0s autopkgtest [16:35:59]: starting date and time: 2025-03-15 16:35:59+0000 0s autopkgtest [16:35:59]: git checkout: 325255d2 Merge branch 'pin-any-arch' into 'ubuntu/production' 0s autopkgtest [16:35:59]: host juju-7f2275-prod-proposed-migration-environment-9; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.sj0ukdv1/out --timeout-copy=6000 --setup-commands 'ln -s /dev/null /etc/systemd/system/bluetooth.service; printf "http_proxy=http://squid.internal:3128\nhttps_proxy=http://squid.internal:3128\nno_proxy=127.0.0.1,127.0.1.1,login.ubuntu.com,localhost,localdomain,novalocal,internal,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,keyserver.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,10.24.0.0/24,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com,radosgw.ps5.canonical.com\n" >> /etc/environment' --apt-pocket=proposed=src:glibc --apt-upgrade r-cran-rrcov --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=glibc/2.41-1ubuntu2 -- lxd -r lxd-armhf-10.145.243.142 lxd-armhf-10.145.243.142:autopkgtest/ubuntu/plucky/armhf 20s autopkgtest [16:36:19]: testbed dpkg architecture: armhf 21s autopkgtest [16:36:20]: testbed apt version: 2.9.33 25s autopkgtest [16:36:24]: @@@@@@@@@@@@@@@@@@@@ test bed setup 27s autopkgtest [16:36:26]: testbed release detected to be: None 34s autopkgtest [16:36:33]: updating testbed package index (apt update) 36s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed InRelease [126 kB] 37s Get:2 http://ftpmaster.internal/ubuntu plucky InRelease [257 kB] 37s Get:3 http://ftpmaster.internal/ubuntu plucky-updates InRelease [126 kB] 37s Get:4 http://ftpmaster.internal/ubuntu plucky-security InRelease [126 kB] 37s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/main Sources [99.7 kB] 38s Get:6 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse Sources [15.8 kB] 38s Get:7 http://ftpmaster.internal/ubuntu plucky-proposed/universe Sources [379 kB] 38s Get:8 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf Packages [114 kB] 38s Get:9 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf c-n-f Metadata [1832 B] 38s Get:10 http://ftpmaster.internal/ubuntu plucky-proposed/restricted armhf c-n-f Metadata [116 B] 38s Get:11 http://ftpmaster.internal/ubuntu plucky-proposed/universe armhf Packages [312 kB] 39s Get:12 http://ftpmaster.internal/ubuntu plucky-proposed/universe armhf c-n-f Metadata [11.1 kB] 39s Get:13 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse armhf Packages [3472 B] 39s Get:14 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse armhf c-n-f Metadata [240 B] 39s Get:15 http://ftpmaster.internal/ubuntu plucky/universe Sources [21.0 MB] 63s Get:16 http://ftpmaster.internal/ubuntu plucky/multiverse Sources [299 kB] 64s Get:17 http://ftpmaster.internal/ubuntu plucky/main Sources [1394 kB] 65s Get:18 http://ftpmaster.internal/ubuntu plucky/main armhf Packages [1378 kB] 67s Get:19 http://ftpmaster.internal/ubuntu plucky/main armhf c-n-f Metadata [29.4 kB] 67s Get:20 http://ftpmaster.internal/ubuntu plucky/restricted armhf c-n-f Metadata [108 B] 67s Get:21 http://ftpmaster.internal/ubuntu plucky/universe armhf Packages [15.1 MB] 85s Get:22 http://ftpmaster.internal/ubuntu plucky/multiverse armhf Packages [172 kB] 87s Fetched 41.0 MB in 50s (816 kB/s) 88s Reading package lists... 94s autopkgtest [16:37:33]: upgrading testbed (apt dist-upgrade and autopurge) 95s Reading package lists... 96s Building dependency tree... 96s Reading state information... 96s Calculating upgrade...Starting pkgProblemResolver with broken count: 0 96s Starting 2 pkgProblemResolver with broken count: 0 96s Done 97s Entering ResolveByKeep 98s 98s Calculating upgrade... 98s The following packages will be upgraded: 98s libc-bin libc6 locales pinentry-curses python3-jinja2 sos strace 98s 7 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 98s Need to get 8683 kB of archives. 98s After this operation, 23.6 kB of additional disk space will be used. 98s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc6 armhf 2.41-1ubuntu2 [2932 kB] 102s Get:2 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc-bin armhf 2.41-1ubuntu2 [545 kB] 102s Get:3 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf locales all 2.41-1ubuntu2 [4246 kB] 107s Get:4 http://ftpmaster.internal/ubuntu plucky/main armhf strace armhf 6.13+ds-1ubuntu1 [445 kB] 108s Get:5 http://ftpmaster.internal/ubuntu plucky/main armhf pinentry-curses armhf 1.3.1-2ubuntu3 [40.6 kB] 108s Get:6 http://ftpmaster.internal/ubuntu plucky/main armhf python3-jinja2 all 3.1.5-2ubuntu1 [109 kB] 108s Get:7 http://ftpmaster.internal/ubuntu plucky/main armhf sos all 4.9.0-5 [365 kB] 109s Preconfiguring packages ... 109s Fetched 8683 kB in 10s (865 kB/s) 109s (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 ... 64655 files and directories currently installed.) 109s Preparing to unpack .../libc6_2.41-1ubuntu2_armhf.deb ... 109s Unpacking libc6:armhf (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 109s Setting up libc6:armhf (2.41-1ubuntu2) ... 110s (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 ... 64655 files and directories currently installed.) 110s Preparing to unpack .../libc-bin_2.41-1ubuntu2_armhf.deb ... 110s Unpacking libc-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 110s Setting up libc-bin (2.41-1ubuntu2) ... 110s (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 ... 64655 files and directories currently installed.) 110s Preparing to unpack .../locales_2.41-1ubuntu2_all.deb ... 110s Unpacking locales (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 110s Preparing to unpack .../strace_6.13+ds-1ubuntu1_armhf.deb ... 110s Unpacking strace (6.13+ds-1ubuntu1) over (6.11-0ubuntu1) ... 110s Preparing to unpack .../pinentry-curses_1.3.1-2ubuntu3_armhf.deb ... 110s Unpacking pinentry-curses (1.3.1-2ubuntu3) over (1.3.1-2ubuntu2) ... 110s Preparing to unpack .../python3-jinja2_3.1.5-2ubuntu1_all.deb ... 111s Unpacking python3-jinja2 (3.1.5-2ubuntu1) over (3.1.5-2) ... 111s Preparing to unpack .../archives/sos_4.9.0-5_all.deb ... 111s Unpacking sos (4.9.0-5) over (4.9.0-4) ... 111s Setting up sos (4.9.0-5) ... 112s Setting up pinentry-curses (1.3.1-2ubuntu3) ... 112s Setting up locales (2.41-1ubuntu2) ... 112s Generating locales (this might take a while)... 114s en_US.UTF-8... done 114s Generation complete. 114s Setting up python3-jinja2 (3.1.5-2ubuntu1) ... 114s Setting up strace (6.13+ds-1ubuntu1) ... 114s Processing triggers for man-db (2.13.0-1) ... 116s Processing triggers for systemd (257.3-1ubuntu3) ... 118s Reading package lists... 118s Building dependency tree... 118s Reading state information... 119s Starting pkgProblemResolver with broken count: 0 119s Starting 2 pkgProblemResolver with broken count: 0 119s Done 119s Solving dependencies... 120s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 122s autopkgtest [16:38:01]: rebooting testbed after setup commands that affected boot 161s autopkgtest [16:38:40]: testbed running kernel: Linux 6.8.0-52-generic #53~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Wed Jan 15 18:10:51 UTC 2 184s autopkgtest [16:39:03]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-rrcov 197s Get:1 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (dsc) [2146 B] 197s Get:2 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (tar) [1542 kB] 197s Get:3 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (diff) [3160 B] 197s gpgv: Signature made Fri Sep 6 03:10:50 2024 UTC 197s gpgv: using RSA key 73471499CC60ED9EEE805946C5BD6C8F2295D502 197s gpgv: issuer "plessy@debian.org" 197s gpgv: Can't check signature: No public key 197s dpkg-source: warning: cannot verify inline signature for ./r-cran-rrcov_1.7-6-1.dsc: no acceptable signature found 197s autopkgtest [16:39:16]: testing package r-cran-rrcov version 1.7-6-1 199s autopkgtest [16:39:18]: build not needed 202s autopkgtest [16:39:21]: test run-unit-test: preparing testbed 204s Reading package lists... 204s Building dependency tree... 204s Reading state information... 205s Starting pkgProblemResolver with broken count: 0 205s Starting 2 pkgProblemResolver with broken count: 0 205s Done 206s The following NEW packages will be installed: 206s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono libblas3 206s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libfreetype6 libgfortran5 206s libgomp1 libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 206s libjpeg8 liblapack3 liblerc4 libpango-1.0-0 libpangocairo-1.0-0 206s libpangoft2-1.0-0 libpaper-utils libpaper2 libpixman-1-0 libsharpyuv0 libsm6 206s libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 206s libxcb-shm0 libxft2 libxrender1 libxss1 libxt6t64 r-base-core 206s r-cran-deoptimr r-cran-lattice r-cran-mass r-cran-mvtnorm r-cran-pcapp 206s r-cran-robustbase r-cran-rrcov unzip x11-common xdg-utils zip 206s 0 upgraded, 52 newly installed, 0 to remove and 0 not upgraded. 206s Need to get 46.8 MB of archives. 206s After this operation, 81.9 MB of additional disk space will be used. 206s Get:1 http://ftpmaster.internal/ubuntu plucky/main armhf libfreetype6 armhf 2.13.3+dfsg-1 [330 kB] 207s Get:2 http://ftpmaster.internal/ubuntu plucky/main armhf fonts-dejavu-mono all 2.37-8 [502 kB] 207s Get:3 http://ftpmaster.internal/ubuntu plucky/main armhf fonts-dejavu-core all 2.37-8 [835 kB] 208s Get:4 http://ftpmaster.internal/ubuntu plucky/main armhf fontconfig-config armhf 2.15.0-2ubuntu1 [37.5 kB] 208s Get:5 http://ftpmaster.internal/ubuntu plucky/main armhf libfontconfig1 armhf 2.15.0-2ubuntu1 [114 kB] 208s Get:6 http://ftpmaster.internal/ubuntu plucky/main armhf fontconfig armhf 2.15.0-2ubuntu1 [190 kB] 208s Get:7 http://ftpmaster.internal/ubuntu plucky/main armhf libblas3 armhf 3.12.1-2 [132 kB] 208s Get:8 http://ftpmaster.internal/ubuntu plucky/main armhf libpixman-1-0 armhf 0.44.0-3 [183 kB] 209s Get:9 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-render0 armhf 1.17.0-2 [15.3 kB] 209s Get:10 http://ftpmaster.internal/ubuntu plucky/main armhf libxcb-shm0 armhf 1.17.0-2 [5774 B] 209s Get:11 http://ftpmaster.internal/ubuntu plucky/main armhf libxrender1 armhf 1:0.9.10-1.1build1 [16.0 kB] 209s Get:12 http://ftpmaster.internal/ubuntu plucky/main armhf libcairo2 armhf 1.18.2-2 [484 kB] 209s Get:13 http://ftpmaster.internal/ubuntu plucky/main armhf libdatrie1 armhf 0.2.13-3build1 [15.7 kB] 209s Get:14 http://ftpmaster.internal/ubuntu plucky/main armhf libdeflate0 armhf 1.23-1 [38.5 kB] 209s Get:15 http://ftpmaster.internal/ubuntu plucky/main armhf libgfortran5 armhf 15-20250222-0ubuntu1 [330 kB] 210s Get:16 http://ftpmaster.internal/ubuntu plucky/main armhf libgomp1 armhf 15-20250222-0ubuntu1 [128 kB] 210s Get:17 http://ftpmaster.internal/ubuntu plucky/main armhf libgraphite2-3 armhf 1.3.14-2ubuntu1 [64.8 kB] 210s Get:18 http://ftpmaster.internal/ubuntu plucky/main armhf libharfbuzz0b armhf 10.2.0-1 [464 kB] 210s Get:19 http://ftpmaster.internal/ubuntu plucky/main armhf x11-common all 1:7.7+23ubuntu3 [21.7 kB] 210s Get:20 http://ftpmaster.internal/ubuntu plucky/main armhf libice6 armhf 2:1.1.1-1 [36.5 kB] 210s Get:21 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg-turbo8 armhf 2.1.5-3ubuntu2 [127 kB] 210s Get:22 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg8 armhf 8c-2ubuntu11 [2148 B] 210s Get:23 http://ftpmaster.internal/ubuntu plucky/main armhf liblapack3 armhf 3.12.1-2 [2091 kB] 213s Get:24 http://ftpmaster.internal/ubuntu plucky/main armhf liblerc4 armhf 4.0.0+ds-5ubuntu1 [160 kB] 213s Get:25 http://ftpmaster.internal/ubuntu plucky/main armhf libthai-data all 0.1.29-2build1 [158 kB] 213s Get:26 http://ftpmaster.internal/ubuntu plucky/main armhf libthai0 armhf 0.1.29-2build1 [15.2 kB] 213s Get:27 http://ftpmaster.internal/ubuntu plucky/main armhf libpango-1.0-0 armhf 1.56.2-1 [216 kB] 213s Get:28 http://ftpmaster.internal/ubuntu plucky/main armhf libpangoft2-1.0-0 armhf 1.56.2-1 [43.6 kB] 213s Get:29 http://ftpmaster.internal/ubuntu plucky/main armhf libpangocairo-1.0-0 armhf 1.56.2-1 [25.1 kB] 214s Get:30 http://ftpmaster.internal/ubuntu plucky/main armhf libpaper2 armhf 2.2.5-0.3 [16.3 kB] 214s Get:31 http://ftpmaster.internal/ubuntu plucky/main armhf libpaper-utils armhf 2.2.5-0.3 [14.2 kB] 214s Get:32 http://ftpmaster.internal/ubuntu plucky/main armhf libsharpyuv0 armhf 1.5.0-0.1 [16.4 kB] 214s Get:33 http://ftpmaster.internal/ubuntu plucky/main armhf libsm6 armhf 2:1.2.4-1 [15.1 kB] 214s Get:34 http://ftpmaster.internal/ubuntu plucky/main armhf libtcl8.6 armhf 8.6.16+dfsg-1 [909 kB] 215s Get:35 http://ftpmaster.internal/ubuntu plucky/main armhf libjbig0 armhf 2.1-6.1ubuntu2 [24.9 kB] 215s Get:36 http://ftpmaster.internal/ubuntu plucky/main armhf libwebp7 armhf 1.5.0-0.1 [188 kB] 215s Get:37 http://ftpmaster.internal/ubuntu plucky/main armhf libtiff6 armhf 4.5.1+git230720-4ubuntu4 [179 kB] 215s Get:38 http://ftpmaster.internal/ubuntu plucky/main armhf libxft2 armhf 2.3.6-1build1 [37.4 kB] 215s Get:39 http://ftpmaster.internal/ubuntu plucky/main armhf libxss1 armhf 1:1.2.3-1build3 [6500 B] 215s Get:40 http://ftpmaster.internal/ubuntu plucky/main armhf libtk8.6 armhf 8.6.16-1 [686 kB] 216s Get:41 http://ftpmaster.internal/ubuntu plucky/main armhf libxt6t64 armhf 1:1.2.1-1.2build1 [145 kB] 216s Get:42 http://ftpmaster.internal/ubuntu plucky/main armhf zip armhf 3.0-14ubuntu2 [164 kB] 216s Get:43 http://ftpmaster.internal/ubuntu plucky/main armhf unzip armhf 6.0-28ubuntu6 [167 kB] 216s Get:44 http://ftpmaster.internal/ubuntu plucky/main armhf xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 216s Get:45 http://ftpmaster.internal/ubuntu plucky/universe armhf r-base-core armhf 4.4.3-1 [28.2 MB] 248s Get:46 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-deoptimr all 1.1-3-1-1 [76.6 kB] 249s Get:47 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-lattice armhf 0.22-6-1 [1363 kB] 251s Get:48 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-mass armhf 7.3-64-1 [1105 kB] 252s Get:49 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-mvtnorm armhf 1.3-3-1 [915 kB] 254s Get:50 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-pcapp armhf 2.0-5-1 [360 kB] 254s Get:51 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-robustbase armhf 0.99-4-1-1 [3025 kB] 259s Get:52 http://ftpmaster.internal/ubuntu plucky/universe armhf r-cran-rrcov armhf 1.7-6-1 [2401 kB] 263s Preconfiguring packages ... 263s Fetched 46.8 MB in 57s (826 kB/s) 263s Selecting previously unselected package libfreetype6:armhf. 263s (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 ... 64655 files and directories currently installed.) 263s Preparing to unpack .../00-libfreetype6_2.13.3+dfsg-1_armhf.deb ... 263s Unpacking libfreetype6:armhf (2.13.3+dfsg-1) ... 263s Selecting previously unselected package fonts-dejavu-mono. 263s Preparing to unpack .../01-fonts-dejavu-mono_2.37-8_all.deb ... 263s Unpacking fonts-dejavu-mono (2.37-8) ... 263s Selecting previously unselected package fonts-dejavu-core. 263s Preparing to unpack .../02-fonts-dejavu-core_2.37-8_all.deb ... 263s Unpacking fonts-dejavu-core (2.37-8) ... 263s Selecting previously unselected package fontconfig-config. 263s Preparing to unpack .../03-fontconfig-config_2.15.0-2ubuntu1_armhf.deb ... 264s Unpacking fontconfig-config (2.15.0-2ubuntu1) ... 264s Selecting previously unselected package libfontconfig1:armhf. 264s Preparing to unpack .../04-libfontconfig1_2.15.0-2ubuntu1_armhf.deb ... 264s Unpacking libfontconfig1:armhf (2.15.0-2ubuntu1) ... 264s Selecting previously unselected package fontconfig. 264s Preparing to unpack .../05-fontconfig_2.15.0-2ubuntu1_armhf.deb ... 264s Unpacking fontconfig (2.15.0-2ubuntu1) ... 264s Selecting previously unselected package libblas3:armhf. 264s Preparing to unpack .../06-libblas3_3.12.1-2_armhf.deb ... 264s Unpacking libblas3:armhf (3.12.1-2) ... 264s Selecting previously unselected package libpixman-1-0:armhf. 264s Preparing to unpack .../07-libpixman-1-0_0.44.0-3_armhf.deb ... 264s Unpacking libpixman-1-0:armhf (0.44.0-3) ... 264s Selecting previously unselected package libxcb-render0:armhf. 264s Preparing to unpack .../08-libxcb-render0_1.17.0-2_armhf.deb ... 264s Unpacking libxcb-render0:armhf (1.17.0-2) ... 264s Selecting previously unselected package libxcb-shm0:armhf. 264s Preparing to unpack .../09-libxcb-shm0_1.17.0-2_armhf.deb ... 264s Unpacking libxcb-shm0:armhf (1.17.0-2) ... 264s Selecting previously unselected package libxrender1:armhf. 264s Preparing to unpack .../10-libxrender1_1%3a0.9.10-1.1build1_armhf.deb ... 264s Unpacking libxrender1:armhf (1:0.9.10-1.1build1) ... 264s Selecting previously unselected package libcairo2:armhf. 264s Preparing to unpack .../11-libcairo2_1.18.2-2_armhf.deb ... 264s Unpacking libcairo2:armhf (1.18.2-2) ... 264s Selecting previously unselected package libdatrie1:armhf. 264s Preparing to unpack .../12-libdatrie1_0.2.13-3build1_armhf.deb ... 264s Unpacking libdatrie1:armhf (0.2.13-3build1) ... 264s Selecting previously unselected package libdeflate0:armhf. 264s Preparing to unpack .../13-libdeflate0_1.23-1_armhf.deb ... 264s Unpacking libdeflate0:armhf (1.23-1) ... 264s Selecting previously unselected package libgfortran5:armhf. 264s Preparing to unpack .../14-libgfortran5_15-20250222-0ubuntu1_armhf.deb ... 264s Unpacking libgfortran5:armhf (15-20250222-0ubuntu1) ... 264s Selecting previously unselected package libgomp1:armhf. 264s Preparing to unpack .../15-libgomp1_15-20250222-0ubuntu1_armhf.deb ... 264s Unpacking libgomp1:armhf (15-20250222-0ubuntu1) ... 264s Selecting previously unselected package libgraphite2-3:armhf. 264s Preparing to unpack .../16-libgraphite2-3_1.3.14-2ubuntu1_armhf.deb ... 264s Unpacking libgraphite2-3:armhf (1.3.14-2ubuntu1) ... 264s Selecting previously unselected package libharfbuzz0b:armhf. 264s Preparing to unpack .../17-libharfbuzz0b_10.2.0-1_armhf.deb ... 264s Unpacking libharfbuzz0b:armhf (10.2.0-1) ... 264s Selecting previously unselected package x11-common. 264s Preparing to unpack .../18-x11-common_1%3a7.7+23ubuntu3_all.deb ... 264s Unpacking x11-common (1:7.7+23ubuntu3) ... 264s Selecting previously unselected package libice6:armhf. 264s Preparing to unpack .../19-libice6_2%3a1.1.1-1_armhf.deb ... 264s Unpacking libice6:armhf (2:1.1.1-1) ... 264s Selecting previously unselected package libjpeg-turbo8:armhf. 264s Preparing to unpack .../20-libjpeg-turbo8_2.1.5-3ubuntu2_armhf.deb ... 264s Unpacking libjpeg-turbo8:armhf (2.1.5-3ubuntu2) ... 264s Selecting previously unselected package libjpeg8:armhf. 264s Preparing to unpack .../21-libjpeg8_8c-2ubuntu11_armhf.deb ... 264s Unpacking libjpeg8:armhf (8c-2ubuntu11) ... 264s Selecting previously unselected package liblapack3:armhf. 264s Preparing to unpack .../22-liblapack3_3.12.1-2_armhf.deb ... 264s Unpacking liblapack3:armhf (3.12.1-2) ... 264s Selecting previously unselected package liblerc4:armhf. 264s Preparing to unpack .../23-liblerc4_4.0.0+ds-5ubuntu1_armhf.deb ... 264s Unpacking liblerc4:armhf (4.0.0+ds-5ubuntu1) ... 264s Selecting previously unselected package libthai-data. 264s Preparing to unpack .../24-libthai-data_0.1.29-2build1_all.deb ... 264s Unpacking libthai-data (0.1.29-2build1) ... 264s Selecting previously unselected package libthai0:armhf. 264s Preparing to unpack .../25-libthai0_0.1.29-2build1_armhf.deb ... 264s Unpacking libthai0:armhf (0.1.29-2build1) ... 264s Selecting previously unselected package libpango-1.0-0:armhf. 264s Preparing to unpack .../26-libpango-1.0-0_1.56.2-1_armhf.deb ... 264s Unpacking libpango-1.0-0:armhf (1.56.2-1) ... 265s Selecting previously unselected package libpangoft2-1.0-0:armhf. 265s Preparing to unpack .../27-libpangoft2-1.0-0_1.56.2-1_armhf.deb ... 265s Unpacking libpangoft2-1.0-0:armhf (1.56.2-1) ... 265s Selecting previously unselected package libpangocairo-1.0-0:armhf. 265s Preparing to unpack .../28-libpangocairo-1.0-0_1.56.2-1_armhf.deb ... 265s Unpacking libpangocairo-1.0-0:armhf (1.56.2-1) ... 265s Selecting previously unselected package libpaper2:armhf. 265s Preparing to unpack .../29-libpaper2_2.2.5-0.3_armhf.deb ... 265s Unpacking libpaper2:armhf (2.2.5-0.3) ... 265s Selecting previously unselected package libpaper-utils. 265s Preparing to unpack .../30-libpaper-utils_2.2.5-0.3_armhf.deb ... 265s Unpacking libpaper-utils (2.2.5-0.3) ... 265s Selecting previously unselected package libsharpyuv0:armhf. 265s Preparing to unpack .../31-libsharpyuv0_1.5.0-0.1_armhf.deb ... 265s Unpacking libsharpyuv0:armhf (1.5.0-0.1) ... 265s Selecting previously unselected package libsm6:armhf. 265s Preparing to unpack .../32-libsm6_2%3a1.2.4-1_armhf.deb ... 265s Unpacking libsm6:armhf (2:1.2.4-1) ... 265s Selecting previously unselected package libtcl8.6:armhf. 265s Preparing to unpack .../33-libtcl8.6_8.6.16+dfsg-1_armhf.deb ... 265s Unpacking libtcl8.6:armhf (8.6.16+dfsg-1) ... 265s Selecting previously unselected package libjbig0:armhf. 265s Preparing to unpack .../34-libjbig0_2.1-6.1ubuntu2_armhf.deb ... 265s Unpacking libjbig0:armhf (2.1-6.1ubuntu2) ... 265s Selecting previously unselected package libwebp7:armhf. 265s Preparing to unpack .../35-libwebp7_1.5.0-0.1_armhf.deb ... 265s Unpacking libwebp7:armhf (1.5.0-0.1) ... 265s Selecting previously unselected package libtiff6:armhf. 265s Preparing to unpack .../36-libtiff6_4.5.1+git230720-4ubuntu4_armhf.deb ... 265s Unpacking libtiff6:armhf (4.5.1+git230720-4ubuntu4) ... 265s Selecting previously unselected package libxft2:armhf. 265s Preparing to unpack .../37-libxft2_2.3.6-1build1_armhf.deb ... 265s Unpacking libxft2:armhf (2.3.6-1build1) ... 265s Selecting previously unselected package libxss1:armhf. 265s Preparing to unpack .../38-libxss1_1%3a1.2.3-1build3_armhf.deb ... 265s Unpacking libxss1:armhf (1:1.2.3-1build3) ... 265s Selecting previously unselected package libtk8.6:armhf. 265s Preparing to unpack .../39-libtk8.6_8.6.16-1_armhf.deb ... 265s Unpacking libtk8.6:armhf (8.6.16-1) ... 265s Selecting previously unselected package libxt6t64:armhf. 265s Preparing to unpack .../40-libxt6t64_1%3a1.2.1-1.2build1_armhf.deb ... 265s Unpacking libxt6t64:armhf (1:1.2.1-1.2build1) ... 265s Selecting previously unselected package zip. 265s Preparing to unpack .../41-zip_3.0-14ubuntu2_armhf.deb ... 265s Unpacking zip (3.0-14ubuntu2) ... 265s Selecting previously unselected package unzip. 265s Preparing to unpack .../42-unzip_6.0-28ubuntu6_armhf.deb ... 265s Unpacking unzip (6.0-28ubuntu6) ... 265s Selecting previously unselected package xdg-utils. 266s Preparing to unpack .../43-xdg-utils_1.2.1-2ubuntu1_all.deb ... 266s Unpacking xdg-utils (1.2.1-2ubuntu1) ... 266s Selecting previously unselected package r-base-core. 266s Preparing to unpack .../44-r-base-core_4.4.3-1_armhf.deb ... 266s Unpacking r-base-core (4.4.3-1) ... 266s Selecting previously unselected package r-cran-deoptimr. 266s Preparing to unpack .../45-r-cran-deoptimr_1.1-3-1-1_all.deb ... 266s Unpacking r-cran-deoptimr (1.1-3-1-1) ... 266s Selecting previously unselected package r-cran-lattice. 266s Preparing to unpack .../46-r-cran-lattice_0.22-6-1_armhf.deb ... 266s Unpacking r-cran-lattice (0.22-6-1) ... 266s Selecting previously unselected package r-cran-mass. 266s Preparing to unpack .../47-r-cran-mass_7.3-64-1_armhf.deb ... 266s Unpacking r-cran-mass (7.3-64-1) ... 266s Selecting previously unselected package r-cran-mvtnorm. 266s Preparing to unpack .../48-r-cran-mvtnorm_1.3-3-1_armhf.deb ... 266s Unpacking r-cran-mvtnorm (1.3-3-1) ... 266s Selecting previously unselected package r-cran-pcapp. 266s Preparing to unpack .../49-r-cran-pcapp_2.0-5-1_armhf.deb ... 266s Unpacking r-cran-pcapp (2.0-5-1) ... 266s Selecting previously unselected package r-cran-robustbase. 266s Preparing to unpack .../50-r-cran-robustbase_0.99-4-1-1_armhf.deb ... 266s Unpacking r-cran-robustbase (0.99-4-1-1) ... 266s Selecting previously unselected package r-cran-rrcov. 266s Preparing to unpack .../51-r-cran-rrcov_1.7-6-1_armhf.deb ... 266s Unpacking r-cran-rrcov (1.7-6-1) ... 266s Setting up libgraphite2-3:armhf (1.3.14-2ubuntu1) ... 266s Setting up libpixman-1-0:armhf (0.44.0-3) ... 266s Setting up libsharpyuv0:armhf (1.5.0-0.1) ... 266s Setting up liblerc4:armhf (4.0.0+ds-5ubuntu1) ... 266s Setting up libxrender1:armhf (1:0.9.10-1.1build1) ... 266s Setting up libdatrie1:armhf (0.2.13-3build1) ... 266s Setting up libxcb-render0:armhf (1.17.0-2) ... 266s Setting up unzip (6.0-28ubuntu6) ... 266s Setting up x11-common (1:7.7+23ubuntu3) ... 266s Setting up libdeflate0:armhf (1.23-1) ... 266s Setting up libxcb-shm0:armhf (1.17.0-2) ... 266s Setting up libgomp1:armhf (15-20250222-0ubuntu1) ... 266s Setting up libjbig0:armhf (2.1-6.1ubuntu2) ... 266s Setting up zip (3.0-14ubuntu2) ... 266s Setting up libblas3:armhf (3.12.1-2) ... 266s update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so.3 to provide /usr/lib/arm-linux-gnueabihf/libblas.so.3 (libblas.so.3-arm-linux-gnueabihf) in auto mode 266s Setting up libfreetype6:armhf (2.13.3+dfsg-1) ... 266s Setting up fonts-dejavu-mono (2.37-8) ... 266s Setting up libtcl8.6:armhf (8.6.16+dfsg-1) ... 266s Setting up fonts-dejavu-core (2.37-8) ... 266s Setting up libjpeg-turbo8:armhf (2.1.5-3ubuntu2) ... 266s Setting up libgfortran5:armhf (15-20250222-0ubuntu1) ... 266s Setting up libwebp7:armhf (1.5.0-0.1) ... 266s Setting up libharfbuzz0b:armhf (10.2.0-1) ... 266s Setting up libthai-data (0.1.29-2build1) ... 266s Setting up libxss1:armhf (1:1.2.3-1build3) ... 266s Setting up libpaper2:armhf (2.2.5-0.3) ... 266s Setting up xdg-utils (1.2.1-2ubuntu1) ... 266s update-alternatives: using /usr/bin/xdg-open to provide /usr/bin/open (open) in auto mode 266s Setting up libjpeg8:armhf (8c-2ubuntu11) ... 266s Setting up libice6:armhf (2:1.1.1-1) ... 266s Setting up liblapack3:armhf (3.12.1-2) ... 266s update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so.3 to provide /usr/lib/arm-linux-gnueabihf/liblapack.so.3 (liblapack.so.3-arm-linux-gnueabihf) in auto mode 266s Setting up fontconfig-config (2.15.0-2ubuntu1) ... 267s Setting up libpaper-utils (2.2.5-0.3) ... 267s Setting up libthai0:armhf (0.1.29-2build1) ... 267s Setting up libtiff6:armhf (4.5.1+git230720-4ubuntu4) ... 267s Setting up libfontconfig1:armhf (2.15.0-2ubuntu1) ... 267s Setting up libsm6:armhf (2:1.2.4-1) ... 267s Setting up fontconfig (2.15.0-2ubuntu1) ... 269s Regenerating fonts cache... done. 269s Setting up libxft2:armhf (2.3.6-1build1) ... 269s Setting up libtk8.6:armhf (8.6.16-1) ... 269s Setting up libpango-1.0-0:armhf (1.56.2-1) ... 269s Setting up libcairo2:armhf (1.18.2-2) ... 269s Setting up libxt6t64:armhf (1:1.2.1-1.2build1) ... 269s Setting up libpangoft2-1.0-0:armhf (1.56.2-1) ... 269s Setting up libpangocairo-1.0-0:armhf (1.56.2-1) ... 269s Setting up r-base-core (4.4.3-1) ... 269s Creating config file /etc/R/Renviron with new version 269s Setting up r-cran-lattice (0.22-6-1) ... 269s Setting up r-cran-deoptimr (1.1-3-1-1) ... 269s Setting up r-cran-mass (7.3-64-1) ... 269s Setting up r-cran-mvtnorm (1.3-3-1) ... 269s Setting up r-cran-robustbase (0.99-4-1-1) ... 269s Setting up r-cran-pcapp (2.0-5-1) ... 269s Setting up r-cran-rrcov (1.7-6-1) ... 269s Processing triggers for libc-bin (2.41-1ubuntu2) ... 269s Processing triggers for man-db (2.13.0-1) ... 270s Processing triggers for install-info (7.1.1-1) ... 277s autopkgtest [16:40:36]: test run-unit-test: [----------------------- 279s BEGIN TEST thubert.R 279s 279s R version 4.4.3 (2025-02-28) -- "Trophy Case" 279s Copyright (C) 2025 The R Foundation for Statistical Computing 279s Platform: arm-unknown-linux-gnueabihf (32-bit) 279s 279s R is free software and comes with ABSOLUTELY NO WARRANTY. 279s You are welcome to redistribute it under certain conditions. 279s Type 'license()' or 'licence()' for distribution details. 279s 279s R is a collaborative project with many contributors. 279s Type 'contributors()' for more information and 279s 'citation()' on how to cite R or R packages in publications. 279s 279s Type 'demo()' for some demos, 'help()' for on-line help, or 279s 'help.start()' for an HTML browser interface to help. 279s Type 'q()' to quit R. 279s 279s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, 279s + method=c("hubert", "hubert.mcd", "locantore", "cov", "classic", 279s + "grid", "proj")) 279s + { 279s + ## Test the PcaXxx() functions on the literature datasets: 279s + ## 279s + ## Call PcaHubert() and the other functions for all regression 279s + ## data sets available in robustbase/rrcov and print: 279s + ## - execution time (if time == TRUE) 279s + ## - loadings 279s + ## - eigenvalues 279s + ## - scores 279s + ## 279s + 279s + dopca <- function(x, xname, nrep=1){ 279s + 279s + n <- dim(x)[1] 279s + p <- dim(x)[2] 279s + if(method == "hubert.mcd") 279s + pca <- PcaHubert(x, k=p) 279s + else if(method == "hubert") 279s + pca <- PcaHubert(x, mcd=FALSE) 279s + else if(method == "locantore") 279s + pca <- PcaLocantore(x) 279s + else if(method == "cov") 279s + pca <- PcaCov(x) 279s + else if(method == "classic") 279s + pca <- PcaClassic(x) 279s + else if(method == "grid") 279s + pca <- PcaGrid(x) 279s + else if(method == "proj") 279s + pca <- PcaProj(x) 279s + else 279s + stop("Undefined PCA method: ", method) 279s + 279s + 279s + e1 <- getEigenvalues(pca)[1] 279s + e2 <- getEigenvalues(pca)[2] 279s + k <- pca@k 279s + 279s + if(time){ 279s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 279s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f %10.3f\n", dim(x)[1], dim(x)[2], k, e1, e2, xtime) 279s + } 279s + else{ 279s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f\n", dim(x)[1], dim(x)[2], k, e1, e2) 279s + } 279s + lpad<-lname-nchar(xname) 279s + cat(pad.right(xname, lpad), xres) 279s + 279s + if(!short){ 279s + cat("Scores: \n") 279s + print(getScores(pca)) 279s + 279s + if(full){ 279s + cat("-------------\n") 279s + show(pca) 279s + } 279s + cat("----------------------------------------------------------\n") 279s + } 279s + } 279s + 279s + stopifnot(length(nrep) == 1, nrep >= 1) 279s + method <- match.arg(method) 279s + 279s + options(digits = 5) 279s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 279s + 279s + lname <- 20 279s + 279s + ## VT::15.09.2013 - this will render the output independent 279s + ## from the version of the package 279s + suppressPackageStartupMessages(library(rrcov)) 279s + 279s + data(Animals, package = "MASS") 279s + brain <- Animals[c(1:24, 26:25, 27:28),] 279s + 279s + tmp <- sys.call() 279s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 279s + 279s + cat("Data Set n p k e1 e2\n") 279s + cat("==========================================================\n") 279s + dopca(heart[, 1:2], data(heart), nrep) 279s + dopca(starsCYG, data(starsCYG), nrep) 279s + dopca(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 279s + dopca(stack.x, data(stackloss), nrep) 279s + ## dopca(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) # differences between the architectures 279s + dopca(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 279s + ## dopca(data.matrix(subset(wood, select = -y)), data(wood), nrep) # differences between the architectures 279s + dopca(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 279s + 279s + ## dopca(brain, "Animals", nrep) 279s + dopca(milk, data(milk), nrep) 279s + dopca(bushfire, data(bushfire), nrep) 279s + cat("==========================================================\n") 279s + } 279s > 279s > dogen <- function(nrep=1, eps=0.49, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 279s + 279s + dopca <- function(x, nrep=1){ 279s + gc() 279s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 279s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 279s + xtime 279s + } 279s + 279s + set.seed(1234) 279s + 279s + ## VT::15.09.2013 - this will render the output independent 279s + ## from the version of the package 279s + suppressPackageStartupMessages(library(rrcov)) 279s + library(MASS) 279s + 279s + method <- match.arg(method) 279s + 279s + ap <- c(2, 5, 10, 20, 30) 279s + an <- c(100, 500, 1000, 10000, 50000) 279s + 279s + tottime <- 0 279s + cat(" n p Time\n") 279s + cat("=====================\n") 279s + for(i in 1:length(an)) { 279s + for(j in 1:length(ap)) { 279s + n <- an[i] 279s + p <- ap[j] 279s + if(5*p <= n){ 279s + xx <- gendata(n, p, eps) 279s + X <- xx$X 279s + ## print(dimnames(X)) 279s + tottime <- tottime + dopca(X, nrep) 279s + } 279s + } 279s + } 279s + 279s + cat("=====================\n") 279s + cat("Total time: ", tottime*nrep, "\n") 279s + } 279s > 279s > dorep <- function(x, nrep=1, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 279s + 279s + method <- match.arg(method) 279s + for(i in 1:nrep) 279s + if(method == "hubert.mcd") 279s + PcaHubert(x) 279s + else if(method == "hubert") 279s + PcaHubert(x, mcd=FALSE) 279s + else if(method == "locantore") 279s + PcaLocantore(x) 279s + else if(method == "cov") 279s + PcaCov(x) 279s + else 279s + stop("Undefined PCA method: ", method) 279s + } 279s > 279s > #### gendata() #### 279s > # Generates a location contaminated multivariate 279s > # normal sample of n observations in p dimensions 279s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 279s > # where 279s > # m = (b,b,...,b) 279s > # Defaults: eps=0 and b=10 279s > # 279s > gendata <- function(n,p,eps=0,b=10){ 279s + 279s + if(missing(n) || missing(p)) 279s + stop("Please specify (n,p)") 279s + if(eps < 0 || eps >= 0.5) 279s + stop(message="eps must be in [0,0.5)") 279s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 279s + nbad <- as.integer(eps * n) 279s + xind <- vector("numeric") 279s + if(nbad > 0){ 279s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 279s + xind <- sample(n,nbad) 279s + X[xind,] <- Xbad 279s + } 279s + list(X=X, xind=xind) 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 > whatis <- function(x){ 279s + if(is.data.frame(x)) 279s + cat("Type: data.frame\n") 279s + else if(is.matrix(x)) 279s + cat("Type: matrix\n") 279s + else if(is.vector(x)) 279s + cat("Type: vector\n") 279s + else 279s + cat("Type: don't know\n") 279s + } 279s > 279s > ################################################################# 279s > ## VT::27.08.2010 279s > ## bug report from Stephen Milborrow 279s > ## 279s > test.case.1 <- function() 279s + { 279s + X <- matrix(c( 279s + -0.79984, -1.00103, 0.899794, 0.00000, 279s + 0.34279, 0.52832, -1.303783, -1.17670, 279s + -0.79984, -1.00103, 0.899794, 0.00000, 279s + 0.34279, 0.52832, -1.303783, -1.17670, 279s + 0.34279, 0.52832, -1.303783, -1.17670, 279s + 1.48542, 0.66735, 0.716162, 1.17670, 279s + -0.79984, -1.00103, 0.899794, 0.00000, 279s + 1.69317, 1.91864, -0.018363, 1.76505, 279s + -1.00759, -0.16684, -0.385626, 0.58835, 279s + -0.79984, -1.00103, 0.899794, 0.00000), ncol=4, byrow=TRUE) 279s + 279s + cc1 <- PcaHubert(X, k=3) 279s + 279s + cc2 <- PcaLocantore(X, k=3) 279s + cc3 <- PcaCov(X, k=3, cov.control=CovControlSest()) 279s + 279s + cc4 <- PcaProj(X, k=2) # with k=3 will produce warnings in .distances - too small eignevalues 279s + cc5 <- PcaGrid(X, k=2) # dito 279s + 279s + list(cc1, cc2, cc3, cc4, cc5) 279s + } 279s > 279s > ################################################################# 279s > ## VT::05.08.2016 279s > ## bug report from Matthieu Lesnoff 279s > ## 279s > test.case.2 <- function() 279s + { 279s + do.test.case.2 <- function(z) 279s + { 279s + if(missing(z)) 279s + { 279s + set.seed(12345678) 279s + n <- 5 279s + z <- data.frame(v1 = rnorm(n), v2 = rnorm(n), v3 = rnorm(n)) 279s + z 279s + } 279s + 279s + fm <- PcaLocantore(z, k = 2, scale = TRUE) 279s + fm@scale 279s + apply(z, MARGIN = 2, FUN = mad) 279s + scale(z, center = fm@center, scale = fm@scale) 279s + 279s + T <- fm@scores 279s + P <- fm@loadings 279s + E <- scale(z, center = fm@center, scale = fm@scale) - T %*% t(P) 279s + d2 <- apply(E^2, MARGIN = 1, FUN = sum) 279s + ## print(sqrt(d2)); print(fm@od) 279s + print(ret <- all.equal(sqrt(d2), fm@od)) 279s + 279s + ret 279s + } 279s + do.test.case.2() 279s + do.test.case.2(phosphor) 279s + do.test.case.2(stackloss) 279s + do.test.case.2(salinity) 279s + do.test.case.2(hbk) 279s + do.test.case.2(milk) 279s + do.test.case.2(bushfire) 279s + data(rice); do.test.case.2(rice) 279s + data(un86); do.test.case.2(un86) 279s + } 279s > 279s > ## VT::15.09.2013 - this will render the output independent 279s > ## from the version of the package 279s > suppressPackageStartupMessages(library(rrcov)) 280s > 280s > dodata(method="classic") 280s 280s Call: dodata(method = "classic") 280s Data Set n p k e1 e2 280s ========================================================== 280s heart 12 2 2 812.379735 9.084962 280s Scores: 280s PC1 PC2 280s 1 2.7072 1.46576 280s 2 59.9990 -1.43041 280s 3 -3.5619 -1.54067 280s 4 -7.7696 2.52687 280s 5 14.7660 -0.95822 280s 6 -20.0489 6.91079 280s 7 1.4189 2.25961 280s 8 -34.3308 -4.23717 280s 9 -6.0487 -0.97859 280s 10 -33.0102 -3.73143 280s 11 -18.6372 0.25821 280s 12 44.5163 -0.54476 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 28.5023 3.0141 280s ---------------------------------------------------------- 280s starsCYG 47 2 2 0.331279 0.079625 280s Scores: 280s PC1 PC2 280s 1 0.2072999 0.089973 280s 2 0.6855999 0.349644 280s 3 -0.0743007 -0.061028 280s 4 0.6855999 0.349644 280s 5 0.1775161 0.015053 280s 6 0.4223986 0.211351 280s 7 -0.2926077 -0.516156 280s 8 0.2188453 0.293607 280s 9 0.5593696 0.028761 280s 10 0.0983878 0.074540 280s 11 0.8258140 -0.711176 280s 12 0.4167063 0.180244 280s 13 0.3799883 0.225541 280s 14 -0.9105236 -0.432014 280s 15 -0.7418831 -0.125322 280s 16 -0.4432862 0.048287 280s 17 -1.0503005 -0.229623 280s 18 -0.8393302 -0.007831 280s 19 -0.8126742 -0.195952 280s 20 0.9842316 -0.688729 280s 21 -0.6230699 -0.108486 280s 22 -0.7814875 -0.130933 280s 23 -0.6017038 0.025840 280s 24 -0.1857772 0.155474 280s 25 -0.0020261 0.070412 280s 26 -0.3640775 0.059510 280s 27 -0.3458392 -0.069204 280s 28 -0.1208393 0.053577 280s 29 -0.6033482 -0.176391 280s 30 1.1440521 -0.676183 280s 31 -0.5960920 -0.013765 280s 32 0.0519296 0.259855 280s 33 0.1861752 0.167779 280s 34 1.3802755 -0.632611 280s 35 -0.6542566 -0.173505 280s 36 0.5583690 0.392215 280s 37 0.0561384 0.230152 280s 38 0.1861752 0.167779 280s 39 0.1353472 0.241376 280s 40 0.5355195 0.197080 280s 41 -0.3980701 0.014294 280s 42 0.0277576 0.145332 280s 43 0.2979736 0.234120 280s 44 0.3049884 0.184614 280s 45 0.4889809 0.311684 280s 46 -0.0514512 0.134108 280s 47 -0.5224950 0.037063 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 0.57557 0.28218 280s ---------------------------------------------------------- 280s phosphor 18 2 2 220.403422 68.346121 280s Scores: 280s PC1 PC2 280s 1 4.04290 -15.3459 280s 2 -22.30489 -1.0004 280s 3 -24.52683 3.2836 280s 4 -12.54839 -6.0848 280s 5 -19.37044 2.2979 280s 6 15.20366 -19.9424 280s 7 0.44222 -3.1379 280s 8 -10.64042 3.6933 280s 9 -11.67967 5.9670 280s 10 14.26805 -7.0221 280s 11 -4.98832 1.5268 280s 12 8.74986 7.9379 280s 13 12.26290 6.0251 280s 14 6.27607 7.5768 280s 15 17.53246 3.1560 280s 16 -10.17024 -5.8994 280s 17 21.05826 5.4492 280s 18 16.39281 11.5191 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 14.8460 8.2672 280s ---------------------------------------------------------- 280s stackloss 21 3 3 99.576089 19.581136 280s Scores: 280s PC1 PC2 PC3 280s 1 20.15352 -4.359452 0.324585 280s 2 19.81554 -5.300468 0.308294 280s 3 15.45222 -1.599136 -0.203125 280s 4 2.40370 -0.145282 2.370302 280s 5 1.89538 0.070566 0.448061 280s 6 2.14954 -0.037358 1.409182 280s 7 4.43153 5.500810 2.468051 280s 8 4.43153 5.500810 2.468051 280s 9 -1.47521 1.245404 2.511773 280s 10 -5.11183 -4.802083 -2.407870 280s 11 -2.07009 3.667055 -2.261247 280s 12 -2.66223 2.833964 -3.238659 280s 13 -4.43589 -2.920053 -2.375287 280s 14 -0.46404 7.323193 -1.234961 280s 15 -9.31959 6.232579 -0.056064 280s 16 -10.33350 3.409533 -0.104938 280s 17 -14.81094 -9.872607 0.628103 280s 18 -12.44514 -3.285499 0.742143 280s 19 -11.85300 -2.452408 1.719555 280s 20 -5.73994 -2.494520 0.098250 280s 21 9.98843 1.484952 -3.614198 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 9.9788 4.4251 1.8986 280s ---------------------------------------------------------- 280s salinity 28 3 3 11.410736 7.075409 280s Scores: 280s PC1 PC2 PC3 280s 1 -0.937789 -2.40535 0.812909 280s 2 -1.752631 -2.57774 2.004437 280s 3 -6.509364 -0.78762 -1.821906 280s 4 -5.619847 -2.41333 -1.586891 280s 5 -7.268242 1.61012 1.563568 280s 6 -4.316558 -3.20411 0.029376 280s 7 -2.379545 -3.32371 0.703101 280s 8 0.013514 -3.50586 1.260502 280s 9 0.265262 -0.16736 -2.886883 280s 10 1.890755 2.43623 -0.986832 280s 11 0.804196 2.56656 0.387577 280s 12 0.935082 -1.03559 -0.074081 280s 13 1.814839 -1.61087 0.612290 280s 14 3.407535 -0.15880 2.026088 280s 15 1.731273 2.95159 -1.840286 280s 16 -6.129708 7.21368 2.632273 280s 17 -0.645124 1.06260 0.028697 280s 18 -1.307532 -2.54679 -0.280273 280s 19 0.483455 -0.55896 -3.097281 280s 20 2.053267 0.47308 -1.858703 280s 21 3.277664 -1.31002 0.453753 280s 22 4.631644 -0.78005 1.519894 280s 23 1.864403 5.32790 -0.849694 280s 24 0.623899 4.29317 0.056461 280s 25 1.301696 0.37871 -0.646220 280s 26 2.852126 -0.79527 -0.347711 280s 27 4.134051 -0.92756 0.449222 280s 28 4.781679 -0.20467 1.736616 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 3.3780 2.6600 1.4836 280s ---------------------------------------------------------- 280s hbk 75 3 3 216.162129 1.981077 280s Scores: 280s PC1 PC2 PC3 280s 1 26.2072 -0.660756 0.503340 280s 2 27.0406 -0.108506 -0.225059 280s 3 28.8351 -1.683721 0.263078 280s 4 29.9221 -0.812174 -0.674480 280s 5 29.3181 -0.909915 -0.121600 280s 6 27.5360 -0.599697 0.916574 280s 7 27.6617 -0.073753 0.676620 280s 8 26.5576 -0.882312 0.159620 280s 9 28.8726 -1.074223 -0.673462 280s 10 27.6643 -1.463829 -0.868593 280s 11 34.2019 -0.664473 -0.567265 280s 12 35.4805 -2.730949 -0.259320 280s 13 34.7544 1.325449 0.749884 280s 14 38.9522 8.171389 0.034382 280s 15 -5.5375 0.390704 1.679172 280s 16 -7.4319 0.803850 1.925633 280s 17 -8.5880 0.957577 -1.010312 280s 18 -6.6022 -0.425109 0.625148 280s 19 -6.5596 1.154721 -0.640680 280s 20 -5.2525 0.812527 1.377832 280s 21 -6.2771 0.067747 0.958907 280s 22 -6.2501 1.325491 -1.104428 280s 23 -7.2419 0.839808 0.728712 280s 24 -7.6489 1.131606 0.154897 280s 25 -9.0763 -0.670721 -0.167577 280s 26 -5.5967 0.999411 -0.810000 280s 27 -5.1460 -0.339018 1.326712 280s 28 -7.1659 -0.993461 0.125933 280s 29 -8.2104 -0.169338 -0.073569 280s 30 -6.2499 -1.689222 -0.877481 280s 31 -7.3180 -0.225795 1.687204 280s 32 -7.9446 1.473868 -0.541790 280s 33 -6.3604 1.237472 0.061800 280s 34 -8.9812 -0.710662 -0.830422 280s 35 -5.1698 -0.435484 1.102817 280s 36 -5.9995 -0.058135 -0.713550 280s 37 -5.8753 0.852882 -1.610556 280s 38 -8.4501 0.334363 0.404813 280s 39 -8.1751 -1.300317 0.633282 280s 40 -7.4495 0.672712 -0.829815 280s 41 -5.6213 -1.106765 1.395315 280s 42 -6.8571 -0.900977 -1.509937 280s 43 -7.0633 1.987372 -1.079934 280s 44 -6.3763 -1.867647 -0.251224 280s 45 -8.6456 -0.866053 0.630132 280s 46 -6.5356 -1.763526 -0.189838 280s 47 -8.2224 -1.183284 1.615150 280s 48 -5.6136 -1.100704 1.079239 280s 49 -5.9907 0.220336 1.443387 280s 50 -5.2675 0.142923 0.194023 280s 51 -7.9324 0.324710 1.113289 280s 52 -7.5544 -1.033884 1.792496 280s 53 -6.7119 -1.712257 -1.711778 280s 54 -7.4679 1.856542 0.046658 280s 55 -7.4666 1.161504 -0.725948 280s 56 -6.7110 1.574868 0.534288 280s 57 -8.2571 -0.399824 0.521995 280s 58 -5.9781 1.312567 0.926790 280s 59 -5.6960 -0.394338 -0.332938 280s 60 -6.1017 -0.797579 -1.679359 280s 61 -5.2628 0.919128 -1.436156 280s 62 -9.1245 -0.516135 -0.229065 280s 63 -7.7140 1.659145 0.068510 280s 64 -4.9886 0.173613 0.865810 280s 65 -6.6157 -1.479786 0.098390 280s 66 -7.9511 0.772770 -0.998321 280s 67 -7.1856 0.459602 0.216588 280s 68 -8.7345 -0.860784 -1.238576 280s 69 -8.5833 -0.313481 0.832074 280s 70 -5.8642 -0.142883 -0.870064 280s 71 -5.8879 0.186456 0.464467 280s 72 -7.1865 0.497156 -0.826767 280s 73 -6.8671 -0.058606 -1.335842 280s 74 -7.1398 0.727642 -1.422331 280s 75 -7.2696 -1.347832 -1.496927 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 14.70245 1.40751 0.95725 280s ---------------------------------------------------------- 280s milk 86 8 8 15.940298 2.771345 280s Scores: 280s PC1 PC2 PC3 PC4 PC5 PC6 PC7 280s 1 6.471620 1.031110 0.469432 0.5736412 1.0294362 -0.6054039 -0.2005117 280s 2 7.439545 0.320597 0.081922 -0.6305898 0.7128977 -1.1601053 -0.1170582 280s 3 1.240654 -1.840458 0.520870 -0.1717469 0.2752079 -0.3815506 0.6004089 280s 4 5.952685 -1.856375 1.638710 0.3358626 -0.5834205 -0.0665348 -0.1580799 280s 5 -0.706973 0.261795 0.423736 0.2916399 -0.5307716 -0.3325563 -0.0062349 280s 6 2.524050 0.293380 -0.572997 0.2466367 -0.3497882 0.0386014 -0.1418131 280s 7 3.136085 -0.050202 -0.818165 -0.0451560 -0.5226337 -0.1597194 0.1669050 280s 8 3.260390 0.312365 -0.110776 0.4908006 -0.5225353 -0.1972222 -0.1068433 280s 9 -0.808914 -2.355785 1.344204 -0.4743284 -0.1394914 -0.1390080 -0.2620731 280s 10 -2.511226 -0.995321 -0.087218 -0.5950040 0.4268321 0.2561918 0.0891170 280s 11 -9.204096 -0.598364 1.587275 0.0833647 0.1865626 0.0358228 0.0920394 280s 12 -12.946774 1.951332 -0.179186 0.2560603 0.1300954 -0.1179820 -0.0999494 280s 13 -10.011603 0.726323 -2.102423 -1.3105560 0.3291550 0.0660007 -0.0794410 280s 14 -11.983644 0.768224 -0.532227 -0.5161201 -0.0817164 -0.4358934 -0.1734612 280s 15 -10.465714 -0.704271 2.035437 0.3713778 -0.0564830 -0.2696432 -0.1940091 280s 16 -2.527619 -0.286939 0.354497 0.8571223 0.1585009 0.2272835 0.4386955 280s 17 -0.514527 -2.895087 1.657181 0.2208239 0.1961109 0.1280496 -0.0182491 280s 18 -1.763931 0.854269 -0.686282 0.2848209 -0.4813608 -0.2623962 0.4757030 280s 19 -1.538419 -0.866477 1.103818 0.3874507 0.2086661 0.1267277 0.2354264 280s 20 0.732842 -1.455594 1.097358 -0.2530588 -0.0302385 0.2654274 0.6093330 280s 21 -2.530155 1.932885 -0.873095 0.6202295 -0.4153607 0.0048383 0.0067484 280s 22 -0.772646 0.675846 -0.259539 0.4844670 -0.0893266 -0.2785557 -0.0424662 280s 23 0.185417 1.413719 0.066135 1.1014470 0.0468093 0.0288637 0.2539994 280s 24 -0.280536 0.908864 0.113221 1.3370381 0.3289929 0.2588134 -0.0356289 280s 25 -3.503626 1.971233 0.203620 1.1975494 -0.3175317 0.1149685 0.0584396 280s 26 -0.639313 1.175503 0.403906 0.9082134 -0.2648165 -0.1238813 -0.0174853 280s 27 -2.923327 -0.365168 0.149478 0.8201430 -0.1544609 -0.4856934 -0.0058424 280s 28 2.505633 3.050292 -0.554424 2.1416405 -0.0378764 0.1002280 -0.3888580 280s 29 4.649504 1.054863 -0.081018 1.1454466 0.1502080 0.4967323 0.0879775 280s 30 1.049282 1.355215 -0.142701 0.7805566 -0.2059790 0.0193142 0.0815524 280s 31 1.962583 1.595396 -2.050642 0.3556747 0.1384801 0.1197984 0.1608247 280s 32 1.554846 0.095644 -1.423054 -0.3175620 0.4260008 -0.1612463 -0.0567196 280s 33 2.248977 0.010348 -0.062469 0.6388269 0.2098648 0.1330250 0.0906704 280s 34 0.993109 -0.828812 0.284059 0.3446686 0.1899096 -0.0515571 -0.2281197 280s 35 -0.335103 1.614093 -0.920661 1.2502617 0.2435013 0.1264875 0.0469238 280s 36 4.346795 1.208134 0.368889 1.1429977 -0.1362052 -0.0158169 -0.0183852 280s 37 0.992634 2.013738 -1.350619 0.8714694 0.0057776 -0.2122691 0.1760918 280s 38 2.213341 1.706516 -0.705418 1.2670281 -0.0707149 0.0670467 -0.1863588 280s 39 -1.213255 0.644062 0.163988 1.1213961 0.2945355 0.1093574 0.0019574 280s 40 3.942604 -1.704266 0.660327 0.1618506 0.4259076 0.0070193 0.3462765 280s 41 4.262054 1.687193 0.351875 0.5396477 1.0052810 -0.9331689 0.0056063 280s 42 6.865198 -1.091248 1.153585 1.1248797 0.0873276 0.2565221 0.0333265 280s 43 3.476720 0.555449 -1.030771 -0.3015720 -0.1748109 -0.1584968 0.4079902 280s 44 5.691730 -0.141240 0.565189 0.3174238 0.6478440 1.0579977 -0.5387916 280s 45 0.327134 0.152011 -0.394798 0.4998430 0.1599781 0.3159518 0.1623656 280s 46 0.280225 1.569387 -0.100397 1.2800976 0.0446645 0.0946513 0.0461599 280s 47 3.119928 -0.384834 -3.325600 -1.8865310 -0.1334744 0.1249987 -0.2561273 280s 48 0.501542 0.739816 -1.384556 -0.1244721 0.2948958 0.4836170 -0.1182802 280s 49 -1.953218 0.269986 -1.726474 -0.8510637 0.5047958 0.4860651 0.2318735 280s 50 3.706878 -2.400570 1.361047 -0.4949076 0.2180352 0.4080879 0.1156540 280s 51 -1.060358 -0.521609 -1.387412 -1.2767491 -0.0521356 0.1665452 -0.0044412 280s 52 -4.900528 0.157011 -1.015880 -0.9941168 0.2069608 0.3239762 -0.1921715 280s 53 -0.388496 0.062051 -0.643721 -0.8544141 -0.1857141 0.0063293 0.2664606 280s 54 0.109234 -0.018709 -0.242825 -0.2064701 -0.0585165 0.1720867 0.1117397 280s 55 1.176175 0.644539 -0.373694 0.0038605 -0.3436524 0.0194450 -0.0838883 280s 56 0.407259 -0.606637 0.222915 -0.3622451 -0.0737834 0.0228104 0.0297333 280s 57 -1.022756 -0.071860 0.741957 0.2273628 -0.1388444 -0.2396467 -0.2327738 280s 58 0.245419 1.167059 0.225934 0.8318795 -0.5365166 -0.0090816 -0.1680757 280s 59 -1.300617 -1.110325 -0.262740 -0.8857801 -0.0816954 -0.1186886 -0.0928322 280s 60 -1.110561 -0.832357 -0.212713 -0.4754481 -0.4105982 -0.1886992 -0.0602872 280s 61 0.381831 -1.475116 0.601047 -0.6260156 -0.1854501 -0.1749306 -0.0013904 280s 62 2.734462 -1.887861 0.813453 -0.5856987 0.2310656 0.1117041 -0.0293373 280s 63 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 280s 64 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 280s 65 0.004744 -2.712679 1.178987 -0.6677199 0.0208119 0.0621903 -0.0655693 280s 66 -2.014851 -1.060090 -0.099959 -0.7225044 -0.1947648 -0.2282902 -0.0505015 280s 67 0.621739 -1.296106 0.255632 -0.3309504 -0.0880200 0.2524306 0.1465779 280s 68 -0.271385 -1.709161 -1.100349 -2.0937671 0.2166264 0.0191278 0.0114174 280s 69 -0.326350 -0.737232 0.021639 -0.3850383 -0.4338287 0.2156624 0.1597594 280s 70 4.187093 9.708082 4.632803 -4.9751240 -0.0881576 0.2392433 0.0568049 280s 71 -1.868507 -1.600166 0.436353 -0.8078214 -0.1530893 0.0479471 -0.1999893 280s 72 2.768081 -0.556824 -0.148923 -0.3197853 -0.5524427 0.0907804 -0.0694488 280s 73 -1.441846 -2.735114 -0.294134 -1.2172969 0.0109453 -0.0562910 0.1505788 280s 74 -10.995490 0.615992 1.950966 1.1687190 0.2798335 0.2713257 0.0652135 280s 75 0.508992 -2.363945 -0.407064 -0.9522316 0.1040307 0.1088110 -0.7368484 280s 76 -1.015714 -0.307662 -1.088162 -1.0181862 -0.0440888 -0.1362208 0.0271200 280s 77 -8.028891 -0.580763 0.933638 0.4619362 0.3379832 -0.1368644 -0.0669441 280s 78 1.763308 -1.336175 -0.127809 -0.7161775 -0.1904861 -0.0900461 0.0037539 280s 79 0.208944 -0.580698 -0.626297 -0.7620610 -0.0262368 -0.2928202 0.0285908 280s 80 -3.230608 1.251352 0.195280 0.8687004 0.1812011 0.2600692 -0.1516375 280s 81 1.498160 0.669731 -0.266114 0.3772866 -0.2769688 -0.1066593 -0.1608395 280s 82 3.232051 -1.776018 0.485524 0.1170945 0.0557260 0.2219872 0.1187681 280s 83 2.999977 -0.228275 -0.467724 -0.4287672 0.0494902 -0.2337809 -0.0718159 280s 84 1.238083 0.320956 -1.806006 -1.0142266 0.2359630 -0.0857149 0.0593938 280s 85 1.276376 -2.081214 2.540850 0.3745805 -0.2596482 -0.1228412 -0.2199985 280s 86 0.930715 0.836457 -1.385153 -0.6074929 -0.2476354 0.1680713 -0.0117324 280s PC8 280s 1 9.0765e-04 280s 2 2.1811e-04 280s 3 1.1834e-03 280s 4 8.4077e-05 280s 5 9.9209e-04 280s 6 1.6277e-03 280s 7 2.4907e-04 280s 8 6.8383e-04 280s 9 -5.0924e-04 280s 10 3.1215e-04 280s 11 3.0654e-04 280s 12 -1.1951e-03 280s 13 -1.2849e-03 280s 14 -9.0801e-04 280s 15 -1.2686e-03 280s 16 -1.8441e-03 280s 17 -2.1068e-03 280s 18 -5.7816e-04 280s 19 -1.2330e-03 280s 20 3.3857e-05 280s 21 3.8623e-04 280s 22 1.3035e-04 280s 23 -3.8648e-04 280s 24 -1.7400e-04 280s 25 -3.9196e-04 280s 26 -7.6996e-04 280s 27 -4.8042e-04 280s 28 -2.0628e-04 280s 29 -4.5672e-04 280s 30 -1.4716e-04 280s 31 -4.6385e-05 280s 32 -2.0481e-04 280s 33 -3.0020e-04 280s 34 -5.8179e-05 280s 35 1.3870e-04 280s 36 -6.7177e-04 280s 37 -3.0799e-04 280s 38 6.2140e-04 280s 39 4.5912e-04 280s 40 -3.7165e-04 280s 41 -5.4362e-04 280s 42 -1.0155e-03 280s 43 1.3449e-04 280s 44 -5.4761e-04 280s 45 1.0300e-03 280s 46 1.1039e-03 280s 47 -6.4858e-04 280s 48 -7.6886e-05 280s 49 3.2590e-04 280s 50 8.6845e-05 280s 51 4.9423e-04 280s 52 9.2973e-04 280s 53 4.4342e-04 280s 54 4.9888e-04 280s 55 7.2171e-04 280s 56 -3.2133e-05 280s 57 -1.8101e-04 280s 58 -5.4969e-06 280s 59 -8.3841e-04 280s 60 5.9446e-05 280s 61 -6.5683e-05 280s 62 -3.4073e-04 280s 63 -6.5145e-04 280s 64 -6.5145e-04 280s 65 1.4986e-04 280s 66 2.8096e-04 280s 67 -6.5170e-05 280s 68 -1.3775e-04 280s 69 6.8225e-06 280s 70 -1.6290e-04 280s 71 3.9009e-04 280s 72 -1.3981e-04 280s 73 6.2613e-04 280s 74 2.6513e-03 280s 75 3.7088e-04 280s 76 9.9539e-04 280s 77 1.2979e-03 280s 78 5.6500e-04 280s 79 3.0940e-04 280s 80 8.7993e-04 280s 81 -3.1353e-04 280s 82 4.9625e-04 280s 83 -6.3951e-04 280s 84 -4.5582e-04 280s 85 5.9440e-04 280s 86 -3.6234e-04 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 3.99253025 1.66473582 1.10660264 0.96987790 0.33004256 0.29263512 0.20843280 280s [8] 0.00074024 280s ---------------------------------------------------------- 280s bushfire 38 5 5 38435.075910 1035.305774 280s Scores: 280s PC1 PC2 PC3 PC4 PC5 280s 1 -111.9345 4.9970 -1.00881 -1.224361 3.180569 280s 2 -113.4128 7.4784 -0.79170 -0.235184 2.385812 280s 3 -105.8364 10.9615 -3.15662 -0.251662 1.017328 280s 4 -89.1684 8.7232 -6.15080 -0.075611 1.431111 280s 5 -58.7216 -1.9543 -12.70661 -0.151328 1.425570 280s 6 -35.0370 -12.8434 -17.06841 -0.525664 3.499743 280s 7 -250.2123 -49.4348 23.31261 -19.070238 0.647348 280s 8 -292.6877 -69.7708 -21.30815 13.093808 -1.288764 280s 9 -294.0765 -70.9903 -23.96326 14.940985 -0.939076 280s 10 -290.0193 -57.3747 3.51346 1.858995 0.083107 280s 11 -289.8168 -43.3207 16.08046 -1.745099 -1.506042 280s 12 -290.8645 6.2503 40.52173 -7.496479 -0.033767 280s 13 -232.6865 41.8090 37.19429 -1.280348 -0.470837 280s 14 9.8483 25.1954 -14.56970 0.538484 1.772046 280s 15 137.1924 11.8521 -37.12452 -5.130459 -0.586695 280s 16 92.9804 10.3923 -24.97267 -7.551314 -1.867125 280s 17 90.4493 10.5630 -21.92735 -5.669651 -1.001362 280s 18 78.6325 5.2211 -19.74718 -6.107880 -1.939986 280s 19 82.1178 3.6913 -21.37810 -4.259855 -1.278838 280s 20 92.9044 7.1961 -21.22900 -4.125571 -0.127089 280s 21 74.9157 10.2991 -16.60924 -5.660751 -0.406343 280s 22 66.7350 12.0460 -16.73298 -4.669080 1.333436 280s 23 -62.1981 22.7394 6.03613 -5.182356 -0.453624 280s 24 -116.5696 32.3182 12.74846 -1.465657 -0.097851 280s 25 -53.8907 22.4278 -2.18861 -2.742014 -0.990071 280s 26 -60.6384 20.2952 -3.05206 -2.953685 -0.629061 280s 27 -74.7621 28.9067 -0.65817 1.473357 -0.443957 280s 28 -50.2202 37.3457 -1.44989 5.530426 -1.073521 280s 29 -38.7483 50.2749 2.34469 10.156457 -0.416262 280s 30 -93.3887 51.7884 20.08872 8.798781 -1.620216 280s 31 35.3096 41.7158 13.46272 14.464358 -0.475973 280s 32 290.8493 3.5924 7.41501 15.244293 2.141354 280s 33 326.7236 -29.8194 15.64898 2.612061 0.064931 280s 34 322.9095 -30.6372 16.21520 1.248005 -0.711322 280s 35 328.5307 -29.9533 16.49656 1.138916 0.974792 280s 36 325.6791 -30.6990 16.83840 -0.050949 -1.211360 280s 37 323.8136 -30.7474 19.55764 -1.545150 -0.267580 280s 38 325.2991 -30.5350 20.31878 -1.928580 -0.120425 280s ------------- 280s Call: 280s PcaClassic(x = x) 280s 280s Standard deviations: 280s [1] 196.0487 32.1762 18.4819 6.9412 1.3510 280s ---------------------------------------------------------- 280s ========================================================== 280s > dodata(method="hubert.mcd") 280s 280s Call: dodata(method = "hubert.mcd") 280s Data Set n p k e1 e2 280s ========================================================== 280s heart 12 2 2 358.175786 4.590630 280s Scores: 280s PC1 PC2 280s 1 -12.2285 0.86283 280s 2 -68.9906 -7.43256 280s 3 -5.7035 -1.53793 280s 4 -1.8988 2.90891 280s 5 -24.0044 -2.68946 280s 6 9.9115 8.43321 280s 7 -11.0210 1.77484 280s 8 25.1826 -1.31573 280s 9 -3.2809 -0.74345 280s 10 23.8200 -0.93701 280s 11 9.1344 1.67701 280s 12 -53.6607 -5.08826 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 18.9255 2.1426 280s ---------------------------------------------------------- 280s starsCYG 47 2 2 0.280653 0.005921 280s Scores: 280s PC1 PC2 280s 1 -0.285731 -0.0899858 280s 2 -0.819689 0.0153191 280s 3 0.028077 -0.1501882 280s 4 -0.819689 0.0153191 280s 5 -0.234971 -0.1526225 280s 6 -0.527231 -0.0382380 280s 7 0.372118 -0.5195605 280s 8 -0.357448 0.1009508 280s 9 -0.603553 -0.2533541 280s 10 -0.177170 -0.0722541 280s 11 -0.637339 -1.0390758 280s 12 -0.512526 -0.0662337 280s 13 -0.490978 -0.0120517 280s 14 0.936868 -0.2550656 280s 15 0.684479 -0.0125787 280s 16 0.347708 0.0641382 280s 17 1.009966 -0.0202111 280s 18 0.742477 0.1286170 280s 19 0.773105 -0.0588983 280s 20 -0.795247 -1.0648673 280s 21 0.566048 -0.0319223 280s 22 0.723956 -0.0061308 280s 23 0.505616 0.0899297 280s 24 0.069956 0.0896997 280s 25 -0.080090 -0.0462652 280s 26 0.268755 0.0512425 280s 27 0.289710 -0.0770574 280s 28 0.038341 -0.0269216 280s 29 0.567463 -0.1026188 280s 30 -0.951542 -1.1005280 280s 31 0.512064 0.0504528 280s 32 -0.188059 0.1184850 280s 33 -0.288758 -0.0094200 280s 34 -1.190016 -1.1293460 280s 35 0.615197 -0.0846898 280s 36 -0.710930 0.0938781 280s 37 -0.183223 0.0888774 280s 38 -0.288758 -0.0094200 280s 39 -0.262177 0.0759816 280s 40 -0.630957 -0.0855773 280s 41 0.314679 0.0182135 280s 42 -0.130850 0.0163715 280s 43 -0.415248 0.0205825 280s 44 -0.407188 -0.0287636 280s 45 -0.620693 0.0376892 280s 46 -0.051896 0.0292672 280s 47 0.426662 0.0770340 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 0.529767 0.076946 280s ---------------------------------------------------------- 280s phosphor 18 2 2 285.985489 32.152099 280s Scores: 280s PC1 PC2 280s 1 -2.89681 -18.08811 280s 2 21.34021 -0.40854 280s 3 22.98065 4.13006 280s 4 12.33544 -6.72947 280s 5 17.99823 2.47611 280s 6 -13.35773 -24.10967 280s 7 -0.92957 -5.51314 280s 8 9.16061 2.71354 280s 9 9.89243 5.10403 280s 10 -14.12600 -11.17832 280s 11 3.84175 -0.17605 280s 12 -10.61905 4.37646 280s 13 -13.85065 2.01919 280s 14 -8.11927 4.34325 280s 15 -18.69805 -1.51673 280s 16 9.95352 -6.85784 280s 17 -22.49433 0.29387 280s 18 -18.66592 6.92359 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 16.9111 5.6703 280s ---------------------------------------------------------- 280s stackloss 21 3 3 78.703690 19.249085 280s Scores: 280s PC1 PC2 PC3 280s 1 -20.323997 10.26124 0.92041 280s 2 -19.761418 11.08797 0.92383 280s 3 -16.469919 6.43190 0.22593 280s 4 -4.171902 1.68262 2.50695 280s 5 -3.756174 1.40774 0.57004 280s 6 -3.964038 1.54518 1.53850 280s 7 -7.547376 -3.27780 2.48643 280s 8 -7.547376 -3.27780 2.48643 280s 9 -0.763294 -0.63699 2.53518 280s 10 4.214079 4.46296 -2.28315 280s 11 -0.849132 -2.97767 -2.31393 280s 12 -0.078689 -2.28838 -3.27896 280s 13 3.088921 2.80948 -2.28999 280s 14 -3.307313 -6.14718 -1.35916 280s 15 5.552354 -7.34201 -0.32057 280s 16 7.240091 -4.86180 -0.31031 280s 17 14.908334 6.84995 0.70603 280s 18 10.970281 1.06279 0.68209 280s 19 