0s autopkgtest [23:02:20]: starting date and time: 2026-02-09 23:02:20+0000 0s autopkgtest [23:02:20]: git checkout: 508d4a25 a-v-ssh wait_for_ssh: demote "ssh connection failed" to a debug message 0s autopkgtest [23:02:20]: host juju-7f2275-prod-proposed-migration-environment-9; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.a7vimvsk/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,localhost,localdomain,internal,login.ubuntu.com,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,keyserver.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com,radosgw.ps5.canonical.com\n" >> /etc/environment' --apt-pocket=proposed=src:lattice --apt-upgrade r-cran-rrcov --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=lattice/0.22-9-1 -- lxd -r lxd-armhf-10.145.243.229 lxd-armhf-10.145.243.229:autopkgtest/ubuntu/resolute/armhf 22s autopkgtest [23:02:42]: testbed dpkg architecture: armhf 23s autopkgtest [23:02:43]: testbed apt version: 3.1.15 28s autopkgtest [23:02:48]: @@@@@@@@@@@@@@@@@@@@ test bed setup 30s autopkgtest [23:02:50]: testbed release detected to be: None 37s autopkgtest [23:02:57]: updating testbed package index (apt update) 39s Get:1 http://ftpmaster.internal/ubuntu resolute-proposed InRelease [124 kB] 40s Get:2 http://ftpmaster.internal/ubuntu resolute InRelease [124 kB] 40s Get:3 http://ftpmaster.internal/ubuntu resolute-updates InRelease [124 kB] 41s Get:4 http://ftpmaster.internal/ubuntu resolute-security InRelease [124 kB] 42s Get:5 http://ftpmaster.internal/ubuntu resolute-proposed/universe Sources [1645 kB] 48s Get:6 http://ftpmaster.internal/ubuntu resolute-proposed/main Sources [176 kB] 49s Get:7 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse Sources [29.4 kB] 49s Get:8 http://ftpmaster.internal/ubuntu resolute-proposed/main armhf Packages [246 kB] 49s Get:9 http://ftpmaster.internal/ubuntu resolute-proposed/universe armhf Packages [1405 kB] 54s Get:10 http://ftpmaster.internal/ubuntu resolute-proposed/multiverse armhf Packages [7452 B] 54s Get:11 http://ftpmaster.internal/ubuntu resolute/universe Sources [21.4 MB] 101s Get:12 http://ftpmaster.internal/ubuntu resolute/restricted Sources [15.1 kB] 101s Get:13 http://ftpmaster.internal/ubuntu resolute/main Sources [1398 kB] 104s Get:14 http://ftpmaster.internal/ubuntu resolute/main armhf Packages [1374 kB] 107s Get:15 http://ftpmaster.internal/ubuntu resolute/universe armhf Packages [15.3 MB] 139s Fetched 43.5 MB in 1min 40s (435 kB/s) 140s Reading package lists... 146s autopkgtest [23:04:46]: upgrading testbed (apt dist-upgrade and autopurge) 148s Reading package lists... 149s Building dependency tree... 149s Reading state information... 150s Calculating upgrade... 151s The following packages will be upgraded: 151s cryptsetup-bin dracut-install iproute2 iptables libcryptsetup12 libip4tc2 151s libip6tc2 libxtables12 wget 151s 9 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 151s Need to get 2407 kB of archives. 151s After this operation, 152 kB of additional disk space will be used. 151s Get:1 http://ftpmaster.internal/ubuntu resolute/main armhf iptables armhf 1.8.11-2ubuntu3 [345 kB] 152s Get:2 http://ftpmaster.internal/ubuntu resolute/main armhf libip4tc2 armhf 1.8.11-2ubuntu3 [22.0 kB] 152s Get:3 http://ftpmaster.internal/ubuntu resolute/main armhf libip6tc2 armhf 1.8.11-2ubuntu3 [22.3 kB] 152s Get:4 http://ftpmaster.internal/ubuntu resolute/main armhf libxtables12 armhf 1.8.11-2ubuntu3 [33.5 kB] 152s Get:5 http://ftpmaster.internal/ubuntu resolute/main armhf iproute2 armhf 6.18.0-1ubuntu1 [1123 kB] 155s Get:6 http://ftpmaster.internal/ubuntu resolute/main armhf libcryptsetup12 armhf 2:2.8.0-1ubuntu3 [254 kB] 155s Get:7 http://ftpmaster.internal/ubuntu resolute/main armhf wget armhf 1.25.0-2ubuntu4 [327 kB] 156s Get:8 http://ftpmaster.internal/ubuntu resolute/main armhf cryptsetup-bin armhf 2:2.8.0-1ubuntu3 [232 kB] 156s Get:9 http://ftpmaster.internal/ubuntu resolute/main armhf dracut-install armhf 109-11ubuntu1 [47.9 kB] 157s Preconfiguring packages ... 157s Fetched 2407 kB in 5s (471 kB/s) 158s (Reading database ... (Reading database ... 5% (Reading database ... 10% (Reading database ... 15% (Reading database ... 20% (Reading database ... 25% (Reading database ... 30% (Reading database ... 35% (Reading database ... 40% (Reading database ... 45% (Reading database ... 50% (Reading database ... 55% (Reading database ... 60% (Reading database ... 65% (Reading database ... 70% (Reading database ... 75% (Reading database ... 80% (Reading database ... 85% (Reading database ... 90% (Reading database ... 95% (Reading database ... 100% (Reading database ... 68683 files and directories currently installed.) 158s Preparing to unpack .../0-iptables_1.8.11-2ubuntu3_armhf.deb ... 158s Unpacking iptables (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 158s Preparing to unpack .../1-libip4tc2_1.8.11-2ubuntu3_armhf.deb ... 158s Unpacking libip4tc2:armhf (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 158s Preparing to unpack .../2-libip6tc2_1.8.11-2ubuntu3_armhf.deb ... 158s Unpacking libip6tc2:armhf (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 158s Preparing to unpack .../3-libxtables12_1.8.11-2ubuntu3_armhf.deb ... 158s Unpacking libxtables12:armhf (1.8.11-2ubuntu3) over (1.8.11-2ubuntu2) ... 158s Preparing to unpack .../4-iproute2_6.18.0-1ubuntu1_armhf.deb ... 158s Unpacking iproute2 (6.18.0-1ubuntu1) over (6.16.0-1ubuntu3) ... 159s Preparing to unpack .../5-libcryptsetup12_2%3a2.8.0-1ubuntu3_armhf.deb ... 159s Unpacking libcryptsetup12:armhf (2:2.8.0-1ubuntu3) over (2:2.8.0-1ubuntu2) ... 159s Preparing to unpack .../6-wget_1.25.0-2ubuntu4_armhf.deb ... 159s Unpacking wget (1.25.0-2ubuntu4) over (1.25.0-2ubuntu3) ... 159s Preparing to unpack .../7-cryptsetup-bin_2%3a2.8.0-1ubuntu3_armhf.deb ... 159s Unpacking cryptsetup-bin (2:2.8.0-1ubuntu3) over (2:2.8.0-1ubuntu2) ... 159s Preparing to unpack .../8-dracut-install_109-11ubuntu1_armhf.deb ... 159s Unpacking dracut-install (109-11ubuntu1) over (109-9ubuntu1) ... 159s Setting up libip4tc2:armhf (1.8.11-2ubuntu3) ... 159s Setting up wget (1.25.0-2ubuntu4) ... 159s Setting up libip6tc2:armhf (1.8.11-2ubuntu3) ... 159s Setting up libxtables12:armhf (1.8.11-2ubuntu3) ... 159s Setting up dracut-install (109-11ubuntu1) ... 159s Setting up libcryptsetup12:armhf (2:2.8.0-1ubuntu3) ... 159s Setting up cryptsetup-bin (2:2.8.0-1ubuntu3) ... 159s Setting up iptables (1.8.11-2ubuntu3) ... 159s Setting up iproute2 (6.18.0-1ubuntu1) ... 159s Processing triggers for man-db (2.13.1-1build1) ... 161s Processing triggers for install-info (7.2-5) ... 161s Processing triggers for libc-bin (2.42-2ubuntu4) ... 164s Reading package lists... 164s Building dependency tree... 164s Reading state information... 165s Solving dependencies... 166s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 169s autopkgtest [23:05:09]: rebooting testbed after setup commands that affected boot 210s autopkgtest [23:05:50]: testbed running kernel: Linux 6.8.0-87-generic #88~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Tue Oct 14 14:00:09 UTC 2 235s autopkgtest [23:06:15]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-rrcov 248s Get:1 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (dsc) [2146 B] 248s Get:2 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (tar) [1542 kB] 248s Get:3 http://ftpmaster.internal/ubuntu resolute/universe r-cran-rrcov 1.7-6-1 (diff) [3160 B] 249s gpgv: Signature made Fri Sep 6 03:10:50 2024 UTC 249s gpgv: using RSA key 73471499CC60ED9EEE805946C5BD6C8F2295D502 249s gpgv: issuer "plessy@debian.org" 249s gpgv: Can't check signature: No public key 249s dpkg-source: warning: cannot verify inline signature for ./r-cran-rrcov_1.7-6-1.dsc: no acceptable signature found 249s autopkgtest [23:06:29]: testing package r-cran-rrcov version 1.7-6-1 251s autopkgtest [23:06:31]: build not needed 255s autopkgtest [23:06:35]: test run-unit-test: preparing testbed 257s Reading package lists... 257s Building dependency tree... 257s Reading state information... 257s Solving dependencies... 258s The following NEW packages will be installed: 258s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono libblas3 258s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libfreetype6 libgfortran5 258s libgomp1 libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 258s libjpeg8 liblapack3 liblerc4 libpango-1.0-0 libpangocairo-1.0-0 258s libpangoft2-1.0-0 libpaper-utils libpaper2 libpixman-1-0 libsharpyuv0 libsm6 258s libtcl8.6 libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 258s libxcb-shm0 libxft2 libxrender1 libxss1 libxt6t64 r-base-core 258s r-cran-deoptimr r-cran-lattice r-cran-mass r-cran-mvtnorm r-cran-pcapp 258s r-cran-robustbase r-cran-rrcov unzip x11-common xdg-utils zip 258s 0 upgraded, 52 newly installed, 0 to remove and 0 not upgraded. 258s Need to get 47.3 MB of archives. 258s After this operation, 83.5 MB of additional disk space will be used. 258s Get:1 http://ftpmaster.internal/ubuntu resolute/main armhf libfreetype6 armhf 2.14.1+dfsg-2 [345 kB] 259s Get:2 http://ftpmaster.internal/ubuntu resolute/main armhf fonts-dejavu-mono all 2.37-8build1 [502 kB] 261s Get:3 http://ftpmaster.internal/ubuntu resolute/main armhf fonts-dejavu-core all 2.37-8build1 [834 kB] 263s Get:4 http://ftpmaster.internal/ubuntu resolute/main armhf fontconfig-config armhf 2.17.1-3ubuntu1 [38.5 kB] 263s Get:5 http://ftpmaster.internal/ubuntu resolute/main armhf libfontconfig1 armhf 2.17.1-3ubuntu1 [117 kB] 264s Get:6 http://ftpmaster.internal/ubuntu resolute/main armhf fontconfig armhf 2.17.1-3ubuntu1 [180 kB] 264s Get:7 http://ftpmaster.internal/ubuntu resolute/main armhf libblas3 armhf 3.12.1-7ubuntu1 [133 kB] 265s Get:8 http://ftpmaster.internal/ubuntu resolute/main armhf libpixman-1-0 armhf 0.46.4-1 [196 kB] 265s Get:9 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-render0 armhf 1.17.0-2ubuntu1 [15.5 kB] 265s Get:10 http://ftpmaster.internal/ubuntu resolute/main armhf libxcb-shm0 armhf 1.17.0-2ubuntu1 [5956 B] 265s Get:11 http://ftpmaster.internal/ubuntu resolute/main armhf libxrender1 armhf 1:0.9.12-1 [16.6 kB] 265s Get:12 http://ftpmaster.internal/ubuntu resolute/main armhf libcairo2 armhf 1.18.4-3 [489 kB] 266s Get:13 http://ftpmaster.internal/ubuntu resolute/main armhf libdatrie1 armhf 0.2.14-1 [16.4 kB] 266s Get:14 http://ftpmaster.internal/ubuntu resolute/main armhf libdeflate0 armhf 1.23-2build1 [38.8 kB] 266s Get:15 http://ftpmaster.internal/ubuntu resolute/main armhf libgfortran5 armhf 15.2.0-12ubuntu1 [334 kB] 267s Get:16 http://ftpmaster.internal/ubuntu resolute/main armhf libgomp1 armhf 15.2.0-12ubuntu1 [129 kB] 267s Get:17 http://ftpmaster.internal/ubuntu resolute/main armhf libgraphite2-3 armhf 1.3.14-11ubuntu1 [65.2 kB] 267s Get:18 http://ftpmaster.internal/ubuntu resolute/main armhf libharfbuzz0b armhf 12.3.2-1 [501 kB] 268s Get:19 http://ftpmaster.internal/ubuntu resolute/main armhf x11-common all 1:7.7+24ubuntu1 [22.4 kB] 268s Get:20 http://ftpmaster.internal/ubuntu resolute/main armhf libice6 armhf 2:1.1.1-1build1 [37.5 kB] 268s Get:21 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg-turbo8 armhf 2.1.5-4ubuntu3 [129 kB] 269s Get:22 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg8 armhf 8c-2ubuntu11 [2148 B] 269s Get:23 http://ftpmaster.internal/ubuntu resolute/main armhf liblapack3 armhf 3.12.1-7ubuntu1 [2090 kB] 273s Get:24 http://ftpmaster.internal/ubuntu resolute/main armhf liblerc4 armhf 4.0.0+ds-5ubuntu2 [162 kB] 274s Get:25 http://ftpmaster.internal/ubuntu resolute/main armhf libthai-data all 0.1.30-1 [155 kB] 274s Get:26 http://ftpmaster.internal/ubuntu resolute/main armhf libthai0 armhf 0.1.30-1 [15.4 kB] 274s Get:27 http://ftpmaster.internal/ubuntu resolute/main armhf libpango-1.0-0 armhf 1.57.0-1 [218 kB] 275s Get:28 http://ftpmaster.internal/ubuntu resolute/main armhf libpangoft2-1.0-0 armhf 1.57.0-1 [45.2 kB] 275s Get:29 http://ftpmaster.internal/ubuntu resolute/main armhf libpangocairo-1.0-0 armhf 1.57.0-1 [25.3 kB] 275s Get:30 http://ftpmaster.internal/ubuntu resolute/main armhf libpaper2 armhf 2.2.5-0.3build1 [16.3 kB] 275s Get:31 http://ftpmaster.internal/ubuntu resolute/main armhf libpaper-utils armhf 2.2.5-0.3build1 [14.2 kB] 275s Get:32 http://ftpmaster.internal/ubuntu resolute/main armhf libsharpyuv0 armhf 1.5.0-0.1build1 [16.3 kB] 275s Get:33 http://ftpmaster.internal/ubuntu resolute/main armhf libsm6 armhf 2:1.2.6-1build1 [15.3 kB] 275s Get:34 http://ftpmaster.internal/ubuntu resolute/main armhf libtcl8.6 armhf 8.6.17+dfsg-1build1 [918 kB] 276s Get:35 http://ftpmaster.internal/ubuntu resolute/main armhf libjbig0 armhf 2.1-6.1ubuntu3 [25.3 kB] 276s Get:36 http://ftpmaster.internal/ubuntu resolute/main armhf libwebp7 armhf 1.5.0-0.1build1 [189 kB] 277s Get:37 http://ftpmaster.internal/ubuntu resolute/main armhf libtiff6 armhf 4.7.0-3ubuntu3 [188 kB] 277s Get:38 http://ftpmaster.internal/ubuntu resolute/main armhf libxft2 armhf 2.3.6-1build2 [37.2 kB] 277s Get:39 http://ftpmaster.internal/ubuntu resolute/main armhf libxss1 armhf 1:1.2.3-1build4 [6328 B] 277s Get:40 http://ftpmaster.internal/ubuntu resolute/main armhf libtk8.6 armhf 8.6.17-1 [694 kB] 278s Get:41 http://ftpmaster.internal/ubuntu resolute/main armhf libxt6t64 armhf 1:1.2.1-1.3 [145 kB] 278s Get:42 http://ftpmaster.internal/ubuntu resolute/main armhf zip armhf 3.0-15ubuntu3 [164 kB] 278s Get:43 http://ftpmaster.internal/ubuntu resolute/main armhf unzip armhf 6.0-29ubuntu1 [167 kB] 278s Get:44 http://ftpmaster.internal/ubuntu resolute/main armhf xdg-utils all 1.2.1-2ubuntu2 [66.1 kB] 279s Get:45 http://ftpmaster.internal/ubuntu resolute/universe armhf r-base-core armhf 4.5.2-1ubuntu2 [28.5 MB] 312s Get:46 http://ftpmaster.internal/ubuntu resolute/universe armhf r-cran-deoptimr all 1.1-4-1 [76.7 kB] 312s Get:47 http://ftpmaster.internal/ubuntu resolute-proposed/universe armhf r-cran-lattice armhf 0.22-9-1 [1399 kB] 313s Get:48 http://ftpmaster.internal/ubuntu resolute/universe armhf r-cran-mass armhf 7.3-65-1 [1108 kB] 314s Get:49 http://ftpmaster.internal/ubuntu resolute/universe armhf r-cran-mvtnorm armhf 1.3-3-1build1 [915 kB] 315s Get:50 http://ftpmaster.internal/ubuntu resolute/universe armhf r-cran-pcapp armhf 2.0-5-1 [360 kB] 316s Get:51 http://ftpmaster.internal/ubuntu resolute/universe armhf r-cran-robustbase armhf 0.99-7-1 [3061 kB] 319s Get:52 http://ftpmaster.internal/ubuntu resolute/universe armhf r-cran-rrcov armhf 1.7-6-1 [2401 kB] 322s Preconfiguring packages ... 322s Fetched 47.3 MB in 1min 3s (747 kB/s) 322s Selecting previously unselected package libfreetype6:armhf. 322s (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 ... 68686 files and directories currently installed.) 322s Preparing to unpack .../00-libfreetype6_2.14.1+dfsg-2_armhf.deb ... 322s Unpacking libfreetype6:armhf (2.14.1+dfsg-2) ... 322s Selecting previously unselected package fonts-dejavu-mono. 322s Preparing to unpack .../01-fonts-dejavu-mono_2.37-8build1_all.deb ... 322s Unpacking fonts-dejavu-mono (2.37-8build1) ... 322s Selecting previously unselected package fonts-dejavu-core. 322s Preparing to unpack .../02-fonts-dejavu-core_2.37-8build1_all.deb ... 322s Unpacking fonts-dejavu-core (2.37-8build1) ... 322s Selecting previously unselected package fontconfig-config. 322s Preparing to unpack .../03-fontconfig-config_2.17.1-3ubuntu1_armhf.deb ... 323s Unpacking fontconfig-config (2.17.1-3ubuntu1) ... 323s Selecting previously unselected package libfontconfig1:armhf. 323s Preparing to unpack .../04-libfontconfig1_2.17.1-3ubuntu1_armhf.deb ... 323s Unpacking libfontconfig1:armhf (2.17.1-3ubuntu1) ... 323s Selecting previously unselected package fontconfig. 323s Preparing to unpack .../05-fontconfig_2.17.1-3ubuntu1_armhf.deb ... 323s Unpacking fontconfig (2.17.1-3ubuntu1) ... 323s Selecting previously unselected package libblas3:armhf. 323s Preparing to unpack .../06-libblas3_3.12.1-7ubuntu1_armhf.deb ... 323s Unpacking libblas3:armhf (3.12.1-7ubuntu1) ... 323s Selecting previously unselected package libpixman-1-0:armhf. 323s Preparing to unpack .../07-libpixman-1-0_0.46.4-1_armhf.deb ... 323s Unpacking libpixman-1-0:armhf (0.46.4-1) ... 323s Selecting previously unselected package libxcb-render0:armhf. 323s Preparing to unpack .../08-libxcb-render0_1.17.0-2ubuntu1_armhf.deb ... 323s Unpacking libxcb-render0:armhf (1.17.0-2ubuntu1) ... 323s Selecting previously unselected package libxcb-shm0:armhf. 323s Preparing to unpack .../09-libxcb-shm0_1.17.0-2ubuntu1_armhf.deb ... 323s Unpacking libxcb-shm0:armhf (1.17.0-2ubuntu1) ... 323s Selecting previously unselected package libxrender1:armhf. 323s Preparing to unpack .../10-libxrender1_1%3a0.9.12-1_armhf.deb ... 323s Unpacking libxrender1:armhf (1:0.9.12-1) ... 323s Selecting previously unselected package libcairo2:armhf. 323s Preparing to unpack .../11-libcairo2_1.18.4-3_armhf.deb ... 323s Unpacking libcairo2:armhf (1.18.4-3) ... 324s Selecting previously unselected package libdatrie1:armhf. 324s Preparing to unpack .../12-libdatrie1_0.2.14-1_armhf.deb ... 324s Unpacking libdatrie1:armhf (0.2.14-1) ... 324s Selecting previously unselected package libdeflate0:armhf. 324s Preparing to unpack .../13-libdeflate0_1.23-2build1_armhf.deb ... 324s Unpacking libdeflate0:armhf (1.23-2build1) ... 324s Selecting previously unselected package libgfortran5:armhf. 324s Preparing to unpack .../14-libgfortran5_15.2.0-12ubuntu1_armhf.deb ... 324s Unpacking libgfortran5:armhf (15.2.0-12ubuntu1) ... 324s Selecting previously unselected package libgomp1:armhf. 324s Preparing to unpack .../15-libgomp1_15.2.0-12ubuntu1_armhf.deb ... 324s Unpacking libgomp1:armhf (15.2.0-12ubuntu1) ... 324s Selecting previously unselected package libgraphite2-3:armhf. 324s Preparing to unpack .../16-libgraphite2-3_1.3.14-11ubuntu1_armhf.deb ... 324s Unpacking libgraphite2-3:armhf (1.3.14-11ubuntu1) ... 324s Selecting previously unselected package libharfbuzz0b:armhf. 324s Preparing to unpack .../17-libharfbuzz0b_12.3.2-1_armhf.deb ... 324s Unpacking libharfbuzz0b:armhf (12.3.2-1) ... 324s Selecting previously unselected package x11-common. 324s Preparing to unpack .../18-x11-common_1%3a7.7+24ubuntu1_all.deb ... 324s Unpacking x11-common (1:7.7+24ubuntu1) ... 324s Selecting previously unselected package libice6:armhf. 