284s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 284s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 284s -------------------------------------------------------- 284s =================================================== 284s > ##dodata(method="suser") 284s > ##dodata(method="surreal") 284s > dodata(method="bisquare") 284s 284s Call: dodata(method = "bisquare") 284s Data Set n p LOG(det) Time 284s =================================================== 284s heart 12 2 7.721793 284s Outliers: 3 284s [1] 2 6 12 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s height weight 284s 36.1 29.4 284s 284s Robust Estimate of Covariance: 284s height weight 284s height 109 177 284s weight 177 307 284s -------------------------------------------------------- 284s starsCYG 47 2 -5.942108 284s Outliers: 7 284s [1] 7 9 11 14 20 30 34 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s log.Te log.light 284s 4.42 4.97 284s 284s Robust Estimate of Covariance: 284s log.Te log.light 284s log.Te 0.0164 0.0574 284s log.light 0.0574 0.3613 284s -------------------------------------------------------- 284s phosphor 18 2 9.269096 284s Outliers: 2 284s [1] 1 6 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s inorg organic 284s 14.1 38.7 284s 284s Robust Estimate of Covariance: 284s inorg organic 284s inorg 173 189 284s organic 189 268 284s -------------------------------------------------------- 284s stackloss 21 3 8.411100 284s Outliers: 3 284s [1] 1 2 3 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s Air.Flow Water.Temp Acid.Conc. 284s 57.5 20.5 86.0 284s 284s Robust Estimate of Covariance: 284s Air.Flow Water.Temp Acid.Conc. 284s Air.Flow 33.82 10.17 20.02 284s Water.Temp 10.17 8.70 6.84 284s Acid.Conc. 20.02 6.84 35.51 284s -------------------------------------------------------- 284s coleman 20 5 4.722046 284s Outliers: 2 284s [1] 6 10 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s salaryP fatherWc sstatus teacherSc motherLev 284s 2.77 45.59 4.14 25.13 6.39 284s 284s Robust Estimate of Covariance: 284s salaryP fatherWc sstatus teacherSc motherLev 284s salaryP 0.2135 1.8732 1.3883 0.2547 0.0648 284s fatherWc 1.8732 905.6704 296.1916 7.9820 21.0848 284s sstatus 1.3883 296.1916 128.9536 4.0196 7.1917 284s teacherSc 0.2547 7.9820 4.0196 0.7321 0.2799 284s motherLev 0.0648 21.0848 7.1917 0.2799 0.5592 284s -------------------------------------------------------- 284s salinity 28 3 4.169963 284s Outliers: 4 284s [1] 5 16 23 24 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s X1 X2 X3 284s 10.30 3.07 22.59 284s 284s Robust Estimate of Covariance: 284s X1 X2 X3 284s X1 12.234 0.748 -3.369 284s X2 0.748 4.115 -1.524 284s X3 -3.369 -1.524 2.655 284s -------------------------------------------------------- 284s wood 20 5 -33.862485 284s Outliers: 5 284s [1] 4 6 8 11 19 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s x1 x2 x3 x4 x5 284s 0.580 0.123 0.530 0.538 0.890 284s 284s Robust Estimate of Covariance: 284s x1 x2 x3 x4 x5 284s x1 5.88e-03 9.96e-04 1.43e-03 -1.96e-04 -5.46e-04 284s x2 9.96e-04 2.86e-04 5.89e-04 -5.78e-05 -2.24e-06 284s x3 1.43e-03 5.89e-04 3.42e-03 -6.95e-04 1.43e-05 284s x4 -1.96e-04 -5.78e-05 -6.95e-04 2.23e-03 1.35e-03 284s x5 -5.46e-04 -2.24e-06 1.43e-05 1.35e-03 1.65e-03 284s -------------------------------------------------------- 284s hbk 75 3 1.472421 284s Outliers: 14 284s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s X1 X2 X3 284s 1.53 1.83 1.66 284s 284s Robust Estimate of Covariance: 284s X1 X2 X3 284s X1 1.6775 0.0447 0.2268 284s X2 0.0447 1.6865 0.2325 284s X3 0.2268 0.2325 1.6032 284s -------------------------------------------------------- 284s Animals 28 2 18.528307 284s Outliers: 11 284s [1] 2 6 7 9 12 14 15 16 24 25 28 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s body brain 284s 30.7 84.1 284s 284s Robust Estimate of Covariance: 284s body brain 284s body 13278 25795 284s brain 25795 58499 284s -------------------------------------------------------- 284s milk 86 8 -24.816943 284s Outliers: 19 284s [1] 1 2 3 11 12 13 14 15 16 17 20 27 41 44 47 70 74 75 77 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s X1 X2 X3 X4 X5 X6 X7 X8 284s 1.03 35.81 32.96 26.04 25.02 24.94 122.79 14.35 284s 284s Robust Estimate of Covariance: 284s X1 X2 X3 X4 X5 X6 X7 284s X1 6.80e-07 2.20e-04 3.70e-04 3.35e-04 3.27e-04 3.30e-04 1.21e-03 284s X2 2.20e-04 1.80e+00 3.96e-01 3.03e-01 2.45e-01 3.27e-01 2.00e+00 284s X3 3.70e-04 3.96e-01 1.27e+00 9.68e-01 9.49e-01 9.56e-01 1.37e+00 284s X4 3.35e-04 3.03e-01 9.68e-01 7.86e-01 7.55e-01 7.57e-01 1.15e+00 284s X5 3.27e-04 2.45e-01 9.49e-01 7.55e-01 7.88e-01 7.61e-01 1.14e+00 284s X6 3.30e-04 3.27e-01 9.56e-01 7.57e-01 7.61e-01 7.90e-01 1.17e+00 284s X7 1.21e-03 2.00e+00 1.37e+00 1.15e+00 1.14e+00 1.17e+00 5.71e+00 284s X8 6.57e-05 2.71e-01 2.30e-01 1.64e-01 1.48e-01 1.57e-01 5.27e-01 284s X8 284s X1 6.57e-05 284s X2 2.71e-01 284s X3 2.30e-01 284s X4 1.64e-01 284s X5 1.48e-01 284s X6 1.57e-01 284s X7 5.27e-01 284s X8 1.62e-01 284s -------------------------------------------------------- 284s bushfire 38 5 21.704243 284s Outliers: 13 284s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s V1 V2 V3 V4 V5 284s 108 149 266 216 278 284s 284s Robust Estimate of Covariance: 284s V1 V2 V3 V4 V5 284s V1 528 398 -2298 -497 -410 284s V2 398 340 -1445 -285 -244 284s V3 -2298 -1445 14026 3348 2687 284s V4 -497 -285 3348 857 676 284s V5 -410 -244 2687 676 537 284s -------------------------------------------------------- 284s rice 105 5 -7.346939 284s Outliers: 8 284s [1] 9 14 40 42 49 57 58 71 284s ------------- 284s 284s Call: 284s CovSest(x = x, method = method) 284s -> Method: S-estimates: bisquare 284s 284s Robust Estimate of Location: 284s Favor Appearance Taste Stickiness Toughness 284s -0.2480 0.1203 -0.1213 0.0710 0.0644 284s 284s Robust Estimate of Covariance: 284s Favor Appearance Taste Stickiness Toughness 284s Favor 0.415 0.338 0.419 0.398 -0.198 284s Appearance 0.338 0.580 0.559 0.539 -0.310 284s Taste 0.419 0.559 0.725 0.693 -0.386 284s Stickiness 0.398 0.539 0.693 0.859 -0.487 284s Toughness -0.198 -0.310 -0.386 -0.487 0.457 284s -------------------------------------------------------- 285s hemophilia 75 2 -7.465173 285s Outliers: 2 285s [1] 11 36 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s AHFactivity AHFantigen 285s -0.2128 -0.0366 285s 285s Robust Estimate of Covariance: 285s AHFactivity AHFantigen 285s AHFactivity 0.0321 0.0115 285s AHFantigen 0.0115 0.0220 285s -------------------------------------------------------- 285s fish 159 6 13.465134 285s Outliers: 35 285s [1] 38 61 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 285s [20] 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 142 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s Weight Length1 Length2 Length3 Height Width 285s 381.4 25.6 27.8 30.8 31.0 14.9 285s 285s Robust Estimate of Covariance: 285s Weight Length1 Length2 Length3 Height Width 285s Weight 111094.92 2440.81 2626.59 2976.92 1129.78 95.85 285s Length1 2440.81 57.63 61.75 68.98 20.67 2.46 285s Length2 2626.59 61.75 66.28 74.24 23.13 2.57 285s Length3 2976.92 68.98 74.24 85.29 34.11 1.65 285s Height 1129.78 20.67 23.13 34.11 52.75 -3.70 285s Width 95.85 2.46 2.57 1.65 -3.70 1.71 285s -------------------------------------------------------- 285s airquality 153 4 21.282926 285s Outliers: 8 285s [1] 7 11 14 23 30 34 77 107 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s Ozone Solar.R Wind Temp 285s 39.40 192.29 9.66 78.74 285s 285s Robust Estimate of Covariance: 285s Ozone Solar.R Wind Temp 285s Ozone 930.566 849.644 -59.157 232.459 285s Solar.R 849.644 9207.569 0.594 168.122 285s Wind -59.157 0.594 10.783 -13.645 285s Temp 232.459 168.122 -13.645 92.048 285s -------------------------------------------------------- 285s attitude 30 7 28.084183 285s Outliers: 6 285s [1] 6 9 14 16 18 24 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s rating complaints privileges learning raises critical 285s 65.7 66.8 51.9 56.1 66.4 76.7 285s advance 285s 43.0 285s 285s Robust Estimate of Covariance: 285s rating complaints privileges learning raises critical advance 285s rating 143.88 114.95 64.97 105.69 83.95 6.96 41.78 285s complaints 114.95 143.84 79.28 115.00 101.48 19.69 66.13 285s privileges 64.97 79.28 126.38 94.70 73.87 5.37 61.07 285s learning 105.69 115.00 94.70 146.14 110.50 21.67 68.49 285s raises 83.95 101.48 73.87 110.50 115.01 24.91 77.16 285s critical 6.96 19.69 5.37 21.67 24.91 71.74 25.88 285s advance 41.78 66.13 61.07 68.49 77.16 25.88 97.71 285s -------------------------------------------------------- 285s attenu 182 5 10.109049 285s Outliers: 35 285s [1] 2 4 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 285s [20] 28 29 30 31 32 64 65 80 93 94 95 96 97 98 99 100 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s event mag station dist accel 285s 16.418 5.850 60.243 27.307 0.134 285s 285s Robust Estimate of Covariance: 285s event mag station dist accel 285s event 41.9000 -2.3543 137.8110 -39.0321 -0.0447 285s mag -2.3543 0.4978 -6.4461 5.2644 0.0118 285s station 137.8110 -6.4461 1283.9675 -90.1657 0.5554 285s dist -39.0321 5.2644 -90.1657 462.3898 -1.3672 285s accel -0.0447 0.0118 0.5554 -1.3672 0.0114 285s -------------------------------------------------------- 285s USJudgeRatings 43 12 -43.367499 285s Outliers: 10 285s [1] 5 7 8 12 13 14 20 23 31 35 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 285s 7.43 8.16 7.75 7.89 7.69 7.76 7.68 7.67 7.52 7.59 8.19 7.87 285s 285s Robust Estimate of Covariance: 285s CONT INTG DMNR DILG CFMG DECI PREP FAMI 285s CONT 0.6895 -0.2399 -0.3728 -0.1514 -0.0461 -0.0801 -0.1419 -0.1577 285s INTG -0.2399 0.5021 0.6746 0.5446 0.4479 0.4254 0.5564 0.5558 285s DMNR -0.3728 0.6746 0.9753 0.7128 0.5992 0.5715 0.7289 0.7181 285s DILG -0.1514 0.5446 0.7128 0.6691 0.5789 0.5501 0.6949 0.6892 285s CFMG -0.0461 0.4479 0.5992 0.5789 0.5468 0.5118 0.6100 0.6049 285s DECI -0.0801 0.4254 0.5715 0.5501 0.5118 0.4965 0.5872 0.5890 285s PREP -0.1419 0.5564 0.7289 0.6949 0.6100 0.5872 0.7497 0.7511 285s FAMI -0.1577 0.5558 0.7181 0.6892 0.6049 0.5890 0.7511 0.7696 285s ORAL -0.1950 0.5848 0.7798 0.6990 0.6143 0.5921 0.7508 0.7610 285s WRIT -0.1866 0.5747 0.7575 0.6946 0.6101 0.5895 0.7470 0.7607 285s PHYS -0.1620 0.3640 0.4878 0.4361 0.3927 0.3910 0.4655 0.4779 285s RTEN -0.2522 0.6268 0.8462 0.7220 0.6210 0.5991 0.7553 0.7599 285s ORAL WRIT PHYS RTEN 285s CONT -0.1950 -0.1866 -0.1620 -0.2522 285s INTG 0.5848 0.5747 0.3640 0.6268 285s DMNR 0.7798 0.7575 0.4878 0.8462 285s DILG 0.6990 0.6946 0.4361 0.7220 285s CFMG 0.6143 0.6101 0.3927 0.6210 285s DECI 0.5921 0.5895 0.3910 0.5991 285s PREP 0.7508 0.7470 0.4655 0.7553 285s FAMI 0.7610 0.7607 0.4779 0.7599 285s ORAL 0.7745 0.7665 0.4893 0.7866 285s WRIT 0.7665 0.7645 0.4823 0.7745 285s PHYS 0.4893 0.4823 0.3620 0.5062 285s RTEN 0.7866 0.7745 0.5062 0.8313 285s -------------------------------------------------------- 285s USArrests 50 4 19.266763 285s Outliers: 4 285s [1] 2 28 33 39 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s Murder Assault UrbanPop Rape 285s 7.04 150.55 64.64 19.34 285s 285s Robust Estimate of Covariance: 285s Murder Assault UrbanPop Rape 285s Murder 23.7 378.9 19.1 29.5 285s Assault 378.9 8388.2 601.3 639.7 285s UrbanPop 19.1 601.3 245.3 77.9 285s Rape 29.5 639.7 77.9 76.3 285s -------------------------------------------------------- 285s longley 16 7 13.789499 285s Outliers: 4 285s [1] 1 2 3 4 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s GNP.deflator GNP Unemployed Armed.Forces Population 285s 107 435 333 293 120 285s Year Employed 285s 1957 67 285s 285s Robust Estimate of Covariance: 285s GNP.deflator GNP Unemployed Armed.Forces Population 285s GNP.deflator 65.05 619.75 734.33 -294.02 48.27 285s GNP 619.75 6112.14 6578.12 -2684.52 474.26 285s Unemployed 734.33 6578.12 12075.90 -3627.79 548.58 285s Armed.Forces -294.02 -2684.52 -3627.79 1797.05 -204.25 285s Population 48.27 474.26 548.58 -204.25 37.36 285s Year 30.58 297.29 351.44 -135.53 23.29 285s Employed 20.36 203.96 186.62 -93.64 15.42 285s Year Employed 285s GNP.deflator 30.58 20.36 285s GNP 297.29 203.96 285s Unemployed 351.44 186.62 285s Armed.Forces -135.53 -93.64 285s Population 23.29 15.42 285s Year 14.70 9.80 285s Employed 9.80 7.36 285s -------------------------------------------------------- 285s Loblolly 84 3 8.518440 285s Outliers: 14 285s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s height age Seed 285s 24.14 9.62 7.51 285s 285s Robust Estimate of Covariance: 285s height age Seed 285s height 464.64 158.43 12.83 285s age 158.43 54.62 2.67 285s Seed 12.83 2.67 22.98 285s -------------------------------------------------------- 285s quakes 1000 4 11.611413 285s Outliers: 234 285s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 285s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 285s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 285s [46] 163 166 170 174 192 205 222 226 230 239 243 250 251 252 254 285s [61] 258 263 267 268 271 283 292 297 300 301 305 311 312 318 320 285s [76] 321 325 328 330 331 334 352 357 360 365 368 376 381 382 384 285s [91] 389 399 400 402 408 413 416 417 418 419 426 429 437 441 443 285s [106] 453 456 467 474 477 490 492 496 504 507 508 509 517 524 527 285s [121] 528 531 532 534 536 538 539 541 542 543 544 545 546 547 552 285s [136] 553 558 560 570 571 581 583 587 593 594 596 597 605 612 613 285s [151] 618 620 625 629 638 642 647 649 653 655 656 672 675 681 686 285s [166] 699 701 702 712 714 716 721 725 726 735 744 753 754 756 759 285s [181] 765 766 769 779 781 782 785 787 797 804 813 825 827 837 840 285s [196] 844 852 853 857 860 865 866 869 870 872 873 883 884 887 888 285s [211] 890 891 893 908 909 912 915 916 921 927 930 952 962 963 969 285s [226] 974 980 982 986 987 988 992 997 1000 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: bisquare 285s 285s Robust Estimate of Location: 285s lat long depth mag 285s -21.54 182.35 369.29 4.54 285s 285s Robust Estimate of Covariance: 285s lat long depth mag 285s lat 2.18e+01 4.82e+00 2.53e+02 -3.54e-01 285s long 4.82e+00 5.87e+00 -4.63e+02 7.45e-02 285s depth 2.53e+02 -4.63e+02 6.51e+04 -2.10e+01 285s mag -3.54e-01 7.45e-02 -2.10e+01 1.83e-01 285s -------------------------------------------------------- 285s =================================================== 285s > dodata(method="rocke") 285s 285s Call: dodata(method = "rocke") 285s Data Set n p LOG(det) Time 285s =================================================== 285s heart 12 2 7.285196 285s Outliers: 3 285s [1] 2 6 12 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s height weight 285s 34.3 26.1 285s 285s Robust Estimate of Covariance: 285s height weight 285s height 105 159 285s weight 159 256 285s -------------------------------------------------------- 285s starsCYG 47 2 -5.929361 285s Outliers: 7 285s [1] 7 9 11 14 20 30 34 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s log.Te log.light 285s 4.42 4.93 285s 285s Robust Estimate of Covariance: 285s log.Te log.light 285s log.Te 0.0193 0.0709 285s log.light 0.0709 0.3987 285s -------------------------------------------------------- 285s phosphor 18 2 8.907518 285s Outliers: 3 285s [1] 1 6 10 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s inorg organic 285s 15.8 39.4 285s 285s Robust Estimate of Covariance: 285s inorg organic 285s inorg 196 252 285s organic 252 360 285s -------------------------------------------------------- 285s stackloss 21 3 8.143313 285s Outliers: 4 285s [1] 1 2 3 21 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s Air.Flow Water.Temp Acid.Conc. 285s 56.8 20.2 86.4 285s 285s Robust Estimate of Covariance: 285s Air.Flow Water.Temp Acid.Conc. 285s Air.Flow 29.26 9.62 14.78 285s Water.Temp 9.62 8.54 6.25 285s Acid.Conc. 14.78 6.25 29.70 285s -------------------------------------------------------- 285s coleman 20 5 4.001659 285s Outliers: 5 285s [1] 2 6 9 10 13 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s salaryP fatherWc sstatus teacherSc motherLev 285s 2.81 40.27 2.11 25.01 6.27 285s 285s Robust Estimate of Covariance: 285s salaryP fatherWc sstatus teacherSc motherLev 285s salaryP 0.2850 1.1473 2.0254 0.3536 0.0737 285s fatherWc 1.1473 798.0714 278.0145 6.4590 18.6357 285s sstatus 2.0254 278.0145 128.7601 4.0666 6.3845 285s teacherSc 0.3536 6.4590 4.0666 0.8749 0.2980 285s motherLev 0.0737 18.6357 6.3845 0.2980 0.4948 285s -------------------------------------------------------- 285s salinity 28 3 3.455146 285s Outliers: 9 285s [1] 3 5 10 11 15 16 17 23 24 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s X1 X2 X3 285s 9.89 3.10 22.46 285s 285s Robust Estimate of Covariance: 285s X1 X2 X3 285s X1 12.710 1.868 -4.135 285s X2 1.868 4.710 -0.663 285s X3 -4.135 -0.663 1.907 285s -------------------------------------------------------- 285s wood 20 5 -35.020244 285s Outliers: 7 285s [1] 4 6 7 8 11 16 19 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s x1 x2 x3 x4 x5 285s 0.588 0.123 0.534 0.535 0.891 285s 285s Robust Estimate of Covariance: 285s x1 x2 x3 x4 x5 285s x1 6.60e-03 1.25e-03 2.16e-03 -3.73e-04 -1.10e-03 