10.199838 0.37350 1.64712 280s 20 4.273564 1.99328 0.14526 280s 21 -11.992249 2.19025 -3.37391 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 8.8715 4.3874 2.1990 280s ---------------------------------------------------------- 280s salinity 28 3 3 11.651966 4.107426 280s Scores: 280s PC1 PC2 PC3 280s 1 1.68712 1.62591 0.19812128 280s 2 2.35772 2.37290 1.24965734 280s 3 6.80132 -2.14412 0.68142276 280s 4 6.41982 -0.61348 -0.31907921 280s 5 6.36697 -1.98030 4.87319903 280s 6 5.22050 1.20864 0.10252555 280s 7 3.34007 2.02950 0.00064329 280s 8 1.06220 2.89801 -0.35658064 280s 9 0.34692 -2.20572 -1.71677710 280s 10 -2.21421 -2.74842 0.76862599 280s 11 -1.40111 -2.16163 2.21124383 280s 12 -0.38242 0.32284 -0.23732191 280s 13 -1.12809 1.33152 -0.28800043 280s 14 -3.24998 1.35943 1.17514969 280s 15 -2.11006 -3.70114 0.45102357 280s 16 3.46920 -5.41242 8.56937909 280s 17 0.46682 -1.46753 1.48992481 280s 18 2.21807 0.99168 -0.61894625 280s 19 0.28525 -2.00333 -2.16450483 280s 20 -1.66639 -1.76768 -1.06946404 280s 21 -2.58106 1.23534 -0.65557612 280s 22 -4.15573 1.71244 0.08170141 280s 23 -3.07670 -4.87628 2.53200755 280s 24 -1.70808 -3.71657 2.99305849 280s 25 -1.08172 -1.05713 0.02468813 280s 26 -2.23187 0.27323 -0.85760867 280s 27 -3.50498 1.07657 -0.68503455 280s 28 -4.49819 1.43219 0.53416609 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 3.4135 2.0267 1.0764 280s ---------------------------------------------------------- 280s hbk 75 3 3 1.459908 1.201048 280s Scores: 280s PC1 PC2 PC3 280s 1 -31.105415 4.714217 10.4566165 280s 2 -31.707650 5.748724 10.7682402 280s 3 -33.366131 4.625897 12.1570167 280s 4 -34.173377 6.069657 12.4466895 280s 5 -33.780418 5.508823 11.9872893 280s 6 -32.493478 4.684595 10.5679819 280s 7 -32.592637 5.235522 10.3765493 280s 8 -31.293363 4.865797 10.9379676 280s 9 -33.160964 5.714260 12.3098920 280s 10 -31.919786 5.384537 12.3374332 280s 11 -38.231962 6.810641 13.5994385 280s 12 -39.290479 5.393906 15.2942554 280s 13 -39.418445 7.326461 11.5194898 280s 14 -43.906584 13.214819 8.3282743 280s 15 -1.906326 -0.716061 -0.8635112 280s 16 -0.263255 -0.926016 -1.9009292 280s 17 1.776489 1.072332 -0.5496140 280s 18 -0.464648 -0.702441 0.0482897 280s 19 -0.267826 1.283779 -0.2925812 280s 20 -2.122108 -0.165970 -0.8924686 280s 21 -0.937217 -0.548532 -0.4132196 280s 22 -0.423273 1.781869 -0.0323061 280s 23 -0.047532 -0.018909 -1.1259327 280s 24 0.490041 0.520202 -1.1065753 280s 25 2.143049 -0.720869 -0.0495474 280s 26 -1.094748 1.459175 0.2226246 280s 27 -2.070705 -0.898573 0.0023229 280s 28 0.294998 -0.830258 0.5929001 280s 29 1.242995 -0.300216 -0.2010507 280s 30 -0.147958 -0.439099 2.0003038 280s 31 -0.170818 -1.440946 -0.9755627 280s 32 0.958531 1.199730 -1.0129867 280s 33 -0.697307 0.874343 -0.7260649 280s 34 2.278946 -0.261106 0.4196544 280s 35 -1.962829 -0.809318 0.2033113 280s 36 -0.626631 0.600666 0.8004036 280s 37 -0.550885 1.881448 0.7382776 280s 38 1.249717 -0.336214 -0.9349845 280s 39 1.106696 -1.569418 0.1869576 280s 40 0.684034 0.939963 -0.1034965 280s 41 -1.559314 -1.551408 0.3660323 280s 42 0.538741 0.447358 1.6361099 280s 43 0.252685 2.080564 -0.7765259 280s 44 -0.217012 -1.027281 1.7015154 280s 45 1.497600 -1.349234 -0.2698932 280s 46 -0.100388 -1.026443 1.5390401 280s 47 0.811117 -2.195271 -0.5208141 280s 48 -1.462210 -1.321318 0.5600144 280s 49 -1.383976 -0.740714 -0.7348906 280s 50 -1.636773 0.215464 0.3195369 280s 51 0.530918 -0.759743 -1.2069247 280s 52 0.109566 -2.107455 -0.5315473 280s 53 0.564334 0.060847 2.3910630 280s 54 0.272234 1.122711 -1.5060028 280s 55 0.608660 1.197219 -0.5255609 280s 56 -0.565430 0.710345 -1.3708230 280s 57 1.115629 -0.888816 -0.4186014 280s 58 -1.351288 0.374815 -1.1980618 280s 59 -0.998016 0.151228 0.9007970 280s 60 -0.124017 0.764846 1.9005963 280s 61 -1.189858 1.905264 0.7721322 280s 62 2.190589 -0.579614 -0.1377914 280s 63 0.518278 0.931130 -1.4534768 280s 64 -2.124566 -0.194391 -0.0327092 280s 65 -0.154218 -1.050861 1.1309885 280s 66 1.197852 1.044147 -0.2265269 280s 67 0.114174 0.094763 -0.5168926 280s 68 2.201115 -0.032271 0.8573493 280s 69 1.307843 -1.104815 -0.7741270 280s 70 -0.691449 0.676665 1.0004603 280s 71 -1.150975 -0.050861 -0.0717068 280s 72 0.457293 0.861871 0.1026350 280s 73 0.392258 0.897451 0.9178065 280s 74 0.584658 1.450471 0.3201857 280s 75 0.972517 0.063777 1.8223995 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 1.2083 1.0959 1.0168 280s ---------------------------------------------------------- 280s milk 86 8 8 5.739740 2.405262 280s Scores: 280s PC1 PC2 PC3 PC4 PC5 PC6 PC7 280s 1 -5.710924 -1.346213 0.01332091 -0.3709242 -0.566813 0.7529298 -1.2525433 280s 2 -6.578612 -0.440749 1.16354746 0.2870685 -0.573207 0.7368064 -1.6101427 280s 3 -0.720902 1.777381 -0.21532020 -0.3213950 0.287603 -0.4764464 -0.5638337 280s 4 -5.545889 1.621147 -0.85212883 0.4380154 0.022241 0.0718035 0.1176140 280s 5 1.323210 -0.143897 -0.78611461 0.5966857 0.043139 -0.0512545 -0.1419726 280s 6 -1.760792 -0.662792 0.46402240 0.2149752 0.130000 0.0797221 0.1916948 280s 7 -2.344198 -0.363657 0.92442296 0.3921371 0.241463 -0.2370967 0.0636268 280s 8 -2.556824 -0.680132 0.04339934 0.4635077 0.154136 0.0371259 0.0260340 280s 9 1.203234 2.712342 -1.00693092 0.1251739 0.170679 0.2231851 -0.0118196 280s 10 3.151858 1.255826 -0.01678562 -0.5087398 -0.087933 0.0115055 -0.0097828 280s 11 9.562891 1.580419 -2.65612113 -0.1748178 -0.153031 -0.0880112 -0.1648752 280s 12 13.617821 -0.999033 -1.92168237 0.0326918 -0.038488 0.0870082 -0.1809687 280s 13 10.958032 -0.097916 0.95915085 -0.2348663 0.147875 0.1219202 0.0419067 280s 14 12.675941 0.158747 -1.04153243 0.3117402 0.302036 0.1187749 -0.2310830 280s 15 10.726828 1.775339 -3.36786799 0.1285422 0.151594 0.0998947 -0.2028458 280s 16 3.042705 0.212589 -1.23921907 -0.5596596 0.277061 -0.5037073 0.0612182 280s 17 0.780071 2.990008 -1.58490147 -0.5441119 0.436485 -0.0603833 0.1016610 280s 18 2.523916 -0.923373 -0.03221722 0.3830822 0.208008 -0.5505270 -0.1252648 280s 19 1.990563 1.062648 -1.42038451 -0.3602257 -0.068006 -0.1932744 -0.1197842 280s 20 -0.243938 1.674555 -0.72225359 -0.1475652 -0.397855 -0.5385123 -0.0559660 280s 21 3.354424 -2.001060 -0.22542149 0.3346180 0.032502 -0.0953825 0.1293148 280s 22 1.477177 -0.777534 -0.35362339 0.1224412 0.203208 0.0514382 -0.2166274 280s 23 0.502055 -1.618511 -0.85013853 -0.1298862 -0.144328 -0.1941806 -0.1923681 280s 24 0.900504 -1.227820 -1.07180474 -0.5851197 0.112657 0.0467164 0.0405544 280s 25 4.161393 -1.869015 -1.54507759 0.2003123 -0.152582 -0.1382908 0.0864320 280s 26 1.277795 -1.185179 -1.13445511 0.2771556 -0.101901 0.0070037 -0.1279016 280s 27 3.447256 0.257652 -1.13407954 -0.0077859 0.853002 -0.1376443 -0.1897380 280s 28 -1.695730 -3.781876 -0.72940594 -0.0956421 0.064475 0.3665470 0.0726448 280s 29 -3.923610 -1.654818 -0.16117226 -0.4242302 -0.303749 -0.0209844 0.1723890 280s 30 -0.309616 -1.564739 -0.39909943 0.1657509 -0.178739 -0.0600221 -0.0571706 280s 31 -0.960838 -2.242733 1.50477679 -0.2957897 0.163758 -0.1034399 0.0257903 280s 32 -0.671285 -0.459839 1.39124475 -0.3669914 0.246127 0.2094780 -0.2681284 280s 33 -1.589089 -0.390812 -0.16505762 -0.3992573 0.086870 -0.0402114 -0.0399923 280s 34 -0.421868 0.636139 -0.42563447 -0.2985726 0.311365 0.2398515 -0.0540852 280s 35 1.118429 -2.116328 -0.22329747 -0.4864401 0.289927 -0.0503006 0.0101706 280s 36 -3.660291 -1.630831 -0.57876280 0.1294792 -0.260224 0.0912904 -0.1565668 280s 37 -0.087686 -2.530609 0.50076931 -0.0319873 0.194898 -0.1233526 -0.2494283 280s 38 -1.418620 -2.303011 -0.09405565 -0.0931745 0.169466 0.1581787 0.0850095 280s 39 1.815225 -0.838968 -1.10222194 -0.4897630 0.180933 0.0096330 -0.0600652 280s 40 -3.420975 1.398516 -0.17143314 -0.5852146 0.090464 -0.2066323 -0.2974177 280s 41 -3.462295 -1.795174 -0.17500650 -0.1610267 -0.595086 0.5981680 -1.5930268 280s 42 -6.401429 0.451242 -0.78723149 -0.4285618 0.055395 -0.0212476 0.0808936 280s 43 -2.583017 -0.871790 1.29937081 0.2422349 -0.190002 -0.2822972 -0.2625721 280s 44 -5.027244 -0.167503 -0.02382957 -0.8288929 -0.852207 0.7399343 0.4606076 280s 45 0.364494 -0.440380 -0.07746564 -0.4552133 0.095711 -0.1662998 0.1566706 280s 46 0.420706 -1.880819 -0.82180986 -0.1823454 -0.022661 -0.0304227 -0.0516440 280s 47 -1.932985 -0.120002 4.00934170 0.0930728 0.295428 0.2787446 0.3766231 280s 48 0.395402 -1.021393 1.07953292 -0.4599764 -0.132386 0.1895780 0.2771755 280s 49 2.886100 -0.276587 1.48851137 -0.6314648 -0.203963 -0.0891955 0.1347804 280s 50 -3.255379 2.479232 -0.37933775 -0.3651497 -0.415000 0.0045750 0.0671055 280s 51 1.939333 0.617579 1.57113225 0.0310866 -0.039226 0.0409183 0.1830694 280s 52 5.727154 0.275898 0.58814711 -0.1739820 -0.222791 0.2553797 0.1959402 280s 53 1.207873 0.131451 0.80899235 0.2872465 -0.353544 -0.1697200 -0.0987230 280s 54 0.612921 0.040062 0.17807459 -0.0053074 -0.202244 -0.0671788 0.0530276 280s 55 -0.399075 -0.727144 0.26196635 0.3657576 -0.192705 0.0903564 0.0641289 280s 56 0.240719 0.733792 -0.05030509 0.0967214 -0.186906 0.0310231 -0.0594812 280s 57 1.589641 0.289427 -1.02478822 0.2723190 -0.048378 0.2599262 -0.2040853 280s 58 0.423483 -1.262515 -0.85026016 0.4749963 -0.082647 0.0752412 0.1352259 280s 59 1.983684 1.335122 0.42593757 0.1345894 0.096456 0.1153107 -0.0385994 280s 60 1.770171 0.935428 0.14901569 0.3641973 0.274015 -0.0280119 0.0690244 280s 61 0.182845 1.706453 -0.18364654 0.2517421 -0.035773 0.0357087 -0.1363470 280s 62 -2.191617 1.966324 -0.03573689 -0.2203900 -0.235704 0.1682332 -0.1145174 280s 63 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 280s 64 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 280s 65 0.407575 2.996346 -0.63021113 -0.1335795 0.087668 0.0627032 0.0486166 280s 66 2.660379 1.322824 0.10122110 0.2420451 0.192938 0.0344019 -0.0771918 280s 67 -0.032273 1.315299 -0.04511689 -0.1293380 -0.025923 -0.1655965 0.1887534 280s 68 1.117637 2.005809 1.97078787 -0.0429209 -0.176568 0.1634287 -0.0916254 280s 69 0.970730 0.837158 0.01621375 0.2347502 -0.071757 -0.2464626 0.2907551 280s 70 -2.688271 -5.335891 -0.64225481 4.1819517 -9.523550 2.0943027 -2.8098426 280s 71 2.428718 1.976051 -0.24749122 0.1308738 0.018276 0.1711292 0.1346284 280s 72 -2.061944 0.405943 0.50472914 0.4393739 -0.056420 -0.0031558 0.2663880 280s 73 2.029606 2.874991 0.68310320 -0.2067254 0.511537 -0.2010371 0.0805608 280s 74 11.293757 0.328931 -3.84783031 -0.4130266 -0.210499 -0.1103148 -0.0381326 280s 75 0.120896 2.287914 0.83639076 -0.2462845 0.551353 0.6629701 0.3789055 280s 76 1.859499 0.422019 1.18435547 0.1546108 0.017266 0.0470615 -0.1071011 280s 77 8.435857 1.147499 -2.19924186 -0.4156770 0.386548 0.0294075 -0.1911399 280s 78 -1.090858 1.311287 0.62897430 0.1727009 0.077341 0.0135972 -0.0096934 280s 79 0.560012 0.623617 0.83727267 0.1680787 0.087477 0.0611949 -0.2588084 280s 80 3.873817 -1.133641 -1.27469019 -0.2717298 -0.165066 0.1696232 0.0635047 280s 81 -0.758664 -0.880260 0.00057124 0.2838720 0.016243 0.1527299 -0.0150514 280s 82 -2.709588 1.464049 -0.12598126 -0.3828567 0.213647 -0.1425385 0.1552827 280s 83 -2.213670 0.059563 0.87565603 0.1255703 -0.082005 0.2189829 -0.2938264 280s 84 -0.242242 -0.483552 2.05089334 -0.0681005 -0.101578 0.1304632 -0.2218093 280s 85 -1.032129 2.375018 -2.19321259 0.2332079 -0.066379 0.1854598 -0.0873859 280s 86 0.015327 -0.948155 1.39530555 0.2701225 -0.268889 0.0578145 0.1608678 280s PC8 280s 1 2.1835e-03 280s 2 1.6801e-03 280s 3 1.6623e-03 280s 4 2.6286e-04 280s 5 9.5884e-04 280s 6 1.4430e-03 280s 7 1.8784e-04 280s 8 6.8473e-04 280s 9 -6.8490e-04 280s 10 1.1565e-04 280s 11 5.6907e-06 280s 12 -1.8395e-03 280s 13 -2.1582e-03 280s 14 -1.6294e-03 280s 15 -1.6964e-03 280s 16 -1.9664e-03 280s 17 -2.2448e-03 280s 18 -6.5884e-04 280s 19 -1.1536e-03 280s 20 2.6887e-04 280s 21 3.3199e-05 280s 22 1.1170e-04 280s 23 -1.7617e-04 280s 24 -2.1577e-04 280s 25 -6.1495e-04 280s 26 -7.2903e-04 280s 27 -6.8773e-04 280s 28 -2.0742e-04 280s 29 -2.6937e-04 280s 30 -6.7472e-05 280s 31 -1.3222e-04 280s 32 -1.6516e-04 280s 33 -1.8836e-04 280s 34 -1.1273e-04 280s 35 3.0703e-05 280s 36 -3.0311e-04 280s 37 -1.9380e-04 280s 38 5.5526e-04 280s 39 4.1987e-04 280s 40 8.4807e-05 280s 41 8.8725e-04 280s 42 -6.5647e-04 280s 43 4.3202e-04 280s 44 -5.3330e-04 280s 45 8.9161e-04 280s 46 1.1588e-03 280s 47 -1.2714e-03 280s 48 -4.0376e-04 280s 49 4.1280e-06 280s 50 3.0116e-04 280s 51 5.8510e-05 280s 52 3.3236e-04 280s 53 4.0982e-04 280s 54 4.0428e-04 280s 55 6.1600e-04 280s 56 -4.0496e-05 280s 57 -1.8342e-04 280s 58 -1.6748e-04 280s 59 -1.0894e-03 280s 60 -2.6876e-04 280s 61 -5.8951e-05 280s 62 -1.5517e-04 280s 63 -7.9933e-04 280s 64 -7.9933e-04 280s 65 2.2592e-05 280s 66 2.4984e-05 280s 67 -2.2714e-04 280s 68 -3.3991e-04 280s 69 -3.0375e-04 280s 70 3.4033e-03 280s 71 2.3288e-05 280s 72 -3.4126e-04 280s 73 2.5528e-04 280s 74 2.2760e-03 280s 75 -2.8985e-04 280s 76 7.9077e-04 280s 77 9.4636e-04 280s 78 4.9099e-04 280s 79 3.0501e-04 280s 80 6.5280e-04 280s 81 -3.6570e-04 280s 82 4.9966e-04 280s 83 -4.3245e-04 280s 84 -4.6152e-04 280s 85 7.4691e-04 280s 86 -6.1103e-04 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 2.39577535 1.55089079 0.92557331 0.33680677 0.19792033 0.17855133 0.16041702 280s [8] 0.00054179 280s ---------------------------------------------------------- 280s bushfire 38 5 5 31248.552973 358.974577 280s Scores: 280s PC1 PC2 PC3 PC4 PC5 280s 1 155.972 1.08098 -23.31135 -1.93015 1.218941 280s 2 157.738 0.35648 -20.95658 -2.42375 0.466415 280s 3 150.667 2.12545 -16.20395 -2.00140 -0.582924 280s 4 133.892 5.25124 -15.88873 -2.78469 -0.275261 280s 5 102.462 13.00611 -21.54096 -4.69409 -0.944176 280s 6 77.694 18.75377 -28.71865 -6.44244 0.446350 280s 7 286.266 -11.36184 -98.67134 10.95233 -3.625338 280s 8 326.627 29.92767 -112.60824 -29.26330 -13.710094 280s 9 327.898 32.39553 -113.34314 -31.65905 -13.830781 280s 10 325.131 5.81628 -105.58927 -13.45695 -8.987971 280s 11 326.458 -7.84562 -94.25242 -6.11547 -8.572845 280s 12 333.171 -37.69907 -50.89207 8.98187 -1.742979 280s 13 279.789 -40.78415 -8.06209 7.65884 0.181748 280s 14 37.714 10.54231 13.46530 -1.55051 2.102662 280s 15 -90.034 34.68964 18.98186 0.69260 0.417573 280s 16 -46.492 23.65086 10.07282 4.36090 -0.748517 280s 17 -43.990 20.36443 9.61049 2.83084 -0.127983 280s 18 -32.938 19.11199 2.64850 2.92879 -1.473988 280s 19 -36.555 20.60142 2.01879 0.63832 -1.235075 280s 20 -46.837 19.89630 6.65142 0.89120 0.271108 280s 21 -28.670 15.29534 6.59311 3.29638 0.402194 280s 22 -20.331 15.06559 7.33721 2.16591 2.006327 280s 23 108.644 -7.92707 -1.45130 6.27388 0.356715 280s 24 163.697 -16.15568 0.61663 4.24231 0.464415 280s 25 100.471 -0.30739 0.87762 2.86452 -0.692735 280s 26 106.922 0.90864 -1.91436 2.54557 -0.565023 280s 27 121.966 -3.29641 4.85626 -0.47676 -0.490047 280s 28 98.650 -4.51455 16.64160 -3.08996 -0.839397 280s 29 88.795 -10.85457 30.46708 -5.37360 0.315657 280s 30 142.981 -27.89100 22.40713 -1.67126 -0.680158 280s 31 14.125 -21.60028 29.80480 -8.25272 -0.019693 280s 32 -244.044 -11.76430 24.53390 -12.52294 2.022312 280s 33 -283.842 -13.21931 -6.23565 -2.63367 -0.080728 280s 34 -280.168 -13.41903 -7.69318 -1.24571 -0.722513 280s 35 -285.666 -13.78452 -6.50318 -1.23756 1.074669 280s 36 -282.938 -13.82281 -7.63902 0.20435 -0.971673 280s 37 -281.129 -16.20408 -8.57154 1.85797 0.234486 280s 38 -282.589 -16.91969 -8.36010 2.35589 0.490630 280s ------------- 280s Call: 280s PcaHubert(x = x, k = p) 280s 280s Standard deviations: 280s [1] 176.77260 18.94662 16.21701 3.95755 0.92761 280s ---------------------------------------------------------- 280s ========================================================== 280s > dodata(method="hubert") 280s 280s Call: dodata(method = "hubert") 280s Data Set n p k e1 e2 280s ========================================================== 280s heart 12 2 1 315.227002 NA 280s Scores: 280s PC1 280s 1 13.2197 280s 2 69.9817 280s 3 6.6946 280s 4 2.8899 280s 5 24.9956 280s 6 -8.9203 280s 7 12.0121 280s 8 -24.1915 280s 9 4.2721 280s 10 -22.8289 280s 11 -8.1433 280s 12 54.6519 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 17.755 280s ---------------------------------------------------------- 280s starsCYG 47 2 1 0.308922 NA 280s Scores: 280s PC1 280s 1 0.224695 280s 2 0.758653 280s 3 -0.089113 280s 4 0.758653 280s 5 0.173934 280s 6 0.466195 280s 7 -0.433154 280s 8 0.296411 280s 9 0.542517 280s 10 0.116133 280s 11 0.576303 280s 12 0.451490 280s 13 0.429942 280s 14 -0.997904 280s 15 -0.745515 280s 16 -0.408745 280s 17 -1.071002 280s 18 -0.803514 280s 19 -0.834141 280s 20 0.734210 280s 21 -0.627085 280s 22 -0.784992 280s 23 -0.566652 280s 24 -0.130992 280s 25 0.019053 280s 26 -0.329791 280s 27 -0.350747 280s 28 -0.099378 280s 29 -0.628499 280s 30 0.890506 280s 31 -0.573100 280s 32 0.127022 280s 33 0.227721 280s 34 1.128979 280s 35 -0.676234 280s 36 0.649894 280s 37 0.122186 280s 38 0.227721 280s 39 0.201140 280s 40 0.569920 280s 41 -0.375716 280s 42 0.069814 280s 43 0.354212 280s 44 0.346152 280s 45 0.559656 280s 46 -0.009140 280s 47 -0.487699 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 0.55581 280s ---------------------------------------------------------- 280s phosphor 18 2 1 215.172048 NA 280s Scores: 280s PC1 280s 1 1.12634 280s 2 -22.10340 280s 3 -23.49216 280s 4 -13.45927 280s 5 -18.60808 280s 6 11.24086 280s 7 -0.14748 280s 8 -9.77075 280s 9 -10.37022 280s 10 12.71798 280s 11 -4.61857 280s 12 10.07037 280s 13 13.16767 280s 14 7.57254 280s 15 17.81362 280s 16 -11.08799 280s 17 21.70358 280s 18 18.24496 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 14.669 280s ---------------------------------------------------------- 280s stackloss 21 3 2 77.038636 18.859777 280s Scores: 280s PC1 PC2 280s 1 -20.334936 10.28081 280s 2 -19.772121 11.10736 280s 3 -16.461573 6.43794 280s 4 -4.258672 1.73213 280s 5 -3.773146 1.41928 280s 6 -4.015909 1.57571 280s 7 -7.635560 -3.22715 280s 8 -7.635560 -3.22715 280s 9 -0.855388 -0.58707 280s 10 4.298129 4.41664 280s 11 -0.767202 -3.02229 280s 12 0.038375 -2.35217 280s 13 3.172500 2.76354 280s 14 -3.261224 -6.17206 280s 15 5.553840 -7.34784 280s 16 7.242284 -4.86820 280s 17 14.878925 6.85989 280s 18 10.939223 1.07406 280s 19 10.133645 0.40394 280s 20 4.267234 1.99501 280s 21 -11.859921 2.12579 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 8.7772 4.3428 280s ---------------------------------------------------------- 280s salinity 28 3 2 8.001175 5.858089 280s Scores: 280s PC1 PC2 280s 1 2.858444 1.04359 280s 2 3.807704 1.55974 280s 3 6.220733 -4.32114 280s 4 6.388841 -2.83649 280s 5 6.077450 -3.70092 280s 6 5.974494 -0.67230 280s 7 4.531584 0.78322 280s 8 2.725849 2.41297 280s 9 0.100501 -2.13615 280s 10 -2.358003 -1.49718 280s 11 -1.317688 -1.15391 280s 12 0.434635 0.58230 280s 13 0.116019 1.79022 280s 14 -1.771501 2.71749 280s 15 -2.630757 -2.44003 280s 16 2.289743 -5.51829 280s 17 0.637985 -1.26452 280s 18 3.076147 0.19883 280s 19 0.097381 -1.95868 280s 20 -1.572471 -0.93003 280s 21 -1.284185 2.21858 280s 22 -2.531713 3.30313 280s 23 -3.865359 -3.01230 280s 24 -2.143461 -2.41918 280s 25 -0.714414 -0.41227 280s 26 -1.327781 1.18373 280s 27 -2.201166 2.41566 280s 28 -2.931988 3.20536 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 2.8286 2.4203 280s ---------------------------------------------------------- 280s hbk 75 3 3 1.459908 1.201048 280s Scores: 280s PC1 PC2 PC3 280s 1 31.105415 -4.714217 -10.4566165 280s 2 31.707650 -5.748724 -10.7682402 280s 3 33.366131 -4.625897 -12.1570167 280s 4 34.173377 -6.069657 -12.4466895 280s 5 33.780418 -5.508823 -11.9872893 280s 6 32.493478 -4.684595 -10.5679819 280s 7 32.592637 -5.235522 -10.3765493 280s 8 31.293363 -4.865797 -10.9379676 280s 9 33.160964 -5.714260 -12.3098920 280s 10 31.919786 -5.384537 -12.3374332 280s 11 38.231962 -6.810641 -13.5994385 280s 12 39.290479 -5.393906 -15.2942554 280s 13 39.418445 -7.326461 -11.5194898 280s 14 43.906584 -13.214819 -8.3282743 280s 15 1.906326 0.716061 0.8635112 280s 16 0.263255 0.926016 1.9009292 280s 17 -1.776489 -1.072332 0.5496140 280s 18 0.464648 0.702441 -0.0482897 280s 19 0.267826 -1.283779 0.2925812 280s 20 2.122108 0.165970 0.8924686 280s 21 0.937217 0.548532 0.4132196 280s 22 0.423273 -1.781869 0.0323061 280s 23 0.047532 0.018909 1.1259327 280s 24 -0.490041 -0.520202 1.1065753 280s 25 -2.143049 0.720869 0.0495474 280s 26 1.094748 -1.459175 -0.2226246 280s 27 2.070705 0.898573 -0.0023229 280s 28 -0.294998 0.830258 -0.5929001 280s 29 -1.242995 0.300216 0.2010507 280s 30 0.147958 0.439099 -2.0003038 280s 31 0.170818 1.440946 0.9755627 280s 32 -0.958531 -1.199730 1.0129867 280s 33 0.697307 -0.874343 0.7260649 280s 34 -2.278946 0.261106 -0.4196544 280s 35 1.962829 0.809318 -0.2033113 280s 36 0.626631 -0.600666 -0.8004036 280s 37 0.550885 -1.881448 -0.7382776 280s 38 -1.249717 0.336214 0.9349845 280s 39 -1.106696 1.569418 -0.1869576 280s 40 -0.684034 -0.939963 0.1034965 280s 41 1.559314 1.551408 -0.3660323 280s 42 -0.538741 -0.447358 -1.6361099 280s 43 -0.252685 -2.080564 0.7765259 280s 44 0.217012 1.027281 -1.7015154 280s 45 -1.497600 1.349234 0.2698932 280s 46 0.100388 1.026443 -1.5390401 280s 47 -0.811117 2.195271 0.5208141 280s 48 1.462210 1.321318 -0.5600144 280s 49 1.383976 0.740714 0.7348906 280s 50 1.636773 -0.215464 -0.3195369 280s 51 -0.530918 0.759743 1.2069247 280s 52 -0.109566 2.107455 0.5315473 280s 53 -0.564334 -0.060847 -2.3910630 280s 54 -0.272234 -1.122711 1.5060028 280s 55 -0.608660 -1.197219 0.5255609 280s 56 0.565430 -0.710345 1.3708230 280s 57 -1.115629 0.888816 0.4186014 280s 58 1.351288 -0.374815 1.1980618 280s 59 0.998016 -0.151228 -0.9007970 280s 60 0.124017 -0.764846 -1.9005963 280s 61 1.189858 -1.905264 -0.7721322 280s 62 -2.190589 0.579614 0.1377914 280s 63 -0.518278 -0.931130 1.4534768 280s 64 2.124566 0.194391 0.0327092 280s 65 0.154218 1.050861 -1.1309885 280s 66 -1.197852 -1.044147 0.2265269 280s 67 -0.114174 -0.094763 0.5168926 280s 68 -2.201115 0.032271 -0.8573493 280s 69 -1.307843 1.104815 0.7741270 280s 70 0.691449 -0.676665 -1.0004603 280s 71 1.150975 0.050861 0.0717068 280s 72 -0.457293 -0.861871 -0.1026350 280s 73 -0.392258 -0.897451 -0.9178065 280s 74 -0.584658 -1.450471 -0.3201857 280s 75 -0.972517 -0.063777 -1.8223995 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 1.2083 1.0959 1.0168 280s ---------------------------------------------------------- 280s milk 86 8 2 6.040806 2.473780 280s Scores: 280s PC1 PC2 280s 1 -5.768003 -0.9174359 280s 2 -6.664422 0.0280812 280s 3 -0.484521 1.7923710 280s 4 -5.211590 2.0747301 280s 5 1.422641 -0.3268437 280s 6 -1.810360 -0.5469828 280s 7 -2.402924 -0.1987041 280s 8 -2.553389 -0.4963662 280s 9 1.583399 2.5410448 280s 10 3.267946 0.9141367 280s 11 9.924771 0.6501301 280s 12 13.628569 -2.3009846 280s 13 10.774550 -1.1628697 280s 14 12.716376 -1.0670330 280s 15 11.176408 0.7403371 280s 16 3.209269 -0.0804317 280s 17 1.256577 2.8931153 280s 18 2.468720 -1.2008647 280s 19 2.253229 0.8379608 280s 20 0.021073 1.6394221 280s 21 3.205298 -2.3518286 280s 22 1.470733 -0.9618655 280s 23 0.475732 -1.7044535 280s 24 0.930144 -1.3288398 280s 25 4.151553 -2.2882554 280s 26 1.314488 -1.3527439 280s 27 3.613405 -0.0813605 280s 28 -1.909178 -3.6473200 280s 29 -3.987263 -1.3255834 280s 30 -0.370601 -1.5855086 280s 31 -1.273254 -2.1892809 280s 32 -0.816634 -0.4514478 280s 33 -1.553394 -0.2792004 280s 34 -0.275027 0.6359374 280s 35 0.980782 -2.2353223 280s 36 -3.678470 -1.3459182 280s 37 -0.327102 -2.5615283 280s 38 -1.563492 -2.2008288 280s 39 1.876146 -1.0292641 280s 40 -3.204182 1.6694332 280s 41 -3.561892 -1.5844770 280s 42 -6.175135 1.0123714 280s 43 -2.736601 -0.7040261 280s 44 -4.981783 0.2434304 280s 45 0.368802 -0.5011413 280s 46 0.369508 -1.9511091 280s 47 -2.306673 -0.0089446 280s 48 0.215195 -1.1000357 280s 49 2.704678 -0.5919929 280s 50 -2.930879 2.7161936 280s 51 1.846250 0.3732500 280s 52 5.661288 -0.3139157 280s 53 1.154929 -0.0575094 280s 54 0.625715 -0.0733934 280s 55 -0.453714 -0.7535924 280s 56 0.343722 0.6460318 280s 57 1.743002 0.0794685 280s 58 0.433705 -1.3500731 280s 59 2.078550 1.0860506 280s 60 1.867913 0.7162287 280s 61 0.392645 1.6184583 280s 62 -1.958732 2.0993596 280s 63 -2.383251 -0.0253919 280s 64 -2.383251 -0.0253919 280s 65 0.780239 2.9018927 280s 66 2.785329 1.0142893 280s 67 0.131210 1.2703167 280s 68 1.110073 1.8140467 280s 69 1.076878 0.6954148 280s 70 -3.260160 -5.6233069 280s 71 2.647036 1.6892084 280s 72 -2.017340 0.5353349 280s 73 2.247524 2.6406249 280s 74 11.649291 -0.7374197 280s 75 0.280544 2.2306959 280s 76 1.791213 0.1796005 280s 77 8.730344 0.3412271 280s 78 -0.987405 1.3467910 280s 79 0.560808 0.5006661 280s 80 3.897879 -1.5270179 280s 81 -0.792759 -0.8649399 280s 82 -2.493611 1.6796838 280s 83 -2.245966 0.1889555 280s 84 -0.468812 -0.5359088 280s 85 -0.538372 2.4105954 280s 86 -0.185347 -1.0176989 280s ------------- 280s Call: 280s PcaHubert(x = x, mcd = FALSE) 280s 280s Standard deviations: 280s [1] 2.4578 1.5728 280s ---------------------------------------------------------- 281s bushfire 38 5 1 38435.075910 NA 281s Scores: 281s PC1 281s 1 -111.9345 281s 2 -113.4128 281s 3 -105.8364 281s 4 -89.1684 281s 5 -58.7216 281s 6 -35.0370 281s 7 -250.2123 281s 8 -292.6877 281s 9 -294.0765 281s 10 -290.0193 281s 11 -289.8168 281s 12 -290.8645 281s 13 -232.6865 281s 14 9.8483 281s 15 137.1924 281s 16 92.9804 281s 17 90.4493 281s 18 78.6325 281s 19 82.1178 281s 20 92.9044 281s 21 74.9157 281s 22 66.7350 281s 23 -62.1981 281s 24 -116.5696 281s 25 -53.8907 281s 26 -60.6384 281s 27 -74.7621 281s 28 -50.2202 281s 29 -38.7483 281s 30 -93.3887 281s 31 35.3096 281s 32 290.8493 281s 33 326.7236 281s 34 322.9095 281s 35 328.5307 281s 36 325.6791 281s 37 323.8136 281s 38 325.2991 281s ------------- 281s Call: 281s PcaHubert(x = x, mcd = FALSE) 281s 281s Standard deviations: 281s [1] 196.05 281s ---------------------------------------------------------- 281s ========================================================== 281s > 281s > dodata(method="locantore") 281s 281s Call: dodata(method = "locantore") 281s Data Set n p k e1 e2 281s ========================================================== 281s heart 12 2 2 1.835912 0.084745 281s Scores: 281s PC1 PC2 281s [1,] 7.3042 1.745289 281s [2,] 64.6474 0.164425 281s [3,] 1.1057 -1.404189 281s [4,] -3.1943 2.565728 281s [5,] 19.4154 -0.401369 281s [6,] -15.5709 6.666752 281s [7,] 5.9980 2.509372 281s [8,] -29.5933 -4.805972 281s [9,] -1.3933 -0.899323 281s [10,] -28.2845 -4.270057 281s [11,] -14.0069 0.048311 281s [12,] 49.1484 0.694598 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 1.35496 0.29111 281s ---------------------------------------------------------- 281s starsCYG 47 2 2 0.779919 0.050341 281s Scores: 281s PC1 PC2 281s [1,] 0.174291 -0.0489127 281s [2,] 0.703776 0.0769650 281s [3,] -0.136954 -0.1212071 281s [4,] 0.703776 0.0769650 281s [5,] 0.125991 -0.1134658 281s [6,] 0.413609 0.0121367 281s [7,] -0.466451 -0.5036094 281s [8,] 0.238569 0.1446547 281s [9,] 0.498194 -0.1998666 281s [10,] 0.065125 -0.0353931 281s [11,] 0.562344 -0.9836936 281s [12,] 0.399997 -0.0164068 281s [13,] 0.376370 0.0369013 281s [14,] -1.041009 -0.2611550 281s [15,] -0.798187 -0.0090880 281s [16,] -0.464636 0.0805967 281s [17,] -1.123135 -0.0293034 281s [18,] -0.861603 0.1297588 281s [19,] -0.884955 -0.0588007 281s [20,] 0.721130 -1.0033585 281s [21,] -0.679097 -0.0238366 281s [22,] -0.837884 -0.0041718 281s [23,] -0.623423 0.1002615 281s [24,] -0.188079 0.1168815 281s [25,] -0.032888 -0.0131784 281s [26,] -0.385242 0.0707643 281s [27,] -0.401220 -0.0582501 281s [28,] -0.151978 0.0015702 281s [29,] -0.677776 -0.0945350 281s [30,] 0.878688 -1.0329475 281s [31,] -0.628339 0.0605648 281s [32,] 0.068629 0.1556245 281s [33,] 0.174199 0.0317098 281s [34,] 1.118098 -1.0525206 281s [35,] -0.726168 -0.0784655 281s [36,] 0.592061 0.1512588 281s [37,] 0.064942 0.1258519 281s [38,] 0.174199 0.0317098 281s [39,] 0.144335 0.1160195 281s [40,] 0.519088 -0.0311555 281s [41,] -0.429855 0.0359837 281s [42,] 0.015412 0.0513747 281s [43,] 0.299435 0.0665821 281s [44,] 0.293289 0.0169612 281s [45,] 0.504064 0.0916219 281s [46,] -0.063981 0.0612071 281s [47,] -0.544029 0.0904291 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 0.88313 0.22437 281s ---------------------------------------------------------- 281s phosphor 18 2 2 0.933905 0.279651 281s Scores: 281s PC1 PC2 281s 1 4.5660 -15.58981 281s 2 -21.2978 -0.38905 281s 3 -23.3783 3.96546 281s 4 -11.7131 -5.79023 281s 5 -18.2569 2.81141 281s 6 15.5702 -20.54935 281s 7 1.3671 -3.27043 281s 8 -9.4859 3.92005 281s 9 -10.4501 6.22662 281s 10 15.0583 -7.60532 281s 11 -3.9078 1.56960 281s 12 10.0330 7.52732 281s 13 13.4815 5.50056 281s 14 7.5487 7.24752 281s 15 18.6543 2.46040 281s 16 -9.3301 -5.68285 281s 17 22.2533 4.63689 281s 18 17.7892 10.85633 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 0.96639 0.52882 281s ---------------------------------------------------------- 281s stackloss 21 3 3 1.137747 0.196704 281s Scores: 281s PC1 PC2 PC3 281s [1,] 19.98046 -6.20875 -3.93576 281s [2,] 19.57014 -7.11509 -4.03666 281s [3,] 15.48729 -3.14247 -3.29600 281s [4,] 3.12341 -1.38969 1.50633 281s [5,] 2.35380 -0.84492 -0.25745 281s [6,] 2.73860 -1.11731 0.62444 281s [7,] 5.58533 4.04837 2.11170 281s [8,] 5.58533 4.04837 2.11170 281s [9,] -0.56851 0.17483 2.46656 281s [10,] -5.36478 -4.80766 -2.64915 281s [11,] -1.67190 3.34943 -1.74110 281s [12,] -2.46702 2.71547 -2.72389 281s [13,] -4.54414 -2.99497 -2.44736 281s [14,] 0.35419 6.70241 -0.45563 281s [15,] -8.28612 5.93369 1.94314 281s [16,] -9.51708 3.21466 1.64046 281s [17,] -14.87676 -9.74652 1.10983 281s [18,] -12.00452 -3.40212 1.81609 281s [19,] -11.20939 -2.76816 2.79887 281s [20,] -5.42808 -2.89367 0.23748 281s [21,] 9.83969 0.74095 -5.30190 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 1.06665 0.44351 0.33935 281s ---------------------------------------------------------- 281s salinity 28 3 3 1.038873 0.621380 281s Scores: 281s PC1 PC2 PC3 281s 1 -2.7215590 -0.98924 0.3594538 281s 2 -3.6251829 -1.03361 1.4973993 281s 3 -6.0588883 4.23861 -1.1012038 281s 4 -6.2741857 2.42372 -1.4875092 281s 5 -5.7274076 5.42190 2.9332011 281s 6 -5.8431892 0.57161 -0.3385363 281s 7 -4.4051377 -0.83292 0.0851817 281s 8 -2.6155827 -2.50739 0.3386166 281s 9 -0.0426575 1.19631 -2.5025726 281s 10 2.5297488 1.65029 -0.0110335 281s 11 1.5528097 1.93255 1.4216262 281s 12 -0.3140451 -0.73269 -0.1961364 281s 13 0.0010783 -1.88658 0.1849912 281s 14 1.9554303 -2.13519 1.8471356 281s 15 2.7897250 2.40211 -0.6327944 281s 16 -1.7665706 8.69449 5.6608836 281s 17 -0.4374125 1.72696 0.7230753 281s 18 -2.9752196 -0.54118 -0.6829760 281s 19 -0.0599346 0.84127 -2.8473543 281s 20 1.6597909 0.34191 -1.4847516 281s 21 1.3857395 -2.43924 0.0039271 281s 22 2.6664754 -3.14291 1.0600254 281s 23 4.1202067 3.81886 1.0608640 281s 24 2.4163743 3.45141 1.6874099 281s 25 0.8493897 0.31424 -0.3073115 281s 26 1.4216265 -1.55310 -0.5455012 281s 27 2.3021676 -2.63392 0.0481451 281s 28 3.0877115 -2.85951 1.4378956 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 1.01925 0.78828 0.36470 281s ---------------------------------------------------------- 281s hbk 75 3 3 1.038833 0.363386 281s Scores: 281s PC1 PC2 PC3 281s 1 32.393698 -3.4318297 0.051248 281s 2 33.103072 -4.4154651 0.294662 281s 3 35.038965 -3.5996035 -0.940929 281s 4 35.955809 -4.9285404 -0.479059 281s 5 35.424918 -4.3076292 -0.366699 281s 6 33.753497 -3.2463136 0.289013 281s 7 33.817375 -3.6819421 0.684167 281s 8 32.717119 -3.7074394 -0.279567 281s 9 34.932190 -4.6939061 -0.738196 281s 10 33.737339 -4.5702346 -1.193206 281s 11 40.202273 -5.4336890 -0.229323 281s 12 41.638189 -4.5304173 -1.996311 281s 13 40.768565 -5.0531048 2.123222 281s 14 44.408749 -8.8448536 8.236462 281s 15 0.977343 1.3057899 0.938694 281s 16 -0.900390 1.6169842 1.382855 281s 17 -2.384467 -0.9835430 0.375495 281s 18 -0.143306 0.7859701 -0.237712 281s 19 -0.344479 -0.9791245 0.733869 281s 20 1.199115 0.8330752 1.216827 281s 21 0.184475 0.8630593 0.351029 281s 22 -0.100389 -1.5084406 0.718236 281s 23 -0.847925 0.4823829 0.958677 281s 24 -1.334366 -0.1021190 1.000300 281s 25 -2.669352 0.4692990 -0.811134 281s 26 0.601538 -1.1984283 0.541627 281s 27 1.373423 1.2098621 0.136249 281s 28 -0.721268 0.6164612 -0.963817 281s 29 -1.832615 0.2543279 -0.297658 281s 30 0.120086 -0.1558590 -1.976558 281s 31 -0.747437 1.7749106 0.342824 281s 32 -1.727558 -0.8325772 1.043088 281s 33 -0.073907 -0.3923823 1.083904 281s 34 -2.646454 -0.1350138 -1.101448 281s 35 1.331096 1.0443905 -0.039328 281s 36 0.281192 -0.6569943 -0.404009 281s 37 0.245349 -1.8406517 0.093656 281s 38 -2.049446 0.5320301 0.347219 281s 39 -1.645547 1.3268749 -1.068792 281s 40 -1.216874 -0.8556007 0.201262 281s 41 0.959445 1.6250030 -0.553881 281s 42 -0.603579 -0.9569812 -1.502730 281s 43 -0.946870 -1.6333180 1.324763 281s 44 0.076217 0.5018427 -1.902369 281s 45 -2.140584 1.2192726 -0.677180 281s 46 -0.081677 0.5389288 -1.785347 281s 47 -1.590461 2.1881067 -0.583771 281s 48 0.931421 1.3321181 -0.669782 281s 49 0.512639 1.2123979 0.683099 281s 50 1.095415 0.0045968 0.143109 281s 51 -1.456417 1.1186245 0.619657 281s 52 -0.917904 2.2084467 -0.366392 281s 53 -0.429654 -0.8524437 -2.326637 281s 54 -1.213858 -0.4996891 1.630709 281s 55 -1.253877 -0.9438354 0.692022 281s 56 -0.390657 -0.0427482 1.571167 281s 57 -1.797537 0.8934866 -0.281980 281s 58 0.396886 0.3227454 1.492494 281s 59 0.646360 -0.2194210 -0.562699 281s 60 0.119900 -1.2480691 -1.459763 281s 61 0.867946 -1.7843458 0.232229 281s 62 -2.733997 0.3604288 -0.692947 281s 63 -1.442683 -0.3732483 1.452800 281s 64 1.444934 0.5727959 0.434633 281s 65 -0.147284 0.7055205 -1.413940 281s 66 -1.739552 -0.9838385 0.220303 281s 67 -0.824644 0.1503195 0.411693 281s 68 -2.437638 -0.4835278 -1.392882 281s 69 -2.091970 1.1865192 -0.088483 281s 70 0.403429 -0.7855276 -0.540161 281s 71 0.507512 0.3152001 0.276885 281s 72 -0.944376 -0.8197825 0.044859 281s 73 -0.648597 -1.1160277 -0.658528 281s 74 -0.979453 -1.4589411 0.029182 281s 75 -0.982282 -0.7226425 -1.917060 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 1.01923 0.60282 0.46137 281s ---------------------------------------------------------- 281s milk 86 8 8 1.175171 0.426506 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 PC6 281s [1,] 6.1907998 0.58762698 0.686510 -0.209679 0.3321757 -1.3424985 281s [2,] 7.0503894 -0.49576086 -0.322697 -0.767415 -0.0165833 -1.4596064 281s [3,] 0.7670594 -1.83556812 0.468814 0.346810 -0.0204610 -0.2115383 281s [4,] 5.4656748 -2.29797862 1.612819 -0.378295 -0.2050232 0.3486957 281s [5,] -1.0291160 0.37303007 0.634604 -0.521527 -0.3299543 0.0859469 281s [6,] 2.2186300 0.39396818 -0.236987 -0.033975 -0.2549238 0.2541221 281s [7,] 2.7938591 -0.01152811 -0.600546 -0.098564 -0.3906602 0.3798516 281s [8,] 2.9544176 0.32646226 0.273051 -0.275073 -0.3982959 0.2377581 281s [9,] -1.3344639 -2.45440308 1.001792 -0.104783 -0.1744718 -0.0887272 281s [10,] -2.9294174 -0.79860558 -0.260533 0.375330 0.3425169 -0.2056682 281s [11,] -9.5810648 -0.09577968 1.565111 -0.112002 0.3143032 -0.3190238 281s [12,] -13.1147240 2.95665890 0.228086 -0.180867 0.0136463 -0.4604390 281s [13,] -10.2989319 1.53220781 -2.244629 0.323950 -0.0398642 -0.3463501 281s [14,] -12.2553418 1.62281167 -0.472862 -0.212983 -0.4124280 -0.4253719 281s [15,] -10.8346894 -0.09781844 2.134079 -0.272304 -0.1090226 -0.3725738 281s [16,] -2.8358474 0.28109809 0.945309 0.603249 0.1615955 0.1762086 281s [17,] -1.0353408 -2.75475311 1.677879 0.598578 0.0078965 0.0228522 281s [18,] -2.0271810 1.25894451 -0.266038 -0.168565 -0.3000200 0.2891774 281s [19,] -1.9279394 -0.68339726 1.264416 0.186749 0.3018226 -0.0869321 281s [20,] 0.2568334 -1.62632029 0.854279 -0.088175 0.5458645 0.2217019 281s [21,] -2.7017404 2.45223507 -0.243639 -0.211402 -0.2102323 0.2140100 281s [22,] -1.0386097 0.99459030 0.188462 -0.033434 -0.2857078 -0.1438517 281s [23,] -0.0198126 1.73285416 0.761979 0.005501 0.1671992 -0.0375468 281s [24,] -0.4909448 1.40982693 0.967440 0.521275 0.1625359 -0.0892501 281s [25,] -3.6632699 2.51414455 0.966410 -0.272694 0.0467958 0.1572715 281s [26,] -0.8733564 1.42247465 0.946038 -0.338985 -0.0804141 -0.0080759 281s [27,] -3.2254798 0.26912538 0.799468 0.372442 -0.6886191 -0.0553515 281s [28,] 2.4675785 3.56128696 0.813964 0.118354 -0.1677073 -0.0303774 281s [29,] 4.4177264 1.13316321 0.613509 0.261488 0.4229929 0.1780620 281s [30,] 0.8240097 1.54163297 0.398148 -0.221825 0.0309586 0.0830110 281s [31,] 1.7735990 2.00615332 -1.399933 0.469158 -0.0740282 0.0692312 281s [32,] 1.2348922 0.28918604 -1.239899 0.470999 -0.1511519 -0.3692504 281s [33,] 1.9407276 0.19123540 0.406623 0.389965 0.0994854 -0.0204286 281s [34,] 0.6225565 -0.65636700 0.565253 0.369897 -0.1612501 -0.1774611 281s [35,] -0.4869219 2.26301333 0.071825 0.588101 -0.0579092 -0.0362009 281s [36,] 4.1117242 1.16638974 0.982790 -0.266009 0.0728797 -0.0018914 281s [37,] 0.8415225 2.46677043 -0.526780 0.167456 -0.2370116 -0.0731483 281s [38,] 2.0528334 2.09648023 0.220912 0.206722 -0.1924842 0.0676382 281s [39,] -1.4493644 1.14916103 0.904194 0.455498 0.0678893 -0.1476540 281s [40,] 3.4867792 -1.82367389 0.730183 0.499859 0.2327704 -0.1518819 281s [41,] 4.0222120 1.34765470 0.580852 -0.453301 0.2482908 -1.5306566 281s [42,] 6.4789035 -1.25599522 1.644194 0.381331 0.1699942 0.1847594 281s [43,] 3.1529354 0.44884526 -0.967114 -0.220364 0.0037036 0.0802727 281s [44,] 5.3344976 -0.47975673 0.642789 0.298705 0.9983145 -0.1310548 281s [45,] 0.0325597 0.49900084 0.076948 0.486521 0.1642679 0.1392696 281s [46,] 0.1014401 1.97657735 0.733879 0.127235 0.0650844 -0.0144271 281s [47,] 2.7217685 -0.37859042 -3.696163 0.355401 -0.4123714 0.2114024 281s [48,] 0.2292225 1.01473918 -1.115726 0.434557 0.2668316 0.0103147 281s [49,] -2.2803784 0.59474034 -1.783003 0.549252 0.4660435 -0.0802352 281s [50,] 3.1560404 -2.84820361 0.913015 0.077151 0.5803961 0.0350246 281s [51,] -1.4680905 -0.43078891 -1.733657 0.074684 0.0026718 0.0819023 281s [52,] -5.2469034 0.48385240 -1.246027 0.081379 0.2380924 -0.1663831 281s [53,] -0.7670982 0.00234561 -0.923030 -0.366820 0.1582141 0.0508747 281s [54,] -0.2428655 0.04714401 -0.217187 -0.059549 0.1762969 0.0806339 281s [55,] 0.8723441 0.66109329 -0.224917 -0.360607 -0.0638127 0.1310131 281s [56,] 0.0019700 -0.67624071 0.081304 -0.182908 0.1045597 -0.0281936 281s [57,] -1.3684663 -0.00045069 0.860560 -0.350684 -0.1443970 -0.2270651 281s [58,] 0.0079047 1.36376727 0.750919 -0.437914 -0.1894910 0.2345556 281s [59,] -1.7430794 -1.06973583 -0.569381 -0.055139 -0.1582790 -0.0873605 281s [60,] -1.5171606 -0.69340281 -0.287048 -0.136559 -0.3871182 0.1606979 281s [61,] -0.0955085 -1.64221260 0.263650 -0.265665 -0.0808644 -0.0476862 281s [62,] 2.2259171 -2.22161516 0.426279 0.027834 0.2924338 -0.1784242 281s [63,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 281s [64,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 281s [65,] -0.5520071 -2.86186682 0.746248 0.109945 0.0556927 -0.0135739 281s [66,] -2.4472964 -0.94969715 -0.329042 -0.113895 -0.2728443 -0.0523337 281s [67,] 0.1790969 -1.29190443 0.146657 0.140234 0.1534048 0.2318353 281s [68,] -0.8017055 -1.93331421 -1.968273 0.017854 0.1287513 -0.2306786 281s [69,] -0.7356418 -0.68868398 -0.075215 -0.156944 0.0302876 0.4232626 281s [70,] 3.8821693 5.16959880 0.215490 -8.985938 5.2189361 -2.8089276 281s [71,] -2.3478937 -1.60220695 0.058822 -0.111845 -0.0539018 0.0087982 281s [72,] 2.3676739 -0.70331436 -0.214457 -0.307311 -0.1582719 0.3995413 281s [73,] -1.9906385 -2.60946629 -0.730312 0.485522 -0.2391998 0.1009341 281s [74,] -11.2435515 1.44868683 2.482678 0.026711 0.4922865 -0.2822136 281s [75,] 0.0044207 -2.29768358 -0.692425 0.538923 -0.4110598 -0.0824903 281s [76,] -1.4045239 -0.22649785 -1.343257 -0.067382 -0.1322233 -0.1072330 281s [77,] -8.3637576 0.14167751 1.267616 0.384528 -0.0728561 -0.4017300 281s [78,] 1.3022939 -1.47457541 -0.394623 -0.068014 -0.1502832 0.0757414 281s [79,] -0.1950676 -0.58254701 -0.824931 -0.088174 -0.2071634 -0.1896613 281s [80,] -3.4432989 1.73593273 0.777996 0.094211 0.2377017 -0.1520088 281s [81,] 1.2167258 0.77512068 0.085803 -0.214850 -0.2201173 0.0432435 281s [82,] 2.7778798 -1.80071342 0.583878 0.465898 0.0648352 0.2148470 281s [83,] 2.6218578 -0.39825539 -0.553372 -0.145721 -0.0977092 -0.2485337 281s [84,] 0.8946018 0.33790104 -1.974267 0.091828 0.0051986 -0.2606274 281s [85,] 0.7759316 -2.34860124 2.423325 -0.384149 -0.0167182 -0.0353374 281s [86,] 0.6266756 0.87099609 -1.407948 -0.237762 0.0361644 0.1675792 281s PC7 PC8 281s [1,] -0.1014312 1.5884e-03 281s [2,] -0.3831443 1.0212e-03 281s [3,] -0.7164683 1.2035e-03 281s [4,] 0.0892864 3.5409e-04 281s [5,] -0.0943992 1.0547e-03 281s [6,] 0.1184847 1.5031e-03 281s [7,] -0.2509793 1.6850e-05 281s [8,] -0.0136880 7.0308e-04 281s [9,] 0.2238736 -1.9164e-04 281s [10,] 0.0754413 1.3614e-04 281s [11,] 0.0784380 3.5175e-04 281s [12,] 0.2033489 -1.3174e-03 281s [13,] 0.2139525 -1.7101e-03 281s [14,] 0.1209735 -9.1070e-04 281s [15,] 0.2119647 -9.2843e-04 281s [16,] -0.3011483 -2.1474e-03 281s [17,] 0.0660858 -1.9036e-03 281s [18,] -0.5199396 -9.4385e-04 281s [19,] -0.1232622 -1.2649e-03 281s [20,] -0.3900208 -2.6927e-04 281s [21,] 0.0264834 7.6074e-05 281s [22,] -0.0736288 1.7240e-04 281s [23,] -0.2156005 -5.5661e-04 281s [24,] 0.1143327 -2.5248e-04 281s [25,] 0.0481580 -6.1531e-04 281s [26,] -0.0084802 -7.5928e-04 281s [27,] -0.2173883 -3.0971e-04 281s [28,] 0.3288873 -1.8975e-04 281s [29,] 0.0788974 -7.2436e-04 281s [30,] -0.0598663 -3.0463e-04 281s [31,] -0.1511658 -4.8751e-04 281s [32,] -0.0532375 -2.5207e-04 281s [33,] -0.0635290 -3.9270e-04 281s [34,] 0.1598240 1.3024e-04 281s [35,] -0.0355175 -8.5374e-05 281s [36,] -0.0174096 -6.3294e-04 281s [37,] -0.2883141 -5.2809e-04 281s [38,] 0.1426412 5.3331e-04 281s [39,] 0.0313308 4.2738e-04 281s [40,] -0.3536195 -3.4170e-04 281s [41,] -0.3925168 1.4588e-04 281s [42,] -0.0056267 -9.1925e-04 281s [43,] -0.4447402 -1.8415e-04 281s [44,] 0.9184385 -5.9685e-04 281s [45,] -0.0340987 7.2924e-04 281s [46,] -0.0162866 9.7800e-04 281s [47,] 0.2428769 -1.1208e-03 281s [48,] 0.3026758 -4.5769e-04 281s [49,] 0.0246345 -2.6207e-04 281s [50,] 0.0857698 7.6439e-05 281s [51,] 0.1136658 1.3013e-04 281s [52,] 0.3993357 6.2796e-04 281s [53,] -0.1765161 1.1329e-04 281s [54,] 0.0016144 2.5870e-04 281s [55,] 0.1064371 5.8188e-04 281s [56,] 0.0207478 -8.7595e-05 281s [57,] 0.1560065 6.3987e-05 281s [58,] 0.1684561 -5.0193e-05 281s [59,] 0.0778732 -8.5458e-04 281s [60,] 0.0037585 1.0429e-05 281s [61,] -0.0296083 3.1526e-05 281s [62,] 0.0913974 -2.2794e-04 281s [63,] 0.0358917 -7.3721e-04 281s [64,] 0.0358917 -7.3721e-04 281s [65,] 0.1209159 2.9398e-04 281s [66,] -0.0027574 2.9380e-04 281s [67,] -0.0091059 -2.7494e-04 281s [68,] 0.0555970 -3.3016e-04 281s [69,] -0.0149255 -3.1228e-04 281s [70,] 0.9282997 4.7859e-05 281s [71,] 0.2630142 4.2617e-04 281s [72,] 0.1063248 -3.0070e-04 281s [73,] -0.1462452 4.9607e-04 281s [74,] 0.2027591 2.6399e-03 281s [75,] 0.6934350 6.0284e-04 281s [76,] -0.0430524 8.1271e-04 281s [77,] 0.0789302 1.4655e-03 281s [78,] -0.0318359 5.2799e-04 281s [79,] -0.1269568 2.9497e-04 281s [80,] 0.2903958 7.8932e-04 281s [81,] 0.0979443 -3.1531e-04 281s [82,] -0.0548155 4.2140e-04 281s [83,] -0.0371550 -5.6653e-04 281s [84,] -0.0835149 -7.0682e-04 281s [85,] 0.1864954 1.0604e-03 281s [86,] 0.1074252 -7.4859e-04 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 1.08405293 0.65307452 0.28970076 0.11162824 0.09072195 0.06659711 0.05888048 281s [8] 0.00022877 281s ---------------------------------------------------------- 281s bushfire 38 5 5 1.464779 0.043290 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 281s [1,] -69.9562 -13.0364 0.98678 1.054123 2.411188 281s [2,] -71.5209 -10.5459 0.31081 1.631208 1.663470 281s [3,] -63.9308 -7.4622 -2.43241 0.671038 0.465836 281s [4,] -47.0413 -9.6343 -3.83609 0.758349 0.683983 281s [5,] -15.9088 -20.1737 -5.55893 1.181744 -0.053563 281s [6,] 8.3484 -30.7646 -5.51541 1.877227 1.338037 281s [7,] -207.7458 -66.2492 34.48519 -5.894885 -1.051729 281s [8,] -246.4327 -97.0433 -9.57057 22.286225 -9.234869 281s [9,] -247.5984 -98.8613 -12.13406 23.948770 -9.250401 281s [10,] -245.8121 -79.2634 12.47990 13.046128 -5.125478 281s [11,] -246.8887 -62.5899 21.21764 9.111011 -5.080985 281s [12,] -251.1354 -9.2115 31.77448 0.236379 0.707528 281s [13,] -194.0239 27.1288 21.05023 0.940913 1.781359 281s [14,] 51.7182 8.5038 -11.22109 -2.132458 1.984807 281s [15,] 180.5597 -4.8151 -21.36630 -9.390663 -0.817036 281s [16,] 135.7246 -5.0756 -11.33517 -10.015567 -1.670831 281s [17,] 133.0151 -4.0344 -8.95540 -7.702087 -0.923277 281s [18,] 121.2619 -9.0627 -5.96042 -7.210971 -2.092872 281s [19,] 124.9038 -10.6649 -7.22555 -5.349553 -1.771009 281s [20,] 135.5410 -6.8146 -7.52834 -5.562769 -0.396924 281s [21,] 117.1950 -3.5643 -4.67473 -6.862117 -0.234551 281s [22,] 108.9944 -2.3344 -5.90349 -5.928299 1.455538 281s [23,] -21.4031 8.0668 6.19525 -4.784890 0.671394 281s [24,] -76.3499 16.7804 6.52545 -1.391250 1.219282 281s [25,] -12.5732 6.1109 -1.45259 -3.512072 -0.375837 281s [26,] -19.1800 3.4685 -2.02243 -3.490028 -0.169127 281s [27,] -33.6733 12.0757 -3.53322 0.048666 0.067468 281s [28,] -9.3966 21.5055 -5.91671 2.650895 -0.449672 281s [29,] 1.4123 35.8559 -5.98222 5.982362 0.613667 281s [30,] -54.2683 39.6029 7.82694 6.759994 0.035048 281s [31,] 74.8866 34.9048 10.03986 12.592158 0.149308 281s [32,] 331.4144 9.3079 27.73391 17.334531 1.015536 281s [33,] 367.6915 -19.5135 48.52753 10.213314 -1.268047 281s [34,] 363.8686 -20.4079 49.32855 8.986581 -1.930673 281s [35,] 369.4371 -19.5074 49.66761 9.001542 -0.179566 281s [36,] 366.5850 -20.2555 50.30290 7.745330 -2.259131 281s [37,] 364.5463 -19.8198 53.00407 6.757796 -1.083372 281s [38,] 365.9709 -19.3753 53.80168 6.467284 -0.854384 281s ------------- 281s Call: 281s PcaLocantore(x = x) 281s 281s Standard deviations: 281s [1] 1.210280 0.208063 0.177790 0.062694 0.014423 281s ---------------------------------------------------------- 281s ========================================================== 281s > dodata(method="cov") 281s 281s Call: dodata(method = "cov") 281s Data Set n p k e1 e2 281s ========================================================== 281s heart 12 2 2 685.776266 13.127306 281s Scores: 281s PC1 PC2 281s 1 8.18562 1.17998 281s 2 65.41185 -2.80723 281s 3 1.86039 -1.70646 281s 4 -2.26910 2.44051 281s 5 20.19603 -1.47331 281s 6 -14.46264 7.05759 281s 7 6.91264 1.99823 281s 8 -28.95436 -3.81624 281s 9 -0.61523 -1.09711 281s 10 -27.62427 -3.33575 281s 11 -13.17788 0.37931 281s 12 49.94879 -1.62675 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 26.1873 3.6232 281s ---------------------------------------------------------- 281s starsCYG 47 2 2 0.280150 0.007389 281s Scores: 281s PC1 PC2 281s 1 0.272263 -0.07964458 281s 2 0.804544 0.03382837 281s 3 -0.040587 -0.14464760 281s 4 0.804544 0.03382837 281s 5 0.222468 -0.14305159 281s 6 0.512941 -0.02420304 281s 7 -0.378928 -0.51924735 281s 8 0.341045 0.11236831 281s 9 0.592550 -0.23812462 281s 10 0.163442 -0.06357822 281s 11 0.638370 -1.02323643 281s 12 0.498667 -0.05242075 281s 13 0.476291 0.00142479 281s 14 -0.947664 -0.26343572 281s 15 -0.699020 -0.01711057 281s 16 -0.363464 0.06475681 281s 17 -1.024352 -0.02972862 281s 18 -0.759174 0.12317995 281s 19 -0.786925 -0.06478250 281s 20 0.796654 -1.04660568 281s 21 -0.580307 -0.03463751 281s 22 -0.738591 -0.01126825 281s 23 -0.521748 0.08812607 281s 24 -0.086135 0.09457052 281s 25 0.065975 -0.03907968 281s 26 -0.284322 0.05307219 281s 27 -0.303309 -0.07553370 281s 28 -0.052738 -0.02155274 281s 29 -0.580638 -0.10534741 281s 30 0.953478 -1.07986770 281s 31 -0.527590 0.04855502 281s 32 0.171408 0.12730538 281s 33 0.274054 0.00095808 281s 34 1.192364 -1.10502882 281s 35 -0.628641 -0.08815176 281s 36 0.694595 0.11071187 281s 37 0.167026 0.09762710 281s 38 0.274054 0.00095808 281s 39 0.246168 0.08594248 281s 40 0.617380 -0.06994769 281s 41 -0.329735 0.01934346 281s 42 0.115770 0.02432733 281s 43 0.400071 0.03289494 281s 44 0.392768 -0.01656886 281s 45 0.605229 0.05314718 281s 46 0.036628 0.03601196 281s 47 -0.442606 0.07644144 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 0.529292 0.085957 281s ---------------------------------------------------------- 281s phosphor 18 2 2 288.018150 22.020514 281s Scores: 281s PC1 PC2 281s 1 2.7987 -19.015683 281s 2 -20.4311 -0.032022 281s 3 -21.8198 4.589809 281s 4 -11.7869 -6.837833 281s 5 -16.9357 2.664785 281s 6 12.9132 -25.602526 281s 7 1.5249 -6.351664 281s 8 -8.0984 2.416616 281s 9 -8.6979 4.843680 281s 10 14.3903 -12.732868 281s 11 -2.9462 -0.760656 281s 12 11.7427 2.991004 281s 13 14.8400 0.459849 281s 14 9.2449 3.095095 281s 15 19.4860 -3.336883 281s 16 -9.4156 -7.096788 281s 17 23.3759 -1.737460 281s 18 19.9173 5.092467 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 16.9711 4.6926 281s ---------------------------------------------------------- 281s stackloss 21 3 3 28.153060 8.925048 281s Scores: 281s PC1 PC2 PC3 281s [1,] 10.538448 13.596944 12.84989 281s [2,] 9.674846 14.098881 12.89733 281s [3,] 8.993255 9.221043 9.94062 281s [4,] 1.744427 3.649104 0.17292 281s [5,] 0.980215 2.223126 1.34874 281s [6,] 1.362321 2.936115 0.76083 281s [7,] 6.926040 0.637480 -0.11170 281s [8,] 6.926040 0.637480 -0.11170 281s [9,] 0.046655 0.977727 -2.46930 281s [10,] -7.909092 0.926343 0.80232 281s [11,] -0.136672 -3.591094 0.37539 281s [12,] -1.382381 -3.802146 1.01074 281s [13,] -6.181887 -0.077532 0.70744 281s [14,] 3.699843 -4.885854 -0.40226 281s [15,] -2.768005 -7.507870 -6.08487 281s [16,] -5.358811 -6.002058 -5.94256 281s [17,] -17.067135 1.738055 -5.86637 281s [18,] -11.021920 -1.775507 -6.19842 281s [19,] -9.776212 -1.564455 -6.83377 281s [20,] -6.075508 0.369252 -2.08345 281s [21,] 6.301743 2.706174 8.79509 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 5.3059 2.9875 1.3020 281s ---------------------------------------------------------- 281s salinity 28 3 3 11.801732 3.961826 281s Scores: 281s PC1 PC2 PC3 281s 1 -1.59888 1.582157 0.135248 281s 2 -2.26975 2.429177 1.107832 281s 3 -6.79543 -2.034636 0.853876 281s 4 -6.36795 -0.602960 -0.267268 281s 5 -6.42044 -1.520259 5.022962 281s 6 -5.13821 1.225470 0.016977 281s 7 -3.24014 1.998671 -0.123418 281s 8 -0.93998 2.789889 -0.515656 281s 9 -0.30856 -2.424345 -1.422752 281s 10 2.20362 -2.800513 1.142127 281s 11 1.38120 -2.076832 2.515630 281s 12 0.44997 0.207439 -0.152835 281s 13 1.21669 1.193701 -0.277116 281s 14 3.31664 1.306627 1.213342 281s 15 2.08484 -3.774814 0.905400 281s 16 -3.64862 -4.677257 9.046484 281s 17 -0.46124 -1.411762 1.706719 281s 18 -2.13038 0.890401 -0.633349 281s 19 -0.23610 -2.262304 -1.885048 281s 20 1.70337 -1.970773 -0.781880 281s 21 2.67273 1.038742 -0.610945 281s 22 4.24561 1.547290 0.108927 281s 23 2.99619 -4.785343 3.094945 281s 24 1.64474 -3.564562 3.432429 281s 25 1.11703 -1.158030 0.237700 281s 26 2.30707 0.069668 -0.735809 281s 27 3.59356 0.860498 -0.611380 281s 28 4.57550 1.300407 0.589307 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 3.43536 1.99043 0.94546 281s ---------------------------------------------------------- 281s hbk 75 3 3 1.436470 1.181766 281s Scores: 281s PC1 PC2 PC3 281s 1 31.105415 -4.714217 10.4566165 281s 2 31.707650 -5.748724 10.7682402 281s 3 33.366131 -4.625897 12.1570167 281s 4 34.173377 -6.069657 12.4466895 281s 5 33.780418 -5.508823 11.9872893 281s 6 32.493478 -4.684595 10.5679819 281s 7 32.592637 -5.235522 10.3765493 281s 8 31.293363 -4.865797 10.9379676 281s 9 33.160964 -5.714260 12.3098920 281s 10 31.919786 -5.384537 12.3374332 281s 11 38.231962 -6.810641 13.5994385 281s 12 39.290479 -5.393906 15.2942554 281s 13 39.418445 -7.326461 11.5194898 281s 14 43.906584 -13.214819 8.3282743 281s 15 1.906326 0.716061 -0.8635112 281s 16 0.263255 0.926016 -1.9009292 281s 17 -1.776489 -1.072332 -0.5496140 281s 18 0.464648 0.702441 0.0482897 281s 19 0.267826 -1.283779 -0.2925812 281s 20 2.122108 0.165970 -0.8924686 281s 21 0.937217 0.548532 -0.4132196 281s 22 0.423273 -1.781869 -0.0323061 281s 23 0.047532 0.018909 -1.1259327 281s 24 -0.490041 -0.520202 -1.1065753 281s 25 -2.143049 0.720869 -0.0495474 281s 26 1.094748 -1.459175 0.2226246 281s 27 2.070705 0.898573 0.0023229 281s 28 -0.294998 0.830258 0.5929001 281s 29 -1.242995 0.300216 -0.2010507 281s 30 0.147958 0.439099 2.0003038 281s 31 0.170818 1.440946 -0.9755627 281s 32 -0.958531 -1.199730 -1.0129867 281s 33 0.697307 -0.874343 -0.7260649 281s 34 -2.278946 0.261106 0.4196544 281s 35 1.962829 0.809318 0.2033113 281s 36 0.626631 -0.600666 0.8004036 281s 37 0.550885 -1.881448 0.7382776 281s 38 -1.249717 0.336214 -0.9349845 281s 39 -1.106696 1.569418 0.1869576 281s 40 -0.684034 -0.939963 -0.1034965 281s 41 1.559314 1.551408 0.3660323 281s 42 -0.538741 -0.447358 1.6361099 281s 43 -0.252685 -2.080564 -0.7765259 281s 44 0.217012 1.027281 1.7015154 281s 45 -1.497600 1.349234 -0.2698932 281s 46 0.100388 1.026443 1.5390401 281s 47 -0.811117 2.195271 -0.5208141 281s 48 1.462210 1.321318 0.5600144 281s 49 1.383976 0.740714 -0.7348906 281s 50 1.636773 -0.215464 0.3195369 281s 51 -0.530918 0.759743 -1.2069247 281s 52 -0.109566 2.107455 -0.5315473 281s 53 -0.564334 -0.060847 2.3910630 281s 54 -0.272234 -1.122711 -1.5060028 281s 55 -0.608660 -1.197219 -0.5255609 281s 56 0.565430 -0.710345 -1.3708230 281s 57 -1.115629 0.888816 -0.4186014 281s 58 1.351288 -0.374815 -1.1980618 281s 59 0.998016 -0.151228 0.9007970 281s 60 0.124017 -0.764846 1.9005963 281s 61 1.189858 -1.905264 0.7721322 281s 62 -2.190589 0.579614 -0.1377914 281s 63 -0.518278 -0.931130 -1.4534768 281s 64 2.124566 0.194391 -0.0327092 281s 65 0.154218 1.050861 1.1309885 281s 66 -1.197852 -1.044147 -0.2265269 281s 67 -0.114174 -0.094763 -0.5168926 281s 68 -2.201115 0.032271 0.8573493 281s 69 -1.307843 1.104815 -0.7741270 281s 70 0.691449 -0.676665 1.0004603 281s 71 1.150975 0.050861 -0.0717068 281s 72 -0.457293 -0.861871 0.1026350 281s 73 -0.392258 -0.897451 0.9178065 281s 74 -0.584658 -1.450471 0.3201857 281s 75 -0.972517 -0.063777 1.8223995 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 1.1985 1.0871 1.0086 281s ---------------------------------------------------------- 281s milk 86 8 8 5.758630 2.224809 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 PC6 281s 1 5.7090867 1.388263 0.0055924 0.3510505 -0.7335114 -1.41950731 281s 2 6.5825186 0.480410 -1.1356236 -0.3250838 -0.7343177 -1.71595400 281s 3 0.7433619 -1.749281 0.2510521 0.3450575 0.2996413 -0.34585702 281s 4 5.5733255 -1.588521 0.8934908 -0.3412408 0.0087626 0.07235942 281s 5 -1.3030839 0.142394 0.8487785 -0.5847851 0.0588053 -0.08968553 281s 6 1.7708705 0.674240 -0.4153759 -0.1915734 0.1382138 0.12454293 281s 7 2.3570866 0.381017 -0.8771357 -0.3739365 0.2918453 0.13437364 281s 8 2.5700714 0.695006 0.0061108 -0.4323695 0.1643797 -0.00469369 281s 9 -1.1725766 -2.713291 1.0677483 -0.0647875 0.1183120 -0.10762785 281s 10 -3.1357225 -1.255175 0.0666017 0.5083690 -0.1096080 -0.00647493 281s 11 -9.5333894 -1.608943 2.7307809 0.1690156 -0.1682415 -0.06597478 281s 12 -13.6028505 0.941083 2.0136258 -0.1076520 -0.0475905 -0.15295614 281s 13 -10.9497471 0.048776 -0.8765307 0.1518572 0.1428294 -0.00064406 281s 14 -12.6558378 -0.219444 1.1396273 -0.3734679 0.2875578 -0.23870524 281s 15 -10.6924790 -1.818075 3.4560731 -0.1177943 0.1101199 -0.19708172 281s 16 -3.0258070 -0.203186 1.2835368 0.5799363 0.3237454 0.23168871 281s 17 -0.7498665 -2.977505 1.6310512 0.6305329 0.3994006 0.06594881 281s 18 -2.5093526 0.924459 0.0899818 -0.4026675 0.2963072 0.11324019 281s 19 -1.9689970 -1.051282 1.4659908 0.3870104 -0.0708083 -0.02148354 281s 20 0.2695886 -1.646440 0.7597630 0.1750131 -0.3418142 0.21515143 281s 21 -3.3470252 1.989939 0.2887021 -0.3599779 0.0771965 0.16867095 281s 22 -1.4659204 0.777242 0.4090149 -0.1248050 0.1916768 -0.23160291 281s 23 -0.4944476 1.634130 0.8915509 0.1222296 -0.1231015 -0.08351169 281s 24 -0.8945477 1.239223 1.1117165 0.6018455 0.0912200 -0.01204668 281s 25 -4.1499992 1.860190 1.6062973 -0.2139736 -0.1140169 0.16632426 281s 26 -1.2647012 1.188058 1.1893430 -0.2740862 -0.0971504 -0.09851714 281s 27 -3.4280131 -0.267150 1.1969552 0.0354366 0.8482718 -0.18977667 281s 28 1.6896630 3.793723 0.7706325 0.1007287 0.0317704 -0.11269816 281s 29 3.9258127 1.691428 0.1850999 0.4485202 -0.2969916 0.16594044 281s 30 0.3178322 1.577233 0.4455231 -0.1687197 -0.1587136 -0.00823174 281s 31 0.9562350 2.258138 -1.4672169 0.2675668 0.1910110 0.03177387 281s 32 0.6738452 0.470764 -1.3496896 0.3524049 0.2008218 -0.36957179 281s 33 1.5980690 0.413899 0.1999664 0.4232293 0.0768479 -0.04627841 281s 34 0.4365091 -0.626490 0.4718364 0.3392252 0.2554060 -0.19018602 281s 35 -1.1184804 2.124234 0.2650931 0.4791171 0.2927791 -0.01579964 281s 36 3.6673986 1.659798 0.6138972 -0.1092158 -0.2705583 -0.16494176 281s 37 0.0867143 2.541765 -0.4572593 0.0024263 0.2163300 -0.20116352 281s 38 1.4191839 2.315690 0.1365887 0.1028375 0.1595780 -0.02049460 281s 39 -1.8062960 0.845438 1.1469588 0.5022406 0.1603011 -0.08751261 281s 40 3.4380914 -1.358545 0.1956896 0.6314649 0.0716078 -0.21591535 281s 41 3.4608782 1.828575 0.2012565 0.1064437 -0.7454169 -1.64629924 281s 42 6.4162310 -0.402642 0.8070441 0.5146855 0.0331594 0.04373032 281s 43 2.5906567 0.897993 -1.2612252 -0.2620162 -0.1432569 -0.10279385 281s 44 5.0299750 0.203721 0.0439110 0.8775684 -0.9536011 0.15153452 281s 45 -0.3555392 0.454930 0.1173992 0.4688991 0.1137820 0.18752442 281s 46 -0.4155426 1.892410 0.8649578 0.1827426 -0.0186113 -0.04029205 281s 47 1.9328817 0.121936 -3.9578157 -0.1135807 0.2971001 0.18733657 281s 48 -0.3947656 1.028405 -1.0370498 0.4467257 -0.1445498 0.16878692 281s 49 -2.8829860 0.279064 -1.4443310 0.5889970 -0.1883118 0.16947945 281s 50 3.2797246 -2.443968 0.4100655 0.4278962 -0.4414712 0.08598366 281s 51 -1.9272930 -0.622137 -1.5136862 -0.0483369 -0.0272502 0.16006066 281s 52 -5.7161590 -0.298434 -0.5216578 0.1385780 -0.2435931 0.10628617 281s 53 -1.1933277 -0.125878 -0.7556261 -0.3129372 -0.3166453 0.03078643 281s 54 -0.5994394 -0.031069 -0.1296378 0.0061490 -0.1869578 0.09839221 281s 55 0.4104586 0.733465 -0.2088065 -0.3645266 -0.1830137 0.04705775 281s 56 -0.2227671 -0.724741 0.1007592 -0.0838897 -0.1939960 -0.04223579 281s 57 -1.5706297 -0.292436 1.0849660 -0.2559591 -0.0917278 -0.27423151 281s 58 -0.4102168 1.263831 0.9082556 -0.4592777 -0.0676902 0.11089798 281s 59 -1.9640736 -1.340173 -0.3652736 -0.1267573 0.0775692 -0.07977644 281s 60 -1.7490968 -0.941370 -0.0849901 -0.3453455 0.2858594 0.06413468 281s 61 -0.1583416 -1.699326 0.2385988 -0.2231496 -0.0513883 -0.12227279 281s 62 2.2124878 -1.942366 0.0743514 0.2627321 -0.2844018 -0.15848039 281s 63 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 281s 64 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 281s 65 -0.3779208 -2.987354 0.6819006 0.1942611 0.0529259 0.01315140 281s 66 -2.6385498 -1.331204 -0.0367809 -0.2327572 0.1845076 -0.08521680 281s 67 0.0526645 -1.301299 0.0912198 0.1634869 -0.0068236 0.24131589 281s 68 -1.1013065 -2.004809 -1.9168056 0.0260663 -0.2029903 -0.12625268 281s 69 -0.9495853 -0.831697 0.0389476 -0.2123483 -0.0202267 0.38463410 281s 70 2.6935893 5.369312 0.6987368 -4.5754846 -9.6833013 -2.32910628 281s 71 -2.4037611 -1.983509 0.3109848 -0.1015686 -0.0071432 0.06410351 281s 72 2.0795505 -0.392730 -0.4534128 -0.4054224 -0.0312781 0.25408988 281s 73 -2.0038405 -2.874605 -0.6269939 0.2408421 0.5184666 0.11140104 281s 74 -11.2683996 -0.361851 3.9219448 0.4045689 -0.2203308 0.05930132 281s 75 -0.1028287 -2.295813 -0.7769187 0.3071821 0.4537196 0.00522380 281s 76 -1.8466137 -0.425825 -1.1261209 -0.1760585 0.0165729 -0.10698465 281s 77 -8.4124493 -1.174820 2.2700712 0.4213953 0.3446597 -0.20636892 281s 78 1.1103236 -1.299480 -0.5787732 -0.1455945 0.0732148 -0.01806218 281s 79 -0.5451834 -0.620170 -0.7830595 -0.1746479 0.0723052 -0.26017118 281s 80 -3.8647223 1.126328 1.3299567 0.2645241 -0.1881443 0.00485531 281s 81 0.7690939 0.887363 0.0513096 -0.2730980 0.0076447 -0.07590882 281s 82 2.7287618 -1.435327 0.1602865 0.4465859 0.2129425 0.16104418 281s 83 2.2241485 -0.042822 -0.8316486 -0.1230697 -0.1193057 -0.35207561 281s 84 0.2452905 0.491732 -2.0050683 0.0286567 -0.1159415 -0.24887542 281s 85 1.0655845 -2.360746 2.2456131 -0.1479972 -0.1186670 -0.14020891 281s 86 -0.0091659 0.952208 -1.3429189 -0.2944676 -0.2433277 0.15354490 281s PC7 PC8 281s 1 -0.09778744 2.3157e-03 281s 2 0.05189698 1.8077e-03 281s 3 0.70506895 1.2838e-03 281s 4 -0.08541140 3.2781e-04 281s 5 0.11768945 8.3496e-04 281s 6 -0.17886391 1.5222e-03 281s 7 0.14143613 1.3261e-04 281s 8 -0.07724578 7.1241e-04 281s 9 -0.12298048 -7.0110e-04 281s 10 0.07569878 2.3093e-05 281s 11 0.29299858 -3.4542e-04 281s 12 0.07764899 -2.1390e-03 281s 13 -0.08945524 -2.2633e-03 281s 14 0.03597787 -1.8891e-03 281s 15 0.11780498 -2.0279e-03 281s 16 0.46501534 -2.3266e-03 281s 17 0.08603290 -2.4073e-03 281s 18 0.52605757 -9.8822e-04 281s 19 0.31007227 -1.3919e-03 281s 20 0.61582059 -2.3549e-05 281s 21 0.01199350 -6.1649e-05 281s 22 0.03654587 1.3302e-05 281s 23 0.27549986 -3.6759e-04 281s 24 -0.04155354 -2.9882e-04 281s 25 0.11473708 -7.9629e-04 281s 26 0.06673183 -8.3728e-04 281s 27 0.16937729 -9.5775e-04 281s 28 -0.41753592 -7.5544e-05 281s 29 -0.03693100 -2.2481e-04 281s 30 0.08461537 -1.3611e-04 281s 31 0.02476253 -1.4319e-04 281s 32 -0.09756048 -1.2234e-04 281s 33 0.06442434 -2.4915e-04 281s 34 -0.17828409 -9.5882e-05 281s 35 0.00881239 -7.1427e-05 281s 36 -0.01041003 -2.8489e-04 281s 37 0.15994729 -3.1472e-04 281s 38 -0.22386895 6.1384e-04 281s 39 0.03666242 2.8506e-04 281s 40 0.35883231 -8.3062e-05 281s 41 0.18521851 8.5509e-04 281s 42 0.00733985 -6.4477e-04 281s 43 0.35466617 3.2923e-04 281s 44 -0.74952524 -7.6869e-05 281s 45 0.09907237 7.9128e-04 281s 46 0.05119980 1.0606e-03 281s 47 -0.48571583 -9.3780e-04 281s 48 -0.27463442 -2.7037e-04 281s 49 0.06787536 -3.0554e-05 281s 50 0.08499400 3.1181e-04 281s 51 -0.09197457 1.1213e-04 281s 52 -0.24513244 3.9100e-04 281s 53 0.24012780 3.2068e-04 281s 54 0.07999888 3.5689e-04 281s 55 -0.09825475 6.6675e-04 281s 56 0.05133674 -7.2984e-05 281s 57 -0.10302363 -2.0693e-04 281s 58 -0.12323360 -1.6620e-04 281s 59 -0.05119989 -1.1016e-03 281s 60 0.00082131 -3.2951e-04 281s 61 0.08128272 -1.1550e-04 281s 62 -0.01789040 -1.1579e-04 281s 63 -0.07188070 -7.8367e-04 281s 64 -0.07188070 -7.8367e-04 281s 65 0.00917085 -2.6800e-05 281s 66 0.03121573 -5.3492e-05 281s 67 0.12202335 -3.0466e-04 281s 68 -0.04764366 -2.6126e-04 281s 69 0.13828337 -3.9331e-04 281s 70 0.10401069 4.2870e-03 281s 71 -0.14369640 3.7669e-05 281s 72 -0.10334451 -2.6456e-04 281s 73 0.17655402 1.0917e-04 281s 74 0.26779696 1.8685e-03 281s 75 -0.75016549 2.1079e-05 281s 76 0.01802016 7.7555e-04 281s 77 0.13081368 6.4286e-04 281s 78 0.01409131 4.9476e-04 281s 79 0.06643384 2.6590e-04 281s 80 -0.12624376 5.9801e-04 281s 81 -0.14074469 -3.2172e-04 281s 82 0.09228230 4.4064e-04 281s 83 -0.06352151 -3.6274e-04 281s 84 -0.02642452 -3.9742e-04 281s 85 -0.03502188 6.9814e-04 281s 86 -0.11749109 -5.1283e-04 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 2.39971451 1.49157920 0.93184037 0.33183258 0.19628996 0.16485446 0.12784351 281s [8] 0.00052622 281s ---------------------------------------------------------- 281s bushfire 38 5 5 11393.979994 197.523453 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 281s 1 -91.383 -16.17804 0.56195 -0.252428 1.261840 281s 2 -93.033 -13.93251 -0.67212 0.042287 0.470924 281s 3 -85.400 -10.72512 -3.09832 -1.224797 -0.504718 281s 4 -68.381 -12.12202 -3.31950 -0.676880 -0.228383 281s 5 -36.742 -21.04171 -1.98872 0.397655 -0.932613 281s 6 -12.095 -30.21719 0.59595 2.100702 0.384714 281s 7 -227.949 -71.40450 35.57308 -7.880296 -2.710415 281s 8 -262.815 -111.81228 -11.04574 2.397832 -13.646407 281s 9 -263.767 -114.13702 -13.71407 3.131736 -13.825200 281s 10 -264.312 -90.69643 9.72320 0.967173 -8.800150 281s 11 -266.681 -72.85993 16.55010 0.291092 -8.373583 281s 12 -274.050 -18.41395 20.74273 -2.464589 -1.505967 281s 13 -218.299 19.16040 7.69765 0.069012 0.054846 281s 14 29.646 10.52526 -7.50754 0.855493 1.966680 281s 15 159.575 3.86633 -6.95837 -2.753953 0.616068 281s 16 114.286 2.47164 0.62690 -3.146317 -0.501623 281s 17 111.289 3.45086 1.97182 -0.303064 -0.094416 281s 18 99.626 -1.80416 4.88197 -0.013096 -1.438397 281s 19 103.353 -3.50426 3.58993 1.578169 -1.317194 281s 20 113.769 0.84544 3.28254 2.204926 0.131167 281s 21 95.186 3.50703 4.97153 0.916181 0.351658 281s 22 86.996 4.00938 2.95209 1.281788 1.920404 281s 23 -44.232 8.50898 6.30689 -1.038871 0.400078 281s 24 -99.527 13.81377 1.75130 -0.260669 0.394804 281s 25 -34.855 5.99709 -0.57224 -1.660513 -0.620158 281s 26 -41.265 2.94659 -1.04825 -2.243950 -0.440017 281s 27 -56.148 10.14428 -5.41858 0.321752 -0.608412 281s 28 -32.366 20.27795 -8.60687 3.806572 -1.267249 281s 29 -22.438 34.73585 -11.19123 8.296154 -0.511610 281s 30 -79.035 37.05713 -1.51591 9.892959 -1.618635 281s 31 49.465 39.37414 5.95714 22.874813 -1.883481 281s 32 304.825 30.19205 37.68900 45.175923 -1.293939 281s 33 341.237 7.04985 65.43451 44.553009 -3.148116 281s 34 337.467 6.16879 66.48222 43.278480 -3.688631 281s 35 342.929 7.38548 66.91291 43.941556 -1.937887 281s 36 340.143 6.70203 67.85433 42.479161 -3.873639 281s 37 337.931 7.43184 70.50828 42.333220 -2.645830 281s 38 339.281 8.07267 71.34405 42.400459 -2.392774 281s ------------- 281s Call: 281s PcaCov(x = x) 281s 281s Standard deviations: 281s [1] 106.7426 14.0543 4.9184 1.8263 1.0193 281s ---------------------------------------------------------- 281s ========================================================== 281s > dodata(method="grid") 281s 281s Call: dodata(method = "grid") 281s Data Set n p k e1 e2 281s ========================================================== 281s heart 12 2 2 516.143549 23.932102 281s Scores: 281s PC1 PC2 281s [1,] 6.4694 3.8179 281s [2,] 61.7387 19.1814 281s [3,] 1.4722 -1.0161 281s [4,] -3.8056 1.5127 281s [5,] 18.6760 5.3303 281s [6,] -16.8411 1.7900 281s [7,] 4.9962 4.1638 281s [8,] -26.8665 -13.3010 281s [9,] -1.0648 -1.2690 281s [10,] -25.7734 -12.4037 281s [11,] -13.3987 -4.0751 281s [12,] 46.7700 15.1272 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 22.719 4.892 281s ---------------------------------------------------------- 281s starsCYG 47 2 2 0.473800 0.026486 281s Scores: 281s PC1 PC2 281s [1,] 0.181489 -0.0300854 281s [2,] 0.695337 0.1492475 281s [3,] -0.120738 -0.1338110 281s [4,] 0.695337 0.1492475 281s [5,] 0.140039 -0.0992368 281s [6,] 0.413314 0.0551030 281s [7,] -0.409428 -0.5478860 281s [8,] 0.225647 0.1690378 281s [9,] 0.519123 -0.1471454 281s [10,] 0.071513 -0.0277935 281s [11,] 0.663045 -0.9203119 281s [12,] 0.402691 0.0253179 281s [13,] 0.373739 0.0759321 281s [14,] -1.005756 -0.3654219 281s [15,] -0.789968 -0.0898580 281s [16,] -0.467328 0.0334465 281s [17,] -1.111148 -0.1431778 281s [18,] -0.867242 0.0417806 281s [19,] -0.871200 -0.1481782 281s [20,] 0.823011 -0.9236455 281s [21,] -0.669994 -0.0923582 281s [22,] -0.829959 -0.0890246 281s [23,] -0.627294 0.0367802 281s [24,] -0.195929 0.0978059 281s [25,] -0.028257 -0.0157122 281s [26,] -0.387346 0.0317797 281s [27,] -0.390054 -0.0981920 281s [28,] -0.148231 -0.0132120 281s [29,] -0.661454 -0.1625514 281s [30,] 0.982767 -0.9369769 281s [31,] -0.628127 -0.0032112 281s [32,] 0.055476 0.1625819 281s [33,] 0.173158 0.0501056 281s [34,] 1.222924 -0.9319795 281s [35,] -0.711235 -0.1515118 281s [36,] 0.576613 0.2117347 281s [37,] 0.054851 0.1325884 281s [38,] 0.173158 0.0501056 281s [39,] 0.134833 0.1309216 281s [40,] 0.522665 0.0228177 281s [41,] -0.428171 -0.0073782 281s [42,] 0.013192 0.0534392 281s [43,] 0.294173 0.0975945 281s [44,] 0.293132 0.0476054 281s [45,] 0.495172 0.1434167 281s [46,] -0.066790 0.0551060 281s [47,] -0.547311 0.0351134 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 0.68833 0.16275 281s ---------------------------------------------------------- 281s phosphor 18 2 2 392.155327 50.657228 281s Scores: 281s PC1 PC2 281s 1 5.6537 -15.2305 281s 2 -21.2150 -1.8862 281s 3 -23.5966 2.3112 281s 4 -11.2742 -6.6000 281s 5 -18.4067 1.5202 281s 6 16.9795 -19.4039 281s 7 1.5964 -3.1666 281s 8 -9.7354 3.2429 281s 9 -10.8594 5.4759 281s 10 15.5585 -6.5279 281s 11 -4.0058 1.2905 281s 12 9.4815 8.2139 281s 13 13.0640 6.4346 281s 14 7.0230 7.7600 281s 15 18.4378 3.7658 281s 16 -8.9047 -6.3253 281s 17 21.8748 6.1900 281s 18 16.9843 12.0801 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 19.8029 7.1174 281s ---------------------------------------------------------- 281s stackloss 21 3 3 109.445054 16.741203 281s Scores: 281s PC1 PC2 PC3 281s [1,] 15.136434 14.82909 -2.0387704 281s [2,] 14.393636 15.46816 -1.8391595 281s [3,] 12.351209 10.12290 -2.3458098 281s [4,] 2.510036 2.07589 1.8251581 281s [5,] 1.767140 1.78527 -0.0088651 281s [6,] 2.138588 1.93058 0.9081465 281s [7,] 6.966825 -1.75851 0.6274924 281s [8,] 6.966825 -1.75851 0.6274924 281s [9,] -0.089513 -1.09062 2.2894224 281s [10,] -7.146340 2.65628 -0.8983590 281s [11,] -0.461157 -3.09532 -2.6948576 281s [12,] -1.575403 -2.60157 -3.4122582 281s [13,] -5.660744 1.37815 -1.2975809 281s [14,] 2.881484 -5.50628 -2.5762898 281s [15,] -4.917360 -9.13772 0.0676942 281s [16,] -7.145755 -7.22052 0.6665270 281s [17,] -17.173481 1.87173 4.3780920 281s [18,] -11.973894 -2.60174 2.9808153 281s [19,] -10.859648 -3.09549 3.6982160 281s [20,] -6.031899 0.15817 1.2270803 281s [21,] 8.451640 4.98077 -5.4038839 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 10.4616 4.0916 2.8271 281s ---------------------------------------------------------- 281s salinity 28 3 3 14.911546 8.034974 281s Scores: 281s PC1 PC2 PC3 281s 1 -2.72400 0.79288 0.688038 281s 2 -3.45684 0.86162 1.941690 281s 3 -5.73471 -4.79507 0.129202 281s 4 -6.17045 -3.04372 -0.352797 281s 5 -4.72453 -5.59543 4.144851 281s 6 -5.75447 -1.07062 0.579975 281s 7 -4.40759 0.47731 0.680203 281s 8 -2.76360 2.30716 0.540271 281s 9 -0.28782 -1.40644 -2.373399 281s 10 2.64361 -1.43362 -0.266957 281s 11 1.91078 -1.66975 1.312215 281s 12 -0.40661 0.68573 -0.200135 281s 13 -0.14911 1.88993 0.044001 281s 14 1.99005 2.43874 1.373229 281s 15 2.88128 -2.21263 -0.863674 281s 16 -0.12935 -8.28831 6.483875 281s 17 -0.16895 -1.68742 0.905190 281s 18 -3.08054 0.23753 -0.269165 281s 19 -0.38685 -1.08501 -2.736860 281s 20 1.45520 -0.33209 -1.686406 281s 21 1.13834 2.53553 -0.381657 281s 22 2.48522 3.42927 0.417050 281s 23 4.56487 -3.36542 0.711908 281s 24 2.94072 -3.08490 1.556939 281s 25 0.82140 -0.26895 -0.406490 281s 26 1.17794 1.61119 -0.863764 281s 27 2.02965 2.80707 -0.489050 281s 28 2.98039 3.21462 0.747622 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 3.86155 2.83460 0.95394 281s ---------------------------------------------------------- 281s hbk 75 3 3 3.714805 3.187126 281s Scores: 281s PC1 PC2 PC3 281s 1 8.423138 24.765818 19.413334 281s 2 7.823138 25.295092 20.356662 281s 3 9.023138 27.411905 20.218454 281s 4 8.223138 28.010236 21.568269 281s 5 8.623138 27.442650 21.123471 281s 6 9.123138 25.601873 20.279943 281s 7 8.823138 25.463855 20.770811 281s 8 8.223138 25.264348 19.451646 281s 9 8.023138 27.373593 20.716984 281s 10 7.623138 26.752275 19.666288 281s 11 9.323138 31.108975 24.313778 281s 12 10.323138 33.179719 23.469966 281s 13 10.323138 29.958667 26.231274 281s 14 9.323138 29.345676 34.207755 281s 15 1.723138 -0.077538 0.754886 281s 16 1.423138 -1.818609 -0.080979 281s 17 -1.676862 -1.872341 -0.686878 281s 18 0.623138 -0.077633 -0.548955 281s 19 -0.876862 -0.576068 0.716574 281s 20 1.423138 -0.016144 1.261078 281s 21 0.923138 -0.223313 0.041619 281s 22 -1.276862 -0.299937 1.038679 281s 23 0.323138 -1.327742 0.057038 281s 24 -0.376862 -1.626860 0.034051 281s 25 -0.676862 -1.550331 -2.266849 281s 26 -0.776862 0.290637 1.184359 281s 27 1.623138 0.750760 0.417361 281s 28 0.123138 -0.016334 -1.346603 281s 29 -0.476862 -1.220468 -1.338846 281s 30 -0.476862 1.387213 -1.339036 281s 31 1.423138 -1.059368 -0.824991 281s 32 -1.176862 -1.833934 0.118433 281s 33 -0.176862 -0.691099 0.908323 281s 34 -1.276862 -1.251213 -2.243862 281s 35 1.423138 0.858128 0.325317 281s 36 -0.576862 0.574335 0.102918 281s 37 -1.576862 0.413330 0.892903 281s 38 -0.176862 -1.841691 -1.085702 281s 39 0.423138 -0.752683 -2.205550 281s 40 -1.176862 -0.905930 -0.211430 281s 41 1.723138 0.819721 -0.479993 281s 42 -1.376862 0.666284 -1.093554 281s 43 -1.576862 -1.304659 1.061761 281s 44 0.123138 1.203126 -1.553772 281s 45 0.223138 -1.358581 -2.151818 281s 46 0.123138 1.003714 -1.569097 281s 47 1.323138 -1.159169 -2.136494 281s 48 1.423138 0.919427 -0.472331 281s 49 1.423138 -0.246300 0.340737 281s 50 0.423138 0.727773 0.716479 281s 51 0.623138 -1.665267 -0.771259 281s 52 1.623138 -0.798657 -1.607314 281s 53 -1.376862 1.310494 -1.645816 281s 54 -0.576862 -1.879908 0.716669 281s 55 -1.176862 -1.235698 0.164407 281s 56 0.123138 -1.296997 0.962055 281s 57 0.123138 -1.304849 -1.545920 281s 58 0.723138 -0.714086 1.207441 281s 59 -0.076862 0.881115 0.026199 281s 60 -1.376862 1.226208 -0.549050 281s 61 -1.276862 0.781504 1.322377 281s 62 -0.776862 -1.657699 -2.174806 281s 63 -0.576862 -1.956627 0.409888 281s 64 1.123138 0.712448 0.915891 281s 65 0.323138 0.689271 -1.392672 281s 66 -1.476862 -1.289430 -0.441492 281s 67 -0.076862 -0.905930 -0.211430 281s 68 -1.576862 -0.852389 -2.213213 281s 69 0.323138 -1.696011 -1.676276 281s 70 -0.676862 0.773747 0.118243 281s 71 0.523138 0.152524 0.371386 281s 72 -1.076862 -0.606812 -0.188443 281s 73 -1.376862 0.114117 -0.433924 281s 74 -1.676862 -0.522431 0.018632 281s 75 -1.376862 0.612552 -1.699453 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 1.9274 1.7853 1.6714 281s ---------------------------------------------------------- 281s milk 86 8 8 9.206694 2.910585 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 PC6 281s [1,] 6.090978 0.590424 1.1644466 -0.3835606 1.0342867 -0.4752288 281s [2,] 6.903009 -0.575027 0.8613622 -1.1221795 0.7221616 -1.3097951 281s [3,] 0.622903 -1.594239 1.2122863 -0.0555128 0.3252629 -0.2799581 281s [4,] 5.282665 -1.815742 2.2543268 0.9824543 -0.5345577 -0.7331037 281s [5,] -1.039753 0.663906 0.3353811 0.3070599 -0.3224317 -0.4056666 281s [6,] 2.247786 0.218255 -0.3382923 0.1270005 -0.0271307 -0.2035021 281s [7,] 2.784293 -0.291678 -0.4897587 0.0198481 0.0752345 -0.5986846 281s [8,] 2.942266 0.315608 0.1603961 0.3568462 -0.0647311 -0.5316127 281s [9,] -1.420086 -1.751212 1.7027572 0.0708340 -0.9226517 0.0738411 281s [10,] -2.921113 -0.727554 0.0113966 -0.3915037 -0.0772913 0.6062573 281s [11,] -9.568075 0.792291 1.0217507 0.2554182 -0.6254883 0.8899897 281s [12,] -12.885166 3.423607 -1.2579351 -0.4300397 -0.4094558 1.1727128 281s [13,] -10.038470 1.274931 -2.6913262 -1.6219658 -0.3284974 1.1228303 281s [14,] -12.044003 2.096254 -1.2859668 -0.9602250 -0.7937418 0.8264019 281s [15,] -10.798341 1.159257 1.4870766 0.3248231 -1.0787537 0.8723637 281s [16,] -2.841629 0.500846 0.4771762 0.5975365 0.3197882 0.5804087 281s [17,] -1.150691 -1.978038 2.3229313 0.5275273 -0.5339514 0.5421631 281s [18,] -1.992369 1.131288 -0.8385615 0.1156462 0.2253010 -0.3393814 281s [19,] -1.999699 -0.252876 1.2229972 0.5081648 0.0082612 0.3373454 281s [20,] 0.091385 -1.439422 1.1836134 0.6297789 0.0961407 -0.2126653 281s [21,] -2.571346 2.280701 -1.2845660 0.1463583 0.0949331 0.0902039 281s [22,] -0.990078 1.087033 -0.1638640 -0.0351472 0.0743205 -0.0040605 281s [23,] -0.010631 1.704171 0.0038808 0.5765418 0.6086460 0.0329995 281s [24,] -0.440350 1.500798 0.2769870 0.5556999 0.4751445 0.6516120 281s [25,] -3.578249 2.672783 -0.3534268 0.7398104 0.1108289 0.2704730 281s [26,] -0.854914 1.626684 0.2301131 0.5530224 0.0662862 -0.0999969 281s [27,] -3.175381 0.762609 0.5101987 0.0849002 -0.2137237 0.2729808 281s [28,] 2.599844 3.370137 -0.5174736 0.7409946 0.6853156 0.2430943 281s [29,] 4.395534 0.823611 0.1610152 0.8184845 0.7665555 0.0779724 281s [30,] 0.843794 1.438263 -0.2366601 0.4600650 0.3424806 -0.1768083 281s [31,] 1.890815 1.266935 -1.8218143 -0.3909337 0.8390127 0.1026821 281s [32,] 1.300145 -0.085976 -0.8965312 -0.8855787 0.4156780 0.1478055 281s [33,] 1.923087 0.137638 0.3487435 0.2958367 0.4245932 0.1566678 281s [34,] 0.615762 -0.390711 0.8107376 0.0295536 -0.1169590 0.2940241 281s [35,] -0.372946 2.037079 -0.7663299 0.1907237 0.6959350 0.5366205 281s [36,] 4.068134 1.129044 0.5492962 0.7640964 0.4799859 -0.4080205 281s [37,] 0.937617 2.048258 -1.2326566 -0.0942856 0.7885267 -0.1004018 281s [38,] 2.141223 1.877022 -0.5178216 0.3750868 0.4767003 0.1240656 281s [39,] -1.403505 1.327163 0.3165610 0.3989824 0.3505825 0.5915956 281s [40,] 3.337528 -1.689495 1.4737175 0.2584843 0.4308444 -0.0810597 281s [41,] 3.938506 1.384908 0.8103687 -0.5875595 1.1616535 -0.6492603 281s [42,] 6.327471 -1.061362 1.9861187 1.1016484 0.3512405 -0.1540592 281s [43,] 3.120160 -0.064108 -0.8370717 -0.2229341 0.5623447 -0.7152184 281s [44,] 5.290520 -0.669008 0.8597130 0.5518503 0.2470856 0.6454703 281s [45,] 0.058291 0.356399 -0.1896007 0.2427518 0.3705541 0.3975085 281s [46,] 0.150881 1.942057 -0.1140726 0.5656469 0.5227623 0.2151825 281s [47,] 2.870881 -1.446283 -2.8450062 -1.7292144 -0.0888429 -0.1347003 281s [48,] 0.335593 0.500884 -1.3154520 -0.3874864 0.3449038 0.5387692 281s [49,] -2.179494 -0.021237 -1.7792344 -0.8445930 0.4435338 0.6547961 281s [50,] 2.968304 -2.588546 1.8552104 0.4590101 -0.1755089 -0.0550378 281s [51,] -1.399208 -0.820296 -1.3660014 -0.8890243 -0.2344105 0.1236943 281s [52,] -5.112989 0.318983 -1.3852993 -0.8461529 -0.3467685 0.7349666 281s [53,] -0.773103 -0.267333 -0.8154896 -0.3783062 0.0113880 -0.3304648 281s [54,] -0.244565 -0.066211 -0.2541557 0.0043037 0.0390890 0.0074067 281s [55,] 0.894921 0.516411 -0.4443369 0.0708354 -0.0637890 -0.2799646 281s [56,] -0.038706 -0.588256 0.3166588 -0.0196663 -0.1793472 -0.1179341 281s [57,] -1.377469 0.428939 0.7502430 0.1458375 -0.3818977 -0.0380258 281s [58,] 0.042787 1.488605 0.0252606 0.6377516 -0.1524172 -0.1898723 281s [59,] -1.734357 -0.966494 -0.1026850 -0.5656888 -0.4831402 0.0308069 281s [60,] -1.501991 -0.544918 -0.0837127 -0.2362486 -0.5382026 -0.1351338 281s [61,] -0.175102 -1.339436 0.8403933 -0.0907428 -0.4846145 -0.2795153 281s [62,] 2.100915 -2.004702 1.3031556 -0.0041957 -0.2067776 -0.0793613 281s [63,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 281s [64,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 281s [65,] -0.665219 -2.325594 1.6287363 0.0607163 -0.6996720 0.1353325 281s [66,] -2.439244 -0.737375 0.0187770 -0.4561269 -0.5425315 -0.0208332 281s [67,] 0.121564 -1.214385 0.4877707 0.1809998 -0.1943262 0.0662506 281s [68,] -0.804267 -2.238327 -0.8547917 -1.3449926 -0.3577254 -0.0293779 281s [69,] -0.761319 -0.676391 -0.0245494 0.2262894 -0.3396872 -0.1166505 281s [70,] 3.385399 4.360467 -0.7946150 -0.0417895 0.4474362 -4.6626174 281s [71,] -2.364955 -1.257673 0.5226907 -0.2346145 -0.7838777 0.1815821 281s [72,] 2.334511 -0.794530 0.0175620 0.1848925 -0.3437761 -0.4522442 281s [73,] -2.023440 -2.449907 0.2525041 -0.6657474 -0.5509480 0.2118442 281s [74,] -11.180192 2.456516 1.1036540 0.8711496 -0.3833194 1.3548314 281s [75,] 0.058297 -2.094811 0.3075211 -0.8052760 -0.9527729 0.5850255 281s [76,] -1.355742 -0.464355 -1.0183333 -0.8525619 -0.1577144 -0.0767323 281s [77,] -8.296881 0.945092 0.8088967 -0.0071463 -0.4527530 1.0614233 281s [78,] 1.251696 -1.460466 0.2511701 -0.2717606 -0.3158308 -0.2964813 281s [79,] -0.192380 -0.662365 -0.3671703 -0.6722658 -0.1243452 -0.2388225 281s [80,] -3.355201 1.915096 -0.1086672 0.3560062 0.0956865 0.6974817 281s [81,] 1.245305 0.736787 -0.1662155 0.1309822 -0.0122872 -0.2182528 281s [82,] 2.679561 -1.666401 1.1576691 0.3960280 -0.0059146 0.0584136 281s [83,] 2.596651 -0.556654 -0.0807307 -0.4468501 0.0964927 -0.3922894 281s [84,] 0.959377 -0.272038 -1.5879803 -1.1153057 0.3412508 -0.1281556 281s [85,] 0.602737 -1.384591 2.8844745 0.9479144 -0.7946454 -0.2014038 281s [86,] 0.698125 0.335743 -1.5248055 -0.4443037 0.0768256 -0.1999790 281s PC7 PC8 281s [1,] 0.9281777 -0.05158594 281s [2,] 0.8397946 -0.04276628 281s [3,] -0.5189230 0.04913688 281s [4,] -0.0178377 0.01578074 281s [5,] -0.0129237 0.01056305 281s [6,] -0.0764270 0.01469518 281s [7,] -0.3059779 0.04237267 281s [8,] -0.0684673 0.02289928 281s [9,] -0.2549733 -0.00832119 281s [10,] -0.0578118 -0.01894694 281s [11,] 0.0415545 -0.03474479 281s [12,] 0.0869267 -0.04485633 281s [13,] -0.2843977 -0.03100709 281s [14,] -0.3375083 -0.02155574 281s [15,] -0.1718828 -0.02996980 281s [16,] -0.4176728 0.03232381 281s [17,] -0.5923252 0.01765700 281s [18,] -0.3190679 0.04476532 281s [19,] -0.0279426 -0.00236626 281s [20,] 0.1299811 0.00586022 281s [21,] 0.0474059 0.00563264 281s [22,] -0.1240299 0.01123557 281s [23,] 0.2232631 0.00551065 281s [24,] 0.0122404 0.00060079 281s [25,] 0.2627442 -0.00824800 281s [26,] 0.2257329 -0.00440907 281s [27,] -0.8496967 0.05266701 281s [28,] 0.3473502 -0.00500580 281s [29,] 0.4172329 -0.00542705 281s [30,] 0.2773880 -0.00014648 281s [31,] -0.1224270 0.02372808 281s [32,] -0.2224748 0.00757892 281s [33,] -0.0633903 0.01236118 281s [34,] -0.2616599 0.00561781 281s [35,] -0.1671986 0.01988458 281s [36,] 0.4502086 -0.00418541 281s [37,] -0.0773232 0.02768282 281s [38,] 0.0464683 0.01134849 281s [39,] -0.0927182 0.00555823 281s [40,] -0.2162796 0.02467605 281s [41,] 0.9440753 -0.04806541 281s [42,] -0.0078920 0.02022925 281s [43,] 0.1152244 0.02074199 281s [44,] 1.0406693 -0.08815111 281s [45,] -0.1376804 0.01424369 281s [46,] 0.1673461 0.00442877 281s [47,] -0.4125225 0.01038694 281s [48,] 0.1556289 -0.02103354 281s [49,] 0.0434415 -0.01782739 281s [50,] 0.2518610 -0.02154540 281s [51,] -0.1186185 -0.00881133 281s [52,] 0.1507435 -0.04523343 281s [53,] 0.2161208 -0.00967982 281s [54,] 0.1374909 -0.00783970 281s [55,] 0.2417108 -0.00895268 281s [56,] 0.1253846 -0.01188643 281s [57,] 0.1390898 -0.01831232 281s [58,] 0.2219634 -0.00364174 281s [59,] -0.2045636 -0.00589047 281s [60,] -0.3679942 0.01673699 281s [61,] -0.0705611 -0.00273407 281s [62,] 0.1447701 -0.02026768 281s [63,] -0.1854788 0.02686899 281s [64,] -0.1854788 0.02686899 281s [65,] -0.2626650 -0.00376657 281s [66,] -0.3044266 0.00484197 281s [67,] -0.1358811 0.00605789 281s [68,] -0.0551482 -0.02379410 281s [69,] -0.0914891 0.00812122 281s [70,] 10.2524854 -0.64367029 281s [71,] -0.1326972 -0.01666774 281s [72,] 0.0051905 0.00656777 281s [73,] -0.8236843 0.03367265 281s [74,] 0.2140104 -0.04092219 281s [75,] -0.5684260 -0.00987116 281s [76,] -0.1225779 -0.00204629 281s [77,] -0.4235612 -0.00450631 281s [78,] -0.1935155 0.00973901 281s [79,] -0.1615883 0.00518643 281s [80,] 0.2915052 -0.02960159 281s [81,] 0.0908823 0.00038216 281s [82,] -0.3392789 0.02605374 281s [83,] 0.1112141 -0.00629308 281s [84,] 0.0510771 -0.00845572 281s [85,] 0.0748700 -0.01174487 281s [86,] 0.2488127 -0.01446339 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 3.034253 1.706044 1.167717 0.670864 0.536071 0.396285 0.266625 0.020768 281s ---------------------------------------------------------- 281s bushfire 38 5 5 38232.614428 1580.825276 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 281s [1,] -67.120 -23.70481 -1.06551 1.129721 1.311630 281s [2,] -69.058 -21.42113 -1.54798 0.983735 0.430774 281s [3,] -61.939 -17.23665 -3.81386 -0.635074 -0.600149 281s [4,] -44.952 -16.53458 -5.16114 0.411753 -0.390518 281s [5,] -12.644 -21.62271 -7.14146 3.519877 -1.211923 281s [6,] 12.820 -27.86930 -7.66114 7.230422 0.040330 281s [7,] -194.634 -100.67730 27.43084 -0.026242 -0.134248 281s [8,] -229.349 -129.75912 -19.46346 25.591651 -18.592601 281s [9,] -230.306 -131.28743 -22.22175 27.251157 -19.214683 281s [10,] -231.118 -115.10815 3.70208 16.303210 -10.573515 281s [11,] -234.540 -100.24984 13.67112 10.325539 -8.727961 281s [12,] -246.507 -51.03515 27.61698 -5.352226 0.514087 281s [13,] -195.712 -5.81324 20.04485 -9.226807 1.721886 281s [14,] 49.881 16.90911 -9.97400 -1.900739 2.190429 281s [15,] 179.545 23.96999 -18.71166 -2.987136 1.332713 281s [16,] 135.356 15.81282 -9.24353 -4.703584 0.971669 281s [17,] 132.350 16.65014 -7.01838 -2.428578 1.346198 281s [18,] 121.499 9.75832 -4.45699 -1.587450 0.131923 281s [19,] 125.222 9.17601 -5.88919 0.582516 -0.061642 281s [20,] 135.112 14.63812 -5.90351 0.411704 1.460488 281s [21,] 116.581 14.47390 -3.04021 -1.842579 2.005998 281s [22,] 108.223 14.62103 -4.47428 -1.196993 3.288463 281s [23,] -22.095 3.26439 6.58391 -6.164581 2.125258 281s [24,] -77.831 3.46616 6.59280 -6.373595 1.545789 281s [25,] -13.092 3.41344 -0.99296 -5.076733 0.299636 281s [26,] -19.206 -0.17007 -1.84209 -4.858675 0.347945 281s [27,] -35.022 6.54155 -3.12767 -3.556587 -0.327873 281s [28,] -12.651 20.14894 -4.61607 -2.025539 -1.214190 281s [29,] -4.404 36.39823 -3.81590 -0.633155 -0.602027 281s [30,] -60.018 30.40980 9.44610 -1.763156 -0.765133 281s [31,] 67.689 47.40087 12.70229 9.791794 -0.671751 281s [32,] 324.134 63.46147 31.52512 30.099817 2.406344 281s [33,] 364.639 38.84260 51.20467 30.648590 3.218678 281s [34,] 361.089 37.09494 52.00522 29.394356 2.861158 281s [35,] 366.403 38.88889 52.31879 29.878844 4.650618 281s [36,] 363.821 37.40859 53.10394 28.286557 2.922632 281s [37,] 361.761 37.21276 55.73012 27.648760 4.477279 281s [38,] 363.106 37.78395 56.56345 27.460078 4.845396 281s ------------- 281s Call: 281s PcaGrid(x = x) 281s 281s Standard deviations: 281s [1] 195.5316 39.7596 11.7329 7.3743 1.7656 281s ---------------------------------------------------------- 281s ========================================================== 281s > 281s > ## IGNORE_RDIFF_BEGIN 281s > dodata(method="proj") 281s 281s Call: dodata(method = "proj") 281s Data Set n p k e1 e2 281s ========================================================== 281s heart 12 2 2 512.772467 29.052346 281s Scores: 281s PC1 PC2 281s [1,] 6.7568 3.2826 281s [2,] 63.0869 14.1293 281s [3,] 1.3852 -1.1318 281s [4,] -3.6709 1.8153 281s [5,] 19.0457 3.8035 281s [6,] -16.6413 3.1452 281s [7,] 5.3163 3.7464 281s [8,] -27.8536 -11.0863 281s [9,] -1.1638 -1.1788 281s [10,] -26.6915 -10.2803 281s [11,] -13.6842 -2.9790 281s [12,] 47.8395 11.2980 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 22.644 5.390 281s ---------------------------------------------------------- 281s starsCYG 47 2 2 0.470874 0.024681 281s Scores: 281s PC1 PC2 281s [1,] 0.181333 -3.1013e-02 281s [2,] 0.696091 1.4569e-01 281s [3,] -0.121421 -1.3319e-01 281s [4,] 0.696091 1.4569e-01 281s [5,] 0.139530 -9.9951e-02 281s [6,] 0.413590 5.2989e-02 281s [7,] -0.412224 -5.4579e-01 281s [8,] 0.226508 1.6788e-01 281s [9,] 0.518364 -1.4980e-01 281s [10,] 0.071370 -2.8159e-02 281s [11,] 0.658332 -9.2369e-01 281s [12,] 0.402815 2.3259e-02 281s [13,] 0.374123 7.4020e-02 281s [14,] -1.007611 -3.6028e-01 281s [15,] -0.790417 -8.5818e-02 281s [16,] -0.467151 3.5835e-02 281s [17,] -1.111866 -1.3750e-01 281s [18,] -0.867017 4.6214e-02 281s [19,] -0.871946 -1.4372e-01 281s [20,] 0.818278 -9.2784e-01 281s [21,] -0.670457 -8.8932e-02 281s [22,] -0.830403 -8.4781e-02 281s [23,] -0.627097 3.9987e-02 281s [24,] -0.195426 9.8806e-02 281s [25,] -0.028337 -1.5568e-02 281s [26,] -0.387178 3.3760e-02 281s [27,] -0.390551 -9.6197e-02 281s [28,] -0.148297 -1.2454e-02 281s [29,] -0.662277 -1.5917e-01 281s [30,] 0.977965 -9.4199e-01 281s [31,] -0.628135 -7.2164e-16 281s [32,] 0.056306 1.6230e-01 281s [33,] 0.173412 4.9220e-02 281s [34,] 1.218143 -9.3822e-01 281s [35,] -0.712000 -1.4787e-01 281s [36,] 0.577688 2.0878e-01 281s [37,] 0.055528 1.3231e-01 281s [38,] 0.173412 4.9220e-02 281s [39,] 0.135501 1.3023e-01 281s [40,] 0.522775 2.0145e-02 281s [41,] -0.428203 -5.1892e-03 281s [42,] 0.013465 5.3371e-02 281s [43,] 0.294668 9.6089e-02 281s [44,] 0.293371 4.6106e-02 281s [45,] 0.495898 1.4088e-01 281s [46,] -0.066508 5.5447e-02 281s [47,] -0.547124 3.7911e-02 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 0.6862 0.1571 281s ---------------------------------------------------------- 281s phosphor 18 2 2 388.639033 51.954664 281s Scores: 281s PC1 PC2 281s 1 5.8164 -15.1691 281s 2 -21.1936 -2.1132 281s 3 -23.6199 2.0585 281s 4 -11.2029 -6.7203 281s 5 -18.4220 1.3231 281s 6 17.1862 -19.2211 281s 7 1.6302 -3.1493 281s 8 -9.7695 3.1385 281s 9 -10.9174 5.3594 281s 10 15.6275 -6.3610 281s 11 -4.0194 1.2476 281s 12 9.3931 8.3149 281s 13 12.9944 6.5741 281s 14 6.9396 7.8348 281s 15 18.3964 3.9629 281s 16 -8.8365 -6.4202 281s 17 21.8073 6.4237 281s 18 16.8541 12.2611 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 19.714 7.208 281s ---------------------------------------------------------- 281s stackloss 21 3 3 97.347030 38.052774 281s Scores: 281s PC1 PC2 PC3 281s [1,] 19.08066 -9.06092 -2.64544 281s [2,] 18.55152 -9.90152 -2.76118 281s [3,] 15.04269 -5.37517 -2.31373 281s [4,] 2.79667 -1.78925 1.70823 281s [5,] 2.21768 -1.17513 -0.10495 281s [6,] 2.50717 -1.48219 0.80164 281s [7,] 5.97151 3.25438 2.40268 281s [8,] 5.97151 3.25438 2.40268 281s [9,] -0.68332 0.30263 2.42495 281s [10,] -5.83478 -4.04630 -2.91819 281s [11,] -1.07253 3.51914 -1.87651 281s [12,] -1.89116 2.98559 -2.89885 281s [13,] -4.77650 -2.36509 -2.68671 281s [14,] 1.33353 6.57450 -0.50696 281s [15,] -7.45351 7.08878 1.37012 281s [16,] -9.04093 4.56697 1.02289 281s [17,] -16.15938 -7.50855 0.30909 281s [18,] -12.45541 -1.62432 1.11929 281s [19,] -11.63677 -1.09077 2.14162 281s [20,] -5.79275 -2.08680 -0.06187 281s [21,] 10.13623 -0.76824 -4.70180 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 9.8665 6.1687 3.2669 281s ---------------------------------------------------------- 281s salinity 28 3 3 12.120566 8.431549 281s Scores: 281s PC1 PC2 PC3 281s 1 -2.52547 1.45945 -1.1943e-01 281s 2 -3.32298 2.15704 8.7594e-01 281s 3 -6.64947 -3.26398 1.0135e+00 281s 4 -6.64427 -1.81382 -1.6392e-01 281s 5 -6.16898 -2.52222 5.1373e+00 281s 6 -5.87594 0.26440 -2.4425e-15 281s 7 -4.23084 1.46250 -2.8008e-01 281s 8 -2.21502 2.76478 -8.3789e-01 281s 9 -0.40186 -2.17785 -1.6702e+00 281s 10 2.27089 -1.84923 7.3391e-01 281s 11 1.37935 -1.29276 2.1418e+00 281s 12 -0.22635 0.60372 -5.0980e-01 281s 13 0.27224 1.73920 -7.0505e-01 281s 14 2.36592 2.40462 6.4320e-01 281s 15 2.37640 -2.83174 5.2669e-01 281s 16 -2.49175 -4.77664 9.0404e+00 281s 17 -0.61250 -1.11672 1.4398e+00 281s 18 -2.91853 0.63310 -8.3666e-01 281s 19 -0.39732 -2.02029 -2.1396e+00 281s 20 1.47554 -1.23407 -1.1712e+00 281s 21 1.70104 1.92401 -1.1292e+00 281s 22 3.14437 2.81928 -5.2415e-01 281s 23 3.62890 -3.51450 2.6740e+00 281s 24 2.04538 -2.63992 3.0718e+00 281s 25 0.77088 -0.54783 -1.3370e-01 281s 26 1.57254 0.89176 -1.2089e+00 281s 27 2.63610 1.97075 -1.1855e+00 281s 28 3.55112 2.67606 -6.0915e-02 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 3.4815 2.9037 1.3810 281s ---------------------------------------------------------- 281s hbk 75 3 3 3.801978 3.574192 281s Scores: 281s PC1 PC2 PC3 281s 1 28.747049 15.134042 2.3959241 281s 2 29.021724 16.318941 2.6207988 281s 3 31.271908 15.869319 3.4420860 281s 4 31.586189 17.508798 3.6246706 281s 5 31.299168 16.838093 3.2402573 281s 6 30.037754 15.591930 2.1421166 281s 7 29.888160 16.139376 1.9750096 281s 8 28.994463 15.350167 2.8226275 281s 9 30.758047 16.820526 3.7269602 281s 10 29.759314 16.079531 4.0486097 281s 11 35.301371 19.637962 3.7433562 281s 12 37.193371 18.709303 4.9915250 281s 13 35.634808 20.497713 1.4740727 281s 14 36.816439 27.523024 -2.3006796 281s 15 1.237203 -0.331072 -1.3801401 281s 16 -0.451166 -1.118847 -1.9707479 281s 17 -2.604733 0.067276 0.0130015 281s 18 0.179177 -0.804398 -0.1285240 281s 19 -0.765512 0.982349 -0.2513990 281s 20 1.236727 0.259123 -1.4210070 281s 21 0.428326 -0.503724 -0.6830690 281s 22 -0.724774 1.507943 -0.0022175 281s 23 -0.745349 -0.330094 -1.0982084 281s 24 -1.407850 -0.011831 -0.8987075 281s 25 -2.190427 -1.732051 0.4497793 281s 26 0.058631 1.444044 0.0446166 281s 27 1.680557 -0.429402 -0.6031146 281s 28 -0.315122 -1.179169 0.5822607 281s 29 -1.563355 -1.026914 0.1040012 281s 30 0.329957 -0.633156 1.8533795 281s 31 -0.110108 -1.617131 -1.0958807 281s 32 -2.035875 0.463421 -0.6346632 281s 33 -0.356033 0.740564 -0.8116369 281s 34 -2.342887 -1.340168 0.9724491 281s 35 1.607131 -0.379763 -0.3747630 281s 36 0.084455 0.486671 0.6551654 281s 37 -0.436144 1.659467 0.7145344 281s 38 -1.754819 -1.076076 -0.6037590 281s 39 -0.904375 -2.161949 0.3436723 281s 40 -1.455274 0.331839 0.1499308 281s 41 1.539788 -1.212921 -0.1715110 281s 42 -0.688338 -0.048173 1.7491184 281s 43 -1.635822 1.539067 -0.5208916 281s 44 0.511762 -1.165641 1.5020865 281s 45 -1.454500 -2.099954 0.0219268 281s 46 0.362645 -1.208389 1.3758464 281s 47 -0.615800 -2.658098 -0.4629006 281s 48 1.426278 -1.027667 0.0582638 281s 49 0.809592 -0.533893 -1.1232120 281s 50 0.996105 0.469082 -0.0988805 281s 51 -1.036368 -1.227376 -1.0843166 281s 52 -0.016464 -2.331540 -0.6477169 281s 53 -0.376625 -0.405855 2.4526088 281s 54 -1.524100 0.621590 -1.2927429 281s 55 -1.588523 0.591668 -0.2559428 281s 56 -0.592710 0.529426 -1.4111404 281s 57 -1.306991 -1.538024 -0.1841717 281s 58 0.275991 0.491888 -1.4739863 281s 59 0.598971 0.196673 0.6208960 281s 60 -0.127953 0.485014 1.8571970 281s 61 0.140584 1.905037 0.5838465 281s 62 -2.305069 -1.617811 0.3880825 281s 63 -1.666479 0.357251 -1.1934779 281s 64 1.480143 0.248671 -0.5959984 281s 65 0.309561 -1.219790 0.9671263 281s 66 -1.986789 0.248245 0.1723620 281s 67 -0.765691 -0.269054 -0.4611368 281s 68 -2.232721 -1.090790 1.3915841 281s 69 -1.502453 -1.813763 -0.4936268 281s 70 0.170883 0.584046 0.8369571 281s 71 0.543623 0.043244 -0.3707674 281s 72 -1.168908 0.341335 0.2837393 281s 73 -0.902885 0.411872 1.0546196 281s 74 -1.425273 0.852445 0.5719123 281s 75 -0.898536 -0.555475 2.0107684 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 1.9499 1.8906 1.2797 281s ---------------------------------------------------------- 281s milk 86 8 8 8.369408 3.530461 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 PC6 281s [1,] 6.337004 -0.245000 0.7704092 -4.9848e-01 -1.6599e-01 1.1763e-01 281s [2,] 7.021899 1.030349 0.2832977 -1.2673e+00 -8.7296e-01 2.0547e-01 281s [3,] 0.600831 1.686247 0.9682032 -3.2663e-02 7.4112e-02 4.7412e-01 281s [4,] 5.206465 2.665956 1.5942253 9.8285e-01 -5.4159e-01 -2.0155e-01 281s [5,] -0.955757 -0.579889 0.3206393 5.1174e-01 -6.1684e-01 -3.8990e-02 281s [6,] 2.198695 0.073770 -0.5712493 1.9440e-01 -1.0237e-01 4.1825e-02 281s [7,] 2.695361 0.644049 -0.8645373 8.1894e-02 -2.6953e-01 1.6884e-01 281s [8,] 2.945361 0.137227 -0.2071463 5.0841e-01 -4.2075e-01 5.8589e-02 281s [9,] -1.539013 1.879894 1.6952390 1.6792e-01 -2.8195e-01 5.0563e-02 281s [10,] -2.977110 0.319666 0.3515636 -5.2496e-01 4.6898e-01 8.5978e-03 281s [11,] -9.375355 -1.638105 1.9026171 4.1237e-01 1.8768e-02 -1.8546e-01 281s [12,] -12.602600 -4.715888 0.0273004 -4.7798e-02 -1.2246e-02 9.6858e-03 281s [13,] -10.114331 -2.487462 -1.6331544 -1.5139e+00 4.1903e-01 2.8313e-01 281s [14,] -11.949336 -3.190157 -0.2146943 -5.0060e-01 -2.9537e-01 3.2160e-01 281s [15,] -10.595396 -1.905517 2.3716887 7.6651e-01 -3.3531e-01 1.9933e-02 281s [16,] -2.735720 -0.748282 0.6750464 7.2415e-01 5.5304e-01 2.2283e-01 281s [17,] -1.248116 2.131195 2.2596886 6.4958e-01 3.5634e-01 2.9021e-01 281s [18,] -1.904210 -1.285804 -0.7746460 3.0198e-01 -2.7407e-01 1.7500e-01 281s [19,] -1.902313 0.095461 1.3824711 5.0369e-01 2.2193e-01 -5.5628e-02 281s [20,] 0.123220 1.399444 1.1517634 3.2546e-01 7.8261e-02 -4.0733e-01 281s [21,] -2.436023 -2.524827 -1.0197416 3.4819e-01 -1.4914e-01 -4.3669e-02 281s [22,] -0.904931 -1.114894 -0.1235807 2.0285e-01 -1.6200e-01 2.5681e-01 281s [23,] 0.220231 -1.767325 0.0482262 6.4418e-01 9.8618e-02 -5.7683e-02 281s [24,] -0.274403 -1.561826 0.3820323 7.0016e-01 5.5220e-01 1.4376e-01 281s [25,] -3.306400 -2.980247 0.0252488 9.4001e-01 -1.0841e-01 -2.5303e-01 281s [26,] -0.658015 -1.625199 0.3021005 7.2702e-01 -3.0299e-01 -1.2339e-01 281s [27,] -3.137066 -0.774218 0.5577497 6.4188e-01 -8.0125e-02 7.7819e-01 281s [28,] 2.867950 -3.099435 -0.6435415 1.0366e+00 1.5908e-01 7.6524e-02 281s [29,] 4.523097 -0.527338 -0.1032516 6.4537e-01 4.7286e-01 -2.7166e-01 281s [30,] 1.002381 -1.376693 -0.2735956 5.0522e-01 -1.2750e-01 -1.6178e-01 281s [31,] 1.894615 -1.296202 -1.9117282 -3.8032e-01 4.6473e-01 3.1085e-01 281s [32,] 1.210291 0.067230 -0.9832930 -8.5379e-01 3.2823e-01 4.9994e-01 281s [33,] 1.964118 0.022175 0.1818518 3.0464e-01 3.5596e-01 1.4985e-01 281s [34,] 0.576738 0.567851 0.6982155 1.8415e-01 1.8695e-01 3.2706e-01 281s [35,] -0.231793 -2.143909 -0.6825523 4.0681e-01 5.4492e-01 3.6259e-01 281s [36,] 4.250883 -0.719760 0.2157706 7.7167e-01 -1.9064e-01 -2.0611e-01 281s [37,] 1.077364 -2.054664 -1.3064867 1.0043e-01 8.6092e-02 3.5416e-01 281s [38,] 2.259260 -1.653588 -0.6730692 5.7300e-01 1.6930e-01 1.6986e-01 281s [39,] -1.251576 -1.451593 0.4671580 5.8957e-01 4.2672e-01 2.2495e-01 281s [40,] 3.304245 1.998193 1.0941231 1.3734e-01 3.7012e-01 2.4142e-01 281s [41,] 4.286315 -1.280951 0.5856744 -6.0980e-01 -4.3090e-01 1.9801e-01 281s [42,] 6.343820 1.801880 1.3481119 1.0355e+00 2.9802e-01 -8.4501e-04 281s [43,] 3.119491 0.214077 -1.1216236 -3.8134e-01 -1.9523e-01 -2.6706e-02 281s [44,] 5.285254 0.938072 0.7440487 1.1539e-02 8.1629e-01 -7.9286e-01 281s [45,] 0.082429 -0.416631 -0.1588203 2.3098e-01 5.1867e-01 9.4503e-02 281s [46,] 0.357862 -1.951997 -0.0731829 7.0393e-01 1.8828e-01 1.5707e-02 281s [47,] 2.428744 1.522538 -3.0467213 -1.9114e+00 2.4638e-01 3.5871e-01 281s [48,] 0.282348 -0.697287 -1.1592508 -5.4929e-01 6.2199e-01 -5.4596e-02 281s [49,] -2.266009 -0.559548 -1.3794914 -1.1300e+00 7.8872e-01 -2.0411e-02 281s [50,] 2.868649 2.860857 1.6128307 6.7382e-02 2.2344e-01 -4.1484e-01 281s [51,] -1.596061 0.546812 -1.1779327 -1.0512e+00 1.3522e-01 -9.4865e-03 281s [52,] -5.186121 -1.000829 -0.7440599 -9.6302e-01 3.0732e-01 -1.7009e-01 281s [53,] -0.800232 0.049087 -0.6946842 -5.8284e-01 -2.1277e-01 -2.7004e-01 281s [54,] -0.246388 -0.030606 -0.1814302 -1.1632e-01 5.7767e-02 -1.8637e-01 281s [55,] 0.914315 -0.428594 -0.4919557 4.5039e-02 -2.7868e-01 -2.2140e-01 281s [56,] -0.061827 0.583572 0.3263056 -1.1589e-01 -1.2973e-01 -1.6518e-01 281s [57,] -1.295979 -0.421943 0.8410805 3.0441e-01 -3.9478e-01 -4.5233e-02 281s [58,] 0.174908 -1.343854 0.0115086 8.0227e-01 -3.9364e-01 -2.2918e-01 281s [59,] -1.869684 0.840823 0.0109543 -5.5536e-01 -1.4155e-01 1.0613e-01 281s [60,] -1.614271 0.557309 -0.0690787 -9.1753e-02 -3.0975e-01 1.6192e-01 281s [61,] -0.258192 1.434984 0.7684636 -1.1998e-01 -3.4662e-01 -4.8808e-02 281s [62,] 2.000275 2.204730 1.1194067 -2.3783e-01 5.9953e-02 -1.5836e-01 281s [63,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 281s [64,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 281s [65,] -0.822201 2.427550 1.5859438 -2.6715e-16 -1.9429e-15 1.0564e-14 281s [66,] -2.545586 0.605953 0.1469837 -3.5318e-01 -2.5871e-01 1.6901e-01 281s [67,] 0.028900 1.253717 0.4474540 5.3595e-02 1.6063e-01 -1.0980e-01 281s [68,] -1.086135 1.968868 -0.7220293 -1.6576e+00 6.2061e-02 -7.0998e-04 281s [69,] -0.836638 0.660453 0.0049966 1.3663e-01 -1.0131e-01 -2.4008e-01 281s [70,] 4.843092 -6.035092 0.8250084 -3.4481e+00 -4.8538e+00 -7.8407e+00 281s [71,] -2.500038 1.146245 0.6967314 -2.4611e-01 -1.4266e-01 -8.2996e-02 281s [72,] 2.220676 1.122951 -0.2444075 1.1066e-01 -3.1540e-01 -2.1344e-01 281s [73,] -2.310518 2.354552 0.2706503 -6.4192e-01 2.0566e-01 4.5520e-01 281s [74,] -10.802799 -3.462655 2.2031446 1.1326e+00 2.8049e-01 -2.9749e-01 281s [75,] -0.301038 2.284366 0.2440764 -6.9450e-01 2.6435e-01 4.3129e-01 281s [76,] -1.477936 0.245154 -0.8869850 -8.9900e-01 -9.8013e-02 1.1983e-01 281s [77,] -8.169236 -1.599780 1.4987144 3.7767e-01 2.4726e-01 3.8246e-01 281s [78,] 1.096654 1.646072 0.0591327 -3.3138e-01 -1.7936e-01 6.2716e-02 281s [79,] -0.289199 0.625796 -0.3974294 -6.6099e-01 -2.0857e-01 2.1190e-01 281s [80,] -3.160557 -2.282579 0.3255355 4.6181e-01 2.7753e-01 -1.5673e-01 281s [81,] 1.284356 -0.548854 -0.2907281 2.4017e-01 -2.5254e-01 -1.4289e-03 281s [82,] 2.562817 2.019485 0.8249162 3.2973e-01 3.3866e-01 1.3889e-01 281s [83,] 2.538825 0.759863 -0.3142506 -5.1028e-01 -2.0539e-01 8.8979e-02 281s [84,] 0.841123 0.110035 -1.5793120 -1.2807e+00 1.2332e-01 1.6224e-01 281s [85,] 0.636271 1.793014 2.6824860 1.0329e+00 -4.8850e-01 -2.3012e-01 281s [86,] 0.633183 -0.426511 -1.4791366 -6.1314e-01 -7.0534e-02 -2.3778e-01 281s PC7 PC8 281s [1,] 1.0196e-01 -1.7180e-03 281s [2,] 2.6131e-01 -8.5191e-03 281s [3,] 6.9637e-01 -8.0573e-03 281s [4,] -1.3548e-01 -1.4969e-03 281s [5,] 3.1443e-02 -2.7307e-03 281s [6,] -2.5079e-01 3.6450e-03 281s [7,] 4.5377e-02 -2.6071e-03 281s [8,] -1.6060e-01 -2.3761e-04 281s [9,] -1.5152e-01 -4.3079e-04 281s [10,] 9.1089e-02 1.9536e-03 281s [11,] 2.5654e-01 -1.4875e-03 281s [12,] -2.3798e-03 -1.0954e-04 281s [13,] -1.3687e-01 2.8402e-03 281s [14,] -6.5248e-02 -1.5114e-03 281s [15,] 3.7695e-02 -2.7827e-03 281s [16,] 3.8131e-01 -3.7990e-03 281s [17,] 4.5661e-02 -1.4965e-03 281s [18,] 3.9910e-01 -7.2703e-03 281s [19,] 2.9353e-01 -3.3342e-03 281s [20,] 6.0915e-01 -6.0837e-03 281s [21,] -1.0079e-01 1.0179e-03 281s [22,] -2.2945e-02 -1.0515e-03 281s [23,] 2.3631e-01 -2.5558e-03 281s [24,] -7.7207e-02 3.4800e-03 281s [25,] 1.4903e-02 -3.2430e-04 281s [26,] 3.8032e-03 -2.1705e-03 281s [27,] 3.7208e-02 -3.0631e-03 281s [28,] -4.8147e-01 6.1089e-03 281s [29,] -4.0388e-02 2.8549e-03 281s [30,] 3.4318e-02 -1.0014e-03 281s [31,] -2.2872e-02 1.8706e-03 281s [32,] -8.4542e-02 1.3368e-03 281s [33,] 4.5274e-02 5.3383e-04 281s [34,] -2.0048e-01 2.4727e-03 281s [35,] -5.6482e-02 2.9923e-03 281s [36,] -2.6046e-02 -1.2910e-03 281s [37,] 9.6038e-02 -1.8897e-03 281s [38,] -2.9035e-01 4.4317e-03 281s [39,] -4.6322e-03 2.4336e-03 281s [40,] 3.8686e-01 -3.9300e-03 281s [41,] 3.7834e-01 -7.8976e-03 281s [42,] -8.2037e-04 -4.3106e-05 281s [43,] 3.3467e-01 -5.2401e-03 281s [44,] -6.2170e-01 1.2840e-02 281s [45,] 5.3557e-02 2.9156e-03 281s [46,] 5.1785e-04 2.0738e-03 281s [47,] -5.2141e-01 5.7206e-03 281s [48,] -2.7669e-01 6.7329e-03 281s [49,] 8.4319e-02 3.8528e-03 281s [50,] 1.4210e-01 1.6961e-04 281s [51,] -1.1871e-01 2.6676e-03 281s [52,] -2.5036e-01 6.4121e-03 281s [53,] 2.2399e-01 -2.8200e-03 281s [54,] 5.6532e-02 4.9304e-04 281s [55,] -1.4343e-01 1.2558e-03 281s [56,] 4.1682e-02 -9.6490e-04 281s [57,] -1.3014e-01 -6.2709e-04 281s [58,] -2.1428e-01 8.2594e-04 281s [59,] -7.9775e-02 -8.9776e-04 281s [60,] -8.6835e-02 -1.0498e-03 281s [61,] 6.2470e-02 -2.7499e-03 281s [62,] 3.3052e-02 -3.2369e-04 281s [63,] -1.7137e-01 -3.1087e-04 281s [64,] -1.7137e-01 -3.1087e-04 281s [65,] 3.5496e-14 2.5975e-12 281s [66,] -2.2016e-02 -1.2206e-03 281s [67,] 8.5160e-02 -1.4837e-04 281s [68,] -2.2535e-03 1.9054e-04 281s [69,] 5.9976e-02 -8.6961e-04 281s [70,] 1.0448e+00 -2.0167e-02 281s [71,] -1.7609e-01 1.9378e-03 281s [72,] -1.7047e-01 2.6076e-04 281s [73,] 1.1885e-01 -8.1624e-04 281s [74,] 2.0942e-01 3.3164e-03 281s [75,] -7.7528e-01 9.9316e-03 281s [76,] -4.6285e-03 2.5153e-04 281s [77,] 7.0218e-02 1.5708e-03 281s [78,] -1.4859e-02 -6.7049e-04 281s [79,] 5.1054e-02 -2.0198e-03 281s [80,] -1.5770e-01 4.9579e-03 281s [81,] -1.9411e-01 4.4401e-04 281s [82,] 6.0634e-02 8.7960e-04 281s [83,] -4.4635e-02 -1.7048e-03 281s [84,] -2.3612e-03 -2.2242e-04 281s [85,] -5.5171e-02 -1.1222e-03 281s [86,] -1.4972e-01 1.4543e-03 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 2.8929930 1.8789522 0.9946460 0.7479403 0.3744197 0.2596328 0.1421387 281s [8] 0.0025753 281s ---------------------------------------------------------- 281s bushfire 38 5 5 37473.439646 1742.633018 281s Scores: 281s PC1 PC2 PC3 PC4 PC5 281s [1,] -67.2152 -2.3010e+01 4.4179e+00 1.0892e+00 1.7536e+00 281s [2,] -69.0225 -2.1417e+01 2.5382e+00 1.1092e+00 9.3919e-01 281s [3,] -61.6651 -1.8580e+01 -6.1022e-01 -8.1124e-01 -1.6462e-01 281s [4,] -44.5883 -1.8234e+01 -3.9899e-01 -5.2145e-01 2.0050e-01 281s [5,] -12.2941 -2.2954e+01 3.5970e+00 1.1037e+00 -2.4384e-01 281s [6,] 13.0282 -2.8133e+01 8.7670e+00 3.4751e+00 1.3728e+00 281s [7,] -199.0774 -7.7956e+01 5.4935e+01 6.3134e+00 -1.9919e+00 281s [8,] -228.2849 -1.3258e+02 2.2340e+01 2.1656e+01 -1.2594e+01 281s [9,] -228.9164 -1.3560e+02 2.0463e+01 2.2625e+01 -1.2743e+01 281s [10,] -232.4703 -1.0661e+02 3.5597e+01 1.7915e+01 -7.7659e+00 281s [11,] -236.7410 -8.8072e+01 3.6632e+01 1.5095e+01 -7.4695e+00 281s [12,] -249.4091 -3.6830e+01 2.4010e+01 4.7317e+00 -1.2986e+00 281s [13,] -197.0450 4.2633e-14 4.9738e-14 1.1657e-13 -1.1369e-13 281s [14,] 50.9487 1.1397e+01 -1.1247e+01 -4.8733e+00 2.4511e+00 281s [15,] 180.7896 1.7571e+01 -8.0454e+00 -1.0582e+01 1.2714e+00 281s [16,] 135.6178 1.4189e+01 -4.9116e-01 -9.2701e+00 1.4021e-01 281s [17,] 132.5344 1.5577e+01 2.2990e-01 -6.4963e+00 7.3370e-01 281s [18,] 121.3422 1.0471e+01 4.5656e+00 -4.9831e+00 -5.2314e-01 281s [19,] 125.2722 9.0272e+00 3.7365e+00 -3.3313e+00 -2.9097e-01 281s [20,] 135.2370 1.4091e+01 2.0639e+00 -3.6800e+00 1.1733e+00 281s [21,] 116.4250 1.5147e+01 2.9085e+00 -4.8084e+00 1.2603e+00 281s [22,] 108.2925 1.4223e+01 7.7165e-01 -4.5065e+00 2.7943e+00 281s [23,] -22.8258 6.4234e+00 2.4654e+00 -3.9627e+00 7.9847e-01 281s [24,] -78.1850 4.6631e+00 -3.6818e+00 -2.7688e+00 5.8508e-01 281s [25,] -13.0417 2.7521e+00 -3.1955e+00 -4.6824e+00 -3.1085e-01 281s [26,] -19.1244 -9.5045e-01 -2.6771e+00 -4.7104e+00 -1.6172e-01 281s [27,] -34.4379 3.2761e+00 -9.2826e+00 -2.9861e+00 -3.3561e-01 281s [28,] -11.5852 1.4506e+01 -1.5649e+01 -1.6260e+00 -8.5347e-01 281s [29,] -2.9366 2.8741e+01 -2.2907e+01 3.9749e-01 3.5861e-02 281s [30,] -59.7518 2.8633e+01 -1.4710e+01 3.5226e+00 -9.9066e-01 281s [31,] 67.8017 4.7241e+01 -9.1255e+00 1.3201e+01 1.3500e-13 281s [32,] 321.9941 7.6188e+01 2.2491e+01 3.1537e+01 3.2368e+00 281s [33,] 359.5155 6.6710e+01 5.6061e+01 3.4541e+01 2.0718e+00 281s [34,] 355.8007 6.5695e+01 5.7430e+01 3.3578e+01 1.4640e+00 281s [35,] 361.1076 6.7577e+01 5.7402e+01 3.3832e+01 3.2618e+00 281s [36,] 358.3592 6.6791e+01 5.8643e+01 3.2720e+01 1.2487e+00 281s [37,] 355.9974 6.8071e+01 6.0927e+01 3.2560e+01 2.4898e+00 281s [38,] 357.2530 6.9073e+01 6.1517e+01 3.2523e+01 2.7558e+00 281s ------------- 281s Call: 281s PcaProj(x = x) 281s 281s Standard deviations: 281s [1] 193.5806 41.7449 16.7665 8.1585 1.6074 281s ---------------------------------------------------------- 281s ========================================================== 281s > ## IGNORE_RDIFF_END 281s > 281s > ## VT::14.11.2018 - commented out - on some platforms PcaHubert will choose only 1 PC 281s > ## and will show difference 281s > ## test.case.1() 281s > 281s > test.case.2() 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s [1] TRUE 281s > 281s BEGIN TEST tlda.R 281s 281s R version 4.4.3 (2025-02-28) -- "Trophy Case" 281s Copyright (C) 2025 The R Foundation for Statistical Computing 281s Platform: arm-unknown-linux-gnueabihf (32-bit) 281s 281s R is free software and comes with ABSOLUTELY NO WARRANTY. 281s You are welcome to redistribute it under certain conditions. 281s Type 'license()' or 'licence()' for distribution details. 281s 281s R is a collaborative project with many contributors. 281s Type 'contributors()' for more information and 281s 'citation()' on how to cite R or R packages in publications. 281s 281s Type 'demo()' for some demos, 'help()' for on-line help, or 281s 'help.start()' for an HTML browser interface to help. 281s Type 'q()' to quit R. 281s 281s > ## VT::15.09.2013 - this will render the output independent 281s > ## from the version of the package 281s > suppressPackageStartupMessages(library(rrcov)) 281s > library(MASS) 282s > 282s > ## VT::14.01.2020 282s > ## On some platforms minor differences are shown - use 282s > ## IGNORE_RDIFF_BEGIN 282s > ## IGNORE_RDIFF_END 282s > 282s > dodata <- function(method) { 282s + 282s + options(digits = 5) 282s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 282s + 282s + tmp <- sys.call() 282s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 282s + cat("===================================================\n") 282s + 282s + cat("\nData: ", "hemophilia\n") 282s + data(hemophilia) 282s + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "anorexia\n") 282s + data(anorexia) 282s + show(rlda <- Linda(Treat~., data=anorexia, method=method)) 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "Pima\n") 282s + data(Pima.tr) 282s + show(rlda <- Linda(type~., data=Pima.tr, method=method)) 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "Forest soils\n") 282s + data(soil) 282s + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) 282s + 282s + ## This will not work within the function, of course 282s + ## - comment it out 282s + ## IGNORE_RDIFF_BEGIN 282s + rlda <- Linda(F~., data=soil1983, method=method) 282s + ## show(rlda) 282s + ## IGNORE_RDIFF_END 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "Raven and Miller diabetes data\n") 282s + data(diabetes) 282s + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "iris\n") 282s + data(iris) 282s + if(method != "mcdA") 282s + { 282s + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) 282s + show(predict(rlda)) 282s + } 282s + 282s + cat("\nData: ", "crabs\n") 282s + data(crabs) 282s + show(rlda <- Linda(sp~., data=crabs, method=method)) 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "fish\n") 282s + data(fish) 282s + fish <- fish[-14,] # remove observation #14 containing missing value 282s + 282s + # The height and width are calculated as percentages 282s + # of the third length variable 282s + fish[,5] <- fish[,5]*fish[,4]/100 282s + fish[,6] <- fish[,6]*fish[,4]/100 282s + 282s + ## There is one class with only 6 observations (p=6). Normally 282s + ## Linda will fail, therefore use l1med=TRUE. 282s + ## This works only for methods mcdB and mcdC 282s + 282s + table(fish$Species) 282s + if(method != "mcdA") 282s + { 282s + ## IGNORE_RDIFF_BEGIN 282s + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) 282s + ## show(rlda) 282s + ## IGNORE_RDIFF_END 282s + show(predict(rlda)) 282s + } 282s + 282s + cat("\nData: ", "pottery\n") 282s + data(pottery) 282s + show(rlda <- Linda(origin~., data=pottery, method=method)) 282s + show(predict(rlda)) 282s + 282s + cat("\nData: ", "olitos\n") 282s + data(olitos) 282s + if(method != "mcdA") 282s + { 282s + ## IGNORE_RDIFF_BEGIN 282s + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) 282s + ## show(rlda) 282s + ## IGNORE_RDIFF_END 282s + show(predict(rlda)) 282s + } 282s + 282s + cat("===================================================\n") 282s + } 282s > 282s > 282s > ## -- now do it: 282s > dodata(method="mcdA") 282s 282s Call: dodata(method = "mcdA") 282s =================================================== 282s 282s Data: hemophilia 282s Call: 282s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 282s 282s Prior Probabilities of Groups: 282s carrier normal 282s 0.6 0.4 282s 282s Group means: 282s AHFactivity AHFantigen 282s carrier -0.30795 -0.0059911 282s normal -0.12920 -0.0603000 282s 282s Within-groups Covariance Matrix: 282s AHFactivity AHFantigen 282s AHFactivity 0.018036 0.011853 282s AHFantigen 0.011853 0.019185 282s 282s Linear Coeficients: 282s AHFactivity AHFantigen 282s carrier -28.4029 17.2368 282s normal -8.5834 2.1602 282s 282s Constants: 282s carrier normal 282s -4.8325 -1.4056 282s 282s Apparent error rate 0.1333 282s 282s Classification table 282s Predicted 282s Actual carrier normal 282s carrier 39 6 282s normal 4 26 282s 282s Confusion matrix 282s Predicted 282s Actual carrier normal 282s carrier 0.867 0.133 282s normal 0.133 0.867 282s 282s Data: anorexia 282s Call: 282s Linda(Treat ~ ., data = anorexia, method = method) 282s 282s Prior Probabilities of Groups: 282s CBT Cont FT 282s 0.40278 0.36111 0.23611 282s 282s Group means: 282s Prewt Postwt 282s CBT 82.633 82.950 282s Cont 81.558 81.108 282s FT 84.331 94.762 282s 282s Within-groups Covariance Matrix: 282s Prewt Postwt 282s Prewt 26.9291 3.3862 282s Postwt 3.3862 18.2368 282s 282s Linear Coeficients: 282s Prewt Postwt 282s CBT 2.5563 4.0738 282s Cont 2.5284 3.9780 282s FT 2.5374 4.7250 282s 282s Constants: 282s CBT Cont FT 282s -275.49 -265.45 -332.31 282s 282s Apparent error rate 0.3889 282s 282s Classification table 282s Predicted 282s Actual CBT Cont FT 282s CBT 16 5 8 282s Cont 11 15 0 282s FT 0 4 13 282s 282s Confusion matrix 282s Predicted 282s Actual CBT Cont FT 282s CBT 0.552 0.172 0.276 282s Cont 0.423 0.577 0.000 282s FT 0.000 0.235 0.765 282s 282s Data: Pima 282s Call: 282s Linda(type ~ ., data = Pima.tr, method = method) 282s 282s Prior Probabilities of Groups: 282s No Yes 282s 0.66 0.34 282s 282s Group means: 282s npreg glu bp skin bmi ped age 282s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 282s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 282s 282s Within-groups Covariance Matrix: 282s npreg glu bp skin bmi ped age 282s npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 282s glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 282s bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 282s skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 282s bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 282s ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 282s age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 282s 282s Linear Coeficients: 282s npreg glu bp skin bmi ped age 282s No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 282s Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 282s 282s Constants: 282s No Yes 282s -37.050 -51.586 282s 282s Apparent error rate 0.22 282s 282s Classification table 282s Predicted 282s Actual No Yes 282s No 107 25 282s Yes 19 49 282s 282s Confusion matrix 282s Predicted 282s Actual No Yes 282s No 0.811 0.189 282s Yes 0.279 0.721 282s 282s Data: Forest soils 282s 282s Apparent error rate 0.3103 282s 282s Classification table 282s Predicted 282s Actual 1 2 3 282s 1 7 2 2 282s 2 3 13 7 282s 3 1 3 20 282s 282s Confusion matrix 282s Predicted 282s Actual 1 2 3 282s 1 0.636 0.182 0.182 282s 2 0.130 0.565 0.304 282s 3 0.042 0.125 0.833 282s 282s Data: Raven and Miller diabetes data 282s Call: 282s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 282s 282s Prior Probabilities of Groups: 282s normal chemical overt 282s 0.52414 0.24828 0.22759 282s 282s Group means: 282s insulin glucose sspg 282s normal 163.939 345.8 99.076 282s chemical 299.448 476.9 223.621 282s overt 95.958 1026.4 343.000 282s 282s Within-groups Covariance Matrix: 282s insulin glucose sspg 282s insulin 7582.0 -1263.1 1095.8 282s glucose -1263.1 18952.4 4919.3 282s sspg 1095.8 4919.3 3351.2 282s 282s Linear Coeficients: 282s insulin glucose sspg 282s normal 0.027694 0.023859 -0.014514 282s chemical 0.040288 0.022532 0.020479 282s overt 0.017144 0.048768 0.025158 282s 282s Constants: 282s normal chemical overt 282s -6.3223 -15.0879 -31.6445 282s 282s Apparent error rate 0.1862 282s 282s Classification table 282s Predicted 282s Actual normal chemical overt 282s normal 69 7 0 282s chemical 13 23 0 282s overt 2 5 26 282s 282s Confusion matrix 282s Predicted 282s Actual normal chemical overt 282s normal 0.908 0.092 0.000 282s chemical 0.361 0.639 0.000 282s overt 0.061 0.152 0.788 282s 282s Data: iris 282s 282s Data: crabs 282s Call: 282s Linda(sp ~ ., data = crabs, method = method) 282s 282s Prior Probabilities of Groups: 282s B O 282s 0.5 0.5 282s 282s Group means: 282s sexM index FL RW CL CW BD 282s B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 282s O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 282s 282s Within-groups Covariance Matrix: 282s sexM index FL RW CL CW BD 282s sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 282s index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 282s FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 282s RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 282s CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 282s CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 282s BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 282s 282s Linear Coeficients: 282s sexM index FL RW CL CW BD 282s B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 282s O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 282s 282s Constants: 282s B O 282s -78.317 -159.259 282s 282s Apparent error rate 0 282s 282s Classification table 282s Predicted 282s Actual B O 282s B 100 0 282s O 0 100 282s 282s Confusion matrix 282s Predicted 282s Actual B O 282s B 1 0 282s O 0 1 282s 282s Data: fish 282s 282s Data: pottery 282s Call: 282s Linda(origin ~ ., data = pottery, method = method) 282s 282s Prior Probabilities of Groups: 282s Attic Eritrean 282s 0.48148 0.51852 282s 282s Group means: 282s SI AL FE MG CA TI 282s Attic 55.36 13.73 9.82 5.45 6.03 0.863 282s Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 282s 282s Within-groups Covariance Matrix: 282s SI AL FE MG CA TI 282s SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 282s AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 282s FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 282s MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 282s CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 282s TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 282s 282s Linear Coeficients: 282s SI AL FE MG CA TI 282s Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 282s Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 282s 282s Constants: 282s Attic Eritrean 282s -1578.14 -901.13 282s 282s Apparent error rate 0.1111 282s 282s Classification table 282s Predicted 282s Actual Attic Eritrean 282s Attic 12 1 282s Eritrean 2 12 282s 282s Confusion matrix 282s Predicted 282s Actual Attic Eritrean 282s Attic 0.923 0.077 282s Eritrean 0.143 0.857 282s 282s Data: olitos 282s =================================================== 282s > dodata(method="mcdB") 282s 282s Call: dodata(method = "mcdB") 282s =================================================== 282s 282s Data: hemophilia 282s Call: 282s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 282s 282s Prior Probabilities of Groups: 282s carrier normal 282s 0.6 0.4 282s 282s Group means: 282s AHFactivity AHFantigen 282s carrier -0.31456 -0.014775 282s normal -0.13582 -0.069084 282s 282s Within-groups Covariance Matrix: 282s AHFactivity AHFantigen 282s AHFactivity 0.0125319 0.0086509 282s AHFantigen 0.0086509 0.0182424 282s 282s Linear Coeficients: 282s AHFactivity AHFantigen 282s carrier -36.486 16.4923 282s normal -12.226 2.0107 282s 282s Constants: 282s carrier normal 282s -6.1276 -1.6771 282s 282s Apparent error rate 0.16 282s 282s Classification table 282s Predicted 282s Actual carrier normal 282s carrier 38 7 282s normal 5 25 282s 282s Confusion matrix 282s Predicted 282s Actual carrier normal 282s carrier 0.844 0.156 282s normal 0.167 0.833 282s 282s Data: anorexia 282s Call: 282s Linda(Treat ~ ., data = anorexia, method = method) 282s 282s Prior Probabilities of Groups: 282s CBT Cont FT 282s 0.40278 0.36111 0.23611 282s 282s Group means: 282s Prewt Postwt 282s CBT 83.254 82.381 282s Cont 82.178 80.539 282s FT 84.951 94.193 282s 282s Within-groups Covariance Matrix: 282s Prewt Postwt 282s Prewt 19.1751 8.8546 282s Postwt 8.8546 25.2326 282s 282s Linear Coeficients: 282s Prewt Postwt 282s CBT 3.3822 2.0780 282s Cont 3.3555 2.0144 282s FT 3.2299 2.5996 282s 282s Constants: 282s CBT Cont FT 282s -227.29 -220.01 -261.06 282s 282s Apparent error rate 0.4444 282s 282s Classification table 282s Predicted 282s Actual CBT Cont FT 282s CBT 16 5 8 282s Cont 12 11 3 282s FT 0 4 13 282s 282s Confusion matrix 282s Predicted 282s Actual CBT Cont FT 282s CBT 0.552 0.172 0.276 282s Cont 0.462 0.423 0.115 282s FT 0.000 0.235 0.765 282s 282s Data: Pima 282s Call: 282s Linda(type ~ ., data = Pima.tr, method = method) 282s 282s Prior Probabilities of Groups: 282s No Yes 282s 0.66 0.34 282s 282s Group means: 282s npreg glu bp skin bmi ped age 282s No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 282s Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 282s 282s Within-groups Covariance Matrix: 282s npreg glu bp skin bmi ped age 282s npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 282s glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 282s bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 282s skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 282s bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 282s ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 282s age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 282s 282s Linear Coeficients: 282s npreg glu bp skin bmi ped age 282s No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 282s Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 282s 282s Constants: 282s No Yes 282s -38.774 -53.654 282s 282s Apparent error rate 0.25 282s 282s Classification table 282s Predicted 282s Actual No Yes 282s No 104 28 282s Yes 22 46 282s 282s Confusion matrix 282s Predicted 282s Actual No Yes 282s No 0.788 0.212 282s Yes 0.324 0.676 282s 282s Data: Forest soils 282s 282s Apparent error rate 0.3448 282s 282s Classification table 282s Predicted 282s Actual 1 2 3 282s 1 4 3 4 282s 2 2 14 7 282s 3 2 2 20 282s 282s Confusion matrix 282s Predicted 282s Actual 1 2 3 282s 1 0.364 0.273 0.364 282s 2 0.087 0.609 0.304 282s 3 0.083 0.083 0.833 282s 282s Data: Raven and Miller diabetes data 282s Call: 282s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 282s 282s Prior Probabilities of Groups: 282s normal chemical overt 282s 0.52414 0.24828 0.22759 282s 282s Group means: 282s insulin glucose sspg 282s normal 152.405 346.55 99.387 282s chemical 288.244 478.80 226.226 282s overt 84.754 1028.28 345.605 282s 282s Within-groups Covariance Matrix: 282s insulin glucose sspg 282s insulin 5061.46 289.69 2071.71 282s glucose 289.69 1983.07 385.31 282s sspg 2071.71 385.31 3000.17 282s 282s Linear Coeficients: 282s insulin glucose sspg 282s normal 0.021952 0.17236 -0.0041671 282s chemical 0.034852 0.23217 0.0215200 282s overt -0.045700 0.50940 0.0813292 282s 282s Constants: 282s normal chemical overt 282s -31.976 -64.433 -275.502 282s 282s Apparent error rate 0.0966 282s 282s Classification table 282s Predicted 282s Actual normal chemical overt 282s normal 73 3 0 282s chemical 4 32 0 282s overt 0 7 26 282s 282s Confusion matrix 282s Predicted 282s Actual normal chemical overt 282s normal 0.961 0.039 0.000 282s chemical 0.111 0.889 0.000 282s overt 0.000 0.212 0.788 282s 282s Data: iris 282s Call: 282s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 282s 282s Prior Probabilities of Groups: 282s setosa versicolor virginica 282s 0.33333 0.33333 0.33333 282s 282s Group means: 282s Sepal.Length Sepal.Width Petal.Length Petal.Width 282s setosa 4.9834 3.4153 1.4532 0.22474 282s versicolor 5.8947 2.8149 4.2263 1.35024 282s virginica 6.5255 3.0017 5.4485 2.06756 282s 282s Within-groups Covariance Matrix: 282s Sepal.Length Sepal.Width Petal.Length Petal.Width 282s Sepal.Length 0.201176 0.084299 0.102984 0.037019 282s Sepal.Width 0.084299 0.108394 0.050253 0.031757 282s Petal.Length 0.102984 0.050253 0.120215 0.045016 282s Petal.Width 0.037019 0.031757 0.045016 0.032825 282s 282s Linear Coeficients: 282s Sepal.Length Sepal.Width Petal.Length Petal.Width 282s setosa 22.536 27.422168 -3.6855 -40.0445 282s versicolor 17.559 6.374082 24.1965 -18.0178 282s virginica 16.488 0.015576 29.9586 3.2926 282s 282s Constants: 282s setosa versicolor virginica 282s -96.901 -100.790 -139.937 282s 282s Apparent error rate 0.0267 282s 282s Classification table 282s Predicted 282s Actual setosa versicolor virginica 282s setosa 50 0 0 282s versicolor 0 48 2 282s virginica 0 2 48 282s 282s Confusion matrix 282s Predicted 282s Actual setosa versicolor virginica 282s setosa 1 0.00 0.00 282s versicolor 0 0.96 0.04 282s virginica 0 0.04 0.96 282s 282s Data: crabs 282s Call: 282s Linda(sp ~ ., data = crabs, method = method) 282s 282s Prior Probabilities of Groups: 282s B O 282s 0.5 0.5 282s 282s Group means: 282s sexM index FL RW CL CW BD 282s B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 282s O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 282s 282s Within-groups Covariance Matrix: 282s sexM index FL RW CL CW BD 282s sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 282s index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 282s FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 282s RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 282s CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 282s CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 282s BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 282s 282s Linear Coeficients: 282s sexM index FL RW CL CW BD 282s B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 282s O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 282s 282s Constants: 282s B O 282s -66.688 -134.375 282s 282s Apparent error rate 0 282s 282s Classification table 282s Predicted 282s Actual B O 282s B 100 0 282s O 0 100 282s 282s Confusion matrix 282s Predicted 282s Actual B O 282s B 1 0 282s O 0 1 282s 282s Data: fish 282s 282s Apparent error rate 0.0949 282s 282s Classification table 282s Predicted 282s Actual 1 2 3 4 5 6 7 282s 1 34 0 0 0 0 0 0 282s 2 0 6 0 0 0 0 0 282s 3 0 0 20 0 0 0 0 282s 4 0 0 0 11 0 0 0 282s 5 0 0 0 0 13 0 1 282s 6 0 0 0 0 0 17 0 282s 7 0 13 0 0 1 0 42 282s 282s Confusion matrix 282s Predicted 282s Actual 1 2 3 4 5 6 7 282s 1 1 0.000 0 0 0.000 0 0.000 282s 2 0 1.000 0 0 0.000 0 0.000 282s 3 0 0.000 1 0 0.000 0 0.000 282s 4 0 0.000 0 1 0.000 0 0.000 282s 5 0 0.000 0 0 0.929 0 0.071 282s 6 0 0.000 0 0 0.000 1 0.000 282s 7 0 0.232 0 0 0.018 0 0.750 282s 282s Data: pottery 282s Call: 282s Linda(origin ~ ., data = pottery, method = method) 282s 282s Prior Probabilities of Groups: 282s Attic Eritrean 282s 0.48148 0.51852 282s 282s Group means: 282s SI AL FE MG CA TI 282s Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 282s Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 282s 282s Within-groups Covariance Matrix: 282s SI AL FE MG CA TI 282s SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 282s AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 282s FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 282s MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 282s CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 282s TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 282s 282s Linear Coeficients: 282s SI AL FE MG CA TI 282s Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 282s Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 282s 282s Constants: 282s Attic Eritrean 282s -3252.6 -1961.9 282s 282s Apparent error rate 0.1111 282s 282s Classification table 282s Predicted 282s Actual Attic Eritrean 282s Attic 12 1 282s Eritrean 2 12 282s 282s Confusion matrix 282s Predicted 282s Actual Attic Eritrean 282s Attic 0.923 0.077 282s Eritrean 0.143 0.857 282s 282s Data: olitos 283s 283s Apparent error rate 0.15 283s 283s Classification table 283s Predicted 283s Actual 1 2 3 4 283s 1 44 1 4 1 283s 2 2 23 0 0 283s 3 6 1 26 1 283s 4 1 1 0 9 283s 283s Confusion matrix 283s Predicted 283s Actual 1 2 3 4 283s 1 0.880 0.020 0.080 0.020 283s 2 0.080 0.920 0.000 0.000 283s 3 0.176 0.029 0.765 0.029 283s 4 0.091 0.091 0.000 0.818 283s =================================================== 283s > dodata(method="mcdC") 283s 283s Call: dodata(method = "mcdC") 283s =================================================== 283s 283s Data: hemophilia 283s Call: 283s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 283s 283s Prior Probabilities of Groups: 283s carrier normal 283s 0.6 0.4 283s 283s Group means: 283s AHFactivity AHFantigen 283s carrier -0.32583 -0.011545 283s normal -0.12783 -0.071377 283s 283s Within-groups Covariance Matrix: 283s AHFactivity AHFantigen 283s AHFactivity 0.0120964 0.0075536 283s AHFantigen 0.0075536 0.0164883 283s 283s Linear Coeficients: 283s AHFactivity AHFantigen 283s carrier -37.117 16.30377 283s normal -11.015 0.71742 283s 283s Constants: 283s carrier normal 283s -6.4636 -1.5947 283s 283s Apparent error rate 0.16 283s 283s Classification table 283s Predicted 283s Actual carrier normal 283s carrier 38 7 283s normal 5 25 283s 283s Confusion matrix 283s Predicted 283s Actual carrier normal 283s carrier 0.844 0.156 283s normal 0.167 0.833 283s 283s Data: anorexia 283s Call: 283s Linda(Treat ~ ., data = anorexia, method = method) 283s 283s Prior Probabilities of Groups: 283s CBT Cont FT 283s 0.40278 0.36111 0.23611 283s 283s Group means: 283s Prewt Postwt 283s CBT 82.477 82.073 283s Cont 82.039 80.835 283s FT 85.242 94.750 283s 283s Within-groups Covariance Matrix: 283s Prewt Postwt 283s Prewt 19.6589 8.3891 283s Postwt 8.3891 22.8805 283s 283s Linear Coeficients: 283s Prewt Postwt 283s CBT 3.1590 2.4288 283s Cont 3.1599 2.3743 283s FT 3.0454 3.0245 283s 283s Constants: 283s CBT Cont FT 283s -230.85 -226.60 -274.53 283s 283s Apparent error rate 0.4583 283s 283s Classification table 283s Predicted 283s Actual CBT Cont FT 283s CBT 16 5 8 283s Cont 14 10 2 283s FT 0 4 13 283s 283s Confusion matrix 283s Predicted 283s Actual CBT Cont FT 283s CBT 0.552 0.172 0.276 283s Cont 0.538 0.385 0.077 283s FT 0.000 0.235 0.765 283s 283s Data: Pima 283s Call: 283s Linda(type ~ ., data = Pima.tr, method = method) 283s 283s Prior Probabilities of Groups: 283s No Yes 283s 0.66 0.34 283s 283s Group means: 283s npreg glu bp skin bmi ped age 283s No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 283s Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 283s 283s Within-groups Covariance Matrix: 283s npreg glu bp skin bmi ped age 283s npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 283s glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 283s bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 283s skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 283s bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 283s ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 283s age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 283s 283s Linear Coeficients: 283s npreg glu bp skin bmi ped age 283s No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 283s Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 283s 283s Constants: 283s No Yes 283s -41.716 -55.056 283s 283s Apparent error rate 0.235 283s 283s Classification table 283s Predicted 283s Actual No Yes 283s No 107 25 283s Yes 22 46 283s 283s Confusion matrix 283s Predicted 283s Actual No Yes 283s No 0.811 0.189 283s Yes 0.324 0.676 283s 283s Data: Forest soils 283s 283s Apparent error rate 0.3276 283s 283s Classification table 283s Predicted 283s Actual 1 2 3 283s 1 5 2 4 283s 2 2 13 8 283s 3 1 2 21 283s 283s Confusion matrix 283s Predicted 283s Actual 1 2 3 283s 1 0.455 0.182 0.364 283s 2 0.087 0.565 0.348 283s 3 0.042 0.083 0.875 283s 283s Data: Raven and Miller diabetes data 283s Call: 283s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 283s 283s Prior Probabilities of Groups: 283s normal chemical overt 283s 0.52414 0.24828 0.22759 283s 283s Group means: 283s insulin glucose sspg 283s normal 167.31 348.69 106.44 283s chemical 247.18 478.18 213.36 283s overt 101.83 932.92 322.42 283s 283s Within-groups Covariance Matrix: 283s insulin glucose sspg 283s insulin 4070.84 118.89 1701.54 283s glucose 118.89 2195.95 426.95 283s sspg 1701.54 426.95 2664.49 283s 283s Linear Coeficients: 283s insulin glucose sspg 283s normal 0.041471 0.15888 -0.011992 283s chemical 0.048103 0.21216 0.015359 283s overt -0.013579 0.41323 0.063462 283s 283s Constants: 283s normal chemical overt 283s -31.177 -59.703 -203.775 283s 283s Apparent error rate 0.0828 283s 283s Classification table 283s Predicted 283s Actual normal chemical overt 283s normal 72 4 0 283s chemical 2 34 0 283s overt 0 6 27 283s 283s Confusion matrix 283s Predicted 283s Actual normal chemical overt 283s normal 0.947 0.053 0.000 283s chemical 0.056 0.944 0.000 283s overt 0.000 0.182 0.818 283s 283s Data: iris 283s Call: 283s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 283s 283s Prior Probabilities of Groups: 283s setosa versicolor virginica 283s 0.33333 0.33333 0.33333 283s 283s Group means: 283s Sepal.Length Sepal.Width Petal.Length Petal.Width 283s setosa 5.0163 3.4510 1.4653 0.2449 283s versicolor 5.9435 2.7891 4.2543 1.3239 283s virginica 6.3867 3.0033 5.3767 2.0700 283s 283s Within-groups Covariance Matrix: 283s Sepal.Length Sepal.Width Petal.Length Petal.Width 283s Sepal.Length 0.186186 0.082478 0.094998 0.035445 283s Sepal.Width 0.082478 0.100137 0.049723 0.030678 283s Petal.Length 0.094998 0.049723 0.113105 0.043078 283s Petal.Width 0.035445 0.030678 0.043078 0.030885 283s 283s Linear Coeficients: 283s Sepal.Length Sepal.Width Petal.Length Petal.Width 283s setosa 23.678 30.2896 -3.1124 -44.9900 283s versicolor 20.342 4.6372 27.3265 -23.2006 283s virginica 18.377 -2.0004 31.4235 4.0906 283s 283s Constants: 283s setosa versicolor virginica 283s -104.96 -110.79 -145.49 283s 283s Apparent error rate 0.0333 283s 283s Classification table 283s Predicted 283s Actual setosa versicolor virginica 283s setosa 50 0 0 283s versicolor 0 48 2 283s virginica 0 3 47 283s 283s Confusion matrix 283s Predicted 283s Actual setosa versicolor virginica 283s setosa 1 0.00 0.00 283s versicolor 0 0.96 0.04 283s virginica 0 0.06 0.94 283s 283s Data: crabs 283s Call: 283s Linda(sp ~ ., data = crabs, method = method) 283s 283s Prior Probabilities of Groups: 283s B O 283s 0.5 0.5 283s 283s Group means: 283s sexM index FL RW CL CW BD 283s B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 283s O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 283s 283s Within-groups Covariance Matrix: 283s sexM index FL RW CL CW BD 283s sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 283s index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 283s FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 283s RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 283s CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 283s CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 283s BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 283s 283s Linear Coeficients: 283s sexM index FL RW CL CW BD 283s B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 283s O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 283s 283s Constants: 283s B O 283s -72.687 -142.365 283s 283s Apparent error rate 0 283s 283s Classification table 283s Predicted 283s Actual B O 283s B 100 0 283s O 0 100 283s 283s Confusion matrix 283s Predicted 283s Actual B O 283s B 1 0 283s O 0 1 283s 283s Data: fish 283s 283s Apparent error rate 0.0316 283s 283s Classification table 283s Predicted 283s Actual 1 2 3 4 5 6 7 283s 1 34 0 0 0 0 0 0 283s 2 0 5 0 0 1 0 0 283s 3 0 0 20 0 0 0 0 283s 4 0 0 0 11 0 0 0 283s 5 0 0 0 0 13 0 1 283s 6 0 0 0 0 0 17 0 283s 7 0 0 0 0 3 0 53 283s 283s Confusion matrix 283s Predicted 283s Actual 1 2 3 4 5 6 7 283s 1 1 0.000 0 0 0.000 0 0.000 283s 2 0 0.833 0 0 0.167 0 0.000 283s 3 0 0.000 1 0 0.000 0 0.000 283s 4 0 0.000 0 1 0.000 0 0.000 283s 5 0 0.000 0 0 0.929 0 0.071 283s 6 0 0.000 0 0 0.000 1 0.000 283s 7 0 0.000 0 0 0.054 0 0.946 283s 283s Data: pottery 283s Call: 283s Linda(origin ~ ., data = pottery, method = method) 283s 283s Prior Probabilities of Groups: 283s Attic Eritrean 283s 0.48148 0.51852 283s 283s Group means: 283s SI AL FE MG CA TI 283s Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 283s Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 283s 283s Within-groups Covariance Matrix: 283s SI AL FE MG CA TI 283s SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 283s AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 283s FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 283s MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 283s CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 283s TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 283s 283s Linear Coeficients: 283s SI AL FE MG CA TI 283s Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 283s Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 283s 283s Constants: 283s Attic Eritrean 283s -5924.2 -3802.9 283s 283s Apparent error rate 0.1111 283s 283s Classification table 283s Predicted 283s Actual Attic Eritrean 283s Attic 12 1 283s Eritrean 2 12 283s 283s Confusion matrix 283s Predicted 283s Actual Attic Eritrean 283s Attic 0.923 0.077 283s Eritrean 0.143 0.857 283s 283s Data: olitos 283s 283s Apparent error rate 0.1667 283s 283s Classification table 283s Predicted 283s Actual 1 2 3 4 283s 1 44 1 2 3 283s 2 2 22 0 1 283s 3 5 2 25 2 283s 4 1 1 0 9 283s 283s Confusion matrix 283s Predicted 283s Actual 1 2 3 4 283s 1 0.880 0.020 0.040 0.060 283s 2 0.080 0.880 0.000 0.040 283s 3 0.147 0.059 0.735 0.059 283s 4 0.091 0.091 0.000 0.818 283s =================================================== 283s > dodata(method="mrcd") 283s 283s Call: dodata(method = "mrcd") 283s =================================================== 283s 283s Data: hemophilia 283s Call: 283s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 283s 283s Prior Probabilities of Groups: 283s carrier normal 283s 0.6 0.4 283s 283s Group means: 283s AHFactivity AHFantigen 283s carrier -0.34048 -0.055943 283s normal -0.13566 -0.081191 283s 283s Within-groups Covariance Matrix: 283s AHFactivity AHFantigen 283s AHFactivity 0.0133676 0.0088055 283s AHFantigen 0.0088055 0.0221225 283s 283s Linear Coeficients: 283s AHFactivity AHFantigen 283s carrier -32.264 10.31334 283s normal -10.478 0.50044 283s 283s Constants: 283s carrier normal 283s -5.7149 -1.6067 283s 283s Apparent error rate 0.16 283s 283s Classification table 283s Predicted 283s Actual carrier normal 283s carrier 38 7 283s normal 5 25 283s 283s Confusion matrix 283s Predicted 283s Actual carrier normal 283s carrier 0.844 0.156 283s normal 0.167 0.833 283s 283s Data: anorexia 283s Call: 283s Linda(Treat ~ ., data = anorexia, method = method) 283s 283s Prior Probabilities of Groups: 283s CBT Cont FT 283s 0.40278 0.36111 0.23611 283s 283s Group means: 283s Prewt Postwt 283s CBT 83.114 84.009 283s Cont 80.327 80.125 283s FT 85.161 94.371 283s 283s Within-groups Covariance Matrix: 283s Prewt Postwt 283s Prewt 22.498 11.860 283s Postwt 11.860 20.426 283s 283s Linear Coeficients: 283s Prewt Postwt 283s CBT 2.1994 2.8357 283s Cont 2.1653 2.6654 283s FT 1.9451 3.4907 283s 283s Constants: 283s CBT Cont FT 283s -211.42 -194.77 -248.97 283s 283s Apparent error rate 0.3889 283s 283s Classification table 283s Predicted 283s Actual CBT Cont FT 283s CBT 15 6 8 283s Cont 6 16 4 283s FT 0 4 13 283s 283s Confusion matrix 283s Predicted 283s Actual CBT Cont FT 283s CBT 0.517 0.207 0.276 283s Cont 0.231 0.615 0.154 283s FT 0.000 0.235 0.765 283s 283s Data: Pima 284s Call: 284s Linda(type ~ ., data = Pima.tr, method = method) 284s 284s Prior Probabilities of Groups: 284s No Yes 284s 0.66 0.34 284s 284s Group means: 284s npreg glu bp skin bmi ped age 284s No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 284s Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 284s 284s Within-groups Covariance Matrix: 284s npreg glu bp skin bmi ped age 284s npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 284s glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 284s bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 284s skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 284s bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 284s ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 284s age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 284s 284s Linear Coeficients: 284s npreg glu bp skin bmi ped age 284s No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 284s Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 284s 284s Constants: 284s No Yes 284s -33.429 -45.434 284s 284s Apparent error rate 0.28 284s 284s Classification table 284s Predicted 284s Actual No Yes 284s No 105 27 284s Yes 29 39 284s 284s Confusion matrix 284s Predicted 284s Actual No Yes 284s No 0.795 0.205 284s Yes 0.426 0.574 284s 284s Data: Forest soils 284s 284s Apparent error rate 0.3448 284s 284s Classification table 284s Predicted 284s Actual 1 2 3 284s 1 7 2 2 284s 2 4 14 5 284s 3 3 4 17 284s 284s Confusion matrix 284s Predicted 284s Actual 1 2 3 284s 1 0.636 0.182 0.182 284s 2 0.174 0.609 0.217 284s 3 0.125 0.167 0.708 284s 284s Data: Raven and Miller diabetes data 284s Call: 284s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 284s 284s Prior Probabilities of Groups: 284s normal chemical overt 284s 0.52414 0.24828 0.22759 284s 284s Group means: 284s insulin glucose sspg 284s normal 154.014 346.07 91.606 284s chemical 248.841 451.10 221.936 284s overt 89.766 1064.16 335.100 284s 284s Within-groups Covariance Matrix: 284s insulin glucose sspg 284s insulin 4948.1 1007.61 1471.12 284s glucose 1007.6 2597.38 358.57 284s sspg 1471.1 358.57 3180.04 284s 284s Linear Coeficients: 284s insulin glucose sspg 284s normal 0.00027839 0.13121 0.013882 284s chemical 0.00148074 0.16615 0.050371 284s overt -0.10102404 0.43466 0.103100 284s 284s Constants: 284s normal chemical overt 284s -24.008 -44.642 -245.497 284s 284s Apparent error rate 0.0966 284s 284s Classification table 284s Predicted 284s Actual normal chemical overt 284s normal 71 5 0 284s chemical 2 34 0 284s overt 0 7 26 284s 284s Confusion matrix 284s Predicted 284s Actual normal chemical overt 284s normal 0.934 0.066 0.000 284s chemical 0.056 0.944 0.000 284s overt 0.000 0.212 0.788 284s 284s Data: iris 284s Call: 284s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 284s 284s Prior Probabilities of Groups: 284s setosa versicolor virginica 284s 0.33333 0.33333 0.33333 284s 284s Group means: 284s Sepal.Length Sepal.Width Petal.Length Petal.Width 284s setosa 4.9755 3.3826 1.4608 0.22053 284s versicolor 5.8868 2.7823 4.2339 1.34603 284s virginica 6.5176 2.9691 5.4560 2.06335 284s 284s Within-groups Covariance Matrix: 284s Sepal.Length Sepal.Width Petal.Length Petal.Width 284s Sepal.Length 0.238417 0.136325 0.086377 0.036955 284s Sepal.Width 0.136325 0.148452 0.067500 0.034804 284s Petal.Length 0.086377 0.067500 0.100934 0.035968 284s Petal.Width 0.036955 0.034804 0.035968 0.023856 284s 284s Linear Coeficients: 284s Sepal.Length Sepal.Width Petal.Length Petal.Width 284s setosa 17.106 15.693 7.8806 -52.031 284s versicolor 21.744 -15.813 38.0139 -11.505 284s virginica 23.032 -26.567 43.6391 23.777 284s 284s Constants: 284s setosa versicolor virginica 284s -70.214 -115.832 -180.294 284s 284s Apparent error rate 0.02 284s 284s Classification table 284s Predicted 284s Actual setosa versicolor virginica 284s setosa 50 0 0 284s versicolor 0 49 1 284s virginica 0 2 48 284s 284s Confusion matrix 284s Predicted 284s Actual setosa versicolor virginica 284s setosa 1 0.00 0.00 284s versicolor 0 0.98 0.02 284s virginica 0 0.04 0.96 284s 284s Data: crabs 284s Call: 284s Linda(sp ~ ., data = crabs, method = method) 284s 284s Prior Probabilities of Groups: 284s B O 284s 0.5 0.5 284s 284s Group means: 284s sexM index FL RW CL CW BD 284s B 0 25.5 13.270 12.138 28.102 32.624 11.816 284s O 1 25.5 16.626 12.262 33.688 37.188 15.324 284s 284s Within-groups Covariance Matrix: 284s sexM index FL RW CL CW BD 284s sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 284s index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 284s FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 284s RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 284s CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 284s CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 284s BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 284s 284s Linear Coeficients: 284s sexM index FL RW CL CW BD 284s B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 284s O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 284s 284s Constants: 284s B O 284s -2.1491e+01 -3.2776e+06 284s 284s Apparent error rate 0.5 284s 284s Classification table 284s Predicted 284s Actual B O 284s B 50 50 284s O 50 50 284s 284s Confusion matrix 284s Predicted 284s Actual B O 284s B 0.5 0.5 284s O 0.5 0.5 284s 284s Data: fish 284s 284s Apparent error rate 0.2532 284s 284s Classification table 284s Predicted 284s Actual 1 2 3 4 5 6 7 284s 1 33 0 0 1 0 0 0 284s 2 0 3 0 0 0 0 3 284s 3 0 2 5 0 0 0 13 284s 4 0 0 0 11 0 0 0 284s 5 0 0 0 0 14 0 0 284s 6 0 0 0 0 0 17 0 284s 7 0 19 0 0 2 0 35 284s 284s Confusion matrix 284s Predicted 284s Actual 1 2 3 4 5 6 7 284s 1 0.971 0.000 0.00 0.029 0.000 0 0.000 284s 2 0.000 0.500 0.00 0.000 0.000 0 0.500 284s 3 0.000 0.100 0.25 0.000 0.000 0 0.650 284s 4 0.000 0.000 0.00 1.000 0.000 0 0.000 284s 5 0.000 0.000 0.00 0.000 1.000 0 0.000 284s 6 0.000 0.000 0.00 0.000 0.000 1 0.000 284s 7 0.000 0.339 0.00 0.000 0.036 0 0.625 284s 284s Data: pottery 284s Call: 284s Linda(origin ~ ., data = pottery, method = method) 284s 284s Prior Probabilities of Groups: 284s Attic Eritrean 284s 0.48148 0.51852 284s 284s Group means: 284s SI AL FE MG CA TI 284s Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 284s Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 284s 284s Within-groups Covariance Matrix: 284s SI AL FE MG CA TI 284s SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 284s AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 284s FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 284s MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 284s CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 284s TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 284s 284s Linear Coeficients: 284s SI AL FE MG CA TI 284s Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 284s Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 284s 284s Constants: 284s Attic Eritrean 284s -4000.1 -3776.1 284s 284s Apparent error rate 0.1111 284s 284s Classification table 284s Predicted 284s Actual Attic Eritrean 284s Attic 12 1 284s Eritrean 2 12 284s 284s Confusion matrix 284s Predicted 284s Actual Attic Eritrean 284s Attic 0.923 0.077 284s Eritrean 0.143 0.857 284s 284s Data: olitos 284s 284s Apparent error rate 0.125 284s 284s Classification table 284s Predicted 284s Actual 1 2 3 4 284s 1 44 2 3 1 284s 2 1 23 1 0 284s 3 4 1 27 2 284s 4 0 0 0 11 284s 284s Confusion matrix 284s Predicted 284s Actual 1 2 3 4 284s 1 0.880 0.040 0.060 0.020 284s 2 0.040 0.920 0.040 0.000 284s 3 0.118 0.029 0.794 0.059 284s 4 0.000 0.000 0.000 1.000 284s =================================================== 284s > dodata(method="ogk") 284s 284s Call: dodata(method = "ogk") 284s =================================================== 284s 284s Data: hemophilia 284s Call: 284s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 284s 284s Prior Probabilities of Groups: 284s carrier normal 284s 0.6 0.4 284s 284s Group means: 284s AHFactivity AHFantigen 284s carrier -0.29043 -0.00052902 284s normal -0.12463 -0.06715037 284s 284s Within-groups Covariance Matrix: 284s AHFactivity AHFantigen 284s AHFactivity 0.015688 0.010544 284s AHFantigen 0.010544 0.016633 284s 284s Linear Coeficients: 284s AHFactivity AHFantigen 284s carrier -32.2203 20.3935 284s normal -9.1149 1.7409 284s 284s Constants: 284s carrier normal 284s -5.1843 -1.4259 284s 284s Apparent error rate 0.1467 284s 284s Classification table 284s Predicted 284s Actual carrier normal 284s carrier 38 7 284s normal 4 26 284s 284s Confusion matrix 284s Predicted 284s Actual carrier normal 284s carrier 0.844 0.156 284s normal 0.133 0.867 284s 284s Data: anorexia 284s Call: 284s Linda(Treat ~ ., data = anorexia, method = method) 284s 284s Prior Probabilities of Groups: 284s CBT Cont FT 284s 0.40278 0.36111 0.23611 284s 284s Group means: 284s Prewt Postwt 284s CBT 82.634 82.060 284s Cont 81.605 80.459 284s FT 85.157 93.822 284s 284s Within-groups Covariance Matrix: 284s Prewt Postwt 284s Prewt 15.8294 4.4663 284s Postwt 4.4663 19.6356 284s 284s Linear Coeficients: 284s Prewt Postwt 284s CBT 4.3183 3.1970 284s Cont 4.2734 3.1256 284s FT 4.3080 3.7983 284s 284s Constants: 284s CBT Cont FT 284s -310.50 -301.12 -363.05 284s 284s Apparent error rate 0.4583 284s 284s Classification table 284s Predicted 284s Actual CBT Cont FT 284s CBT 15 5 9 284s Cont 14 11 1 284s FT 0 4 13 284s 284s Confusion matrix 284s Predicted 284s Actual CBT Cont FT 284s CBT 0.517 0.172 0.310 284s Cont 0.538 0.423 0.038 284s FT 0.000 0.235 0.765 284s 284s Data: Pima 284s Call: 284s Linda(type ~ ., data = Pima.tr, method = method) 284s 284s Prior Probabilities of Groups: 284s No Yes 284s 0.66 0.34 284s 284s Group means: 284s npreg glu bp skin bmi ped age 284s No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 284s Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 284s 284s Within-groups Covariance Matrix: 284s npreg glu bp skin bmi ped age 284s npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 284s glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 284s bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 284s skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 284s bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 284s ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 284s age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 284s 284s Linear Coeficients: 284s npreg glu bp skin bmi ped age 284s No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 284s Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 284s 284s Constants: 284s No Yes 284s -43.449 -60.176 284s 284s Apparent error rate 0.23 284s 284s Classification table 284s Predicted 284s Actual No Yes 284s No 108 24 284s Yes 22 46 284s 284s Confusion matrix 284s Predicted 284s Actual No Yes 284s No 0.818 0.182 284s Yes 0.324 0.676 284s 284s Data: Forest soils 284s 284s Apparent error rate 0.3621 284s 284s Classification table 284s Predicted 284s Actual 1 2 3 284s 1 7 3 1 284s 2 4 13 6 284s 3 3 4 17 284s 284s Confusion matrix 284s Predicted 284s Actual 1 2 3 284s 1 0.636 0.273 0.091 284s 2 0.174 0.565 0.261 284s 3 0.125 0.167 0.708 284s 284s Data: Raven and Miller diabetes data 284s Call: 284s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 284s 284s Prior Probabilities of Groups: 284s normal chemical overt 284s 0.52414 0.24828 0.22759 284s 284s Group means: 284s insulin glucose sspg 284s normal 159.540 344.06 99.486 284s chemical 252.992 478.16 219.442 284s overt 79.635 1094.96 338.517 284s 284s Within-groups Covariance Matrix: 284s insulin glucose sspg 284s insulin 3844.877 67.238 1456.55 284s glucose 67.238 2228.396 324.21 284s sspg 1456.548 324.205 2181.73 284s 284s Linear Coeficients: 284s insulin glucose sspg 284s normal 0.040407 0.15379 -0.0042303 284s chemical 0.047858 0.20764 0.0377766 284s overt -0.026158 0.47736 0.1016873 284s 284s Constants: 284s normal chemical overt 284s -30.115 -61.233 -278.996 284s 284s Apparent error rate 0.0966 284s 284s Classification table 284s Predicted 284s Actual normal chemical overt 284s normal 71 5 0 284s chemical 2 34 0 284s overt 0 7 26 284s 284s Confusion matrix 284s Predicted 284s Actual normal chemical overt 284s normal 0.934 0.066 0.000 284s chemical 0.056 0.944 0.000 284s overt 0.000 0.212 0.788 284s 284s Data: iris 284s Call: 284s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 284s 284s Prior Probabilities of Groups: 284s setosa versicolor virginica 284s 0.33333 0.33333 0.33333 284s 284s Group means: 284s Sepal.Length Sepal.Width Petal.Length Petal.Width 284s setosa 4.9654 3.3913 1.4507 0.21639 284s versicolor 5.8767 2.7909 4.2238 1.34189 284s virginica 6.5075 2.9777 5.4459 2.05921 284s 284s Within-groups Covariance Matrix: 284s Sepal.Length Sepal.Width Petal.Length Petal.Width 284s Sepal.Length 0.180280 0.068876 0.101512 0.036096 284s Sepal.Width 0.068876 0.079556 0.047722 0.029409 284s Petal.Length 0.101512 0.047722 0.111492 0.038658 284s Petal.Width 0.036096 0.029409 0.038658 0.029965 284s 284s Linear Coeficients: 284s Sepal.Length Sepal.Width Petal.Length Petal.Width 284s setosa 28.582 46.5236 -13.859 -54.9877 284s versicolor 19.837 11.9601 20.865 -17.7704 284s virginica 16.999 1.9046 29.808 7.9189 284s 284s Constants: 284s setosa versicolor virginica 284s -134.94 -108.22 -148.56 284s 284s Apparent error rate 0.0133 284s 284s Classification table 284s Predicted 284s Actual setosa versicolor virginica 284s setosa 50 0 0 284s versicolor 0 49 1 284s virginica 0 1 49 284s 284s Confusion matrix 284s Predicted 284s Actual setosa versicolor virginica 284s setosa 1 0.00 0.00 284s versicolor 0 0.98 0.02 284s virginica 0 0.02 0.98 284s 284s Data: crabs 284s Call: 284s Linda(sp ~ ., data = crabs, method = method) 284s 284s Prior Probabilities of Groups: 284s B O 284s 0.5 0.5 284s 284s Group means: 284s sexM index FL RW CL CW BD 284s B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 284s O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 284s 284s Within-groups Covariance Matrix: 284s sexM index FL RW CL CW BD 284s sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 284s index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 284s FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 284s RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 284s CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 284s CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 284s BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 284s 284s Linear Coeficients: 284s sexM index FL RW CL CW BD 284s B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 284s O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 284s 284s Constants: 284s B O 284s -77.695 -147.287 284s 284s Apparent error rate 0 284s 284s Classification table 284s Predicted 284s Actual B O 284s B 100 0 284s O 0 100 284s 284s Confusion matrix 284s Predicted 284s Actual B O 284s B 1 0 284s O 0 1 284s 284s Data: fish 284s 284s Apparent error rate 0.0063 284s 284s Classification table 284s Predicted 284s Actual 1 2 3 4 5 6 7 284s 1 34 0 0 0 0 0 0 284s 2 0 6 0 0 0 0 0 284s 3 0 0 20 0 0 0 0 284s 4 0 0 0 11 0 0 0 284s 5 0 0 0 0 14 0 0 284s 6 0 0 0 0 0 17 0 284s 7 0 0 0 0 1 0 55 284s 284s Confusion matrix 284s Predicted 284s Actual 1 2 3 4 5 6 7 284s 1 1 0 0 0 0.000 0 0.000 284s 2 0 1 0 0 0.000 0 0.000 284s 3 0 0 1 0 0.000 0 0.000 284s 4 0 0 0 1 0.000 0 0.000 284s 5 0 0 0 0 1.000 0 0.000 284s 6 0 0 0 0 0.000 1 0.000 284s 7 0 0 0 0 0.018 0 0.982 284s 284s Data: pottery 284s Call: 284s Linda(origin ~ ., data = pottery, method = method) 284s 284s Prior Probabilities of Groups: 284s Attic Eritrean 284s 0.48148 0.51852 284s 284s Group means: 284s SI AL FE MG CA TI 284s Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 284s Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 284s 284s Within-groups Covariance Matrix: 284s SI AL FE MG CA TI 284s SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 284s AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 284s FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 284s MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 284s CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 284s TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 284s 284s Linear Coeficients: 284s SI AL FE MG CA TI 284s Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 284s Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 284s 284s Constants: 284s Attic Eritrean 284s -4346 -4139 284s 284s Apparent error rate 0.1111 284s 284s Classification table 284s Predicted 284s Actual Attic Eritrean 284s Attic 12 1 284s Eritrean 2 12 284s 284s Confusion matrix 284s Predicted 284s Actual Attic Eritrean 284s Attic 0.923 0.077 284s Eritrean 0.143 0.857 284s 284s Data: olitos 284s 284s Apparent error rate 0.1 284s 284s Classification table 284s Predicted 284s Actual 1 2 3 4 284s 1 45 2 2 1 284s 2 0 25 0 0 284s 3 4 1 27 2 284s 4 0 0 0 11 284s 284s Confusion matrix 284s Predicted 284s Actual 1 2 3 4 284s 1 0.900 0.040 0.040 0.020 284s 2 0.000 1.000 0.000 0.000 284s 3 0.118 0.029 0.794 0.059 284s 4 0.000 0.000 0.000 1.000 284s =================================================== 284s > #dodata(method="fsa") 284s > 284s BEGIN TEST tldapp.R 284s 284s R version 4.4.3 (2025-02-28) -- "Trophy Case" 284s Copyright (C) 2025 The R Foundation for Statistical Computing 284s Platform: arm-unknown-linux-gnueabihf (32-bit) 284s 284s R is free software and comes with ABSOLUTELY NO WARRANTY. 284s You are welcome to redistribute it under certain conditions. 284s Type 'license()' or 'licence()' for distribution details. 284s 284s R is a collaborative project with many contributors. 284s Type 'contributors()' for more information and 284s 'citation()' on how to cite R or R packages in publications. 284s 284s Type 'demo()' for some demos, 'help()' for on-line help, or 284s 'help.start()' for an HTML browser interface to help. 284s Type 'q()' to quit R. 284s 284s > ## VT::15.09.2013 - this will render the output independent 284s > ## from the version of the package 284s > suppressPackageStartupMessages(library(rrcov)) 285s > library(MASS) 285s > 285s > dodata <- function(method) { 285s + 285s + options(digits = 5) 285s + set.seed(101) 285s + 285s + tmp <- sys.call() 285s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 285s + cat("===================================================\n") 285s + 285s + data(pottery); show(lda <- LdaPP(origin~., data=pottery, method=method)); show(predict(lda)) 285s + data(hemophilia); show(lda <- LdaPP(as.factor(gr)~., data=hemophilia, method=method)); show(predict(lda)) 285s + data(anorexia); show(lda <- LdaPP(Treat~., data=anorexia, method=method)); show(predict(lda)) 285s + data(Pima.tr); show(lda <- LdaPP(type~., data=Pima.tr, method=method)); show(predict(lda)) 285s + data(crabs); show(lda <- LdaPP(sp~., data=crabs, method=method)); show(predict(lda)) 285s + 285s + cat("===================================================\n") 285s + } 285s > 285s > 285s > ## -- now do it: 285s > 285s > ## Commented out - still to slow 285s > ##dodata(method="huber") 285s > ##dodata(method="sest") 285s > 285s > ## VT::14.11.2018 - Commented out: too slow 285s > ## dodata(method="mad") 285s > ## dodata(method="class") 285s > 285s BEGIN TEST tmcd4.R 285s 285s R version 4.4.3 (2025-02-28) -- "Trophy Case" 285s Copyright (C) 2025 The R Foundation for Statistical Computing 285s Platform: arm-unknown-linux-gnueabihf (32-bit) 285s 285s R is free software and comes with ABSOLUTELY NO WARRANTY. 285s You are welcome to redistribute it under certain conditions. 285s Type 'license()' or 'licence()' for distribution details. 285s 285s R is a collaborative project with many contributors. 285s Type 'contributors()' for more information and 285s 'citation()' on how to cite R or R packages in publications. 285s 285s Type 'demo()' for some demos, 'help()' for on-line help, or 285s 'help.start()' for an HTML browser interface to help. 285s Type 'q()' to quit R. 285s 285s > ## Test the exact fit property of CovMcd 285s > doexactfit <- function(){ 285s + exact <-function(seed=1234){ 285s + 285s + set.seed(seed) 285s + 285s + n1 <- 45 285s + p <- 2 285s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 285s + x1[,p] <- x1[,p] + 3 285s + n2 <- 55 285s + m1 <- 0 285s + m2 <- 3 285s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 285s + x<-rbind(x1,x2) 285s + colnames(x) <- c("X1","X2") 285s + x 285s + } 285s + print(CovMcd(exact())) 285s + } 285s > 285s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 285s + ##@bdescr 285s + ## Test the function covMcd() on the literature datasets: 285s + ## 285s + ## Call CovMcd() for all regression datasets available in rrcov and print: 285s + ## - execution time (if time == TRUE) 285s + ## - objective fucntion 285s + ## - best subsample found (if short == false) 285s + ## - outliers identified (with cutoff 0.975) (if short == false) 285s + ## - estimated center and covarinance matrix if full == TRUE) 285s + ## 285s + ##@edescr 285s + ## 285s + ##@in nrep : [integer] number of repetitions to use for estimating the 285s + ## (average) execution time 285s + ##@in time : [boolean] whether to evaluate the execution time 285s + ##@in short : [boolean] whether to do short output (i.e. only the 285s + ## objective function value). If short == FALSE, 285s + ## the best subsample and the identified outliers are 285s + ## printed. See also the parameter full below 285s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 285s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 285s + 285s + doest <- function(x, xname, nrep=1){ 285s + n <- dim(x)[1] 285s + p <- dim(x)[2] 285s + if(method == "MASS"){ 285s + mcd<-cov.mcd(x) 285s + quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2) 285s + } 285s + else{ 285s + mcd <- if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 285s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 285s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 285s + else CovMcd(x, trace=FALSE) 285s + quan <- as.integer(mcd@quan) 285s + } 285s + 285s + crit <- mcd@crit 285s + 285s + if(time){ 285s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 285s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 285s + } 285s + else{ 285s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 285s + } 285s + lpad<-lname-nchar(xname) 285s + cat(pad.right(xname,lpad), xres) 285s + 285s + if(!short){ 285s + cat("Best subsample: \n") 285s + if(length(mcd@best) > 150) 285s + cat("Too long... \n") 285s + else 285s + print(mcd@best) 285s + 285s + ibad <- which(mcd@wt==0) 285s + names(ibad) <- NULL 285s + nbad <- length(ibad) 285s + cat("Outliers: ",nbad,"\n") 285s + if(nbad > 0 & nbad < 150) 285s + print(ibad) 285s + else 285s + cat("Too many to print ... \n") 285s + if(full){ 285s + cat("-------------\n") 285s + show(mcd) 285s + } 285s + cat("--------------------------------------------------------\n") 285s + } 285s + } 285s + 285s + options(digits = 5) 285s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 285s + 285s + lname <- 20 285s + 285s + ## VT::15.09.2013 - this will render the output independent 285s + ## from the version of the package 285s + suppressPackageStartupMessages(library(rrcov)) 285s + 285s + method <- match.arg(method) 285s + if(method == "MASS") 285s + library(MASS) 285s + 285s + data(Animals, package = "MASS") 285s + brain <- Animals[c(1:24, 26:25, 27:28),] 285s + 285s + data(fish) 285s + data(pottery) 285s + data(rice) 285s + data(un86) 285s + data(wages) 285s + 285s + tmp <- sys.call() 285s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 285s + 285s + cat("Data Set n p Half LOG(obj) Time\n") 285s + cat("========================================================\n") 285s + 285s + if(method=="exact") 285s + { 285s + ## only small data sets 285s + doest(heart[, 1:2], data(heart), nrep) 285s + doest(starsCYG, data(starsCYG), nrep) 285s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 285s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 285s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 285s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 285s + doest(brain, "Animals", nrep) 285s + doest(lactic, data(lactic), nrep) 285s + doest(pension, data(pension), nrep) 285s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 285s + doest(stack.x, data(stackloss), nrep) 285s + doest(pilot, data(pilot), nrep) 285s + } else 285s + { 285s + doest(heart[, 1:2], data(heart), nrep) 285s + doest(starsCYG, data(starsCYG), nrep) 285s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 285s + doest(stack.x, data(stackloss), nrep) 285s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 285s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 285s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 285s + doest(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 285s + 285s + doest(brain, "Animals", nrep) 285s + ## doest(milk, data(milk), nrep) # difference between 386 and x64 285s + doest(bushfire, data(bushfire), nrep) 285s + 285s + doest(lactic, data(lactic), nrep) 285s + doest(pension, data(pension), nrep) 285s + ## doest(pilot, data(pilot), nrep) # difference between 386 and x64 285s + 285s + if(method != "MRCD") # these two are quite slow for MRCD, especially the second one 285s + { 285s + doest(radarImage, data(radarImage), nrep) 285s + doest(NOxEmissions, data(NOxEmissions), nrep) 285s + } 285s + 285s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 285s + doest(data.matrix(subset(wagnerGrowth, select = -Period)), data(wagnerGrowth), nrep) 285s + 285s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep) 285s + doest(data.matrix(subset(pottery, select = -origin)), data(pottery), nrep) 285s + doest(rice, data(rice), nrep) 285s + doest(un86, data(un86), nrep) 285s + 285s + doest(wages, data(wages), nrep) 285s + 285s + ## from package 'datasets' 285s + doest(airquality[,1:4], data(airquality), nrep) 285s + doest(attitude, data(attitude), nrep) 285s + doest(attenu, data(attenu), nrep) 285s + doest(USJudgeRatings, data(USJudgeRatings), nrep) 285s + doest(USArrests, data(USArrests), nrep) 285s + doest(longley, data(longley), nrep) 285s + doest(Loblolly, data(Loblolly), nrep) 285s + doest(quakes[,1:4], data(quakes), nrep) 285s + } 285s + cat("========================================================\n") 285s + } 285s > 285s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMCD", "MASS")){ 285s + 285s + doest <- function(x, nrep=1){ 285s + gc() 285s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 285s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 285s + xtime 285s + } 285s + 285s + set.seed(1234) 285s + 285s + ## VT::15.09.2013 - this will render the output independent 285s + ## from the version of the package 285s + suppressPackageStartupMessages(library(rrcov)) 285s + 285s + library(MASS) 285s + method <- match.arg(method) 285s + 285s + ap <- c(2, 5, 10, 20, 30) 285s + an <- c(100, 500, 1000, 10000, 50000) 285s + 285s + tottime <- 0 285s + cat(" n p Time\n") 285s + cat("=====================\n") 285s + for(i in 1:length(an)) { 285s + for(j in 1:length(ap)) { 285s + n <- an[i] 285s + p <- ap[j] 285s + if(5*p <= n){ 285s + xx <- gendata(n, p, eps) 285s + X <- xx$X 285s + tottime <- tottime + doest(X, nrep) 285s + } 285s + } 285s + } 285s + 285s + cat("=====================\n") 285s + cat("Total time: ", tottime*nrep, "\n") 285s + } 285s > 285s > docheck <- function(n, p, eps){ 285s + xx <- gendata(n,p,eps) 285s + mcd <- CovMcd(xx$X) 285s + check(mcd, xx$xind) 285s + } 285s > 285s > check <- function(mcd, xind){ 285s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 285s + ## did not get zero weight 285s + mymatch <- xind %in% which(mcd@wt == 0) 285s + length(xind) - length(which(mymatch)) 285s + } 285s > 285s > dorep <- function(x, nrep=1, method=c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 285s + 285s + method <- match.arg(method) 285s + for(i in 1:nrep) 285s + if(method == "MASS") 285s + cov.mcd(x) 285s + else 285s + { 285s + if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 285s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 285s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 285s + else CovMcd(x, trace=FALSE) 285s + } 285s + } 285s > 285s > #### gendata() #### 285s > # Generates a location contaminated multivariate 285s > # normal sample of n observations in p dimensions 285s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 285s > # where 285s > # m = (b,b,...,b) 285s > # Defaults: eps=0 and b=10 285s > # 285s > gendata <- function(n,p,eps=0,b=10){ 285s + 285s + if(missing(n) || missing(p)) 285s + stop("Please specify (n,p)") 285s + if(eps < 0 || eps >= 0.5) 285s + stop(message="eps must be in [0,0.5)") 285s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 285s + nbad <- as.integer(eps * n) 285s + if(nbad > 0){ 285s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 285s + xind <- sample(n,nbad) 285s + X[xind,] <- Xbad 285s + } 285s + list(X=X, xind=xind) 285s + } 285s > 285s > pad.right <- function(z, pads) 285s + { 285s + ### Pads spaces to right of text 285s + padding <- paste(rep(" ", pads), collapse = "") 285s + paste(z, padding, sep = "") 285s + } 285s > 285s > whatis<-function(x){ 285s + if(is.data.frame(x)) 285s + cat("Type: data.frame\n") 285s + else if(is.matrix(x)) 285s + cat("Type: matrix\n") 285s + else if(is.vector(x)) 285s + cat("Type: vector\n") 285s + else 285s + cat("Type: don't know\n") 285s + } 285s > 285s > ## VT::15.09.2013 - this will render the output independent 285s > ## from the version of the package 285s > suppressPackageStartupMessages(library(rrcov)) 285s > 285s > dodata() 286s 286s Call: dodata() 286s Data Set n p Half LOG(obj) Time 286s ======================================================== 286s heart 12 2 7 5.678742 286s Best subsample: 286s [1] 1 3 4 5 7 9 11 286s Outliers: 0 286s Too many to print ... 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s height weight 286s 38.3 33.1 286s 286s Robust Estimate of Covariance: 286s height weight 286s height 135 259 286s weight 259 564 286s -------------------------------------------------------- 286s starsCYG 47 2 25 -8.031215 286s Best subsample: 286s [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 286s Outliers: 7 286s [1] 7 9 11 14 20 30 34 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s log.Te log.light 286s 4.41 4.95 286s 286s Robust Estimate of Covariance: 286s log.Te log.light 286s log.Te 0.0132 0.0394 286s log.light 0.0394 0.2743 286s -------------------------------------------------------- 286s phosphor 18 2 10 6.878847 286s Best subsample: 286s [1] 3 5 8 9 11 12 13 14 15 17 286s Outliers: 3 286s [1] 1 6 10 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s inorg organic 286s 13.4 38.8 286s 286s Robust Estimate of Covariance: 286s inorg organic 286s inorg 129 130 286s organic 130 182 286s -------------------------------------------------------- 286s stackloss 21 3 12 5.472581 286s Best subsample: 286s [1] 4 5 6 7 8 9 10 11 12 13 14 20 286s Outliers: 9 286s [1] 1 2 3 15 16 17 18 19 21 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Air.Flow Water.Temp Acid.Conc. 286s 59.5 20.8 87.3 286s 286s Robust Estimate of Covariance: 286s Air.Flow Water.Temp Acid.Conc. 286s Air.Flow 6.29 5.85 5.74 286s Water.Temp 5.85 9.23 6.14 286s Acid.Conc. 5.74 6.14 23.25 286s -------------------------------------------------------- 286s coleman 20 5 13 1.286808 286s Best subsample: 286s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 286s Outliers: 7 286s [1] 1 6 9 10 11 15 18 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s salaryP fatherWc sstatus teacherSc motherLev 286s 2.76 48.38 6.12 25.00 6.40 286s 286s Robust Estimate of Covariance: 286s salaryP fatherWc sstatus teacherSc motherLev 286s salaryP 0.253 1.786 -0.266 0.151 0.075 286s fatherWc 1.786 1303.382 330.496 12.604 34.503 286s sstatus -0.266 330.496 119.888 3.833 10.131 286s teacherSc 0.151 12.604 3.833 0.785 0.555 286s motherLev 0.075 34.503 10.131 0.555 1.043 286s -------------------------------------------------------- 286s salinity 28 3 16 1.326364 286s Best subsample: 286s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 286s Outliers: 4 286s [1] 5 16 23 24 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s X1 X2 X3 286s 10.08 2.78 22.78 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 286s X1 10.44 1.01 -3.19 286s X2 1.01 3.83 -1.44 286s X3 -3.19 -1.44 2.39 286s -------------------------------------------------------- 286s wood 20 5 13 -36.270094 286s Best subsample: 286s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 286s Outliers: 7 286s [1] 4 6 7 8 11 16 19 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s x1 x2 x3 x4 x5 286s 0.587 0.122 0.531 0.538 0.892 286s 286s Robust Estimate of Covariance: 286s x1 x2 x3 x4 x5 286s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 286s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 286s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 286s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 286s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 286s -------------------------------------------------------- 286s hbk 75 3 39 -1.047858 286s Best subsample: 286s [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 286s [26] 55 56 58 59 61 63 64 66 67 70 71 72 73 74 286s Outliers: 14 286s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=39); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s X1 X2 X3 286s 1.54 1.78 1.69 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 286s X1 1.227 0.055 0.127 286s X2 0.055 1.249 0.153 286s X3 0.127 0.153 1.160 286s -------------------------------------------------------- 286s Animals 28 2 15 14.555543 286s Best subsample: 286s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 286s Outliers: 14 286s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s body brain 286s 18.7 64.9 286s 286s Robust Estimate of Covariance: 286s body brain 286s body 929 1576 286s brain 1576 5646 286s -------------------------------------------------------- 286s bushfire 38 5 22 18.135810 286s Best subsample: 286s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 286s Outliers: 16 286s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=22); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s V1 V2 V3 V4 V5 286s 105 147 274 218 279 286s 286s Robust Estimate of Covariance: 286s V1 V2 V3 V4 V5 286s V1 346 268 -1692 -381 -311 286s V2 268 236 -1125 -230 -194 286s V3 -1692 -1125 9993 2455 1951 286s V4 -381 -230 2455 647 505 286s V5 -311 -194 1951 505 398 286s -------------------------------------------------------- 286s lactic 20 2 11 0.359580 286s Best subsample: 286s [1] 1 2 3 4 5 7 8 9 10 11 12 286s Outliers: 4 286s [1] 17 18 19 20 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s X Y 286s 3.86 5.01 286s 286s Robust Estimate of Covariance: 286s X Y 286s X 10.6 14.6 286s Y 14.6 21.3 286s -------------------------------------------------------- 286s pension 18 2 10 16.675508 286s Best subsample: 286s [1] 1 2 3 4 5 6 8 9 11 12 286s Outliers: 5 286s [1] 14 15 16 17 18 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Income Reserves 286s 52.3 560.9 286s 286s Robust Estimate of Covariance: 286s Income Reserves 286s Income 1420 11932 286s Reserves 11932 208643 286s -------------------------------------------------------- 286s radarImage 1573 5 789 36.694425 286s Best subsample: 286s Too long... 286s Outliers: 117 286s [1] 164 237 238 242 261 262 351 450 451 462 480 481 509 516 535 286s [16] 542 572 597 620 643 654 669 697 737 802 803 804 818 832 833 286s [31] 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 1202 1223 286s [46] 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 1376 1393 286s [61] 1394 1402 1403 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1479 1487 286s [76] 1492 1504 1510 1511 1512 1517 1518 1519 1520 1521 1522 1525 1526 1527 1528 286s [91] 1530 1532 1534 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 286s [106] 1557 1558 1561 1562 1564 1565 1566 1567 1569 1570 1571 1573 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=789); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s X.coord Y.coord Band.1 Band.2 Band.3 286s 52.80 35.12 6.77 18.44 8.90 286s 286s Robust Estimate of Covariance: 286s X.coord Y.coord Band.1 Band.2 Band.3 286s X.coord 123.6 23.0 -361.9 -197.1 -22.5 286s Y.coord 23.0 400.6 34.3 -191.1 -39.1 286s Band.1 -361.9 34.3 27167.9 8178.8 473.7 286s Band.2 -197.1 -191.1 8178.8 26021.8 952.4 286s Band.3 -22.5 -39.1 473.7 952.4 4458.4 286s -------------------------------------------------------- 286s NOxEmissions 8088 4 4046 2.474539 286s Best subsample: 286s Too long... 286s Outliers: 2156 286s Too many to print ... 