324s Preparing to unpack .../19-libice6_2%3a1.1.1-1build1_armhf.deb ... 324s Unpacking libice6:armhf (2:1.1.1-1build1) ... 324s Selecting previously unselected package libjpeg-turbo8:armhf. 324s Preparing to unpack .../20-libjpeg-turbo8_2.1.5-4ubuntu3_armhf.deb ... 324s Unpacking libjpeg-turbo8:armhf (2.1.5-4ubuntu3) ... 324s Selecting previously unselected package libjpeg8:armhf. 324s Preparing to unpack .../21-libjpeg8_8c-2ubuntu11_armhf.deb ... 324s Unpacking libjpeg8:armhf (8c-2ubuntu11) ... 324s Selecting previously unselected package liblapack3:armhf. 324s Preparing to unpack .../22-liblapack3_3.12.1-7ubuntu1_armhf.deb ... 324s Unpacking liblapack3:armhf (3.12.1-7ubuntu1) ... 324s Selecting previously unselected package liblerc4:armhf. 324s Preparing to unpack .../23-liblerc4_4.0.0+ds-5ubuntu2_armhf.deb ... 324s Unpacking liblerc4:armhf (4.0.0+ds-5ubuntu2) ... 324s Selecting previously unselected package libthai-data. 324s Preparing to unpack .../24-libthai-data_0.1.30-1_all.deb ... 324s Unpacking libthai-data (0.1.30-1) ... 324s Selecting previously unselected package libthai0:armhf. 324s Preparing to unpack .../25-libthai0_0.1.30-1_armhf.deb ... 324s Unpacking libthai0:armhf (0.1.30-1) ... 324s Selecting previously unselected package libpango-1.0-0:armhf. 324s Preparing to unpack .../26-libpango-1.0-0_1.57.0-1_armhf.deb ... 324s Unpacking libpango-1.0-0:armhf (1.57.0-1) ... 325s Selecting previously unselected package libpangoft2-1.0-0:armhf. 325s Preparing to unpack .../27-libpangoft2-1.0-0_1.57.0-1_armhf.deb ... 325s Unpacking libpangoft2-1.0-0:armhf (1.57.0-1) ... 325s Selecting previously unselected package libpangocairo-1.0-0:armhf. 325s Preparing to unpack .../28-libpangocairo-1.0-0_1.57.0-1_armhf.deb ... 325s Unpacking libpangocairo-1.0-0:armhf (1.57.0-1) ... 325s Selecting previously unselected package libpaper2:armhf. 325s Preparing to unpack .../29-libpaper2_2.2.5-0.3build1_armhf.deb ... 325s Unpacking libpaper2:armhf (2.2.5-0.3build1) ... 325s Selecting previously unselected package libpaper-utils. 325s Preparing to unpack .../30-libpaper-utils_2.2.5-0.3build1_armhf.deb ... 325s Unpacking libpaper-utils (2.2.5-0.3build1) ... 325s Selecting previously unselected package libsharpyuv0:armhf. 325s Preparing to unpack .../31-libsharpyuv0_1.5.0-0.1build1_armhf.deb ... 325s Unpacking libsharpyuv0:armhf (1.5.0-0.1build1) ... 325s Selecting previously unselected package libsm6:armhf. 325s Preparing to unpack .../32-libsm6_2%3a1.2.6-1build1_armhf.deb ... 325s Unpacking libsm6:armhf (2:1.2.6-1build1) ... 325s Selecting previously unselected package libtcl8.6:armhf. 325s Preparing to unpack .../33-libtcl8.6_8.6.17+dfsg-1build1_armhf.deb ... 325s Unpacking libtcl8.6:armhf (8.6.17+dfsg-1build1) ... 325s 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Setting up r-base-core (4.5.2-1ubuntu2) ... 330s Creating config file /etc/R/Renviron with new version 330s Setting up r-cran-lattice (0.22-9-1) ... 330s Setting up r-cran-deoptimr (1.1-4-1) ... 330s Setting up r-cran-mass (7.3-65-1) ... 330s Setting up r-cran-mvtnorm (1.3-3-1build1) ... 330s Setting up r-cran-robustbase (0.99-7-1) ... 330s Setting up r-cran-pcapp (2.0-5-1) ... 330s Setting up r-cran-rrcov (1.7-6-1) ... 330s Processing triggers for libc-bin (2.42-2ubuntu4) ... 331s Processing triggers for man-db (2.13.1-1build1) ... 332s Processing triggers for install-info (7.2-5) ... 340s autopkgtest [23:08:00]: test run-unit-test: [----------------------- 342s BEGIN TEST thubert.R 342s 342s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 342s Copyright (C) 2025 The R Foundation for Statistical Computing 342s Platform: arm-unknown-linux-gnueabihf (32-bit) 342s 342s R is free software and comes with ABSOLUTELY NO WARRANTY. 342s You are welcome to redistribute it under certain conditions. 342s Type 'license()' or 'licence()' for distribution details. 342s 342s R is a collaborative project with many contributors. 342s Type 'contributors()' for more information and 342s 'citation()' on how to cite R or R packages in publications. 342s 342s Type 'demo()' for some demos, 'help()' for on-line help, or 342s 'help.start()' for an HTML browser interface to help. 342s Type 'q()' to quit R. 342s 342s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, 342s + method=c("hubert", "hubert.mcd", "locantore", "cov", "classic", 342s + "grid", "proj")) 342s + { 342s + ## Test the PcaXxx() functions on the literature datasets: 342s + ## 342s + ## Call PcaHubert() and the other functions for all regression 342s + ## data sets available in robustbase/rrcov and print: 342s + ## - execution time (if time == TRUE) 342s + ## - loadings 342s + ## - eigenvalues 342s + ## - scores 342s + ## 342s + 342s + dopca <- function(x, xname, nrep=1){ 342s + 342s + n <- dim(x)[1] 342s + p <- dim(x)[2] 342s + if(method == "hubert.mcd") 342s + pca <- PcaHubert(x, k=p) 342s + else if(method == "hubert") 342s + pca <- PcaHubert(x, mcd=FALSE) 342s + else if(method == "locantore") 342s + pca <- PcaLocantore(x) 342s + else if(method == "cov") 342s + pca <- PcaCov(x) 342s + else if(method == "classic") 342s + pca <- PcaClassic(x) 342s + else if(method == "grid") 342s + pca <- PcaGrid(x) 342s + else if(method == "proj") 342s + pca <- PcaProj(x) 342s + else 342s + stop("Undefined PCA method: ", method) 342s + 342s + 342s + e1 <- getEigenvalues(pca)[1] 342s + e2 <- getEigenvalues(pca)[2] 342s + k <- pca@k 342s + 342s + if(time){ 342s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 342s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f %10.3f\n", dim(x)[1], dim(x)[2], k, e1, e2, xtime) 342s + } 342s + else{ 342s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f\n", dim(x)[1], dim(x)[2], k, e1, e2) 342s + } 342s + lpad<-lname-nchar(xname) 342s + cat(pad.right(xname, lpad), xres) 342s + 342s + if(!short){ 342s + cat("Scores: \n") 342s + print(getScores(pca)) 342s + 342s + if(full){ 342s + cat("-------------\n") 342s + show(pca) 342s + } 342s + cat("----------------------------------------------------------\n") 342s + } 342s + } 342s + 342s + stopifnot(length(nrep) == 1, nrep >= 1) 342s + method <- match.arg(method) 342s + 342s + options(digits = 5) 342s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 342s + 342s + lname <- 20 342s + 342s + ## VT::15.09.2013 - this will render the output independent 342s + ## from the version of the package 342s + suppressPackageStartupMessages(library(rrcov)) 342s + 342s + data(Animals, package = "MASS") 342s + brain <- Animals[c(1:24, 26:25, 27:28),] 342s + 342s + tmp <- sys.call() 342s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 342s + 342s + cat("Data Set n p k e1 e2\n") 342s + cat("==========================================================\n") 342s + dopca(heart[, 1:2], data(heart), nrep) 342s + dopca(starsCYG, data(starsCYG), nrep) 342s + dopca(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 342s + dopca(stack.x, data(stackloss), nrep) 342s + ## dopca(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) # differences between the architectures 342s + dopca(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 342s + ## dopca(data.matrix(subset(wood, select = -y)), data(wood), nrep) # differences between the architectures 342s + dopca(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 342s + 342s + ## dopca(brain, "Animals", nrep) 342s + dopca(milk, data(milk), nrep) 342s + dopca(bushfire, data(bushfire), nrep) 342s + cat("==========================================================\n") 342s + } 342s > 342s > dogen <- function(nrep=1, eps=0.49, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 342s + 342s + dopca <- function(x, nrep=1){ 342s + gc() 342s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 342s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 342s + xtime 342s + } 342s + 342s + set.seed(1234) 342s + 342s + ## VT::15.09.2013 - this will render the output independent 342s + ## from the version of the package 342s + suppressPackageStartupMessages(library(rrcov)) 342s + library(MASS) 342s + 342s + method <- match.arg(method) 342s + 342s + ap <- c(2, 5, 10, 20, 30) 342s + an <- c(100, 500, 1000, 10000, 50000) 342s + 342s + tottime <- 0 342s + cat(" n p Time\n") 342s + cat("=====================\n") 342s + for(i in 1:length(an)) { 342s + for(j in 1:length(ap)) { 342s + n <- an[i] 342s + p <- ap[j] 342s + if(5*p <= n){ 342s + xx <- gendata(n, p, eps) 342s + X <- xx$X 342s + ## print(dimnames(X)) 342s + tottime <- tottime + dopca(X, nrep) 342s + } 342s + } 342s + } 342s + 342s + cat("=====================\n") 342s + cat("Total time: ", tottime*nrep, "\n") 342s + } 342s > 342s > dorep <- function(x, nrep=1, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 342s + 342s + method <- match.arg(method) 342s + for(i in 1:nrep) 342s + if(method == "hubert.mcd") 342s + PcaHubert(x) 342s + else if(method == "hubert") 342s + PcaHubert(x, mcd=FALSE) 342s + else if(method == "locantore") 342s + PcaLocantore(x) 342s + else if(method == "cov") 342s + PcaCov(x) 342s + else 342s + stop("Undefined PCA method: ", method) 342s + } 342s > 342s > #### gendata() #### 342s > # Generates a location contaminated multivariate 342s > # normal sample of n observations in p dimensions 342s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 342s > # where 342s > # m = (b,b,...,b) 342s > # Defaults: eps=0 and b=10 342s > # 342s > gendata <- function(n,p,eps=0,b=10){ 342s + 342s + if(missing(n) || missing(p)) 342s + stop("Please specify (n,p)") 342s + if(eps < 0 || eps >= 0.5) 342s + stop(message="eps must be in [0,0.5)") 342s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 342s + nbad <- as.integer(eps * n) 342s + xind <- vector("numeric") 342s + if(nbad > 0){ 342s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 342s + xind <- sample(n,nbad) 342s + X[xind,] <- Xbad 342s + } 342s + list(X=X, xind=xind) 342s + } 342s > 342s > pad.right <- function(z, pads) 342s + { 342s + ### Pads spaces to right of text 342s + padding <- paste(rep(" ", pads), collapse = "") 342s + paste(z, padding, sep = "") 342s + } 342s > 342s > whatis <- function(x){ 342s + if(is.data.frame(x)) 342s + cat("Type: data.frame\n") 342s + else if(is.matrix(x)) 342s + cat("Type: matrix\n") 342s + else if(is.vector(x)) 342s + cat("Type: vector\n") 342s + else 342s + cat("Type: don't know\n") 342s + } 342s > 342s > ################################################################# 342s > ## VT::27.08.2010 342s > ## bug report from Stephen Milborrow 342s > ## 342s > test.case.1 <- function() 342s + { 342s + X <- matrix(c( 342s + -0.79984, -1.00103, 0.899794, 0.00000, 342s + 0.34279, 0.52832, -1.303783, -1.17670, 342s + -0.79984, -1.00103, 0.899794, 0.00000, 342s + 0.34279, 0.52832, -1.303783, -1.17670, 342s + 0.34279, 0.52832, -1.303783, -1.17670, 342s + 1.48542, 0.66735, 0.716162, 1.17670, 342s + -0.79984, -1.00103, 0.899794, 0.00000, 342s + 1.69317, 1.91864, -0.018363, 1.76505, 342s + -1.00759, -0.16684, -0.385626, 0.58835, 342s + -0.79984, -1.00103, 0.899794, 0.00000), ncol=4, byrow=TRUE) 342s + 342s + cc1 <- PcaHubert(X, k=3) 342s + 342s + cc2 <- PcaLocantore(X, k=3) 342s + cc3 <- PcaCov(X, k=3, cov.control=CovControlSest()) 342s + 342s + cc4 <- PcaProj(X, k=2) # with k=3 will produce warnings in .distances - too small eignevalues 342s + cc5 <- PcaGrid(X, k=2) # dito 342s + 342s + list(cc1, cc2, cc3, cc4, cc5) 342s + } 342s > 342s > ################################################################# 342s > ## VT::05.08.2016 342s > ## bug report from Matthieu Lesnoff 342s > ## 342s > test.case.2 <- function() 342s + { 342s + do.test.case.2 <- function(z) 342s + { 342s + if(missing(z)) 342s + { 342s + set.seed(12345678) 342s + n <- 5 342s + z <- data.frame(v1 = rnorm(n), v2 = rnorm(n), v3 = rnorm(n)) 342s + z 342s + } 342s + 342s + fm <- PcaLocantore(z, k = 2, scale = TRUE) 342s + fm@scale 342s + apply(z, MARGIN = 2, FUN = mad) 342s + scale(z, center = fm@center, scale = fm@scale) 342s + 342s + T <- fm@scores 342s + P <- fm@loadings 342s + E <- scale(z, center = fm@center, scale = fm@scale) - T %*% t(P) 342s + d2 <- apply(E^2, MARGIN = 1, FUN = sum) 342s + ## print(sqrt(d2)); print(fm@od) 342s + print(ret <- all.equal(sqrt(d2), fm@od)) 342s + 342s + ret 342s + } 342s + do.test.case.2() 342s + do.test.case.2(phosphor) 342s + do.test.case.2(stackloss) 342s + do.test.case.2(salinity) 342s + do.test.case.2(hbk) 342s + do.test.case.2(milk) 342s + do.test.case.2(bushfire) 342s + data(rice); do.test.case.2(rice) 342s + data(un86); do.test.case.2(un86) 342s + } 342s > 342s > ## VT::15.09.2013 - this will render the output independent 342s > ## from the version of the package 342s > suppressPackageStartupMessages(library(rrcov)) 342s > 342s > dodata(method="classic") 343s 343s Call: dodata(method = "classic") 343s Data Set n p k e1 e2 343s ========================================================== 343s heart 12 2 2 812.379735 9.084962 343s Scores: 343s PC1 PC2 343s 1 2.7072 1.46576 343s 2 59.9990 -1.43041 343s 3 -3.5619 -1.54067 343s 4 -7.7696 2.52687 343s 5 14.7660 -0.95822 343s 6 -20.0489 6.91079 343s 7 1.4189 2.25961 343s 8 -34.3308 -4.23717 343s 9 -6.0487 -0.97859 343s 10 -33.0102 -3.73143 343s 11 -18.6372 0.25821 343s 12 44.5163 -0.54476 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 28.5023 3.0141 343s ---------------------------------------------------------- 343s starsCYG 47 2 2 0.331279 0.079625 343s Scores: 343s PC1 PC2 343s 1 0.2072999 0.089973 343s 2 0.6855999 0.349644 343s 3 -0.0743007 -0.061028 343s 4 0.6855999 0.349644 343s 5 0.1775161 0.015053 343s 6 0.4223986 0.211351 343s 7 -0.2926077 -0.516156 343s 8 0.2188453 0.293607 343s 9 0.5593696 0.028761 343s 10 0.0983878 0.074540 343s 11 0.8258140 -0.711176 343s 12 0.4167063 0.180244 343s 13 0.3799883 0.225541 343s 14 -0.9105236 -0.432014 343s 15 -0.7418831 -0.125322 343s 16 -0.4432862 0.048287 343s 17 -1.0503005 -0.229623 343s 18 -0.8393302 -0.007831 343s 19 -0.8126742 -0.195952 343s 20 0.9842316 -0.688729 343s 21 -0.6230699 -0.108486 343s 22 -0.7814875 -0.130933 343s 23 -0.6017038 0.025840 343s 24 -0.1857772 0.155474 343s 25 -0.0020261 0.070412 343s 26 -0.3640775 0.059510 343s 27 -0.3458392 -0.069204 343s 28 -0.1208393 0.053577 343s 29 -0.6033482 -0.176391 343s 30 1.1440521 -0.676183 343s 31 -0.5960920 -0.013765 343s 32 0.0519296 0.259855 343s 33 0.1861752 0.167779 343s 34 1.3802755 -0.632611 343s 35 -0.6542566 -0.173505 343s 36 0.5583690 0.392215 343s 37 0.0561384 0.230152 343s 38 0.1861752 0.167779 343s 39 0.1353472 0.241376 343s 40 0.5355195 0.197080 343s 41 -0.3980701 0.014294 343s 42 0.0277576 0.145332 343s 43 0.2979736 0.234120 343s 44 0.3049884 0.184614 343s 45 0.4889809 0.311684 343s 46 -0.0514512 0.134108 343s 47 -0.5224950 0.037063 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 0.57557 0.28218 343s ---------------------------------------------------------- 343s phosphor 18 2 2 220.403422 68.346121 343s Scores: 343s PC1 PC2 343s 1 4.04290 -15.3459 343s 2 -22.30489 -1.0004 343s 3 -24.52683 3.2836 343s 4 -12.54839 -6.0848 343s 5 -19.37044 2.2979 343s 6 15.20366 -19.9424 343s 7 0.44222 -3.1379 343s 8 -10.64042 3.6933 343s 9 -11.67967 5.9670 343s 10 14.26805 -7.0221 343s 11 -4.98832 1.5268 343s 12 8.74986 7.9379 343s 13 12.26290 6.0251 343s 14 6.27607 7.5768 343s 15 17.53246 3.1560 343s 16 -10.17024 -5.8994 343s 17 21.05826 5.4492 343s 18 16.39281 11.5191 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 14.8460 8.2672 343s ---------------------------------------------------------- 343s stackloss 21 3 3 99.576089 19.581136 343s Scores: 343s PC1 PC2 PC3 343s 1 20.15352 -4.359452 0.324585 343s 2 19.81554 -5.300468 0.308294 343s 3 15.45222 -1.599136 -0.203125 343s 4 2.40370 -0.145282 2.370302 343s 5 1.89538 0.070566 0.448061 343s 6 2.14954 -0.037358 1.409182 343s 7 4.43153 5.500810 2.468051 343s 8 4.43153 5.500810 2.468051 343s 9 -1.47521 1.245404 2.511773 343s 10 -5.11183 -4.802083 -2.407870 343s 11 -2.07009 3.667055 -2.261247 343s 12 -2.66223 2.833964 -3.238659 343s 13 -4.43589 -2.920053 -2.375287 343s 14 -0.46404 7.323193 -1.234961 343s 15 -9.31959 6.232579 -0.056064 343s 16 -10.33350 3.409533 -0.104938 343s 17 -14.81094 -9.872607 0.628103 343s 18 -12.44514 -3.285499 0.742143 343s 19 -11.85300 -2.452408 1.719555 343s 20 -5.73994 -2.494520 0.098250 343s 21 9.98843 1.484952 -3.614198 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 9.9788 4.4251 1.8986 343s ---------------------------------------------------------- 343s salinity 28 3 3 11.410736 7.075409 343s Scores: 343s PC1 PC2 PC3 343s 1 -0.937789 -2.40535 0.812909 343s 2 -1.752631 -2.57774 2.004437 343s 3 -6.509364 -0.78762 -1.821906 343s 4 -5.619847 -2.41333 -1.586891 343s 5 -7.268242 1.61012 1.563568 343s 6 -4.316558 -3.20411 0.029376 343s 7 -2.379545 -3.32371 0.703101 343s 8 0.013514 -3.50586 1.260502 343s 9 0.265262 -0.16736 -2.886883 343s 10 1.890755 2.43623 -0.986832 343s 11 0.804196 2.56656 0.387577 343s 12 0.935082 -1.03559 -0.074081 343s 13 1.814839 -1.61087 0.612290 343s 14 3.407535 -0.15880 2.026088 343s 15 1.731273 2.95159 -1.840286 343s 16 -6.129708 7.21368 2.632273 343s 17 -0.645124 1.06260 0.028697 343s 18 -1.307532 -2.54679 -0.280273 343s 19 0.483455 -0.55896 -3.097281 343s 20 2.053267 0.47308 -1.858703 343s 21 3.277664 -1.31002 0.453753 343s 22 4.631644 -0.78005 1.519894 343s 23 1.864403 5.32790 -0.849694 343s 24 0.623899 4.29317 0.056461 343s 25 1.301696 0.37871 -0.646220 343s 26 2.852126 -0.79527 -0.347711 343s 27 4.134051 -0.92756 0.449222 343s 28 4.781679 -0.20467 1.736616 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 3.3780 2.6600 1.4836 343s ---------------------------------------------------------- 343s hbk 75 3 3 216.162129 1.981077 343s Scores: 343s PC1 PC2 PC3 343s 1 26.2072 -0.660756 0.503340 343s 2 27.0406 -0.108506 -0.225059 343s 3 28.8351 -1.683721 0.263078 343s 4 29.9221 -0.812174 -0.674480 343s 5 29.3181 -0.909915 -0.121600 343s 6 27.5360 -0.599697 0.916574 343s 7 27.6617 -0.073753 0.676620 343s 8 26.5576 -0.882312 0.159620 343s 9 28.8726 -1.074223 -0.673462 343s 10 27.6643 -1.463829 -0.868593 343s 11 34.2019 -0.664473 -0.567265 343s 12 35.4805 -2.730949 -0.259320 343s 13 34.7544 1.325449 0.749884 343s 14 38.9522 8.171389 0.034382 343s 15 -5.5375 0.390704 1.679172 343s 16 -7.4319 0.803850 1.925633 343s 17 -8.5880 0.957577 -1.010312 343s 18 -6.6022 -0.425109 0.625148 343s 19 -6.5596 1.154721 -0.640680 343s 20 -5.2525 0.812527 1.377832 343s 21 -6.2771 0.067747 0.958907 343s 22 -6.2501 1.325491 -1.104428 343s 23 -7.2419 0.839808 0.728712 343s 24 -7.6489 1.131606 0.154897 343s 25 -9.0763 -0.670721 -0.167577 343s 26 -5.5967 0.999411 -0.810000 343s 27 -5.1460 -0.339018 1.326712 343s 28 -7.1659 -0.993461 0.125933 343s 29 -8.2104 -0.169338 -0.073569 343s 30 -6.2499 -1.689222 -0.877481 343s 31 -7.3180 -0.225795 1.687204 343s 32 -7.9446 1.473868 -0.541790 343s 33 -6.3604 1.237472 0.061800 343s 34 -8.9812 -0.710662 -0.830422 343s 35 -5.1698 -0.435484 1.102817 343s 36 -5.9995 -0.058135 -0.713550 343s 37 -5.8753 0.852882 -1.610556 343s 38 -8.4501 0.334363 0.404813 343s 39 -8.1751 -1.300317 0.633282 343s 40 -7.4495 0.672712 -0.829815 343s 41 -5.6213 -1.106765 1.395315 343s 42 -6.8571 -0.900977 -1.509937 343s 43 -7.0633 1.987372 -1.079934 343s 44 -6.3763 -1.867647 -0.251224 343s 45 -8.6456 -0.866053 0.630132 343s 46 -6.5356 -1.763526 -0.189838 343s 47 -8.2224 -1.183284 1.615150 343s 48 -5.6136 -1.100704 1.079239 343s 49 -5.9907 0.220336 1.443387 343s 50 -5.2675 0.142923 0.194023 343s 51 -7.9324 0.324710 1.113289 343s 52 -7.5544 -1.033884 1.792496 343s 53 -6.7119 -1.712257 -1.711778 343s 54 -7.4679 1.856542 0.046658 343s 55 -7.4666 1.161504 -0.725948 343s 56 -6.7110 1.574868 0.534288 343s 57 -8.2571 -0.399824 0.521995 343s 58 -5.9781 1.312567 0.926790 343s 59 -5.6960 -0.394338 -0.332938 343s 60 -6.1017 -0.797579 -1.679359 343s 61 -5.2628 0.919128 -1.436156 343s 62 -9.1245 -0.516135 -0.229065 343s 63 -7.7140 1.659145 0.068510 343s 64 -4.9886 0.173613 0.865810 343s 65 -6.6157 -1.479786 0.098390 343s 66 -7.9511 0.772770 -0.998321 343s 67 -7.1856 0.459602 0.216588 343s 68 -8.7345 -0.860784 -1.238576 343s 69 -8.5833 -0.313481 0.832074 343s 70 -5.8642 -0.142883 -0.870064 343s 71 -5.8879 0.186456 0.464467 343s 72 -7.1865 0.497156 -0.826767 343s 73 -6.8671 -0.058606 -1.335842 343s 74 -7.1398 0.727642 -1.422331 343s 75 -7.2696 -1.347832 -1.496927 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 14.70245 1.40751 0.95725 343s ---------------------------------------------------------- 343s milk 86 8 8 15.940298 2.771345 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 PC6 PC7 343s 1 6.471620 1.031110 0.469432 0.5736412 1.0294362 -0.6054039 -0.2005117 343s 2 7.439545 0.320597 0.081922 -0.6305898 0.7128977 -1.1601053 -0.1170582 343s 3 1.240654 -1.840458 0.520870 -0.1717469 0.2752079 -0.3815506 0.6004089 343s 4 5.952685 -1.856375 1.638710 0.3358626 -0.5834205 -0.0665348 -0.1580799 343s 5 -0.706973 0.261795 0.423736 0.2916399 -0.5307716 -0.3325563 -0.0062349 343s 6 2.524050 0.293380 -0.572997 0.2466367 -0.3497882 0.0386014 -0.1418131 343s 7 3.136085 -0.050202 -0.818165 -0.0451560 -0.5226337 -0.1597194 0.1669050 343s 8 3.260390 0.312365 -0.110776 0.4908006 -0.5225353 -0.1972222 -0.1068433 343s 9 -0.808914 -2.355785 1.344204 -0.4743284 -0.1394914 -0.1390080 -0.2620731 343s 10 -2.511226 -0.995321 -0.087218 -0.5950040 0.4268321 0.2561918 0.0891170 343s 11 -9.204096 -0.598364 1.587275 0.0833647 0.1865626 0.0358228 0.0920394 343s 12 -12.946774 1.951332 -0.179186 0.2560603 0.1300954 -0.1179820 -0.0999494 343s 13 -10.011603 0.726323 -2.102423 -1.3105560 0.3291550 0.0660007 -0.0794410 343s 14 -11.983644 0.768224 -0.532227 -0.5161201 -0.0817164 -0.4358934 -0.1734612 343s 15 -10.465714 -0.704271 2.035437 0.3713778 -0.0564830 -0.2696432 -0.1940091 343s 16 -2.527619 -0.286939 0.354497 0.8571223 0.1585009 0.2272835 0.4386955 343s 17 -0.514527 -2.895087 1.657181 0.2208239 0.1961109 0.1280496 -0.0182491 343s 18 -1.763931 0.854269 -0.686282 0.2848209 -0.4813608 -0.2623962 0.4757030 343s 19 -1.538419 -0.866477 1.103818 0.3874507 0.2086661 0.1267277 0.2354264 343s 20 0.732842 -1.455594 1.097358 -0.2530588 -0.0302385 0.2654274 0.6093330 343s 21 -2.530155 1.932885 -0.873095 0.6202295 -0.4153607 0.0048383 0.0067484 343s 22 -0.772646 0.675846 -0.259539 0.4844670 -0.0893266 -0.2785557 -0.0424662 343s 23 0.185417 1.413719 0.066135 1.1014470 0.0468093 0.0288637 0.2539994 343s 24 -0.280536 0.908864 0.113221 1.3370381 0.3289929 0.2588134 -0.0356289 343s 25 -3.503626 1.971233 0.203620 1.1975494 -0.3175317 0.1149685 0.0584396 343s 26 -0.639313 1.175503 0.403906 0.9082134 -0.2648165 -0.1238813 -0.0174853 343s 27 -2.923327 -0.365168 0.149478 0.8201430 -0.1544609 -0.4856934 -0.0058424 343s 28 2.505633 3.050292 -0.554424 2.1416405 -0.0378764 0.1002280 -0.3888580 343s 29 4.649504 1.054863 -0.081018 1.1454466 0.1502080 0.4967323 0.0879775 343s 30 1.049282 1.355215 -0.142701 0.7805566 -0.2059790 0.0193142 0.0815524 343s 31 1.962583 1.595396 -2.050642 0.3556747 0.1384801 0.1197984 0.1608247 343s 32 1.554846 0.095644 -1.423054 -0.3175620 0.4260008 -0.1612463 -0.0567196 343s 33 2.248977 0.010348 -0.062469 0.6388269 0.2098648 0.1330250 0.0906704 343s 34 0.993109 -0.828812 0.284059 0.3446686 0.1899096 -0.0515571 -0.2281197 343s 35 -0.335103 1.614093 -0.920661 1.2502617 0.2435013 0.1264875 0.0469238 343s 36 4.346795 1.208134 0.368889 1.1429977 -0.1362052 -0.0158169 -0.0183852 343s 37 0.992634 2.013738 -1.350619 0.8714694 0.0057776 -0.2122691 0.1760918 343s 38 2.213341 1.706516 -0.705418 1.2670281 -0.0707149 0.0670467 -0.1863588 343s 39 -1.213255 0.644062 0.163988 1.1213961 0.2945355 0.1093574 0.0019574 343s 40 3.942604 -1.704266 0.660327 0.1618506 0.4259076 0.0070193 0.3462765 343s 41 4.262054 1.687193 0.351875 0.5396477 1.0052810 -0.9331689 0.0056063 343s 42 6.865198 -1.091248 1.153585 1.1248797 0.0873276 0.2565221 0.0333265 343s 43 3.476720 0.555449 -1.030771 -0.3015720 -0.1748109 -0.1584968 0.4079902 343s 44 5.691730 -0.141240 0.565189 0.3174238 0.6478440 1.0579977 -0.5387916 343s 45 0.327134 0.152011 -0.394798 0.4998430 0.1599781 0.3159518 0.1623656 343s 46 0.280225 1.569387 -0.100397 1.2800976 0.0446645 0.0946513 0.0461599 343s 47 3.119928 -0.384834 -3.325600 -1.8865310 -0.1334744 0.1249987 -0.2561273 343s 48 0.501542 0.739816 -1.384556 -0.1244721 0.2948958 0.4836170 -0.1182802 343s 49 -1.953218 0.269986 -1.726474 -0.8510637 0.5047958 0.4860651 0.2318735 343s 50 3.706878 -2.400570 1.361047 -0.4949076 0.2180352 0.4080879 0.1156540 343s 51 -1.060358 -0.521609 -1.387412 -1.2767491 -0.0521356 0.1665452 -0.0044412 343s 52 -4.900528 0.157011 -1.015880 -0.9941168 0.2069608 0.3239762 -0.1921715 343s 53 -0.388496 0.062051 -0.643721 -0.8544141 -0.1857141 0.0063293 0.2664606 343s 54 0.109234 -0.018709 -0.242825 -0.2064701 -0.0585165 0.1720867 0.1117397 343s 55 1.176175 0.644539 -0.373694 0.0038605 -0.3436524 0.0194450 -0.0838883 343s 56 0.407259 -0.606637 0.222915 -0.3622451 -0.0737834 0.0228104 0.0297333 343s 57 -1.022756 -0.071860 0.741957 0.2273628 -0.1388444 -0.2396467 -0.2327738 343s 58 0.245419 1.167059 0.225934 0.8318795 -0.5365166 -0.0090816 -0.1680757 343s 59 -1.300617 -1.110325 -0.262740 -0.8857801 -0.0816954 -0.1186886 -0.0928322 343s 60 -1.110561 -0.832357 -0.212713 -0.4754481 -0.4105982 -0.1886992 -0.0602872 343s 61 0.381831 -1.475116 0.601047 -0.6260156 -0.1854501 -0.1749306 -0.0013904 343s 62 2.734462 -1.887861 0.813453 -0.5856987 0.2310656 0.1117041 -0.0293373 343s 63 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 343s 64 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 343s 65 0.004744 -2.712679 1.178987 -0.6677199 0.0208119 0.0621903 -0.0655693 343s 66 -2.014851 -1.060090 -0.099959 -0.7225044 -0.1947648 -0.2282902 -0.0505015 343s 67 0.621739 -1.296106 0.255632 -0.3309504 -0.0880200 0.2524306 0.1465779 343s 68 -0.271385 -1.709161 -1.100349 -2.0937671 0.2166264 0.0191278 0.0114174 343s 69 -0.326350 -0.737232 0.021639 -0.3850383 -0.4338287 0.2156624 0.1597594 343s 70 4.187093 9.708082 4.632803 -4.9751240 -0.0881576 0.2392433 0.0568049 343s 71 -1.868507 -1.600166 0.436353 -0.8078214 -0.1530893 0.0479471 -0.1999893 343s 72 2.768081 -0.556824 -0.148923 -0.3197853 -0.5524427 0.0907804 -0.0694488 343s 73 -1.441846 -2.735114 -0.294134 -1.2172969 0.0109453 -0.0562910 0.1505788 343s 74 -10.995490 0.615992 1.950966 1.1687190 0.2798335 0.2713257 0.0652135 343s 75 0.508992 -2.363945 -0.407064 -0.9522316 0.1040307 0.1088110 -0.7368484 343s 76 -1.015714 -0.307662 -1.088162 -1.0181862 -0.0440888 -0.1362208 0.0271200 343s 77 -8.028891 -0.580763 0.933638 0.4619362 0.3379832 -0.1368644 -0.0669441 343s 78 1.763308 -1.336175 -0.127809 -0.7161775 -0.1904861 -0.0900461 0.0037539 343s 79 0.208944 -0.580698 -0.626297 -0.7620610 -0.0262368 -0.2928202 0.0285908 343s 80 -3.230608 1.251352 0.195280 0.8687004 0.1812011 0.2600692 -0.1516375 343s 81 1.498160 0.669731 -0.266114 0.3772866 -0.2769688 -0.1066593 -0.1608395 343s 82 3.232051 -1.776018 0.485524 0.1170945 0.0557260 0.2219872 0.1187681 343s 83 2.999977 -0.228275 -0.467724 -0.4287672 0.0494902 -0.2337809 -0.0718159 343s 84 1.238083 0.320956 -1.806006 -1.0142266 0.2359630 -0.0857149 0.0593938 343s 85 1.276376 -2.081214 2.540850 0.3745805 -0.2596482 -0.1228412 -0.2199985 343s 86 0.930715 0.836457 -1.385153 -0.6074929 -0.2476354 0.1680713 -0.0117324 343s PC8 343s 1 9.0765e-04 343s 2 2.1811e-04 343s 3 1.1834e-03 343s 4 8.4077e-05 343s 5 9.9209e-04 343s 6 1.6277e-03 343s 7 2.4907e-04 343s 8 6.8383e-04 343s 9 -5.0924e-04 343s 10 3.1215e-04 343s 11 3.0654e-04 343s 12 -1.1951e-03 343s 13 -1.2849e-03 343s 14 -9.0801e-04 343s 15 -1.2686e-03 343s 16 -1.8441e-03 343s 17 -2.1068e-03 343s 18 -5.7816e-04 343s 19 -1.2330e-03 343s 20 3.3857e-05 343s 21 3.8623e-04 343s 22 1.3035e-04 343s 23 -3.8648e-04 343s 24 -1.7400e-04 343s 25 -3.9196e-04 343s 26 -7.6996e-04 343s 27 -4.8042e-04 343s 28 -2.0628e-04 343s 29 -4.5672e-04 343s 30 -1.4716e-04 343s 31 -4.6385e-05 343s 32 -2.0481e-04 343s 33 -3.0020e-04 343s 34 -5.8179e-05 343s 35 1.3870e-04 343s 36 -6.7177e-04 343s 37 -3.0799e-04 343s 38 6.2140e-04 343s 39 4.5912e-04 343s 40 -3.7165e-04 343s 41 -5.4362e-04 343s 42 -1.0155e-03 343s 43 1.3449e-04 343s 44 -5.4761e-04 343s 45 1.0300e-03 343s 46 1.1039e-03 343s 47 -6.4858e-04 343s 48 -7.6886e-05 343s 49 3.2590e-04 343s 50 8.6845e-05 343s 51 4.9423e-04 343s 52 9.2973e-04 343s 53 4.4342e-04 343s 54 4.9888e-04 343s 55 7.2171e-04 343s 56 -3.2133e-05 343s 57 -1.8101e-04 343s 58 -5.4969e-06 343s 59 -8.3841e-04 343s 60 5.9446e-05 343s 61 -6.5683e-05 343s 62 -3.4073e-04 343s 63 -6.5145e-04 343s 64 -6.5145e-04 343s 65 1.4986e-04 343s 66 2.8096e-04 343s 67 -6.5170e-05 343s 68 -1.3775e-04 343s 69 6.8225e-06 343s 70 -1.6290e-04 343s 71 3.9009e-04 343s 72 -1.3981e-04 343s 73 6.2613e-04 343s 74 2.6513e-03 343s 75 3.7088e-04 343s 76 9.9539e-04 343s 77 1.2979e-03 343s 78 5.6500e-04 343s 79 3.0940e-04 343s 80 8.7993e-04 343s 81 -3.1353e-04 343s 82 4.9625e-04 343s 83 -6.3951e-04 343s 84 -4.5582e-04 343s 85 5.9440e-04 343s 86 -3.6234e-04 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 3.99253025 1.66473582 1.10660264 0.96987790 0.33004256 0.29263512 0.20843280 343s [8] 0.00074024 343s ---------------------------------------------------------- 343s bushfire 38 5 5 38435.075910 1035.305774 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 343s 1 -111.9345 4.9970 -1.00881 -1.224361 3.180569 343s 2 -113.4128 7.4784 -0.79170 -0.235184 2.385812 343s 3 -105.8364 10.9615 -3.15662 -0.251662 1.017328 343s 4 -89.1684 8.7232 -6.15080 -0.075611 1.431111 343s 5 -58.7216 -1.9543 -12.70661 -0.151328 1.425570 343s 6 -35.0370 -12.8434 -17.06841 -0.525664 3.499743 343s 7 -250.2123 -49.4348 23.31261 -19.070238 0.647348 343s 8 -292.6877 -69.7708 -21.30815 13.093808 -1.288764 343s 9 -294.0765 -70.9903 -23.96326 14.940985 -0.939076 343s 10 -290.0193 -57.3747 3.51346 1.858995 0.083107 343s 11 -289.8168 -43.3207 16.08046 -1.745099 -1.506042 343s 12 -290.8645 6.2503 40.52173 -7.496479 -0.033767 343s 13 -232.6865 41.8090 37.19429 -1.280348 -0.470837 343s 14 9.8483 25.1954 -14.56970 0.538484 1.772046 343s 15 137.1924 11.8521 -37.12452 -5.130459 -0.586695 343s 16 92.9804 10.3923 -24.97267 -7.551314 -1.867125 343s 17 90.4493 10.5630 -21.92735 -5.669651 -1.001362 343s 18 78.6325 5.2211 -19.74718 -6.107880 -1.939986 343s 19 82.1178 3.6913 -21.37810 -4.259855 -1.278838 343s 20 92.9044 7.1961 -21.22900 -4.125571 -0.127089 343s 21 74.9157 10.2991 -16.60924 -5.660751 -0.406343 343s 22 66.7350 12.0460 -16.73298 -4.669080 1.333436 343s 23 -62.1981 22.7394 6.03613 -5.182356 -0.453624 343s 24 -116.5696 32.3182 12.74846 -1.465657 -0.097851 343s 25 -53.8907 22.4278 -2.18861 -2.742014 -0.990071 343s 26 -60.6384 20.2952 -3.05206 -2.953685 -0.629061 343s 27 -74.7621 28.9067 -0.65817 1.473357 -0.443957 343s 28 -50.2202 37.3457 -1.44989 5.530426 -1.073521 343s 29 -38.7483 50.2749 2.34469 10.156457 -0.416262 343s 30 -93.3887 51.7884 20.08872 8.798781 -1.620216 343s 31 35.3096 41.7158 13.46272 14.464358 -0.475973 343s 32 290.8493 3.5924 7.41501 15.244293 2.141354 343s 33 326.7236 -29.8194 15.64898 2.612061 0.064931 343s 34 322.9095 -30.6372 16.21520 1.248005 -0.711322 343s 35 328.5307 -29.9533 16.49656 1.138916 0.974792 343s 36 325.6791 -30.6990 16.83840 -0.050949 -1.211360 343s 37 323.8136 -30.7474 19.55764 -1.545150 -0.267580 343s 38 325.2991 -30.5350 20.31878 -1.928580 -0.120425 343s ------------- 343s Call: 343s PcaClassic(x = x) 343s 343s Standard deviations: 343s [1] 196.0487 32.1762 18.4819 6.9412 1.3510 343s ---------------------------------------------------------- 343s ========================================================== 343s > dodata(method="hubert.mcd") 343s 343s Call: dodata(method = "hubert.mcd") 343s Data Set n p k e1 e2 343s ========================================================== 343s heart 12 2 2 358.175786 4.590630 343s Scores: 343s PC1 PC2 343s 1 -12.2285 0.86283 343s 2 -68.9906 -7.43256 343s 3 -5.7035 -1.53793 343s 4 -1.8988 2.90891 343s 5 -24.0044 -2.68946 343s 6 9.9115 8.43321 343s 7 -11.0210 1.77484 343s 8 25.1826 -1.31573 343s 9 -3.2809 -0.74345 343s 10 23.8200 -0.93701 343s 11 9.1344 1.67701 343s 12 -53.6607 -5.08826 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 18.9255 2.1426 343s ---------------------------------------------------------- 343s starsCYG 47 2 2 0.280653 0.005921 343s Scores: 343s PC1 PC2 343s 1 -0.285731 -0.0899858 343s 2 -0.819689 0.0153191 343s 3 0.028077 -0.1501882 343s 4 -0.819689 0.0153191 343s 5 -0.234971 -0.1526225 343s 6 -0.527231 -0.0382380 343s 7 0.372118 -0.5195605 343s 8 -0.357448 0.1009508 343s 9 -0.603553 -0.2533541 343s 10 -0.177170 -0.0722541 343s 11 -0.637339 -1.0390758 343s 12 -0.512526 -0.0662337 343s 13 -0.490978 -0.0120517 343s 14 0.936868 -0.2550656 343s 15 0.684479 -0.0125787 343s 16 0.347708 0.0641382 343s 17 1.009966 -0.0202111 343s 18 0.742477 0.1286170 343s 19 0.773105 -0.0588983 343s 20 -0.795247 -1.0648673 343s 21 0.566048 -0.0319223 343s 22 0.723956 -0.0061308 343s 23 0.505616 0.0899297 343s 24 0.069956 0.0896997 343s 25 -0.080090 -0.0462652 343s 26 0.268755 0.0512425 343s 27 0.289710 -0.0770574 343s 28 0.038341 -0.0269216 343s 29 0.567463 -0.1026188 343s 30 -0.951542 -1.1005280 343s 31 0.512064 0.0504528 343s 32 -0.188059 0.1184850 343s 33 -0.288758 -0.0094200 343s 34 -1.190016 -1.1293460 343s 35 0.615197 -0.0846898 343s 36 -0.710930 0.0938781 343s 37 -0.183223 0.0888774 343s 38 -0.288758 -0.0094200 343s 39 -0.262177 0.0759816 343s 40 -0.630957 -0.0855773 343s 41 0.314679 0.0182135 343s 42 -0.130850 0.0163715 343s 43 -0.415248 0.0205825 343s 44 -0.407188 -0.0287636 343s 45 -0.620693 0.0376892 343s 46 -0.051896 0.0292672 343s 47 0.426662 0.0770340 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 0.529767 0.076946 343s ---------------------------------------------------------- 343s phosphor 18 2 2 285.985489 32.152099 343s Scores: 343s PC1 PC2 343s 1 -2.89681 -18.08811 343s 2 21.34021 -0.40854 343s 3 22.98065 4.13006 343s 4 12.33544 -6.72947 343s 5 17.99823 2.47611 343s 6 -13.35773 -24.10967 343s 7 -0.92957 -5.51314 343s 8 9.16061 2.71354 343s 9 9.89243 5.10403 343s 10 -14.12600 -11.17832 343s 11 3.84175 -0.17605 343s 12 -10.61905 4.37646 343s 13 -13.85065 2.01919 343s 14 -8.11927 4.34325 343s 15 -18.69805 -1.51673 343s 16 9.95352 -6.85784 343s 17 -22.49433 0.29387 343s 18 -18.66592 6.92359 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 16.9111 5.6703 343s ---------------------------------------------------------- 343s stackloss 21 3 3 78.703690 19.249085 343s Scores: 343s PC1 PC2 PC3 343s 1 -20.323997 10.26124 0.92041 343s 2 -19.761418 11.08797 0.92383 343s 3 -16.469919 6.43190 0.22593 343s 4 -4.171902 1.68262 2.50695 343s 5 -3.756174 1.40774 0.57004 343s 6 -3.964038 1.54518 1.53850 343s 7 -7.547376 -3.27780 2.48643 343s 8 -7.547376 -3.27780 2.48643 343s 9 -0.763294 -0.63699 2.53518 343s 10 4.214079 4.46296 -2.28315 343s 11 -0.849132 -2.97767 -2.31393 343s 12 -0.078689 -2.28838 -3.27896 343s 13 3.088921 2.80948 -2.28999 343s 14 -3.307313 -6.14718 -1.35916 343s 15 5.552354 -7.34201 -0.32057 343s 16 7.240091 -4.86180 -0.31031 343s 17 14.908334 6.84995 0.70603 343s 18 10.970281 1.06279 0.68209 343s 19 10.199838 0.37350 1.64712 343s 20 4.273564 1.99328 0.14526 343s 21 -11.992249 2.19025 -3.37391 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 8.8715 4.3874 2.1990 343s ---------------------------------------------------------- 343s salinity 28 3 3 11.651966 4.107426 343s Scores: 343s PC1 PC2 PC3 343s 1 1.68712 1.62591 0.19812128 343s 2 2.35772 2.37290 1.24965734 343s 3 6.80132 -2.14412 0.68142276 343s 4 6.41982 -0.61348 -0.31907921 343s 5 6.36697 -1.98030 4.87319903 343s 6 5.22050 1.20864 0.10252555 343s 7 3.34007 2.02950 0.00064329 343s 8 1.06220 2.89801 -0.35658064 343s 9 0.34692 -2.20572 -1.71677710 343s 10 -2.21421 -2.74842 0.76862599 343s 11 -1.40111 -2.16163 2.21124383 343s 12 -0.38242 0.32284 -0.23732191 343s 13 -1.12809 1.33152 -0.28800043 343s 14 -3.24998 1.35943 1.17514969 343s 15 -2.11006 -3.70114 0.45102357 343s 16 3.46920 -5.41242 8.56937909 343s 17 0.46682 -1.46753 1.48992481 343s 18 2.21807 0.99168 -0.61894625 343s 19 0.28525 -2.00333 -2.16450483 343s 20 -1.66639 -1.76768 -1.06946404 343s 21 -2.58106 1.23534 -0.65557612 343s 22 -4.15573 1.71244 0.08170141 343s 23 -3.07670 -4.87628 2.53200755 343s 24 -1.70808 -3.71657 2.99305849 343s 25 -1.08172 -1.05713 0.02468813 343s 26 -2.23187 0.27323 -0.85760867 343s 27 -3.50498 1.07657 -0.68503455 343s 28 -4.49819 1.43219 0.53416609 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 3.4135 2.0267 1.0764 343s ---------------------------------------------------------- 343s hbk 75 3 3 1.459908 1.201048 343s