285s x2 1.25e-03 3.30e-04 8.91e-04 -1.23e-05 2.62e-05 285s x3 2.16e-03 8.91e-04 4.55e-03 -4.90e-04 1.93e-04 285s x4 -3.73e-04 -1.23e-05 -4.90e-04 2.01e-03 1.36e-03 285s x5 -1.10e-03 2.62e-05 1.93e-04 1.36e-03 1.95e-03 285s -------------------------------------------------------- 285s hbk 75 3 1.413303 285s Outliers: 14 285s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s X1 X2 X3 285s 1.56 1.77 1.68 285s 285s Robust Estimate of Covariance: 285s X1 X2 X3 285s X1 1.6483 0.0825 0.2133 285s X2 0.0825 1.6928 0.2334 285s X3 0.2133 0.2334 1.5334 285s -------------------------------------------------------- 285s Animals 28 2 17.787210 285s Outliers: 11 285s [1] 2 6 7 9 12 14 15 16 24 25 28 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s body brain 285s 60.6 150.2 285s 285s Robust Estimate of Covariance: 285s body brain 285s body 10670 19646 285s brain 19646 41147 285s -------------------------------------------------------- 285s milk 86 8 -25.169970 285s Outliers: 22 285s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 28 41 44 47 70 73 74 75 77 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s X1 X2 X3 X4 X5 X6 X7 X8 285s 1.03 35.87 33.14 26.19 25.17 25.11 123.16 14.41 285s 285s Robust Estimate of Covariance: 285s X1 X2 X3 X4 X5 X6 X7 285s X1 4.47e-07 1.77e-04 1.94e-04 1.79e-04 1.60e-04 1.45e-04 6.45e-04 285s X2 1.77e-04 2.36e+00 4.03e-01 3.08e-01 2.08e-01 3.45e-01 2.18e+00 285s X3 1.94e-04 4.03e-01 1.13e+00 8.31e-01 8.08e-01 7.79e-01 9.83e-01 285s X4 1.79e-04 3.08e-01 8.31e-01 6.62e-01 6.22e-01 5.95e-01 7.82e-01 285s X5 1.60e-04 2.08e-01 8.08e-01 6.22e-01 6.51e-01 5.93e-01 7.60e-01 285s X6 1.45e-04 3.45e-01 7.79e-01 5.95e-01 5.93e-01 5.88e-01 7.81e-01 285s X7 6.45e-04 2.18e+00 9.83e-01 7.82e-01 7.60e-01 7.81e-01 4.81e+00 285s X8 2.47e-05 2.57e-01 2.00e-01 1.37e-01 1.13e-01 1.28e-01 4.38e-01 285s X8 285s X1 2.47e-05 285s X2 2.57e-01 285s X3 2.00e-01 285s X4 1.37e-01 285s X5 1.13e-01 285s X6 1.28e-01 285s X7 4.38e-01 285s X8 1.61e-01 285s -------------------------------------------------------- 285s bushfire 38 5 21.641566 285s Outliers: 13 285s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s V1 V2 V3 V4 V5 285s 111 150 256 214 276 285s 285s Robust Estimate of Covariance: 285s V1 V2 V3 V4 V5 285s V1 554 408 -2321 -464 -393 285s V2 408 343 -1361 -244 -215 285s V3 -2321 -1361 14690 3277 2684 285s V4 -464 -244 3277 783 629 285s V5 -393 -215 2684 629 509 285s -------------------------------------------------------- 285s rice 105 5 -7.208835 285s Outliers: 8 285s [1] 9 14 40 42 49 57 58 71 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s Favor Appearance Taste Stickiness Toughness 285s -0.21721 0.20948 -0.04581 0.15355 -0.00254 285s 285s Robust Estimate of Covariance: 285s Favor Appearance Taste Stickiness Toughness 285s Favor 0.432 0.337 0.417 0.382 -0.201 285s Appearance 0.337 0.591 0.553 0.510 -0.295 285s Taste 0.417 0.553 0.735 0.683 -0.385 285s Stickiness 0.382 0.510 0.683 0.834 -0.462 285s Toughness -0.201 -0.295 -0.385 -0.462 0.408 285s -------------------------------------------------------- 285s hemophilia 75 2 -7.453807 285s Outliers: 2 285s [1] 46 53 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s AHFactivity AHFantigen 285s -0.2276 -0.0637 285s 285s Robust Estimate of Covariance: 285s AHFactivity AHFantigen 285s AHFactivity 0.0405 0.0221 285s AHFantigen 0.0221 0.0263 285s -------------------------------------------------------- 285s fish 159 6 13.110263 285s Outliers: 47 285s [1] 38 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 285s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 285s [39] 98 99 100 101 102 103 104 140 142 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s Weight Length1 Length2 Length3 Height Width 285s 452.1 27.2 29.5 32.6 30.8 15.0 285s 285s Robust Estimate of Covariance: 285s Weight Length1 Length2 Length3 Height Width 285s Weight 132559.85 2817.97 3035.69 3369.07 1231.68 112.19 285s Length1 2817.97 64.16 68.74 75.36 22.52 2.37 285s Length2 3035.69 68.74 73.77 81.12 25.57 2.47 285s Length3 3369.07 75.36 81.12 91.65 37.39 1.40 285s Height 1231.68 22.52 25.57 37.39 50.91 -3.92 285s Width 112.19 2.37 2.47 1.40 -3.92 1.87 285s -------------------------------------------------------- 285s airquality 153 4 21.181656 285s Outliers: 13 285s [1] 6 7 11 14 17 20 23 30 34 53 63 77 107 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s Ozone Solar.R Wind Temp 285s 40.21 198.33 9.76 79.35 285s 285s Robust Estimate of Covariance: 285s Ozone Solar.R Wind Temp 285s Ozone 885.7 581.1 -57.3 226.4 285s Solar.R 581.1 8870.9 26.2 -15.1 285s Wind -57.3 26.2 11.8 -13.4 285s Temp 226.4 -15.1 -13.4 89.4 285s -------------------------------------------------------- 285s attitude 30 7 27.836398 285s Outliers: 8 285s [1] 1 9 13 14 17 18 24 26 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s rating complaints privileges learning raises critical 285s 64.0 65.4 50.5 54.9 63.1 72.6 285s advance 285s 40.5 285s 285s Robust Estimate of Covariance: 285s rating complaints privileges learning raises critical advance 285s rating 180.10 153.16 42.04 128.90 90.25 18.75 39.81 285s complaints 153.16 192.38 58.32 142.48 94.29 8.13 45.33 285s privileges 42.04 58.32 113.65 82.31 69.53 23.13 61.96 285s learning 128.90 142.48 82.31 156.99 101.74 13.22 49.64 285s raises 90.25 94.29 69.53 101.74 110.85 47.84 55.76 285s critical 18.75 8.13 23.13 13.22 47.84 123.00 36.97 285s advance 39.81 45.33 61.96 49.64 55.76 36.97 53.59 285s -------------------------------------------------------- 285s attenu 182 5 9.726797 285s Outliers: 44 285s [1] 1 2 4 5 6 7 8 9 10 11 13 15 16 19 20 21 22 23 24 285s [20] 25 27 28 29 30 31 32 40 45 60 61 64 65 78 80 81 93 94 95 285s [39] 96 97 98 99 100 108 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s event mag station dist accel 285s 16.39 5.82 60.89 27.97 0.12 285s 285s Robust Estimate of Covariance: 285s event mag station dist accel 285s event 4.20e+01 -1.97e+00 1.44e+02 -3.50e+01 4.05e-02 285s mag -1.97e+00 5.05e-01 -4.78e+00 4.63e+00 4.19e-03 285s station 1.44e+02 -4.78e+00 1.47e+03 -5.74e+01 7.88e-01 285s dist -3.50e+01 4.63e+00 -5.74e+01 3.99e+02 -1.18e+00 285s accel 4.05e-02 4.19e-03 7.88e-01 -1.18e+00 7.71e-03 285s -------------------------------------------------------- 285s USJudgeRatings 43 12 -46.356873 285s Outliers: 15 285s [1] 1 5 7 8 12 13 14 17 20 21 23 30 31 35 42 285s ------------- 285s 285s Call: 285s CovSest(x = x, method = method) 285s -> Method: S-estimates: Rocke type 285s 285s Robust Estimate of Location: 285s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 285s 7.56 8.12 7.70 7.91 7.74 7.82 7.66 7.66 7.50 7.58 8.22 7.86 285s 285s Robust Estimate of Covariance: 285s CONT INTG DMNR DILG CFMG DECI PREP 285s CONT 0.63426 -0.20121 -0.31858 -0.09578 0.00521 -0.00436 -0.07140 285s INTG -0.20121 0.28326 0.37540 0.27103 0.20362 0.19838 0.25706 285s DMNR -0.31858 0.37540 0.58265 0.33615 0.25649 0.24804 0.31696 285s DILG -0.09578 0.27103 0.33615 0.32588 0.27022 0.26302 0.32236 285s CFMG 0.00521 0.20362 0.25649 0.27022 0.25929 0.24217 0.27784 285s DECI -0.00436 0.19838 0.24804 0.26302 0.24217 0.23830 0.27284 285s PREP -0.07140 0.25706 0.31696 0.32236 0.27784 0.27284 0.35071 285s FAMI -0.07118 0.25858 0.29511 0.32582 0.27863 0.27657 0.35941 285s ORAL -0.11149 0.27055 0.33919 0.31768 0.27339 0.26739 0.34200 285s WRIT -0.10050 0.26857 0.32570 0.32327 0.27860 0.27201 0.34399 285s PHYS -0.09693 0.15339 0.18416 0.17089 0.13837 0.14895 0.18472 285s RTEN -0.15643 0.31793 0.40884 0.33863 0.27073 0.26854 0.34049 285s FAMI ORAL WRIT PHYS RTEN 285s CONT -0.07118 -0.11149 -0.10050 -0.09693 -0.15643 285s INTG 0.25858 0.27055 0.26857 0.15339 0.31793 285s DMNR 0.29511 0.33919 0.32570 0.18416 0.40884 285s DILG 0.32582 0.31768 0.32327 0.17089 0.33863 285s CFMG 0.27863 0.27339 0.27860 0.13837 0.27073 285s DECI 0.27657 0.26739 0.27201 0.14895 0.26854 285s PREP 0.35941 0.34200 0.34399 0.18472 0.34049 285s FAMI 0.38378 0.35617 0.36094 0.19998 0.35048 285s ORAL 0.35617 0.34918 0.34808 0.19759 0.35217 285s WRIT 0.36094 0.34808 0.35242 0.19666 0.35090 285s PHYS 0.19998 0.19759 0.19666 0.14770 0.20304 285s RTEN 0.35048 0.35217 0.35090 0.20304 0.39451 285s -------------------------------------------------------- 286s USArrests 50 4 19.206310 286s Outliers: 4 286s [1] 2 28 33 39 286s ------------- 286s 286s Call: 286s CovSest(x = x, method = method) 286s -> Method: S-estimates: Rocke type 286s 286s Robust Estimate of Location: 286s Murder Assault UrbanPop Rape 286s 7.55 160.94 65.10 19.97 286s 286s Robust Estimate of Covariance: 286s Murder