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=4046); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s julday LNOx LNOxEm sqrtWS 286s 168.19 4.73 7.91 1.37 286s 286s Robust Estimate of Covariance: 286s julday LNOx LNOxEm sqrtWS 286s julday 9180.6297 12.0306 0.7219 -10.1273 286s LNOx 12.0306 0.4721 0.1418 -0.1526 286s LNOxEm 0.7219 0.1418 0.2516 0.0438 286s sqrtWS -10.1273 -0.1526 0.0438 0.2073 286s -------------------------------------------------------- 286s vaso 39 2 21 -3.972244 286s Best subsample: 286s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 286s Outliers: 4 286s [1] 1 2 17 31 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Volume Rate 286s 1.16 1.72 286s 286s Robust Estimate of Covariance: 286s Volume Rate 286s Volume 0.313 -0.167 286s Rate -0.167 0.728 286s -------------------------------------------------------- 286s wagnerGrowth 63 6 35 6.572208 286s Best subsample: 286s [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 286s [26] 48 51 52 53 54 55 56 57 60 62 286s Outliers: 13 286s [1] 1 8 15 21 22 28 29 33 42 43 46 50 63 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=35); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Region PA GPA HS GHS y 286s 11.00 33.66 -2.00 2.48 0.31 7.48 286s 286s Robust Estimate of Covariance: 286s Region PA GPA HS GHS y 286s Region 35.5615 17.9337 -0.5337 -0.9545 -0.3093 -14.0090 286s PA 17.9337 27.7333 -4.9017 -1.4174 0.0343 -28.7040 286s GPA -0.5337 -4.9017 5.3410 0.2690 -0.1484 4.0006 286s HS -0.9545 -1.4174 0.2690 0.8662 -0.0454 2.9024 286s GHS -0.3093 0.0343 -0.1484 -0.0454 0.1772 0.7457 286s y -14.0090 -28.7040 4.0006 2.9024 0.7457 82.6877 286s -------------------------------------------------------- 286s fish 159 6 82 8.879005 286s Best subsample: 286s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 286s [20] 20 21 22 23 24 25 26 27 28 30 32 35 36 37 42 43 44 45 46 286s [39] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 107 109 110 111 113 286s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 286s [77] 134 135 136 137 138 139 286s Outliers: 63 286s [1] 30 39 40 41 42 62 63 64 65 66 68 69 70 73 74 75 76 77 78 286s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 286s [39] 98 99 100 101 102 103 104 105 141 143 144 145 147 148 149 150 151 152 153 286s [58] 154 155 156 157 158 159 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=82); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Weight Length1 Length2 Length3 Height Width 286s 329.9 24.5 26.6 29.7 31.1 14.7 286s 286s Robust Estimate of Covariance: 286s Weight Length1 Length2 Length3 Height Width 286s Weight 69082.99 1477.81 1613.64 1992.62 1439.32 -62.12 286s Length1 1477.81 34.68 37.61 45.51 28.82 -1.31 286s Length2 1613.64 37.61 40.88 49.52 31.81 -1.40 286s Length3 1992.62 45.51 49.52 61.16 42.65 -2.25 286s Height 1439.32 28.82 31.81 42.65 46.74 -2.82 286s Width -62.12 -1.31 -1.40 -2.25 -2.82 1.01 286s -------------------------------------------------------- 286s pottery 27 6 17 -10.586933 286s Best subsample: 286s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 286s Outliers: 9 286s [1] 3 8 12 16 17 18 23 24 25 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=17); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s SI AL FE MG CA TI 286s 54.983 15.206 9.700 3.817 5.211 0.859 286s 286s Robust Estimate of Covariance: 286s SI AL FE MG CA TI 286s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 286s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 286s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 286s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 286s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 286s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 286s -------------------------------------------------------- 286s rice 105 6 56 -14.463986 286s Best subsample: 286s [1] 2 4 6 8 10 12 15 18 21 22 24 29 30 31 32 33 34 36 37 286s [20] 38 41 44 45 47 51 52 53 54 55 59 61 65 67 68 69 70 72 76 286s [39] 78 79 80 81 82 83 84 85 86 92 93 94 95 97 98 99 102 105 286s Outliers: 13 286s [1] 9 14 19 28 40 42 49 58 62 71 75 77 89 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=56); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Favor Appearance Taste Stickiness 286s -0.2731 0.0600 -0.1468 0.0646 286s Toughness Overall_evaluation 286s 0.0894 -0.2192 286s 286s Robust Estimate of Covariance: 286s Favor Appearance Taste Stickiness Toughness 286s Favor 0.388 0.323 0.393 0.389 -0.195 286s Appearance 0.323 0.503 0.494 0.494 -0.270 286s Taste 0.393 0.494 0.640 0.629 -0.361 286s Stickiness 0.389 0.494 0.629 0.815 -0.486 286s Toughness -0.195 -0.270 -0.361 -0.486 0.451 286s Overall_evaluation 0.471 0.575 0.723 0.772 -0.457 286s Overall_evaluation 286s Favor 0.471 286s Appearance 0.575 286s Taste 0.723 286s Stickiness 0.772 286s Toughness -0.457 286s Overall_evaluation 0.882 286s -------------------------------------------------------- 286s un86 73 7 40 17.009322 286s Best subsample: 286s [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 286s [26] 51 52 55 56 60 61 62 63 64 65 67 70 71 72 73 286s Outliers: 30 286s [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 286s [26] 58 59 66 68 69 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s POP MOR CAR DR GNP DEN TB 286s 20.740 71.023 6.435 0.817 1.146 56.754 0.441 286s 286s Robust Estimate of Covariance: 286s POP MOR CAR DR GNP DEN 286s POP 582.4034 224.9343 -12.6722 -1.6729 -3.3664 226.1952 286s MOR 224.9343 2351.3907 -286.9504 -32.0743 -35.5649 -527.4684 286s CAR -12.6722 -286.9504 58.1190 5.7393 6.6365 83.6180 286s DR -1.6729 -32.0743 5.7393 0.8339 0.5977 12.1938 286s GNP -3.3664 -35.5649 6.6365 0.5977 1.4175 13.0709 286s DEN 226.1952 -527.4684 83.6180 12.1938 13.0709 2041.5809 286s TB 0.4002 -1.1807 0.2701 0.0191 0.0058 -0.9346 286s TB 286s POP 0.4002 286s MOR -1.1807 286s CAR 0.2701 286s DR 0.0191 286s GNP 0.0058 286s DEN -0.9346 286s TB 0.0184 286s -------------------------------------------------------- 286s wages 39 10 19 22.994272 286s Best subsample: 286s [1] 1 2 6 7 8 9 10 11 12 13 14 15 17 18 19 25 26 27 28 286s Outliers: 9 286s [1] 4 5 6 24 28 30 32 33 34 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 286s 2153.37 2.87 1129.16 297.53 360.58 6876.58 39.48 2.36 286s RACE SCHOOL 286s 38.88 10.17 286s 286s Robust Estimate of Covariance: 286s HRS RATE ERSP ERNO NEIN ASSET 286s HRS 6.12e+03 1.73e+01 -1.67e+03 -2.06e+03 9.10e+03 2.02e+05 286s RATE 1.73e+01 2.52e-01 2.14e+01 -3.54e+00 5.85e+01 1.37e+03 286s ERSP -1.67e+03 2.14e+01 1.97e+04 7.76e+01 -1.71e+03 -1.41e+04 286s ERNO -2.06e+03 -3.54e+00 7.76e+01 2.06e+03 -2.02e+03 -4.83e+04 286s NEIN 9.10e+03 5.85e+01 -1.71e+03 -2.02e+03 2.02e+04 4.54e+05 286s ASSET 2.02e+05 1.37e+03 -1.41e+04 -4.83e+04 4.54e+05 1.03e+07 286s AGE -6.29e+01 -2.61e-01 4.83e+00 2.44e+01 -1.08e+02 -2.46e+03 286s DEP -6.17e+00 -7.05e-02 -2.13e+01 2.29e+00 -1.30e+01 -3.16e+02 286s RACE -2.17e+03 -9.46e+00 7.19e+02 5.59e+02 -3.95e+03 -8.77e+04 286s SCHOOL 7.12e+01 5.87e-01 5.39e+01 -2.14e+01 1.63e+02 3.79e+03 286s AGE DEP RACE SCHOOL 286s HRS -6.29e+01 -6.17e+00 -2.17e+03 7.12e+01 286s RATE -2.61e-01 -7.05e-02 -9.46e+00 5.87e-01 286s ERSP 4.83e+00 -2.13e+01 7.19e+02 5.39e+01 286s ERNO 2.44e+01 2.29e+00 5.59e+02 -2.14e+01 286s NEIN -1.08e+02 -1.30e+01 -3.95e+03 1.63e+02 286s ASSET -2.46e+03 -3.16e+02 -8.77e+04 3.79e+03 286s AGE 1.01e+00 7.03e-02 2.39e+01 -9.52e-01 286s DEP 7.03e-02 4.62e-02 2.72e+00 -1.94e-01 286s RACE 2.39e+01 2.72e+00 8.74e+02 -3.09e+01 286s SCHOOL -9.52e-01 -1.94e-01 -3.09e+01 1.62e+00 286s -------------------------------------------------------- 286s airquality 153 4 58 18.213499 286s Best subsample: 286s [1] 3 22 24 25 28 29 32 33 35 36 37 38 39 40 41 42 43 44 46 286s [20] 47 48 49 50 52 56 57 58 59 60 64 66 67 68 69 71 72 73 74 286s [39] 76 78 80 82 83 84 86 87 89 90 91 92 93 94 95 97 98 105 109 286s [58] 110 286s Outliers: 14 286s [1] 8 9 15 18 20 21 23 24 28 30 48 62 117 148 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=58); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Ozone Solar.R Wind Temp 286s 43.2 192.9 9.6 80.5 286s 286s Robust Estimate of Covariance: 286s Ozone Solar.R Wind Temp 286s Ozone 959.69 771.68 -60.92 198.38 286s Solar.R 771.68 7089.72 -1.72 95.75 286s Wind -60.92 -1.72 10.71 -11.96 286s Temp 198.38 95.75 -11.96 62.78 286s -------------------------------------------------------- 286s attitude 30 7 19 24.442803 286s Best subsample: 286s [1] 2 3 4 5 7 8 10 12 15 17 19 20 22 23 25 27 28 29 30 286s Outliers: 10 286s [1] 1 6 9 13 14 16 18 21 24 26 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s rating complaints privileges learning raises critical 286s 67.1 68.0 52.4 57.6 67.2 77.4 286s advance 286s 43.4 286s 286s Robust Estimate of Covariance: 286s rating complaints privileges learning raises critical advance 286s rating 169.34 127.83 40.48 110.26 91.71 -3.59 53.84 286s complaints 127.83 156.80 52.65 110.97 96.56 7.27 76.03 286s privileges 40.48 52.65 136.91 92.38 69.00 9.53 87.98 286s learning 110.26 110.97 92.38 157.77 112.92 6.74 75.51 286s raises 91.71 96.56 69.00 112.92 112.79 4.91 70.22 286s critical -3.59 7.27 9.53 6.74 4.91 52.25 15.00 286s advance 53.84 76.03 87.98 75.51 70.22 15.00 93.11 286s -------------------------------------------------------- 286s attenu 182 5 86 6.440834 286s Best subsample: 286s [1] 68 69 70 71 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 286s [20] 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 115 116 117 118 286s [39] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 286s [58] 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 286s [77] 157 158 159 160 161 162 163 164 165 166 286s Outliers: 61 286s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 286s [20] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 286s [39] 40 45 46 47 54 55 56 57 58 59 60 61 64 65 82 97 98 100 101 286s [58] 102 103 104 105 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=86); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s event mag station dist accel 286s 18.624 5.752 67.861 22.770 0.141 286s 286s Robust Estimate of Covariance: 286s event mag station dist accel 286s event 1.64e+01 -1.22e+00 5.59e+01 9.98e+00 -8.37e-02 286s mag -1.22e+00 4.13e-01 -3.19e+00 1.35e+00 1.22e-02 286s station 5.59e+01 -3.19e+00 1.03e+03 7.00e+01 5.56e-01 286s dist 9.98e+00 1.35e+00 7.00e+01 2.21e+02 -9.24e-01 286s accel -8.37e-02 1.22e-02 5.56e-01 -9.24e-01 9.62e-03 286s -------------------------------------------------------- 286s USJudgeRatings 43 12 28 -47.889993 286s Best subsample: 286s [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 286s [26] 38 41 43 286s Outliers: 14 286s [1] 5 7 8 12 13 14 20 21 23 30 31 35 40 42 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=28); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 286s 7.40 8.19 7.80 7.96 7.74 7.82 7.74 7.73 7.57 7.63 8.25 7.94 286s 286s Robust Estimate of Covariance: 286s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL 286s CONT 0.852 -0.266 -0.422 -0.155 -0.049 -0.074 -0.117 -0.119 -0.177 286s INTG -0.266 0.397 0.537 0.406 0.340 0.325 0.404 0.409 0.430 286s DMNR -0.422 0.537 0.824 0.524 0.458 0.437 0.520 0.504 0.569 286s DILG -0.155 0.406 0.524 0.486 0.426 0.409 0.506 0.515 0.511 286s CFMG -0.049 0.340 0.458 0.426 0.427 0.403 0.466 0.476 0.478 286s DECI -0.074 0.325 0.437 0.409 0.403 0.396 0.449 0.462 0.460 286s PREP -0.117 0.404 0.520 0.506 0.466 0.449 0.552 0.565 0.551 286s FAMI -0.119 0.409 0.504 0.515 0.476 0.462 0.565 0.594 0.571 286s ORAL -0.177 0.430 0.569 0.511 0.478 0.460 0.551 0.571 0.575 286s WRIT -0.159 0.427 0.549 0.515 0.480 0.461 0.556 0.580 0.574 286s PHYS -0.184 0.269 0.362 0.308 0.298 0.307 0.335 0.358 0.369 286s RTEN -0.260 0.472 0.642 0.519 0.467 0.455 0.539 0.554 0.573 286s WRIT PHYS RTEN 286s CONT -0.159 -0.184 -0.260 286s INTG 0.427 0.269 0.472 286s DMNR 0.549 0.362 0.642 286s DILG 0.515 0.308 0.519 286s CFMG 0.480 0.298 0.467 286s DECI 0.461 0.307 0.455 286s PREP 0.556 0.335 0.539 286s FAMI 0.580 0.358 0.554 286s ORAL 0.574 0.369 0.573 286s WRIT 0.580 0.365 0.567 286s PHYS 0.365 0.300 0.378 286s RTEN 0.567 0.378 0.615 286s -------------------------------------------------------- 286s USArrests 50 4 27 15.391648 286s Best subsample: 286s [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 286s [26] 49 50 286s Outliers: 11 286s [1] 2 3 5 6 10 18 24 28 33 37 47 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s Murder Assault UrbanPop Rape 286s 6.71 145.42 65.06 17.88 286s 286s Robust Estimate of Covariance: 286s Murder Assault UrbanPop Rape 286s Murder 16.1 269.3 20.3 25.2 286s Assault 269.3 6613.0 567.8 453.7 286s UrbanPop 20.3 567.8 225.4 47.7 286s Rape 25.2 453.7 47.7 50.9 286s -------------------------------------------------------- 286s longley 16 7 12 12.747678 286s Best subsample: 286s [1] 5 6 7 8 9 10 11 12 13 14 15 16 286s Outliers: 4 286s [1] 1 2 3 4 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s GNP.deflator GNP Unemployed Armed.Forces Population 286s 106.5 430.6 328.2 295.0 120.2 286s Year Employed 286s 1956.5 66.9 286s 286s Robust Estimate of Covariance: 286s GNP.deflator GNP Unemployed Armed.Forces Population 286s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 286s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 286s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 286s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 286s Population 81.4 803.4 929.1 -327.4 63.5 286s Year 51.6 504.3 595.6 -216.7 39.7 286s Employed 34.2 344.1 323.6 -149.5 26.2 286s Year Employed 286s GNP.deflator 51.6 34.2 286s GNP 504.3 344.1 286s Unemployed 595.6 323.6 286s Armed.Forces -216.7 -149.5 286s Population 39.7 26.2 286s Year 25.1 16.7 286s Employed 16.7 12.4 286s -------------------------------------------------------- 286s Loblolly 84 3 44 4.898174 286s Best subsample: 286s [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 286s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 286s Outliers: 31 286s [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 286s [26] 72 76 77 78 83 84 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=44); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s height age Seed 286s 20.44 8.19 7.72 286s 286s Robust Estimate of Covariance: 286s height age Seed 286s height 247.8 79.5 11.9 286s age 79.5 25.7 3.0 286s Seed 11.9 3.0 17.1 286s -------------------------------------------------------- 286s quakes 1000 4 502 8.274369 286s Best subsample: 286s Too long... 286s Outliers: 265 286s Too many to print ... 286s ------------- 286s 286s Call: 286s CovMcd(x = x, trace = FALSE) 286s -> Method: Fast MCD(alpha=0.5 ==> h=502); nsamp = 500; (n,k)mini = (300,5) 286s 286s Robust Estimate of Location: 286s lat long depth mag 286s -21.31 182.48 361.35 4.54 286s 286s Robust Estimate of Covariance: 286s lat long depth mag 286s lat 1.47e+01 3.53e+00 1.34e+02 -2.52e-01 286s long 3.53e+00 4.55e+00 -3.63e+02 4.36e-02 286s depth 1.34e+02 -3.63e+02 4.84e+04 -1.29e+01 286s mag -2.52e-01 4.36e-02 -1.29e+01 1.38e-01 286s -------------------------------------------------------- 286s ======================================================== 286s > dodata(method="deterministic") 286s 286s Call: dodata(method = "deterministic") 286s Data Set n p Half LOG(obj) Time 286s ======================================================== 286s heart 12 2 7 5.678742 286s Best subsample: 286s [1] 1 3 4 5 7 9 11 286s Outliers: 0 286s Too many to print ... 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=7) 286s 286s Robust Estimate of Location: 286s height weight 286s 38.3 33.1 286s 286s Robust Estimate of Covariance: 286s height weight 286s height 135 259 286s weight 259 564 286s -------------------------------------------------------- 286s starsCYG 47 2 25 -8.028718 286s Best subsample: 286s [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 286s Outliers: 7 286s [1] 7 9 11 14 20 30 34 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=25) 286s 286s Robust Estimate of Location: 286s log.Te log.light 286s 4.41 4.95 286s 286s Robust Estimate of Covariance: 286s log.Te log.light 286s log.Te 0.0132 0.0394 286s log.light 0.0394 0.2743 286s -------------------------------------------------------- 286s phosphor 18 2 10 7.732906 286s Best subsample: 286s [1] 2 4 5 7 8 9 11 12 14 16 286s Outliers: 1 286s [1] 6 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 286s 286s Robust Estimate of Location: 286s inorg organic 286s 12.5 40.8 286s 286s Robust Estimate of Covariance: 286s inorg organic 286s inorg 124 101 286s organic 101 197 286s -------------------------------------------------------- 286s stackloss 21 3 12 6.577286 286s Best subsample: 286s [1] 4 5 6 7 8 9 11 13 16 18 19 20 286s Outliers: 2 286s [1] 1 2 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 286s 286s Robust Estimate of Location: 286s Air.Flow Water.Temp Acid.Conc. 286s 58.4 20.5 86.1 286s 286s Robust Estimate of Covariance: 286s Air.Flow Water.Temp Acid.Conc. 286s Air.Flow 56.28 13.33 26.68 286s Water.Temp 13.33 8.28 6.98 286s Acid.Conc. 26.68 6.98 37.97 286s -------------------------------------------------------- 286s coleman 20 5 13 2.149184 286s Best subsample: 286s [1] 3 4 5 7 8 12 13 14 16 17 18 19 20 286s Outliers: 2 286s [1] 6 10 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 286s 286s Robust Estimate of Location: 286s salaryP fatherWc sstatus teacherSc motherLev 286s 2.76 41.08 2.76 25.01 6.27 286s 286s Robust Estimate of Covariance: 286s salaryP fatherWc sstatus teacherSc motherLev 286s salaryP 0.391 2.956 2.146 0.447 0.110 286s fatherWc 2.956 1358.640 442.724 12.235 32.842 286s sstatus 2.146 442.724 205.590 6.464 11.382 286s teacherSc 0.447 12.235 6.464 1.179 0.510 286s motherLev 0.110 32.842 11.382 0.510 0.919 286s -------------------------------------------------------- 286s salinity 28 3 16 1.940763 286s Best subsample: 286s [1] 1 8 10 12 13 14 15 17 18 20 21 22 25 26 27 28 286s Outliers: 2 286s [1] 5 16 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=16) 286s 286s Robust Estimate of Location: 286s X1 X2 X3 286s 10.50 2.58 23.12 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 286s X1 10.90243 -0.00457 -1.46156 286s X2 -0.00457 3.85051 -1.94604 286s X3 -1.46156 -1.94604 3.21424 286s -------------------------------------------------------- 286s wood 20 5 13 -35.240819 286s Best subsample: 286s [1] 1 2 3 5 9 11 12 13 14 15 17 18 20 286s Outliers: 4 286s [1] 4 6 8 19 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 286s 286s Robust Estimate of Location: 286s x1 x2 x3 x4 x5 286s 0.582 0.125 0.530 0.534 0.888 286s 286s Robust Estimate of Covariance: 286s x1 x2 x3 x4 x5 286s x1 1.05e-02 1.81e-03 2.08e-03 -6.41e-04 -9.61e-04 286s x2 1.81e-03 5.55e-04 8.76e-04 -2.03e-04 -4.70e-05 286s x3 2.08e-03 8.76e-04 5.60e-03 -1.11e-03 -1.26e-05 286s x4 -6.41e-04 -2.03e-04 -1.11e-03 4.27e-03 2.60e-03 286s x5 -9.61e-04 -4.70e-05 -1.26e-05 2.60e-03 2.95e-03 286s -------------------------------------------------------- 286s hbk 75 3 39 -1.045501 286s Best subsample: 286s [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 286s [26] 54 55 56 58 59 63 64 66 67 70 71 72 73 74 286s Outliers: 14 286s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=39) 286s 286s Robust Estimate of Location: 286s X1 X2 X3 286s 1.54 1.78 1.69 286s 286s Robust Estimate of Covariance: 286s X1 X2 X3 286s X1 1.227 0.055 0.127 286s X2 0.055 1.249 0.153 286s X3 0.127 0.153 1.160 286s -------------------------------------------------------- 286s Animals 28 2 15 14.555543 286s Best subsample: 286s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 286s Outliers: 14 286s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=15) 286s 286s Robust Estimate of Location: 286s body brain 286s 18.7 64.9 286s 286s Robust Estimate of Covariance: 286s body brain 286s body 929 1576 286s brain 1576 5646 286s -------------------------------------------------------- 286s bushfire 38 5 22 18.135810 286s Best subsample: 286s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 286s Outliers: 16 286s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=22) 286s 286s Robust Estimate of Location: 286s V1 V2 V3 V4 V5 286s 105 147 274 218 279 286s 286s Robust Estimate of Covariance: 286s V1 V2 V3 V4 V5 286s V1 346 268 -1692 -381 -311 286s V2 268 236 -1125 -230 -194 286s V3 -1692 -1125 9993 2455 1951 286s V4 -381 -230 2455 647 505 286s V5 -311 -194 1951 505 398 286s -------------------------------------------------------- 286s lactic 20 2 11 0.359580 286s Best subsample: 286s [1] 1 2 3 4 5 7 8 9 10 11 12 286s Outliers: 4 286s [1] 17 18 19 20 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=11) 286s 286s Robust Estimate of Location: 286s X Y 286s 3.86 5.01 286s 286s Robust Estimate of Covariance: 286s X Y 286s X 10.6 14.6 286s Y 14.6 21.3 286s -------------------------------------------------------- 286s pension 18 2 10 16.675508 286s Best subsample: 286s [1] 1 2 3 4 5 6 8 9 11 12 286s Outliers: 5 286s [1] 14 15 16 17 18 286s ------------- 286s 286s Call: 286s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 286s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 286s 286s Robust Estimate of Location: 286s Income Reserves 286s 52.3 560.9 286s 286s Robust Estimate of Covariance: 286s Income Reserves 286s Income 1420 11932 286s Reserves 11932 208643 286s -------------------------------------------------------- 287s radarImage 1573 5 789 36.694865 287s Best subsample: 287s Too long... 287s Outliers: 114 287s [1] 164 237 238 242 261 262 351 450 451 462 463 480 481 509 516 287s [16] 535 542 572 597 620 643 654 669 679 697 737 802 803 804 818 287s [31] 832 833 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 287s [46] 1202 1223 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 287s [61] 1376 1393 1394 1402 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1504 287s [76] 1510 1511 1512 1518 1519 1520 1521 1522 1525 1526 1527 1528 1530 1532 1534 287s [91] 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1557 1558 1561 287s [106] 1562 1564 1565 1566 1567 1569 1570 1571 1573 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=789) 287s 287s Robust Estimate of Location: 287s X.coord Y.coord Band.1 Band.2 Band.3 287s 52.78 35.37 7.12 18.81 9.09 287s 287s Robust Estimate of Covariance: 287s X.coord Y.coord Band.1 Band.2 Band.3 287s X.coord 123.2 21.5 -363.9 -200.1 -24.3 287s Y.coord 21.5 410.7 46.5 -177.3 -33.4 287s Band.1 -363.9 46.5 27051.1 8138.9 469.3 287s Band.2 -200.1 -177.3 8138.9 25938.0 946.2 287s Band.3 -24.3 -33.4 469.3 946.2 4470.1 287s -------------------------------------------------------- 287s NOxEmissions 8088 4 4046 2.474536 287s Best subsample: 287s Too long... 287s Outliers: 2152 287s Too many to print ... 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=4046) 287s 287s Robust Estimate of Location: 287s julday LNOx LNOxEm sqrtWS 287s 168.20 4.73 7.91 1.37 287s 287s Robust Estimate of Covariance: 287s julday LNOx LNOxEm sqrtWS 287s julday 9176.2934 12.0355 0.7022 -10.1387 287s LNOx 12.0355 0.4736 0.1430 -0.1528 287s LNOxEm 0.7022 0.1430 0.2527 0.0436 287s sqrtWS -10.1387 -0.1528 0.0436 0.2074 287s -------------------------------------------------------- 287s vaso 39 2 21 -3.972244 287s Best subsample: 287s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 287s Outliers: 4 287s [1] 1 2 17 31 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=21) 287s 287s Robust Estimate of Location: 287s Volume Rate 287s 1.16 1.72 287s 287s Robust Estimate of Covariance: 287s Volume Rate 287s Volume 0.313 -0.167 287s Rate -0.167 0.728 287s -------------------------------------------------------- 287s wagnerGrowth 63 6 35 6.511864 287s Best subsample: 287s [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 287s [26] 48 51 52 53 54 55 56 57 60 62 287s Outliers: 15 287s [1] 1 8 15 21 22 28 29 33 39 42 43 46 49 50 63 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=35) 287s 287s Robust Estimate of Location: 287s Region PA GPA HS GHS y 287s 10.91 33.65 -2.05 2.43 0.31 6.98 287s 287s Robust Estimate of Covariance: 287s Region PA GPA HS GHS y 287s Region 35.1365 17.7291 -1.4003 -0.6554 -0.4728 -14.9305 287s PA 17.7291 28.4297 -5.5245 -1.2444 -0.0452 -29.6181 287s GPA -1.4003 -5.5245 5.2170 0.3954 -0.2152 3.8252 287s HS -0.6554 -1.2444 0.3954 0.7273 -0.0107 2.1514 287s GHS -0.4728 -0.0452 -0.2152 -0.0107 0.1728 0.8440 287s y -14.9305 -29.6181 3.8252 2.1514 0.8440 79.0511 287s -------------------------------------------------------- 287s fish 159 6 82 8.880459 287s Best subsample: 287s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 287s [20] 20 21 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 287s [39] 50 51 52 53 54 55 56 57 58 59 60 106 107 108 109 110 111 112 113 287s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 287s [77] 134 135 136 137 138 139 287s Outliers: 64 287s [1] 30 39 40 41 62 63 64 65 66 68 69 70 73 74 75 76 77 78 79 287s [20] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 287s [39] 99 100 101 102 103 104 105 141 142 143 144 145 146 147 148 149 150 151 152 287s [58] 153 154 155 156 157 158 159 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=82) 287s 287s Robust Estimate of Location: 287s Weight Length1 Length2 Length3 Height Width 287s 316.3 24.1 26.3 29.3 31.0 14.7 287s 287s Robust Estimate of Covariance: 287s Weight Length1 Length2 Length3 Height Width 287s Weight 64662.19 1412.34 1541.95 1917.21 1420.83 -61.15 287s Length1 1412.34 34.14 37.04 45.07 29.25 -1.26 287s Length2 1541.95 37.04 40.26 49.04 32.21 -1.34 287s Length3 1917.21 45.07 49.04 60.82 43.03 -2.15 287s Height 1420.83 29.25 32.21 43.03 46.50 -2.66 287s Width -61.15 -1.26 -1.34 -2.15 -2.66 1.02 287s -------------------------------------------------------- 287s pottery 27 6 17 -10.586933 287s Best subsample: 287s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 287s Outliers: 9 287s [1] 3 8 12 16 17 18 23 24 25 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=17) 287s 287s Robust Estimate of Location: 287s SI AL FE MG CA TI 287s 54.983 15.206 9.700 3.817 5.211 0.859 287s 287s Robust Estimate of Covariance: 287s SI AL FE MG CA TI 287s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 287s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 287s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 287s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 287s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 287s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 287s -------------------------------------------------------- 287s rice 105 6 56 -14.423048 287s Best subsample: 287s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 287s [20] 38 44 45 47 51 52 53 55 59 60 65 66 67 70 72 74 76 78 79 287s [39] 80 81 82 83 84 85 86 90 92 93 94 95 97 98 99 100 101 105 287s Outliers: 13 287s [1] 9 19 28 40 42 43 49 58 62 64 71 75 77 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=56) 287s 287s Robust Estimate of Location: 287s Favor Appearance Taste Stickiness 287s -0.2950 0.0799 -0.1555 0.0363 287s Toughness Overall_evaluation 287s 0.0530 -0.2284 287s 287s Robust Estimate of Covariance: 287s Favor Appearance Taste Stickiness Toughness 287s Favor 0.466 0.389 0.471 0.447 -0.198 287s Appearance 0.389 0.610 0.592 0.570 -0.293 287s Taste 0.471 0.592 0.760 0.718 -0.356 287s Stickiness 0.447 0.570 0.718 0.820 -0.419 287s Toughness -0.198 -0.293 -0.356 -0.419 0.400 287s Overall_evaluation 0.557 0.669 0.838 0.846 -0.425 287s Overall_evaluation 287s Favor 0.557 287s Appearance 0.669 287s Taste 0.838 287s Stickiness 0.846 287s Toughness -0.425 287s Overall_evaluation 0.987 287s -------------------------------------------------------- 287s un86 73 7 40 17.117142 287s Best subsample: 287s [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 287s [26] 52 55 56 57 60 61 62 63 64 65 67 70 71 72 73 287s Outliers: 30 287s [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 287s [26] 58 59 66 68 69 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=40) 287s 287s Robust Estimate of Location: 287s POP MOR CAR DR GNP DEN TB 287s 17.036 68.512 6.444 0.877 1.134 64.140 0.433 287s 287s Robust Estimate of Covariance: 287s POP MOR CAR DR GNP DEN 287s POP 3.61e+02 1.95e+02 -6.28e+00 -1.91e-02 -2.07e+00 5.79e+01 287s MOR 1.95e+02 2.39e+03 -2.79e+02 -3.37e+01 -3.39e+01 -9.21e+02 287s CAR -6.28e+00 -2.79e+02 5.76e+01 5.77e+00 6.59e+00 7.81e+01 287s DR -1.91e-02 -3.37e+01 5.77e+00 9.07e-01 5.66e-01 1.69e+01 287s GNP -2.07e+00 -3.39e+01 6.59e+00 5.66e-01 1.42e+00 9.28e+00 287s DEN 5.79e+01 -9.21e+02 7.81e+01 1.69e+01 9.28e+00 3.53e+03 287s TB -6.09e-02 -9.93e-01 2.50e-01 1.98e-02 6.82e-03 -9.75e-01 287s TB 287s POP -6.09e-02 287s MOR -9.93e-01 287s CAR 2.50e-01 287s DR 1.98e-02 287s GNP 6.82e-03 287s DEN -9.75e-01 287s TB 1.64e-02 287s -------------------------------------------------------- 287s wages 39 10 19 23.119456 287s Best subsample: 287s [1] 1 2 5 6 7 9 10 11 12 13 14 15 19 21 23 25 26 27 28 287s Outliers: 9 287s [1] 4 5 9 24 25 26 28 32 34 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 287s 287s Robust Estimate of Location: 287s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 287s 2161.89 2.95 1114.21 297.68 374.00 7269.37 39.13 2.43 287s RACE SCHOOL 287s 36.13 10.39 287s 287s Robust Estimate of Covariance: 287s HRS RATE ERSP ERNO NEIN ASSET 287s HRS 3.53e+03 8.31e+00 -5.96e+03 -6.43e+02 5.15e+03 1.12e+05 287s RATE 8.31e+00 1.78e-01 8.19e+00 2.70e+00 3.90e+01 8.94e+02 287s ERSP -5.96e+03 8.19e+00 1.90e+04 1.13e+03 -4.73e+03 -9.49e+04 287s ERNO -6.43e+02 2.70e+00 1.13e+03 1.80e+03 -3.56e+02 -7.33e+03 287s NEIN 5.15e+03 3.90e+01 -4.73e+03 -3.56e+02 1.38e+04 3.00e+05 287s ASSET 1.12e+05 8.94e+02 -9.49e+04 -7.33e+03 3.00e+05 6.62e+06 287s AGE -3.33e+01 -6.55e-02 8.33e+01 1.50e+00 -3.28e+01 -7.55e+02 287s DEP 4.50e+00 -4.01e-02 -2.77e+01 1.31e+00 -8.09e+00 -1.61e+02 287s RACE -1.30e+03 -6.06e+00 1.80e+03 1.48e+02 -2.58e+03 -5.59e+04 287s SCHOOL 3.01e+01 3.58e-01 -5.57e+00 2.84e+00 9.26e+01 2.10e+03 287s AGE DEP RACE SCHOOL 287s HRS -3.33e+01 4.50e+00 -1.30e+03 3.01e+01 287s RATE -6.55e-02 -4.01e-02 -6.06e+00 3.58e-01 287s ERSP 8.33e+01 -2.77e+01 1.80e+03 -5.57e+00 287s ERNO 1.50e+00 1.31e+00 1.48e+02 2.84e+00 287s NEIN -3.28e+01 -8.09e+00 -2.58e+03 9.26e+01 287s ASSET -7.55e+02 -1.61e+02 -5.59e+04 2.10e+03 287s AGE 6.57e-01 -1.64e-01 1.13e+01 -2.67e-01 287s DEP -1.64e-01 9.20e-02 2.38e-01 -6.01e-02 287s RACE 1.13e+01 2.38e-01 5.73e+02 -1.67e+01 287s SCHOOL -2.67e-01 -6.01e-02 -1.67e+01 7.95e-01 287s -------------------------------------------------------- 287s airquality 153 4 58 18.316848 287s Best subsample: 287s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 287s [20] 47 48 49 50 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 287s [39] 81 82 84 86 87 89 90 91 92 95 97 98 100 101 105 106 108 109 110 287s [58] 111 287s Outliers: 10 287s [1] 8 9 15 18 24 30 48 62 117 148 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=58) 287s 287s Robust Estimate of Location: 287s Ozone Solar.R Wind Temp 287s 40.80 189.37 9.66 78.81 287s 287s Robust Estimate of Covariance: 287s Ozone Solar.R Wind Temp 287s Ozone 935.54 857.76 -56.30 220.48 287s Solar.R 857.76 8507.83 1.36 155.13 287s Wind -56.30 1.36 9.90 -11.61 287s Temp 220.48 155.13 -11.61 84.00 287s -------------------------------------------------------- 287s attitude 30 7 19 24.464288 287s Best subsample: 287s [1] 2 3 4 5 7 8 10 11 12 15 17 19 21 22 23 25 27 28 29 287s Outliers: 8 287s [1] 6 9 13 14 16 18 24 26 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 287s 287s Robust Estimate of Location: 287s rating complaints privileges learning raises critical 287s 64.4 65.2 51.0 55.5 65.9 77.4 287s advance 287s 43.2 287s 287s Robust Estimate of Covariance: 287s rating complaints privileges learning raises critical advance 287s rating 199.95 162.36 115.83 160.44 128.87 -13.55 66.20 287s complaints 162.36 204.84 130.33 170.66 150.19 16.28 96.66 287s privileges 115.83 130.33 181.31 152.63 106.56 4.52 91.44 287s learning 160.44 170.66 152.63 213.06 156.57 9.92 88.31 287s raises 128.87 150.19 106.56 156.57 152.05 23.10 84.00 287s critical -13.55 16.28 4.52 9.92 23.10 80.22 27.15 287s advance 66.20 96.66 91.44 88.31 84.00 27.15 95.51 287s -------------------------------------------------------- 287s attenu 182 5 86 6.593068 287s Best subsample: 287s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 287s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 287s [39] 115 116 117 119 120 121 122 124 125 126 127 128 129 130 131 132 133 134 135 287s [58] 136 137 138 139 140 141 144 145 146 147 148 149 150 151 152 153 154 155 156 287s [77] 157 158 159 160 161 162 163 164 165 166 287s Outliers: 49 287s [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 287s [20] 23 24 25 27 28 29 30 31 32 33 40 45 47 59 60 61 64 65 78 287s [39] 82 83 97 98 100 101 102 103 104 105 117 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=86) 287s 287s Robust Estimate of Location: 287s event mag station dist accel 287s 17.122 5.798 63.461 25.015 0.131 287s 287s Robust Estimate of Covariance: 287s event mag station dist accel 287s event 2.98e+01 -1.58e+00 9.49e+01 -8.36e+00 -3.59e-02 287s mag -1.58e+00 4.26e-01 -3.88e+00 3.13e+00 5.30e-03 287s station 9.49e+01 -3.88e+00 1.10e+03 2.60e+01 5.38e-01 287s dist -8.36e+00 3.13e+00 2.60e+01 2.66e+02 -9.23e-01 287s accel -3.59e-02 5.30e-03 5.38e-01 -9.23e-01 7.78e-03 287s -------------------------------------------------------- 287s USJudgeRatings 43 12 28 -47.886937 287s Best subsample: 287s [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 287s [26] 40 41 43 287s Outliers: 14 287s [1] 1 5 7 8 12 13 14 17 20 21 23 31 35 42 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=28) 287s 287s Robust Estimate of Location: 287s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 287s 7.46 8.26 7.88 8.06 7.85 7.92 7.84 7.83 7.67 7.74 8.31 8.03 287s 287s Robust Estimate of Covariance: 287s CONT INTG DMNR DILG CFMG DECI PREP FAMI 287s CONT 0.7363 -0.2916 -0.4193 -0.1943 -0.0555 -0.0690 -0.1703 -0.1727 287s INTG -0.2916 0.4179 0.5511 0.4167 0.3176 0.3102 0.4247 0.4279 287s DMNR -0.4193 0.5511 0.8141 0.5256 0.4092 0.3934 0.5294 0.5094 287s DILG -0.1943 0.4167 0.5256 0.4820 0.3904 0.3819 0.5054 0.5104 287s CFMG -0.0555 0.3176 0.4092 0.3904 0.3595 0.3368 0.4180 0.4206 287s DECI -0.0690 0.3102 0.3934 0.3819 0.3368 0.3310 0.4135 0.4194 287s PREP -0.1703 0.4247 0.5294 0.5054 0.4180 0.4135 0.5647 0.5752 287s FAMI -0.1727 0.4279 0.5094 0.5104 0.4206 0.4194 0.5752 0.6019 287s ORAL -0.2109 0.4453 0.5646 0.5054 0.4200 0.4121 0.5575 0.5735 287s WRIT -0.2033 0.4411 0.5466 0.5087 0.4222 0.4147 0.5592 0.5787 287s PHYS -0.1624 0.2578 0.3163 0.2833 0.2268 0.2362 0.3108 0.3284 287s RTEN -0.2622 0.4872 0.6324 0.5203 0.4145 0.4081 0.5488 0.5595 287s ORAL WRIT PHYS RTEN 287s CONT -0.2109 -0.2033 -0.1624 -0.2622 287s INTG 0.4453 0.4411 0.2578 0.4872 287s DMNR 0.5646 0.5466 0.3163 0.6324 287s DILG 0.5054 0.5087 0.2833 0.5203 287s CFMG 0.4200 0.4222 0.2268 0.4145 287s DECI 0.4121 0.4147 0.2362 0.4081 287s PREP 0.5575 0.5592 0.3108 0.5488 287s FAMI 0.5735 0.5787 0.3284 0.5595 287s ORAL 0.5701 0.5677 0.3283 0.5688 287s WRIT 0.5677 0.5715 0.3268 0.5645 287s PHYS 0.3283 0.3268 0.2302 0.3308 287s RTEN 0.5688 0.5645 0.3308 0.6057 287s -------------------------------------------------------- 287s USArrests 50 4 27 15.438912 287s Best subsample: 287s [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 287s [26] 49 50 287s Outliers: 7 287s [1] 2 5 6 10 24 28 33 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=27) 287s 287s Robust Estimate of Location: 287s Murder Assault UrbanPop Rape 287s 6.91 150.10 65.88 18.75 287s 287s Robust Estimate of Covariance: 287s Murder Assault UrbanPop Rape 287s Murder 17.9 285.4 17.6 25.0 287s Assault 285.4 6572.8 524.9 465.0 287s UrbanPop 17.6 524.9 211.9 50.5 287s Rape 25.0 465.0 50.5 56.4 287s -------------------------------------------------------- 287s longley 16 7 12 12.747678 287s Best subsample: 287s [1] 5 6 7 8 9 10 11 12 13 14 15 16 287s Outliers: 4 287s [1] 1 2 3 4 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 287s 287s Robust Estimate of Location: 287s GNP.deflator GNP Unemployed Armed.Forces Population 287s 106.5 430.6 328.2 295.0 120.2 287s Year Employed 287s 1956.5 66.9 287s 287s Robust Estimate of Covariance: 287s GNP.deflator GNP Unemployed Armed.Forces Population 287s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 287s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 287s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 287s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 287s Population 81.4 803.4 929.1 -327.4 63.5 287s Year 51.6 504.3 595.6 -216.7 39.7 287s Employed 34.2 344.1 323.6 -149.5 26.2 287s Year Employed 287s GNP.deflator 51.6 34.2 287s GNP 504.3 344.1 287s Unemployed 595.6 323.6 287s Armed.Forces -216.7 -149.5 287s Population 39.7 26.2 287s Year 25.1 16.7 287s Employed 16.7 12.4 287s -------------------------------------------------------- 287s Loblolly 84 3 44 4.898174 287s Best subsample: 287s [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 287s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 287s Outliers: 31 287s [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 287s [26] 72 76 77 78 83 84 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=44) 287s 287s Robust Estimate of Location: 287s height age Seed 287s 20.44 8.19 7.72 287s 287s Robust Estimate of Covariance: 287s height age Seed 287s height 247.8 79.5 11.9 287s age 79.5 25.7 3.0 287s Seed 11.9 3.0 17.1 287s -------------------------------------------------------- 287s quakes 1000 4 502 8.274209 287s Best subsample: 287s Too long... 287s Outliers: 266 287s Too many to print ... 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 287s -> Method: Deterministic MCD(alpha=0.5 ==> h=502) 287s 287s Robust Estimate of Location: 287s lat long depth mag 287s -21.34 182.47 360.58 4.54 287s 287s Robust Estimate of Covariance: 287s lat long depth mag 287s lat 1.50e+01 3.58e+00 1.37e+02 -2.66e-01 287s long 3.58e+00 4.55e+00 -3.61e+02 4.64e-02 287s depth 1.37e+02 -3.61e+02 4.84e+04 -1.36e+01 287s mag -2.66e-01 4.64e-02 -1.36e+01 1.34e-01 287s -------------------------------------------------------- 287s ======================================================== 287s > dodata(method="exact") 287s 287s Call: dodata(method = "exact") 287s Data Set n p Half LOG(obj) Time 287s ======================================================== 287s heart 12 2 7 5.678742 287s Best subsample: 287s [1] 1 3 4 5 7 9 11 287s Outliers: 0 287s Too many to print ... 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "exact", trace = FALSE) 287s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = exact; (n,k)mini = (300,5) 287s 287s Robust Estimate of Location: 287s height weight 287s 38.3 33.1 287s 287s Robust Estimate of Covariance: 287s height weight 287s height 135 259 287s weight 259 564 287s -------------------------------------------------------- 287s starsCYG 47 2 25 -8.031215 287s Best subsample: 287s [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 287s Outliers: 7 287s [1] 7 9 11 14 20 30 34 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "exact", trace = FALSE) 287s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = exact; (n,k)mini = (300,5) 287s 287s Robust Estimate of Location: 287s log.Te log.light 287s 4.41 4.95 287s 287s Robust Estimate of Covariance: 287s log.Te log.light 287s log.Te 0.0132 0.0394 287s log.light 0.0394 0.2743 287s -------------------------------------------------------- 287s phosphor 18 2 10 6.878847 287s Best subsample: 287s [1] 3 5 8 9 11 12 13 14 15 17 287s Outliers: 3 287s [1] 1 6 10 287s ------------- 287s 287s Call: 287s CovMcd(x = x, nsamp = "exact", trace = FALSE) 287s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 287s 287s Robust Estimate of Location: 287s inorg organic 287s 13.4 38.8 287s 287s Robust Estimate of Covariance: 287s inorg organic 287s inorg 129 130 287s organic 130 182 287s -------------------------------------------------------- 288s coleman 20 5 13 1.286808 288s Best subsample: 288s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 288s Outliers: 7 288s [1] 1 6 9 10 11 15 18 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s salaryP fatherWc sstatus teacherSc motherLev 288s 2.76 48.38 6.12 25.00 6.40 288s 288s Robust Estimate of Covariance: 288s salaryP fatherWc sstatus teacherSc motherLev 288s salaryP 0.253 1.786 -0.266 0.151 0.075 288s fatherWc 1.786 1303.382 330.496 12.604 34.503 288s sstatus -0.266 330.496 119.888 3.833 10.131 288s teacherSc 0.151 12.604 3.833 0.785 0.555 288s motherLev 0.075 34.503 10.131 0.555 1.043 288s -------------------------------------------------------- 288s salinity 28 3 16 1.326364 288s Best subsample: 288s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 288s Outliers: 4 288s [1] 5 16 23 24 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s X1 X2 X3 288s 10.08 2.78 22.78 288s 288s Robust Estimate of Covariance: 288s X1 X2 X3 288s X1 10.44 1.01 -3.19 288s X2 1.01 3.83 -1.44 288s X3 -3.19 -1.44 2.39 288s -------------------------------------------------------- 288s wood 20 5 13 -36.270094 288s Best subsample: 288s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 288s Outliers: 7 288s [1] 4 6 7 8 11 16 19 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s x1 x2 x3 x4 x5 288s 0.587 0.122 0.531 0.538 0.892 288s 288s Robust Estimate of Covariance: 288s x1 x2 x3 x4 x5 288s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 288s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 288s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 288s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 288s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 288s -------------------------------------------------------- 288s Animals 28 2 15 14.555543 288s Best subsample: 288s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 288s Outliers: 14 288s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s body brain 288s 18.7 64.9 288s 288s Robust Estimate of Covariance: 288s body brain 288s body 929 1576 288s brain 1576 5646 288s -------------------------------------------------------- 288s lactic 20 2 11 0.359580 288s Best subsample: 288s [1] 1 2 3 4 5 7 8 9 10 11 12 288s Outliers: 4 288s [1] 17 18 19 20 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s X Y 288s 3.86 5.01 288s 288s Robust Estimate of Covariance: 288s X Y 288s X 10.6 14.6 288s Y 14.6 21.3 288s -------------------------------------------------------- 288s pension 18 2 10 16.675508 288s Best subsample: 288s [1] 1 2 3 4 5 6 8 9 11 12 288s Outliers: 5 288s [1] 14 15 16 17 18 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s Income Reserves 288s 52.3 560.9 288s 288s Robust Estimate of Covariance: 288s Income Reserves 288s Income 1420 11932 288s Reserves 11932 208643 288s -------------------------------------------------------- 288s vaso 39 2 21 -3.972244 288s Best subsample: 288s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 288s Outliers: 4 288s [1] 1 2 17 31 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s Volume Rate 288s 1.16 1.72 288s 288s Robust Estimate of Covariance: 288s Volume Rate 288s Volume 0.313 -0.167 288s Rate -0.167 0.728 288s -------------------------------------------------------- 288s stackloss 21 3 12 5.472581 288s Best subsample: 288s [1] 4 5 6 7 8 9 10 11 12 13 14 20 288s Outliers: 9 288s [1] 1 2 3 15 16 17 18 19 21 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s Air.Flow Water.Temp Acid.Conc. 288s 59.5 20.8 87.3 288s 288s Robust Estimate of Covariance: 288s Air.Flow Water.Temp Acid.Conc. 288s Air.Flow 6.29 5.85 5.74 288s Water.Temp 5.85 9.23 6.14 288s Acid.Conc. 5.74 6.14 23.25 288s -------------------------------------------------------- 288s pilot 20 2 11 6.487287 288s Best subsample: 288s [1] 2 3 6 7 9 12 15 16 17 18 20 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMcd(x = x, nsamp = "exact", trace = FALSE) 288s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 288s 288s Robust Estimate of Location: 288s X Y 288s 101.1 67.7 288s 288s Robust Estimate of Covariance: 288s X Y 288s X 3344 1070 288s Y 1070 343 288s -------------------------------------------------------- 288s ======================================================== 288s > dodata(method="MRCD") 288s 288s Call: dodata(method = "MRCD") 288s Data Set n p Half LOG(obj) Time 288s ======================================================== 288s heart 12 2 6 7.446266 288s Best subsample: 288s [1] 1 3 4 7 9 11 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=6) 288s 288s Robust Estimate of Location: 288s height weight 288s 38.8 33.0 288s 288s Robust Estimate of Covariance: 288s height weight 288s height 47.4 75.2 288s weight 75.2 155.4 288s -------------------------------------------------------- 288s starsCYG 47 2 24 -5.862050 288s Best subsample: 288s [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 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=24) 288s 288s Robust Estimate of Location: 288s log.Te log.light 288s 4.44 5.05 288s 288s Robust Estimate of Covariance: 288s log.Te log.light 288s log.Te 0.00867 0.02686 288s log.light 0.02686 0.41127 288s -------------------------------------------------------- 288s phosphor 18 2 9 9.954788 288s Best subsample: 288s [1] 4 7 8 9 11 12 13 14 16 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 288s 288s Robust Estimate of Location: 288s inorg organic 288s 12.5 39.0 288s 288s Robust Estimate of Covariance: 288s inorg organic 288s inorg 236 140 288s organic 140 172 288s -------------------------------------------------------- 288s stackloss 21 3 11 7.991165 288s Best subsample: 288s [1] 4 5 6 7 8 9 10 13 18 19 20 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=11) 288s 288s Robust Estimate of Location: 288s Air.Flow Water.Temp Acid.Conc. 288s 58.2 21.4 85.2 288s 288s Robust Estimate of Covariance: 288s Air.Flow Water.Temp Acid.Conc. 288s Air.Flow 49.8 17.2 42.7 288s Water.Temp 17.2 13.8 25.2 288s Acid.Conc. 42.7 25.2 58.2 288s -------------------------------------------------------- 288s coleman 20 5 10 5.212156 288s Best subsample: 288s [1] 3 4 5 7 8 9 14 16 19 20 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 288s 288s Robust Estimate of Location: 288s salaryP fatherWc sstatus teacherSc motherLev 288s 2.78 59.44 9.28 25.41 6.70 288s 288s Robust Estimate of Covariance: 288s salaryP fatherWc sstatus teacherSc motherLev 288s salaryP 0.1582 -0.2826 0.4112 0.1754 0.0153 288s fatherWc -0.2826 902.9210 201.5815 -2.1236 18.8736 288s sstatus 0.4112 201.5815 65.4580 -0.3876 4.7794 288s teacherSc 0.1754 -2.1236 -0.3876 0.7233 -0.0322 288s motherLev 0.0153 18.8736 4.7794 -0.0322 0.5417 288s -------------------------------------------------------- 288s salinity 28 3 14 3.586919 288s Best subsample: 288s [1] 1 7 8 12 13 14 18 20 21 22 25 26 27 28 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 288s 288s Robust Estimate of Location: 288s X1 X2 X3 288s 10.95 3.71 21.99 288s 288s Robust Estimate of Covariance: 288s X1 X2 X3 288s X1 14.153 0.718 -3.359 288s X2 0.718 3.565 -0.722 288s X3 -3.359 -0.722 1.607 288s -------------------------------------------------------- 288s wood 20 5 10 -33.100492 288s Best subsample: 288s [1] 1 2 3 5 11 14 15 17 18 20 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 288s 288s Robust Estimate of Location: 288s x1 x2 x3 x4 x5 288s 0.572 0.120 0.504 0.545 0.899 288s 288s Robust Estimate of Covariance: 288s x1 x2 x3 x4 x5 288s x1 0.007543 0.001720 0.000412 -0.001230 -0.001222 288s x2 0.001720 0.000568 0.000355 -0.000533 -0.000132 288s x3 0.000412 0.000355 0.002478 0.000190 0.000811 288s x4 -0.001230 -0.000533 0.000190 0.002327 0.000967 288s x5 -0.001222 -0.000132 0.000811 0.000967 0.001894 288s -------------------------------------------------------- 288s hbk 75 3 38 1.539545 288s Best subsample: 288s [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 288s [26] 55 56 58 59 63 64 66 67 70 71 72 73 74 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=38) 288s 288s Robust Estimate of Location: 288s X1 X2 X3 288s 1.60 2.37 1.64 288s 288s Robust Estimate of Covariance: 288s X1 X2 X3 288s X1 2.810 0.124 1.248 288s X2 0.124 1.017 0.208 288s X3 1.248 0.208 2.218 288s -------------------------------------------------------- 288s Animals 28 2 14 16.278395 288s Best subsample: 288s [1] 1 3 4 5 10 11 18 19 20 21 22 23 26 27 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 288s 288s Robust Estimate of Location: 288s body brain 288s 19.5 56.8 288s 288s Robust Estimate of Covariance: 288s body brain 288s body 2802 5179 288s brain 5179 13761 288s -------------------------------------------------------- 288s bushfire 38 5 19 28.483413 288s Best subsample: 288s [1] 1 2 3 4 5 14 15 16 17 18 19 20 21 22 23 24 25 26 27 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=19) 288s 288s Robust Estimate of Location: 288s V1 V2 V3 V4 V5 288s 103 145 287 221 281 288s 288s Robust Estimate of Covariance: 288s V1 V2 V3 V4 V5 288s V1 366 249 -1993 -503 -396 288s V2 249 252 -1223 -291 -233 288s V3 -1993 -1223 14246 3479 2718 288s V4 -503 -291 3479 1083 748 288s V5 -396 -233 2718 748 660 288s -------------------------------------------------------- 288s lactic 20 2 10 2.593141 288s Best subsample: 288s [1] 1 2 3 4 5 7 8 9 10 11 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 288s 288s Robust Estimate of Location: 288s X Y 288s 2.60 3.63 288s 288s Robust Estimate of Covariance: 288s X Y 288s X 8.13 13.54 288s Y 13.54 24.17 288s -------------------------------------------------------- 288s pension 18 2 9 18.931204 288s Best subsample: 288s [1] 2 3 4 5 6 8 9 11 12 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 288s 288s Robust Estimate of Location: 288s Income Reserves 288s 45.7 466.9 288s 288s Robust Estimate of Covariance: 288s Income Reserves 288s Income 2127 23960 288s Reserves 23960 348275 288s -------------------------------------------------------- 288s vaso 39 2 20 -1.864710 288s Best subsample: 288s [1] 3 4 8 14 18 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=20) 288s 288s Robust Estimate of Location: 288s Volume Rate 288s 1.14 1.77 288s 288s Robust Estimate of Covariance: 288s Volume Rate 288s Volume 0.44943 -0.00465 288s Rate -0.00465 0.34480 288s -------------------------------------------------------- 288s wagnerGrowth 63 6 32 9.287760 288s Best subsample: 288s [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 288s [26] 53 54 55 56 57 60 62 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=32) 288s 288s Robust Estimate of Location: 288s Region PA GPA HS GHS y 288s 10.719 33.816 -2.144 2.487 0.293 4.918 288s 288s Robust Estimate of Covariance: 288s Region PA GPA HS GHS y 288s Region 56.7128 17.4919 -2.9710 -0.6491 -0.4545 -10.4287 288s PA 17.4919 29.9968 -7.6846 -1.3141 0.5418 -35.6434 288s GPA -2.9710 -7.6846 6.3238 1.1257 -0.4757 12.4707 288s HS -0.6491 -1.3141 1.1257 1.1330 -0.0915 3.3617 288s GHS -0.4545 0.5418 -0.4757 -0.0915 0.1468 -1.1228 288s y -10.4287 -35.6434 12.4707 3.3617 -1.1228 67.4215 288s -------------------------------------------------------- 288s fish 159 6 79 22.142828 288s Best subsample: 288s [1] 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 288s [20] 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 50 51 288s [39] 52 53 54 55 56 57 58 59 60 71 105 106 107 109 110 111 113 114 115 288s [58] 116 117 118 119 120 122 123 124 125 126 127 128 129 130 131 132 134 135 136 288s [77] 137 138 139 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=79) 288s 288s Robust Estimate of Location: 288s Weight Length1 Length2 Length3 Height Width 288s 291.7 23.8 25.9 28.9 30.4 14.7 288s 288s Robust Estimate of Covariance: 288s Weight Length1 Length2 Length3 Height Width 288s Weight 77155.07 1567.55 1713.74 2213.16 1912.62 -103.97 288s Length1 1567.55 45.66 41.57 52.14 38.66 -2.39 288s Length2 1713.74 41.57 54.26 56.77 42.72 -2.55 288s Length3 2213.16 52.14 56.77 82.57 58.84 -3.65 288s Height 1912.62 38.66 42.72 58.84 70.51 -3.80 288s Width -103.97 -2.39 -2.55 -3.65 -3.80 1.19 288s -------------------------------------------------------- 288s pottery 27 6 14 -6.897459 288s Best subsample: 288s [1] 1 2 4 5 6 10 11 13 14 15 19 21 22 26 288s Outliers: 0 288s Too many to print ... 288s ------------- 288s 288s Call: 288s CovMrcd(x = x, trace = FALSE) 288s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 288s 288s Robust Estimate of Location: 288s SI AL FE MG CA TI 288s 54.39 14.93 9.78 3.82 5.11 0.86 288s 288s Robust Estimate of Covariance: 288s SI AL FE MG CA TI 288s SI 17.47469 -0.16656 0.39943 4.48192 -0.71153 0.06515 288s AL -0.16656 3.93154 -0.35738 -2.29899 0.14770 -0.02050 288s FE 0.39943 -0.35738 0.20434 0.37562 -0.22460 0.00943 288s MG 4.48192 -2.29899 0.37562 2.82339 -0.16027 0.02943 288s CA -0.71153 0.14770 -0.22460 -0.16027 0.88443 -0.01711 288s TI 0.06515 -0.02050 0.00943 0.02943 -0.01711 0.00114 288s -------------------------------------------------------- 289s rice 105 6 53 -8.916472 289s Best subsample: 289s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 289s [20] 38 44 45 47 51 52 53 54 55 59 60 65 67 70 72 76 79 80 81 289s [39] 82 83 84 85 86 90 92 93 94 95 97 98 99 101 105 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=53) 289s 289s Robust Estimate of Location: 289s Favor Appearance Taste Stickiness 289s -0.1741 0.0774 -0.0472 0.1868 289s Toughness Overall_evaluation 289s -0.0346 -0.0683 289s 289s Robust Estimate of Covariance: 289s Favor Appearance Taste Stickiness Toughness 289s Favor 0.402 0.306 0.378 0.364 -0.134 289s Appearance 0.306 0.508 0.474 0.407 -0.146 289s Taste 0.378 0.474 0.708 0.611 -0.258 289s Stickiness 0.364 0.407 0.611 0.795 -0.320 289s Toughness -0.134 -0.146 -0.258 -0.320 0.302 289s Overall_evaluation 0.453 0.536 0.746 0.745 -0.327 289s Overall_evaluation 289s Favor 0.453 289s Appearance 0.536 289s Taste 0.746 289s Stickiness 0.745 289s Toughness -0.327 289s Overall_evaluation 0.963 289s -------------------------------------------------------- 289s un86 73 7 37 19.832993 289s Best subsample: 289s [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 289s [26] 56 57 60 62 63 64 65 67 70 71 72 73 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=37) 289s 289s Robust Estimate of Location: 289s POP MOR CAR DR GNP DEN TB 289s 14.462 66.892 6.670 0.858 1.251 55.518 0.429 289s 289s Robust Estimate of Covariance: 289s POP MOR CAR DR GNP DEN 289s POP 3.00e+02 1.58e+02 9.83e+00 2.74e+00 5.51e-01 6.87e+01 289s MOR 1.58e+02 2.96e+03 -4.24e+02 -4.72e+01 -5.40e+01 -1.01e+03 289s CAR 9.83e+00 -4.24e+02 9.12e+01 8.71e+00 1.13e+01 1.96e+02 289s DR 2.74e+00 -4.72e+01 8.71e+00 1.25e+00 1.03e+00 2.74e+01 289s GNP 5.51e-01 -5.40e+01 1.13e+01 1.03e+00 2.31e+00 2.36e+01 289s DEN 6.87e+01 -1.01e+03 1.96e+02 2.74e+01 2.36e+01 3.12e+03 289s TB 2.04e-02 -1.81e+00 3.42e-01 2.57e-02 2.09e-02 -6.88e-01 289s TB 289s POP 2.04e-02 289s MOR -1.81e+00 289s CAR 3.42e-01 289s DR 2.57e-02 289s GNP 2.09e-02 289s DEN -6.88e-01 289s TB 2.59e-02 289s -------------------------------------------------------- 289s wages 39 10 14 35.698016 289s Best subsample: 289s [1] 1 2 5 6 9 10 11 13 15 19 23 25 26 28 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 289s 289s Robust Estimate of Location: 289s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 289s 2167.71 2.96 1113.50 300.43 382.29 7438.00 39.06 2.41 289s RACE SCHOOL 289s 33.00 10.45 289s 289s Robust Estimate of Covariance: 289s HRS RATE ERSP ERNO NEIN ASSET 289s HRS 1.97e+03 -4.14e-01 -4.71e+03 -6.58e+02 1.81e+03 3.84e+04 289s RATE -4.14e-01 1.14e-01 1.79e+01 3.08e+00 1.40e+01 3.57e+02 289s ERSP -4.71e+03 1.79e+01 1.87e+04 2.33e+03 -2.06e+03 -3.57e+04 289s ERNO -6.58e+02 3.08e+00 2.33e+03 5.36e+02 -3.42e+02 -5.56e+03 289s NEIN 1.81e+03 1.40e+01 -2.06e+03 -3.42e+02 5.77e+03 1.10e+05 289s ASSET 3.84e+04 3.57e+02 -3.57e+04 -5.56e+03 1.10e+05 2.86e+06 289s AGE -1.83e+01 1.09e-02 6.69e+01 8.78e+00 -5.07e+00 -1.51e+02 289s DEP 4.82e+00 -3.14e-02 -2.52e+01 -2.96e+00 -5.33e+00 -1.03e+02 289s RACE -5.67e+02 -1.33e+00 1.21e+03 1.81e+02 -9.13e+02 -1.96e+04 289s SCHOOL 5.33e+00 1.87e-01 1.86e+01 3.12e+00 3.20e+01 7.89e+02 289s AGE DEP RACE SCHOOL 289s HRS -1.83e+01 4.82e+00 -5.67e+02 5.33e+00 289s RATE 1.09e-02 -3.14e-02 -1.33e+00 1.87e-01 289s ERSP 6.69e+01 -2.52e+01 1.21e+03 1.86e+01 289s ERNO 8.78e+00 -2.96e+00 1.81e+02 3.12e+00 289s NEIN -5.07e+00 -5.33e+00 -9.13e+02 3.20e+01 289s ASSET -1.51e+02 -1.03e+02 -1.96e+04 7.89e+02 289s AGE 5.71e-01 -1.56e-01 4.58e+00 -5.00e-02 289s DEP -1.56e-01 8.08e-02 -3.02e-01 -4.47e-02 289s RACE 4.58e+00 -3.02e-01 2.36e+02 -4.54e+00 289s SCHOOL -5.00e-02 -4.47e-02 -4.54e+00 4.23e-01 289s -------------------------------------------------------- 289s airquality 153 4 56 21.136376 289s Best subsample: 289s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 289s [20] 47 48 49 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 81 289s [39] 82 84 86 87 89 90 91 92 96 97 98 100 101 105 106 109 110 111 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=56) 289s 289s Robust Estimate of Location: 289s Ozone Solar.R Wind Temp 289s 41.84 197.21 8.93 80.39 289s 289s Robust Estimate of Covariance: 289s Ozone Solar.R Wind Temp 289s Ozone 1480.7 1562.8 -99.9 347.3 289s Solar.R 1562.8 11401.2 -35.2 276.8 289s Wind -99.9 -35.2 11.4 -23.5 289s Temp 347.3 276.8 -23.5 107.7 289s -------------------------------------------------------- 289s attitude 30 7 15 27.040805 289s Best subsample: 289s [1] 2 3 4 5 7 8 10 12 15 19 22 23 25 27 28 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=15) 289s 289s Robust Estimate of Location: 289s rating complaints privileges learning raises critical 289s 65.8 66.5 50.1 56.1 66.7 78.1 289s advance 289s 41.7 289s 289s Robust Estimate of Covariance: 289s rating complaints privileges learning raises critical advance 289s rating 138.77 80.02 59.22 107.33 95.83 -1.24 54.36 289s complaints 80.02 97.23 50.59 99.50 79.15 -2.71 42.81 289s privileges 59.22 50.59 84.92 90.03 60.88 22.39 44.93 289s learning 107.33 99.50 90.03 187.67 128.71 15.48 63.67 289s raises 95.83 79.15 60.88 128.71 123.94 -1.46 49.98 289s critical -1.24 -2.71 22.39 15.48 -1.46 61.23 12.88 289s advance 54.36 42.81 44.93 63.67 49.98 12.88 48.61 289s -------------------------------------------------------- 289s attenu 182 5 83 9.710111 289s Best subsample: 289s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 289s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 289s [39] 115 116 117 121 122 124 125 126 127 128 129 130 131 132 133 134 135 136 137 289s [58] 138 139 140 141 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 289s [77] 160 161 162 163 164 165 166 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=83) 289s 289s Robust Estimate of Location: 289s event mag station dist accel 289s 18.940 5.741 67.988 23.365 0.124 289s 289s Robust Estimate of Covariance: 289s event mag station dist accel 289s event 2.86e+01 -2.31e+00 1.02e+02 2.68e+01 -1.99e-01 289s mag -2.31e+00 6.17e-01 -7.03e+00 4.67e-01 2.59e-02 289s station 1.02e+02 -7.03e+00 1.66e+03 1.62e+02 7.96e-02 289s dist 2.68e+01 4.67e-01 1.62e+02 3.61e+02 -1.23e+00 289s accel -1.99e-01 2.59e-02 7.96e-02 -1.23e+00 9.42e-03 289s -------------------------------------------------------- 289s USJudgeRatings 43 12 22 -23.463708 289s Best subsample: 289s [1] 2 3 4 6 9 11 15 16 18 19 24 25 26 27 28 29 32 33 34 36 37 38 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=22) 289s 289s Robust Estimate of Location: 289s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 289s 7.24 8.42 8.10 8.19 7.95 8.00 7.96 7.96 7.81 7.89 8.40 8.20 289s 289s Robust Estimate of Covariance: 289s CONT INTG DMNR DILG CFMG DECI PREP 289s CONT 0.61805 -0.05601 -0.09540 0.00694 0.09853 0.06261 0.03939 289s INTG -0.05601 0.23560 0.27537 0.20758 0.16603 0.17281 0.21128 289s DMNR -0.09540 0.27537 0.55349 0.28872 0.24014 0.24293 0.28886 289s DILG 0.00694 0.20758 0.28872 0.34099 0.23502 0.23917 0.29672 289s CFMG 0.09853 0.16603 0.24014 0.23502 0.31649 0.23291 0.27651 289s DECI 0.06261 0.17281 0.24293 0.23917 0.23291 0.30681 0.27737 289s PREP 0.03939 0.21128 0.28886 0.29672 0.27651 0.27737 0.42020 289s FAMI 0.04588 0.20388 0.26072 0.29037 0.27179 0.27737 0.34857 289s ORAL 0.03000 0.21379 0.29606 0.28764 0.27338 0.27424 0.33503 289s WRIT 0.03261 0.20258 0.26931 0.27962 0.26382 0.26610 0.32677 289s PHYS -0.04485 0.13598 0.17659 0.16834 0.14554 0.16467 0.18948 289s RTEN 0.01543 0.22654 0.32117 0.27307 0.23826 0.24669 0.29450 289s FAMI ORAL WRIT PHYS RTEN 289s CONT 0.04588 0.03000 0.03261 -0.04485 0.01543 289s INTG 0.20388 0.21379 0.20258 0.13598 0.22654 289s DMNR 0.26072 0.29606 0.26931 0.17659 0.32117 289s DILG 0.29037 0.28764 0.27962 0.16834 0.27307 289s CFMG 0.27179 0.27338 0.26382 0.14554 0.23826 289s DECI 0.27737 0.27424 0.26610 0.16467 0.24669 289s PREP 0.34857 0.33503 0.32677 0.18948 0.29450 289s FAMI 0.47232 0.33762 0.33420 0.19759 0.29015 289s ORAL 0.33762 0.40361 0.32208 0.19794 0.29544 289s WRIT 0.33420 0.32208 0.38733 0.19276 0.28184 289s PHYS 0.19759 0.19794 0.19276 0.20284 0.18097 289s RTEN 0.29015 0.29544 0.28184 0.18097 0.36877 289s -------------------------------------------------------- 289s USArrests 50 4 25 17.834643 289s Best subsample: 289s [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 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=25) 289s 289s Robust Estimate of Location: 289s Murder Assault UrbanPop Rape 289s 5.38 121.68 63.80 16.33 289s 289s Robust Estimate of Covariance: 289s Murder Assault UrbanPop Rape 289s Murder 17.8 316.3 48.5 31.1 289s Assault 316.3 6863.0 1040.0 548.9 289s UrbanPop 48.5 1040.0 424.8 93.6 289s Rape 31.1 548.9 93.6 63.8 289s -------------------------------------------------------- 289s longley 16 7 8 31.147844 289s Best subsample: 289s [1] 5 6 7 9 10 11 13 14 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=8) 289s 289s Robust Estimate of Location: 289s GNP.deflator GNP Unemployed Armed.Forces Population 289s 104.3 410.8 278.8 300.1 118.2 289s Year Employed 289s 1955.4 66.5 289s 289s Robust Estimate of Covariance: 289s GNP.deflator GNP Unemployed Armed.Forces Population 289s GNP.deflator 85.0 652.3 784.4 -370.7 48.7 289s GNP 652.3 7502.9 7328.6 -3414.2 453.9 289s Unemployed 784.4 7328.6 10760.3 -4646.7 548.1 289s Armed.Forces -370.7 -3414.2 -4646.7 2824.3 -253.9 289s Population 48.7 453.9 548.1 -253.9 40.2 289s Year 33.5 312.7 378.8 -176.1 23.4 289s Employed 23.9 224.8 263.6 -128.3 16.8 289s Year Employed 289s GNP.deflator 33.5 23.9 289s GNP 312.7 224.8 289s Unemployed 378.8 263.6 289s Armed.Forces -176.1 -128.3 289s Population 23.4 16.8 289s Year 18.9 11.7 289s Employed 11.7 10.3 289s -------------------------------------------------------- 289s Loblolly 84 3 42 11.163448 289s Best subsample: 289s [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 289s [26] 53 54 57 58 59 63 64 65 66 70 71 76 77 81 82 83 84 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=42) 289s 289s Robust Estimate of Location: 289s height age Seed 289s 44.20 17.26 6.76 289s 289s Robust Estimate of Covariance: 289s height age Seed 289s height 326.74 139.18 3.50 289s age 139.18 68.48 -2.72 289s Seed 3.50 -2.72 25.43 289s -------------------------------------------------------- 289s quakes 1000 4 500 11.802478 289s Best subsample: 289s Too long... 289s Outliers: 0 289s Too many to print ... 