Scores: 343s PC1 PC2 PC3 343s 1 -31.105415 4.714217 10.4566165 343s 2 -31.707650 5.748724 10.7682402 343s 3 -33.366131 4.625897 12.1570167 343s 4 -34.173377 6.069657 12.4466895 343s 5 -33.780418 5.508823 11.9872893 343s 6 -32.493478 4.684595 10.5679819 343s 7 -32.592637 5.235522 10.3765493 343s 8 -31.293363 4.865797 10.9379676 343s 9 -33.160964 5.714260 12.3098920 343s 10 -31.919786 5.384537 12.3374332 343s 11 -38.231962 6.810641 13.5994385 343s 12 -39.290479 5.393906 15.2942554 343s 13 -39.418445 7.326461 11.5194898 343s 14 -43.906584 13.214819 8.3282743 343s 15 -1.906326 -0.716061 -0.8635112 343s 16 -0.263255 -0.926016 -1.9009292 343s 17 1.776489 1.072332 -0.5496140 343s 18 -0.464648 -0.702441 0.0482897 343s 19 -0.267826 1.283779 -0.2925812 343s 20 -2.122108 -0.165970 -0.8924686 343s 21 -0.937217 -0.548532 -0.4132196 343s 22 -0.423273 1.781869 -0.0323061 343s 23 -0.047532 -0.018909 -1.1259327 343s 24 0.490041 0.520202 -1.1065753 343s 25 2.143049 -0.720869 -0.0495474 343s 26 -1.094748 1.459175 0.2226246 343s 27 -2.070705 -0.898573 0.0023229 343s 28 0.294998 -0.830258 0.5929001 343s 29 1.242995 -0.300216 -0.2010507 343s 30 -0.147958 -0.439099 2.0003038 343s 31 -0.170818 -1.440946 -0.9755627 343s 32 0.958531 1.199730 -1.0129867 343s 33 -0.697307 0.874343 -0.7260649 343s 34 2.278946 -0.261106 0.4196544 343s 35 -1.962829 -0.809318 0.2033113 343s 36 -0.626631 0.600666 0.8004036 343s 37 -0.550885 1.881448 0.7382776 343s 38 1.249717 -0.336214 -0.9349845 343s 39 1.106696 -1.569418 0.1869576 343s 40 0.684034 0.939963 -0.1034965 343s 41 -1.559314 -1.551408 0.3660323 343s 42 0.538741 0.447358 1.6361099 343s 43 0.252685 2.080564 -0.7765259 343s 44 -0.217012 -1.027281 1.7015154 343s 45 1.497600 -1.349234 -0.2698932 343s 46 -0.100388 -1.026443 1.5390401 343s 47 0.811117 -2.195271 -0.5208141 343s 48 -1.462210 -1.321318 0.5600144 343s 49 -1.383976 -0.740714 -0.7348906 343s 50 -1.636773 0.215464 0.3195369 343s 51 0.530918 -0.759743 -1.2069247 343s 52 0.109566 -2.107455 -0.5315473 343s 53 0.564334 0.060847 2.3910630 343s 54 0.272234 1.122711 -1.5060028 343s 55 0.608660 1.197219 -0.5255609 343s 56 -0.565430 0.710345 -1.3708230 343s 57 1.115629 -0.888816 -0.4186014 343s 58 -1.351288 0.374815 -1.1980618 343s 59 -0.998016 0.151228 0.9007970 343s 60 -0.124017 0.764846 1.9005963 343s 61 -1.189858 1.905264 0.7721322 343s 62 2.190589 -0.579614 -0.1377914 343s 63 0.518278 0.931130 -1.4534768 343s 64 -2.124566 -0.194391 -0.0327092 343s 65 -0.154218 -1.050861 1.1309885 343s 66 1.197852 1.044147 -0.2265269 343s 67 0.114174 0.094763 -0.5168926 343s 68 2.201115 -0.032271 0.8573493 343s 69 1.307843 -1.104815 -0.7741270 343s 70 -0.691449 0.676665 1.0004603 343s 71 -1.150975 -0.050861 -0.0717068 343s 72 0.457293 0.861871 0.1026350 343s 73 0.392258 0.897451 0.9178065 343s 74 0.584658 1.450471 0.3201857 343s 75 0.972517 0.063777 1.8223995 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 1.2083 1.0959 1.0168 343s ---------------------------------------------------------- 343s milk 86 8 8 5.739740 2.405262 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 PC6 PC7 343s 1 -5.710924 -1.346213 0.01332091 -0.3709242 -0.566813 0.7529298 -1.2525433 343s 2 -6.578612 -0.440749 1.16354746 0.2870685 -0.573207 0.7368064 -1.6101427 343s 3 -0.720902 1.777381 -0.21532020 -0.3213950 0.287603 -0.4764464 -0.5638337 343s 4 -5.545889 1.621147 -0.85212883 0.4380154 0.022241 0.0718035 0.1176140 343s 5 1.323210 -0.143897 -0.78611461 0.5966857 0.043139 -0.0512545 -0.1419726 343s 6 -1.760792 -0.662792 0.46402240 0.2149752 0.130000 0.0797221 0.1916948 343s 7 -2.344198 -0.363657 0.92442296 0.3921371 0.241463 -0.2370967 0.0636268 343s 8 -2.556824 -0.680132 0.04339934 0.4635077 0.154136 0.0371259 0.0260340 343s 9 1.203234 2.712342 -1.00693092 0.1251739 0.170679 0.2231851 -0.0118196 343s 10 3.151858 1.255826 -0.01678562 -0.5087398 -0.087933 0.0115055 -0.0097828 343s 11 9.562891 1.580419 -2.65612113 -0.1748178 -0.153031 -0.0880112 -0.1648752 343s 12 13.617821 -0.999033 -1.92168237 0.0326918 -0.038488 0.0870082 -0.1809687 343s 13 10.958032 -0.097916 0.95915085 -0.2348663 0.147875 0.1219202 0.0419067 343s 14 12.675941 0.158747 -1.04153243 0.3117402 0.302036 0.1187749 -0.2310830 343s 15 10.726828 1.775339 -3.36786799 0.1285422 0.151594 0.0998947 -0.2028458 343s 16 3.042705 0.212589 -1.23921907 -0.5596596 0.277061 -0.5037073 0.0612182 343s 17 0.780071 2.990008 -1.58490147 -0.5441119 0.436485 -0.0603833 0.1016610 343s 18 2.523916 -0.923373 -0.03221722 0.3830822 0.208008 -0.5505270 -0.1252648 343s 19 1.990563 1.062648 -1.42038451 -0.3602257 -0.068006 -0.1932744 -0.1197842 343s 20 -0.243938 1.674555 -0.72225359 -0.1475652 -0.397855 -0.5385123 -0.0559660 343s 21 3.354424 -2.001060 -0.22542149 0.3346180 0.032502 -0.0953825 0.1293148 343s 22 1.477177 -0.777534 -0.35362339 0.1224412 0.203208 0.0514382 -0.2166274 343s 23 0.502055 -1.618511 -0.85013853 -0.1298862 -0.144328 -0.1941806 -0.1923681 343s 24 0.900504 -1.227820 -1.07180474 -0.5851197 0.112657 0.0467164 0.0405544 343s 25 4.161393 -1.869015 -1.54507759 0.2003123 -0.152582 -0.1382908 0.0864320 343s 26 1.277795 -1.185179 -1.13445511 0.2771556 -0.101901 0.0070037 -0.1279016 343s 27 3.447256 0.257652 -1.13407954 -0.0077859 0.853002 -0.1376443 -0.1897380 343s 28 -1.695730 -3.781876 -0.72940594 -0.0956421 0.064475 0.3665470 0.0726448 343s 29 -3.923610 -1.654818 -0.16117226 -0.4242302 -0.303749 -0.0209844 0.1723890 343s 30 -0.309616 -1.564739 -0.39909943 0.1657509 -0.178739 -0.0600221 -0.0571706 343s 31 -0.960838 -2.242733 1.50477679 -0.2957897 0.163758 -0.1034399 0.0257903 343s 32 -0.671285 -0.459839 1.39124475 -0.3669914 0.246127 0.2094780 -0.2681284 343s 33 -1.589089 -0.390812 -0.16505762 -0.3992573 0.086870 -0.0402114 -0.0399923 343s 34 -0.421868 0.636139 -0.42563447 -0.2985726 0.311365 0.2398515 -0.0540852 343s 35 1.118429 -2.116328 -0.22329747 -0.4864401 0.289927 -0.0503006 0.0101706 343s 36 -3.660291 -1.630831 -0.57876280 0.1294792 -0.260224 0.0912904 -0.1565668 343s 37 -0.087686 -2.530609 0.50076931 -0.0319873 0.194898 -0.1233526 -0.2494283 343s 38 -1.418620 -2.303011 -0.09405565 -0.0931745 0.169466 0.1581787 0.0850095 343s 39 1.815225 -0.838968 -1.10222194 -0.4897630 0.180933 0.0096330 -0.0600652 343s 40 -3.420975 1.398516 -0.17143314 -0.5852146 0.090464 -0.2066323 -0.2974177 343s 41 -3.462295 -1.795174 -0.17500650 -0.1610267 -0.595086 0.5981680 -1.5930268 343s 42 -6.401429 0.451242 -0.78723149 -0.4285618 0.055395 -0.0212476 0.0808936 343s 43 -2.583017 -0.871790 1.29937081 0.2422349 -0.190002 -0.2822972 -0.2625721 343s 44 -5.027244 -0.167503 -0.02382957 -0.8288929 -0.852207 0.7399343 0.4606076 343s 45 0.364494 -0.440380 -0.07746564 -0.4552133 0.095711 -0.1662998 0.1566706 343s 46 0.420706 -1.880819 -0.82180986 -0.1823454 -0.022661 -0.0304227 -0.0516440 343s 47 -1.932985 -0.120002 4.00934170 0.0930728 0.295428 0.2787446 0.3766231 343s 48 0.395402 -1.021393 1.07953292 -0.4599764 -0.132386 0.1895780 0.2771755 343s 49 2.886100 -0.276587 1.48851137 -0.6314648 -0.203963 -0.0891955 0.1347804 343s 50 -3.255379 2.479232 -0.37933775 -0.3651497 -0.415000 0.0045750 0.0671055 343s 51 1.939333 0.617579 1.57113225 0.0310866 -0.039226 0.0409183 0.1830694 343s 52 5.727154 0.275898 0.58814711 -0.1739820 -0.222791 0.2553797 0.1959402 343s 53 1.207873 0.131451 0.80899235 0.2872465 -0.353544 -0.1697200 -0.0987230 343s 54 0.612921 0.040062 0.17807459 -0.0053074 -0.202244 -0.0671788 0.0530276 343s 55 -0.399075 -0.727144 0.26196635 0.3657576 -0.192705 0.0903564 0.0641289 343s 56 0.240719 0.733792 -0.05030509 0.0967214 -0.186906 0.0310231 -0.0594812 343s 57 1.589641 0.289427 -1.02478822 0.2723190 -0.048378 0.2599262 -0.2040853 343s 58 0.423483 -1.262515 -0.85026016 0.4749963 -0.082647 0.0752412 0.1352259 343s 59 1.983684 1.335122 0.42593757 0.1345894 0.096456 0.1153107 -0.0385994 343s 60 1.770171 0.935428 0.14901569 0.3641973 0.274015 -0.0280119 0.0690244 343s 61 0.182845 1.706453 -0.18364654 0.2517421 -0.035773 0.0357087 -0.1363470 343s 62 -2.191617 1.966324 -0.03573689 -0.2203900 -0.235704 0.1682332 -0.1145174 343s 63 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 343s 64 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 343s 65 0.407575 2.996346 -0.63021113 -0.1335795 0.087668 0.0627032 0.0486166 343s 66 2.660379 1.322824 0.10122110 0.2420451 0.192938 0.0344019 -0.0771918 343s 67 -0.032273 1.315299 -0.04511689 -0.1293380 -0.025923 -0.1655965 0.1887534 343s 68 1.117637 2.005809 1.97078787 -0.0429209 -0.176568 0.1634287 -0.0916254 343s 69 0.970730 0.837158 0.01621375 0.2347502 -0.071757 -0.2464626 0.2907551 343s 70 -2.688271 -5.335891 -0.64225481 4.1819517 -9.523550 2.0943027 -2.8098426 343s 71 2.428718 1.976051 -0.24749122 0.1308738 0.018276 0.1711292 0.1346284 343s 72 -2.061944 0.405943 0.50472914 0.4393739 -0.056420 -0.0031558 0.2663880 343s 73 2.029606 2.874991 0.68310320 -0.2067254 0.511537 -0.2010371 0.0805608 343s 74 11.293757 0.328931 -3.84783031 -0.4130266 -0.210499 -0.1103148 -0.0381326 343s 75 0.120896 2.287914 0.83639076 -0.2462845 0.551353 0.6629701 0.3789055 343s 76 1.859499 0.422019 1.18435547 0.1546108 0.017266 0.0470615 -0.1071011 343s 77 8.435857 1.147499 -2.19924186 -0.4156770 0.386548 0.0294075 -0.1911399 343s 78 -1.090858 1.311287 0.62897430 0.1727009 0.077341 0.0135972 -0.0096934 343s 79 0.560012 0.623617 0.83727267 0.1680787 0.087477 0.0611949 -0.2588084 343s 80 3.873817 -1.133641 -1.27469019 -0.2717298 -0.165066 0.1696232 0.0635047 343s 81 -0.758664 -0.880260 0.00057124 0.2838720 0.016243 0.1527299 -0.0150514 343s 82 -2.709588 1.464049 -0.12598126 -0.3828567 0.213647 -0.1425385 0.1552827 343s 83 -2.213670 0.059563 0.87565603 0.1255703 -0.082005 0.2189829 -0.2938264 343s 84 -0.242242 -0.483552 2.05089334 -0.0681005 -0.101578 0.1304632 -0.2218093 343s 85 -1.032129 2.375018 -2.19321259 0.2332079 -0.066379 0.1854598 -0.0873859 343s 86 0.015327 -0.948155 1.39530555 0.2701225 -0.268889 0.0578145 0.1608678 343s PC8 343s 1 2.1835e-03 343s 2 1.6801e-03 343s 3 1.6623e-03 343s 4 2.6286e-04 343s 5 9.5884e-04 343s 6 1.4430e-03 343s 7 1.8784e-04 343s 8 6.8473e-04 343s 9 -6.8490e-04 343s 10 1.1565e-04 343s 11 5.6907e-06 343s 12 -1.8395e-03 343s 13 -2.1582e-03 343s 14 -1.6294e-03 343s 15 -1.6964e-03 343s 16 -1.9664e-03 343s 17 -2.2448e-03 343s 18 -6.5884e-04 343s 19 -1.1536e-03 343s 20 2.6887e-04 343s 21 3.3199e-05 343s 22 1.1170e-04 343s 23 -1.7617e-04 343s 24 -2.1577e-04 343s 25 -6.1495e-04 343s 26 -7.2903e-04 343s 27 -6.8773e-04 343s 28 -2.0742e-04 343s 29 -2.6937e-04 343s 30 -6.7472e-05 343s 31 -1.3222e-04 343s 32 -1.6516e-04 343s 33 -1.8836e-04 343s 34 -1.1273e-04 343s 35 3.0703e-05 343s 36 -3.0311e-04 343s 37 -1.9380e-04 343s 38 5.5526e-04 343s 39 4.1987e-04 343s 40 8.4807e-05 343s 41 8.8725e-04 343s 42 -6.5647e-04 343s 43 4.3202e-04 343s 44 -5.3330e-04 343s 45 8.9161e-04 343s 46 1.1588e-03 343s 47 -1.2714e-03 343s 48 -4.0376e-04 343s 49 4.1280e-06 343s 50 3.0116e-04 343s 51 5.8510e-05 343s 52 3.3236e-04 343s 53 4.0982e-04 343s 54 4.0428e-04 343s 55 6.1600e-04 343s 56 -4.0496e-05 343s 57 -1.8342e-04 343s 58 -1.6748e-04 343s 59 -1.0894e-03 343s 60 -2.6876e-04 343s 61 -5.8951e-05 343s 62 -1.5517e-04 343s 63 -7.9933e-04 343s 64 -7.9933e-04 343s 65 2.2592e-05 343s 66 2.4984e-05 343s 67 -2.2714e-04 343s 68 -3.3991e-04 343s 69 -3.0375e-04 343s 70 3.4033e-03 343s 71 2.3288e-05 343s 72 -3.4126e-04 343s 73 2.5528e-04 343s 74 2.2760e-03 343s 75 -2.8985e-04 343s 76 7.9077e-04 343s 77 9.4636e-04 343s 78 4.9099e-04 343s 79 3.0501e-04 343s 80 6.5280e-04 343s 81 -3.6570e-04 343s 82 4.9966e-04 343s 83 -4.3245e-04 343s 84 -4.6152e-04 343s 85 7.4691e-04 343s 86 -6.1103e-04 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 2.39577535 1.55089079 0.92557331 0.33680677 0.19792033 0.17855133 0.16041702 343s [8] 0.00054179 343s ---------------------------------------------------------- 343s bushfire 38 5 5 31248.552973 358.974577 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 343s 1 155.972 1.08098 -23.31135 -1.93015 1.218941 343s 2 157.738 0.35648 -20.95658 -2.42375 0.466415 343s 3 150.667 2.12545 -16.20395 -2.00140 -0.582924 343s 4 133.892 5.25124 -15.88873 -2.78469 -0.275261 343s 5 102.462 13.00611 -21.54096 -4.69409 -0.944176 343s 6 77.694 18.75377 -28.71865 -6.44244 0.446350 343s 7 286.266 -11.36184 -98.67134 10.95233 -3.625338 343s 8 326.627 29.92767 -112.60824 -29.26330 -13.710094 343s 9 327.898 32.39553 -113.34314 -31.65905 -13.830781 343s 10 325.131 5.81628 -105.58927 -13.45695 -8.987971 343s 11 326.458 -7.84562 -94.25242 -6.11547 -8.572845 343s 12 333.171 -37.69907 -50.89207 8.98187 -1.742979 343s 13 279.789 -40.78415 -8.06209 7.65884 0.181748 343s 14 37.714 10.54231 13.46530 -1.55051 2.102662 343s 15 -90.034 34.68964 18.98186 0.69260 0.417573 343s 16 -46.492 23.65086 10.07282 4.36090 -0.748517 343s 17 -43.990 20.36443 9.61049 2.83084 -0.127983 343s 18 -32.938 19.11199 2.64850 2.92879 -1.473988 343s 19 -36.555 20.60142 2.01879 0.63832 -1.235075 343s 20 -46.837 19.89630 6.65142 0.89120 0.271108 343s 21 -28.670 15.29534 6.59311 3.29638 0.402194 343s 22 -20.331 15.06559 7.33721 2.16591 2.006327 343s 23 108.644 -7.92707 -1.45130 6.27388 0.356715 343s 24 163.697 -16.15568 0.61663 4.24231 0.464415 343s 25 100.471 -0.30739 0.87762 2.86452 -0.692735 343s 26 106.922 0.90864 -1.91436 2.54557 -0.565023 343s 27 121.966 -3.29641 4.85626 -0.47676 -0.490047 343s 28 98.650 -4.51455 16.64160 -3.08996 -0.839397 343s 29 88.795 -10.85457 30.46708 -5.37360 0.315657 343s 30 142.981 -27.89100 22.40713 -1.67126 -0.680158 343s 31 14.125 -21.60028 29.80480 -8.25272 -0.019693 343s 32 -244.044 -11.76430 24.53390 -12.52294 2.022312 343s 33 -283.842 -13.21931 -6.23565 -2.63367 -0.080728 343s 34 -280.168 -13.41903 -7.69318 -1.24571 -0.722513 343s 35 -285.666 -13.78452 -6.50318 -1.23756 1.074669 343s 36 -282.938 -13.82281 -7.63902 0.20435 -0.971673 343s 37 -281.129 -16.20408 -8.57154 1.85797 0.234486 343s 38 -282.589 -16.91969 -8.36010 2.35589 0.490630 343s ------------- 343s Call: 343s PcaHubert(x = x, k = p) 343s 343s Standard deviations: 343s [1] 176.77260 18.94662 16.21701 3.95755 0.92761 343s ---------------------------------------------------------- 343s ========================================================== 343s > dodata(method="hubert") 343s 343s Call: dodata(method = "hubert") 343s Data Set n p k e1 e2 343s ========================================================== 343s heart 12 2 1 315.227002 NA 343s Scores: 343s PC1 343s 1 13.2197 343s 2 69.9817 343s 3 6.6946 343s 4 2.8899 343s 5 24.9956 343s 6 -8.9203 343s 7 12.0121 343s 8 -24.1915 343s 9 4.2721 343s 10 -22.8289 343s 11 -8.1433 343s 12 54.6519 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 17.755 343s ---------------------------------------------------------- 343s starsCYG 47 2 1 0.308922 NA 343s Scores: 343s PC1 343s 1 0.224695 343s 2 0.758653 343s 3 -0.089113 343s 4 0.758653 343s 5 0.173934 343s 6 0.466195 343s 7 -0.433154 343s 8 0.296411 343s 9 0.542517 343s 10 0.116133 343s 11 0.576303 343s 12 0.451490 343s 13 0.429942 343s 14 -0.997904 343s 15 -0.745515 343s 16 -0.408745 343s 17 -1.071002 343s 18 -0.803514 343s 19 -0.834141 343s 20 0.734210 343s 21 -0.627085 343s 22 -0.784992 343s 23 -0.566652 343s 24 -0.130992 343s 25 0.019053 343s 26 -0.329791 343s 27 -0.350747 343s 28 -0.099378 343s 29 -0.628499 343s 30 0.890506 343s 31 -0.573100 343s 32 0.127022 343s 33 0.227721 343s 34 1.128979 343s 35 -0.676234 343s 36 0.649894 343s 37 0.122186 343s 38 0.227721 343s 39 0.201140 343s 40 0.569920 343s 41 -0.375716 343s 42 0.069814 343s 43 0.354212 343s 44 0.346152 343s 45 0.559656 343s 46 -0.009140 343s 47 -0.487699 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 0.55581 343s ---------------------------------------------------------- 343s phosphor 18 2 1 215.172048 NA 343s Scores: 343s PC1 343s 1 1.12634 343s 2 -22.10340 343s 3 -23.49216 343s 4 -13.45927 343s 5 -18.60808 343s 6 11.24086 343s 7 -0.14748 343s 8 -9.77075 343s 9 -10.37022 343s 10 12.71798 343s 11 -4.61857 343s 12 10.07037 343s 13 13.16767 343s 14 7.57254 343s 15 17.81362 343s 16 -11.08799 343s 17 21.70358 343s 18 18.24496 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 14.669 343s ---------------------------------------------------------- 343s stackloss 21 3 2 77.038636 18.859777 343s Scores: 343s PC1 PC2 343s 1 -20.334936 10.28081 343s 2 -19.772121 11.10736 343s 3 -16.461573 6.43794 343s 4 -4.258672 1.73213 343s 5 -3.773146 1.41928 343s 6 -4.015909 1.57571 343s 7 -7.635560 -3.22715 343s 8 -7.635560 -3.22715 343s 9 -0.855388 -0.58707 343s 10 4.298129 4.41664 343s 11 -0.767202 -3.02229 343s 12 0.038375 -2.35217 343s 13 3.172500 2.76354 343s 14 -3.261224 -6.17206 343s 15 5.553840 -7.34784 343s 16 7.242284 -4.86820 343s 17 14.878925 6.85989 343s 18 10.939223 1.07406 343s 19 10.133645 0.40394 343s 20 4.267234 1.99501 343s 21 -11.859921 2.12579 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 8.7772 4.3428 343s ---------------------------------------------------------- 343s salinity 28 3 2 8.001175 5.858089 343s Scores: 343s PC1 PC2 343s 1 2.858444 1.04359 343s 2 3.807704 1.55974 343s 3 6.220733 -4.32114 343s 4 6.388841 -2.83649 343s 5 6.077450 -3.70092 343s 6 5.974494 -0.67230 343s 7 4.531584 0.78322 343s 8 2.725849 2.41297 343s 9 0.100501 -2.13615 343s 10 -2.358003 -1.49718 343s 11 -1.317688 -1.15391 343s 12 0.434635 0.58230 343s 13 0.116019 1.79022 343s 14 -1.771501 2.71749 343s 15 -2.630757 -2.44003 343s 16 2.289743 -5.51829 343s 17 0.637985 -1.26452 343s 18 3.076147 0.19883 343s 19 0.097381 -1.95868 343s 20 -1.572471 -0.93003 343s 21 -1.284185 2.21858 343s 22 -2.531713 3.30313 343s 23 -3.865359 -3.01230 343s 24 -2.143461 -2.41918 343s 25 -0.714414 -0.41227 343s 26 -1.327781 1.18373 343s 27 -2.201166 2.41566 343s 28 -2.931988 3.20536 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 2.8286 2.4203 343s ---------------------------------------------------------- 343s hbk 75 3 3 1.459908 1.201048 343s Scores: 343s PC1 PC2 PC3 343s 1 31.105415 -4.714217 -10.4566165 343s 2 31.707650 -5.748724 -10.7682402 343s 3 33.366131 -4.625897 -12.1570167 343s 4 34.173377 -6.069657 -12.4466895 343s 5 33.780418 -5.508823 -11.9872893 343s 6 32.493478 -4.684595 -10.5679819 343s 7 32.592637 -5.235522 -10.3765493 343s 8 31.293363 -4.865797 -10.9379676 343s 9 33.160964 -5.714260 -12.3098920 343s 10 31.919786 -5.384537 -12.3374332 343s 11 38.231962 -6.810641 -13.5994385 