Assault UrbanPop Rape 286s Murder 25.6 409.5 23.4 32.1 286s Assault 409.5 8530.9 676.9 669.4 286s UrbanPop 23.4 676.9 269.9 76.6 286s Rape 32.1 669.4 76.6 76.6 286s -------------------------------------------------------- 286s longley 16 7 13.387132 286s Outliers: 4 286s [1] 1 2 3 4 286s ------------- 286s 286s Call: 286s CovSest(x = x, method = method) 286s -> Method: S-estimates: Rocke type 286s 286s Robust Estimate of Location: 286s GNP.deflator GNP Unemployed Armed.Forces Population 286s 105.5 422.4 318.3 299.7 119.5 286s Year Employed 286s 1956.1 66.5 286s 286s Robust Estimate of Covariance: 286s GNP.deflator GNP Unemployed Armed.Forces Population 286s GNP.deflator 59.97 582.66 694.99 -237.75 46.12 286s GNP 582.66 5849.82 6383.68 -2207.26 461.15 286s Unemployed 694.99 6383.68 11155.03 -3104.18 534.25 286s Armed.Forces -237.75 -2207.26 -3104.18 1429.11 -171.28 286s Population 46.12 461.15 534.25 -171.28 36.79 286s Year 29.01 287.48 340.95 -112.61 22.85 286s Employed 18.99 193.66 186.31 -76.88 14.94 286s Year Employed 286s GNP.deflator 29.01 18.99 286s GNP 287.48 193.66 286s Unemployed 340.95 186.31 286s Armed.Forces -112.61 -76.88 286s Population 22.85 14.94 286s Year 14.36 9.45 286s Employed 9.45 6.90 286s -------------------------------------------------------- 286s Loblolly 84 3 7.757906 286s Outliers: 27 286s [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 286s [26] 83 84 286s ------------- 286s 286s Call: 286s CovSest(x = x, method = method) 286s -> Method: S-estimates: Rocke type 286s 286s Robust Estimate of Location: 286s height age Seed 286s 21.72 8.60 7.58 286s 286s Robust Estimate of Covariance: 286s height age Seed 286s height 316.590 102.273 5.939 286s age 102.273 33.465 -0.121 286s Seed 5.939 -0.121 27.203 286s -------------------------------------------------------- 286s quakes 1000 4 11.473431 286s Outliers: 237 286s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 286s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 286s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 286s [46] 163 166 170 174 176 192 205 222 226 230 239 243 244 250 251 286s [61] 252 254 258 263 267 268 271 283 292 297 300 301 305 311 312 286s [76] 318 320 321 325 328 330 331 334 352 357 360 365 368 376 381 286s [91] 382 384 389 399 400 402 408 410 413 416 417 418 419 426 429 286s [106] 437 441 443 453 456 467 474 477 490 492 496 504 507 508 509 286s [121] 517 524 527 528 531 532 534 536 538 539 541 542 543 544 545 286s [136] 546 547 552 553 558 560 570 571 581 583 587 593 594 596 597 286s [151] 605 612 613 618 620 625 629 638 642 647 649 653 655 656 672 286s [166] 675 681 686 699 701 702 712 714 716 721 725 726 735 744 753 286s [181] 754 756 759 765 766 769 779 781 782 785 787 797 804 813 825 286s [196] 827 837 840 844 852 853 857 860 865 866 869 870 872 873 883 286s [211] 884 887 888 890 891 893 908 909 912 915 916 921 927 930 952 286s [226] 962 963 969 974 980 982 986 987 988 992 997 1000 286s ------------- 286s 286s Call: 286s CovSest(x = x, method = method) 286s -> Method: S-estimates: Rocke type 286s 286s Robust Estimate of Location: 286s lat long depth mag 286s -21.45 182.54 351.18 4.55 286s 286s Robust Estimate of Covariance: 286s lat long depth mag 286s lat 2.10e+01 4.66e+00 2.45e+02 -3.38e-01 286s long 4.66e+00 5.88e+00 -4.63e+02 9.36e-02 286s depth 2.45e+02 -4.63e+02 6.38e+04 -2.02e+01 286s mag -3.38e-01 9.36e-02 -2.02e+01 1.78e-01 286s -------------------------------------------------------- 286s =================================================== 286s > dodata(method="MM") 286s 286s Call: dodata(method = "MM") 286s Data Set n p LOG(det) Time 286s =================================================== 286s heart 12 2 2.017701 286s Outliers: 1 286s [1] 6 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s height weight 286s 40.0 37.7 286s 286s Robust Estimate of Covariance: 286s height weight 286s height 99.2 205.7 286s weight 205.7 458.9 286s -------------------------------------------------------- 286s starsCYG 47 2 -1.450032 286s Outliers: 7 286s [1] 7 9 11 14 20 30 34 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s log.Te log.light 286s 4.41 4.94 286s 286s Robust Estimate of Covariance: 286s log.Te log.light 286s log.Te 0.0180 0.0526 286s log.light 0.0526 0.3217 286s -------------------------------------------------------- 286s phosphor 18 2 2.320721 286s Outliers: 1 286s [1] 6 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s inorg organic 286s 12.3 41.4 286s 286s Robust Estimate of Covariance: 286s inorg organic 286s inorg 94.2 67.2 286s organic 67.2 162.1 286s -------------------------------------------------------- 286s stackloss 21 3 1.470031 286s Outliers: 0 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s Air.Flow Water.Temp Acid.Conc. 286s 60.2 21.0 86.4 286s 286s Robust Estimate of Covariance: 286s Air.Flow Water.Temp Acid.Conc. 286s Air.Flow 81.13 21.99 23.15 286s Water.Temp 21.99 10.01 6.43 286s Acid.Conc. 23.15 6.43 27.22 286s -------------------------------------------------------- 286s coleman 20 5 0.491419 286s Outliers: 1 286s [1] 10 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s salaryP fatherWc sstatus teacherSc motherLev 286s 2.74 43.14 3.65 25.07 6.32 286s 286s Robust Estimate of Covariance: 286s salaryP fatherWc sstatus teacherSc motherLev 286s salaryP 0.1878 2.0635 1.0433 0.2721 0.0582 286s fatherWc 2.0635 670.2232 211.0609 4.3625 15.6083 286s sstatus 1.0433 211.0609 92.8743 2.6532 5.1816 286s teacherSc 0.2721 4.3625 2.6532 1.2757 0.1613 286s motherLev 0.0582 15.6083 5.1816 0.1613 0.4192 286s -------------------------------------------------------- 286s salinity 28 3 0.734619 286s Outliers: 2 286s [1] 5 16 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s X1 X2 X3 286s 10.46 2.66 23.15 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 286s X1 10.079 -0.024 -1.899 286s X2 -0.024 3.466 -1.817 286s X3 -1.899 -1.817 3.665 286s -------------------------------------------------------- 286s wood 20 5 -3.202636 286s Outliers: 0 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s x1 x2 x3 x4 x5 286s 0.550 0.133 0.506 0.511 0.909 286s 286s Robust Estimate of Covariance: 286s x1 x2 x3 x4 x5 286s x1 0.008454 -0.000377 0.003720 0.002874 -0.003065 286s x2 -0.000377 0.000516 -0.000399 -0.000933 0.000645 286s x3 0.003720 -0.000399 0.004186 0.001720 -0.001714 286s x4 0.002874 -0.000933 0.001720 0.003993 -0.001028 286s x5 -0.003065 0.000645 -0.001714 -0.001028 0.002744 286s -------------------------------------------------------- 286s hbk 75 3 0.283145 286s Outliers: 14 286s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s X1 X2 X3 286s 1.54 1.79 1.68 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 286s X1 1.8016 0.0739 0.2000 286s X2 0.0739 1.8301 0.2295 286s X3 0.2000 0.2295 1.7101 286s -------------------------------------------------------- 286s Animals 28 2 4.685129 286s Outliers: 10 286s [1] 2 6 7 9 12 14 15 16 24 25 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s body brain 286s 82 148 286s 286s Robust Estimate of Covariance: 286s body brain 286s body 21050 24534 286s brain 24534 35135 286s -------------------------------------------------------- 286s milk 86 8 -1.437863 286s Outliers: 12 286s [1] 1 2 3 12 13 17 41 44 47 70 74 75 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s X1 X2 X3 X4 X5 X6 X7 X8 286s 1.03 35.73 32.87 25.96 24.94 24.85 122.55 14.33 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 X4 X5 X6 X7 286s X1 1.08e-06 5.36e-04 6.80e-04 5.96e-04 5.87e-04 5.91e-04 2.22e-03 286s X2 5.36e-04 2.42e+00 7.07e-01 5.51e-01 4.89e-01 5.70e-01 3.08e+00 286s X3 6.80e-04 7.07e-01 1.64e+00 1.28e+00 1.25e+00 1.26e+00 2.38e+00 286s X4 5.96e-04 5.51e-01 1.28e+00 1.05e+00 1.01e+00 1.02e+00 2.01e+00 286s X5 5.87e-04 4.89e-01 1.25e+00 1.01e+00 1.05e+00 1.02e+00 1.96e+00 286s X6 5.91e-04 5.70e-01 1.26e+00 1.02e+00 1.02e+00 1.05e+00 2.01e+00 286s X7 2.22e-03 3.08e+00 2.38e+00 2.01e+00 1.96e+00 2.01e+00 9.22e+00 286s X8 1.68e-04 4.13e-01 3.37e-01 2.53e-01 2.34e-01 2.43e-01 8.81e-01 286s X8 286s X1 1.68e-04 286s X2 4.13e-01 286s X3 3.37e-01 286s X4 2.53e-01 286s X5 2.34e-01 286s X6 2.43e-01 286s X7 8.81e-01 286s X8 2.11e-01 286s -------------------------------------------------------- 286s bushfire 38 5 2.443148 286s Outliers: 12 286s [1] 8 9 10 11 31 32 33 34 35 36 37 38 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s V1 V2 V3 V4 V5 286s 109 149 258 215 276 286s 286s Robust Estimate of Covariance: 286s V1 V2 V3 V4 V5 286s V1 708 538 -2705 -558 -464 286s V2 538 497 -1376 -248 -216 286s V3 -2705 -1376 20521 4833 3914 286s