289s ------------- 289s 289s Call: 289s CovMrcd(x = x, trace = FALSE) 289s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=500) 289s 289s Robust Estimate of Location: 289s lat long depth mag 289s -20.59 182.13 432.46 4.42 289s 289s Robust Estimate of Covariance: 289s lat long depth mag 289s lat 15.841 5.702 -106.720 -0.441 289s long 5.702 7.426 -577.189 -0.136 289s depth -106.720 -577.189 66701.479 3.992 289s mag -0.441 -0.136 3.992 0.144 289s -------------------------------------------------------- 289s ======================================================== 289s > ##doexactfit() 289s > 289s BEGIN TEST tmest4.R 289s 289s R version 4.4.3 (2025-02-28) -- "Trophy Case" 289s Copyright (C) 2025 The R Foundation for Statistical Computing 289s Platform: arm-unknown-linux-gnueabihf (32-bit) 289s 289s R is free software and comes with ABSOLUTELY NO WARRANTY. 289s You are welcome to redistribute it under certain conditions. 289s Type 'license()' or 'licence()' for distribution details. 289s 289s R is a collaborative project with many contributors. 289s Type 'contributors()' for more information and 289s 'citation()' on how to cite R or R packages in publications. 289s 289s Type 'demo()' for some demos, 'help()' for on-line help, or 289s 'help.start()' for an HTML browser interface to help. 289s Type 'q()' to quit R. 289s 289s > ## VT::15.09.2013 - this will render the output independent 289s > ## from the version of the package 289s > suppressPackageStartupMessages(library(rrcov)) 290s > 290s > library(MASS) 290s > dodata <- function(nrep = 1, time = FALSE, full = TRUE) { 290s + domest <- function(x, xname, nrep = 1) { 290s + n <- dim(x)[1] 290s + p <- dim(x)[2] 290s + mm <- CovMest(x) 290s + crit <- log(mm@crit) 290s + ## c1 <- mm@psi@c1 290s + ## M <- mm$psi@M 290s + 290s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 290s + lpad <- lname-nchar(xname) 290s + cat(pad.right(xname,lpad), xres) 290s + 290s + dist <- getDistance(mm) 290s + quantiel <- qchisq(0.975, p) 290s + ibad <- which(dist >= quantiel) 290s + names(ibad) <- NULL 290s + nbad <- length(ibad) 290s + cat("Outliers: ",nbad,"\n") 290s + if(nbad > 0) 290s + print(ibad) 290s + cat("-------------\n") 290s + show(mm) 290s + cat("--------------------------------------------------------\n") 290s + } 290s + 290s + options(digits = 5) 290s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 290s + 290s + lname <- 20 290s + 290s + data(heart) 290s + data(starsCYG) 290s + data(phosphor) 290s + data(stackloss) 290s + data(coleman) 290s + data(salinity) 290s + data(wood) 290s + data(hbk) 290s + 290s + data(Animals, package = "MASS") 290s + brain <- Animals[c(1:24, 26:25, 27:28),] 290s + data(milk) 290s + data(bushfire) 290s + 290s + tmp <- sys.call() 290s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 290s + 290s + cat("Data Set n p c1 M LOG(det) Time\n") 290s + cat("======================================================================\n") 290s + domest(heart[, 1:2], data(heart), nrep) 290s + domest(starsCYG, data(starsCYG), nrep) 290s + domest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 290s + domest(stack.x, data(stackloss), nrep) 290s + domest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 290s + domest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 290s + domest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 290s + domest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 290s + 290s + 290s + domest(brain, "Animals", nrep) 290s + domest(milk, data(milk), nrep) 290s + domest(bushfire, data(bushfire), nrep) 290s + cat("======================================================================\n") 290s + } 290s > 290s > # generate contaminated data using the function gendata with different 290s > # number of outliers and check if the M-estimate breaks - i.e. the 290s > # largest eigenvalue is larger than e.g. 5. 290s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 290s > # outliers grater than 20. 290s > dogen <- function(){ 290s + eig <- vector("numeric",26) 290s + for(i in 0:25) { 290s + gg <- gendata(eps=i) 290s + mm <- CovMest(gg$x, t0=gg$tgood, S0=gg$sgood, arp=0.001) 290s + eig[i+1] <- ev <- getEvals(mm)[1] 290s + # cat(i, ev, "\n") 290s + 290s + stopifnot(ev < 5 || i > 20) 290s + } 290s + # plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 290s + } 290s > 290s > # 290s > # generate data 50x10 as multivariate normal N(0,I) and add 290s > # eps % outliers by adding d=5.0 to each component. 290s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 290s > # - if eps >= 1, it is the number of outliers 290s > # - use the center and cov of the good data as good start 290s > # - use the center and the cov of all data as a bad start 290s > # If using a good start, the M-estimate must iterate to 290s > # the good solution: the largest eigenvalue is less then e.g. 5 290s > # 290s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 290s + 290s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 290s + stop("eps is out of range") 290s + 290s + library(MASS) 290s + 290s + x <- mvrnorm(n, rep(0,p), diag(p)) 290s + bad <- vector("numeric") 290s + nbad = if(eps < 1) eps*n else eps 290s + if(nbad > 0){ 290s + bad <- sample(n, nbad) 290s + x[bad,] <- x[bad,] + d 290s + } 290s + cov1 <- cov.wt(x) 290s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 290s + 290s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 290s + } 290s > 290s > pad.right <- function(z, pads) 290s + { 290s + ## Pads spaces to right of text 290s + padding <- paste(rep(" ", pads), collapse = "") 290s + paste(z, padding, sep = "") 290s + } 290s > 290s > 290s > ## -- now do it: 290s > dodata() 290s 290s Call: dodata() 290s Data Set n p c1 M LOG(det) Time 290s ====================================================================== 290s heart 12 2 7.160341 290s Outliers: 3 290s [1] 2 6 12 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s height weight 290s 34.9 27.0 290s 290s Robust Estimate of Covariance: 290s height weight 290s height 102 155 290s weight 155 250 290s -------------------------------------------------------- 290s starsCYG 47 2 -5.994588 290s Outliers: 7 290s [1] 7 9 11 14 20 30 34 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s log.Te log.light 290s 4.42 4.95 290s 290s Robust Estimate of Covariance: 290s log.Te log.light 290s log.Te 0.0169 0.0587 290s log.light 0.0587 0.3523 290s -------------------------------------------------------- 290s phosphor 18 2 8.867522 290s Outliers: 3 290s [1] 1 6 10 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s inorg organic 290s 15.4 39.1 290s 290s Robust Estimate of Covariance: 290s inorg organic 290s inorg 169 213 290s organic 213 308 290s -------------------------------------------------------- 290s stackloss 21 3 7.241400 290s Outliers: 9 290s [1] 1 2 3 15 16 17 18 19 21 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s Air.Flow Water.Temp Acid.Conc. 290s 59.5 20.8 87.3 290s 290s Robust Estimate of Covariance: 290s Air.Flow Water.Temp Acid.Conc. 290s Air.Flow 9.34 8.69 8.52 290s Water.Temp 8.69 13.72 9.13 290s Acid.Conc. 8.52 9.13 34.54 290s -------------------------------------------------------- 290s coleman 20 5 2.574752 290s Outliers: 7 290s [1] 2 6 9 10 12 13 15 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s salaryP fatherWc sstatus teacherSc motherLev 290s 2.82 48.44 5.30 25.19 6.51 290s 290s Robust Estimate of Covariance: 290s salaryP fatherWc sstatus teacherSc motherLev 290s salaryP 0.2850 0.1045 1.7585 0.3074 0.0355 290s fatherWc 0.1045 824.8305 260.7062 3.7507 17.7959 290s sstatus 1.7585 260.7062 105.6135 4.1140 5.7714 290s teacherSc 0.3074 3.7507 4.1140 0.6753 0.1563 290s motherLev 0.0355 17.7959 5.7714 0.1563 0.4147 290s -------------------------------------------------------- 290s salinity 28 3 3.875096 290s Outliers: 9 290s [1] 3 5 10 11 15 16 17 23 24 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s X1 X2 X3 290s 10.02 3.21 22.36 290s 290s Robust Estimate of Covariance: 290s X1 X2 X3 290s X1 15.353 1.990 -5.075 290s X2 1.990 5.210 -0.769 290s X3 -5.075 -0.769 2.314 290s -------------------------------------------------------- 290s wood 20 5 -35.156305 290s Outliers: 7 290s [1] 4 6 7 8 11 16 19 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s x1 x2 x3 x4 x5 290s 0.587 0.122 0.531 0.538 0.892 290s 290s Robust Estimate of Covariance: 290s x1 x2 x3 x4 x5 290s x1 6.45e-03 1.21e-03 2.03e-03 -3.77e-04 -1.05e-03 290s x2 1.21e-03 3.12e-04 8.16e-04 -3.34e-05 1.52e-05 290s x3 2.03e-03 8.16e-04 4.27e-03 -5.60e-04 2.27e-04 290s x4 -3.77e-04 -3.34e-05 -5.60e-04 1.83e-03 1.18e-03 290s x5 -1.05e-03 1.52e-05 2.27e-04 1.18e-03 1.78e-03 290s -------------------------------------------------------- 290s hbk 75 3 1.432485 290s Outliers: 14 290s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s X1 X2 X3 290s 1.54 1.78 1.69 290s 290s Robust Estimate of Covariance: 290s X1 X2 X3 290s X1 1.6485 0.0739 0.1709 290s X2 0.0739 1.6780 0.2049 290s X3 0.1709 0.2049 1.5584 290s -------------------------------------------------------- 290s Animals 28 2 18.194822 290s Outliers: 10 290s [1] 2 6 7 9 12 14 15 16 25 28 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s body brain 290s 18.7 64.9 290s 290s Robust Estimate of Covariance: 290s body brain 290s body 4993 8466 290s brain 8466 30335 290s -------------------------------------------------------- 290s milk 86 8 -25.041802 290s Outliers: 20 290s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s X1 X2 X3 X4 X5 X6 X7 X8 290s 1.03 35.88 33.04 26.11 25.09 25.02 123.12 14.39 290s 290s Robust Estimate of Covariance: 290s X1 X2 X3 X4 X5 X6 X7 290s X1 4.89e-07 9.64e-05 1.83e-04 1.76e-04 1.57e-04 1.48e-04 6.53e-04 290s X2 9.64e-05 2.05e+00 3.38e-01 2.37e-01 1.70e-01 2.71e-01 1.91e+00 290s X3 1.83e-04 3.38e-01 1.16e+00 8.56e-01 8.48e-01 8.31e-01 8.85e-01 290s X4 1.76e-04 2.37e-01 8.56e-01 6.83e-01 6.55e-01 6.40e-01 6.91e-01 290s X5 1.57e-04 1.70e-01 8.48e-01 6.55e-01 6.93e-01 6.52e-01 6.90e-01 290s X6 1.48e-04 2.71e-01 8.31e-01 6.40e-01 6.52e-01 6.61e-01 6.95e-01 290s X7 6.53e-04 1.91e+00 8.85e-01 6.91e-01 6.90e-01 6.95e-01 4.40e+00 290s X8 5.56e-06 2.60e-01 1.98e-01 1.29e-01 1.12e-01 1.19e-01 4.12e-01 290s X8 290s X1 5.56e-06 290s X2 2.60e-01 290s X3 1.98e-01 290s X4 1.29e-01 290s X5 1.12e-01 290s X6 1.19e-01 290s X7 4.12e-01 290s X8 1.65e-01 290s -------------------------------------------------------- 290s bushfire 38 5 23.457490 290s Outliers: 15 290s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 290s ------------- 290s 290s Call: 290s CovMest(x = x) 290s -> Method: M-Estimates 290s 290s Robust Estimate of Location: 290s V1 V2 V3 V4 V5 290s 107 147 263 215 277 290s 290s Robust Estimate of Covariance: 290s V1 V2 V3 V4 V5 290s V1 775 560 -4179 -925 -759 290s V2 560 478 -2494 -510 -431 290s V3 -4179 -2494 27433 6441 5196 290s V4 -925 -510 6441 1607 1276 290s V5 -759 -431 5196 1276 1020 290s -------------------------------------------------------- 290s ====================================================================== 290s > dogen() 290s > #cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 290s > 290s BEGIN TEST tmve4.R 290s 290s R version 4.4.3 (2025-02-28) -- "Trophy Case" 290s Copyright (C) 2025 The R Foundation for Statistical Computing 290s Platform: arm-unknown-linux-gnueabihf (32-bit) 290s 290s R is free software and comes with ABSOLUTELY NO WARRANTY. 290s You are welcome to redistribute it under certain conditions. 290s Type 'license()' or 'licence()' for distribution details. 290s 290s R is a collaborative project with many contributors. 290s Type 'contributors()' for more information and 290s 'citation()' on how to cite R or R packages in publications. 290s 290s Type 'demo()' for some demos, 'help()' for on-line help, or 290s 'help.start()' for an HTML browser interface to help. 290s Type 'q()' to quit R. 290s 290s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMVE","MASS")){ 290s + ##@bdescr 290s + ## Test the function covMve() on the literature datasets: 290s + ## 290s + ## Call covMve() for all regression datasets available in rrco/robustbasev and print: 290s + ## - execution time (if time == TRUE) 290s + ## - objective fucntion 290s + ## - best subsample found (if short == false) 290s + ## - outliers identified (with cutoff 0.975) (if short == false) 290s + ## - estimated center and covarinance matrix if full == TRUE) 290s + ## 290s + ##@edescr 290s + ## 290s + ##@in nrep : [integer] number of repetitions to use for estimating the 290s + ## (average) execution time 290s + ##@in time : [boolean] whether to evaluate the execution time 290s + ##@in short : [boolean] whether to do short output (i.e. only the 290s + ## objective function value). If short == FALSE, 290s + ## the best subsample and the identified outliers are 290s + ## printed. See also the parameter full below 290s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 290s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 290s + 290s + domve <- function(x, xname, nrep=1){ 290s + n <- dim(x)[1] 290s + p <- dim(x)[2] 290s + alpha <- 0.5 290s + h <- h.alpha.n(alpha, n, p) 290s + if(method == "MASS"){ 290s + mve <- cov.mve(x, quantile.used=h) 290s + quan <- h #default: floor((n+p+1)/2) 290s + crit <- mve$crit 290s + best <- mve$best 290s + mah <- mahalanobis(x, mve$center, mve$cov) 290s + quantiel <- qchisq(0.975, p) 290s + wt <- as.numeric(mah < quantiel) 290s + } 290s + else{ 290s + mve <- CovMve(x, trace=FALSE) 290s + quan <- as.integer(mve@quan) 290s + crit <- log(mve@crit) 290s + best <- mve@best 290s + wt <- mve@wt 290s + } 290s + 290s + 290s + if(time){ 290s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 290s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 290s + } 290s + else{ 290s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 290s + } 290s + 290s + lpad<-lname-nchar(xname) 290s + cat(pad.right(xname,lpad), xres) 290s + 290s + if(!short){ 290s + cat("Best subsample: \n") 290s + print(best) 290s + 290s + ibad <- which(wt == 0) 290s + names(ibad) <- NULL 290s + nbad <- length(ibad) 290s + cat("Outliers: ", nbad, "\n") 290s + if(nbad > 0) 290s + print(ibad) 290s + if(full){ 290s + cat("-------------\n") 290s + show(mve) 290s + } 290s + cat("--------------------------------------------------------\n") 290s + } 290s + } 290s + 290s + options(digits = 5) 290s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 290s + 290s + lname <- 20 290s + 290s + ## VT::15.09.2013 - this will render the output independent 290s + ## from the version of the package 290s + suppressPackageStartupMessages(library(rrcov)) 290s + 290s + method <- match.arg(method) 290s + if(method == "MASS") 290s + library(MASS) 290s + 290s + 290s + data(heart) 290s + data(starsCYG) 290s + data(phosphor) 290s + data(stackloss) 290s + data(coleman) 291s + data(salinity) 291s + data(wood) 291s + 291s + data(hbk) 291s + 291s + data(Animals, package = "MASS") 291s + brain <- Animals[c(1:24, 26:25, 27:28),] 291s + data(milk) 291s + data(bushfire) 291s + 291s + tmp <- sys.call() 291s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 291s + 291s + cat("Data Set n p Half LOG(obj) Time\n") 291s + cat("========================================================\n") 291s + domve(heart[, 1:2], data(heart), nrep) 291s + domve(starsCYG, data(starsCYG), nrep) 291s + domve(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 291s + domve(stack.x, data(stackloss), nrep) 291s + domve(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 291s + domve(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 291s + domve(data.matrix(subset(wood, select = -y)), data(wood), nrep) 291s + domve(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 291s + 291s + domve(brain, "Animals", nrep) 291s + domve(milk, data(milk), nrep) 291s + domve(bushfire, data(bushfire), nrep) 291s + cat("========================================================\n") 291s + } 291s > 291s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMVE", "MASS")){ 291s + 291s + domve <- function(x, nrep=1){ 291s + gc() 291s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 291s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 291s + xtime 291s + } 291s + 291s + set.seed(1234) 291s + 291s + ## VT::15.09.2013 - this will render the output independent 291s + ## from the version of the package 291s + suppressPackageStartupMessages(library(rrcov)) 291s + library(MASS) 291s + 291s + method <- match.arg(method) 291s + 291s + ap <- c(2, 5, 10, 20, 30) 291s + an <- c(100, 500, 1000, 10000, 50000) 291s + 291s + tottime <- 0 291s + cat(" n p Time\n") 291s + cat("=====================\n") 291s + for(i in 1:length(an)) { 291s + for(j in 1:length(ap)) { 291s + n <- an[i] 291s + p <- ap[j] 291s + if(5*p <= n){ 291s + xx <- gendata(n, p, eps) 291s + X <- xx$X 291s + tottime <- tottime + domve(X, nrep) 291s + } 291s + } 291s + } 291s + 291s + cat("=====================\n") 291s + cat("Total time: ", tottime*nrep, "\n") 291s + } 291s > 291s > docheck <- function(n, p, eps){ 291s + xx <- gendata(n,p,eps) 291s + mve <- CovMve(xx$X) 291s + check(mve, xx$xind) 291s + } 291s > 291s > check <- function(mcd, xind){ 291s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 291s + ## did not get zero weight 291s + mymatch <- xind %in% which(mcd@wt == 0) 291s + length(xind) - length(which(mymatch)) 291s + } 291s > 291s > dorep <- function(x, nrep=1, method=c("FASTMVE","MASS")){ 291s + 291s + method <- match.arg(method) 291s + for(i in 1:nrep) 291s + if(method == "MASS") 291s + cov.mve(x) 291s + else 291s + CovMve(x) 291s + } 291s > 291s > #### gendata() #### 291s > # Generates a location contaminated multivariate 291s > # normal sample of n observations in p dimensions 291s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 291s > # where 291s > # m = (b,b,...,b) 291s > # Defaults: eps=0 and b=10 291s > # 291s > gendata <- function(n,p,eps=0,b=10){ 291s + 291s + if(missing(n) || missing(p)) 291s + stop("Please specify (n,p)") 291s + if(eps < 0 || eps >= 0.5) 291s + stop(message="eps must be in [0,0.5)") 291s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 291s + nbad <- as.integer(eps * n) 291s + if(nbad > 0){ 291s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 291s + xind <- sample(n,nbad) 291s + X[xind,] <- Xbad 291s + } 291s + list(X=X, xind=xind) 291s + } 291s > 291s > pad.right <- function(z, pads) 291s + { 291s + ### Pads spaces to right of text 291s + padding <- paste(rep(" ", pads), collapse = "") 291s + paste(z, padding, sep = "") 291s + } 291s > 291s > whatis<-function(x){ 291s + if(is.data.frame(x)) 291s + cat("Type: data.frame\n") 291s + else if(is.matrix(x)) 291s + cat("Type: matrix\n") 291s + else if(is.vector(x)) 291s + cat("Type: vector\n") 291s + else 291s + cat("Type: don't know\n") 291s + } 291s > 291s > ## VT::15.09.2013 - this will render the output independent 291s > ## from the version of the package 291s > suppressPackageStartupMessages(library(rrcov)) 291s > 291s > dodata() 291s 291s Call: dodata() 291s Data Set n p Half LOG(obj) Time 291s ======================================================== 291s heart 12 2 7 3.827606 291s Best subsample: 291s [1] 1 4 7 8 9 10 11 291s Outliers: 3 291s [1] 2 6 12 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s height weight 291s 34.9 27.0 291s 291s Robust Estimate of Covariance: 291s height weight 291s height 142 217 291s weight 217 350 291s -------------------------------------------------------- 291s starsCYG 47 2 25 -2.742997 291s Best subsample: 291s [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 291s Outliers: 7 291s [1] 7 9 11 14 20 30 34 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s log.Te log.light 291s 4.41 4.93 291s 291s Robust Estimate of Covariance: 291s log.Te log.light 291s log.Te 0.0173 0.0578 291s log.light 0.0578 0.3615 291s -------------------------------------------------------- 291s phosphor 18 2 10 4.443101 291s Best subsample: 291s [1] 3 5 8 9 11 12 13 14 15 17 291s Outliers: 3 291s [1] 1 6 10 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s inorg organic 291s 15.2 39.4 291s 291s Robust Estimate of Covariance: 291s inorg organic 291s inorg 188 230 291s organic 230 339 291s -------------------------------------------------------- 291s stackloss 21 3 12 3.327582 291s Best subsample: 291s [1] 4 5 6 7 8 9 10 11 12 13 14 20 291s Outliers: 3 291s [1] 1 2 3 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s Air.Flow Water.Temp Acid.Conc. 291s 56.7 20.2 85.5 291s 291s Robust Estimate of Covariance: 291s Air.Flow Water.Temp Acid.Conc. 291s Air.Flow 34.31 11.07 23.54 291s Water.Temp 11.07 9.23 7.85 291s Acid.Conc. 23.54 7.85 47.35 291s -------------------------------------------------------- 291s coleman 20 5 13 2.065143 291s Best subsample: 291s [1] 1 3 4 5 7 8 11 14 16 17 18 19 20 291s Outliers: 5 291s [1] 2 6 9 10 13 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s salaryP fatherWc sstatus teacherSc motherLev 291s 2.79 44.26 3.59 25.08 6.38 291s 291s Robust Estimate of Covariance: 291s salaryP fatherWc sstatus teacherSc motherLev 291s salaryP 0.2920 1.1188 2.0421 0.3487 0.0748 291s fatherWc 1.1188 996.7540 338.6587 7.1673 23.1783 291s sstatus 2.0421 338.6587 148.2501 4.4894 7.8135 291s teacherSc 0.3487 7.1673 4.4894 0.9082 0.3204 291s motherLev 0.0748 23.1783 7.8135 0.3204 0.6024 291s -------------------------------------------------------- 291s salinity 28 3 16 2.002555 291s Best subsample: 291s [1] 1 7 8 9 12 13 14 18 19 20 21 22 25 26 27 28 291s Outliers: 5 291s [1] 5 11 16 23 24 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s X1 X2 X3 291s 10.2 3.1 22.4 291s 291s Robust Estimate of Covariance: 291s X1 X2 X3 291s X1 14.387 1.153 -4.072 291s X2 1.153 5.005 -0.954 291s X3 -4.072 -0.954 2.222 291s -------------------------------------------------------- 291s wood 20 5 13 -5.471407 291s Best subsample: 291s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 291s Outliers: 5 291s [1] 4 6 8 11 19 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s x1 x2 x3 x4 x5 291s 0.576 0.123 0.531 0.538 0.889 291s 291s Robust Estimate of Covariance: 291s x1 x2 x3 x4 x5 291s x1 7.45e-03 1.11e-03 1.83e-03 -2.90e-05 -5.65e-04 291s x2 1.11e-03 3.11e-04 7.68e-04 3.37e-05 3.85e-05 291s x3 1.83e-03 7.68e-04 4.30e-03 -9.96e-04 -6.27e-05 291s x4 -2.90e-05 3.37e-05 -9.96e-04 3.02e-03 1.91e-03 291s x5 -5.65e-04 3.85e-05 -6.27e-05 1.91e-03 2.25e-03 291s -------------------------------------------------------- 291s hbk 75 3 39 1.096831 291s Best subsample: 291s [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 291s [26] 55 56 58 59 64 65 66 67 70 71 72 73 74 75 291s Outliers: 14 291s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s X1 X2 X3 291s 1.48 1.86 1.73 291s 291s Robust Estimate of Covariance: 291s X1 X2 X3 291s X1 1.695 0.230 0.265 291s X2 0.230 1.679 0.119 291s X3 0.265 0.119 1.683 291s -------------------------------------------------------- 291s Animals 28 2 15 8.945423 291s Best subsample: 291s [1] 1 3 4 5 10 11 17 18 21 22 23 24 26 27 28 291s Outliers: 9 291s [1] 2 6 7 9 12 14 15 16 25 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s body brain 291s 48.3 127.3 291s 291s Robust Estimate of Covariance: 291s body brain 291s body 10767 16872 291s brain 16872 46918 291s -------------------------------------------------------- 291s milk 86 8 47 -1.160085 291s Best subsample: 291s [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 291s [26] 46 54 56 57 59 60 61 62 63 64 65 66 67 69 72 76 78 79 81 82 83 85 291s Outliers: 18 291s [1] 1 2 3 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s X1 X2 X3 X4 X5 X6 X7 X8 291s 1.03 35.91 33.02 26.08 25.06 24.99 122.93 14.38 291s 291s Robust Estimate of Covariance: 291s X1 X2 X3 X4 X5 X6 X7 291s X1 6.00e-07 1.51e-04 3.34e-04 3.09e-04 2.82e-04 2.77e-04 1.09e-03 291s X2 1.51e-04 2.03e+00 3.83e-01 3.04e-01 2.20e-01 3.51e-01 2.18e+00 291s X3 3.34e-04 3.83e-01 1.58e+00 1.21e+00 1.18e+00 1.20e+00 1.60e+00 291s X4 3.09e-04 3.04e-01 1.21e+00 9.82e-01 9.39e-01 9.53e-01 1.36e+00 291s X5 2.82e-04 2.20e-01 1.18e+00 9.39e-01 9.67e-01 9.52e-01 1.34e+00 291s X6 2.77e-04 3.51e-01 1.20e+00 9.53e-01 9.52e-01 9.92e-01 1.38e+00 291s X7 1.09e-03 2.18e+00 1.60e+00 1.36e+00 1.34e+00 1.38e+00 6.73e+00 291s X8 3.33e-05 2.92e-01 2.65e-01 1.83e-01 1.65e-01 1.76e-01 5.64e-01 291s X8 291s X1 3.33e-05 291s X2 2.92e-01 291s X3 2.65e-01 291s X4 1.83e-01 291s X5 1.65e-01 291s X6 1.76e-01 291s X7 5.64e-01 291s X8 1.80e-01 291s -------------------------------------------------------- 291s bushfire 38 5 22 5.644315 291s Best subsample: 291s [1] 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 291s Outliers: 15 291s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 291s ------------- 291s 291s Call: 291s CovMve(x = x, trace = FALSE) 291s -> Method: Minimum volume ellipsoid estimator 291s 291s Robust Estimate of Location: 291s V1 V2 V3 V4 V5 291s 107 147 263 215 277 291s 291s Robust Estimate of Covariance: 291s V1 V2 V3 V4 V5 291s V1 519 375 -2799 -619 -509 291s V2 375 320 -1671 -342 -289 291s V3 -2799 -1671 18373 4314 3480 291s V4 -619 -342 4314 1076 854 291s V5 -509 -289 3480 854 683 291s -------------------------------------------------------- 291s ======================================================== 291s > 291s BEGIN TEST togk4.R 291s 291s R version 4.4.3 (2025-02-28) -- "Trophy Case" 291s Copyright (C) 2025 The R Foundation for Statistical Computing 291s Platform: arm-unknown-linux-gnueabihf (32-bit) 291s 291s R is free software and comes with ABSOLUTELY NO WARRANTY. 291s You are welcome to redistribute it under certain conditions. 291s Type 'license()' or 'licence()' for distribution details. 291s 291s R is a collaborative project with many contributors. 291s Type 'contributors()' for more information and 291s 'citation()' on how to cite R or R packages in publications. 291s 291s Type 'demo()' for some demos, 'help()' for on-line help, or 291s 'help.start()' for an HTML browser interface to help. 291s Type 'q()' to quit R. 291s 291s > ## VT::15.09.2013 - this will render the output independent 291s > ## from the version of the package 291s > suppressPackageStartupMessages(library(rrcov)) 291s > 291s > ## VT::14.01.2020 291s > ## On some platforms minor differences are shown - use 291s > ## IGNORE_RDIFF_BEGIN 291s > ## IGNORE_RDIFF_END 291s > 291s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS")){ 291s + domcd <- function(x, xname, nrep=1){ 291s + n <- dim(x)[1] 291s + p <- dim(x)[2] 291s + 291s + mcd<-CovOgk(x) 291s + 291s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 291s + 291s + lpad<-lname-nchar(xname) 291s + cat(pad.right(xname,lpad), xres) 291s + 291s + dist <- getDistance(mcd) 291s + quantiel <- qchisq(0.975, p) 291s + ibad <- which(dist >= quantiel) 291s + names(ibad) <- NULL 291s + nbad <- length(ibad) 291s + cat("Outliers: ",nbad,"\n") 291s + if(nbad > 0) 291s + print(ibad) 291s + cat("-------------\n") 291s + show(mcd) 291s + cat("--------------------------------------------------------\n") 291s + } 291s + 291s + lname <- 20 291s + 291s + ## VT::15.09.2013 - this will render the output independent 291s + ## from the version of the package 291s + suppressPackageStartupMessages(library(rrcov)) 291s + 291s + method <- match.arg(method) 291s + 291s + data(heart) 291s + data(starsCYG) 291s + data(phosphor) 291s + data(stackloss) 291s + data(coleman) 291s + data(salinity) 291s + data(wood) 291s + 291s + data(hbk) 291s + 291s + data(Animals, package = "MASS") 291s + brain <- Animals[c(1:24, 26:25, 27:28),] 291s + data(milk) 291s + data(bushfire) 291s + 291s + tmp <- sys.call() 291s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 291s + 291s + cat("Data Set n p Half LOG(obj) Time\n") 291s + cat("========================================================\n") 291s + domcd(heart[, 1:2], data(heart), nrep) 291s + ## This will not work within the function, of course 291s + ## - comment it out 291s + ## IGNORE_RDIFF_BEGIN 291s + ## domcd(starsCYG,data(starsCYG), nrep) 291s + ## IGNORE_RDIFF_END 291s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 291s + domcd(stack.x,data(stackloss), nrep) 291s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 291s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 291s + ## IGNORE_RDIFF_BEGIN 291s + ## domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 291s + ## IGNORE_RDIFF_END 291s + domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 291s + 291s + domcd(brain, "Animals", nrep) 291s + domcd(milk, data(milk), nrep) 292s + domcd(bushfire, data(bushfire), nrep) 292s + cat("========================================================\n") 292s + } 292s > 292s > pad.right <- function(z, pads) 292s + { 292s + ### Pads spaces to right of text 292s + padding <- paste(rep(" ", pads), collapse = "") 292s + paste(z, padding, sep = "") 292s + } 292s > 292s > dodata() 292s 292s Call: dodata() 292s Data Set n p Half LOG(obj) Time 292s ======================================================== 292s heart 12 2 292s Outliers: 5 292s [1] 2 6 8 10 12 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s height weight 292s 39.76 35.71 292s 292s Robust Estimate of Covariance: 292s height weight 292s height 15.88 32.07 292s weight 32.07 78.28 292s -------------------------------------------------------- 292s phosphor 18 2 292s Outliers: 2 292s [1] 1 6 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s inorg organic 292s 13.31 40.00 292s 292s Robust Estimate of Covariance: 292s inorg organic 292s inorg 92.82 93.24 292s organic 93.24 152.62 292s -------------------------------------------------------- 292s stackloss 21 3 292s Outliers: 2 292s [1] 1 2 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s Air.Flow Water.Temp Acid.Conc. 292s 57.72 20.50 85.78 292s 292s Robust Estimate of Covariance: 292s Air.Flow Water.Temp Acid.Conc. 292s Air.Flow 38.423 11.306 18.605 292s Water.Temp 11.306 6.806 5.889 292s Acid.Conc. 18.605 5.889 29.840 292s -------------------------------------------------------- 292s coleman 20 5 292s Outliers: 3 292s [1] 1 6 10 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s salaryP fatherWc sstatus teacherSc motherLev 292s 2.723 43.202 2.912 25.010 6.290 292s 292s Robust Estimate of Covariance: 292s salaryP fatherWc sstatus teacherSc motherLev 292s salaryP 0.12867 2.80048 0.92026 0.15118 0.06413 292s fatherWc 2.80048 678.72549 227.36415 9.30826 16.15102 292s sstatus 0.92026 227.36415 101.39094 3.38013 5.63283 292s teacherSc 0.15118 9.30826 3.38013 0.57112 0.27701 292s motherLev 0.06413 16.15102 5.63283 0.27701 0.44801 292s -------------------------------------------------------- 292s salinity 28 3 292s Outliers: 3 292s [1] 3 5 16 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s X1 X2 X3 292s 10.74 2.68 22.99 292s 292s Robust Estimate of Covariance: 292s X1 X2 X3 292s X1 8.1047 -0.6365 -0.4720 292s X2 -0.6365 3.0976 -1.3520 292s X3 -0.4720 -1.3520 2.3648 292s -------------------------------------------------------- 292s hbk 75 3 292s Outliers: 14 292s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s X1 X2 X3 292s 1.538 1.780 1.687 292s 292s Robust Estimate of Covariance: 292s X1 X2 X3 292s X1 1.11350 0.04992 0.11541 292s X2 0.04992 1.13338 0.13843 292s X3 0.11541 0.13843 1.05261 292s -------------------------------------------------------- 292s Animals 28 2 292s Outliers: 12 292s [1] 2 6 7 9 12 14 15 16 17 24 25 28 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s body brain 292s 39.65 105.83 292s 292s Robust Estimate of Covariance: 292s body brain 292s body 3981 7558 292s brain 7558 16594 292s -------------------------------------------------------- 292s milk 86 8 292s Outliers: 22 292s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 50 70 74 75 77 85 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s X1 X2 X3 X4 X5 X6 X7 X8 292s 1.03 35.80 33.10 26.15 25.13 25.06 123.06 14.39 292s 292s Robust Estimate of Covariance: 292s X1 X2 X3 X4 X5 X6 X7 292s X1 4.074e-07 5.255e-05 1.564e-04 1.506e-04 1.340e-04 1.234e-04 5.308e-04 292s X2 5.255e-05 1.464e+00 3.425e-01 2.465e-01 1.847e-01 2.484e-01 1.459e+00 292s X3 1.564e-04 3.425e-01 1.070e+00 7.834e-01 7.665e-01 7.808e-01 7.632e-01 292s X4 1.506e-04 2.465e-01 7.834e-01 6.178e-01 5.868e-01 5.959e-01 5.923e-01 292s X5 1.340e-04 1.847e-01 7.665e-01 5.868e-01 6.124e-01 5.967e-01 5.868e-01 292s X6 1.234e-04 2.484e-01 7.808e-01 5.959e-01 5.967e-01 6.253e-01 5.819e-01 292s X7 5.308e-04 1.459e+00 7.632e-01 5.923e-01 5.868e-01 5.819e-01 3.535e+00 292s X8 1.990e-07 1.851e-01 1.861e-01 1.210e-01 1.041e-01 1.116e-01 3.046e-01 292s X8 292s X1 1.990e-07 292s X2 1.851e-01 292s X3 1.861e-01 292s X4 1.210e-01 292s X5 1.041e-01 292s X6 1.116e-01 292s X7 3.046e-01 292s X8 1.292e-01 292s -------------------------------------------------------- 292s bushfire 38 5 292s Outliers: 17 292s [1] 7 8 9 10 11 12 28 29 30 31 32 33 34 35 36 37 38 292s ------------- 292s 292s Call: 292s CovOgk(x = x) 292s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 292s 292s Robust Estimate of Location: 292s V1 V2 V3 V4 V5 292s 104.5 146.0 275.6 217.8 279.3 292s 292s Robust Estimate of Covariance: 292s V1 V2 V3 V4 V5 292s V1 266.8 203.2 -1380.7 -311.1 -252.2 292s V2 203.2 178.4 -910.9 -185.9 -155.9 292s V3 -1380.7 -910.9 8279.7 2035.5 1615.4 292s V4 -311.1 -185.9 2035.5 536.5 418.6 292s V5 -252.2 -155.9 1615.4 418.6 329.2 292s -------------------------------------------------------- 292s ======================================================== 292s > 292s BEGIN TEST tqda.R 292s 292s R version 4.4.3 (2025-02-28) -- "Trophy Case" 292s Copyright (C) 2025 The R Foundation for Statistical Computing 292s Platform: arm-unknown-linux-gnueabihf (32-bit) 292s 292s R is free software and comes with ABSOLUTELY NO WARRANTY. 292s You are welcome to redistribute it under certain conditions. 292s Type 'license()' or 'licence()' for distribution details. 292s 292s R is a collaborative project with many contributors. 292s Type 'contributors()' for more information and 292s 'citation()' on how to cite R or R packages in publications. 292s 292s Type 'demo()' for some demos, 'help()' for on-line help, or 292s 'help.start()' for an HTML browser interface to help. 292s Type 'q()' to quit R. 292s 292s > ## VT::15.09.2013 - this will render the output independent 292s > ## from the version of the package 292s > suppressPackageStartupMessages(library(rrcov)) 292s > 292s > dodata <- function(method) { 292s + 292s + options(digits = 5) 292s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 292s + 292s + tmp <- sys.call() 292s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 292s + cat("===================================================\n") 292s + 292s + data(hemophilia); show(QdaCov(as.factor(gr)~., data=hemophilia, method=method)) 292s + data(anorexia, package="MASS"); show(QdaCov(Treat~., data=anorexia, method=method)) 292s + data(Pima.tr, package="MASS"); show(QdaCov(type~., data=Pima.tr, method=method)) 292s + data(iris); # show(QdaCov(Species~., data=iris, method=method)) 292s + data(crabs, package="MASS"); # show(QdaCov(sp~., data=crabs, method=method)) 292s + 292s + show(QdaClassic(as.factor(gr)~., data=hemophilia)) 292s + show(QdaClassic(Treat~., data=anorexia)) 292s + show(QdaClassic(type~., data=Pima.tr)) 292s + show(QdaClassic(Species~., data=iris)) 292s + ## show(QdaClassic(sp~., data=crabs)) 292s + cat("===================================================\n") 292s + } 292s > 292s > 292s > ## -- now do it: 292s > dodata(method="mcd") 292s 292s Call: dodata(method = "mcd") 292s =================================================== 292s Call: 292s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 292s 292s Prior Probabilities of Groups: 292s carrier normal 292s 0.6 0.4 292s 292s Group means: 292s AHFactivity AHFantigen 292s carrier -0.30795 -0.0059911 292s normal -0.12920 -0.0603000 292s 292s Group: carrier 292s AHFactivity AHFantigen 292s AHFactivity 0.023784 0.015376 292s AHFantigen 0.015376 0.024035 292s 292s Group: normal 292s AHFactivity AHFantigen 292s AHFactivity 0.0057546 0.0042606 292s AHFantigen 0.0042606 0.0084914 292s Call: 292s QdaCov(Treat ~ ., data = anorexia, method = method) 292s 292s Prior Probabilities of Groups: 292s CBT Cont FT 292s 0.40278 0.36111 0.23611 292s 292s Group means: 292s Prewt Postwt 292s CBT 82.633 82.950 292s Cont 81.558 81.108 292s FT 84.331 94.762 292s 292s Group: CBT 292s Prewt Postwt 292s Prewt 9.8671 8.6611 292s Postwt 8.6611 11.8966 292s 292s Group: Cont 292s Prewt Postwt 292s Prewt 32.5705 -4.3705 292s Postwt -4.3705 22.5079 292s 292s Group: FT 292s Prewt Postwt 292s Prewt 33.056 10.814 292s Postwt 10.814 14.265 292s Call: 292s QdaCov(type ~ ., data = Pima.tr, method = method) 292s 292s Prior Probabilities of Groups: 292s No Yes 292s 0.66 0.34 292s 292s Group means: 292s npreg glu bp skin bmi ped age 292s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 292s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 292s 292s Group: No 292s npreg glu bp skin bmi ped age 292s npreg 2.221983 -0.18658 1.86507 -0.44427 0.1725348 -0.0683616 2.63439 292s glu -0.186582 471.88789 45.28021 8.95404 30.6551510 -0.6359899 3.50218 292s bp 1.865066 45.28021 110.09787 26.11192 14.4739180 -0.2104074 13.23392 292s skin -0.444272 8.95404 26.11192 118.30521 52.3115719 -0.2995751 8.65861 292s bmi 0.172535 30.65515 14.47392 52.31157 43.3140415 0.0079866 6.75720 292s ped -0.068362 -0.63599 -0.21041 -0.29958 0.0079866 0.0587710 -0.18683 292s age 2.634387 3.50218 13.23392 8.65861 6.7572019 -0.1868284 12.09493 292s 292s Group: Yes 292s npreg glu bp skin bmi ped age 292s npreg 17.875215 -13.740021 9.03580 4.498580 1.787458 0.079504 26.92283 292s glu -13.740021 917.719003 55.30399 27.976265 10.755113 0.092673 38.94970 292s bp 9.035798 55.303991 129.97953 34.130200 10.104275 0.198342 32.95351 292s skin 4.498580 27.976265 34.13020 101.842647 30.297210 0.064739 3.59427 292s bmi 1.787458 10.755113 10.10428 30.297210 22.529467 0.084369 -6.64317 292s ped 0.079504 0.092673 0.19834 0.064739 0.084369 0.066667 0.11199 292s age 26.922828 38.949697 32.95351 3.594266 -6.643165 0.111992 143.69752 292s Call: 292s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 292s 292s Prior Probabilities of Groups: 292s carrier normal 292s 0.6 0.4 292s 292s Group means: 292s AHFactivity AHFantigen 292s carrier -0.30795 -0.0059911 292s normal -0.13487 -0.0778567 292s 292s Group: carrier 292s AHFactivity AHFantigen 292s AHFactivity 0.023784 0.015376 292s AHFantigen 0.015376 0.024035 292s 292s Group: normal 292s AHFactivity AHFantigen 292s AHFactivity 0.020897 0.015515 292s AHFantigen 0.015515 0.017920 292s Call: 292s QdaClassic(Treat ~ ., data = anorexia) 292s 292s Prior Probabilities of Groups: 292s CBT Cont FT 292s 0.40278 0.36111 0.23611 292s 292s Group means: 292s Prewt Postwt 292s CBT 82.690 85.697 292s Cont 81.558 81.108 292s FT 83.229 90.494 292s 292s Group: CBT 292s Prewt Postwt 292s Prewt 23.479 19.910 292s Postwt 19.910 69.755 292s 292s Group: Cont 292s Prewt Postwt 292s Prewt 32.5705 -4.3705 292s Postwt -4.3705 22.5079 292s 292s Group: FT 292s Prewt Postwt 292s Prewt 25.167 22.883 292s Postwt 22.883 71.827 292s Call: 292s QdaClassic(type ~ ., data = Pima.tr) 292s 292s Prior Probabilities of Groups: 292s No Yes 292s 0.66 0.34 292s 292s Group means: 292s npreg glu bp skin bmi ped age 292s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 292s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 292s 292s Group: No 292s npreg glu bp skin bmi ped age 292s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 292s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 292s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 292s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 292s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 292s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 292s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 292s 292s Group: Yes 292s npreg glu bp skin bmi ped age 292s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 292s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 292s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 292s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 292s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 292s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 292s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 292s Call: 292s QdaClassic(Species ~ ., data = iris) 292s 292s Prior Probabilities of Groups: 292s setosa versicolor virginica 292s 0.33333 0.33333 0.33333 292s 292s Group means: 292s Sepal.Length Sepal.Width Petal.Length Petal.Width 292s setosa 5.006 3.428 1.462 0.246 292s versicolor 5.936 2.770 4.260 1.326 292s virginica 6.588 2.974 5.552 2.026 292s 292s Group: setosa 292s Sepal.Length Sepal.Width Petal.Length Petal.Width 292s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 292s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 292s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 292s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 292s 292s Group: versicolor 292s Sepal.Length Sepal.Width Petal.Length Petal.Width 292s Sepal.Length 0.266433 0.085184 0.182898 0.055780 292s Sepal.Width 0.085184 0.098469 0.082653 0.041204 292s Petal.Length 0.182898 0.082653 0.220816 0.073102 292s Petal.Width 0.055780 0.041204 0.073102 0.039106 292s 292s Group: virginica 292s Sepal.Length Sepal.Width Petal.Length Petal.Width 292s Sepal.Length 0.404343 0.093763 0.303290 0.049094 292s Sepal.Width 0.093763 0.104004 0.071380 0.047629 292s Petal.Length 0.303290 0.071380 0.304588 0.048824 292s Petal.Width 0.049094 0.047629 0.048824 0.075433 292s =================================================== 292s > dodata(method="m") 292s 292s Call: dodata(method = "m") 292s =================================================== 292s Call: 292s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 292s 292s Prior Probabilities of Groups: 292s carrier normal 292s 0.6 0.4 292s 292s Group means: 292s AHFactivity AHFantigen 292s carrier -0.29810 -0.0028222 292s normal -0.13081 -0.0675283 292s 292s Group: carrier 292s AHFactivity AHFantigen 292s AHFactivity 0.026018 0.017653 292s AHFantigen 0.017653 0.030128 292s 292s Group: normal 292s AHFactivity AHFantigen 292s AHFactivity 0.0081933 0.0065737 292s AHFantigen 0.0065737 0.0118565 293s Call: 293s QdaCov(Treat ~ ., data = anorexia, method = method) 293s 293s Prior Probabilities of Groups: 293s CBT Cont FT 293s 0.40278 0.36111 0.23611 293s 293s Group means: 293s Prewt Postwt 293s CBT 82.436 82.631 293s Cont 81.559 80.272 293s FT 85.120 94.657 293s 293s Group: CBT 293s Prewt Postwt 293s Prewt 23.630 25.128 293s Postwt 25.128 38.142 293s 293s Group: Cont 293s Prewt Postwt 293s Prewt 35.8824 -8.2405 293s Postwt -8.2405 23.7416 293s 293s Group: FT 293s Prewt Postwt 293s Prewt 33.805 18.206 293s Postwt 18.206 24.639 293s Call: 293s QdaCov(type ~ ., data = Pima.tr, method = method) 293s 293s Prior Probabilities of Groups: 293s No Yes 293s 0.66 0.34 293s 293s Group means: 293s npreg glu bp skin bmi ped age 293s No 2.5225 111.26 68.081 26.640 30.801 0.40452 26.306 293s Yes 5.0702 144.32 75.088 31.982 34.267 0.47004 37.140 293s 293s Group: No 293s npreg glu bp skin bmi ped age 293s npreg 5.74219 14.47051 6.63766 4.98559 0.826570 -0.128106 10.71303 293s glu 14.47051 591.08717 68.81219 44.73311 40.658393 -0.545716 38.01918 293s bp 6.63766 68.81219 121.02716 30.46466 16.789801 -0.320065 25.29371 293s skin 4.98559 44.73311 30.46466 136.52176 56.604475 -0.250711 19.73259 293s bmi 0.82657 40.65839 16.78980 56.60447 47.859747 0.046358 6.94523 293s ped -0.12811 -0.54572 -0.32006 -0.25071 0.046358 0.061485 -0.34653 293s age 10.71303 38.01918 25.29371 19.73259 6.945227 -0.346527 35.66101 293s 293s Group: Yes 293s npreg glu bp skin bmi ped age 293s npreg 15.98861 -1.2430 10.48556 9.05947 2.425316 0.162453 30.149872 293s glu -1.24304 867.1076 46.43838 25.92297 5.517382 1.044360 31.152650 293s bp 10.48556 46.4384 130.12536 17.21407 6.343942 -0.235121 41.091494 293s skin 9.05947 25.9230 17.21407 85.96314 26.089017 0.170061 14.562516 293s bmi 2.42532 5.5174 6.34394 26.08902 22.051976 0.097786 -5.297971 293s ped 0.16245 1.0444 -0.23512 0.17006 0.097786 0.057112 0.055286 293s age 30.14987 31.1527 41.09149 14.56252 -5.297971 0.055286 137.440921 293s Call: 293s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 293s 293s Prior Probabilities of Groups: 293s carrier normal 293s 0.6 0.4 293s 293s Group means: 293s AHFactivity AHFantigen 293s carrier -0.30795 -0.0059911 293s normal -0.13487 -0.0778567 293s 293s Group: carrier 293s AHFactivity AHFantigen 293s AHFactivity 0.023784 0.015376 293s AHFantigen 0.015376 0.024035 293s 293s Group: normal 293s AHFactivity AHFantigen 293s AHFactivity 0.020897 0.015515 293s AHFantigen 0.015515 0.017920 293s Call: 293s QdaClassic(Treat ~ ., data = anorexia) 293s 293s Prior Probabilities of Groups: 293s CBT Cont FT 293s 0.40278 0.36111 0.23611 293s 293s Group means: 293s Prewt Postwt 293s CBT 82.690 85.697 293s Cont 81.558 81.108 293s FT 83.229 90.494 293s 293s Group: CBT 293s Prewt Postwt 293s Prewt 23.479 19.910 293s Postwt 19.910 69.755 293s 293s Group: Cont 293s Prewt Postwt 293s Prewt 32.5705 -4.3705 293s Postwt -4.3705 22.5079 293s 293s Group: FT 293s Prewt Postwt 293s Prewt 25.167 22.883 293s Postwt 22.883 71.827 293s Call: 293s QdaClassic(type ~ ., data = Pima.tr) 293s 293s Prior Probabilities of Groups: 293s No Yes 293s 0.66 0.34 293s 293s Group means: 293s npreg glu bp skin bmi ped age 293s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 293s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 293s 293s Group: No 293s npreg glu bp skin bmi ped age 293s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 293s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 293s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 293s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 293s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 293s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 293s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 293s 293s Group: Yes 293s npreg glu bp skin bmi ped age 293s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 293s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 293s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 293s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 293s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 293s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 293s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 293s Call: 293s QdaClassic(Species ~ ., data = iris) 293s 293s Prior Probabilities of Groups: 293s setosa versicolor virginica 293s 0.33333 0.33333 0.33333 293s 293s Group means: 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s setosa 5.006 3.428 1.462 0.246 293s versicolor 5.936 2.770 4.260 1.326 293s virginica 6.588 2.974 5.552 2.026 293s 293s Group: setosa 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 293s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 293s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 293s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 293s 293s Group: versicolor 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.266433 0.085184 0.182898 0.055780 293s Sepal.Width 0.085184 0.098469 0.082653 0.041204 293s Petal.Length 0.182898 0.082653 0.220816 0.073102 293s Petal.Width 0.055780 0.041204 0.073102 0.039106 293s 293s Group: virginica 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.404343 0.093763 0.303290 0.049094 293s Sepal.Width 0.093763 0.104004 0.071380 0.047629 293s Petal.Length 0.303290 0.071380 0.304588 0.048824 293s Petal.Width 0.049094 0.047629 0.048824 0.075433 293s =================================================== 293s > dodata(method="ogk") 293s 293s Call: dodata(method = "ogk") 293s =================================================== 293s Call: 293s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 293s 293s Prior Probabilities of Groups: 293s carrier normal 293s 0.6 0.4 293s 293s Group means: 293s AHFactivity AHFantigen 293s carrier -0.29324 0.00033953 293s normal -0.12744 -0.06628182 293s 293s Group: carrier 293s AHFactivity AHFantigen 293s AHFactivity 0.019260 0.013026 293s AHFantigen 0.013026 0.021889 293s 293s Group: normal 293s AHFactivity AHFantigen 293s AHFactivity 0.0049651 0.0039707 293s AHFantigen 0.0039707 0.0066084 293s Call: 293s QdaCov(Treat ~ ., data = anorexia, method = method) 293s 293s Prior Probabilities of Groups: 293s CBT Cont FT 293s 0.40278 0.36111 0.23611 293s 293s Group means: 293s Prewt Postwt 293s CBT 82.587 82.709 293s Cont 81.558 81.108 293s FT 85.110 94.470 293s 293s Group: CBT 293s Prewt Postwt 293s Prewt 10.452 15.115 293s Postwt 15.115 37.085 293s 293s Group: Cont 293s Prewt Postwt 293s Prewt 31.3178 -4.2024 293s Postwt -4.2024 21.6422 293s 293s Group: FT 293s Prewt Postwt 293s Prewt 5.5309 1.4813 293s Postwt 1.4813 7.5501 293s Call: 293s QdaCov(type ~ ., data = Pima.tr, method = method) 293s 293s Prior Probabilities of Groups: 293s No Yes 293s 0.66 0.34 293s 293s Group means: 293s npreg glu bp skin bmi ped age 293s No 2.4286 110.35 67.495 25.905 30.275 0.39587 26.248 293s Yes 5.1964 142.71 75.357 32.732 34.809 0.48823 37.607 293s 293s Group: No 293s npreg glu bp skin bmi ped age 293s npreg 3.97823 8.70612 4.58776 4.16463 0.250612 -0.117238 8.21769 293s glu 8.70612 448.91392 51.71120 38.66213 21.816345 -0.296524 24.29370 293s bp 4.58776 51.71120 99.41188 24.27574 10.491311 -0.290753 20.02975 293s skin 4.16463 38.66213 24.27574 98.61950 41.682404 -0.335213 16.60454 293s bmi 0.25061 21.81634 10.49131 41.68240 35.237101 -0.019774 5.12042 293s ped -0.11724 -0.29652 -0.29075 -0.33521 -0.019774 0.051431 -0.36275 293s age 8.21769 24.29370 20.02975 16.60454 5.120417 -0.362748 31.32916 293s 293s Group: Yes 293s npreg glu bp skin bmi ped age 293s npreg 15.26499 6.30612 3.01913 3.76690 0.94825 0.12076 22.64860 293s glu 6.30612 688.16837 22.22704 12.81633 3.55791 0.68833 32.28061 293s bp 3.01913 22.22704 103.97959 9.95281 2.09860 0.45672 31.17602 293s skin 3.76690 12.81633 9.95281 67.51754 19.51489 0.59831 -2.35523 293s bmi 0.94825 3.55791 2.09860 19.51489 17.20331 0.24347 -6.88221 293s ped 0.12076 0.68833 0.45672 0.59831 0.24347 0.05933 0.43798 293s age 22.64860 32.28061 31.17602 -2.35523 -6.88221 0.43798 111.16709 293s Call: 293s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 293s 293s Prior Probabilities of Groups: 293s carrier normal 293s 0.6 0.4 293s 293s Group means: 293s AHFactivity AHFantigen 293s carrier -0.30795 -0.0059911 293s normal -0.13487 -0.0778567 293s 293s Group: carrier 293s AHFactivity AHFantigen 293s AHFactivity 0.023784 0.015376 293s AHFantigen 0.015376 0.024035 293s 293s Group: normal 293s AHFactivity AHFantigen 293s AHFactivity 0.020897 0.015515 293s AHFantigen 0.015515 0.017920 293s Call: 293s QdaClassic(Treat ~ ., data = anorexia) 293s 293s Prior Probabilities of Groups: 293s CBT Cont FT 293s 0.40278 0.36111 0.23611 293s 293s Group means: 293s Prewt Postwt 293s CBT 82.690 85.697 293s Cont 81.558 81.108 293s FT 83.229 90.494 293s 293s Group: CBT 293s Prewt Postwt 293s Prewt 23.479 19.910 293s Postwt 19.910 69.755 293s 293s Group: Cont 293s Prewt Postwt 293s Prewt 32.5705 -4.3705 293s Postwt -4.3705 22.5079 293s 293s Group: FT 293s Prewt Postwt 293s Prewt 25.167 22.883 293s Postwt 22.883 71.827 293s Call: 293s QdaClassic(type ~ ., data = Pima.tr) 293s 293s Prior Probabilities of Groups: 293s No Yes 293s 0.66 0.34 293s 293s Group means: 293s npreg glu bp skin bmi ped age 293s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 293s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 293s 293s Group: No 293s npreg glu bp skin bmi ped age 293s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 293s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 293s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 293s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 293s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 293s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 293s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 293s 293s Group: Yes 293s npreg glu bp skin bmi ped age 293s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 293s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 293s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 293s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 293s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 293s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 293s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 293s Call: 293s QdaClassic(Species ~ ., data = iris) 293s 293s Prior Probabilities of Groups: 293s setosa versicolor virginica 293s 0.33333 0.33333 0.33333 293s 293s Group means: 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s setosa 5.006 3.428 1.462 0.246 293s versicolor 5.936 2.770 4.260 1.326 293s virginica 6.588 2.974 5.552 2.026 293s 293s Group: setosa 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 293s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 293s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 293s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 293s 293s Group: versicolor 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.266433 0.085184 0.182898 0.055780 293s Sepal.Width 0.085184 0.098469 0.082653 0.041204 293s Petal.Length 0.182898 0.082653 0.220816 0.073102 293s Petal.Width 0.055780 0.041204 0.073102 0.039106 293s 293s Group: virginica 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.404343 0.093763 0.303290 0.049094 293s Sepal.Width 0.093763 0.104004 0.071380 0.047629 293s Petal.Length 0.303290 0.071380 0.304588 0.048824 293s Petal.Width 0.049094 0.047629 0.048824 0.075433 293s =================================================== 293s > dodata(method="sde") 293s 293s Call: dodata(method = "sde") 293s =================================================== 293s Call: 293s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 293s 293s Prior Probabilities of Groups: 293s carrier normal 293s 0.6 0.4 293s 293s Group means: 293s AHFactivity AHFantigen 293s carrier -0.29834 -0.0032286 293s normal -0.12944 -0.0676930 293s 293s Group: carrier 293s AHFactivity AHFantigen 293s AHFactivity 0.025398 0.017810 293s AHFantigen 0.017810 0.030639 293s 293s Group: normal 293s AHFactivity AHFantigen 293s AHFactivity 0.0083435 0.0067686 293s AHFantigen 0.0067686 0.0119565 293s Call: 293s QdaCov(Treat ~ ., data = anorexia, method = method) 293s 293s Prior Probabilities of Groups: 293s CBT Cont FT 293s 0.40278 0.36111 0.23611 293s 293s Group means: 293s Prewt Postwt 293s CBT 82.949 83.323 293s Cont 81.484 80.840 293s FT 84.596 93.835 293s 293s Group: CBT 293s Prewt Postwt 293s Prewt 22.283 17.084 293s Postwt 17.084 25.308 293s 293s Group: Cont 293s Prewt Postwt 293s Prewt 37.1864 -8.8896 293s Postwt -8.8896 31.1930 293s 293s Group: FT 293s Prewt Postwt 293s Prewt 20.7108 3.1531 293s Postwt 3.1531 25.7046 293s Call: 293s QdaCov(type ~ ., data = Pima.tr, method = method) 293s 293s Prior Probabilities of Groups: 293s No Yes 293s 0.66 0.34 293s 293s Group means: 293s npreg glu bp skin bmi ped age 293s No 2.2567 109.91 67.538 25.484 30.355 0.38618 25.628 293s Yes 5.2216 141.64 75.048 32.349 34.387 0.47742 37.634 293s 293s Group: No 293s npreg glu bp skin bmi ped age 293s npreg 4.396832 10.20629 5.43346 4.38313 7.9891e-01 -0.09389257 7.45638 293s glu 10.206286 601.12211 56.62047 49.67071 3.3829e+01 -0.31896741 31.64508 293s bp 5.433462 56.62047 120.38052 34.38984 1.4817e+01 -0.21784446 26.44853 293s skin 4.383134 49.67071 34.38984 136.94931 6.1576e+01 -0.47532490 17.74141 293s bmi 0.798908 33.82928 14.81668 61.57578 5.1441e+01 0.00061983 8.56856 293s ped -0.093893 -0.31897 -0.21784 -0.47532 6.1983e-04 0.06012655 -0.26872 293s age 7.456376 31.64508 26.44853 17.74141 8.5686e+00 -0.26872005 29.93856 293s 293s Group: Yes 293s npreg glu bp skin bmi ped age 293s npreg 15.91978 7.7491 7.24229 10.46802 5.40627 0.320434 25.88314 293s glu 7.74907 856.4955 58.59554 29.65331 11.44745 1.388745 38.24430 293s bp 7.24229 58.5955 89.66288 21.36597 6.46859 0.764486 36.30462 293s skin 10.46802 29.6533 21.36597 86.79253 26.22071 0.620654 5.28665 293s bmi 5.40627 11.4475 6.46859 26.22071 20.12351 0.211701 0.71583 293s ped 0.32043 1.3887 0.76449 0.62065 0.21170 0.062727 0.93743 293s age 25.88314 38.2443 36.30462 5.28665 0.71583 0.937430 136.24335 293s Call: 293s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 293s 293s Prior Probabilities of Groups: 293s carrier normal 293s 0.6 0.4 293s 293s Group means: 293s AHFactivity AHFantigen 293s carrier -0.30795 -0.0059911 293s normal -0.13487 -0.0778567 293s 293s Group: carrier 293s AHFactivity AHFantigen 293s AHFactivity 0.023784 0.015376 293s AHFantigen 0.015376 0.024035 293s 293s Group: normal 293s AHFactivity AHFantigen 293s AHFactivity 0.020897 0.015515 293s AHFantigen 0.015515 0.017920 293s Call: 293s QdaClassic(Treat ~ ., data = anorexia) 293s 293s Prior Probabilities of Groups: 293s CBT Cont FT 293s 0.40278 0.36111 0.23611 293s 293s Group means: 293s Prewt Postwt 293s CBT 82.690 85.697 293s Cont 81.558 81.108 293s FT 83.229 90.494 293s 293s Group: CBT 293s Prewt Postwt 293s Prewt 23.479 19.910 293s Postwt 19.910 69.755 293s 293s Group: Cont 293s Prewt Postwt 293s Prewt 32.5705 -4.3705 293s Postwt -4.3705 22.5079 293s 293s Group: FT 293s Prewt Postwt 293s Prewt 25.167 22.883 293s Postwt 22.883 71.827 293s Call: 293s QdaClassic(type ~ ., data = Pima.tr) 293s 293s Prior Probabilities of Groups: 293s No Yes 293s 0.66 0.34 293s 293s Group means: 293s npreg glu bp skin bmi ped age 293s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 293s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 293s 293s Group: No 293s npreg glu bp skin bmi ped age 293s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 293s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 293s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 293s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 293s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 293s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 293s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 293s 293s Group: Yes 293s npreg glu bp skin bmi ped age 293s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 293s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 293s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 293s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 293s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 293s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 293s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 293s Call: 293s QdaClassic(Species ~ ., data = iris) 293s 293s Prior Probabilities of Groups: 293s setosa versicolor virginica 293s 0.33333 0.33333 0.33333 293s 293s Group means: 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s setosa 5.006 3.428 1.462 0.246 293s versicolor 5.936 2.770 4.260 1.326 293s virginica 6.588 2.974 5.552 2.026 293s 293s Group: setosa 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 293s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 293s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 293s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 293s 293s Group: versicolor 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.266433 0.085184 0.182898 0.055780 293s Sepal.Width 0.085184 0.098469 0.082653 0.041204 293s Petal.Length 0.182898 0.082653 0.220816 0.073102 293s Petal.Width 0.055780 0.041204 0.073102 0.039106 293s 293s Group: virginica 293s Sepal.Length Sepal.Width Petal.Length Petal.Width 293s Sepal.Length 0.404343 0.093763 0.303290 0.049094 293s Sepal.Width 0.093763 0.104004 0.071380 0.047629 293s Petal.Length 0.303290 0.071380 0.304588 0.048824 293s Petal.Width 0.049094 0.047629 0.048824 0.075433 293s =================================================== 293s > 293s BEGIN TEST tsde.R 293s 293s R version 4.4.3 (2025-02-28) -- "Trophy Case" 293s Copyright (C) 2025 The R Foundation for Statistical Computing 293s Platform: arm-unknown-linux-gnueabihf (32-bit) 293s 293s R is free software and comes with ABSOLUTELY NO WARRANTY. 