343s 12 39.290479 -5.393906 -15.2942554 343s 13 39.418445 -7.326461 -11.5194898 343s 14 43.906584 -13.214819 -8.3282743 343s 15 1.906326 0.716061 0.8635112 343s 16 0.263255 0.926016 1.9009292 343s 17 -1.776489 -1.072332 0.5496140 343s 18 0.464648 0.702441 -0.0482897 343s 19 0.267826 -1.283779 0.2925812 343s 20 2.122108 0.165970 0.8924686 343s 21 0.937217 0.548532 0.4132196 343s 22 0.423273 -1.781869 0.0323061 343s 23 0.047532 0.018909 1.1259327 343s 24 -0.490041 -0.520202 1.1065753 343s 25 -2.143049 0.720869 0.0495474 343s 26 1.094748 -1.459175 -0.2226246 343s 27 2.070705 0.898573 -0.0023229 343s 28 -0.294998 0.830258 -0.5929001 343s 29 -1.242995 0.300216 0.2010507 343s 30 0.147958 0.439099 -2.0003038 343s 31 0.170818 1.440946 0.9755627 343s 32 -0.958531 -1.199730 1.0129867 343s 33 0.697307 -0.874343 0.7260649 343s 34 -2.278946 0.261106 -0.4196544 343s 35 1.962829 0.809318 -0.2033113 343s 36 0.626631 -0.600666 -0.8004036 343s 37 0.550885 -1.881448 -0.7382776 343s 38 -1.249717 0.336214 0.9349845 343s 39 -1.106696 1.569418 -0.1869576 343s 40 -0.684034 -0.939963 0.1034965 343s 41 1.559314 1.551408 -0.3660323 343s 42 -0.538741 -0.447358 -1.6361099 343s 43 -0.252685 -2.080564 0.7765259 343s 44 0.217012 1.027281 -1.7015154 343s 45 -1.497600 1.349234 0.2698932 343s 46 0.100388 1.026443 -1.5390401 343s 47 -0.811117 2.195271 0.5208141 343s 48 1.462210 1.321318 -0.5600144 343s 49 1.383976 0.740714 0.7348906 343s 50 1.636773 -0.215464 -0.3195369 343s 51 -0.530918 0.759743 1.2069247 343s 52 -0.109566 2.107455 0.5315473 343s 53 -0.564334 -0.060847 -2.3910630 343s 54 -0.272234 -1.122711 1.5060028 343s 55 -0.608660 -1.197219 0.5255609 343s 56 0.565430 -0.710345 1.3708230 343s 57 -1.115629 0.888816 0.4186014 343s 58 1.351288 -0.374815 1.1980618 343s 59 0.998016 -0.151228 -0.9007970 343s 60 0.124017 -0.764846 -1.9005963 343s 61 1.189858 -1.905264 -0.7721322 343s 62 -2.190589 0.579614 0.1377914 343s 63 -0.518278 -0.931130 1.4534768 343s 64 2.124566 0.194391 0.0327092 343s 65 0.154218 1.050861 -1.1309885 343s 66 -1.197852 -1.044147 0.2265269 343s 67 -0.114174 -0.094763 0.5168926 343s 68 -2.201115 0.032271 -0.8573493 343s 69 -1.307843 1.104815 0.7741270 343s 70 0.691449 -0.676665 -1.0004603 343s 71 1.150975 0.050861 0.0717068 343s 72 -0.457293 -0.861871 -0.1026350 343s 73 -0.392258 -0.897451 -0.9178065 343s 74 -0.584658 -1.450471 -0.3201857 343s 75 -0.972517 -0.063777 -1.8223995 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 1.2083 1.0959 1.0168 343s ---------------------------------------------------------- 343s milk 86 8 2 6.040806 2.473780 343s Scores: 343s PC1 PC2 343s 1 -5.768003 -0.9174359 343s 2 -6.664422 0.0280812 343s 3 -0.484521 1.7923710 343s 4 -5.211590 2.0747301 343s 5 1.422641 -0.3268437 343s 6 -1.810360 -0.5469828 343s 7 -2.402924 -0.1987041 343s 8 -2.553389 -0.4963662 343s 9 1.583399 2.5410448 343s 10 3.267946 0.9141367 343s 11 9.924771 0.6501301 343s 12 13.628569 -2.3009846 343s 13 10.774550 -1.1628697 343s 14 12.716376 -1.0670330 343s 15 11.176408 0.7403371 343s 16 3.209269 -0.0804317 343s 17 1.256577 2.8931153 343s 18 2.468720 -1.2008647 343s 19 2.253229 0.8379608 343s 20 0.021073 1.6394221 343s 21 3.205298 -2.3518286 343s 22 1.470733 -0.9618655 343s 23 0.475732 -1.7044535 343s 24 0.930144 -1.3288398 343s 25 4.151553 -2.2882554 343s 26 1.314488 -1.3527439 343s 27 3.613405 -0.0813605 343s 28 -1.909178 -3.6473200 343s 29 -3.987263 -1.3255834 343s 30 -0.370601 -1.5855086 343s 31 -1.273254 -2.1892809 343s 32 -0.816634 -0.4514478 343s 33 -1.553394 -0.2792004 343s 34 -0.275027 0.6359374 343s 35 0.980782 -2.2353223 343s 36 -3.678470 -1.3459182 343s 37 -0.327102 -2.5615283 343s 38 -1.563492 -2.2008288 343s 39 1.876146 -1.0292641 343s 40 -3.204182 1.6694332 343s 41 -3.561892 -1.5844770 343s 42 -6.175135 1.0123714 343s 43 -2.736601 -0.7040261 343s 44 -4.981783 0.2434304 343s 45 0.368802 -0.5011413 343s 46 0.369508 -1.9511091 343s 47 -2.306673 -0.0089446 343s 48 0.215195 -1.1000357 343s 49 2.704678 -0.5919929 343s 50 -2.930879 2.7161936 343s 51 1.846250 0.3732500 343s 52 5.661288 -0.3139157 343s 53 1.154929 -0.0575094 343s 54 0.625715 -0.0733934 343s 55 -0.453714 -0.7535924 343s 56 0.343722 0.6460318 343s 57 1.743002 0.0794685 343s 58 0.433705 -1.3500731 343s 59 2.078550 1.0860506 343s 60 1.867913 0.7162287 343s 61 0.392645 1.6184583 343s 62 -1.958732 2.0993596 343s 63 -2.383251 -0.0253919 343s 64 -2.383251 -0.0253919 343s 65 0.780239 2.9018927 343s 66 2.785329 1.0142893 343s 67 0.131210 1.2703167 343s 68 1.110073 1.8140467 343s 69 1.076878 0.6954148 343s 70 -3.260160 -5.6233069 343s 71 2.647036 1.6892084 343s 72 -2.017340 0.5353349 343s 73 2.247524 2.6406249 343s 74 11.649291 -0.7374197 343s 75 0.280544 2.2306959 343s 76 1.791213 0.1796005 343s 77 8.730344 0.3412271 343s 78 -0.987405 1.3467910 343s 79 0.560808 0.5006661 343s 80 3.897879 -1.5270179 343s 81 -0.792759 -0.8649399 343s 82 -2.493611 1.6796838 343s 83 -2.245966 0.1889555 343s 84 -0.468812 -0.5359088 343s 85 -0.538372 2.4105954 343s 86 -0.185347 -1.0176989 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 2.4578 1.5728 343s ---------------------------------------------------------- 343s bushfire 38 5 1 38435.075910 NA 343s Scores: 343s PC1 343s 1 -111.9345 343s 2 -113.4128 343s 3 -105.8364 343s 4 -89.1684 343s 5 -58.7216 343s 6 -35.0370 343s 7 -250.2123 343s 8 -292.6877 343s 9 -294.0765 343s 10 -290.0193 343s 11 -289.8168 343s 12 -290.8645 343s 13 -232.6865 343s 14 9.8483 343s 15 137.1924 343s 16 92.9804 343s 17 90.4493 343s 18 78.6325 343s 19 82.1178 343s 20 92.9044 343s 21 74.9157 343s 22 66.7350 343s 23 -62.1981 343s 24 -116.5696 343s 25 -53.8907 343s 26 -60.6384 343s 27 -74.7621 343s 28 -50.2202 343s 29 -38.7483 343s 30 -93.3887 343s 31 35.3096 343s 32 290.8493 343s 33 326.7236 343s 34 322.9095 343s 35 328.5307 343s 36 325.6791 343s 37 323.8136 343s 38 325.2991 343s ------------- 343s Call: 343s PcaHubert(x = x, mcd = FALSE) 343s 343s Standard deviations: 343s [1] 196.05 343s ---------------------------------------------------------- 343s ========================================================== 343s > 343s > dodata(method="locantore") 343s 343s Call: dodata(method = "locantore") 343s Data Set n p k e1 e2 343s ========================================================== 343s heart 12 2 2 1.835912 0.084745 343s Scores: 343s PC1 PC2 343s [1,] 7.3042 1.745289 343s [2,] 64.6474 0.164425 343s [3,] 1.1057 -1.404189 343s [4,] -3.1943 2.565728 343s [5,] 19.4154 -0.401369 343s [6,] -15.5709 6.666752 343s [7,] 5.9980 2.509372 343s [8,] -29.5933 -4.805972 343s [9,] -1.3933 -0.899323 343s [10,] -28.2845 -4.270057 343s [11,] -14.0069 0.048311 343s [12,] 49.1484 0.694598 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 1.35496 0.29111 343s ---------------------------------------------------------- 343s starsCYG 47 2 2 0.779919 0.050341 343s Scores: 343s PC1 PC2 343s [1,] 0.174291 -0.0489127 343s [2,] 0.703776 0.0769650 343s [3,] -0.136954 -0.1212071 343s [4,] 0.703776 0.0769650 343s [5,] 0.125991 -0.1134658 343s [6,] 0.413609 0.0121367 343s [7,] -0.466451 -0.5036094 343s [8,] 0.238569 0.1446547 343s [9,] 0.498194 -0.1998666 343s [10,] 0.065125 -0.0353931 343s [11,] 0.562344 -0.9836936 343s [12,] 0.399997 -0.0164068 343s [13,] 0.376370 0.0369013 343s [14,] -1.041009 -0.2611550 343s [15,] -0.798187 -0.0090880 343s [16,] -0.464636 0.0805967 343s [17,] -1.123135 -0.0293034 343s [18,] -0.861603 0.1297588 343s [19,] -0.884955 -0.0588007 343s [20,] 0.721130 -1.0033585 343s [21,] -0.679097 -0.0238366 343s [22,] -0.837884 -0.0041718 343s [23,] -0.623423 0.1002615 343s [24,] -0.188079 0.1168815 343s [25,] -0.032888 -0.0131784 343s [26,] -0.385242 0.0707643 343s [27,] -0.401220 -0.0582501 343s [28,] -0.151978 0.0015702 343s [29,] -0.677776 -0.0945350 343s [30,] 0.878688 -1.0329475 343s [31,] -0.628339 0.0605648 343s [32,] 0.068629 0.1556245 343s [33,] 0.174199 0.0317098 343s [34,] 1.118098 -1.0525206 343s [35,] -0.726168 -0.0784655 343s [36,] 0.592061 0.1512588 343s [37,] 0.064942 0.1258519 343s [38,] 0.174199 0.0317098 343s [39,] 0.144335 0.1160195 343s [40,] 0.519088 -0.0311555 343s [41,] -0.429855 0.0359837 343s [42,] 0.015412 0.0513747 343s [43,] 0.299435 0.0665821 343s [44,] 0.293289 0.0169612 343s [45,] 0.504064 0.0916219 343s [46,] -0.063981 0.0612071 343s [47,] -0.544029 0.0904291 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 0.88313 0.22437 343s ---------------------------------------------------------- 343s phosphor 18 2 2 0.933905 0.279651 343s Scores: 343s PC1 PC2 343s 1 4.5660 -15.58981 343s 2 -21.2978 -0.38905 343s 3 -23.3783 3.96546 343s 4 -11.7131 -5.79023 343s 5 -18.2569 2.81141 343s 6 15.5702 -20.54935 343s 7 1.3671 -3.27043 343s 8 -9.4859 3.92005 343s 9 -10.4501 6.22662 343s 10 15.0583 -7.60532 343s 11 -3.9078 1.56960 343s 12 10.0330 7.52732 343s 13 13.4815 5.50056 343s 14 7.5487 7.24752 343s 15 18.6543 2.46040 343s 16 -9.3301 -5.68285 343s 17 22.2533 4.63689 343s 18 17.7892 10.85633 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 0.96639 0.52882 343s ---------------------------------------------------------- 343s stackloss 21 3 3 1.137747 0.196704 343s Scores: 343s PC1 PC2 PC3 343s [1,] 19.98046 -6.20875 -3.93576 343s [2,] 19.57014 -7.11509 -4.03666 343s [3,] 15.48729 -3.14247 -3.29600 343s [4,] 3.12341 -1.38969 1.50633 343s [5,] 2.35380 -0.84492 -0.25745 343s [6,] 2.73860 -1.11731 0.62444 343s [7,] 5.58533 4.04837 2.11170 343s [8,] 5.58533 4.04837 2.11170 343s [9,] -0.56851 0.17483 2.46656 343s [10,] -5.36478 -4.80766 -2.64915 343s [11,] -1.67190 3.34943 -1.74110 343s [12,] -2.46702 2.71547 -2.72389 343s [13,] -4.54414 -2.99497 -2.44736 343s [14,] 0.35419 6.70241 -0.45563 343s [15,] -8.28612 5.93369 1.94314 343s [16,] -9.51708 3.21466 1.64046 343s [17,] -14.87676 -9.74652 1.10983 343s [18,] -12.00452 -3.40212 1.81609 343s [19,] -11.20939 -2.76816 2.79887 343s [20,] -5.42808 -2.89367 0.23748 343s [21,] 9.83969 0.74095 -5.30190 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 1.06665 0.44351 0.33935 343s ---------------------------------------------------------- 343s salinity 28 3 3 1.038873 0.621380 343s Scores: 343s PC1 PC2 PC3 343s 1 -2.7215590 -0.98924 0.3594538 343s 2 -3.6251829 -1.03361 1.4973993 343s 3 -6.0588883 4.23861 -1.1012038 343s 4 -6.2741857 2.42372 -1.4875092 343s 5 -5.7274076 5.42190 2.9332011 343s 6 -5.8431892 0.57161 -0.3385363 343s 7 -4.4051377 -0.83292 0.0851817 343s 8 -2.6155827 -2.50739 0.3386166 343s 9 -0.0426575 1.19631 -2.5025726 343s 10 2.5297488 1.65029 -0.0110335 343s 11 1.5528097 1.93255 1.4216262 343s 12 -0.3140451 -0.73269 -0.1961364 343s 13 0.0010783 -1.88658 0.1849912 343s 14 1.9554303 -2.13519 1.8471356 343s 15 2.7897250 2.40211 -0.6327944 343s 16 -1.7665706 8.69449 5.6608836 343s 17 -0.4374125 1.72696 0.7230753 343s 18 -2.9752196 -0.54118 -0.6829760 343s 19 -0.0599346 0.84127 -2.8473543 343s 20 1.6597909 0.34191 -1.4847516 343s 21 1.3857395 -2.43924 0.0039271 343s 22 2.6664754 -3.14291 1.0600254 343s 23 4.1202067 3.81886 1.0608640 343s 24 2.4163743 3.45141 1.6874099 343s 25 0.8493897 0.31424 -0.3073115 343s 26 1.4216265 -1.55310 -0.5455012 343s 27 2.3021676 -2.63392 0.0481451 343s 28 3.0877115 -2.85951 1.4378956 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 1.01925 0.78828 0.36470 343s ---------------------------------------------------------- 343s hbk 75 3 3 1.038833 0.363386 343s Scores: 343s PC1 PC2 PC3 343s 1 32.393698 -3.4318297 0.051248 343s 2 33.103072 -4.4154651 0.294662 343s 3 35.038965 -3.5996035 -0.940929 343s 4 35.955809 -4.9285404 -0.479059 343s 5 35.424918 -4.3076292 -0.366699 343s 6 33.753497 -3.2463136 0.289013 343s 7 33.817375 -3.6819421 0.684167 343s 8 32.717119 -3.7074394 -0.279567 343s 9 34.932190 -4.6939061 -0.738196 343s 10 33.737339 -4.5702346 -1.193206 343s 11 40.202273 -5.4336890 -0.229323 343s 12 41.638189 -4.5304173 -1.996311 343s 13 40.768565 -5.0531048 2.123222 343s 14 44.408749 -8.8448536 8.236462 343s 15 0.977343 1.3057899 0.938694 343s 16 -0.900390 1.6169842 1.382855 343s 17 -2.384467 -0.9835430 0.375495 343s 18 -0.143306 0.7859701 -0.237712 343s 19 -0.344479 -0.9791245 0.733869 343s 20 1.199115 0.8330752 1.216827 343s 21 0.184475 0.8630593 0.351029 343s 22 -0.100389 -1.5084406 0.718236 343s 23 -0.847925 0.4823829 0.958677 343s 24 -1.334366 -0.1021190 1.000300 343s 25 -2.669352 0.4692990 -0.811134 343s 26 0.601538 -1.1984283 0.541627 343s 27 1.373423 1.2098621 0.136249 343s 28 -0.721268 0.6164612 -0.963817 343s 29 -1.832615 0.2543279 -0.297658 343s 30 0.120086 -0.1558590 -1.976558 343s 31 -0.747437 1.7749106 0.342824 343s 32 -1.727558 -0.8325772 1.043088 343s 33 -0.073907 -0.3923823 1.083904 343s 34 -2.646454 -0.1350138 -1.101448 343s 35 1.331096 1.0443905 -0.039328 343s 36 0.281192 -0.6569943 -0.404009 343s 37 0.245349 -1.8406517 0.093656 343s 38 -2.049446 0.5320301 0.347219 343s 39 -1.645547 1.3268749 -1.068792 343s 40 -1.216874 -0.8556007 0.201262 343s 41 0.959445 1.6250030 -0.553881 343s 42 -0.603579 -0.9569812 -1.502730 343s 43 -0.946870 -1.6333180 1.324763 343s 44 0.076217 0.5018427 -1.902369 343s 45 -2.140584 1.2192726 -0.677180 343s 46 -0.081677 0.5389288 -1.785347 343s 47 -1.590461 2.1881067 -0.583771 343s 48 0.931421 1.3321181 -0.669782 343s 49 0.512639 1.2123979 0.683099 343s 50 1.095415 0.0045968 0.143109 343s 51 -1.456417 1.1186245 0.619657 343s 52 -0.917904 2.2084467 -0.366392 343s 53 -0.429654 -0.8524437 -2.326637 343s 54 -1.213858 -0.4996891 1.630709 343s 55 -1.253877 -0.9438354 0.692022 343s 56 -0.390657 -0.0427482 1.571167 343s 57 -1.797537 0.8934866 -0.281980 343s 58 0.396886 0.3227454 1.492494 343s 59 0.646360 -0.2194210 -0.562699 343s 60 0.119900 -1.2480691 -1.459763 343s 61 0.867946 -1.7843458 0.232229 343s 62 -2.733997 0.3604288 -0.692947 343s 63 -1.442683 -0.3732483 1.452800 343s 64 1.444934 0.5727959 0.434633 343s 65 -0.147284 0.7055205 -1.413940 343s 66 -1.739552 -0.9838385 0.220303 343s 67 -0.824644 0.1503195 0.411693 343s 68 -2.437638 -0.4835278 -1.392882 343s 69 -2.091970 1.1865192 -0.088483 343s 70 0.403429 -0.7855276 -0.540161 343s 71 0.507512 0.3152001 0.276885 343s 72 -0.944376 -0.8197825 0.044859 343s 73 -0.648597 -1.1160277 -0.658528 343s 74 -0.979453 -1.4589411 0.029182 343s 75 -0.982282 -0.7226425 -1.917060 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 1.01923 0.60282 0.46137 343s ---------------------------------------------------------- 343s milk 86 8 8 1.175171 0.426506 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 PC6 343s [1,] 6.1907998 0.58762698 0.686510 -0.209679 0.3321757 -1.3424985 343s [2,] 7.0503894 -0.49576086 -0.322697 -0.767415 -0.0165833 -1.4596064 343s [3,] 0.7670594 -1.83556812 0.468814 0.346810 -0.0204610 -0.2115383 343s [4,] 5.4656748 -2.29797862 1.612819 -0.378295 -0.2050232 0.3486957 343s [5,] -1.0291160 0.37303007 0.634604 -0.521527 -0.3299543 0.0859469 343s [6,] 2.2186300 0.39396818 -0.236987 -0.033975 -0.2549238 0.2541221 343s [7,] 2.7938591 -0.01152811 -0.600546 -0.098564 -0.3906602 0.3798516 343s [8,] 2.9544176 0.32646226 0.273051 -0.275073 -0.3982959 0.2377581 343s [9,] -1.3344639 -2.45440308 1.001792 -0.104783 -0.1744718 -0.0887272 343s [10,] -2.9294174 -0.79860558 -0.260533 0.375330 0.3425169 -0.2056682 343s [11,] -9.5810648 -0.09577968 1.565111 -0.112002 0.3143032 -0.3190238 343s [12,] -13.1147240 2.95665890 0.228086 -0.180867 0.0136463 -0.4604390 343s [13,] -10.2989319 1.53220781 -2.244629 0.323950 -0.0398642 -0.3463501 343s [14,] -12.2553418 1.62281167 -0.472862 -0.212983 -0.4124280 -0.4253719 343s [15,] -10.8346894 -0.09781844 2.134079 -0.272304 -0.1090226 -0.3725738 343s [16,] -2.8358474 0.28109809 0.945309 0.603249 0.1615955 0.1762086 343s [17,] -1.0353408 -2.75475311 1.677879 0.598578 0.0078965 0.0228522 343s [18,] -2.0271810 1.25894451 -0.266038 -0.168565 -0.3000200 0.2891774 343s [19,] -1.9279394 -0.68339726 1.264416 0.186749 0.3018226 -0.0869321 343s [20,] 0.2568334 -1.62632029 0.854279 -0.088175 0.5458645 0.2217019 343s [21,] -2.7017404 2.45223507 -0.243639 -0.211402 -0.2102323 0.2140100 343s [22,] -1.0386097 0.99459030 0.188462 -0.033434 -0.2857078 -0.1438517 343s [23,] -0.0198126 1.73285416 0.761979 0.005501 0.1671992 -0.0375468 343s [24,] -0.4909448 1.40982693 0.967440 0.521275 0.1625359 -0.0892501 343s [25,] -3.6632699 2.51414455 0.966410 -0.272694 0.0467958 0.1572715 343s [26,] -0.8733564 1.42247465 0.946038 -0.338985 -0.0804141 -0.0080759 343s [27,] -3.2254798 0.26912538 0.799468 0.372442 -0.6886191 -0.0553515 343s [28,] 2.4675785 3.56128696 0.813964 0.118354 -0.1677073 -0.0303774 343s [29,] 4.4177264 1.13316321 0.613509 0.261488 0.4229929 0.1780620 343s [30,] 0.8240097 1.54163297 0.398148 -0.221825 0.0309586 0.0830110 343s [31,] 1.7735990 2.00615332 -1.399933 0.469158 -0.0740282 0.0692312 343s [32,] 1.2348922 0.28918604 -1.239899 0.470999 -0.1511519 -0.3692504 343s [33,] 1.9407276 0.19123540 0.406623 0.389965 0.0994854 -0.0204286 343s [34,] 0.6225565 -0.65636700 0.565253 0.369897 -0.1612501 -0.1774611 343s [35,] -0.4869219 2.26301333 0.071825 0.588101 -0.0579092 -0.0362009 343s [36,] 4.1117242 1.16638974 0.982790 -0.266009 0.0728797 -0.0018914 343s [37,] 0.8415225 2.46677043 -0.526780 0.167456 -0.2370116 -0.0731483 343s [38,] 2.0528334 2.09648023 0.220912 0.206722 -0.1924842 0.0676382 343s [39,] -1.4493644 1.14916103 0.904194 0.455498 0.0678893 -0.1476540 343s [40,] 3.4867792 -1.82367389 0.730183 0.499859 0.2327704 -0.1518819 343s [41,] 4.0222120 1.34765470 0.580852 -0.453301 0.2482908 -1.5306566 343s [42,] 6.4789035 -1.25599522 1.644194 0.381331 0.1699942 0.1847594 343s [43,] 3.1529354 0.44884526 -0.967114 -0.220364 0.0037036 0.0802727 343s [44,] 5.3344976 -0.47975673 0.642789 0.298705 0.9983145 -0.1310548 343s [45,] 0.0325597 0.49900084 0.076948 0.486521 0.1642679 0.1392696 343s [46,] 0.1014401 1.97657735 0.733879 0.127235 0.0650844 -0.0144271 343s [47,] 2.7217685 -0.37859042 -3.696163 0.355401 -0.4123714 0.2114024 343s [48,] 0.2292225 1.01473918 -1.115726 0.434557 0.2668316 0.0103147 343s [49,] -2.2803784 0.59474034 -1.783003 0.549252 0.4660435 -0.0802352 343s [50,] 3.1560404 -2.84820361 0.913015 0.077151 0.5803961 0.0350246 343s [51,] -1.4680905 -0.43078891 -1.733657 0.074684 0.0026718 0.0819023 343s [52,] -5.2469034 0.48385240 -1.246027 0.081379 0.2380924 -0.1663831 343s [53,] -0.7670982 0.00234561 -0.923030 -0.366820 0.1582141 0.0508747 343s [54,] -0.2428655 0.04714401 -0.217187 -0.059549 