V4 -558 -248 4833 1217 969 286s V5 -464 -216 3914 969 778 286s -------------------------------------------------------- 286s rice 105 5 -0.724874 286s Outliers: 5 286s [1] 9 42 49 58 71 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s Favor Appearance Taste Stickiness Toughness 286s -0.2653 0.0969 -0.1371 0.0483 0.0731 286s 286s Robust Estimate of Covariance: 286s Favor Appearance Taste Stickiness Toughness 286s Favor 0.421 0.349 0.427 0.405 -0.191 286s Appearance 0.349 0.605 0.565 0.553 -0.316 286s Taste 0.427 0.565 0.725 0.701 -0.378 286s Stickiness 0.405 0.553 0.701 0.868 -0.484 286s Toughness -0.191 -0.316 -0.378 -0.484 0.464 286s -------------------------------------------------------- 286s hemophilia 75 2 -1.868949 286s Outliers: 2 286s [1] 11 36 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s AHFactivity AHFantigen 286s -0.2342 -0.0333 286s 286s Robust Estimate of Covariance: 286s AHFactivity AHFantigen 286s AHFactivity 0.0309 0.0122 286s AHFantigen 0.0122 0.0231 286s -------------------------------------------------------- 286s fish 159 6 1.285876 286s Outliers: 20 286s [1] 61 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 286s [20] 142 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s Weight Length1 Length2 Length3 Height Width 286s 352.7 24.3 26.4 29.2 29.7 14.6 286s 286s Robust Estimate of Covariance: 286s Weight Length1 Length2 Length3 Height Width 286s Weight 1.20e+05 2.89e+03 3.12e+03 3.51e+03 1.49e+03 2.83e+02 286s Length1 2.89e+03 7.73e+01 8.35e+01 9.28e+01 3.73e+01 9.26e+00 286s Length2 3.12e+03 8.35e+01 9.04e+01 1.01e+02 4.16e+01 1.01e+01 286s Length3 3.51e+03 9.28e+01 1.01e+02 1.14e+02 5.37e+01 1.01e+01 286s Height 1.49e+03 3.73e+01 4.16e+01 5.37e+01 6.75e+01 3.22e+00 286s Width 2.83e+02 9.26e+00 1.01e+01 1.01e+01 3.22e+00 4.18e+00 286s -------------------------------------------------------- 286s airquality 153 4 2.684374 286s Outliers: 6 286s [1] 7 14 23 30 34 77 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s Ozone Solar.R Wind Temp 286s 40.35 186.21 9.86 78.09 286s 286s Robust Estimate of Covariance: 286s Ozone Solar.R Wind Temp 286s Ozone 951.0 959.9 -62.5 224.6 286s Solar.R 959.9 8629.9 -28.1 244.9 286s Wind -62.5 -28.1 11.6 -15.8 286s Temp 224.6 244.9 -15.8 93.1 286s -------------------------------------------------------- 286s attitude 30 7 2.091968 286s Outliers: 4 286s [1] 14 16 18 24 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s rating complaints privileges learning raises critical 286s 65.0 66.5 52.4 56.2 65.3 75.6 286s advance 286s 42.7 286s 286s Robust Estimate of Covariance: 286s rating complaints privileges learning raises critical advance 286s rating 143.5 123.4 62.4 92.5 79.2 17.7 28.2 286s complaints 123.4 159.8 83.9 99.7 96.0 27.3 44.0 286s privileges 62.4 83.9 133.5 78.6 62.0 13.4 46.4 286s learning 92.5 99.7 78.6 136.0 90.9 18.9 62.6 286s raises 79.2 96.0 62.0 90.9 107.6 34.6 63.3 286s critical 17.7 27.3 13.4 18.9 34.6 84.9 25.9 286s advance 28.2 44.0 46.4 62.6 63.3 25.9 94.4 286s -------------------------------------------------------- 286s attenu 182 5 1.148032 286s Outliers: 21 286s [1] 2 7 8 9 10 11 15 16 24 25 28 29 30 31 32 64 65 94 95 286s [20] 96 100 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s event mag station dist accel 286s 15.36 5.95 58.11 33.56 0.14 286s 286s Robust Estimate of Covariance: 286s event mag station dist accel 286s event 4.88e+01 -2.74e+00 1.53e+02 -1.14e+02 5.95e-02 286s mag -2.74e+00 5.32e-01 -6.29e+00 1.10e+01 9.37e-03 286s station 1.53e+02 -6.29e+00 1.29e+03 -2.95e+02 1.04e+00 286s dist -1.14e+02 1.10e+01 -2.95e+02 1.13e+03 -2.41e+00 286s accel 5.95e-02 9.37e-03 1.04e+00 -2.41e+00 1.70e-02 286s -------------------------------------------------------- 286s USJudgeRatings 43 12 -1.683847 286s Outliers: 7 286s [1] 5 7 12 13 14 23 31 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 286s 7.45 8.15 7.74 7.87 7.67 7.74 7.65 7.65 7.50 7.57 8.17 7.85 286s 286s Robust Estimate of Covariance: 286s CONT INTG DMNR DILG CFMG DECI PREP FAMI 286s CONT 0.9403 -0.2500 -0.3953 -0.1418 -0.0176 -0.0620 -0.1304 -0.1517 286s INTG -0.2500 0.6314 0.8479 0.6889 0.5697 0.5386 0.7007 0.6985 286s DMNR -0.3953 0.8479 1.2186 0.9027 0.7613 0.7232 0.9191 0.9055 286s DILG -0.1418 0.6889 0.9027 0.8474 0.7344 0.6949 0.8751 0.8655 286s CFMG -0.0176 0.5697 0.7613 0.7344 0.6904 0.6442 0.7683 0.7594 286s DECI -0.0620 0.5386 0.7232 0.6949 0.6442 0.6219 0.7362 0.7360 286s PREP -0.1304 0.7007 0.9191 0.8751 0.7683 0.7362 0.9370 0.9357 286s FAMI -0.1517 0.6985 0.9055 0.8655 0.7594 0.7360 0.9357 0.9547 286s ORAL -0.1866 0.7375 0.9841 0.8816 0.7747 0.7433 0.9400 0.9496 286s WRIT -0.1881 0.7208 0.9516 0.8711 0.7646 0.7357 0.9302 0.9439 286s PHYS -0.1407 0.4673 0.6261 0.5661 0.5105 0.5039 0.5996 0.6112 286s RTEN -0.2494 0.7921 1.0688 0.9167 0.7902 0.7585 0.9533 0.9561 286s ORAL WRIT PHYS RTEN 286s CONT -0.1866 -0.1881 -0.1407 -0.2494 286s INTG 0.7375 0.7208 0.4673 0.7921 286s DMNR 0.9841 0.9516 0.6261 1.0688 286s DILG 0.8816 0.8711 0.5661 0.9167 286s CFMG 0.7747 0.7646 0.5105 0.7902 286s DECI 0.7433 0.7357 0.5039 0.7585 286s PREP 0.9400 0.9302 0.5996 0.9533 286s FAMI 0.9496 0.9439 0.6112 0.9561 286s ORAL 0.9712 0.9558 0.6271 0.9933 286s WRIT 0.9558 0.9483 0.6135 0.9725 286s PHYS 0.6271 0.6135 0.4816 0.6549 286s RTEN 0.9933 0.9725 0.6549 1.0540 286s -------------------------------------------------------- 286s USArrests 50 4 2.411726 286s Outliers: 3 286s [1] 2 33 39 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s Murder Assault UrbanPop Rape 286s 7.52 163.86 65.66 20.64 286s 286s Robust Estimate of Covariance: 286s Murder Assault UrbanPop Rape 286s Murder 19.05 295.96 8.32 23.40 286s Assault 295.96 6905.03 396.53 523.49 286s UrbanPop 8.32 396.53 202.98 62.81 286s Rape 23.40 523.49 62.81 79.10 286s -------------------------------------------------------- 286s longley 16 7 1.038316 286s Outliers: 5 286s [1] 1 2 3 4 5 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s GNP.deflator GNP Unemployed Armed.Forces Population 286s 107.5 440.4 339.4 293.0 120.9 286s Year Employed 286s 1957.0 67.2 286s 286s Robust Estimate of Covariance: 286s GNP.deflator GNP Unemployed Armed.Forces Population 286s GNP.deflator 100.4 953.8 1140.8 -501.8 74.3 286s GNP 953.8 9434.3 10084.3 -4573.8 731.3 286s Unemployed 1140.8 10084.3 19644.6 -6296.3 848.4 286s Armed.Forces -501.8 -4573.8 -6296.3 3192.3 -348.5 286s Population 74.3 731.3 848.4 -348.5 57.7 286s Year 46.3 450.7 537.0 -230.7 35.3 286s Employed 30.8 310.2 273.8 -159.4 23.3 286s Year Employed 286s GNP.deflator 46.3 30.8 286s GNP 450.7 310.2 286s Unemployed 537.0 273.8 286s Armed.Forces -230.7 -159.4 286s Population 35.3 23.3 286s Year 21.9 14.6 286s Employed 14.6 11.2 286s -------------------------------------------------------- 286s Loblolly 84 3 1.481317 286s Outliers: 0 286s ------------- 286s 286s Call: 286s CovMMest(x = x) 286s -> Method: MM-estimates 286s 286s Robust Estimate of Location: 286s height age Seed 286s 31.93 12.79 7.48 286s 286s Robust Estimate of Covariance: 286s height age Seed 286s height 440.644 165.652 6.958 286s age 165.652 63.500 0.681 286s Seed 6.958 0.681 16.564 286s -------------------------------------------------------- 287s quakes 1000 4 1.576855 287s Outliers: 218 287s [1] 7 12 15 17 22 27 32 37 40 41 45 48 53 63 64 287s [16] 73 78 87 91 92 94 99 108 110 117 118 119 120 121 122 287s [31] 126 133 136 141 143 145 148 152 154 155 157 159 160 163 170 287s [46] 192 205 222 226 230 239 243 250 251 252 254 258 263 267 268 287s [61] 271 283 292 300 301 305 311 312 318 320 321 325 328 330 334 287s [76] 352 357 360 365 381 382 384 389 400 402 408 413 416 417 419 287s [91] 429 437 441 443 453 456 467 474 477 490 492 496 504 507 508 287s [106] 509 517 524 527 528 531 532 534 536 538 539 541 542 543 544 287s [121] 545 546 547 552 553 560 571 581 583 587 593 594 596 597 605 287s [136] 612 613 618 620 625 629 638 642 647 649 653 655 656 672 675 287s [151] 681 686 699 701 702 712 714 716 721 725 726 735 744 754 756 287s [166] 759 765 766 769 779 781 782 785 787 797 804 813 825 827 837 287s [181] 840 844 852 853 857 860 865 866 869 870 872 873 883 884 887 287s [196] 888 890 891 893 908 909 912 915 916 921 927 930 962 963 969 287s [211] 974 980 982 986 987 988 997 1000 287s ------------- 287s 287s Call: 287s CovMMest(x = x) 287s -> Method: MM-estimates 287s 287s Robust Estimate of Location: 287s lat long depth mag 287s -21.74 182.37 356.37 4.56 287s 287s Robust Estimate of Covariance: 287s lat long depth mag 287s lat 2.97e+01 6.53e+00 3.46e+02 -4.66e-01 287s long 6.53e+00 6.92e+00 -5.05e+02 5.62e-02 287s depth 3.46e+02 -5.05e+02 7.39e+04 -2.51e+01 287s mag -4.66e-01 5.62e-02 -2.51e+01 2.32e-01 287s -------------------------------------------------------- 287s =================================================== 287s > ##dogen() 287s > ##cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 287s > 287s autopkgtest [22:55:35]: test run-unit-test: -----------------------] 287s run-unit-test PASS 287s autopkgtest [22:55:35]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 288s autopkgtest [22:55:36]: test pkg-r-autopkgtest: preparing testbed 288s Reading package lists... 