293s You are welcome to redistribute it under certain conditions. 293s Type 'license()' or 'licence()' for distribution details. 293s 293s R is a collaborative project with many contributors. 293s Type 'contributors()' for more information and 293s 'citation()' on how to cite R or R packages in publications. 293s 293s Type 'demo()' for some demos, 'help()' for on-line help, or 293s 'help.start()' for an HTML browser interface to help. 293s Type 'q()' to quit R. 293s 293s > ## Test for singularity 293s > doexact <- function(){ 293s + exact <-function(){ 293s + n1 <- 45 293s + p <- 2 293s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 293s + x1[,p] <- x1[,p] + 3 293s + ## library(MASS) 293s + ## x1 <- mvrnorm(n=n1, mu=c(0,3), Sigma=diag(1,nrow=p)) 293s + 293s + n2 <- 55 293s + m1 <- 0 293s + m2 <- 3 293s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 293s + x<-rbind(x1,x2) 293s + colnames(x) <- c("X1","X2") 293s + x 293s + } 293s + print(CovSde(exact())) 293s + } 293s > 293s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE){ 293s + 293s + domcd <- function(x, xname, nrep=1){ 293s + n <- dim(x)[1] 293s + p <- dim(x)[2] 293s + 293s + mcd<-CovSde(x) 293s + 293s + if(time){ 293s + xtime <- system.time(dorep(x, nrep))[1]/nrep 293s + xres <- sprintf("%3d %3d %3d\n", dim(x)[1], dim(x)[2], xtime) 293s + } 293s + else{ 293s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 293s + } 293s + lpad<-lname-nchar(xname) 293s + cat(pad.right(xname,lpad), xres) 293s + 293s + if(!short){ 293s + 293s + ibad <- which(mcd@wt==0) 293s + names(ibad) <- NULL 293s + nbad <- length(ibad) 293s + cat("Outliers: ",nbad,"\n") 293s + if(nbad > 0) 293s + print(ibad) 293s + if(full){ 293s + cat("-------------\n") 293s + show(mcd) 293s + } 293s + cat("--------------------------------------------------------\n") 293s + } 293s + } 293s + 293s + options(digits = 5) 293s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 293s + 293s + lname <- 20 293s + 293s + ## VT::15.09.2013 - this will render the output independent 293s + ## from the version of the package 293s + suppressPackageStartupMessages(library(rrcov)) 293s + 293s + data(heart) 293s + data(starsCYG) 293s + data(phosphor) 293s + data(stackloss) 293s + data(coleman) 293s + data(salinity) 293s + data(wood) 293s + 293s + data(hbk) 293s + 293s + data(Animals, package = "MASS") 293s + brain <- Animals[c(1:24, 26:25, 27:28),] 293s + data(milk) 293s + data(bushfire) 293s + 293s + tmp <- sys.call() 293s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 293s + 293s + cat("Data Set n p Half LOG(obj) Time\n") 293s + cat("========================================================\n") 293s + domcd(heart[, 1:2], data(heart), nrep) 293s + domcd(starsCYG, data(starsCYG), nrep) 293s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 293s + domcd(stack.x, data(stackloss), nrep) 293s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 293s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 293s + domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 293s + domcd(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 293s + 293s + domcd(brain, "Animals", nrep) 293s + domcd(milk, data(milk), nrep) 293s + domcd(bushfire, data(bushfire), nrep) 293s + ## VT::19.07.2010: test the univariate SDE 293s + for(i in 1:ncol(bushfire)) 293s + domcd(bushfire[i], data(bushfire), nrep) 293s + cat("========================================================\n") 293s + } 293s > 293s > dogen <- function(nrep=1, eps=0.49){ 293s + 293s + library(MASS) 293s + domcd <- function(x, nrep=1){ 293s + gc() 293s + xtime <- system.time(dorep(x, nrep))[1]/nrep 293s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 293s + xtime 293s + } 293s + 293s + set.seed(1234) 293s + 293s + ## VT::15.09.2013 - this will render the output independent 293s + ## from the version of the package 293s + suppressPackageStartupMessages(library(rrcov)) 293s + 293s + ap <- c(2, 5, 10, 20, 30) 293s + an <- c(100, 500, 1000, 10000, 50000) 293s + 293s + tottime <- 0 293s + cat(" n p Time\n") 293s + cat("=====================\n") 293s + for(i in 1:length(an)) { 293s + for(j in 1:length(ap)) { 293s + n <- an[i] 293s + p <- ap[j] 293s + if(5*p <= n){ 293s + xx <- gendata(n, p, eps) 293s + X <- xx$X 293s + tottime <- tottime + domcd(X, nrep) 293s + } 293s + } 293s + } 293s + 293s + cat("=====================\n") 293s + cat("Total time: ", tottime*nrep, "\n") 293s + } 293s > 293s > docheck <- function(n, p, eps){ 293s + xx <- gendata(n,p,eps) 293s + mcd <- CovSde(xx$X) 293s + check(mcd, xx$xind) 293s + } 293s > 293s > check <- function(mcd, xind){ 293s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 293s + ## did not get zero weight 293s + mymatch <- xind %in% which(mcd@wt == 0) 293s + length(xind) - length(which(mymatch)) 293s + } 293s > 293s > dorep <- function(x, nrep=1){ 293s + 293s + for(i in 1:nrep) 293s + CovSde(x) 293s + } 293s > 293s > #### gendata() #### 293s > # Generates a location contaminated multivariate 293s > # normal sample of n observations in p dimensions 293s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 293s > # where 293s > # m = (b,b,...,b) 293s > # Defaults: eps=0 and b=10 293s > # 293s > gendata <- function(n,p,eps=0,b=10){ 293s + 293s + if(missing(n) || missing(p)) 293s + stop("Please specify (n,p)") 293s + if(eps < 0 || eps >= 0.5) 293s + stop(message="eps must be in [0,0.5)") 293s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 293s + nbad <- as.integer(eps * n) 293s + if(nbad > 0){ 293s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 293s + xind <- sample(n,nbad) 293s + X[xind,] <- Xbad 293s + } 293s + list(X=X, xind=xind) 293s + } 293s > 293s > pad.right <- function(z, pads) 293s + { 293s + ### Pads spaces to right of text 293s + padding <- paste(rep(" ", pads), collapse = "") 293s + paste(z, padding, sep = "") 293s + } 293s > 293s > whatis<-function(x){ 293s + if(is.data.frame(x)) 293s + cat("Type: data.frame\n") 293s + else if(is.matrix(x)) 293s + cat("Type: matrix\n") 293s + else if(is.vector(x)) 293s + cat("Type: vector\n") 293s + else 293s + cat("Type: don't know\n") 293s + } 293s > 293s > ## VT::15.09.2013 - this will render the output independent 293s > ## from the version of the package 293s > suppressPackageStartupMessages(library(rrcov)) 293s > 293s > dodata() 293s 293s Call: dodata() 293s Data Set n p Half LOG(obj) Time 293s ======================================================== 293s heart 12 2 293s Outliers: 5 293s [1] 2 6 8 10 12 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s height weight 293s 39.8 35.7 293s 293s Robust Estimate of Covariance: 293s height weight 293s height 38.2 77.1 293s weight 77.1 188.1 293s -------------------------------------------------------- 293s starsCYG 47 2 293s Outliers: 7 293s [1] 7 9 11 14 20 30 34 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s log.Te log.light 293s 4.42 4.96 293s 293s Robust Estimate of Covariance: 293s log.Te log.light 293s log.Te 0.0163 0.0522 293s log.light 0.0522 0.3243 293s -------------------------------------------------------- 293s phosphor 18 2 293s Outliers: 2 293s [1] 1 6 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s inorg organic 293s 13.3 39.7 293s 293s Robust Estimate of Covariance: 293s inorg organic 293s inorg 133 134 293s organic 134 204 293s -------------------------------------------------------- 293s stackloss 21 3 293s Outliers: 6 293s [1] 1 2 3 15 17 21 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s Air.Flow Water.Temp Acid.Conc. 293s 57.8 20.7 86.4 293s 293s Robust Estimate of Covariance: 293s Air.Flow Water.Temp Acid.Conc. 293s Air.Flow 39.7 15.6 25.0 293s Water.Temp 15.6 13.0 11.9 293s Acid.Conc. 25.0 11.9 40.3 293s -------------------------------------------------------- 293s coleman 20 5 293s Outliers: 8 293s [1] 1 2 6 10 11 12 15 18 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s salaryP fatherWc sstatus teacherSc motherLev 293s 2.78 58.64 9.09 25.37 6.69 293s 293s Robust Estimate of Covariance: 293s salaryP fatherWc sstatus teacherSc motherLev 293s salaryP 0.2556 -1.0144 0.6599 0.2673 0.0339 293s fatherWc -1.0144 1615.9192 382.7846 -4.8287 36.0227 293s sstatus 0.6599 382.7846 108.1781 -0.7904 9.1027 293s teacherSc 0.2673 -4.8287 -0.7904 0.9346 -0.0239 293s motherLev 0.0339 36.0227 9.1027 -0.0239 0.9155 293s -------------------------------------------------------- 293s salinity 28 3 293s Outliers: 9 293s [1] 3 4 5 9 11 16 19 23 24 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s X1 X2 X3 293s 10.84 3.35 22.48 293s 293s Robust Estimate of Covariance: 293s X1 X2 X3 293s X1 10.75 -1.62 -2.05 293s X2 -1.62 4.21 -1.43 293s X3 -2.05 -1.43 2.63 293s -------------------------------------------------------- 293s wood 20 5 293s Outliers: 11 293s [1] 4 6 7 8 9 10 12 13 16 19 20 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s x1 x2 x3 x4 x5 293s 0.573 0.119 0.517 0.549 0.904 293s 293s Robust Estimate of Covariance: 293s x1 x2 x3 x4 x5 293s x1 0.025185 0.004279 -0.001262 -0.000382 -0.001907 293s x2 0.004279 0.001011 0.000197 -0.000117 0.000247 293s x3 -0.001262 0.000197 0.003042 0.002034 0.001773 293s x4 -0.000382 -0.000117 0.002034 0.007943 0.001137 293s x5 -0.001907 0.000247 0.001773 0.001137 0.005392 293s -------------------------------------------------------- 293s hbk 75 3 293s Outliers: 15 293s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 53 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s X1 X2 X3 293s 1.59 1.79 1.67 293s 293s Robust Estimate of Covariance: 293s X1 X2 X3 293s X1 1.6354 0.0793 0.2284 293s X2 0.0793 1.6461 0.3186 293s X3 0.2284 0.3186 1.5673 293s -------------------------------------------------------- 293s Animals 28 2 293s Outliers: 13 293s [1] 2 6 7 8 9 12 13 14 15 16 24 25 28 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s body brain 293s 18.7 64.9 293s 293s Robust Estimate of Covariance: 293s body brain 293s body 4702 7973 293s brain 7973 28571 293s -------------------------------------------------------- 293s milk 86 8 293s Outliers: 21 293s [1] 1 2 3 6 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s X1 X2 X3 X4 X5 X6 X7 X8 293s 1.03 35.90 33.04 26.11 25.10 25.02 123.06 14.37 293s 293s Robust Estimate of Covariance: 293s X1 X2 X3 X4 X5 X6 X7 293s X1 4.73e-07 6.57e-05 1.79e-04 1.71e-04 1.62e-04 1.42e-04 6.85e-04 293s X2 6.57e-05 1.57e+00 1.36e-01 9.28e-02 4.18e-02 1.30e-01 1.58e+00 293s X3 1.79e-04 1.36e-01 1.12e+00 8.20e-01 8.27e-01 8.00e-01 6.66e-01 293s X4 1.71e-04 9.28e-02 8.20e-01 6.57e-01 6.41e-01 6.18e-01 5.47e-01 293s X5 1.62e-04 4.18e-02 8.27e-01 6.41e-01 6.93e-01 6.44e-01 5.71e-01 293s X6 1.42e-04 1.30e-01 8.00e-01 6.18e-01 6.44e-01 6.44e-01 5.55e-01 293s X7 6.85e-04 1.58e+00 6.66e-01 5.47e-01 5.71e-01 5.55e-01 4.17e+00 293s X8 1.40e-05 2.33e-01 1.74e-01 1.06e-01 9.44e-02 9.86e-02 3.54e-01 293s X8 293s X1 1.40e-05 293s X2 2.33e-01 293s X3 1.74e-01 293s X4 1.06e-01 293s X5 9.44e-02 293s X6 9.86e-02 293s X7 3.54e-01 293s X8 1.57e-01 293s -------------------------------------------------------- 293s bushfire 38 5 293s Outliers: 23 293s [1] 1 5 6 7 8 9 10 11 12 13 15 16 28 29 30 31 32 33 34 35 36 37 38 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s V1 V2 V3 V4 V5 293s 105 148 287 223 283 293s 293s Robust Estimate of Covariance: 293s V1 V2 V3 V4 V5 293s V1 1964 1712 -10230 -2504 -2066 293s V2 1712 1526 -8732 -2145 -1763 293s V3 -10230 -8732 56327 13803 11472 293s V4 -2504 -2145 13803 3509 2894 293s V5 -2066 -1763 11472 2894 2404 293s -------------------------------------------------------- 293s bushfire 38 1 293s Outliers: 2 293s [1] 13 30 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s V1 293s 98.5 293s 293s Robust Estimate of Covariance: 293s V1 293s V1 431 293s -------------------------------------------------------- 293s bushfire 38 1 293s Outliers: 6 293s [1] 33 34 35 36 37 38 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s V2 293s 141 293s 293s Robust Estimate of Covariance: 293s V2 293s V2 528 293s -------------------------------------------------------- 293s bushfire 38 1 293s Outliers: 0 293s ------------- 293s 293s Call: 293s CovSde(x = x) 293s -> Method: Stahel-Donoho estimator 293s 293s Robust Estimate of Location: 293s V3 293s 238 293s 293s Robust Estimate of Covariance: 294s V3 294s V3 37148 294s -------------------------------------------------------- 294s bushfire 38 1 294s Outliers: 9 294s [1] 8 9 32 33 34 35 36 37 38 294s ------------- 294s 294s Call: 294s CovSde(x = x) 294s -> Method: Stahel-Donoho estimator 294s 294s Robust Estimate of Location: 294s V4 294s 210 294s 294s Robust Estimate of Covariance: 294s V4 294s V4 2543 294s -------------------------------------------------------- 294s bushfire 38 1 294s Outliers: 9 294s [1] 8 9 32 33 34 35 36 37 38 294s ------------- 294s 294s Call: 294s CovSde(x = x) 294s -> Method: Stahel-Donoho estimator 294s 294s Robust Estimate of Location: 294s V5 294s 273 294s 294s Robust Estimate of Covariance: 294s V5 294s V5 1575 294s -------------------------------------------------------- 294s ======================================================== 294s > ##doexact() 294s > 294s BEGIN TEST tsest.R 294s 294s R version 4.4.3 (2025-02-28) -- "Trophy Case" 294s Copyright (C) 2025 The R Foundation for Statistical Computing 294s Platform: arm-unknown-linux-gnueabihf (32-bit) 294s 294s R is free software and comes with ABSOLUTELY NO WARRANTY. 294s You are welcome to redistribute it under certain conditions. 294s Type 'license()' or 'licence()' for distribution details. 294s 294s R is a collaborative project with many contributors. 294s Type 'contributors()' for more information and 294s 'citation()' on how to cite R or R packages in publications. 294s 294s Type 'demo()' for some demos, 'help()' for on-line help, or 294s 'help.start()' for an HTML browser interface to help. 294s Type 'q()' to quit R. 294s 294s > ## VT::15.09.2013 - this will render the output independent 294s > ## from the version of the package 294s > suppressPackageStartupMessages(library(rrcov)) 294s > 294s > library(MASS) 294s > 294s > dodata <- function(nrep = 1, time = FALSE, full = TRUE, method) { 294s + doest <- function(x, xname, nrep = 1, method=c("sfast", "surreal", "bisquare", "rocke", "suser", "MM", "sdet")) { 294s + 294s + method <- match.arg(method) 294s + 294s + lname <- 20 294s + n <- dim(x)[1] 294s + p <- dim(x)[2] 294s + 294s + mm <- if(method == "MM") CovMMest(x) else CovSest(x, method=method) 294s + 294s + crit <- log(mm@crit) 294s + 294s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 294s + lpad <- lname-nchar(xname) 294s + cat(pad.right(xname,lpad), xres) 294s + 294s + dist <- getDistance(mm) 294s + quantiel <- qchisq(0.975, p) 294s + ibad <- which(dist >= quantiel) 294s + names(ibad) <- NULL 294s + nbad <- length(ibad) 294s + cat("Outliers: ",nbad,"\n") 294s + if(nbad > 0) 294s + print(ibad) 294s + cat("-------------\n") 294s + show(mm) 294s + cat("--------------------------------------------------------\n") 294s + } 294s + 294s + options(digits = 5) 294s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 294s + 294s + data(heart) 294s + data(starsCYG) 294s + data(phosphor) 294s + data(stackloss) 294s + data(coleman) 294s + data(salinity) 294s + data(wood) 294s + data(hbk) 294s + 294s + data(Animals, package = "MASS") 294s + brain <- Animals[c(1:24, 26:25, 27:28),] 294s + data(milk) 294s + data(bushfire) 294s + ### 294s + data(rice) 294s + data(hemophilia) 294s + data(fish) 294s + 294s + tmp <- sys.call() 294s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 294s + 294s + cat("Data Set n p LOG(det) Time\n") 294s + cat("===================================================\n") 294s + doest(heart[, 1:2], data(heart), nrep, method=method) 294s + doest(starsCYG, data(starsCYG), nrep, method=method) 294s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep, method=method) 294s + doest(stack.x, data(stackloss), nrep, method=method) 294s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep, method=method) 294s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep, method=method) 294s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep, method=method) 294s + doest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep, method=method) 294s + 294s + 294s + doest(brain, "Animals", nrep, method=method) 294s + doest(milk, data(milk), nrep, method=method) 294s + doest(bushfire, data(bushfire), nrep, method=method) 294s + 294s + doest(data.matrix(subset(rice, select = -Overall_evaluation)), data(rice), nrep, method=method) 294s + doest(data.matrix(subset(hemophilia, select = -gr)), data(hemophilia), nrep, method=method) 294s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep, method=method) 294s + 294s + ## from package 'datasets' 294s + doest(airquality[,1:4], data(airquality), nrep, method=method) 294s + doest(attitude, data(attitude), nrep, method=method) 294s + doest(attenu, data(attenu), nrep, method=method) 294s + doest(USJudgeRatings, data(USJudgeRatings), nrep, method=method) 294s + doest(USArrests, data(USArrests), nrep, method=method) 294s + doest(longley, data(longley), nrep, method=method) 294s + doest(Loblolly, data(Loblolly), nrep, method=method) 294s + doest(quakes[,1:4], data(quakes), nrep, method=method) 294s + 294s + cat("===================================================\n") 294s + } 294s > 294s > # generate contaminated data using the function gendata with different 294s > # number of outliers and check if the M-estimate breaks - i.e. the 294s > # largest eigenvalue is larger than e.g. 5. 294s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 294s > # outliers grater than 20. 294s > dogen <- function(){ 294s + eig <- vector("numeric",26) 294s + for(i in 0:25) { 294s + gg <- gendata(eps=i) 294s + mm <- CovSRocke(gg$x, t0=gg$tgood, S0=gg$sgood) 294s + eig[i+1] <- ev <- getEvals(mm)[1] 294s + cat(i, ev, "\n") 294s + 294s + ## stopifnot(ev < 5 || i > 20) 294s + } 294s + plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 294s + } 294s > 294s > # 294s > # generate data 50x10 as multivariate normal N(0,I) and add 294s > # eps % outliers by adding d=5.0 to each component. 294s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 294s > # - if eps >= 1, it is the number of outliers 294s > # - use the center and cov of the good data as good start 294s > # - use the center and the cov of all data as a bad start 294s > # If using a good start, the M-estimate must iterate to 294s > # the good solution: the largest eigenvalue is less then e.g. 5 294s > # 294s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 294s + 294s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 294s + stop("eps is out of range") 294s + 294s + library(MASS) 294s + 294s + x <- mvrnorm(n, rep(0,p), diag(p)) 294s + bad <- vector("numeric") 294s + nbad = if(eps < 1) eps*n else eps 294s + if(nbad > 0){ 294s + bad <- sample(n, nbad) 294s + x[bad,] <- x[bad,] + d 294s + } 294s + cov1 <- cov.wt(x) 294s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 294s + 294s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 294s + } 294s > 294s > pad.right <- function(z, pads) 294s + { 294s + ## Pads spaces to right of text 294s + padding <- paste(rep(" ", pads), collapse = "") 294s + paste(z, padding, sep = "") 294s + } 294s > 294s > 294s > ## -- now do it: 294s > dodata(method="sfast") 294s 294s Call: dodata(method = "sfast") 294s Data Set n p LOG(det) Time 294s =================================================== 294s heart 12 2 2.017701 294s Outliers: 3 294s [1] 2 6 12 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 36.1 29.5 294s 294s Robust Estimate of Covariance: 294s height weight 294s height 129 210 294s weight 210 365 294s -------------------------------------------------------- 294s starsCYG 47 2 -1.450032 294s Outliers: 7 294s [1] 7 9 11 14 20 30 34 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 4.42 4.97 294s 294s Robust Estimate of Covariance: 294s log.Te log.light 294s log.Te 0.0176 0.0617 294s log.light 0.0617 0.3880 294s -------------------------------------------------------- 294s phosphor 18 2 2.320721 294s Outliers: 2 294s [1] 1 6 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 14.1 38.8 294s 294s Robust Estimate of Covariance: 294s inorg organic 294s inorg 174 190 294s organic 190 268 294s -------------------------------------------------------- 294s stackloss 21 3 1.470031 294s Outliers: 3 294s [1] 1 2 3 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 57.5 20.5 86.0 294s 294s Robust Estimate of Covariance: 294s Air.Flow Water.Temp Acid.Conc. 294s Air.Flow 38.94 11.66 22.89 294s Water.Temp 11.66 9.96 7.81 294s Acid.Conc. 22.89 7.81 40.48 294s -------------------------------------------------------- 294s coleman 20 5 0.491419 294s Outliers: 2 294s [1] 6 10 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 2.77 45.58 4.13 25.13 6.39 294s 294s Robust Estimate of Covariance: 294s salaryP fatherWc sstatus teacherSc motherLev 294s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 294s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 294s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 294s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 294s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 294s -------------------------------------------------------- 294s salinity 28 3 0.734619 294s Outliers: 4 294s [1] 5 16 23 24 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 10.31 3.07 22.60 294s 294s Robust Estimate of Covariance: 294s X1 X2 X3 294s X1 13.200 0.784 -3.611 294s X2 0.784 4.441 -1.658 294s X3 -3.611 -1.658 2.877 294s -------------------------------------------------------- 294s wood 20 5 -3.202636 294s Outliers: 2 294s [1] 7 9 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 0.551 0.135 0.496 0.511 0.916 294s 294s Robust Estimate of Covariance: 294s x1 x2 x3 x4 x5 294s x1 0.011361 -0.000791 0.005473 0.004204 -0.004747 294s x2 -0.000791 0.000701 -0.000534 -0.001452 0.000864 294s x3 0.005473 -0.000534 0.004905 0.002960 -0.001914 294s x4 0.004204 -0.001452 0.002960 0.005154 -0.002187 294s x5 -0.004747 0.000864 -0.001914 -0.002187 0.003214 294s -------------------------------------------------------- 294s hbk 75 3 0.283145 294s Outliers: 14 294s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 1.53 1.83 1.66 294s 294s Robust Estimate of Covariance: 294s X1 X2 X3 294s X1 1.8091 0.0479 0.2446 294s X2 0.0479 1.8190 0.2513 294s X3 0.2446 0.2513 1.7288 294s -------------------------------------------------------- 294s Animals 28 2 4.685129 294s Outliers: 10 294s [1] 2 6 7 9 12 14 15 16 24 25 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 30.8 84.2 294s 294s Robust Estimate of Covariance: 294s body brain 294s body 14806 28767 294s brain 28767 65195 294s -------------------------------------------------------- 294s milk 86 8 -1.437863 294s Outliers: 15 294s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 294s 294s Robust Estimate of Covariance: 294s X1 X2 X3 X4 X5 X6 X7 294s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 294s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 294s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 294s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 294s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 294s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 294s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 294s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 294s X8 294s X1 7.45e-05 294s X2 3.33e-01 294s X3 2.82e-01 294s X4 2.01e-01 294s X5 1.80e-01 294s X6 1.91e-01 294s X7 6.38e-01 294s X8 2.01e-01 294s -------------------------------------------------------- 294s bushfire 38 5 2.443148 294s Outliers: 13 294s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 108 149 266 216 278 294s 294s Robust Estimate of Covariance: 294s V1 V2 V3 V4 V5 294s V1 911 688 -3961 -856 -707 294s V2 688 587 -2493 -492 -420 294s V3 -3961 -2493 24146 5765 4627 294s V4 -856 -492 5765 1477 1164 294s V5 -707 -420 4627 1164 925 294s -------------------------------------------------------- 294s rice 105 5 -0.724874 294s Outliers: 7 294s [1] 9 40 42 49 57 58 71 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 294s 294s Robust Estimate of Covariance: 294s Favor Appearance Taste Stickiness Toughness 294s Favor 0.423 0.345 0.427 0.405 -0.202 294s Appearance 0.345 0.592 0.570 0.549 -0.316 294s Taste 0.427 0.570 0.739 0.706 -0.393 294s Stickiness 0.405 0.549 0.706 0.876 -0.497 294s Toughness -0.202 -0.316 -0.393 -0.497 0.467 294s -------------------------------------------------------- 294s hemophilia 75 2 -1.868949 294s Outliers: 2 294s [1] 11 36 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] -0.2126 -0.0357 294s 294s Robust Estimate of Covariance: 294s AHFactivity AHFantigen 294s AHFactivity 0.0317 0.0112 294s AHFantigen 0.0112 0.0218 294s -------------------------------------------------------- 294s fish 159 6 1.285876 294s Outliers: 21 294s [1] 61 62 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 294s [20] 103 142 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 358.3 24.7 26.9 29.7 30.0 14.7 294s 294s Robust Estimate of Covariance: 294s Weight Length1 Length2 Length3 Height Width 294s Weight 1.33e+05 3.09e+03 3.34e+03 3.78e+03 1.72e+03 2.24e+02 294s Length1 3.09e+03 7.92e+01 8.54e+01 9.55e+01 4.04e+01 7.43e+00 294s Length2 3.34e+03 8.54e+01 9.22e+01 1.03e+02 4.49e+01 8.07e+00 294s Length3 3.78e+03 9.55e+01 1.03e+02 1.18e+02 5.92e+01 7.65e+00 294s Height 1.72e+03 4.04e+01 4.49e+01 5.92e+01 7.12e+01 8.51e-01 294s Width 2.24e+02 7.43e+00 8.07e+00 7.65e+00 8.51e-01 3.57e+00 294s -------------------------------------------------------- 294s airquality 153 4 2.684374 294s Outliers: 7 294s [1] 7 14 23 30 34 77 107 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 39.34 192.12 9.67 78.71 294s 294s Robust Estimate of Covariance: 294s Ozone Solar.R Wind Temp 294s Ozone 973.104 894.011 -61.856 243.560 294s Solar.R 894.011 9677.269 0.388 179.429 294s Wind -61.856 0.388 11.287 -14.310 294s Temp 243.560 179.429 -14.310 96.714 294s -------------------------------------------------------- 294s attitude 30 7 2.091968 294s Outliers: 4 294s [1] 14 16 18 24 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 294s 294s Robust Estimate of Covariance: 294s rating complaints privileges learning raises critical advance 294s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 294s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 294s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 294s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 294s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 294s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 294s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 294s -------------------------------------------------------- 294s attenu 182 5 1.148032 294s Outliers: 31 294s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 294s [20] 29 30 31 32 64 65 80 94 95 96 97 100 294s ------------- 294s 294s Call: 294s CovSest(x = x, method = method) 294s -> Method: S-estimates: S-FAST 294s 294s Robust Estimate of Location: 294s [1] 16.432 5.849 60.297 27.144 0.134 294s 294s Robust Estimate of Covariance: 294s event mag station dist accel 294s event 54.9236 -3.0733 181.0954 -49.4194 -0.0628 294s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 294s station 181.0954 -8.4388 1689.7161 -114.6319 0.7285 294s dist -49.4194 6.7388 -114.6319 597.3606 -1.7988 294s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 294s -------------------------------------------------------- 295s USJudgeRatings 43 12 -1.683847 295s Outliers: 7 295s [1] 5 7 12 13 14 23 31 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: S-FAST 295s 295s Robust Estimate of Location: 295s [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 295s 295s Robust Estimate of Covariance: 295s CONT INTG DMNR DILG CFMG DECI PREP FAMI 295s CONT 0.8710 -0.3019 -0.4682 -0.1893 -0.0569 -0.0992 -0.1771 -0.1975 295s INTG -0.3019 0.6401 0.8598 0.6955 0.5732 0.5439 0.7091 0.7084 295s DMNR -0.4682 0.8598 1.2412 0.9107 0.7668 0.7305 0.9292 0.9158 295s DILG -0.1893 0.6955 0.9107 0.8554 0.7408 0.7036 0.8865 0.8791 295s CFMG -0.0569 0.5732 0.7668 0.7408 0.6994 0.6545 0.7788 0.7721 295s DECI -0.0992 0.5439 0.7305 0.7036 0.6545 0.6342 0.7492 0.7511 295s PREP -0.1771 0.7091 0.9292 0.8865 0.7788 0.7492 0.9541 0.9556 295s FAMI -0.1975 0.7084 0.9158 0.8791 0.7721 0.7511 0.9556 0.9785 295s ORAL -0.2444 0.7453 0.9939 0.8917 0.7842 0.7551 0.9554 0.9680 295s WRIT -0.2344 0.7319 0.9649 0.8853 0.7781 0.7511 0.9498 0.9668 295s PHYS -0.1983 0.4676 0.6263 0.5629 0.5073 0.5039 0.5990 0.6140 295s RTEN -0.3152 0.8000 1.0798 0.9234 0.7952 0.7663 0.9637 0.9693 295s ORAL WRIT PHYS RTEN 295s CONT -0.2444 -0.2344 -0.1983 -0.3152 295s INTG 0.7453 0.7319 0.4676 0.8000 295s DMNR 0.9939 0.9649 0.6263 1.0798 295s DILG 0.8917 0.8853 0.5629 0.9234 295s CFMG 0.7842 0.7781 0.5073 0.7952 295s DECI 0.7551 0.7511 0.5039 0.7663 295s PREP 0.9554 0.9498 0.5990 0.9637 295s FAMI 0.9680 0.9668 0.6140 0.9693 295s ORAL 0.9853 0.9744 0.6280 1.0032 295s WRIT 0.9744 0.9711 0.6184 0.9870 295s PHYS 0.6280 0.6184 0.4716 0.6520 295s RTEN 1.0032 0.9870 0.6520 1.0622 295s -------------------------------------------------------- 295s USArrests 50 4 2.411726 295s Outliers: 4 295s [1] 2 28 33 39 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: S-FAST 295s 295s Robust Estimate of Location: 295s [1] 7.05 150.66 64.66 19.37 295s 295s Robust Estimate of Covariance: 295s Murder Assault UrbanPop Rape 295s Murder 23.8 380.8 19.2 29.7 295s Assault 380.8 8436.2 605.6 645.3 295s UrbanPop 19.2 605.6 246.5 78.8 295s Rape 29.7 645.3 78.8 77.3 295s -------------------------------------------------------- 295s longley 16 7 1.038316 295s Outliers: 5 295s [1] 1 2 3 4 5 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: S-FAST 295s 295s Robust Estimate of Location: 295s [1] 107.6 440.8 339.7 292.5 121.0 1957.1 67.2 295s 295s Robust Estimate of Covariance: 295s GNP.deflator GNP Unemployed Armed.Forces Population 295s GNP.deflator 100.6 954.7 1147.1 -507.6 74.2 295s GNP 954.7 9430.9 10115.8 -4616.5 730.1 295s Unemployed 1147.1 10115.8 19838.3 -6376.9 850.6 295s Armed.Forces -507.6 -4616.5 -6376.9 3240.2 -351.3 295s Population 74.2 730.1 850.6 -351.3 57.5 295s Year 46.4 450.8 539.5 -233.0 35.3 295s Employed 30.8 310.5 274.0 -160.8 23.3 295s Year Employed 295s GNP.deflator 46.4 30.8 295s GNP 450.8 310.5 295s Unemployed 539.5 274.0 295s Armed.Forces -233.0 -160.8 295s Population 35.3 23.3 295s Year 21.9 14.6 295s Employed 14.6 11.2 295s -------------------------------------------------------- 295s Loblolly 84 3 1.481317 295s Outliers: 14 295s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: S-FAST 295s 295s Robust Estimate of Location: 295s [1] 24.22 9.65 7.50 295s 295s Robust Estimate of Covariance: 295s height age Seed 295s height 525.08 179.21 14.27 295s age 179.21 61.85 2.94 295s Seed 14.27 2.94 25.86 295s -------------------------------------------------------- 295s quakes 1000 4 1.576855 295s Outliers: 223 295s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 295s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 295s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 295s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 295s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 295s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 295s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 295s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 295s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 295s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 295s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 295s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 295s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 295s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 295s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: S-FAST 295s 295s Robust Estimate of Location: 295s [1] -21.54 182.35 369.21 4.54 295s 295s Robust Estimate of Covariance: 295s lat long depth mag 295s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 295s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 295s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 295s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 295s -------------------------------------------------------- 295s =================================================== 295s > dodata(method="sdet") 295s 295s Call: dodata(method = "sdet") 295s Data Set n p LOG(det) Time 295s =================================================== 295s heart 12 2 2.017701 295s Outliers: 3 295s [1] 2 6 12 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: DET-S 295s 295s Robust Estimate of Location: 295s [1] 36.1 29.5 295s 295s Robust Estimate of Covariance: 295s height weight 295s height 129 210 295s weight 210 365 295s -------------------------------------------------------- 295s starsCYG 47 2 -1.450032 295s Outliers: 7 295s [1] 7 9 11 14 20 30 34 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: DET-S 295s 295s Robust Estimate of Location: 295s [1] 4.42 4.97 295s 295s Robust Estimate of Covariance: 295s log.Te log.light 295s log.Te 0.0176 0.0617 295s log.light 0.0617 0.3880 295s -------------------------------------------------------- 295s phosphor 18 2 2.320721 295s Outliers: 2 295s [1] 1 6 295s ------------- 295s 295s Call: 295s CovSest(x = x, method = method) 295s -> Method: S-estimates: DET-S 295s 295s Robust Estimate of Location: 295s [1] 14.1 38.8 295s 295s Robust Estimate of Covariance: 295s inorg organic 295s inorg 174 190 295s organic 190 268 295s -------------------------------------------------------- 296s stackloss 21 3 1.470031 296s Outliers: 3 296s [1] 1 2 3 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 57.5 20.5 86.0 296s 296s Robust Estimate of Covariance: 296s Air.Flow Water.Temp Acid.Conc. 296s Air.Flow 38.94 11.66 22.89 296s Water.Temp 11.66 9.96 7.81 296s Acid.Conc. 22.89 7.81 40.48 296s -------------------------------------------------------- 296s coleman 20 5 0.491419 296s Outliers: 2 296s [1] 6 10 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 2.77 45.58 4.13 25.13 6.39 296s 296s Robust Estimate of Covariance: 296s salaryP fatherWc sstatus teacherSc motherLev 296s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 296s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 296s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 296s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 296s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 296s -------------------------------------------------------- 296s salinity 28 3 0.734619 296s Outliers: 4 296s [1] 5 16 23 24 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 10.31 3.07 22.60 296s 296s Robust Estimate of Covariance: 296s X1 X2 X3 296s X1 13.200 0.784 -3.611 296s X2 0.784 4.441 -1.658 296s X3 -3.611 -1.658 2.877 296s -------------------------------------------------------- 296s wood 20 5 -3.220754 296s Outliers: 4 296s [1] 4 6 8 19 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 0.580 0.123 0.530 0.538 0.890 296s 296s Robust Estimate of Covariance: 296s x1 x2 x3 x4 x5 296s x1 8.16e-03 1.39e-03 1.97e-03 -2.82e-04 -7.61e-04 296s x2 1.39e-03 4.00e-04 8.14e-04 -8.51e-05 -5.07e-06 296s x3 1.97e-03 8.14e-04 4.74e-03 -9.59e-04 2.06e-05 296s x4 -2.82e-04 -8.51e-05 -9.59e-04 3.09e-03 1.87e-03 296s x5 -7.61e-04 -5.07e-06 2.06e-05 1.87e-03 2.28e-03 296s -------------------------------------------------------- 296s hbk 75 3 0.283145 296s Outliers: 14 296s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 1.53 1.83 1.66 296s 296s Robust Estimate of Covariance: 296s X1 X2 X3 296s X1 1.8091 0.0479 0.2446 296s X2 0.0479 1.8190 0.2513 296s X3 0.2446 0.2513 1.7288 296s -------------------------------------------------------- 296s Animals 28 2 4.685129 296s Outliers: 10 296s [1] 2 6 7 9 12 14 15 16 24 25 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 30.8 84.2 296s 296s Robust Estimate of Covariance: 296s body brain 296s body 14806 28767 296s brain 28767 65194 296s -------------------------------------------------------- 296s milk 86 8 -1.437863 296s Outliers: 15 296s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 296s ------------- 296s 296s Call: 296s CovSest(x = x, method = method) 296s -> Method: S-estimates: DET-S 296s 296s Robust Estimate of Location: 296s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 296s 296s Robust Estimate of Covariance: 296s X1 X2 X3 X4 X5 X6 X7 296s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 296s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 296s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 296s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 296s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 296s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 296s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 296s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 296s X8 296s X1 7.45e-05 296s X2 3.33e-01 296s X3 2.82e-01 296s X4 2.01e-01 296s X5 1.80e-01 296s X6 1.91e-01 296s X7 6.38e-01 296s X8 2.01e-01 296s -------------------------------------------------------- 297s bushfire 38 5 2.443148 297s Outliers: 13 297s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 297s ------------- 297s 297s Call: 297s CovSest(x = x, method = method) 297s -> Method: S-estimates: DET-S 297s 297s Robust Estimate of Location: 297s [1] 108 149 266 216 278 297s 297s Robust Estimate of Covariance: 297s V1 V2 V3 V4 V5 297s V1 911 688 -3961 -856 -707 297s V2 688 587 -2493 -492 -420 297s V3 -3961 -2493 24146 5765 4627 297s V4 -856 -492 5765 1477 1164 297s V5 -707 -420 4627 1164 925 297s -------------------------------------------------------- 297s rice 105 5 -0.724874 297s Outliers: 7 297s [1] 9 40 42 49 57 58 71 297s ------------- 297s 297s Call: 297s CovSest(x = x, method = method) 297s -> Method: S-estimates: DET-S 297s 297s Robust Estimate of Location: 297s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 297s 297s Robust Estimate of Covariance: 297s Favor Appearance Taste Stickiness Toughness 297s Favor 0.423 0.345 0.427 0.405 -0.202 297s Appearance 0.345 0.592 0.570 0.549 -0.316 297s Taste 0.427 0.570 0.739 0.706 -0.393 297s Stickiness 0.405 0.549 0.706 0.876 -0.497 297s Toughness -0.202 -0.316 -0.393 -0.497 0.467 297s -------------------------------------------------------- 297s hemophilia 75 2 -1.868949 297s Outliers: 2 297s [1] 11 36 297s ------------- 297s 297s Call: 297s CovSest(x = x, method = method) 297s -> Method: S-estimates: DET-S 297s 297s Robust Estimate of Location: 297s [1] -0.2126 -0.0357 297s 297s Robust Estimate of Covariance: 297s AHFactivity AHFantigen 297s AHFactivity 0.0317 0.0112 297s AHFantigen 0.0112 0.0218 297s -------------------------------------------------------- 298s fish 159 6 1.267294 298s Outliers: 33 298s [1] 61 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 298s [20] 91 92 93 94 95 96 97 98 99 100 101 102 103 142 298s ------------- 298s 298s Call: 298s CovSest(x = x, method = method) 298s -> Method: S-estimates: DET-S 298s 298s Robust Estimate of Location: 298s [1] 381.2 25.6 27.8 30.8 31.0 14.9 298s 298s Robust Estimate of Covariance: 298s Weight Length1 Length2 Length3 Height Width 298s Weight 148372.04 3260.48 3508.71 3976.93 1507.43 127.94 298s Length1 3260.48 77.00 82.52 92.18 27.56 3.29 298s Length2 3508.71 82.52 88.57 99.20 30.83 3.43 298s Length3 3976.93 92.18 99.20 113.97 45.50 2.21 298s Height 1507.43 27.56 30.83 45.50 70.54 -4.95 298s Width 127.94 3.29 3.43 2.21 -4.95 2.28 298s -------------------------------------------------------- 298s airquality 153 4 2.684374 298s Outliers: 7 298s [1] 7 14 23 30 34 77 107 298s ------------- 298s 298s Call: 298s CovSest(x = x, method = method) 298s -> Method: S-estimates: DET-S 298s 298s Robust Estimate of Location: 298s [1] 39.34 192.12 9.67 78.71 298s 298s Robust Estimate of Covariance: 298s Ozone Solar.R Wind Temp 298s Ozone 973.104 894.011 -61.856 243.560 298s Solar.R 894.011 9677.269 0.388 179.429 298s Wind -61.856 0.388 11.287 -14.310 298s Temp 243.560 179.429 -14.310 96.714 298s -------------------------------------------------------- 298s attitude 30 7 2.091968 298s Outliers: 4 298s [1] 14 16 18 24 298s ------------- 298s 298s Call: 298s CovSest(x = x, method = method) 298s -> Method: S-estimates: DET-S 298s 298s Robust Estimate of Location: 298s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 298s 298s Robust Estimate of Covariance: 298s rating complaints privileges learning raises critical advance 298s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 298s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 298s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 298s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 298s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 298s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 298s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 298s -------------------------------------------------------- 298s attenu 182 5 1.148032 298s Outliers: 31 298s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 298s [20] 29 30 31 32 64 65 80 94 95 96 97 100 298s ------------- 298s 298s Call: 298s CovSest(x = x, method = method) 298s -> Method: S-estimates: DET-S 298s 298s Robust Estimate of Location: 298s [1] 16.432 5.849 60.297 27.144 0.134 298s 298s Robust Estimate of Covariance: 298s event mag station dist accel 298s event 54.9236 -3.0733 181.0954 -49.4195 -0.0628 298s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 298s station 181.0954 -8.4388 1689.7161 -114.6321 0.7285 298s dist -49.4195 6.7388 -114.6321 597.3609 -1.7988 298s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 298s -------------------------------------------------------- 299s USJudgeRatings 43 12 -1.683847 299s Outliers: 7 299s [1] 5 7 12 13 14 23 31 299s ------------- 299s 299s Call: 299s CovSest(x = x, method = method) 299s -> Method: S-estimates: DET-S 299s 299s Robust Estimate of Location: 299s [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 299s 299s Robust Estimate of Covariance: 299s CONT INTG DMNR DILG CFMG DECI PREP FAMI 299s CONT 0.8715 -0.3020 -0.4683 -0.1894 -0.0569 -0.0993 -0.1772 -0.1976 299s INTG -0.3020 0.6403 0.8600 0.6956 0.5733 0.5440 0.7093 0.7086 299s DMNR -0.4683 0.8600 1.2416 0.9109 0.7669 0.7307 0.9295 0.9161 299s DILG -0.1894 0.6956 0.9109 0.8555 0.7410 0.7037 0.8867 0.8793 299s CFMG -0.0569 0.5733 0.7669 0.7410 0.6995 0.6546 0.7789 0.7723 299s DECI -0.0993 0.5440 0.7307 0.7037 0.6546 0.6343 0.7493 0.7513 299s PREP -0.1772 0.7093 0.9295 0.8867 0.7789 0.7493 0.9543 0.9559 299s FAMI -0.1976 0.7086 0.9161 0.8793 0.7723 0.7513 0.9559 0.9788 299s ORAL -0.2445 0.7456 0.9942 0.8919 0.7844 0.7553 0.9557 0.9683 299s WRIT -0.2345 0.7321 0.9652 0.8856 0.7783 0.7513 0.9501 0.9671 299s PHYS -0.1986 0.4676 0.6264 0.5628 0.5072 0.5038 0.5990 0.6140 299s RTEN -0.3154 0.8002 1.0801 0.9236 0.7954 0.7665 0.9639 0.9695 299s ORAL WRIT PHYS RTEN 299s CONT -0.2445 -0.2345 -0.1986 -0.3154 299s INTG 0.7456 0.7321 0.4676 0.8002 299s DMNR 0.9942 0.9652 0.6264 1.0801 299s DILG 0.8919 0.8856 0.5628 0.9236 299s CFMG 0.7844 0.7783 0.5072 0.7954 299s DECI 0.7553 0.7513 0.5038 0.7665 299s PREP 0.9557 0.9501 0.5990 0.9639 299s FAMI 0.9683 0.9671 0.6140 0.9695 299s ORAL 0.9856 0.9748 0.6281 1.0035 299s WRIT 0.9748 0.9714 0.6184 0.9873 299s PHYS 0.6281 0.6184 0.4713 0.6520 299s RTEN 1.0035 0.9873 0.6520 1.0624 299s -------------------------------------------------------- 299s USArrests 50 4 2.411726 299s Outliers: 4 299s [1] 2 28 33 39 299s ------------- 299s 299s Call: 299s CovSest(x = x, method = method) 299s -> Method: S-estimates: DET-S 299s 299s Robust Estimate of Location: 299s [1] 7.05 150.66 64.66 19.37 299s 299s Robust Estimate of Covariance: 299s Murder Assault UrbanPop Rape 299s Murder 23.8 380.8 19.2 29.7 299s Assault 380.8 8436.2 605.6 645.3 299s UrbanPop 19.2 605.6 246.5 78.8 299s Rape 29.7 645.3 78.8 77.3 299s -------------------------------------------------------- 299s longley 16 7 1.143113 299s Outliers: 4 299s [1] 1 2 3 4 299s ------------- 299s 299s Call: 299s CovSest(x = x, method = method) 299s -> Method: S-estimates: DET-S 299s 299s Robust Estimate of Location: 299s [1] 107 435 334 293 120 1957 67 299s 299s Robust Estimate of Covariance: 299s GNP.deflator GNP Unemployed Armed.Forces Population 299s GNP.deflator 89.2 850.1 1007.4 -404.4 66.2 299s GNP 850.1 8384.4 9020.8 -3692.0 650.5 299s Unemployed 1007.4 9020.8 16585.4 -4990.7 752.5 299s Armed.Forces -404.4 -3692.0 -4990.7 2474.2 -280.9 299s Population 66.2 650.5 752.5 -280.9 51.2 299s Year 41.9 407.6 481.9 -186.4 31.9 299s Employed 27.9 279.7 255.6 -128.8 21.1 299s Year Employed 299s GNP.deflator 41.9 27.9 299s GNP 407.6 279.7 299s Unemployed 481.9 255.6 299s Armed.Forces -186.4 -128.8 299s Population 31.9 21.1 299s Year 20.2 13.4 299s Employed 13.4 10.1 299s -------------------------------------------------------- 300s Loblolly 84 3 1.481317 300s Outliers: 14 300s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: DET-S 300s 300s Robust Estimate of Location: 300s [1] 24.22 9.65 7.50 300s 300s Robust Estimate of Covariance: 300s height age Seed 300s height 525.08 179.21 14.27 300s age 179.21 61.85 2.94 300s Seed 14.27 2.94 25.86 300s -------------------------------------------------------- 300s quakes 1000 4 1.576855 300s Outliers: 223 300s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 300s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 300s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 300s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 300s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 300s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 300s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 300s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 300s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 300s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 300s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 300s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 300s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 300s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 300s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: DET-S 300s 300s Robust Estimate of Location: 300s [1] -21.54 182.35 369.21 4.54 300s 300s Robust Estimate of Covariance: 300s lat long depth mag 300s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 300s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 300s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 300s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 300s -------------------------------------------------------- 300s =================================================== 300s > ##dodata(method="suser") 300s > ##dodata(method="surreal") 300s > dodata(method="bisquare") 300s 300s Call: dodata(method = "bisquare") 300s Data Set n p LOG(det) Time 300s =================================================== 300s heart 12 2 7.721793 300s Outliers: 3 300s [1] 2 6 12 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s height weight 300s 36.1 29.4 300s 300s Robust Estimate of Covariance: 300s height weight 300s height 109 177 300s weight 177 307 300s -------------------------------------------------------- 300s starsCYG 47 2 -5.942108 300s Outliers: 7 300s [1] 7 9 11 14 20 30 34 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s log.Te log.light 300s 4.42 4.97 300s 300s Robust Estimate of Covariance: 300s log.Te log.light 300s log.Te 0.0164 0.0574 300s log.light 0.0574 0.3613 300s -------------------------------------------------------- 300s phosphor 18 2 9.269096 300s Outliers: 2 300s [1] 1 6 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s inorg organic 300s 14.1 38.7 300s 300s Robust Estimate of Covariance: 300s inorg organic 300s inorg 173 189 300s organic 189 268 300s -------------------------------------------------------- 300s stackloss 21 3 8.411100 300s Outliers: 3 300s [1] 1 2 3 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s Air.Flow Water.Temp Acid.Conc. 300s 57.5 20.5 86.0 300s 300s Robust Estimate of Covariance: 300s Air.Flow Water.Temp Acid.Conc. 300s Air.Flow 33.82 10.17 20.02 300s Water.Temp 10.17 8.70 6.84 300s Acid.Conc. 20.02 6.84 35.51 300s -------------------------------------------------------- 300s coleman 20 5 4.722046 300s Outliers: 2 300s [1] 6 10 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s salaryP fatherWc sstatus teacherSc motherLev 300s 2.77 45.59 4.14 25.13 6.39 300s 300s Robust Estimate of Covariance: 300s salaryP fatherWc sstatus teacherSc motherLev 300s salaryP 0.2135 1.8732 1.3883 0.2547 0.0648 300s fatherWc 1.8732 905.6704 296.1916 7.9820 21.0848 300s sstatus 1.3883 296.1916 128.9536 4.0196 7.1917 300s teacherSc 0.2547 7.9820 4.0196 0.7321 0.2799 300s motherLev 0.0648 21.0848 7.1917 0.2799 0.5592 300s -------------------------------------------------------- 300s salinity 28 3 4.169963 300s Outliers: 4 300s [1] 5 16 23 24 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s X1 X2 X3 300s 10.30 3.07 22.59 300s 300s Robust Estimate of Covariance: 300s X1 X2 X3 300s X1 12.234 0.748 -3.369 300s X2 0.748 4.115 -1.524 300s X3 -3.369 -1.524 2.655 300s -------------------------------------------------------- 300s wood 20 5 -33.862485 300s Outliers: 5 300s [1] 4 6 8 11 19 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s x1 x2 x3 x4 x5 300s 0.580 0.123 0.530 0.538 0.890 300s 300s Robust Estimate of Covariance: 300s x1 x2 x3 x4 x5 300s x1 5.88e-03 9.96e-04 1.43e-03 -1.96e-04 -5.46e-04 300s x2 9.96e-04 2.86e-04 5.89e-04 -5.78e-05 -2.24e-06 300s x3 1.43e-03 5.89e-04 3.42e-03 -6.95e-04 1.43e-05 300s x4 -1.96e-04 -5.78e-05 -6.95e-04 2.23e-03 1.35e-03 300s x5 -5.46e-04 -2.24e-06 1.43e-05 1.35e-03 1.65e-03 300s -------------------------------------------------------- 300s hbk 75 3 1.472421 300s Outliers: 14 300s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s X1 X2 X3 300s 1.53 1.83 1.66 300s 300s Robust Estimate of Covariance: 300s X1 X2 X3 300s X1 1.6775 0.0447 0.2268 300s X2 0.0447 1.6865 0.2325 300s X3 0.2268 0.2325 1.6032 300s -------------------------------------------------------- 300s Animals 28 2 18.528307 300s Outliers: 11 300s [1] 2 6 7 9 12 14 15 16 24 25 28 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s body brain 300s 30.7 84.1 300s 300s Robust Estimate of Covariance: 300s body brain 300s body 13278 25795 300s brain 25795 58499 300s -------------------------------------------------------- 300s milk 86 8 -24.816943 300s Outliers: 19 300s [1] 1 2 3 11 12 13 14 15 16 17 20 27 41 44 47 70 74 75 77 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s X1 X2 X3 X4 X5 X6 X7 X8 300s 1.03 35.81 32.96 26.04 25.02 24.94 122.79 14.35 300s 300s Robust Estimate of Covariance: 300s X1 X2 X3 X4 X5 X6 X7 300s X1 6.80e-07 2.20e-04 3.70e-04 3.35e-04 3.27e-04 3.30e-04 1.21e-03 300s X2 2.20e-04 1.80e+00 3.96e-01 3.03e-01 2.45e-01 3.27e-01 2.00e+00 300s X3 3.70e-04 3.96e-01 1.27e+00 9.68e-01 9.49e-01 9.56e-01 1.37e+00 300s X4 3.35e-04 3.03e-01 9.68e-01 7.86e-01 7.55e-01 7.57e-01 1.15e+00 300s X5 3.27e-04 2.45e-01 9.49e-01 7.55e-01 7.88e-01 7.61e-01 1.14e+00 300s X6 3.30e-04 3.27e-01 9.56e-01 7.57e-01 7.61e-01 7.90e-01 1.17e+00 300s X7 1.21e-03 2.00e+00 1.37e+00 1.15e+00 1.14e+00 1.17e+00 5.71e+00 300s X8 6.57e-05 2.71e-01 2.30e-01 1.64e-01 1.48e-01 1.57e-01 5.27e-01 300s X8 300s X1 6.57e-05 300s X2 2.71e-01 300s X3 2.30e-01 300s X4 1.64e-01 300s X5 1.48e-01 300s X6 1.57e-01 300s X7 5.27e-01 300s X8 1.62e-01 300s -------------------------------------------------------- 300s bushfire 38 5 21.704243 300s Outliers: 13 300s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s V1 V2 V3 V4 V5 300s 108 149 266 216 278 300s 300s Robust Estimate of Covariance: 300s V1 V2 V3 V4 V5 300s V1 528 398 -2298 -497 -410 300s V2 398 340 -1445 -285 -244 300s V3 -2298 -1445 14026 3348 2687 300s V4 -497 -285 3348 857 676 300s V5 -410 -244 2687 676 537 300s -------------------------------------------------------- 300s rice 105 5 -7.346939 300s Outliers: 8 300s [1] 9 14 40 42 49 57 58 71 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s Favor Appearance Taste Stickiness Toughness 300s -0.2480 0.1203 -0.1213 0.0710 0.0644 300s 300s Robust Estimate of Covariance: 300s Favor Appearance Taste Stickiness Toughness 300s Favor 0.415 0.338 0.419 0.398 -0.198 300s Appearance 0.338 0.580 0.559 0.539 -0.310 300s Taste 0.419 0.559 0.725 0.693 -0.386 300s Stickiness 0.398 0.539 0.693 0.859 -0.487 300s Toughness -0.198 -0.310 -0.386 -0.487 0.457 300s -------------------------------------------------------- 300s hemophilia 75 2 -7.465173 300s Outliers: 2 300s [1] 11 36 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s AHFactivity AHFantigen 300s -0.2128 -0.0366 300s 300s Robust Estimate of Covariance: 300s AHFactivity AHFantigen 300s AHFactivity 0.0321 0.0115 300s AHFantigen 0.0115 0.0220 300s -------------------------------------------------------- 300s fish 159 6 13.465134 300s Outliers: 35 300s [1] 38 61 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 300s [20] 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 142 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s Weight Length1 Length2 Length3 Height Width 300s 381.4 25.6 27.8 30.8 31.0 14.9 300s 300s Robust Estimate of Covariance: 300s Weight Length1 Length2 Length3 Height Width 300s Weight 111094.92 2440.81 2626.59 2976.92 1129.78 95.85 300s Length1 2440.81 57.63 61.75 68.98 20.67 2.46 300s Length2 2626.59 61.75 66.28 74.24 23.13 2.57 300s Length3 2976.92 68.98 74.24 85.29 34.11 1.65 300s Height 1129.78 20.67 23.13 34.11 52.75 -3.70 300s Width 95.85 2.46 2.57 1.65 -3.70 1.71 300s -------------------------------------------------------- 300s airquality 153 4 21.282926 300s Outliers: 8 300s [1] 7 11 14 23 30 34 77 107 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s Ozone Solar.R Wind Temp 300s 39.40 192.29 9.66 78.74 300s 300s Robust Estimate of Covariance: 300s Ozone Solar.R Wind Temp 300s Ozone 930.566 849.644 -59.157 232.459 300s Solar.R 849.644 9207.569 0.594 168.122 300s Wind -59.157 0.594 10.783 -13.645 300s Temp 232.459 168.122 -13.645 92.048 300s -------------------------------------------------------- 300s