0.1762969 0.0806339 343s [55,] 0.8723441 0.66109329 -0.224917 -0.360607 -0.0638127 0.1310131 343s [56,] 0.0019700 -0.67624071 0.081304 -0.182908 0.1045597 -0.0281936 343s [57,] -1.3684663 -0.00045069 0.860560 -0.350684 -0.1443970 -0.2270651 343s [58,] 0.0079047 1.36376727 0.750919 -0.437914 -0.1894910 0.2345556 343s [59,] -1.7430794 -1.06973583 -0.569381 -0.055139 -0.1582790 -0.0873605 343s [60,] -1.5171606 -0.69340281 -0.287048 -0.136559 -0.3871182 0.1606979 343s [61,] -0.0955085 -1.64221260 0.263650 -0.265665 -0.0808644 -0.0476862 343s [62,] 2.2259171 -2.22161516 0.426279 0.027834 0.2924338 -0.1784242 343s [63,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 343s [64,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 343s [65,] -0.5520071 -2.86186682 0.746248 0.109945 0.0556927 -0.0135739 343s [66,] -2.4472964 -0.94969715 -0.329042 -0.113895 -0.2728443 -0.0523337 343s [67,] 0.1790969 -1.29190443 0.146657 0.140234 0.1534048 0.2318353 343s [68,] -0.8017055 -1.93331421 -1.968273 0.017854 0.1287513 -0.2306786 343s [69,] -0.7356418 -0.68868398 -0.075215 -0.156944 0.0302876 0.4232626 343s [70,] 3.8821693 5.16959880 0.215490 -8.985938 5.2189361 -2.8089276 343s [71,] -2.3478937 -1.60220695 0.058822 -0.111845 -0.0539018 0.0087982 343s [72,] 2.3676739 -0.70331436 -0.214457 -0.307311 -0.1582719 0.3995413 343s [73,] -1.9906385 -2.60946629 -0.730312 0.485522 -0.2391998 0.1009341 343s [74,] -11.2435515 1.44868683 2.482678 0.026711 0.4922865 -0.2822136 343s [75,] 0.0044207 -2.29768358 -0.692425 0.538923 -0.4110598 -0.0824903 343s [76,] -1.4045239 -0.22649785 -1.343257 -0.067382 -0.1322233 -0.1072330 343s [77,] -8.3637576 0.14167751 1.267616 0.384528 -0.0728561 -0.4017300 343s [78,] 1.3022939 -1.47457541 -0.394623 -0.068014 -0.1502832 0.0757414 343s [79,] -0.1950676 -0.58254701 -0.824931 -0.088174 -0.2071634 -0.1896613 343s [80,] -3.4432989 1.73593273 0.777996 0.094211 0.2377017 -0.1520088 343s [81,] 1.2167258 0.77512068 0.085803 -0.214850 -0.2201173 0.0432435 343s [82,] 2.7778798 -1.80071342 0.583878 0.465898 0.0648352 0.2148470 343s [83,] 2.6218578 -0.39825539 -0.553372 -0.145721 -0.0977092 -0.2485337 343s [84,] 0.8946018 0.33790104 -1.974267 0.091828 0.0051986 -0.2606274 343s [85,] 0.7759316 -2.34860124 2.423325 -0.384149 -0.0167182 -0.0353374 343s [86,] 0.6266756 0.87099609 -1.407948 -0.237762 0.0361644 0.1675792 343s PC7 PC8 343s [1,] -0.1014312 1.5884e-03 343s [2,] -0.3831443 1.0212e-03 343s [3,] -0.7164683 1.2035e-03 343s [4,] 0.0892864 3.5409e-04 343s [5,] -0.0943992 1.0547e-03 343s [6,] 0.1184847 1.5031e-03 343s [7,] -0.2509793 1.6850e-05 343s [8,] -0.0136880 7.0308e-04 343s [9,] 0.2238736 -1.9164e-04 343s [10,] 0.0754413 1.3614e-04 343s [11,] 0.0784380 3.5175e-04 343s [12,] 0.2033489 -1.3174e-03 343s [13,] 0.2139525 -1.7101e-03 343s [14,] 0.1209735 -9.1070e-04 343s [15,] 0.2119647 -9.2843e-04 343s [16,] -0.3011483 -2.1474e-03 343s [17,] 0.0660858 -1.9036e-03 343s [18,] -0.5199396 -9.4385e-04 343s [19,] -0.1232622 -1.2649e-03 343s [20,] -0.3900208 -2.6927e-04 343s [21,] 0.0264834 7.6074e-05 343s [22,] -0.0736288 1.7240e-04 343s [23,] -0.2156005 -5.5661e-04 343s [24,] 0.1143327 -2.5248e-04 343s [25,] 0.0481580 -6.1531e-04 343s [26,] -0.0084802 -7.5928e-04 343s [27,] -0.2173883 -3.0971e-04 343s [28,] 0.3288873 -1.8975e-04 343s [29,] 0.0788974 -7.2436e-04 343s [30,] -0.0598663 -3.0463e-04 343s [31,] -0.1511658 -4.8751e-04 343s [32,] -0.0532375 -2.5207e-04 343s [33,] -0.0635290 -3.9270e-04 343s [34,] 0.1598240 1.3024e-04 343s [35,] -0.0355175 -8.5374e-05 343s [36,] -0.0174096 -6.3294e-04 343s [37,] -0.2883141 -5.2809e-04 343s [38,] 0.1426412 5.3331e-04 343s [39,] 0.0313308 4.2738e-04 343s [40,] -0.3536195 -3.4170e-04 343s [41,] -0.3925168 1.4588e-04 343s [42,] -0.0056267 -9.1925e-04 343s [43,] -0.4447402 -1.8415e-04 343s [44,] 0.9184385 -5.9685e-04 343s [45,] -0.0340987 7.2924e-04 343s [46,] -0.0162866 9.7800e-04 343s [47,] 0.2428769 -1.1208e-03 343s [48,] 0.3026758 -4.5769e-04 343s [49,] 0.0246345 -2.6207e-04 343s [50,] 0.0857698 7.6439e-05 343s [51,] 0.1136658 1.3013e-04 343s [52,] 0.3993357 6.2796e-04 343s [53,] -0.1765161 1.1329e-04 343s [54,] 0.0016144 2.5870e-04 343s [55,] 0.1064371 5.8188e-04 343s [56,] 0.0207478 -8.7595e-05 343s [57,] 0.1560065 6.3987e-05 343s [58,] 0.1684561 -5.0193e-05 343s [59,] 0.0778732 -8.5458e-04 343s [60,] 0.0037585 1.0429e-05 343s [61,] -0.0296083 3.1526e-05 343s [62,] 0.0913974 -2.2794e-04 343s [63,] 0.0358917 -7.3721e-04 343s [64,] 0.0358917 -7.3721e-04 343s [65,] 0.1209159 2.9398e-04 343s [66,] -0.0027574 2.9380e-04 343s [67,] -0.0091059 -2.7494e-04 343s [68,] 0.0555970 -3.3016e-04 343s [69,] -0.0149255 -3.1228e-04 343s [70,] 0.9282997 4.7859e-05 343s [71,] 0.2630142 4.2617e-04 343s [72,] 0.1063248 -3.0070e-04 343s [73,] -0.1462452 4.9607e-04 343s [74,] 0.2027591 2.6399e-03 343s [75,] 0.6934350 6.0284e-04 343s [76,] -0.0430524 8.1271e-04 343s [77,] 0.0789302 1.4655e-03 343s [78,] -0.0318359 5.2799e-04 343s [79,] -0.1269568 2.9497e-04 343s [80,] 0.2903958 7.8932e-04 343s [81,] 0.0979443 -3.1531e-04 343s [82,] -0.0548155 4.2140e-04 343s [83,] -0.0371550 -5.6653e-04 343s [84,] -0.0835149 -7.0682e-04 343s [85,] 0.1864954 1.0604e-03 343s [86,] 0.1074252 -7.4859e-04 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 1.08405293 0.65307452 0.28970076 0.11162824 0.09072195 0.06659711 0.05888048 343s [8] 0.00022877 343s ---------------------------------------------------------- 343s bushfire 38 5 5 1.464779 0.043290 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 343s [1,] -69.9562 -13.0364 0.98678 1.054123 2.411188 343s [2,] -71.5209 -10.5459 0.31081 1.631208 1.663470 343s [3,] -63.9308 -7.4622 -2.43241 0.671038 0.465836 343s [4,] -47.0413 -9.6343 -3.83609 0.758349 0.683983 343s [5,] -15.9088 -20.1737 -5.55893 1.181744 -0.053563 343s [6,] 8.3484 -30.7646 -5.51541 1.877227 1.338037 343s [7,] -207.7458 -66.2492 34.48519 -5.894885 -1.051729 343s [8,] -246.4327 -97.0433 -9.57057 22.286225 -9.234869 343s [9,] -247.5984 -98.8613 -12.13406 23.948770 -9.250401 343s [10,] -245.8121 -79.2634 12.47990 13.046128 -5.125478 343s [11,] -246.8887 -62.5899 21.21764 9.111011 -5.080985 343s [12,] -251.1354 -9.2115 31.77448 0.236379 0.707528 343s [13,] -194.0239 27.1288 21.05023 0.940913 1.781359 343s [14,] 51.7182 8.5038 -11.22109 -2.132458 1.984807 343s [15,] 180.5597 -4.8151 -21.36630 -9.390663 -0.817036 343s [16,] 135.7246 -5.0756 -11.33517 -10.015567 -1.670831 343s [17,] 133.0151 -4.0344 -8.95540 -7.702087 -0.923277 343s [18,] 121.2619 -9.0627 -5.96042 -7.210971 -2.092872 343s [19,] 124.9038 -10.6649 -7.22555 -5.349553 -1.771009 343s [20,] 135.5410 -6.8146 -7.52834 -5.562769 -0.396924 343s [21,] 117.1950 -3.5643 -4.67473 -6.862117 -0.234551 343s [22,] 108.9944 -2.3344 -5.90349 -5.928299 1.455538 343s [23,] -21.4031 8.0668 6.19525 -4.784890 0.671394 343s [24,] -76.3499 16.7804 6.52545 -1.391250 1.219282 343s [25,] -12.5732 6.1109 -1.45259 -3.512072 -0.375837 343s [26,] -19.1800 3.4685 -2.02243 -3.490028 -0.169127 343s [27,] -33.6733 12.0757 -3.53322 0.048666 0.067468 343s [28,] -9.3966 21.5055 -5.91671 2.650895 -0.449672 343s [29,] 1.4123 35.8559 -5.98222 5.982362 0.613667 343s [30,] -54.2683 39.6029 7.82694 6.759994 0.035048 343s [31,] 74.8866 34.9048 10.03986 12.592158 0.149308 343s [32,] 331.4144 9.3079 27.73391 17.334531 1.015536 343s [33,] 367.6915 -19.5135 48.52753 10.213314 -1.268047 343s [34,] 363.8686 -20.4079 49.32855 8.986581 -1.930673 343s [35,] 369.4371 -19.5074 49.66761 9.001542 -0.179566 343s [36,] 366.5850 -20.2555 50.30290 7.745330 -2.259131 343s [37,] 364.5463 -19.8198 53.00407 6.757796 -1.083372 343s [38,] 365.9709 -19.3753 53.80168 6.467284 -0.854384 343s ------------- 343s Call: 343s PcaLocantore(x = x) 343s 343s Standard deviations: 343s [1] 1.210280 0.208063 0.177790 0.062694 0.014423 343s ---------------------------------------------------------- 343s ========================================================== 343s > dodata(method="cov") 343s 343s Call: dodata(method = "cov") 343s Data Set n p k e1 e2 343s ========================================================== 343s heart 12 2 2 685.776266 13.127306 343s Scores: 343s PC1 PC2 343s 1 8.18562 1.17998 343s 2 65.41185 -2.80723 343s 3 1.86039 -1.70646 343s 4 -2.26910 2.44051 343s 5 20.19603 -1.47331 343s 6 -14.46264 7.05759 343s 7 6.91264 1.99823 343s 8 -28.95436 -3.81624 343s 9 -0.61523 -1.09711 343s 10 -27.62427 -3.33575 343s 11 -13.17788 0.37931 343s 12 49.94879 -1.62675 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 26.1873 3.6232 343s ---------------------------------------------------------- 343s starsCYG 47 2 2 0.280150 0.007389 343s Scores: 343s PC1 PC2 343s 1 0.272263 -0.07964458 343s 2 0.804544 0.03382837 343s 3 -0.040587 -0.14464760 343s 4 0.804544 0.03382837 343s 5 0.222468 -0.14305159 343s 6 0.512941 -0.02420304 343s 7 -0.378928 -0.51924735 343s 8 0.341045 0.11236831 343s 9 0.592550 -0.23812462 343s 10 0.163442 -0.06357822 343s 11 0.638370 -1.02323643 343s 12 0.498667 -0.05242075 343s 13 0.476291 0.00142479 343s 14 -0.947664 -0.26343572 343s 15 -0.699020 -0.01711057 343s 16 -0.363464 0.06475681 343s 17 -1.024352 -0.02972862 343s 18 -0.759174 0.12317995 343s 19 -0.786925 -0.06478250 343s 20 0.796654 -1.04660568 343s 21 -0.580307 -0.03463751 343s 22 -0.738591 -0.01126825 343s 23 -0.521748 0.08812607 343s 24 -0.086135 0.09457052 343s 25 0.065975 -0.03907968 343s 26 -0.284322 0.05307219 343s 27 -0.303309 -0.07553370 343s 28 -0.052738 -0.02155274 343s 29 -0.580638 -0.10534741 343s 30 0.953478 -1.07986770 343s 31 -0.527590 0.04855502 343s 32 0.171408 0.12730538 343s 33 0.274054 0.00095808 343s 34 1.192364 -1.10502882 343s 35 -0.628641 -0.08815176 343s 36 0.694595 0.11071187 343s 37 0.167026 0.09762710 343s 38 0.274054 0.00095808 343s 39 0.246168 0.08594248 343s 40 0.617380 -0.06994769 343s 41 -0.329735 0.01934346 343s 42 0.115770 0.02432733 343s 43 0.400071 0.03289494 343s 44 0.392768 -0.01656886 343s 45 0.605229 0.05314718 343s 46 0.036628 0.03601196 343s 47 -0.442606 0.07644144 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 0.529292 0.085957 343s ---------------------------------------------------------- 343s phosphor 18 2 2 288.018150 22.020514 343s Scores: 343s PC1 PC2 343s 1 2.7987 -19.015683 343s 2 -20.4311 -0.032022 343s 3 -21.8198 4.589809 343s 4 -11.7869 -6.837833 343s 5 -16.9357 2.664785 343s 6 12.9132 -25.602526 343s 7 1.5249 -6.351664 343s 8 -8.0984 2.416616 343s 9 -8.6979 4.843680 343s 10 14.3903 -12.732868 343s 11 -2.9462 -0.760656 343s 12 11.7427 2.991004 343s 13 14.8400 0.459849 343s 14 9.2449 3.095095 343s 15 19.4860 -3.336883 343s 16 -9.4156 -7.096788 343s 17 23.3759 -1.737460 343s 18 19.9173 5.092467 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 16.9711 4.6926 343s ---------------------------------------------------------- 343s stackloss 21 3 3 28.153060 8.925048 343s Scores: 343s PC1 PC2 PC3 343s [1,] 10.538448 13.596944 12.84989 343s [2,] 9.674846 14.098881 12.89733 343s [3,] 8.993255 9.221043 9.94062 343s [4,] 1.744427 3.649104 0.17292 343s [5,] 0.980215 2.223126 1.34874 343s [6,] 1.362321 2.936115 0.76083 343s [7,] 6.926040 0.637480 -0.11170 343s [8,] 6.926040 0.637480 -0.11170 343s [9,] 0.046655 0.977727 -2.46930 343s [10,] -7.909092 0.926343 0.80232 343s [11,] -0.136672 -3.591094 0.37539 343s [12,] -1.382381 -3.802146 1.01074 343s [13,] -6.181887 -0.077532 0.70744 343s [14,] 3.699843 -4.885854 -0.40226 343s [15,] -2.768005 -7.507870 -6.08487 343s [16,] -5.358811 -6.002058 -5.94256 343s [17,] -17.067135 1.738055 -5.86637 343s [18,] -11.021920 -1.775507 -6.19842 343s [19,] -9.776212 -1.564455 -6.83377 343s [20,] -6.075508 0.369252 -2.08345 343s [21,] 6.301743 2.706174 8.79509 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 5.3059 2.9875 1.3020 343s ---------------------------------------------------------- 343s salinity 28 3 3 11.801732 3.961826 343s Scores: 343s PC1 PC2 PC3 343s 1 -1.59888 1.582157 0.135248 343s 2 -2.26975 2.429177 1.107832 343s 3 -6.79543 -2.034636 0.853876 343s 4 -6.36795 -0.602960 -0.267268 343s 5 -6.42044 -1.520259 5.022962 343s 6 -5.13821 1.225470 0.016977 343s 7 -3.24014 1.998671 -0.123418 343s 8 -0.93998 2.789889 -0.515656 343s 9 -0.30856 -2.424345 -1.422752 343s 10 2.20362 -2.800513 1.142127 343s 11 1.38120 -2.076832 2.515630 343s 12 0.44997 0.207439 -0.152835 343s 13 1.21669 1.193701 -0.277116 343s 14 3.31664 1.306627 1.213342 343s 15 2.08484 -3.774814 0.905400 343s 16 -3.64862 -4.677257 9.046484 343s 17 -0.46124 -1.411762 1.706719 343s 18 -2.13038 0.890401 -0.633349 343s 19 -0.23610 -2.262304 -1.885048 343s 20 1.70337 -1.970773 -0.781880 343s 21 2.67273 1.038742 -0.610945 343s 22 4.24561 1.547290 0.108927 343s 23 2.99619 -4.785343 3.094945 343s 24 1.64474 -3.564562 3.432429 343s 25 1.11703 -1.158030 0.237700 343s 26 2.30707 0.069668 -0.735809 343s 27 3.59356 0.860498 -0.611380 343s 28 4.57550 1.300407 0.589307 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 3.43536 1.99043 0.94546 343s ---------------------------------------------------------- 343s hbk 75 3 3 1.436470 1.181766 343s Scores: 343s PC1 PC2 PC3 343s 1 31.105415 -4.714217 10.4566165 343s 2 31.707650 -5.748724 10.7682402 343s 3 33.366131 -4.625897 12.1570167 343s 4 34.173377 -6.069657 12.4466895 343s 5 33.780418 -5.508823 11.9872893 343s 6 32.493478 -4.684595 10.5679819 343s 7 32.592637 -5.235522 10.3765493 343s 8 31.293363 -4.865797 10.9379676 343s 9 33.160964 -5.714260 12.3098920 343s 10 31.919786 -5.384537 12.3374332 343s 11 38.231962 -6.810641 13.5994385 343s 12 39.290479 -5.393906 15.2942554 343s 13 39.418445 -7.326461 11.5194898 343s 14 43.906584 -13.214819 8.3282743 343s 15 1.906326 0.716061 -0.8635112 343s 16 0.263255 0.926016 -1.9009292 343s 17 -1.776489 -1.072332 -0.5496140 343s 18 0.464648 0.702441 0.0482897 343s 19 0.267826 -1.283779 -0.2925812 343s 20 2.122108 0.165970 -0.8924686 343s 21 0.937217 0.548532 -0.4132196 343s 22 0.423273 -1.781869 -0.0323061 343s 23 0.047532 0.018909 -1.1259327 343s 24 -0.490041 -0.520202 -1.1065753 343s 25 -2.143049 0.720869 -0.0495474 343s 26 1.094748 -1.459175 0.2226246 343s 27 2.070705 0.898573 0.0023229 343s 28 -0.294998 0.830258 0.5929001 343s 29 -1.242995 0.300216 -0.2010507 343s 30 0.147958 0.439099 2.0003038 343s 31 0.170818 1.440946 -0.9755627 343s 32 -0.958531 -1.199730 -1.0129867 343s 33 0.697307 -0.874343 -0.7260649 343s 34 -2.278946 0.261106 0.4196544 343s 35 1.962829 0.809318 0.2033113 343s 36 0.626631 -0.600666 0.8004036 343s 37 0.550885 -1.881448 0.7382776 343s 38 -1.249717 0.336214 -0.9349845 343s 39 -1.106696 1.569418 0.1869576 343s 40 -0.684034 -0.939963 -0.1034965 343s 41 1.559314 1.551408 0.3660323 343s 42 -0.538741 -0.447358 1.6361099 343s 43 -0.252685 -2.080564 -0.7765259 343s 44 0.217012 1.027281 1.7015154 343s 45 -1.497600 1.349234 -0.2698932 343s 46 0.100388 1.026443 1.5390401 343s 47 -0.811117 2.195271 -0.5208141 343s 48 1.462210 1.321318 0.5600144 343s 49 1.383976 0.740714 -0.7348906 343s 50 1.636773 -0.215464 0.3195369 343s 51 -0.530918 0.759743 -1.2069247 343s 52 -0.109566 2.107455 -0.5315473 343s 53 -0.564334 -0.060847 2.3910630 343s 54 -0.272234 -1.122711 -1.5060028 343s 55 -0.608660 -1.197219 -0.5255609 343s 56 0.565430 -0.710345 -1.3708230 343s 57 -1.115629 0.888816 -0.4186014 343s 58 1.351288 -0.374815 -1.1980618 343s 59 0.998016 -0.151228 0.9007970 343s 60 0.124017 -0.764846 1.9005963 343s 61 1.189858 -1.905264 0.7721322 343s 62 -2.190589 0.579614 -0.1377914 343s 63 -0.518278 -0.931130 -1.4534768 343s 64 2.124566 0.194391 -0.0327092 343s 65 0.154218 1.050861 1.1309885 343s 66 -1.197852 -1.044147 -0.2265269 343s 67 -0.114174 -0.094763 -0.5168926 343s 68 -2.201115 0.032271 0.8573493 343s 69 -1.307843 1.104815 -0.7741270 343s 70 0.691449 -0.676665 1.0004603 343s 71 1.150975 0.050861 -0.0717068 343s 72 -0.457293 -0.861871 0.1026350 343s 73 -0.392258 -0.897451 0.9178065 343s 74 -0.584658 -1.450471 0.3201857 343s 75 -0.972517 -0.063777 1.8223995 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 1.1985 1.0871 1.0086 343s ---------------------------------------------------------- 343s milk 86 8 8 5.758630 2.224809 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 PC6 343s 1 5.7090867 1.388263 0.0055924 0.3510505 -0.7335114 -1.41950731 343s 2 6.5825186 0.480410 -1.1356236 -0.3250838 -0.7343177 -1.71595400 343s 3 0.7433619 -1.749281 0.2510521 0.3450575 0.2996413 -0.34585702 343s 4 5.5733255 -1.588521 0.8934908 -0.3412408 0.0087626 0.07235942 343s 5 -1.3030839 0.142394 0.8487785 -0.5847851 0.0588053 -0.08968553 343s 6 1.7708705 0.674240 -0.4153759 -0.1915734 0.1382138 0.12454293 343s 7 2.3570866 0.381017 -0.8771357 -0.3739365 0.2918453 0.13437364 343s 8 2.5700714 0.695006 0.0061108 -0.4323695 0.1643797 -0.00469369 343s 9 -1.1725766 -2.713291 1.0677483 -0.0647875 0.1183120 -0.10762785 343s 10 -3.1357225 -1.255175 0.0666017 0.5083690 -0.1096080 -0.00647493 343s 11 -9.5333894 -1.608943 2.7307809 0.1690156 -0.1682415 -0.06597478 343s 12 -13.6028505 0.941083 2.0136258 -0.1076520 -0.0475905 -0.15295614 343s 13 -10.9497471 0.048776 -0.8765307 0.1518572 0.1428294 -0.00064406 343s 14 -12.6558378 -0.219444 1.1396273 -0.3734679 0.2875578 -0.23870524 343s 15 -10.6924790 -1.818075 3.4560731 -0.1177943 0.1101199 -0.19708172 343s 16 -3.0258070 -0.203186 1.2835368 0.5799363 0.3237454 0.23168871 343s 17 -0.7498665 -2.977505 1.6310512 0.6305329 0.3994006 0.06594881 343s 18 -2.5093526 0.924459 0.0899818 -0.4026675 0.2963072 0.11324019 343s 19 -1.9689970 -1.051282 1.4659908 0.3870104 -0.0708083 -0.02148354 343s 20 0.2695886 -1.646440 0.7597630 0.1750131 -0.3418142 0.21515143 343s 21 -3.3470252 1.989939 0.2887021 -0.3599779 0.0771965 0.16867095 343s 22 -1.4659204 0.777242 0.4090149 -0.1248050 0.1916768 -0.23160291 343s 23 -0.4944476 1.634130 0.8915509 0.1222296 -0.1231015 -0.08351169 343s 24 -0.8945477 1.239223 1.1117165 0.6018455 0.0912200 -0.01204668 343s 25 -4.1499992 1.860190 1.6062973 -0.2139736 -0.1140169 0.16632426 343s 26 -1.2647012 1.188058 1.1893430 -0.2740862 -0.0971504 -0.09851714 343s 27 -3.4280131 -0.267150 1.1969552 0.0354366 0.8482718 -0.18977667 343s 28 1.6896630 3.793723 0.7706325 0.1007287 0.0317704 -0.11269816 343s 29 3.9258127 1.691428 0.1850999 0.4485202 -0.2969916 0.16594044 343s 30 0.3178322 1.577233 0.4455231 -0.1687197 -0.1587136 -0.00823174 343s 31 0.9562350 2.258138 -1.4672169 0.2675668 0.1910110 0.03177387 343s 32 0.6738452 