288s Building dependency tree... 288s Reading state information... 288s Solving dependencies... 288s The following NEW packages will be installed: 288s build-essential cpp cpp-15 cpp-15-powerpc64le-linux-gnu 288s cpp-powerpc64le-linux-gnu dctrl-tools g++ g++-15 288s g++-15-powerpc64le-linux-gnu g++-powerpc64le-linux-gnu gcc gcc-15 288s gcc-15-powerpc64le-linux-gnu gcc-powerpc64le-linux-gnu gfortran gfortran-15 288s gfortran-15-powerpc64le-linux-gnu gfortran-powerpc64le-linux-gnu 288s icu-devtools libasan8 libblas-dev libbz2-dev libc-dev-bin libc6-dev libcc1-0 288s libcrypt-dev libdeflate-dev libgcc-15-dev libgfortran-15-dev libicu-dev 288s libisl23 libitm1 libjpeg-dev libjpeg-turbo8-dev libjpeg8-dev liblapack-dev 288s liblsan0 liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 libpcre2-32-0 288s libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev libquadmath0 288s libreadline-dev libstdc++-15-dev libtirpc-dev libtsan2 libubsan1 libzstd-dev 288s linux-libc-dev pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev rpcsvc-proto 288s zlib1g-dev 288s 0 upgraded, 60 newly installed, 0 to remove and 0 not upgraded. 288s Need to get 104 MB of archives. 288s After this operation, 388 MB of additional disk space will be used. 288s Get:1 http://ftpmaster.internal/ubuntu resolute/main ppc64el libc-dev-bin ppc64el 2.42-2ubuntu4 [23.9 kB] 288s Get:2 http://ftpmaster.internal/ubuntu resolute/main ppc64el linux-libc-dev ppc64el 6.19.0-3.3 [1832 kB] 291s Get:3 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcrypt-dev ppc64el 1:4.5.1-1 [162 kB] 291s Get:4 http://ftpmaster.internal/ubuntu resolute/main ppc64el rpcsvc-proto ppc64el 1.4.3-1build1 [84.2 kB] 291s Get:5 http://ftpmaster.internal/ubuntu resolute/main ppc64el libc6-dev ppc64el 2.42-2ubuntu4 [2080 kB] 294s Get:6 http://ftpmaster.internal/ubuntu resolute/main ppc64el libisl23 ppc64el 0.27-1build1 [893 kB] 295s Get:7 http://ftpmaster.internal/ubuntu resolute/main ppc64el libmpc3 ppc64el 1.3.1-2 [62.5 kB] 295s Get:8 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [11.4 MB] 312s Get:9 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp-15 ppc64el 15.2.0-12ubuntu1 [1038 B] 312s Get:10 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [5746 B] 312s Get:11 http://ftpmaster.internal/ubuntu resolute/main ppc64el cpp ppc64el 4:15.2.0-4ubuntu1 [22.4 kB] 312s Get:12 http://ftpmaster.internal/ubuntu resolute/main ppc64el libcc1-0 ppc64el 15.2.0-12ubuntu1 [49.0 kB] 312s Get:13 http://ftpmaster.internal/ubuntu resolute/main ppc64el libitm1 ppc64el 15.2.0-12ubuntu1 [32.2 kB] 312s Get:14 http://ftpmaster.internal/ubuntu resolute/main ppc64el libasan8 ppc64el 15.2.0-12ubuntu1 [3006 kB] 316s Get:15 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblsan0 ppc64el 15.2.0-12ubuntu1 [1374 kB] 318s Get:16 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtsan2 ppc64el 15.2.0-12ubuntu1 [2729 kB] 323s Get:17 http://ftpmaster.internal/ubuntu resolute/main ppc64el libubsan1 ppc64el 15.2.0-12ubuntu1 [1231 kB] 323s Get:18 http://ftpmaster.internal/ubuntu resolute/main ppc64el libquadmath0 ppc64el 15.2.0-12ubuntu1 [160 kB] 323s Get:19 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgcc-15-dev ppc64el 15.2.0-12ubuntu1 [1670 kB] 326s Get:20 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [22.4 MB] 360s Get:21 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc-15 ppc64el 15.2.0-12ubuntu1 [530 kB] 361s Get:22 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [1220 B] 361s Get:23 http://ftpmaster.internal/ubuntu resolute/main ppc64el gcc ppc64el 4:15.2.0-4ubuntu1 [5032 B] 361s Get:24 http://ftpmaster.internal/ubuntu resolute/main ppc64el libstdc++-15-dev ppc64el 15.2.0-12ubuntu1 [2747 kB] 365s Get:25 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [13.0 MB] 385s Get:26 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++-15 ppc64el 15.2.0-12ubuntu1 [25.3 kB] 385s Get:27 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [970 B] 385s Get:28 http://ftpmaster.internal/ubuntu resolute/main ppc64el g++ ppc64el 4:15.2.0-4ubuntu1 [1092 B] 385s Get:29 http://ftpmaster.internal/ubuntu resolute/main ppc64el build-essential ppc64el 12.12ubuntu2 [5256 B] 385s Get:30 http://ftpmaster.internal/ubuntu resolute/main ppc64el dctrl-tools ppc64el 2.24-3build4 [108 kB] 386s Get:31 http://ftpmaster.internal/ubuntu resolute/main ppc64el libgfortran-15-dev ppc64el 15.2.0-12ubuntu1 [651 kB] 387s Get:32 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran-15-powerpc64le-linux-gnu ppc64el 15.2.0-12ubuntu1 [12.3 MB] 405s Get:33 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran-15 ppc64el 15.2.0-12ubuntu1 [18.1 kB] 405s Get:34 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran-powerpc64le-linux-gnu ppc64el 4:15.2.0-4ubuntu1 [1020 B] 405s Get:35 http://ftpmaster.internal/ubuntu resolute/main ppc64el gfortran ppc64el 4:15.2.0-4ubuntu1 [1166 B] 405s Get:36 http://ftpmaster.internal/ubuntu resolute/main ppc64el icu-devtools ppc64el 78.2-1ubuntu1 [246 kB] 405s Get:37 http://ftpmaster.internal/ubuntu resolute/main ppc64el libblas-dev ppc64el 3.12.1-7ubuntu1 [306 kB] 405s Get:38 http://ftpmaster.internal/ubuntu resolute/main ppc64el libbz2-dev ppc64el 1.0.8-6build2 [50.0 kB] 405s Get:39 http://ftpmaster.internal/ubuntu resolute/main ppc64el libdeflate-dev ppc64el 1.23-2build1 [71.8 kB] 405s Get:40 http://ftpmaster.internal/ubuntu resolute/main ppc64el libicu-dev ppc64el 78.2-1ubuntu1 [13.3 MB] 425s Get:41 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg-turbo8-dev ppc64el 2.1.5-4ubuntu3 [358 kB] 425s Get:42 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg8-dev ppc64el 8c-2ubuntu11 [1484 B] 425s Get:43 http://ftpmaster.internal/ubuntu resolute/main ppc64el libjpeg-dev ppc64el 8c-2ubuntu11 [1486 B] 425s Get:44 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblapack-dev ppc64el 3.12.1-7ubuntu1 [6357 kB] 435s Get:45 http://ftpmaster.internal/ubuntu resolute/main ppc64el libncurses-dev ppc64el 6.6+20251231-1 [505 kB] 435s Get:46 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpcre2-16-0 ppc64el 10.46-1 [292 kB] 435s Get:47 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpcre2-32-0 ppc64el 10.46-1 [275 kB] 435s Get:48 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpcre2-posix3 ppc64el 10.46-1 [7334 B] 435s Get:49 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpcre2-dev ppc64el 10.46-1 [937 kB] 437s Get:50 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpkgconf3 ppc64el 1.8.1-4build1 [37.9 kB] 437s Get:51 http://ftpmaster.internal/ubuntu resolute/main ppc64el zlib1g-dev ppc64el 1:1.3.dfsg+really1.3.1-1ubuntu2 [903 kB] 438s Get:52 http://ftpmaster.internal/ubuntu resolute/main ppc64el libpng-dev ppc64el 1.6.54-1 [326 kB] 438s Get:53 http://ftpmaster.internal/ubuntu resolute/main ppc64el libreadline-dev ppc64el 8.3-3 [252 kB] 438s Get:54 http://ftpmaster.internal/ubuntu resolute/main ppc64el libzstd-dev ppc64el 1.5.7+dfsg-3 [528 kB] 439s Get:55 http://ftpmaster.internal/ubuntu resolute/main ppc64el liblzma-dev ppc64el 5.8.2-2 [210 kB] 439s Get:56 http://ftpmaster.internal/ubuntu resolute/main ppc64el pkgconf-bin ppc64el 1.8.1-4build1 [22.7 kB] 439s Get:57 http://ftpmaster.internal/ubuntu resolute/main ppc64el pkgconf ppc64el 1.8.1-4build1 [16.8 kB] 439s Get:58 http://ftpmaster.internal/ubuntu resolute/main ppc64el libtirpc-dev ppc64el 1.3.6+ds-1 [223 kB] 439s Get:59 http://ftpmaster.internal/ubuntu resolute/universe ppc64el r-base-dev all 4.5.2-1ubuntu2 [1880 B] 439s Get:60 http://ftpmaster.internal/ubuntu resolute/universe ppc64el pkg-r-autopkgtest all 20250812 [6158 B] 439s Fetched 104 MB in 2min 31s (689 kB/s) 439s Selecting previously unselected package libc-dev-bin. 439s (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 ... 124634 files and directories currently installed.) 439s Preparing to unpack .../00-libc-dev-bin_2.42-2ubuntu4_ppc64el.deb ... 439s Unpacking libc-dev-bin (2.42-2ubuntu4) ... 