attitude 30 7 28.084183 300s Outliers: 6 300s [1] 6 9 14 16 18 24 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s rating complaints privileges learning raises critical 300s 65.7 66.8 51.9 56.1 66.4 76.7 300s advance 300s 43.0 300s 300s Robust Estimate of Covariance: 300s rating complaints privileges learning raises critical advance 300s rating 143.88 114.95 64.97 105.69 83.95 6.96 41.78 300s complaints 114.95 143.84 79.28 115.00 101.48 19.69 66.13 300s privileges 64.97 79.28 126.38 94.70 73.87 5.37 61.07 300s learning 105.69 115.00 94.70 146.14 110.50 21.67 68.49 300s raises 83.95 101.48 73.87 110.50 115.01 24.91 77.16 300s critical 6.96 19.69 5.37 21.67 24.91 71.74 25.88 300s advance 41.78 66.13 61.07 68.49 77.16 25.88 97.71 300s -------------------------------------------------------- 300s attenu 182 5 10.109049 300s Outliers: 35 300s [1] 2 4 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 300s [20] 28 29 30 31 32 64 65 80 93 94 95 96 97 98 99 100 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s event mag station dist accel 300s 16.418 5.850 60.243 27.307 0.134 300s 300s Robust Estimate of Covariance: 300s event mag station dist accel 300s event 41.9000 -2.3543 137.8110 -39.0321 -0.0447 300s mag -2.3543 0.4978 -6.4461 5.2644 0.0118 300s station 137.8110 -6.4461 1283.9675 -90.1657 0.5554 300s dist -39.0321 5.2644 -90.1657 462.3898 -1.3672 300s accel -0.0447 0.0118 0.5554 -1.3672 0.0114 300s -------------------------------------------------------- 300s USJudgeRatings 43 12 -43.367499 300s Outliers: 10 300s [1] 5 7 8 12 13 14 20 23 31 35 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 300s 7.43 8.16 7.75 7.89 7.69 7.76 7.68 7.67 7.52 7.59 8.19 7.87 300s 300s Robust Estimate of Covariance: 300s CONT INTG DMNR DILG CFMG DECI PREP FAMI 300s CONT 0.6895 -0.2399 -0.3728 -0.1514 -0.0461 -0.0801 -0.1419 -0.1577 300s INTG -0.2399 0.5021 0.6746 0.5446 0.4479 0.4254 0.5564 0.5558 300s DMNR -0.3728 0.6746 0.9753 0.7128 0.5992 0.5715 0.7289 0.7181 300s DILG -0.1514 0.5446 0.7128 0.6691 0.5789 0.5501 0.6949 0.6892 300s CFMG -0.0461 0.4479 0.5992 0.5789 0.5468 0.5118 0.6100 0.6049 300s DECI -0.0801 0.4254 0.5715 0.5501 0.5118 0.4965 0.5872 0.5890 300s PREP -0.1419 0.5564 0.7289 0.6949 0.6100 0.5872 0.7497 0.7511 300s FAMI -0.1577 0.5558 0.7181 0.6892 0.6049 0.5890 0.7511 0.7696 300s ORAL -0.1950 0.5848 0.7798 0.6990 0.6143 0.5921 0.7508 0.7610 300s WRIT -0.1866 0.5747 0.7575 0.6946 0.6101 0.5895 0.7470 0.7607 300s PHYS -0.1620 0.3640 0.4878 0.4361 0.3927 0.3910 0.4655 0.4779 300s RTEN -0.2522 0.6268 0.8462 0.7220 0.6210 0.5991 0.7553 0.7599 300s ORAL WRIT PHYS RTEN 300s CONT -0.1950 -0.1866 -0.1620 -0.2522 300s INTG 0.5848 0.5747 0.3640 0.6268 300s DMNR 0.7798 0.7575 0.4878 0.8462 300s DILG 0.6990 0.6946 0.4361 0.7220 300s CFMG 0.6143 0.6101 0.3927 0.6210 300s DECI 0.5921 0.5895 0.3910 0.5991 300s PREP 0.7508 0.7470 0.4655 0.7553 300s FAMI 0.7610 0.7607 0.4779 0.7599 300s ORAL 0.7745 0.7665 0.4893 0.7866 300s WRIT 0.7665 0.7645 0.4823 0.7745 300s PHYS 0.4893 0.4823 0.3620 0.5062 300s RTEN 0.7866 0.7745 0.5062 0.8313 300s -------------------------------------------------------- 300s USArrests 50 4 19.266763 300s Outliers: 4 300s [1] 2 28 33 39 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s Murder Assault UrbanPop Rape 300s 7.04 150.55 64.64 19.34 300s 300s Robust Estimate of Covariance: 300s Murder Assault UrbanPop Rape 300s Murder 23.7 378.9 19.1 29.5 300s Assault 378.9 8388.2 601.3 639.7 300s UrbanPop 19.1 601.3 245.3 77.9 300s Rape 29.5 639.7 77.9 76.3 300s -------------------------------------------------------- 300s longley 16 7 13.789499 300s Outliers: 4 300s [1] 1 2 3 4 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s GNP.deflator GNP Unemployed Armed.Forces Population 300s 107 435 333 293 120 300s Year Employed 300s 1957 67 300s 300s Robust Estimate of Covariance: 300s GNP.deflator GNP Unemployed Armed.Forces Population 300s GNP.deflator 65.05 619.75 734.33 -294.02 48.27 300s GNP 619.75 6112.14 6578.12 -2684.52 474.26 300s Unemployed 734.33 6578.12 12075.90 -3627.79 548.58 300s Armed.Forces -294.02 -2684.52 -3627.79 1797.05 -204.25 300s Population 48.27 474.26 548.58 -204.25 37.36 300s Year 30.58 297.29 351.44 -135.53 23.29 300s Employed 20.36 203.96 186.62 -93.64 15.42 300s Year Employed 300s GNP.deflator 30.58 20.36 300s GNP 297.29 203.96 300s Unemployed 351.44 186.62 300s Armed.Forces -135.53 -93.64 300s Population 23.29 15.42 300s Year 14.70 9.80 300s Employed 9.80 7.36 300s -------------------------------------------------------- 300s Loblolly 84 3 8.518440 300s Outliers: 14 300s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s height age Seed 300s 24.14 9.62 7.51 300s 300s Robust Estimate of Covariance: 300s height age Seed 300s height 464.64 158.43 12.83 300s age 158.43 54.62 2.67 300s Seed 12.83 2.67 22.98 300s -------------------------------------------------------- 300s quakes 1000 4 11.611413 300s Outliers: 234 300s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 300s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 300s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 300s [46] 163 166 170 174 192 205 222 226 230 239 243 250 251 252 254 300s [61] 258 263 267 268 271 283 292 297 300 301 305 311 312 318 320 300s [76] 321 325 328 330 331 334 352 357 360 365 368 376 381 382 384 300s [91] 389 399 400 402 408 413 416 417 418 419 426 429 437 441 443 300s [106] 453 456 467 474 477 490 492 496 504 507 508 509 517 524 527 300s [121] 528 531 532 534 536 538 539 541 542 543 544 545 546 547 552 300s [136] 553 558 560 570 571 581 583 587 593 594 596 597 605 612 613 300s [151] 618 620 625 629 638 642 647 649 653 655 656 672 675 681 686 300s [166] 699 701 702 712 714 716 721 725 726 735 744 753 754 756 759 300s [181] 765 766 769 779 781 782 785 787 797 804 813 825 827 837 840 300s [196] 844 852 853 857 860 865 866 869 870 872 873 883 884 887 888 300s [211] 890 891 893 908 909 912 915 916 921 927 930 952 962 963 969 300s [226] 974 980 982 986 987 988 992 997 1000 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: bisquare 300s 300s Robust Estimate of Location: 300s lat long depth mag 300s -21.54 182.35 369.29 4.54 300s 300s Robust Estimate of Covariance: 300s lat long depth mag 300s lat 2.18e+01 4.82e+00 2.53e+02 -3.54e-01 300s long 4.82e+00 5.87e+00 -4.63e+02 7.45e-02 300s depth 2.53e+02 -4.63e+02 6.51e+04 -2.10e+01 300s mag -3.54e-01 7.45e-02 -2.10e+01 1.83e-01 300s -------------------------------------------------------- 300s =================================================== 300s > dodata(method="rocke") 300s 300s Call: dodata(method = "rocke") 300s Data Set n p LOG(det) Time 300s =================================================== 300s heart 12 2 7.285196 300s Outliers: 3 300s [1] 2 6 12 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: Rocke type 300s 300s Robust Estimate of Location: 300s height weight 300s 34.3 26.1 300s 300s Robust Estimate of Covariance: 300s height weight 300s height 105 159 300s weight 159 256 300s -------------------------------------------------------- 300s starsCYG 47 2 -5.929361 300s Outliers: 7 300s [1] 7 9 11 14 20 30 34 300s ------------- 300s 300s Call: 300s CovSest(x = x, method = method) 300s -> Method: S-estimates: Rocke type 300s 300s Robust Estimate of Location: 300s log.Te log.light 300s 4.42 4.93 300s 300s Robust Estimate of Covariance: 300s log.Te log.light 300s log.Te 0.0193 0.0709 300s log.light 0.0709 0.3987 300s -------------------------------------------------------- 301s phosphor 18 2 8.907518 301s Outliers: 3 301s [1] 1 6 10 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s inorg organic 301s 15.8 39.4 301s 301s Robust Estimate of Covariance: 301s inorg organic 301s inorg 196 252 301s organic 252 360 301s -------------------------------------------------------- 301s stackloss 21 3 8.143313 301s Outliers: 4 301s [1] 1 2 3 21 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s Air.Flow Water.Temp Acid.Conc. 301s 56.8 20.2 86.4 301s 301s Robust Estimate of Covariance: 301s Air.Flow Water.Temp Acid.Conc. 301s Air.Flow 29.26 9.62 14.78 301s Water.Temp 9.62 8.54 6.25 301s Acid.Conc. 14.78 6.25 29.70 301s -------------------------------------------------------- 301s coleman 20 5 4.001659 301s Outliers: 5 301s [1] 2 6 9 10 13 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s salaryP fatherWc sstatus teacherSc motherLev 301s 2.81 40.27 2.11 25.01 6.27 301s 301s Robust Estimate of Covariance: 301s salaryP fatherWc sstatus teacherSc motherLev 301s salaryP 0.2850 1.1473 2.0254 0.3536 0.0737 301s fatherWc 1.1473 798.0714 278.0145 6.4590 18.6357 301s sstatus 2.0254 278.0145 128.7601 4.0666 6.3845 301s teacherSc 0.3536 6.4590 4.0666 0.8749 0.2980 301s motherLev 0.0737 18.6357 6.3845 0.2980 0.4948 301s -------------------------------------------------------- 301s salinity 28 3 3.455146 301s Outliers: 9 301s [1] 3 5 10 11 15 16 17 23 24 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s X1 X2 X3 301s 9.89 3.10 22.46 301s 301s Robust Estimate of Covariance: 301s X1 X2 X3 301s X1 12.710 1.868 -4.135 301s X2 1.868 4.710 -0.663 301s X3 -4.135 -0.663 1.907 301s -------------------------------------------------------- 301s wood 20 5 -35.020244 301s Outliers: 7 301s [1] 4 6 7 8 11 16 19 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s x1 x2 x3 x4 x5 301s 0.588 0.123 0.534 0.535 0.891 301s 301s Robust Estimate of Covariance: 301s x1 x2 x3 x4 x5 301s x1 6.60e-03 1.25e-03 2.16e-03 -3.73e-04 -1.10e-03 301s x2 1.25e-03 3.30e-04 8.91e-04 -1.23e-05 2.62e-05 301s x3 2.16e-03 8.91e-04 4.55e-03 -4.90e-04 1.93e-04 301s x4 -3.73e-04 -1.23e-05 -4.90e-04 2.01e-03 1.36e-03 301s x5 -1.10e-03 2.62e-05 1.93e-04 1.36e-03 1.95e-03 301s -------------------------------------------------------- 301s hbk 75 3 1.413303 301s Outliers: 14 301s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s X1 X2 X3 301s 1.56 1.77 1.68 301s 301s Robust Estimate of Covariance: 301s X1 X2 X3 301s X1 1.6483 0.0825 0.2133 301s X2 0.0825 1.6928 0.2334 301s X3 0.2133 0.2334 1.5334 301s -------------------------------------------------------- 301s Animals 28 2 17.787210 301s Outliers: 11 301s [1] 2 6 7 9 12 14 15 16 24 25 28 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s body brain 301s 60.6 150.2 301s 301s Robust Estimate of Covariance: 301s body brain 301s body 10670 19646 301s brain 19646 41147 301s -------------------------------------------------------- 301s milk 86 8 -25.169970 301s Outliers: 22 301s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 28 41 44 47 70 73 74 75 77 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s X1 X2 X3 X4 X5 X6 X7 X8 301s 1.03 35.87 33.14 26.19 25.17 25.11 123.16 14.41 301s 301s Robust Estimate of Covariance: 301s X1 X2 X3 X4 X5 X6 X7 301s X1 4.47e-07 1.77e-04 1.94e-04 1.79e-04 1.60e-04 1.45e-04 6.45e-04 301s X2 1.77e-04 2.36e+00 4.03e-01 3.08e-01 2.08e-01 3.45e-01 2.18e+00 301s X3 1.94e-04 4.03e-01 1.13e+00 8.31e-01 8.08e-01 7.79e-01 9.83e-01 301s X4 1.79e-04 3.08e-01 8.31e-01 6.62e-01 6.22e-01 5.95e-01 7.82e-01 301s X5 1.60e-04 2.08e-01 8.08e-01 6.22e-01 6.51e-01 5.93e-01 7.60e-01 301s X6 1.45e-04 3.45e-01 7.79e-01 5.95e-01 5.93e-01 5.88e-01 7.81e-01 301s X7 6.45e-04 2.18e+00 9.83e-01 7.82e-01 7.60e-01 7.81e-01 4.81e+00 301s X8 2.47e-05 2.57e-01 2.00e-01 1.37e-01 1.13e-01 1.28e-01 4.38e-01 301s X8 301s X1 2.47e-05 301s X2 2.57e-01 301s X3 2.00e-01 301s X4 1.37e-01 301s X5 1.13e-01 301s X6 1.28e-01 301s X7 4.38e-01 301s X8 1.61e-01 301s -------------------------------------------------------- 301s bushfire 38 5 21.641566 301s Outliers: 13 301s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s V1 V2 V3 V4 V5 301s 111 150 256 214 276 301s 301s Robust Estimate of Covariance: 301s V1 V2 V3 V4 V5 301s V1 554 408 -2321 -464 -393 301s V2 408 343 -1361 -244 -215 301s V3 -2321 -1361 14690 3277 2684 301s V4 -464 -244 3277 783 629 301s V5 -393 -215 2684 629 509 301s -------------------------------------------------------- 301s rice 105 5 -7.208835 301s Outliers: 8 301s [1] 9 14 40 42 49 57 58 71 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s Favor Appearance Taste Stickiness Toughness 301s -0.21721 0.20948 -0.04581 0.15355 -0.00254 301s 301s Robust Estimate of Covariance: 301s Favor Appearance Taste Stickiness Toughness 301s Favor 0.432 0.337 0.417 0.382 -0.201 301s Appearance 0.337 0.591 0.553 0.510 -0.295 301s Taste 0.417 0.553 0.735 0.683 -0.385 301s Stickiness 0.382 0.510 0.683 0.834 -0.462 301s Toughness -0.201 -0.295 -0.385 -0.462 0.408 301s -------------------------------------------------------- 301s hemophilia 75 2 -7.453807 301s Outliers: 2 301s [1] 46 53 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s AHFactivity AHFantigen 301s -0.2276 -0.0637 301s 301s Robust Estimate of Covariance: 301s AHFactivity AHFantigen 301s AHFactivity 0.0405 0.0221 301s AHFantigen 0.0221 0.0263 301s -------------------------------------------------------- 301s fish 159 6 13.110263 301s Outliers: 47 301s [1] 38 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 301s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 301s [39] 98 99 100 101 102 103 104 140 142 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s Weight Length1 Length2 Length3 Height Width 301s 452.1 27.2 29.5 32.6 30.8 15.0 301s 301s Robust Estimate of Covariance: 301s Weight Length1 Length2 Length3 Height Width 301s Weight 132559.85 2817.97 3035.69 3369.07 1231.68 112.19 301s Length1 2817.97 64.16 68.74 75.36 22.52 2.37 301s Length2 3035.69 68.74 73.77 81.12 25.57 2.47 301s Length3 3369.07 75.36 81.12 91.65 37.39 1.40 301s Height 1231.68 22.52 25.57 37.39 50.91 -3.92 301s Width 112.19 2.37 2.47 1.40 -3.92 1.87 301s -------------------------------------------------------- 301s airquality 153 4 21.181656 301s Outliers: 13 301s [1] 6 7 11 14 17 20 23 30 34 53 63 77 107 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s Ozone Solar.R Wind Temp 301s 40.21 198.33 9.76 79.35 301s 301s Robust Estimate of Covariance: 301s Ozone Solar.R Wind Temp 301s Ozone 885.7 581.1 -57.3 226.4 301s Solar.R 581.1 8870.9 26.2 -15.1 301s Wind -57.3 26.2 11.8 -13.4 301s Temp 226.4 -15.1 -13.4 89.4 301s -------------------------------------------------------- 301s attitude 30 7 27.836398 301s Outliers: 8 301s [1] 1 9 13 14 17 18 24 26 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s rating complaints privileges learning raises critical 301s 64.0 65.4 50.5 54.9 63.1 72.6 301s advance 301s 40.5 301s 301s Robust Estimate of Covariance: 301s rating complaints privileges learning raises critical advance 301s rating 180.10 153.16 42.04 128.90 90.25 18.75 39.81 301s complaints 153.16 192.38 58.32 142.48 94.29 8.13 45.33 301s privileges 42.04 58.32 113.65 82.31 69.53 23.13 61.96 301s learning 128.90 142.48 82.31 156.99 101.74 13.22 49.64 301s raises 90.25 94.29 69.53 101.74 110.85 47.84 55.76 301s critical 18.75 8.13 23.13 13.22 47.84 123.00 36.97 301s advance 39.81 45.33 61.96 49.64 55.76 36.97 53.59 301s -------------------------------------------------------- 301s attenu 182 5 9.726797 301s Outliers: 44 301s [1] 1 2 4 5 6 7 8 9 10 11 13 15 16 19 20 21 22 23 24 301s [20] 25 27 28 29 30 31 32 40 45 60 61 64 65 78 80 81 93 94 95 301s [39] 96 97 98 99 100 108 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s event mag station dist accel 301s 16.39 5.82 60.89 27.97 0.12 301s 301s Robust Estimate of Covariance: 301s event mag station dist accel 301s event 4.20e+01 -1.97e+00 1.44e+02 -3.50e+01 4.05e-02 301s mag -1.97e+00 5.05e-01 -4.78e+00 4.63e+00 4.19e-03 301s station 1.44e+02 -4.78e+00 1.47e+03 -5.74e+01 7.88e-01 301s dist -3.50e+01 4.63e+00 -5.74e+01 3.99e+02 -1.18e+00 301s accel 4.05e-02 4.19e-03 7.88e-01 -1.18e+00 7.71e-03 301s -------------------------------------------------------- 301s USJudgeRatings 43 12 -46.356873 301s Outliers: 15 301s [1] 1 5 7 8 12 13 14 17 20 21 23 30 31 35 42 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 301s 7.56 8.12 7.70 7.91 7.74 7.82 7.66 7.66 7.50 7.58 8.22 7.86 301s 301s Robust Estimate of Covariance: 301s CONT INTG DMNR DILG CFMG DECI PREP 301s CONT 0.63426 -0.20121 -0.31858 -0.09578 0.00521 -0.00436 -0.07140 301s INTG -0.20121 0.28326 0.37540 0.27103 0.20362 0.19838 0.25706 301s DMNR -0.31858 0.37540 0.58265 0.33615 0.25649 0.24804 0.31696 301s DILG -0.09578 0.27103 0.33615 0.32588 0.27022 0.26302 0.32236 301s CFMG 0.00521 0.20362 0.25649 0.27022 0.25929 0.24217 0.27784 301s DECI -0.00436 0.19838 0.24804 0.26302 0.24217 0.23830 0.27284 301s PREP -0.07140 0.25706 0.31696 0.32236 0.27784 0.27284 0.35071 301s FAMI -0.07118 0.25858 0.29511 0.32582 0.27863 0.27657 0.35941 301s ORAL -0.11149 0.27055 0.33919 0.31768 0.27339 0.26739 0.34200 301s WRIT -0.10050 0.26857 0.32570 0.32327 0.27860 0.27201 0.34399 301s PHYS -0.09693 0.15339 0.18416 0.17089 0.13837 0.14895 0.18472 301s RTEN -0.15643 0.31793 0.40884 0.33863 0.27073 0.26854 0.34049 301s FAMI ORAL WRIT PHYS RTEN 301s CONT -0.07118 -0.11149 -0.10050 -0.09693 -0.15643 301s INTG 0.25858 0.27055 0.26857 0.15339 0.31793 301s DMNR 0.29511 0.33919 0.32570 0.18416 0.40884 301s DILG 0.32582 0.31768 0.32327 0.17089 0.33863 301s CFMG 0.27863 0.27339 0.27860 0.13837 0.27073 301s DECI 0.27657 0.26739 0.27201 0.14895 0.26854 301s PREP 0.35941 0.34200 0.34399 0.18472 0.34049 301s FAMI 0.38378 0.35617 0.36094 0.19998 0.35048 301s ORAL 0.35617 0.34918 0.34808 0.19759 0.35217 301s WRIT 0.36094 0.34808 0.35242 0.19666 0.35090 301s PHYS 0.19998 0.19759 0.19666 0.14770 0.20304 301s RTEN 0.35048 0.35217 0.35090 0.20304 0.39451 301s -------------------------------------------------------- 301s USArrests 50 4 19.206310 301s Outliers: 4 301s [1] 2 28 33 39 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s Murder Assault UrbanPop Rape 301s 7.55 160.94 65.10 19.97 301s 301s Robust Estimate of Covariance: 301s Murder Assault UrbanPop Rape 301s Murder 25.6 409.5 23.4 32.1 301s Assault 409.5 8530.9 676.9 669.4 301s UrbanPop 23.4 676.9 269.9 76.6 301s Rape 32.1 669.4 76.6 76.6 301s -------------------------------------------------------- 301s longley 16 7 13.387132 301s Outliers: 4 301s [1] 1 2 3 4 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s GNP.deflator GNP Unemployed Armed.Forces Population 301s 105.5 422.4 318.3 299.7 119.5 301s Year Employed 301s 1956.1 66.5 301s 301s Robust Estimate of Covariance: 301s GNP.deflator GNP Unemployed Armed.Forces Population 301s GNP.deflator 59.97 582.66 694.99 -237.75 46.12 301s GNP 582.66 5849.82 6383.68 -2207.26 461.15 301s Unemployed 694.99 6383.68 11155.03 -3104.18 534.25 301s Armed.Forces -237.75 -2207.26 -3104.18 1429.11 -171.28 301s Population 46.12 461.15 534.25 -171.28 36.79 301s Year 29.01 287.48 340.95 -112.61 22.85 301s Employed 18.99 193.66 186.31 -76.88 14.94 301s Year Employed 301s GNP.deflator 29.01 18.99 301s GNP 287.48 193.66 301s Unemployed 340.95 186.31 301s Armed.Forces -112.61 -76.88 301s Population 22.85 14.94 301s Year 14.36 9.45 301s Employed 9.45 6.90 301s -------------------------------------------------------- 301s Loblolly 84 3 7.757906 301s Outliers: 27 301s [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 301s [26] 83 84 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s height age Seed 301s 21.72 8.60 7.58 301s 301s Robust Estimate of Covariance: 301s height age Seed 301s height 316.590 102.273 5.939 301s age 102.273 33.465 -0.121 301s Seed 5.939 -0.121 27.203 301s -------------------------------------------------------- 301s quakes 1000 4 11.473431 301s Outliers: 237 301s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 301s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 301s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 301s [46] 163 166 170 174 176 192 205 222 226 230 239 243 244 250 251 301s [61] 252 254 258 263 267 268 271 283 292 297 300 301 305 311 312 301s [76] 318 320 321 325 328 330 331 334 352 357 360 365 368 376 381 301s [91] 382 384 389 399 400 402 408 410 413 416 417 418 419 426 429 301s [106] 437 441 443 453 456 467 474 477 490 492 496 504 507 508 509 301s [121] 517 524 527 528 531 532 534 536 538 539 541 542 543 544 545 301s [136] 546 547 552 553 558 560 570 571 581 583 587 593 594 596 597 301s [151] 605 612 613 618 620 625 629 638 642 647 649 653 655 656 672 301s [166] 675 681 686 699 701 702 712 714 716 721 725 726 735 744 753 301s [181] 754 756 759 765 766 769 779 781 782 785 787 797 804 813 825 301s [196] 827 837 840 844 852 853 857 860 865 866 869 870 872 873 883 301s [211] 884 887 888 890 891 893 908 909 912 915 916 921 927 930 952 301s [226] 962 963 969 974 980 982 986 987 988 992 997 1000 301s ------------- 301s 301s Call: 301s CovSest(x = x, method = method) 301s -> Method: S-estimates: Rocke type 301s 301s Robust Estimate of Location: 301s lat long depth mag 301s -21.45 182.54 351.18 4.55 301s 301s Robust Estimate of Covariance: 301s lat long depth mag 301s lat 2.10e+01 4.66e+00 2.45e+02 -3.38e-01 301s long 4.66e+00 5.88e+00 -4.63e+02 9.36e-02 301s depth 2.45e+02 -4.63e+02 6.38e+04 -2.02e+01 301s mag -3.38e-01 9.36e-02 -2.02e+01 1.78e-01 301s -------------------------------------------------------- 301s =================================================== 301s > dodata(method="MM") 301s 301s Call: dodata(method = "MM") 301s Data Set n p LOG(det) Time 301s =================================================== 301s heart 12 2 2.017701 301s Outliers: 1 301s [1] 6 301s ------------- 301s 301s Call: 301s CovMMest(x = x) 301s -> Method: MM-estimates 301s 301s Robust Estimate of Location: 301s height weight 301s 40.0 37.7 301s 301s Robust Estimate of Covariance: 301s height weight 301s height 99.2 205.7 301s weight 205.7 458.9 301s -------------------------------------------------------- 301s starsCYG 47 2 -1.450032 301s Outliers: 7 301s [1] 7 9 11 14 20 30 34 301s ------------- 301s 301s Call: 301s CovMMest(x = x) 301s -> Method: MM-estimates 301s 301s Robust Estimate of Location: 301s log.Te log.light 301s 4.41 4.94 301s 301s Robust Estimate of Covariance: 301s log.Te log.light 301s log.Te 0.0180 0.0526 301s log.light 0.0526 0.3217 301s -------------------------------------------------------- 301s phosphor 18 2 2.320721 301s Outliers: 1 301s [1] 6 301s ------------- 301s 301s Call: 301s CovMMest(x = x) 301s -> Method: MM-estimates 301s 301s Robust Estimate of Location: 301s inorg organic 301s 12.3 41.4 301s 301s Robust Estimate of Covariance: 301s inorg organic 301s inorg 94.2 67.2 301s organic 67.2 162.1 301s -------------------------------------------------------- 301s stackloss 21 3 1.470031 301s Outliers: 0 301s ------------- 301s 301s Call: 301s CovMMest(x = x) 301s -> Method: MM-estimates 301s 301s Robust Estimate of Location: 301s Air.Flow Water.Temp Acid.Conc. 301s 60.2 21.0 86.4 301s 301s Robust Estimate of Covariance: 301s Air.Flow Water.Temp Acid.Conc. 301s Air.Flow 81.13 21.99 23.15 301s Water.Temp 21.99 10.01 6.43 301s Acid.Conc. 23.15 6.43 27.22 301s -------------------------------------------------------- 301s coleman 20 5 0.491419 301s Outliers: 1 301s [1] 10 301s ------------- 301s 301s Call: 301s CovMMest(x = x) 301s -> Method: MM-estimates 301s 301s Robust Estimate of Location: 301s salaryP fatherWc sstatus teacherSc motherLev 301s 2.74 43.14 3.65 25.07 6.32 301s 301s Robust Estimate of Covariance: 301s salaryP fatherWc sstatus teacherSc motherLev 301s salaryP 0.1878 2.0635 1.0433 0.2721 0.0582 301s fatherWc 2.0635 670.2232 211.0609 4.3625 15.6083 301s sstatus 1.0433 211.0609 92.8743 2.6532 5.1816 301s teacherSc 0.2721 4.3625 2.6532 1.2757 0.1613 301s motherLev 0.0582 15.6083 5.1816 0.1613 0.4192 301s -------------------------------------------------------- 302s salinity 28 3 0.734619 302s Outliers: 2 302s [1] 5 16 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s X1 X2 X3 302s 10.46 2.66 23.15 302s 302s Robust Estimate of Covariance: 302s X1 X2 X3 302s X1 10.079 -0.024 -1.899 302s X2 -0.024 3.466 -1.817 302s X3 -1.899 -1.817 3.665 302s -------------------------------------------------------- 302s wood 20 5 -3.202636 302s Outliers: 0 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s x1 x2 x3 x4 x5 302s 0.550 0.133 0.506 0.511 0.909 302s 302s Robust Estimate of Covariance: 302s x1 x2 x3 x4 x5 302s x1 0.008454 -0.000377 0.003720 0.002874 -0.003065 302s x2 -0.000377 0.000516 -0.000399 -0.000933 0.000645 302s x3 0.003720 -0.000399 0.004186 0.001720 -0.001714 302s x4 0.002874 -0.000933 0.001720 0.003993 -0.001028 302s x5 -0.003065 0.000645 -0.001714 -0.001028 0.002744 302s -------------------------------------------------------- 302s hbk 75 3 0.283145 302s Outliers: 14 302s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s X1 X2 X3 302s 1.54 1.79 1.68 302s 302s Robust Estimate of Covariance: 302s X1 X2 X3 302s X1 1.8016 0.0739 0.2000 302s X2 0.0739 1.8301 0.2295 302s X3 0.2000 0.2295 1.7101 302s -------------------------------------------------------- 302s Animals 28 2 4.685129 302s Outliers: 10 302s [1] 2 6 7 9 12 14 15 16 24 25 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s body brain 302s 82 148 302s 302s Robust Estimate of Covariance: 302s body brain 302s body 21050 24534 302s brain 24534 35135 302s -------------------------------------------------------- 302s milk 86 8 -1.437863 302s Outliers: 12 302s [1] 1 2 3 12 13 17 41 44 47 70 74 75 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s X1 X2 X3 X4 X5 X6 X7 X8 302s 1.03 35.73 32.87 25.96 24.94 24.85 122.55 14.33 302s 302s Robust Estimate of Covariance: 302s X1 X2 X3 X4 X5 X6 X7 302s X1 1.08e-06 5.36e-04 6.80e-04 5.96e-04 5.87e-04 5.91e-04 2.22e-03 302s X2 5.36e-04 2.42e+00 7.07e-01 5.51e-01 4.89e-01 5.70e-01 3.08e+00 302s X3 6.80e-04 7.07e-01 1.64e+00 1.28e+00 1.25e+00 1.26e+00 2.38e+00 302s X4 5.96e-04 5.51e-01 1.28e+00 1.05e+00 1.01e+00 1.02e+00 2.01e+00 302s X5 5.87e-04 4.89e-01 1.25e+00 1.01e+00 1.05e+00 1.02e+00 1.96e+00 302s X6 5.91e-04 5.70e-01 1.26e+00 1.02e+00 1.02e+00 1.05e+00 2.01e+00 302s X7 2.22e-03 3.08e+00 2.38e+00 2.01e+00 1.96e+00 2.01e+00 9.22e+00 302s X8 1.68e-04 4.13e-01 3.37e-01 2.53e-01 2.34e-01 2.43e-01 8.81e-01 302s X8 302s X1 1.68e-04 302s X2 4.13e-01 302s X3 3.37e-01 302s X4 2.53e-01 302s X5 2.34e-01 302s X6 2.43e-01 302s X7 8.81e-01 302s X8 2.11e-01 302s -------------------------------------------------------- 302s bushfire 38 5 2.443148 302s Outliers: 12 302s [1] 8 9 10 11 31 32 33 34 35 36 37 38 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s V1 V2 V3 V4 V5 302s 109 149 258 215 276 302s 302s Robust Estimate of Covariance: 302s V1 V2 V3 V4 V5 302s V1 708 538 -2705 -558 -464 302s V2 538 497 -1376 -248 -216 302s V3 -2705 -1376 20521 4833 3914 302s V4 -558 -248 4833 1217 969 302s V5 -464 -216 3914 969 778 302s -------------------------------------------------------- 302s rice 105 5 -0.724874 302s Outliers: 5 302s [1] 9 42 49 58 71 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s Favor Appearance Taste Stickiness Toughness 302s -0.2653 0.0969 -0.1371 0.0483 0.0731 302s 302s Robust Estimate of Covariance: 302s Favor Appearance Taste Stickiness Toughness 302s Favor 0.421 0.349 0.427 0.405 -0.191 302s Appearance 0.349 0.605 0.565 0.553 -0.316 302s Taste 0.427 0.565 0.725 0.701 -0.378 302s Stickiness 0.405 0.553 0.701 0.868 -0.484 302s Toughness -0.191 -0.316 -0.378 -0.484 0.464 302s -------------------------------------------------------- 302s hemophilia 75 2 -1.868949 302s Outliers: 2 302s [1] 11 36 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s AHFactivity AHFantigen 302s -0.2342 -0.0333 302s 302s Robust Estimate of Covariance: 302s AHFactivity AHFantigen 302s AHFactivity 0.0309 0.0122 302s AHFantigen 0.0122 0.0231 302s -------------------------------------------------------- 302s fish 159 6 1.285876 302s Outliers: 20 302s [1] 61 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 302s [20] 142 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s Weight Length1 Length2 Length3 Height Width 302s 352.7 24.3 26.4 29.2 29.7 14.6 302s 302s Robust Estimate of Covariance: 302s Weight Length1 Length2 Length3 Height Width 302s Weight 1.20e+05 2.89e+03 3.12e+03 3.51e+03 1.49e+03 2.83e+02 302s Length1 2.89e+03 7.73e+01 8.35e+01 9.28e+01 3.73e+01 9.26e+00 302s Length2 3.12e+03 8.35e+01 9.04e+01 1.01e+02 4.16e+01 1.01e+01 302s Length3 3.51e+03 9.28e+01 1.01e+02 1.14e+02 5.37e+01 1.01e+01 302s Height 1.49e+03 3.73e+01 4.16e+01 5.37e+01 6.75e+01 3.22e+00 302s Width 2.83e+02 9.26e+00 1.01e+01 1.01e+01 3.22e+00 4.18e+00 302s -------------------------------------------------------- 302s airquality 153 4 2.684374 302s Outliers: 6 302s [1] 7 14 23 30 34 77 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s Ozone Solar.R Wind Temp 302s 40.35 186.21 9.86 78.09 302s 302s Robust Estimate of Covariance: 302s Ozone Solar.R Wind Temp 302s Ozone 951.0 959.9 -62.5 224.6 302s Solar.R 959.9 8629.9 -28.1 244.9 302s Wind -62.5 -28.1 11.6 -15.8 302s Temp 224.6 244.9 -15.8 93.1 302s -------------------------------------------------------- 302s attitude 30 7 2.091968 302s Outliers: 4 302s [1] 14 16 18 24 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s rating complaints privileges learning raises critical 302s 65.0 66.5 52.4 56.2 65.3 75.6 302s advance 302s 42.7 302s 302s Robust Estimate of Covariance: 302s rating complaints privileges learning raises critical advance 302s rating 143.5 123.4 62.4 92.5 79.2 17.7 28.2 302s complaints 123.4 159.8 83.9 99.7 96.0 27.3 44.0 302s privileges 62.4 83.9 133.5 78.6 62.0 13.4 46.4 302s learning 92.5 99.7 78.6 136.0 90.9 18.9 62.6 302s raises 79.2 96.0 62.0 90.9 107.6 34.6 63.3 302s critical 17.7 27.3 13.4 18.9 34.6 84.9 25.9 302s advance 28.2 44.0 46.4 62.6 63.3 25.9 94.4 302s -------------------------------------------------------- 302s attenu 182 5 1.148032 302s Outliers: 21 302s [1] 2 7 8 9 10 11 15 16 24 25 28 29 30 31 32 64 65 94 95 302s [20] 96 100 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s event mag station dist accel 302s 15.36 5.95 58.11 33.56 0.14 302s 302s Robust Estimate of Covariance: 302s event mag station dist accel 302s event 4.88e+01 -2.74e+00 1.53e+02 -1.14e+02 5.95e-02 302s mag -2.74e+00 5.32e-01 -6.29e+00 1.10e+01 9.37e-03 302s station 1.53e+02 -6.29e+00 1.29e+03 -2.95e+02 1.04e+00 302s dist -1.14e+02 1.10e+01 -2.95e+02 1.13e+03 -2.41e+00 302s accel 5.95e-02 9.37e-03 1.04e+00 -2.41e+00 1.70e-02 302s -------------------------------------------------------- 302s USJudgeRatings 43 12 -1.683847 302s Outliers: 7 302s [1] 5 7 12 13 14 23 31 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 302s 7.45 8.15 7.74 7.87 7.67 7.74 7.65 7.65 7.50 7.57 8.17 7.85 302s 302s Robust Estimate of Covariance: 302s CONT INTG DMNR DILG CFMG DECI PREP FAMI 302s CONT 0.9403 -0.2500 -0.3953 -0.1418 -0.0176 -0.0620 -0.1304 -0.1517 302s INTG -0.2500 0.6314 0.8479 0.6889 0.5697 0.5386 0.7007 0.6985 302s DMNR -0.3953 0.8479 1.2186 0.9027 0.7613 0.7232 0.9191 0.9055 302s DILG -0.1418 0.6889 0.9027 0.8474 0.7344 0.6949 0.8751 0.8655 302s CFMG -0.0176 0.5697 0.7613 0.7344 0.6904 0.6442 0.7683 0.7594 302s DECI -0.0620 0.5386 0.7232 0.6949 0.6442 0.6219 0.7362 0.7360 302s PREP -0.1304 0.7007 0.9191 0.8751 0.7683 0.7362 0.9370 0.9357 302s FAMI -0.1517 0.6985 0.9055 0.8655 0.7594 0.7360 0.9357 0.9547 302s ORAL -0.1866 0.7375 0.9841 0.8816 0.7747 0.7433 0.9400 0.9496 302s WRIT -0.1881 0.7208 0.9516 0.8711 0.7646 0.7357 0.9302 0.9439 302s PHYS -0.1407 0.4673 0.6261 0.5661 0.5105 0.5039 0.5996 0.6112 302s RTEN -0.2494 0.7921 1.0688 0.9167 0.7902 0.7585 0.9533 0.9561 302s ORAL WRIT PHYS RTEN 302s CONT -0.1866 -0.1881 -0.1407 -0.2494 302s INTG 0.7375 0.7208 0.4673 0.7921 302s DMNR 0.9841 0.9516 0.6261 1.0688 302s DILG 0.8816 0.8711 0.5661 0.9167 302s CFMG 0.7747 0.7646 0.5105 0.7902 302s DECI 0.7433 0.7357 0.5039 0.7585 302s PREP 0.9400 0.9302 0.5996 0.9533 302s FAMI 0.9496 0.9439 0.6112 0.9561 302s ORAL 0.9712 0.9558 0.6271 0.9933 302s WRIT 0.9558 0.9483 0.6135 0.9725 302s PHYS 0.6271 0.6135 0.4816 0.6549 302s RTEN 0.9933 0.9725 0.6549 1.0540 302s -------------------------------------------------------- 302s USArrests 50 4 2.411726 302s Outliers: 3 302s [1] 2 33 39 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s Murder Assault UrbanPop Rape 302s 7.52 163.86 65.66 20.64 302s 302s Robust Estimate of Covariance: 302s Murder Assault UrbanPop Rape 302s Murder 19.05 295.96 8.32 23.40 302s Assault 295.96 6905.03 396.53 523.49 302s UrbanPop 8.32 396.53 202.98 62.81 302s Rape 23.40 523.49 62.81 79.10 302s -------------------------------------------------------- 302s longley 16 7 1.038316 302s Outliers: 5 302s [1] 1 2 3 4 5 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s GNP.deflator GNP Unemployed Armed.Forces Population 302s 107.5 440.4 339.4 293.0 120.9 302s Year Employed 302s 1957.0 67.2 302s 302s Robust Estimate of Covariance: 302s GNP.deflator GNP Unemployed Armed.Forces Population 302s GNP.deflator 100.4 953.8 1140.8 -501.8 74.3 302s GNP 953.8 9434.3 10084.3 -4573.8 731.3 302s Unemployed 1140.8 10084.3 19644.6 -6296.3 848.4 302s Armed.Forces -501.8 -4573.8 -6296.3 3192.3 -348.5 302s Population 74.3 731.3 848.4 -348.5 57.7 302s Year 46.3 450.7 537.0 -230.7 35.3 302s Employed 30.8 310.2 273.8 -159.4 23.3 302s Year Employed 302s GNP.deflator 46.3 30.8 302s GNP 450.7 310.2 302s Unemployed 537.0 273.8 302s Armed.Forces -230.7 -159.4 302s Population 35.3 23.3 302s Year 21.9 14.6 302s Employed 14.6 11.2 302s -------------------------------------------------------- 302s Loblolly 84 3 1.481317 302s Outliers: 0 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s height age Seed 302s 31.93 12.79 7.48 302s 302s Robust Estimate of Covariance: 302s height age Seed 302s height 440.644 165.652 6.958 302s age 165.652 63.500 0.681 302s Seed 6.958 0.681 16.564 302s -------------------------------------------------------- 302s quakes 1000 4 1.576855 302s Outliers: 218 302s [1] 7 12 15 17 22 27 32 37 40 41 45 48 53 63 64 302s [16] 73 78 87 91 92 94 99 108 110 117 118 119 120 121 122 302s [31] 126 133 136 141 143 145 148 152 154 155 157 159 160 163 170 302s [46] 192 205 222 226 230 239 243 250 251 252 254 258 263 267 268 302s [61] 271 283 292 300 301 305 311 312 318 320 321 325 328 330 334 302s [76] 352 357 360 365 381 382 384 389 400 402 408 413 416 417 419 302s [91] 429 437 441 443 453 456 467 474 477 490 492 496 504 507 508 302s [106] 509 517 524 527 528 531 532 534 536 538 539 541 542 543 544 302s [121] 545 546 547 552 553 560 571 581 583 587 593 594 596 597 605 302s [136] 612 613 618 620 625 629 638 642 647 649 653 655 656 672 675 302s [151] 681 686 699 701 702 712 714 716 721 725 726 735 744 754 756 302s [166] 759 765 766 769 779 781 782 785 787 797 804 813 825 827 837 302s [181] 840 844 852 853 857 860 865 866 869 870 872 873 883 884 887 302s [196] 888 890 891 893 908 909 912 915 916 921 927 930 962 963 969 302s [211] 974 980 982 986 987 988 997 1000 302s ------------- 302s 302s Call: 302s CovMMest(x = x) 302s -> Method: MM-estimates 302s 302s Robust Estimate of Location: 302s lat long depth mag 302s -21.74 182.37 356.37 4.56 302s 302s Robust Estimate of Covariance: 302s lat long depth mag 302s lat 2.97e+01 6.53e+00 3.46e+02 -4.66e-01 302s long 6.53e+00 6.92e+00 -5.05e+02 5.62e-02 302s depth 3.46e+02 -5.05e+02 7.39e+04 -2.51e+01 302s mag -4.66e-01 5.62e-02 -2.51e+01 2.32e-01 302s -------------------------------------------------------- 302s =================================================== 302s > ##dogen() 302s > ##cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 302s > 302s autopkgtest [16:41:01]: test run-unit-test: -----------------------] 307s run-unit-test PASS 307s autopkgtest [16:41:06]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 310s autopkgtest [16:41:09]: test pkg-r-autopkgtest: preparing testbed 312s Reading package lists... 312s Building dependency tree... 312s Reading state information... 313s Starting pkgProblemResolver with broken count: 0 313s Starting 2 pkgProblemResolver with broken count: 0 313s Done 314s The following NEW packages will be installed: 314s build-essential cpp cpp-14 cpp-14-arm-linux-gnueabihf 314s cpp-arm-linux-gnueabihf dctrl-tools g++ g++-14 g++-14-arm-linux-gnueabihf 314s g++-arm-linux-gnueabihf gcc gcc-14 gcc-14-arm-linux-gnueabihf 314s gcc-arm-linux-gnueabihf gfortran gfortran-14 gfortran-14-arm-linux-gnueabihf 314s gfortran-arm-linux-gnueabihf icu-devtools libasan8 libblas-dev libbz2-dev 314s libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libdeflate-dev libgcc-14-dev 314s libgfortran-14-dev libicu-dev libisl23 libjpeg-dev libjpeg-turbo8-dev 314s libjpeg8-dev liblapack-dev liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 314s libpcre2-32-0 libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev 314s libreadline-dev libstdc++-14-dev libtirpc-dev libubsan1 linux-libc-dev 314s pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev rpcsvc-proto zlib1g-dev 314s 0 upgraded, 55 newly installed, 0 to remove and 0 not upgraded. 314s Need to get 78.1 MB of archives. 314s After this operation, 243 MB of additional disk space will be used. 314s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc-dev-bin armhf 2.41-1ubuntu2 [23.0 kB] 314s Get:2 http://ftpmaster.internal/ubuntu plucky/main armhf linux-libc-dev armhf 6.14.0-10.10 [1683 kB] 316s Get:3 http://ftpmaster.internal/ubuntu plucky/main armhf libcrypt-dev armhf 1:4.4.38-1 [120 kB] 316s Get:4 http://ftpmaster.internal/ubuntu plucky/main armhf rpcsvc-proto armhf 1.4.2-0ubuntu7 [62.2 kB] 316s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/main armhf libc6-dev armhf 2.41-1ubuntu2 [1396 kB] 318s Get:6 http://ftpmaster.internal/ubuntu plucky/main armhf libisl23 armhf 0.27-1 [546 kB] 318s Get:7 http://ftpmaster.internal/ubuntu plucky/main armhf libmpc3 armhf 1.3.1-1build2 [47.1 kB] 318s Get:8 http://ftpmaster.internal/ubuntu plucky/main armhf cpp-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [9220 kB] 334s Get:9 http://ftpmaster.internal/ubuntu plucky/main armhf cpp-14 armhf 14.2.0-17ubuntu3 [1030 B] 334s Get:10 http://ftpmaster.internal/ubuntu plucky/main armhf cpp-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [5578 B] 334s Get:11 http://ftpmaster.internal/ubuntu plucky/main armhf cpp armhf 4:14.2.0-1ubuntu1 [22.4 kB] 334s Get:12 http://ftpmaster.internal/ubuntu plucky/main armhf libcc1-0 armhf 15-20250222-0ubuntu1 [38.9 kB] 334s Get:13 http://ftpmaster.internal/ubuntu plucky/main armhf libasan8 armhf 15-20250222-0ubuntu1 [2955 kB] 337s Get:14 http://ftpmaster.internal/ubuntu plucky/main armhf libubsan1 armhf 15-20250222-0ubuntu1 [1191 kB] 338s Get:15 http://ftpmaster.internal/ubuntu plucky/main armhf libgcc-14-dev armhf 14.2.0-17ubuntu3 [897 kB] 339s Get:16 http://ftpmaster.internal/ubuntu plucky/main armhf gcc-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [18.0 MB] 357s Get:17 http://ftpmaster.internal/ubuntu plucky/main armhf gcc-14 armhf 14.2.0-17ubuntu3 [506 kB] 358s Get:18 http://ftpmaster.internal/ubuntu plucky/main armhf gcc-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [1218 B] 358s Get:19 http://ftpmaster.internal/ubuntu plucky/main armhf gcc armhf 4:14.2.0-1ubuntu1 [5004 B] 358s Get:20 http://ftpmaster.internal/ubuntu plucky/main armhf libstdc++-14-dev armhf 14.2.0-17ubuntu3 [2573 kB] 360s Get:21 http://ftpmaster.internal/ubuntu plucky/main armhf g++-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [10.5 MB] 370s Get:22 http://ftpmaster.internal/ubuntu plucky/main armhf g++-14 armhf 14.2.0-17ubuntu3 [21.8 kB] 370s Get:23 http://ftpmaster.internal/ubuntu plucky/main armhf g++-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [966 B] 370s Get:24 http://ftpmaster.internal/ubuntu plucky/main armhf g++ armhf 4:14.2.0-1ubuntu1 [1084 B] 370s Get:25 http://ftpmaster.internal/ubuntu plucky/main armhf build-essential armhf 12.10ubuntu1 [4928 B] 370s Get:26 http://ftpmaster.internal/ubuntu plucky/main armhf dctrl-tools armhf 2.24-3build3 [94.7 kB] 370s Get:27 http://ftpmaster.internal/ubuntu plucky/main armhf libgfortran-14-dev armhf 14.2.0-17ubuntu3 [370 kB] 370s Get:28 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran-14-arm-linux-gnueabihf armhf 14.2.0-17ubuntu3 [9763 kB] 380s Get:29 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran-14 armhf 14.2.0-17ubuntu3 [13.6 kB] 380s Get:30 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran-arm-linux-gnueabihf armhf 4:14.2.0-1ubuntu1 [1026 B] 380s Get:31 http://ftpmaster.internal/ubuntu plucky/main armhf gfortran armhf 4:14.2.0-1ubuntu1 [1166 B] 380s Get:32 http://ftpmaster.internal/ubuntu plucky/main armhf icu-devtools armhf 76.1-1ubuntu2 [206 kB] 380s Get:33 http://ftpmaster.internal/ubuntu plucky/main armhf libblas-dev armhf 3.12.1-2 [141 kB] 381s Get:34 http://ftpmaster.internal/ubuntu plucky/main armhf libbz2-dev armhf 1.0.8-6 [30.9 kB] 381s Get:35 http://ftpmaster.internal/ubuntu plucky/main armhf libdeflate-dev armhf 1.23-1 [45.0 kB] 381s Get:36 http://ftpmaster.internal/ubuntu plucky/main armhf libicu-dev armhf 76.1-1ubuntu2 [12.0 MB] 393s Get:37 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg-turbo8-dev armhf 2.1.5-3ubuntu2 [265 kB] 393s Get:38 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg8-dev armhf 8c-2ubuntu11 [1484 B] 393s Get:39 http://ftpmaster.internal/ubuntu plucky/main armhf libjpeg-dev armhf 8c-2ubuntu11 [1482 B] 393s Get:40 http://ftpmaster.internal/ubuntu plucky/main armhf liblapack-dev armhf 3.12.1-2 [2207 kB] 396s Get:41 http://ftpmaster.internal/ubuntu plucky/main armhf libncurses-dev armhf 6.5+20250216-2 [345 kB] 396s Get:42 http://ftpmaster.internal/ubuntu plucky/main armhf libpcre2-16-0 armhf 10.45-1 [207 kB] 396s Get:43 http://ftpmaster.internal/ubuntu plucky/main armhf libpcre2-32-0 armhf 10.45-1 [197 kB] 396s Get:44 http://ftpmaster.internal/ubuntu plucky/main armhf libpcre2-posix3 armhf 10.45-1 [6300 B] 396s Get:45 http://ftpmaster.internal/ubuntu plucky/main armhf libpcre2-dev armhf 10.45-1 [752 kB] 397s Get:46 http://ftpmaster.internal/ubuntu plucky/main armhf libpkgconf3 armhf 1.8.1-4 [26.6 kB] 397s Get:47 http://ftpmaster.internal/ubuntu plucky/main armhf zlib1g-dev armhf 1:1.3.dfsg+really1.3.1-1ubuntu1 [880 kB] 398s Get:48 http://ftpmaster.internal/ubuntu plucky/main armhf libpng-dev armhf 1.6.47-1 [251 kB] 398s Get:49 http://ftpmaster.internal/ubuntu plucky/main armhf libreadline-dev armhf 8.2-6 [153 kB] 398s Get:50 http://ftpmaster.internal/ubuntu plucky/main armhf liblzma-dev armhf 5.6.4-1 [166 kB] 398s Get:51 http://ftpmaster.internal/ubuntu plucky/main armhf pkgconf-bin armhf 1.8.1-4 [21.2 kB] 398s Get:52 http://ftpmaster.internal/ubuntu plucky/main armhf pkgconf armhf 1.8.1-4 [16.8 kB] 398s Get:53 http://ftpmaster.internal/ubuntu plucky/main armhf libtirpc-dev armhf 1.3.4+ds-1.3 [184 kB] 399s Get:54 http://ftpmaster.internal/ubuntu plucky/universe armhf r-base-dev all 4.4.3-1 [4176 B] 399s Get:55 http://ftpmaster.internal/ubuntu plucky/universe armhf pkg-r-autopkgtest all 20231212ubuntu1 [6448 B] 399s Fetched 78.1 MB in 1min 25s (921 kB/s) 399s Selecting previously unselected package libc-dev-bin. 399s (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 ... 67266 files and directories currently installed.) 399s Preparing to unpack .../00-libc-dev-bin_2.41-1ubuntu2_armhf.deb ... 399s Unpacking libc-dev-bin (2.41-1ubuntu2) ... 399s Selecting previously unselected package linux-libc-dev:armhf. 399s Preparing to unpack .../01-linux-libc-dev_6.14.0-10.10_armhf.deb ... 399s Unpacking linux-libc-dev:armhf (6.14.0-10.10) ... 400s Selecting previously unselected package libcrypt-dev:armhf. 400s Preparing to unpack .../02-libcrypt-dev_1%3a4.4.38-1_armhf.deb ... 400s Unpacking libcrypt-dev:armhf (1:4.4.38-1) ... 400s Selecting previously unselected package rpcsvc-proto. 400s Preparing to unpack .../03-rpcsvc-proto_1.4.2-0ubuntu7_armhf.deb ... 400s Unpacking rpcsvc-proto (1.4.2-0ubuntu7) ... 400s Selecting previously unselected package libc6-dev:armhf. 400s Preparing to unpack .../04-libc6-dev_2.41-1ubuntu2_armhf.deb ... 400s Unpacking libc6-dev:armhf (2.41-1ubuntu2) ... 400s Selecting previously unselected package libisl23:armhf. 400s Preparing to unpack .../05-libisl23_0.27-1_armhf.deb ... 400s Unpacking libisl23:armhf (0.27-1) ... 400s Selecting previously unselected package libmpc3:armhf. 400s Preparing to unpack .../06-libmpc3_1.3.1-1build2_armhf.deb ... 400s Unpacking libmpc3:armhf (1.3.1-1build2) ... 400s Selecting previously unselected package cpp-14-arm-linux-gnueabihf. 400s Preparing to unpack .../07-cpp-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ... 400s Unpacking cpp-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 400s Selecting previously unselected package cpp-14. 400s Preparing to unpack .../08-cpp-14_14.2.0-17ubuntu3_armhf.deb ... 400s Unpacking cpp-14 (14.2.0-17ubuntu3) ... 400s Selecting previously unselected package cpp-arm-linux-gnueabihf. 400s Preparing to unpack .../09-cpp-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ... 400s Unpacking cpp-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 400s Selecting previously unselected package cpp. 400s Preparing to unpack .../10-cpp_4%3a14.2.0-1ubuntu1_armhf.deb ... 400s Unpacking cpp (4:14.2.0-1ubuntu1) ... 400s Selecting previously unselected package libcc1-0:armhf. 400s Preparing to unpack .../11-libcc1-0_15-20250222-0ubuntu1_armhf.deb ... 400s Unpacking libcc1-0:armhf (15-20250222-0ubuntu1) ... 400s Selecting previously unselected package libasan8:armhf. 400s Preparing to unpack .../12-libasan8_15-20250222-0ubuntu1_armhf.deb ... 400s Unpacking libasan8:armhf (15-20250222-0ubuntu1) ... 401s Selecting previously unselected package libubsan1:armhf. 401s Preparing to unpack .../13-libubsan1_15-20250222-0ubuntu1_armhf.deb ... 401s Unpacking libubsan1:armhf (15-20250222-0ubuntu1) ... 401s Selecting previously unselected package libgcc-14-dev:armhf. 401s Preparing to unpack .../14-libgcc-14-dev_14.2.0-17ubuntu3_armhf.deb ... 401s Unpacking libgcc-14-dev:armhf (14.2.0-17ubuntu3) ... 401s Selecting previously unselected package gcc-14-arm-linux-gnueabihf. 401s Preparing to unpack .../15-gcc-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ... 401s Unpacking gcc-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 401s Selecting previously unselected package gcc-14. 401s Preparing to unpack .../16-gcc-14_14.2.0-17ubuntu3_armhf.deb ... 401s Unpacking gcc-14 (14.2.0-17ubuntu3) ... 401s Selecting previously unselected package gcc-arm-linux-gnueabihf. 401s Preparing to unpack .../17-gcc-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ... 401s Unpacking gcc-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 401s Selecting previously unselected package gcc. 401s Preparing to unpack .../18-gcc_4%3a14.2.0-1ubuntu1_armhf.deb ... 401s Unpacking gcc (4:14.2.0-1ubuntu1) ... 401s Selecting previously unselected package libstdc++-14-dev:armhf. 401s Preparing to unpack .../19-libstdc++-14-dev_14.2.0-17ubuntu3_armhf.deb ... 401s Unpacking libstdc++-14-dev:armhf (14.2.0-17ubuntu3) ... 401s Selecting previously unselected package g++-14-arm-linux-gnueabihf. 401s Preparing to unpack .../20-g++-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ... 401s Unpacking g++-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 402s Selecting previously unselected package g++-14. 402s Preparing to unpack .../21-g++-14_14.2.0-17ubuntu3_armhf.deb ... 402s Unpacking g++-14 (14.2.0-17ubuntu3) ... 402s Selecting previously unselected package g++-arm-linux-gnueabihf. 402s Preparing to unpack .../22-g++-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ... 402s Unpacking g++-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 402s Selecting previously unselected package g++. 402s Preparing to unpack .../23-g++_4%3a14.2.0-1ubuntu1_armhf.deb ... 402s Unpacking g++ (4:14.2.0-1ubuntu1) ... 402s Selecting previously unselected package build-essential. 402s Preparing to unpack .../24-build-essential_12.10ubuntu1_armhf.deb ... 402s Unpacking build-essential (12.10ubuntu1) ... 402s Selecting previously unselected package dctrl-tools. 402s Preparing to unpack .../25-dctrl-tools_2.24-3build3_armhf.deb ... 402s Unpacking dctrl-tools (2.24-3build3) ... 402s Selecting previously unselected package libgfortran-14-dev:armhf. 402s Preparing to unpack .../26-libgfortran-14-dev_14.2.0-17ubuntu3_armhf.deb ... 402s Unpacking libgfortran-14-dev:armhf (14.2.0-17ubuntu3) ... 402s Selecting previously unselected package gfortran-14-arm-linux-gnueabihf. 402s Preparing to unpack .../27-gfortran-14-arm-linux-gnueabihf_14.2.0-17ubuntu3_armhf.deb ... 402s Unpacking gfortran-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 402s Selecting previously unselected package gfortran-14. 402s Preparing to unpack .../28-gfortran-14_14.2.0-17ubuntu3_armhf.deb ... 402s Unpacking gfortran-14 (14.2.0-17ubuntu3) ... 402s Selecting previously unselected package gfortran-arm-linux-gnueabihf. 402s Preparing to unpack .../29-gfortran-arm-linux-gnueabihf_4%3a14.2.0-1ubuntu1_armhf.deb ... 402s Unpacking gfortran-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 402s Selecting previously unselected package gfortran. 402s Preparing to unpack .../30-gfortran_4%3a14.2.0-1ubuntu1_armhf.deb ... 402s Unpacking gfortran (4:14.2.0-1ubuntu1) ... 402s Selecting previously unselected package icu-devtools. 402s Preparing to unpack .../31-icu-devtools_76.1-1ubuntu2_armhf.deb ... 402s Unpacking icu-devtools (76.1-1ubuntu2) ... 402s Selecting previously unselected package libblas-dev:armhf. 402s Preparing to unpack .../32-libblas-dev_3.12.1-2_armhf.deb ... 402s Unpacking libblas-dev:armhf (3.12.1-2) ... 402s Selecting previously unselected package libbz2-dev:armhf. 402s Preparing to unpack .../33-libbz2-dev_1.0.8-6_armhf.deb ... 402s Unpacking libbz2-dev:armhf (1.0.8-6) ... 402s Selecting previously unselected package libdeflate-dev:armhf. 402s Preparing to unpack .../34-libdeflate-dev_1.23-1_armhf.deb ... 402s Unpacking libdeflate-dev:armhf (1.23-1) ... 402s Selecting previously unselected package libicu-dev:armhf. 402s Preparing to unpack .../35-libicu-dev_76.1-1ubuntu2_armhf.deb ... 402s Unpacking libicu-dev:armhf (76.1-1ubuntu2) ... 403s Selecting previously unselected package libjpeg-turbo8-dev:armhf. 403s Preparing to unpack .../36-libjpeg-turbo8-dev_2.1.5-3ubuntu2_armhf.deb ... 403s Unpacking libjpeg-turbo8-dev:armhf (2.1.5-3ubuntu2) ... 403s Selecting previously unselected package libjpeg8-dev:armhf. 403s Preparing to unpack .../37-libjpeg8-dev_8c-2ubuntu11_armhf.deb ... 403s Unpacking libjpeg8-dev:armhf (8c-2ubuntu11) ... 403s Selecting previously unselected package libjpeg-dev:armhf. 403s Preparing to unpack .../38-libjpeg-dev_8c-2ubuntu11_armhf.deb ... 403s Unpacking libjpeg-dev:armhf (8c-2ubuntu11) ... 403s Selecting previously unselected package liblapack-dev:armhf. 403s Preparing to unpack .../39-liblapack-dev_3.12.1-2_armhf.deb ... 403s Unpacking liblapack-dev:armhf (3.12.1-2) ... 403s Selecting previously unselected package libncurses-dev:armhf. 403s Preparing to unpack .../40-libncurses-dev_6.5+20250216-2_armhf.deb ... 403s Unpacking libncurses-dev:armhf (6.5+20250216-2) ... 403s Selecting previously unselected package libpcre2-16-0:armhf. 403s Preparing to unpack .../41-libpcre2-16-0_10.45-1_armhf.deb ... 403s Unpacking libpcre2-16-0:armhf (10.45-1) ... 403s Selecting previously unselected package libpcre2-32-0:armhf. 403s Preparing to unpack .../42-libpcre2-32-0_10.45-1_armhf.deb ... 403s Unpacking libpcre2-32-0:armhf (10.45-1) ... 403s Selecting previously unselected package libpcre2-posix3:armhf. 403s Preparing to unpack .../43-libpcre2-posix3_10.45-1_armhf.deb ... 403s Unpacking libpcre2-posix3:armhf (10.45-1) ... 403s Selecting previously unselected package libpcre2-dev:armhf. 403s Preparing to unpack .../44-libpcre2-dev_10.45-1_armhf.deb ... 403s Unpacking libpcre2-dev:armhf (10.45-1) ... 403s Selecting previously unselected package libpkgconf3:armhf. 403s Preparing to unpack .../45-libpkgconf3_1.8.1-4_armhf.deb ... 403s Unpacking libpkgconf3:armhf (1.8.1-4) ... 403s Selecting previously unselected package zlib1g-dev:armhf. 403s Preparing to unpack .../46-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu1_armhf.deb ... 403s Unpacking zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1ubuntu1) ... 403s Selecting previously unselected package libpng-dev:armhf. 403s Preparing to unpack .../47-libpng-dev_1.6.47-1_armhf.deb ... 403s Unpacking libpng-dev:armhf (1.6.47-1) ... 403s Selecting previously unselected package libreadline-dev:armhf. 403s Preparing to unpack .../48-libreadline-dev_8.2-6_armhf.deb ... 403s Unpacking libreadline-dev:armhf (8.2-6) ... 403s Selecting previously unselected package liblzma-dev:armhf. 403s Preparing to unpack .../49-liblzma-dev_5.6.4-1_armhf.deb ... 403s Unpacking liblzma-dev:armhf (5.6.4-1) ... 403s Selecting previously unselected package pkgconf-bin. 403s Preparing to unpack .../50-pkgconf-bin_1.8.1-4_armhf.deb ... 403s Unpacking pkgconf-bin (1.8.1-4) ... 403s Selecting previously unselected package pkgconf:armhf. 403s Preparing to unpack .../51-pkgconf_1.8.1-4_armhf.deb ... 403s Unpacking pkgconf:armhf (1.8.1-4) ... 403s Selecting previously unselected package libtirpc-dev:armhf. 403s Preparing to unpack .../52-libtirpc-dev_1.3.4+ds-1.3_armhf.deb ... 403s Unpacking libtirpc-dev:armhf (1.3.4+ds-1.3) ... 403s Selecting previously unselected package r-base-dev. 403s Preparing to unpack .../53-r-base-dev_4.4.3-1_all.deb ... 403s Unpacking r-base-dev (4.4.3-1) ... 404s Selecting previously unselected package pkg-r-autopkgtest. 404s Preparing to unpack .../54-pkg-r-autopkgtest_20231212ubuntu1_all.deb ... 404s Unpacking pkg-r-autopkgtest (20231212ubuntu1) ... 404s Setting up linux-libc-dev:armhf (6.14.0-10.10) ... 404s Setting up libpcre2-16-0:armhf (10.45-1) ... 404s Setting up libpcre2-32-0:armhf (10.45-1) ... 404s Setting up libtirpc-dev:armhf (1.3.4+ds-1.3) ... 404s Setting up libpkgconf3:armhf (1.8.1-4) ... 404s Setting up rpcsvc-proto (1.4.2-0ubuntu7) ... 404s Setting up libmpc3:armhf (1.3.1-1build2) ... 404s Setting up icu-devtools (76.1-1ubuntu2) ... 404s Setting up pkgconf-bin (1.8.1-4) ... 404s Setting up liblzma-dev:armhf (5.6.4-1) ... 404s Setting up libubsan1:armhf (15-20250222-0ubuntu1) ... 404s Setting up libpcre2-posix3:armhf (10.45-1) ... 404s Setting up libcrypt-dev:armhf (1:4.4.38-1) ... 404s Setting up libasan8:armhf (15-20250222-0ubuntu1) ... 404s Setting up libgcc-14-dev:armhf (14.2.0-17ubuntu3) ... 404s Setting up libisl23:armhf (0.27-1) ... 404s Setting up libc-dev-bin (2.41-1ubuntu2) ... 404s Setting up libdeflate-dev:armhf (1.23-1) ... 404s Setting up libcc1-0:armhf (15-20250222-0ubuntu1) ... 404s Setting up libblas-dev:armhf (3.12.1-2) ... 404s update-alternatives: using /usr/lib/arm-linux-gnueabihf/blas/libblas.so to provide /usr/lib/arm-linux-gnueabihf/libblas.so (libblas.so-arm-linux-gnueabihf) in auto mode 404s Setting up dctrl-tools (2.24-3build3) ... 404s Setting up cpp-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 404s Setting up libgfortran-14-dev:armhf (14.2.0-17ubuntu3) ... 404s Setting up gcc-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 404s Setting up pkgconf:armhf (1.8.1-4) ... 404s Setting up liblapack-dev:armhf (3.12.1-2) ... 404s update-alternatives: using /usr/lib/arm-linux-gnueabihf/lapack/liblapack.so to provide /usr/lib/arm-linux-gnueabihf/liblapack.so (liblapack.so-arm-linux-gnueabihf) in auto mode 404s Setting up cpp-14 (14.2.0-17ubuntu3) ... 404s Setting up libc6-dev:armhf (2.41-1ubuntu2) ... 404s Setting up libstdc++-14-dev:armhf (14.2.0-17ubuntu3) ... 404s Setting up libicu-dev:armhf (76.1-1ubuntu2) ... 404s Setting up cpp-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 404s Setting up gfortran-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 404s Setting up libbz2-dev:armhf (1.0.8-6) ... 404s Setting up gcc-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 404s Setting up g++-14-arm-linux-gnueabihf (14.2.0-17ubuntu3) ... 404s Setting up libjpeg-turbo8-dev:armhf (2.1.5-3ubuntu2) ... 404s Setting up libncurses-dev:armhf (6.5+20250216-2) ... 404s Setting up libpcre2-dev:armhf (10.45-1) ... 404s Setting up libreadline-dev:armhf (8.2-6) ... 404s Setting up gcc-14 (14.2.0-17ubuntu3) ... 404s Setting up gfortran-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 404s Setting up zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1ubuntu1) ... 404s Setting up cpp (4:14.2.0-1ubuntu1) ... 404s Setting up g++-14 (14.2.0-17ubuntu3) ... 404s Setting up libjpeg8-dev:armhf (8c-2ubuntu11) ... 404s Setting up gfortran-14 (14.2.0-17ubuntu3) ... 404s Setting up g++-arm-linux-gnueabihf (4:14.2.0-1ubuntu1) ... 404s Setting up libpng-dev:armhf (1.6.47-1) ... 404s Setting up libjpeg-dev:armhf (8c-2ubuntu11) ... 404s Setting up gcc (4:14.2.0-1ubuntu1) ... 404s Setting up g++ (4:14.2.0-1ubuntu1) ... 404s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 404s Setting up build-essential (12.10ubuntu1) ... 404s Setting up gfortran (4:14.2.0-1ubuntu1) ... 404s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 404s 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 404s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 404s 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 404s Setting up r-base-dev (4.4.3-1) ... 404s Setting up pkg-r-autopkgtest (20231212ubuntu1) ... 404s Processing triggers for libc-bin (2.41-1ubuntu2) ... 404s Processing triggers for man-db (2.13.0-1) ... 405s Processing triggers for install-info (7.1.1-1) ... 413s autopkgtest [16:42:52]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 413s autopkgtest [16:42:52]: test pkg-r-autopkgtest: [----------------------- 414s Test: Try to load the R library rrcov 415s 415s R version 4.4.3 (2025-02-28) -- "Trophy Case" 415s Copyright (C) 2025 The R Foundation for Statistical Computing 415s Platform: arm-unknown-linux-gnueabihf (32-bit) 415s 415s R is free software and comes with ABSOLUTELY NO WARRANTY. 415s You are welcome to redistribute it under certain conditions. 415s Type 'license()' or 'licence()' for distribution details. 415s 415s R is a collaborative project with many contributors. 415s Type 'contributors()' for more information and 415s 'citation()' on how to cite R or R packages in publications. 415s 415s Type 'demo()' for some demos, 'help()' for on-line help, or 415s 'help.start()' for an HTML browser interface to help. 415s Type 'q()' to quit R. 415s 415s Loading required package: robustbase 415s > library('rrcov') 415s Scalable Robust Estimators with High Breakdown Point (version 1.7-6) 415s 415s > 415s > 415s Other tests are currently unsupported! 415s They will be progressively added. 415s autopkgtest [16:42:54]: test pkg-r-autopkgtest: -----------------------] 419s pkg-r-autopkgtest PASS 419s autopkgtest [16:42:58]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 423s autopkgtest [16:43:02]: @@@@@@@@@@@@@@@@@@@@ summary 423s run-unit-test PASS 423s pkg-r-autopkgtest PASS