0.470764 -1.3496896 0.3524049 0.2008218 -0.36957179 343s 33 1.5980690 0.413899 0.1999664 0.4232293 0.0768479 -0.04627841 343s 34 0.4365091 -0.626490 0.4718364 0.3392252 0.2554060 -0.19018602 343s 35 -1.1184804 2.124234 0.2650931 0.4791171 0.2927791 -0.01579964 343s 36 3.6673986 1.659798 0.6138972 -0.1092158 -0.2705583 -0.16494176 343s 37 0.0867143 2.541765 -0.4572593 0.0024263 0.2163300 -0.20116352 343s 38 1.4191839 2.315690 0.1365887 0.1028375 0.1595780 -0.02049460 343s 39 -1.8062960 0.845438 1.1469588 0.5022406 0.1603011 -0.08751261 343s 40 3.4380914 -1.358545 0.1956896 0.6314649 0.0716078 -0.21591535 343s 41 3.4608782 1.828575 0.2012565 0.1064437 -0.7454169 -1.64629924 343s 42 6.4162310 -0.402642 0.8070441 0.5146855 0.0331594 0.04373032 343s 43 2.5906567 0.897993 -1.2612252 -0.2620162 -0.1432569 -0.10279385 343s 44 5.0299750 0.203721 0.0439110 0.8775684 -0.9536011 0.15153452 343s 45 -0.3555392 0.454930 0.1173992 0.4688991 0.1137820 0.18752442 343s 46 -0.4155426 1.892410 0.8649578 0.1827426 -0.0186113 -0.04029205 343s 47 1.9328817 0.121936 -3.9578157 -0.1135807 0.2971001 0.18733657 343s 48 -0.3947656 1.028405 -1.0370498 0.4467257 -0.1445498 0.16878692 343s 49 -2.8829860 0.279064 -1.4443310 0.5889970 -0.1883118 0.16947945 343s 50 3.2797246 -2.443968 0.4100655 0.4278962 -0.4414712 0.08598366 343s 51 -1.9272930 -0.622137 -1.5136862 -0.0483369 -0.0272502 0.16006066 343s 52 -5.7161590 -0.298434 -0.5216578 0.1385780 -0.2435931 0.10628617 343s 53 -1.1933277 -0.125878 -0.7556261 -0.3129372 -0.3166453 0.03078643 343s 54 -0.5994394 -0.031069 -0.1296378 0.0061490 -0.1869578 0.09839221 343s 55 0.4104586 0.733465 -0.2088065 -0.3645266 -0.1830137 0.04705775 343s 56 -0.2227671 -0.724741 0.1007592 -0.0838897 -0.1939960 -0.04223579 343s 57 -1.5706297 -0.292436 1.0849660 -0.2559591 -0.0917278 -0.27423151 343s 58 -0.4102168 1.263831 0.9082556 -0.4592777 -0.0676902 0.11089798 343s 59 -1.9640736 -1.340173 -0.3652736 -0.1267573 0.0775692 -0.07977644 343s 60 -1.7490968 -0.941370 -0.0849901 -0.3453455 0.2858594 0.06413468 343s 61 -0.1583416 -1.699326 0.2385988 -0.2231496 -0.0513883 -0.12227279 343s 62 2.2124878 -1.942366 0.0743514 0.2627321 -0.2844018 -0.15848039 343s 63 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 343s 64 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 343s 65 -0.3779208 -2.987354 0.6819006 0.1942611 0.0529259 0.01315140 343s 66 -2.6385498 -1.331204 -0.0367809 -0.2327572 0.1845076 -0.08521680 343s 67 0.0526645 -1.301299 0.0912198 0.1634869 -0.0068236 0.24131589 343s 68 -1.1013065 -2.004809 -1.9168056 0.0260663 -0.2029903 -0.12625268 343s 69 -0.9495853 -0.831697 0.0389476 -0.2123483 -0.0202267 0.38463410 343s 70 2.6935893 5.369312 0.6987368 -4.5754846 -9.6833013 -2.32910628 343s 71 -2.4037611 -1.983509 0.3109848 -0.1015686 -0.0071432 0.06410351 343s 72 2.0795505 -0.392730 -0.4534128 -0.4054224 -0.0312781 0.25408988 343s 73 -2.0038405 -2.874605 -0.6269939 0.2408421 0.5184666 0.11140104 343s 74 -11.2683996 -0.361851 3.9219448 0.4045689 -0.2203308 0.05930132 343s 75 -0.1028287 -2.295813 -0.7769187 0.3071821 0.4537196 0.00522380 343s 76 -1.8466137 -0.425825 -1.1261209 -0.1760585 0.0165729 -0.10698465 343s 77 -8.4124493 -1.174820 2.2700712 0.4213953 0.3446597 -0.20636892 343s 78 1.1103236 -1.299480 -0.5787732 -0.1455945 0.0732148 -0.01806218 343s 79 -0.5451834 -0.620170 -0.7830595 -0.1746479 0.0723052 -0.26017118 343s 80 -3.8647223 1.126328 1.3299567 0.2645241 -0.1881443 0.00485531 343s 81 0.7690939 0.887363 0.0513096 -0.2730980 0.0076447 -0.07590882 343s 82 2.7287618 -1.435327 0.1602865 0.4465859 0.2129425 0.16104418 343s 83 2.2241485 -0.042822 -0.8316486 -0.1230697 -0.1193057 -0.35207561 343s 84 0.2452905 0.491732 -2.0050683 0.0286567 -0.1159415 -0.24887542 343s 85 1.0655845 -2.360746 2.2456131 -0.1479972 -0.1186670 -0.14020891 343s 86 -0.0091659 0.952208 -1.3429189 -0.2944676 -0.2433277 0.15354490 343s PC7 PC8 343s 1 -0.09778744 2.3157e-03 343s 2 0.05189698 1.8077e-03 343s 3 0.70506895 1.2838e-03 343s 4 -0.08541140 3.2781e-04 343s 5 0.11768945 8.3496e-04 343s 6 -0.17886391 1.5222e-03 343s 7 0.14143613 1.3261e-04 343s 8 -0.07724578 7.1241e-04 343s 9 -0.12298048 -7.0110e-04 343s 10 0.07569878 2.3093e-05 343s 11 0.29299858 -3.4542e-04 343s 12 0.07764899 -2.1390e-03 343s 13 -0.08945524 -2.2633e-03 343s 14 0.03597787 -1.8891e-03 343s 15 0.11780498 -2.0279e-03 343s 16 0.46501534 -2.3266e-03 343s 17 0.08603290 -2.4073e-03 343s 18 0.52605757 -9.8822e-04 343s 19 0.31007227 -1.3919e-03 343s 20 0.61582059 -2.3549e-05 343s 21 0.01199350 -6.1649e-05 343s 22 0.03654587 1.3302e-05 343s 23 0.27549986 -3.6759e-04 343s 24 -0.04155354 -2.9882e-04 343s 25 0.11473708 -7.9629e-04 343s 26 0.06673183 -8.3728e-04 343s 27 0.16937729 -9.5775e-04 343s 28 -0.41753592 -7.5544e-05 343s 29 -0.03693100 -2.2481e-04 343s 30 0.08461537 -1.3611e-04 343s 31 0.02476253 -1.4319e-04 343s 32 -0.09756048 -1.2234e-04 343s 33 0.06442434 -2.4915e-04 343s 34 -0.17828409 -9.5882e-05 343s 35 0.00881239 -7.1427e-05 343s 36 -0.01041003 -2.8489e-04 343s 37 0.15994729 -3.1472e-04 343s 38 -0.22386895 6.1384e-04 343s 39 0.03666242 2.8506e-04 343s 40 0.35883231 -8.3062e-05 343s 41 0.18521851 8.5509e-04 343s 42 0.00733985 -6.4477e-04 343s 43 0.35466617 3.2923e-04 343s 44 -0.74952524 -7.6869e-05 343s 45 0.09907237 7.9128e-04 343s 46 0.05119980 1.0606e-03 343s 47 -0.48571583 -9.3780e-04 343s 48 -0.27463442 -2.7037e-04 343s 49 0.06787536 -3.0554e-05 343s 50 0.08499400 3.1181e-04 343s 51 -0.09197457 1.1213e-04 343s 52 -0.24513244 3.9100e-04 343s 53 0.24012780 3.2068e-04 343s 54 0.07999888 3.5689e-04 343s 55 -0.09825475 6.6675e-04 343s 56 0.05133674 -7.2984e-05 343s 57 -0.10302363 -2.0693e-04 343s 58 -0.12323360 -1.6620e-04 343s 59 -0.05119989 -1.1016e-03 343s 60 0.00082131 -3.2951e-04 343s 61 0.08128272 -1.1550e-04 343s 62 -0.01789040 -1.1579e-04 343s 63 -0.07188070 -7.8367e-04 343s 64 -0.07188070 -7.8367e-04 343s 65 0.00917085 -2.6800e-05 343s 66 0.03121573 -5.3492e-05 343s 67 0.12202335 -3.0466e-04 343s 68 -0.04764366 -2.6126e-04 343s 69 0.13828337 -3.9331e-04 343s 70 0.10401069 4.2870e-03 343s 71 -0.14369640 3.7669e-05 343s 72 -0.10334451 -2.6456e-04 343s 73 0.17655402 1.0917e-04 343s 74 0.26779696 1.8685e-03 343s 75 -0.75016549 2.1079e-05 343s 76 0.01802016 7.7555e-04 343s 77 0.13081368 6.4286e-04 343s 78 0.01409131 4.9476e-04 343s 79 0.06643384 2.6590e-04 343s 80 -0.12624376 5.9801e-04 343s 81 -0.14074469 -3.2172e-04 343s 82 0.09228230 4.4064e-04 343s 83 -0.06352151 -3.6274e-04 343s 84 -0.02642452 -3.9742e-04 343s 85 -0.03502188 6.9814e-04 343s 86 -0.11749109 -5.1283e-04 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 2.39971451 1.49157920 0.93184037 0.33183258 0.19628996 0.16485446 0.12784351 343s [8] 0.00052622 343s ---------------------------------------------------------- 343s bushfire 38 5 5 11393.979994 197.523453 343s Scores: 343s PC1 PC2 PC3 PC4 PC5 343s 1 -91.383 -16.17804 0.56195 -0.252428 1.261840 343s 2 -93.033 -13.93251 -0.67212 0.042287 0.470924 343s 3 -85.400 -10.72512 -3.09832 -1.224797 -0.504718 343s 4 -68.381 -12.12202 -3.31950 -0.676880 -0.228383 343s 5 -36.742 -21.04171 -1.98872 0.397655 -0.932613 343s 6 -12.095 -30.21719 0.59595 2.100702 0.384714 343s 7 -227.949 -71.40450 35.57308 -7.880296 -2.710415 343s 8 -262.815 -111.81228 -11.04574 2.397832 -13.646407 343s 9 -263.767 -114.13702 -13.71407 3.131736 -13.825200 343s 10 -264.312 -90.69643 9.72320 0.967173 -8.800150 343s 11 -266.681 -72.85993 16.55010 0.291092 -8.373583 343s 12 -274.050 -18.41395 20.74273 -2.464589 -1.505967 343s 13 -218.299 19.16040 7.69765 0.069012 0.054846 343s 14 29.646 10.52526 -7.50754 0.855493 1.966680 343s 15 159.575 3.86633 -6.95837 -2.753953 0.616068 343s 16 114.286 2.47164 0.62690 -3.146317 -0.501623 343s 17 111.289 3.45086 1.97182 -0.303064 -0.094416 343s 18 99.626 -1.80416 4.88197 -0.013096 -1.438397 343s 19 103.353 -3.50426 3.58993 1.578169 -1.317194 343s 20 113.769 0.84544 3.28254 2.204926 0.131167 343s 21 95.186 3.50703 4.97153 0.916181 0.351658 343s 22 86.996 4.00938 2.95209 1.281788 1.920404 343s 23 -44.232 8.50898 6.30689 -1.038871 0.400078 343s 24 -99.527 13.81377 1.75130 -0.260669 0.394804 343s 25 -34.855 5.99709 -0.57224 -1.660513 -0.620158 343s 26 -41.265 2.94659 -1.04825 -2.243950 -0.440017 343s 27 -56.148 10.14428 -5.41858 0.321752 -0.608412 343s 28 -32.366 20.27795 -8.60687 3.806572 -1.267249 343s 29 -22.438 34.73585 -11.19123 8.296154 -0.511610 343s 30 -79.035 37.05713 -1.51591 9.892959 -1.618635 343s 31 49.465 39.37414 5.95714 22.874813 -1.883481 343s 32 304.825 30.19205 37.68900 45.175923 -1.293939 343s 33 341.237 7.04985 65.43451 44.553009 -3.148116 343s 34 337.467 6.16879 66.48222 43.278480 -3.688631 343s 35 342.929 7.38548 66.91291 43.941556 -1.937887 343s 36 340.143 6.70203 67.85433 42.479161 -3.873639 343s 37 337.931 7.43184 70.50828 42.333220 -2.645830 343s 38 339.281 8.07267 71.34405 42.400459 -2.392774 343s ------------- 343s Call: 343s PcaCov(x = x) 343s 343s Standard deviations: 343s [1] 106.7426 14.0543 4.9184 1.8263 1.0193 343s ---------------------------------------------------------- 343s ========================================================== 343s > dodata(method="grid") 343s 343s Call: dodata(method = "grid") 343s Data Set n p k e1 e2 343s ========================================================== 343s heart 12 2 2 516.143549 23.932102 343s Scores: 343s PC1 PC2 343s [1,] 6.4694 3.8179 343s [2,] 61.7387 19.1814 343s [3,] 1.4722 -1.0161 343s [4,] -3.8056 1.5127 343s [5,] 18.6760 5.3303 343s [6,] -16.8411 1.7900 343s [7,] 4.9962 4.1638 343s [8,] -26.8665 -13.3010 343s [9,] -1.0648 -1.2690 343s [10,] -25.7734 -12.4037 343s [11,] -13.3987 -4.0751 343s [12,] 46.7700 15.1272 343s ------------- 343s Call: 343s PcaGrid(x = x) 343s 343s Standard deviations: 343s [1] 22.719 4.892 343s ---------------------------------------------------------- 343s starsCYG 47 2 2 0.473800 0.026486 343s Scores: 343s PC1 PC2 343s [1,] 0.181489 -0.0300854 343s [2,] 0.695337 0.1492475 343s [3,] -0.120738 -0.1338110 343s [4,] 0.695337 0.1492475 343s [5,] 0.140039 -0.0992368 343s [6,] 0.413314 0.0551030 343s [7,] -0.409428 -0.5478860 343s [8,] 0.225647 0.1690378 343s [9,] 0.519123 -0.1471454 343s [10,] 0.071513 -0.0277935 343s [11,] 0.663045 -0.9203119 343s [12,] 0.402691 0.0253179 343s [13,] 0.373739 0.0759321 343s [14,] -1.005756 -0.3654219 343s [15,] -0.789968 -0.0898580 343s [16,] -0.467328 0.0334465 343s [17,] -1.111148 -0.1431778 343s [18,] -0.867242 0.0417806 343s [19,] -0.871200 -0.1481782 343s [20,] 0.823011 -0.9236455 343s [21,] -0.669994 -0.0923582 343s [22,] -0.829959 -0.0890246 343s [23,] -0.627294 0.0367802 343s [24,] -0.195929 0.0978059 343s [25,] -0.028257 -0.0157122 343s [26,] -0.387346 0.0317797 343s [27,] -0.390054 -0.0981920 343s [28,] -0.148231 -0.0132120 343s [29,] -0.661454 -0.1625514 343s [30,] 0.982767 -0.9369769 343s [31,] -0.628127 -0.0032112 343s [32,] 0.055476 0.1625819 343s [33,] 0.173158 0.0501056 343s [34,] 1.222924 -0.9319795 343s [35,] -0.711235 -0.1515118 343s [36,] 0.576613 0.2117347 343s [37,] 0.054851 0.1325884 343s [38,] 0.173158 0.0501056 343s [39,] 0.134833 0.1309216 343s [40,] 0.522665 0.0228177 343s [41,] -0.428171 -0.0073782 343s [42,] 0.013192 0.0534392 343s [43,] 0.294173 0.0975945 343s [44,] 0.293132 0.0476054 343s [45,] 0.495172 0.1434167 343s [46,] -0.066790 0.0551060 343s [47,] -0.547311 0.0351134 343s ------------- 343s Call: 343s PcaGrid(x = x) 343s 343s Standard deviations: 343s [1] 0.68833 0.16275 343s ---------------------------------------------------------- 343s phosphor 18 2 2 392.155327 50.657228 343s Scores: 343s PC1 PC2 343s 1 5.6537 -15.2305 343s 2 -21.2150 -1.8862 343s 3 -23.5966 2.3112 343s 4 -11.2742 -6.6000 343s 5 -18.4067 1.5202 343s 6 16.9795 -19.4039 343s 7 1.5964 -3.1666 343s 8 -9.7354 3.2429 343s 9 -10.8594 5.4759 343s 10 15.5585 -6.5279 343s 11 -4.0058 1.2905 343s 12 9.4815 8.2139 343s 13 13.0640 6.4346 343s 14 7.0230 7.7600 343s 15 18.4378 3.7658 343s 16 -8.9047 -6.3253 343s 17 21.8748 6.1900 343s 18 16.9843 12.0801 343s ------------- 343s Call: 343s PcaGrid(x = x) 343s 343s Standard deviations: 343s [1] 19.8029 7.1174 343s ---------------------------------------------------------- 343s stackloss 21 3 3 109.445054 16.741203 343s Scores: 343s PC1 PC2 PC3 343s [1,] 15.136434 14.82909 -2.0387704 343s [2,] 14.393636 15.46816 -1.8391595 343s [3,] 12.351209 10.12290 -2.3458098 343s [4,] 2.510036 2.07589 1.8251581 343s [5,] 1.767140 1.78527 -0.0088651 343s [6,] 2.138588 1.93058 0.9081465 343s [7,] 6.966825 -1.75851 0.6274924 343s [8,] 6.966825 -1.75851 0.6274924 343s [9,] -0.089513 -1.09062 2.2894224 343s [10,] -7.146340 2.65628 -0.8983590 343s [11,] -0.461157 -3.09532 -2.6948576 343s [12,] -1.575403 -2.60157 -3.4122582 343s [13,] -5.660744 1.37815 -1.2975809 343s [14,] 2.881484 -5.50628 -2.5762898 343s [15,] -4.917360 -9.13772 0.0676942 343s [16,] -7.145755 -7.22052 0.6665270 343s [17,] -17.173481 1.87173 4.3780920 343s [18,] -11.973894 -2.60174 2.9808153 343s [19,] -10.859648 -3.09549 3.6982160 343s [20,] -6.031899 0.15817 1.2270803 343s [21,] 8.451640 4.98077 -5.4038839 343s ------------- 343s Call: 343s PcaGrid(x = x) 343s 343s Standard deviations: 343s [1] 10.4616 4.0916 2.8271 343s ---------------------------------------------------------- 343s salinity 28 3 3 14.911546 8.034974 343s Scores: 343s PC1 PC2 PC3 343s 1 -2.72400 0.79288 0.688038 343s 2 -3.45684 0.86162 1.941690 343s 3 -5.73471 -4.79507 0.129202 343s 4 -6.17045 -3.04372 -0.352797 343s 5 -4.72453 -5.59543 4.144851 343s 6 -5.75447 -1.07062 0.579975 343s 7 -4.40759 0.47731 0.680203 343s 8 -2.76360 2.30716 0.540271 343s 9 -0.28782 -1.40644 -2.373399 343s 10 2.64361 -1.43362 -0.266957 343s 11 1.91078 -1.66975 1.312215 343s 12 -0.40661 0.68573 -0.200135 343s 13 -0.14911 1.88993 0.044001 343s 14 1.99005 2.43874 1.373229 343s 15 2.88128 -2.21263 -0.863674 343s 16 -0.12935 -8.28831 6.483875 343s 17 -0.16895 -1.68742 0.905190 343s 18 -3.08054 0.23753 -0.269165 343s 19 -0.38685 -1.08501 -2.736860 343s 20 1.45520 -0.33209 -1.686406 343s 21 1.13834 2.53553 -0.381657 343s 22 2.48522 3.42927 0.417050 343s 23 4.56487 -3.36542 0.711908 343s 24 2.94072 -3.08490 1.556939 343s 25 0.82140 -0.26895 -0.406490 343s 26 1.17794 1.61119 -0.863764 343s 27 2.02965 2.80707 -0.489050 343s 28 2.98039 3.21462 0.747622 343s ------------- 343s Call: 343s PcaGrid(x = x) 343s 343s Standard deviations: 343s [1] 3.86155 2.83460 0.95394 343s ---------------------------------------------------------- 343s hbk 75 3 3 3.714805 3.187126 343s Scores: 343s PC1 PC2 PC3 343s 1 8.423138 24.765818 19.413334 343s 2 7.823138 25.295092 20.356662 343s 3 9.023138 27.411905 20.218454 343s 4 8.223138 28.010236 21.568269 343s 5 8.623138 27.442650 21.123471 343s 6 9.123138 25.601873 20.279943 343s 7 8.823138 25.463855 20.770811 343s 8 8.223138 25.264348 19.451646 343s 9 8.023138 27.373593 20.716984 343s 10 7.623138 26.752275 19.666288 343s 11 9.323138 31.108975 24.313778 343s 12 10.323138 33.179719 23.469966 343s 13 10.323138 29.958667 26.231274 343s 14 9.323138 29.345676 34.207755 343s 15 1.723138 -0.077538 0.754886 343s 16 1.423138 -1.818609 -0.080979 343s 17 -1.676862 -1.872341 -0.686878 343s 18 0.623138 -0.077633 -0.548955 343s 19 -0.876862 -0.576068 0.716574 343s 20 1.423138 -0.016144 1.261078 343s 21 0.923138 -0.223313 0.041619 343s 22 -1.276862 -0.299937 1.038679 343s 23 0.323138 -1.327742 0.057038 343s 24 -0.376862 -1.626860 0.034051 343s 25 -0.676862 -1.550331 -2.266849 343s 26 -0.776862 0.290637 1.184359 343s 27 1.623138 0.750760 0.417361 343s 28 0.123138 -0.016334 -1.346603 343s 29 -0.476862 -1.220468 -1.338846 343s 30 -0.476862 1.387213 -1.339036 343s 31 1.423138 -1.059368 -0.824991 343s 32 -1.176862 -1.833934 0.118433 343s 33 -0.176862 -0.691099 0.908323 343s 34 -1.276862 -1.251213 -2.243862 343s 35 1.423138 0.858128 0.325317 343s 36 -0.576862 0.574335 0.102918 343s 37 -1.576862 0.413330 0.892903 343s 38 -0.176862 -1.841691 -1.085702 343s 39 0.423138 -0.752683 -2.205550 343s 40 -1.176862 -0.905930 -0.211430 343s 41 1.723138 0.819721 -0.479993 343s 42 -1.376862 0.666284 -1.093554 343s 43 -1.576862 -1.304659 1.061761 343s 44 0.123138 1.203126 -1.553772 343s 45 0.223138 -1.358581 -2.151818 343s 46 0.123138 1.003714 -1.569097 343s 47 1.323138 -1.159169 -2.136494 343s 48 1.423138 0.919427 -0.472331 343s 49 1.423138 -0.246300 0.340737 343s 50 0.423138 0.727773 0.716479 343s 51 0.623138 -1.665267 -0.771259 343s 52 1.623138 -0.798657 -1.607314 343s 53 -1.376862 1.310494 -1.645816 343s 54 -0.576862 -1.879908 0.716669 343s 55 -1.176862 -1.235698 0.164407 343s 56 0.123138 -1.296997 0.962055 343s 57 0.123138 -1.304849 -1.545920 343s 58 0.723138 -0.714086 1.207441 343s 59 -0.076862 0.881115 0.026199 343s 60 -1.376862 1.226208 -0.549050 343s 61 -1.276862 0.781504 1.322377 343s 62 -0.776862 -1.657699 -2.174806 343s 63 -0.576862 -1.956627 0.409888 343s 64 1.123138 0.712448 0.915891 343s 65 0.323138 0.689271 -1.392672 343s 66 -1.476862 -1.289430 -0.441492 343s 67 -0.076862 -0.905930 -0.211430 343s 68 -1.576862 -0.852389 -2.213213 343s 69 0.323138 -1.696011 -1.676276 343s 70 -0.676862 0.773747 0.118243 343s 71 0.523138 0.152524 0.371386 343s 72 -1.076862 -0.606812 -0.188443 343s 73 -1.376862 0.114117 -0.433924 343s 74 -1.676862 -0.522431 0.018632 343s 75 -1.376862 0.612552 -1.699453 343s ------------- 343s Call: 343s PcaGrid(x = x) 343s 343s Standard deviations: 343s [1] 1.9274 1.7853 1.6714 343s ---------------------------------------------------------- 344s milk 86 8 8 9.206694 2.910585 344s Scores: 344s PC1 PC2 PC3 PC4 PC5 PC6 344s [1,] 6.090978 0.590424 1.1644466 -0.3835606 1.0342867 -0.4752288 344s [2,] 6.903009 -0.575027 0.8613622 -1.1221795 0.7221616 -1.3097951 344s [3,] 0.622903 -1.594239 1.2122863 -0.0555128 0.3252629 -0.2799581 344s [4,] 5.282665 -1.815742 2.2543268 0.9824543 -0.5345577 -0.7331037 344s [5,] -1.039753 0.663906 0.3353811 0.3070599 -0.3224317 -0.4056666 344s [6,] 2.247786 0.218255 -0.3382923 0.1270005 -0.0271307 -0.2035021 344s [7,] 2.784293 -0.291678 -0.4897587 0.0198481 0.0752345 -0.5986846 344s [8,] 2.942266 0.315608 0.1603961 0.3568462 -0.0647311 -0.5316127 344s [9,] -1.420086 -1.751212 1.7027572 0.0708340 -0.9226517 0.0738411 344s [10,] -2.921113 -0.727554 0.0113966 -0.3915037 -0.0772913 0.6062573 344s [11,] -9.568075 0.792291 1.0217507 0.2554182 -0.6254883 0.8899897 344s [12,] -12.885166 