439s Selecting previously unselected package linux-libc-dev:ppc64el. 439s Preparing to unpack .../01-linux-libc-dev_6.19.0-3.3_ppc64el.deb ... 439s Unpacking linux-libc-dev:ppc64el (6.19.0-3.3) ... 439s Selecting previously unselected package libcrypt-dev:ppc64el. 439s Preparing to unpack .../02-libcrypt-dev_1%3a4.5.1-1_ppc64el.deb ... 439s Unpacking libcrypt-dev:ppc64el (1:4.5.1-1) ... 439s Selecting previously unselected package rpcsvc-proto. 439s Preparing to unpack .../03-rpcsvc-proto_1.4.3-1build1_ppc64el.deb ... 439s Unpacking rpcsvc-proto (1.4.3-1build1) ... 439s Selecting previously unselected package libc6-dev:ppc64el. 439s Preparing to unpack .../04-libc6-dev_2.42-2ubuntu4_ppc64el.deb ... 439s Unpacking libc6-dev:ppc64el (2.42-2ubuntu4) ... 439s Selecting previously unselected package libisl23:ppc64el. 439s Preparing to unpack .../05-libisl23_0.27-1build1_ppc64el.deb ... 439s Unpacking libisl23:ppc64el (0.27-1build1) ... 439s Selecting previously unselected package libmpc3:ppc64el. 439s Preparing to unpack .../06-libmpc3_1.3.1-2_ppc64el.deb ... 439s Unpacking libmpc3:ppc64el (1.3.1-2) ... 439s Selecting previously unselected package cpp-15-powerpc64le-linux-gnu. 440s Preparing to unpack .../07-cpp-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking cpp-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package cpp-15. 440s Preparing to unpack .../08-cpp-15_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking cpp-15 (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package cpp-powerpc64le-linux-gnu. 440s Preparing to unpack .../09-cpp-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 440s Unpacking cpp-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 440s Selecting previously unselected package cpp. 440s Preparing to unpack .../10-cpp_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 440s Unpacking cpp (4:15.2.0-4ubuntu1) ... 440s Selecting previously unselected package libcc1-0:ppc64el. 440s Preparing to unpack .../11-libcc1-0_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libcc1-0:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package libitm1:ppc64el. 440s Preparing to unpack .../12-libitm1_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libitm1:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package libasan8:ppc64el. 440s Preparing to unpack .../13-libasan8_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libasan8:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package liblsan0:ppc64el. 440s Preparing to unpack .../14-liblsan0_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking liblsan0:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package libtsan2:ppc64el. 440s Preparing to unpack .../15-libtsan2_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libtsan2:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package libubsan1:ppc64el. 440s Preparing to unpack .../16-libubsan1_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libubsan1:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package libquadmath0:ppc64el. 440s Preparing to unpack .../17-libquadmath0_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libquadmath0:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package libgcc-15-dev:ppc64el. 440s Preparing to unpack .../18-libgcc-15-dev_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libgcc-15-dev:ppc64el (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package gcc-15-powerpc64le-linux-gnu. 440s Preparing to unpack .../19-gcc-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking gcc-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package gcc-15. 440s Preparing to unpack .../20-gcc-15_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking gcc-15 (15.2.0-12ubuntu1) ... 440s Selecting previously unselected package gcc-powerpc64le-linux-gnu. 440s Preparing to unpack .../21-gcc-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 440s Unpacking gcc-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 440s Selecting previously unselected package gcc. 440s Preparing to unpack .../22-gcc_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 440s Unpacking gcc (4:15.2.0-4ubuntu1) ... 440s Selecting previously unselected package libstdc++-15-dev:ppc64el. 440s Preparing to unpack .../23-libstdc++-15-dev_15.2.0-12ubuntu1_ppc64el.deb ... 440s Unpacking libstdc++-15-dev:ppc64el (15.2.0-12ubuntu1) ... 441s Selecting previously unselected package g++-15-powerpc64le-linux-gnu. 441s Preparing to unpack .../24-g++-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ... 441s Unpacking g++-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 441s Selecting previously unselected package g++-15. 441s Preparing to unpack .../25-g++-15_15.2.0-12ubuntu1_ppc64el.deb ... 441s Unpacking g++-15 (15.2.0-12ubuntu1) ... 441s Selecting previously unselected package g++-powerpc64le-linux-gnu. 441s Preparing to unpack .../26-g++-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 441s Unpacking g++-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 441s Selecting previously unselected package g++. 441s Preparing to unpack .../27-g++_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 441s Unpacking g++ (4:15.2.0-4ubuntu1) ... 441s Selecting previously unselected package build-essential. 441s Preparing to unpack .../28-build-essential_12.12ubuntu2_ppc64el.deb ... 441s Unpacking build-essential (12.12ubuntu2) ... 441s Selecting previously unselected package dctrl-tools. 441s Preparing to unpack .../29-dctrl-tools_2.24-3build4_ppc64el.deb ... 441s Unpacking dctrl-tools (2.24-3build4) ... 441s Selecting previously unselected package libgfortran-15-dev:ppc64el. 441s Preparing to unpack .../30-libgfortran-15-dev_15.2.0-12ubuntu1_ppc64el.deb ... 441s Unpacking libgfortran-15-dev:ppc64el (15.2.0-12ubuntu1) ... 441s Selecting previously unselected package gfortran-15-powerpc64le-linux-gnu. 441s Preparing to unpack .../31-gfortran-15-powerpc64le-linux-gnu_15.2.0-12ubuntu1_ppc64el.deb ... 441s Unpacking gfortran-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 441s Selecting previously unselected package gfortran-15. 441s Preparing to unpack .../32-gfortran-15_15.2.0-12ubuntu1_ppc64el.deb ... 441s Unpacking gfortran-15 (15.2.0-12ubuntu1) ... 441s Selecting previously unselected package gfortran-powerpc64le-linux-gnu. 441s Preparing to unpack .../33-gfortran-powerpc64le-linux-gnu_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 441s Unpacking gfortran-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 441s Selecting previously unselected package gfortran. 441s Preparing to unpack .../34-gfortran_4%3a15.2.0-4ubuntu1_ppc64el.deb ... 441s Unpacking gfortran (4:15.2.0-4ubuntu1) ... 441s Selecting previously unselected package icu-devtools. 441s Preparing to unpack .../35-icu-devtools_78.2-1ubuntu1_ppc64el.deb ... 441s Unpacking icu-devtools (78.2-1ubuntu1) ... 441s Selecting previously unselected package libblas-dev:ppc64el. 441s Preparing to unpack .../36-libblas-dev_3.12.1-7ubuntu1_ppc64el.deb ... 441s Unpacking libblas-dev:ppc64el (3.12.1-7ubuntu1) ... 441s Selecting previously unselected package libbz2-dev:ppc64el. 441s Preparing to unpack .../37-libbz2-dev_1.0.8-6build2_ppc64el.deb ... 441s Unpacking libbz2-dev:ppc64el (1.0.8-6build2) ... 441s Selecting previously unselected package libdeflate-dev:ppc64el. 441s Preparing to unpack .../38-libdeflate-dev_1.23-2build1_ppc64el.deb ... 441s Unpacking libdeflate-dev:ppc64el (1.23-2build1) ... 441s Selecting previously unselected package libicu-dev:ppc64el. 441s Preparing to unpack .../39-libicu-dev_78.2-1ubuntu1_ppc64el.deb ... 441s Unpacking libicu-dev:ppc64el (78.2-1ubuntu1) ... 441s Selecting previously unselected package libjpeg-turbo8-dev:ppc64el. 442s Preparing to unpack .../40-libjpeg-turbo8-dev_2.1.5-4ubuntu3_ppc64el.deb ... 442s Unpacking libjpeg-turbo8-dev:ppc64el (2.1.5-4ubuntu3) ... 442s Selecting previously unselected package libjpeg8-dev:ppc64el. 442s Preparing to unpack .../41-libjpeg8-dev_8c-2ubuntu11_ppc64el.deb ... 442s Unpacking libjpeg8-dev:ppc64el (8c-2ubuntu11) ... 442s Selecting previously unselected package libjpeg-dev:ppc64el. 442s Preparing to unpack .../42-libjpeg-dev_8c-2ubuntu11_ppc64el.deb ... 442s Unpacking libjpeg-dev:ppc64el (8c-2ubuntu11) ... 442s Selecting previously unselected package liblapack-dev:ppc64el. 442s Preparing to unpack .../43-liblapack-dev_3.12.1-7ubuntu1_ppc64el.deb ... 442s Unpacking liblapack-dev:ppc64el (3.12.1-7ubuntu1) ... 442s Selecting previously unselected package libncurses-dev:ppc64el. 442s Preparing to unpack .../44-libncurses-dev_6.6+20251231-1_ppc64el.deb ... 442s Unpacking libncurses-dev:ppc64el (6.6+20251231-1) ... 442s Selecting previously unselected package libpcre2-16-0:ppc64el. 442s Preparing to unpack .../45-libpcre2-16-0_10.46-1_ppc64el.deb ... 442s Unpacking libpcre2-16-0:ppc64el (10.46-1) ... 442s Selecting previously unselected package libpcre2-32-0:ppc64el. 442s Preparing to unpack .../46-libpcre2-32-0_10.46-1_ppc64el.deb ... 442s Unpacking libpcre2-32-0:ppc64el (10.46-1) ... 442s Selecting previously unselected package libpcre2-posix3:ppc64el. 442s Preparing to unpack .../47-libpcre2-posix3_10.46-1_ppc64el.deb ... 442s Unpacking libpcre2-posix3:ppc64el (10.46-1) ... 442s Selecting previously unselected package libpcre2-dev:ppc64el. 442s Preparing to unpack .../48-libpcre2-dev_10.46-1_ppc64el.deb ... 442s Unpacking libpcre2-dev:ppc64el (10.46-1) ... 442s Selecting previously unselected package libpkgconf3:ppc64el. 442s Preparing to unpack .../49-libpkgconf3_1.8.1-4build1_ppc64el.deb ... 442s Unpacking libpkgconf3:ppc64el (1.8.1-4build1) ... 442s Selecting previously unselected package zlib1g-dev:ppc64el. 442s Preparing to unpack .../50-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu2_ppc64el.deb ... 442s Unpacking zlib1g-dev:ppc64el (1:1.3.dfsg+really1.3.1-1ubuntu2) ... 442s Selecting previously unselected package libpng-dev:ppc64el. 442s Preparing to unpack .../51-libpng-dev_1.6.54-1_ppc64el.deb ... 442s Unpacking libpng-dev:ppc64el (1.6.54-1) ... 442s Selecting previously unselected package libreadline-dev:ppc64el. 442s Preparing to unpack .../52-libreadline-dev_8.3-3_ppc64el.deb ... 442s Unpacking libreadline-dev:ppc64el (8.3-3) ... 442s Selecting previously unselected package libzstd-dev:ppc64el. 442s Preparing to unpack .../53-libzstd-dev_1.5.7+dfsg-3_ppc64el.deb ... 442s Unpacking libzstd-dev:ppc64el (1.5.7+dfsg-3) ... 442s Selecting previously unselected package liblzma-dev:ppc64el. 442s Preparing to unpack .../54-liblzma-dev_5.8.2-2_ppc64el.deb ... 442s Unpacking liblzma-dev:ppc64el (5.8.2-2) ... 442s Selecting previously unselected package pkgconf-bin. 442s Preparing to unpack .../55-pkgconf-bin_1.8.1-4build1_ppc64el.deb ... 442s Unpacking pkgconf-bin (1.8.1-4build1) ... 442s Selecting previously unselected package pkgconf:ppc64el. 442s Preparing to unpack .../56-pkgconf_1.8.1-4build1_ppc64el.deb ... 442s Unpacking pkgconf:ppc64el (1.8.1-4build1) ... 442s Selecting previously unselected package libtirpc-dev:ppc64el. 442s Preparing to unpack .../57-libtirpc-dev_1.3.6+ds-1_ppc64el.deb ... 442s Unpacking libtirpc-dev:ppc64el (1.3.6+ds-1) ... 442s Selecting previously unselected package r-base-dev. 442s Preparing to unpack .../58-r-base-dev_4.5.2-1ubuntu2_all.deb ... 442s Unpacking r-base-dev (4.5.2-1ubuntu2) ... 442s Selecting previously unselected package pkg-r-autopkgtest. 442s Preparing to unpack .../59-pkg-r-autopkgtest_20250812_all.deb ... 442s Unpacking pkg-r-autopkgtest (20250812) ... 442s Setting up libzstd-dev:ppc64el (1.5.7+dfsg-3) ... 442s Setting up linux-libc-dev:ppc64el (6.19.0-3.3) ... 442s Setting up libpcre2-16-0:ppc64el (10.46-1) ... 442s Setting up libpcre2-32-0:ppc64el (10.46-1) ... 442s Setting up libtirpc-dev:ppc64el (1.3.6+ds-1) ... 442s Setting up libpkgconf3:ppc64el (1.8.1-4build1) ... 442s Setting up rpcsvc-proto (1.4.3-1build1) ... 442s Setting up libquadmath0:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libmpc3:ppc64el (1.3.1-2) ... 442s Setting up icu-devtools (78.2-1ubuntu1) ... 442s Setting up pkgconf-bin (1.8.1-4build1) ... 442s Setting up liblzma-dev:ppc64el (5.8.2-2) ... 442s Setting up libubsan1:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libpcre2-posix3:ppc64el (10.46-1) ... 442s Setting up libcrypt-dev:ppc64el (1:4.5.1-1) ... 442s Setting up libasan8:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libtsan2:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libisl23:ppc64el (0.27-1build1) ... 442s Setting up libc-dev-bin (2.42-2ubuntu4) ... 442s Setting up libdeflate-dev:ppc64el (1.23-2build1) ... 442s Setting up libcc1-0:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up liblsan0:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libblas-dev:ppc64el (3.12.1-7ubuntu1) ... 442s update-alternatives: using /usr/lib/powerpc64le-linux-gnu/blas/libblas.so to provide /usr/lib/powerpc64le-linux-gnu/libblas.so (libblas.so-powerpc64le-linux-gnu) in auto mode 442s Setting up dctrl-tools (2.24-3build4) ... 442s Setting up libitm1:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libgcc-15-dev:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up libgfortran-15-dev:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up pkgconf:ppc64el (1.8.1-4build1) ... 442s Setting up cpp-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 442s Setting up liblapack-dev:ppc64el (3.12.1-7ubuntu1) ... 442s update-alternatives: using /usr/lib/powerpc64le-linux-gnu/lapack/liblapack.so to provide /usr/lib/powerpc64le-linux-gnu/liblapack.so (liblapack.so-powerpc64le-linux-gnu) in auto mode 442s Setting up cpp-15 (15.2.0-12ubuntu1) ... 442s Setting up libc6-dev:ppc64el (2.42-2ubuntu4) ... 442s Setting up libicu-dev:ppc64el (78.2-1ubuntu1) ... 442s Setting up cpp-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 442s Setting up libbz2-dev:ppc64el (1.0.8-6build2) ... 442s Setting up libjpeg-turbo8-dev:ppc64el (2.1.5-4ubuntu3) ... 442s Setting up libncurses-dev:ppc64el (6.6+20251231-1) ... 442s Setting up libpcre2-dev:ppc64el (10.46-1) ... 442s Setting up gcc-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 442s Setting up libreadline-dev:ppc64el (8.3-3) ... 442s Setting up gcc-15 (15.2.0-12ubuntu1) ... 442s Setting up libstdc++-15-dev:ppc64el (15.2.0-12ubuntu1) ... 442s Setting up gcc-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 442s Setting up gfortran-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 442s Setting up zlib1g-dev:ppc64el (1:1.3.dfsg+really1.3.1-1ubuntu2) ... 442s Setting up cpp (4:15.2.0-4ubuntu1) ... 442s Setting up libjpeg8-dev:ppc64el (8c-2ubuntu11) ... 442s Setting up gfortran-15 (15.2.0-12ubuntu1) ... 442s Setting up g++-15-powerpc64le-linux-gnu (15.2.0-12ubuntu1) ... 442s Setting up libpng-dev:ppc64el (1.6.54-1) ... 442s Setting up libjpeg-dev:ppc64el (8c-2ubuntu11) ... 442s Setting up gcc (4:15.2.0-4ubuntu1) ... 442s Setting up gfortran-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 442s Setting up g++-15 (15.2.0-12ubuntu1) ... 442s Setting up g++-powerpc64le-linux-gnu (4:15.2.0-4ubuntu1) ... 442s Setting up gfortran (4:15.2.0-4ubuntu1) ... 442s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 442s 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 442s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 442s 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 442s Setting up g++ (4:15.2.0-4ubuntu1) ... 442s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 442s Setting up build-essential (12.12ubuntu2) ... 442s Setting up r-base-dev (4.5.2-1ubuntu2) ... 442s Setting up pkg-r-autopkgtest (20250812) ... 442s Processing triggers for libc-bin (2.42-2ubuntu4) ... 442s Processing triggers for man-db (2.13.1-1build1) ... 443s Processing triggers for install-info (7.2-5) ... 446s autopkgtest [22:58:14]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 446s autopkgtest [22:58:14]: test pkg-r-autopkgtest: [----------------------- 446s Test: Try to load the R library rrcov 446s 446s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 446s Copyright (C) 2025 The R Foundation for Statistical Computing 446s Platform: powerpc64le-unknown-linux-gnu 446s 446s R is free software and comes with ABSOLUTELY NO WARRANTY. 446s You are welcome to redistribute it under certain conditions. 446s Type 'license()' or 'licence()' for distribution details. 446s 446s R is a collaborative project with many contributors. 446s Type 'contributors()' for more information and 446s 'citation()' on how to cite R or R packages in publications. 446s 446s Type 'demo()' for some demos, 'help()' for on-line help, or 446s 'help.start()' for an HTML browser interface to help. 446s Type 'q()' to quit R. 446s 446s > library('rrcov') 446s Loading required package: robustbase 447s Scalable Robust Estimators with High Breakdown Point (version 1.7-6) 447s 447s > 447s autopkgtest [22:58:15]: test pkg-r-autopkgtest: -----------------------] 447s pkg-r-autopkgtest PASS 447s autopkgtest [22:58:15]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 448s autopkgtest [22:58:16]: @@@@@@@@@@@@@@@@@@@@ summary 448s run-unit-test PASS 448s pkg-r-autopkgtest PASS