3.423607 -1.2579351 -0.4300397 -0.4094558 1.1727128 344s [13,] -10.038470 1.274931 -2.6913262 -1.6219658 -0.3284974 1.1228303 344s [14,] -12.044003 2.096254 -1.2859668 -0.9602250 -0.7937418 0.8264019 344s [15,] -10.798341 1.159257 1.4870766 0.3248231 -1.0787537 0.8723637 344s [16,] -2.841629 0.500846 0.4771762 0.5975365 0.3197882 0.5804087 344s [17,] -1.150691 -1.978038 2.3229313 0.5275273 -0.5339514 0.5421631 344s [18,] -1.992369 1.131288 -0.8385615 0.1156462 0.2253010 -0.3393814 344s [19,] -1.999699 -0.252876 1.2229972 0.5081648 0.0082612 0.3373454 344s [20,] 0.091385 -1.439422 1.1836134 0.6297789 0.0961407 -0.2126653 344s [21,] -2.571346 2.280701 -1.2845660 0.1463583 0.0949331 0.0902039 344s [22,] -0.990078 1.087033 -0.1638640 -0.0351472 0.0743205 -0.0040605 344s [23,] -0.010631 1.704171 0.0038808 0.5765418 0.6086460 0.0329995 344s [24,] -0.440350 1.500798 0.2769870 0.5556999 0.4751445 0.6516120 344s [25,] -3.578249 2.672783 -0.3534268 0.7398104 0.1108289 0.2704730 344s [26,] -0.854914 1.626684 0.2301131 0.5530224 0.0662862 -0.0999969 344s [27,] -3.175381 0.762609 0.5101987 0.0849002 -0.2137237 0.2729808 344s [28,] 2.599844 3.370137 -0.5174736 0.7409946 0.6853156 0.2430943 344s [29,] 4.395534 0.823611 0.1610152 0.8184845 0.7665555 0.0779724 344s [30,] 0.843794 1.438263 -0.2366601 0.4600650 0.3424806 -0.1768083 344s [31,] 1.890815 1.266935 -1.8218143 -0.3909337 0.8390127 0.1026821 344s [32,] 1.300145 -0.085976 -0.8965312 -0.8855787 0.4156780 0.1478055 344s [33,] 1.923087 0.137638 0.3487435 0.2958367 0.4245932 0.1566678 344s [34,] 0.615762 -0.390711 0.8107376 0.0295536 -0.1169590 0.2940241 344s [35,] -0.372946 2.037079 -0.7663299 0.1907237 0.6959350 0.5366205 344s [36,] 4.068134 1.129044 0.5492962 0.7640964 0.4799859 -0.4080205 344s [37,] 0.937617 2.048258 -1.2326566 -0.0942856 0.7885267 -0.1004018 344s [38,] 2.141223 1.877022 -0.5178216 0.3750868 0.4767003 0.1240656 344s [39,] -1.403505 1.327163 0.3165610 0.3989824 0.3505825 0.5915956 344s [40,] 3.337528 -1.689495 1.4737175 0.2584843 0.4308444 -0.0810597 344s [41,] 3.938506 1.384908 0.8103687 -0.5875595 1.1616535 -0.6492603 344s [42,] 6.327471 -1.061362 1.9861187 1.1016484 0.3512405 -0.1540592 344s [43,] 3.120160 -0.064108 -0.8370717 -0.2229341 0.5623447 -0.7152184 344s [44,] 5.290520 -0.669008 0.8597130 0.5518503 0.2470856 0.6454703 344s [45,] 0.058291 0.356399 -0.1896007 0.2427518 0.3705541 0.3975085 344s [46,] 0.150881 1.942057 -0.1140726 0.5656469 0.5227623 0.2151825 344s [47,] 2.870881 -1.446283 -2.8450062 -1.7292144 -0.0888429 -0.1347003 344s [48,] 0.335593 0.500884 -1.3154520 -0.3874864 0.3449038 0.5387692 344s [49,] -2.179494 -0.021237 -1.7792344 -0.8445930 0.4435338 0.6547961 344s [50,] 2.968304 -2.588546 1.8552104 0.4590101 -0.1755089 -0.0550378 344s [51,] -1.399208 -0.820296 -1.3660014 -0.8890243 -0.2344105 0.1236943 344s [52,] -5.112989 0.318983 -1.3852993 -0.8461529 -0.3467685 0.7349666 344s [53,] -0.773103 -0.267333 -0.8154896 -0.3783062 0.0113880 -0.3304648 344s [54,] -0.244565 -0.066211 -0.2541557 0.0043037 0.0390890 0.0074067 344s [55,] 0.894921 0.516411 -0.4443369 0.0708354 -0.0637890 -0.2799646 344s [56,] -0.038706 -0.588256 0.3166588 -0.0196663 -0.1793472 -0.1179341 344s [57,] -1.377469 0.428939 0.7502430 0.1458375 -0.3818977 -0.0380258 344s [58,] 0.042787 1.488605 0.0252606 0.6377516 -0.1524172 -0.1898723 344s [59,] -1.734357 -0.966494 -0.1026850 -0.5656888 -0.4831402 0.0308069 344s [60,] -1.501991 -0.544918 -0.0837127 -0.2362486 -0.5382026 -0.1351338 344s [61,] -0.175102 -1.339436 0.8403933 -0.0907428 -0.4846145 -0.2795153 344s [62,] 2.100915 -2.004702 1.3031556 -0.0041957 -0.2067776 -0.0793613 344s [63,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 344s [64,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 344s [65,] -0.665219 -2.325594 1.6287363 0.0607163 -0.6996720 0.1353325 344s [66,] -2.439244 -0.737375 0.0187770 -0.4561269 -0.5425315 -0.0208332 344s [67,] 0.121564 -1.214385 0.4877707 0.1809998 -0.1943262 0.0662506 344s [68,] -0.804267 -2.238327 -0.8547917 -1.3449926 -0.3577254 -0.0293779 344s [69,] -0.761319 -0.676391 -0.0245494 0.2262894 -0.3396872 -0.1166505 344s [70,] 3.385399 4.360467 -0.7946150 -0.0417895 0.4474362 -4.6626174 344s [71,] -2.364955 -1.257673 0.5226907 -0.2346145 -0.7838777 0.1815821 344s [72,] 2.334511 -0.794530 0.0175620 0.1848925 -0.3437761 -0.4522442 344s [73,] -2.023440 -2.449907 0.2525041 -0.6657474 -0.5509480 0.2118442 344s [74,] -11.180192 2.456516 1.1036540 0.8711496 -0.3833194 1.3548314 344s [75,] 0.058297 -2.094811 0.3075211 -0.8052760 -0.9527729 0.5850255 344s [76,] -1.355742 -0.464355 -1.0183333 -0.8525619 -0.1577144 -0.0767323 344s [77,] -8.296881 0.945092 0.8088967 -0.0071463 -0.4527530 1.0614233 344s [78,] 1.251696 -1.460466 0.2511701 -0.2717606 -0.3158308 -0.2964813 344s [79,] -0.192380 -0.662365 -0.3671703 -0.6722658 -0.1243452 -0.2388225 344s [80,] -3.355201 1.915096 -0.1086672 0.3560062 0.0956865 0.6974817 344s [81,] 1.245305 0.736787 -0.1662155 0.1309822 -0.0122872 -0.2182528 344s [82,] 2.679561 -1.666401 1.1576691 0.3960280 -0.0059146 0.0584136 344s [83,] 2.596651 -0.556654 -0.0807307 -0.4468501 0.0964927 -0.3922894 344s [84,] 0.959377 -0.272038 -1.5879803 -1.1153057 0.3412508 -0.1281556 344s [85,] 0.602737 -1.384591 2.8844745 0.9479144 -0.7946454 -0.2014038 344s [86,] 0.698125 0.335743 -1.5248055 -0.4443037 0.0768256 -0.1999790 344s PC7 PC8 344s [1,] 0.9281777 -0.05158594 344s [2,] 0.8397946 -0.04276628 344s [3,] -0.5189230 0.04913688 344s [4,] -0.0178377 0.01578074 344s [5,] -0.0129237 0.01056305 344s [6,] -0.0764270 0.01469518 344s [7,] -0.3059779 0.04237267 344s [8,] -0.0684673 0.02289928 344s [9,] -0.2549733 -0.00832119 344s [10,] -0.0578118 -0.01894694 344s [11,] 0.0415545 -0.03474479 344s [12,] 0.0869267 -0.04485633 344s [13,] -0.2843977 -0.03100709 344s [14,] -0.3375083 -0.02155574 344s [15,] -0.1718828 -0.02996980 344s [16,] -0.4176728 0.03232381 344s [17,] -0.5923252 0.01765700 344s [18,] -0.3190679 0.04476532 344s [19,] -0.0279426 -0.00236626 344s [20,] 0.1299811 0.00586022 344s [21,] 0.0474059 0.00563264 344s [22,] -0.1240299 0.01123557 344s [23,] 0.2232631 0.00551065 344s [24,] 0.0122404 0.00060079 344s [25,] 0.2627442 -0.00824800 344s [26,] 0.2257329 -0.00440907 344s [27,] -0.8496967 0.05266701 344s [28,] 0.3473502 -0.00500580 344s [29,] 0.4172329 -0.00542705 344s [30,] 0.2773880 -0.00014648 344s [31,] -0.1224270 0.02372808 344s [32,] -0.2224748 0.00757892 344s [33,] -0.0633903 0.01236118 344s [34,] -0.2616599 0.00561781 344s [35,] -0.1671986 0.01988458 344s [36,] 0.4502086 -0.00418541 344s [37,] -0.0773232 0.02768282 344s [38,] 0.0464683 0.01134849 344s [39,] -0.0927182 0.00555823 344s [40,] -0.2162796 0.02467605 344s [41,] 0.9440753 -0.04806541 344s [42,] -0.0078920 0.02022925 344s [43,] 0.1152244 0.02074199 344s [44,] 1.0406693 -0.08815111 344s [45,] -0.1376804 0.01424369 344s [46,] 0.1673461 0.00442877 344s [47,] -0.4125225 0.01038694 344s [48,] 0.1556289 -0.02103354 344s [49,] 0.0434415 -0.01782739 344s [50,] 0.2518610 -0.02154540 344s [51,] -0.1186185 -0.00881133 344s [52,] 0.1507435 -0.04523343 344s [53,] 0.2161208 -0.00967982 344s [54,] 0.1374909 -0.00783970 344s [55,] 0.2417108 -0.00895268 344s [56,] 0.1253846 -0.01188643 344s [57,] 0.1390898 -0.01831232 344s [58,] 0.2219634 -0.00364174 344s [59,] -0.2045636 -0.00589047 344s [60,] -0.3679942 0.01673699 344s [61,] -0.0705611 -0.00273407 344s [62,] 0.1447701 -0.02026768 344s [63,] -0.1854788 0.02686899 344s [64,] -0.1854788 0.02686899 344s [65,] -0.2626650 -0.00376657 344s [66,] -0.3044266 0.00484197 344s [67,] -0.1358811 0.00605789 344s [68,] -0.0551482 -0.02379410 344s [69,] -0.0914891 0.00812122 344s [70,] 10.2524854 -0.64367029 344s [71,] -0.1326972 -0.01666774 344s [72,] 0.0051905 0.00656777 344s [73,] -0.8236843 0.03367265 344s [74,] 0.2140104 -0.04092219 344s [75,] -0.5684260 -0.00987116 344s [76,] -0.1225779 -0.00204629 344s [77,] -0.4235612 -0.00450631 344s [78,] -0.1935155 0.00973901 344s [79,] -0.1615883 0.00518643 344s [80,] 0.2915052 -0.02960159 344s [81,] 0.0908823 0.00038216 344s [82,] -0.3392789 0.02605374 344s [83,] 0.1112141 -0.00629308 344s [84,] 0.0510771 -0.00845572 344s [85,] 0.0748700 -0.01174487 344s [86,] 0.2488127 -0.01446339 344s ------------- 344s Call: 344s PcaGrid(x = x) 344s 344s Standard deviations: 344s [1] 3.034253 1.706044 1.167717 0.670864 0.536071 0.396285 0.266625 0.020768 344s ---------------------------------------------------------- 344s bushfire 38 5 5 38232.614428 1580.825276 344s Scores: 344s PC1 PC2 PC3 PC4 PC5 344s [1,] -67.120 -23.70481 -1.06551 1.129721 1.311630 344s [2,] -69.058 -21.42113 -1.54798 0.983735 0.430774 344s [3,] -61.939 -17.23665 -3.81386 -0.635074 -0.600149 344s [4,] -44.952 -16.53458 -5.16114 0.411753 -0.390518 344s [5,] -12.644 -21.62271 -7.14146 3.519877 -1.211923 344s [6,] 12.820 -27.86930 -7.66114 7.230422 0.040330 344s [7,] -194.634 -100.67730 27.43084 -0.026242 -0.134248 344s [8,] -229.349 -129.75912 -19.46346 25.591651 -18.592601 344s [9,] -230.306 -131.28743 -22.22175 27.251157 -19.214683 344s [10,] -231.118 -115.10815 3.70208 16.303210 -10.573515 344s [11,] -234.540 -100.24984 13.67112 10.325539 -8.727961 344s [12,] -246.507 -51.03515 27.61698 -5.352226 0.514087 344s [13,] -195.712 -5.81324 20.04485 -9.226807 1.721886 344s [14,] 49.881 16.90911 -9.97400 -1.900739 2.190429 344s [15,] 179.545 23.96999 -18.71166 -2.987136 1.332713 344s [16,] 135.356 15.81282 -9.24353 -4.703584 0.971669 344s [17,] 132.350 16.65014 -7.01838 -2.428578 1.346198 344s [18,] 121.499 9.75832 -4.45699 -1.587450 0.131923 344s [19,] 125.222 9.17601 -5.88919 0.582516 -0.061642 344s [20,] 135.112 14.63812 -5.90351 0.411704 1.460488 344s [21,] 116.581 14.47390 -3.04021 -1.842579 2.005998 344s [22,] 108.223 14.62103 -4.47428 -1.196993 3.288463 344s [23,] -22.095 3.26439 6.58391 -6.164581 2.125258 344s [24,] -77.831 3.46616 6.59280 -6.373595 1.545789 344s [25,] -13.092 3.41344 -0.99296 -5.076733 0.299636 344s [26,] -19.206 -0.17007 -1.84209 -4.858675 0.347945 344s [27,] -35.022 6.54155 -3.12767 -3.556587 -0.327873 344s [28,] -12.651 20.14894 -4.61607 -2.025539 -1.214190 344s [29,] -4.404 36.39823 -3.81590 -0.633155 -0.602027 344s [30,] -60.018 30.40980 9.44610 -1.763156 -0.765133 344s [31,] 67.689 47.40087 12.70229 9.791794 -0.671751 344s [32,] 324.134 63.46147 31.52512 30.099817 2.406344 344s [33,] 364.639 38.84260 51.20467 30.648590 3.218678 344s [34,] 361.089 37.09494 52.00522 29.394356 2.861158 344s [35,] 366.403 38.88889 52.31879 29.878844 4.650618 344s [36,] 363.821 37.40859 53.10394 28.286557 2.922632 344s [37,] 361.761 37.21276 55.73012 27.648760 4.477279 344s [38,] 363.106 37.78395 56.56345 27.460078 4.845396 344s ------------- 344s Call: 344s PcaGrid(x = x) 344s 344s Standard deviations: 344s [1] 195.5316 39.7596 11.7329 7.3743 1.7656 344s ---------------------------------------------------------- 344s ========================================================== 344s > 344s > ## IGNORE_RDIFF_BEGIN 344s > dodata(method="proj") 344s 344s Call: dodata(method = "proj") 344s Data Set n p k e1 e2 344s ========================================================== 344s heart 12 2 2 512.772467 29.052346 344s Scores: 344s PC1 PC2 344s [1,] 6.7568 3.2826 344s [2,] 63.0869 14.1293 344s [3,] 1.3852 -1.1318 344s [4,] -3.6709 1.8153 344s [5,] 19.0457 3.8035 344s [6,] -16.6413 3.1452 344s [7,] 5.3163 3.7464 344s [8,] -27.8536 -11.0863 344s [9,] -1.1638 -1.1788 344s [10,] -26.6915 -10.2803 344s [11,] -13.6842 -2.9790 344s [12,] 47.8395 11.2980 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 22.644 5.390 344s ---------------------------------------------------------- 344s starsCYG 47 2 2 0.470874 0.024681 344s Scores: 344s PC1 PC2 344s [1,] 0.181333 -3.1013e-02 344s [2,] 0.696091 1.4569e-01 344s [3,] -0.121421 -1.3319e-01 344s [4,] 0.696091 1.4569e-01 344s [5,] 0.139530 -9.9951e-02 344s [6,] 0.413590 5.2989e-02 344s [7,] -0.412224 -5.4579e-01 344s [8,] 0.226508 1.6788e-01 344s [9,] 0.518364 -1.4980e-01 344s [10,] 0.071370 -2.8159e-02 344s [11,] 0.658332 -9.2369e-01 344s [12,] 0.402815 2.3259e-02 344s [13,] 0.374123 7.4020e-02 344s [14,] -1.007611 -3.6028e-01 344s [15,] -0.790417 -8.5818e-02 344s [16,] -0.467151 3.5835e-02 344s [17,] -1.111866 -1.3750e-01 344s [18,] -0.867017 4.6214e-02 344s [19,] -0.871946 -1.4372e-01 344s [20,] 0.818278 -9.2784e-01 344s [21,] -0.670457 -8.8932e-02 344s [22,] -0.830403 -8.4781e-02 344s [23,] -0.627097 3.9987e-02 344s [24,] -0.195426 9.8806e-02 344s [25,] -0.028337 -1.5568e-02 344s [26,] -0.387178 3.3760e-02 344s [27,] -0.390551 -9.6197e-02 344s [28,] -0.148297 -1.2454e-02 344s [29,] -0.662277 -1.5917e-01 344s [30,] 0.977965 -9.4199e-01 344s [31,] -0.628135 -7.2164e-16 344s [32,] 0.056306 1.6230e-01 344s [33,] 0.173412 4.9220e-02 344s [34,] 1.218143 -9.3822e-01 344s [35,] -0.712000 -1.4787e-01 344s [36,] 0.577688 2.0878e-01 344s [37,] 0.055528 1.3231e-01 344s [38,] 0.173412 4.9220e-02 344s [39,] 0.135501 1.3023e-01 344s [40,] 0.522775 2.0145e-02 344s [41,] -0.428203 -5.1892e-03 344s [42,] 0.013465 5.3371e-02 344s [43,] 0.294668 9.6089e-02 344s [44,] 0.293371 4.6106e-02 344s [45,] 0.495898 1.4088e-01 344s [46,] -0.066508 5.5447e-02 344s [47,] -0.547124 3.7911e-02 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 0.6862 0.1571 344s ---------------------------------------------------------- 344s phosphor 18 2 2 388.639033 51.954664 344s Scores: 344s PC1 PC2 344s 1 5.8164 -15.1691 344s 2 -21.1936 -2.1132 344s 3 -23.6199 2.0585 344s 4 -11.2029 -6.7203 344s 5 -18.4220 1.3231 344s 6 17.1862 -19.2211 344s 7 1.6302 -3.1493 344s 8 -9.7695 3.1385 344s 9 -10.9174 5.3594 344s 10 15.6275 -6.3610 344s 11 -4.0194 1.2476 344s 12 9.3931 8.3149 344s 13 12.9944 6.5741 344s 14 6.9396 7.8348 344s 15 18.3964 3.9629 344s 16 -8.8365 -6.4202 344s 17 21.8073 6.4237 344s 18 16.8541 12.2611 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 19.714 7.208 344s ---------------------------------------------------------- 344s stackloss 21 3 3 97.347030 38.052774 344s Scores: 344s PC1 PC2 PC3 344s [1,] 19.08066 -9.06092 -2.64544 344s [2,] 18.55152 -9.90152 -2.76118 344s [3,] 15.04269 -5.37517 -2.31373 344s [4,] 2.79667 -1.78925 1.70823 344s [5,] 2.21768 -1.17513 -0.10495 344s [6,] 2.50717 -1.48219 0.80164 344s [7,] 5.97151 3.25438 2.40268 344s [8,] 5.97151 3.25438 2.40268 344s [9,] -0.68332 0.30263 2.42495 344s [10,] -5.83478 -4.04630 -2.91819 344s [11,] -1.07253 3.51914 -1.87651 344s [12,] -1.89116 2.98559 -2.89885 344s [13,] -4.77650 -2.36509 -2.68671 344s [14,] 1.33353 6.57450 -0.50696 344s [15,] -7.45351 7.08878 1.37012 344s [16,] -9.04093 4.56697 1.02289 344s [17,] -16.15938 -7.50855 0.30909 344s [18,] -12.45541 -1.62432 1.11929 344s [19,] -11.63677 -1.09077 2.14162 344s [20,] -5.79275 -2.08680 -0.06187 344s [21,] 10.13623 -0.76824 -4.70180 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 9.8665 6.1687 3.2669 344s ---------------------------------------------------------- 344s salinity 28 3 3 12.120566 8.431549 344s Scores: 344s PC1 PC2 PC3 344s 1 -2.52547 1.45945 -1.1943e-01 344s 2 -3.32298 2.15704 8.7594e-01 344s 3 -6.64947 -3.26398 1.0135e+00 344s 4 -6.64427 -1.81382 -1.6392e-01 344s 5 -6.16898 -2.52222 5.1373e+00 344s 6 -5.87594 0.26440 -2.4425e-15 344s 7 -4.23084 1.46250 -2.8008e-01 344s 8 -2.21502 2.76478 -8.3789e-01 344s 9 -0.40186 -2.17785 -1.6702e+00 344s 10 2.27089 -1.84923 7.3391e-01 344s 11 1.37935 -1.29276 2.1418e+00 344s 12 -0.22635 0.60372 -5.0980e-01 344s 13 0.27224 1.73920 -7.0505e-01 344s 14 2.36592 2.40462 6.4320e-01 344s 15 2.37640 -2.83174 5.2669e-01 344s 16 -2.49175 -4.77664 9.0404e+00 344s 17 -0.61250 -1.11672 1.4398e+00 344s 18 -2.91853 0.63310 -8.3666e-01 344s 19 -0.39732 -2.02029 -2.1396e+00 344s 20 1.47554 -1.23407 -1.1712e+00 344s 21 1.70104 1.92401 -1.1292e+00 344s 22 3.14437 2.81928 -5.2415e-01 344s 23 3.62890 -3.51450 2.6740e+00 344s 24 2.04538 -2.63992 3.0718e+00 344s 25 0.77088 -0.54783 -1.3370e-01 344s 26 1.57254 0.89176 -1.2089e+00 344s 27 2.63610 1.97075 -1.1855e+00 344s 28 3.55112 2.67606 -6.0915e-02 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 3.4815 2.9037 1.3810 344s ---------------------------------------------------------- 344s hbk 75 3 3 3.801978 3.574192 344s Scores: 344s PC1 PC2 PC3 344s 1 28.747049 15.134042 2.3959241 344s 2 29.021724 16.318941 2.6207988 344s 3 31.271908 15.869319 3.4420860 344s 4 31.586189 17.508798 3.6246706 344s 5 31.299168 16.838093 3.2402573 344s 6 30.037754 15.591930 2.1421166 344s 7 29.888160 16.139376 1.9750096 344s 8 28.994463 15.350167 2.8226275 344s 9 30.758047 16.820526 3.7269602 344s 10 29.759314 16.079531 4.0486097 344s 11 35.301371 19.637962 3.7433562 344s 12 37.193371 18.709303 4.9915250 344s 13 35.634808 20.497713 1.4740727 344s 14 36.816439 27.523024 -2.3006796 344s 15 1.237203 -0.331072 -1.3801401 344s 16 -0.451166 -1.118847 -1.9707479 344s 17 -2.604733 0.067276 0.0130015 344s 18 0.179177 -0.804398 -0.1285240 344s 19 -0.765512 0.982349 -0.2513990 344s 20 1.236727 0.259123 -1.4210070 344s 21 0.428326 -0.503724 -0.6830690 344s 22 -0.724774 1.507943 -0.0022175 344s 23 -0.745349 -0.330094 -1.0982084 344s 24 -1.407850 -0.011831 -0.8987075 344s 25 -2.190427 -1.732051 0.4497793 344s 26 0.058631 1.444044 0.0446166 344s 27 1.680557 -0.429402 -0.6031146 344s 28 -0.315122 -1.179169 0.5822607 344s 29 -1.563355 -1.026914 0.1040012 344s 30 0.329957 -0.633156 1.8533795 344s 31 -0.110108 -1.617131 -1.0958807 344s 32 -2.035875 0.463421 -0.6346632 344s 33 -0.356033 0.740564 -0.8116369 344s 34 -2.342887 -1.340168 0.9724491 344s 35 1.607131 -0.379763 -0.3747630 344s 36 0.084455 0.486671 0.6551654 344s 37 -0.436144 1.659467 0.7145344 344s 38 -1.754819 -1.076076 -0.6037590 344s 39 -0.904375 -2.161949 0.3436723 344s 40 -1.455274 0.331839 0.1499308 344s 41 1.539788 -1.212921 -0.1715110 344s 42 -0.688338 -0.048173 1.7491184 344s 43 -1.635822 1.539067 -0.5208916 344s 44 0.511762 -1.165641 1.5020865 344s 45 -1.454500 -2.099954 0.0219268 344s 46 0.362645 -1.208389 1.3758464 344s 47 -0.615800 -2.658098 -0.4629006 344s 48 1.426278 -1.027667 0.0582638 344s 49 0.809592 -0.533893 -1.1232120 344s 50 0.996105 0.469082 -0.0988805 344s 51 -1.036368 -1.227376 -1.0843166 344s 52 -0.016464 -2.331540 -0.6477169 344s 53 -0.376625 -0.405855 2.4526088 344s 54 -1.524100 0.621590 -1.2927429 344s 55 -1.588523 0.591668 -0.2559428 344s 56 -0.592710 0.529426 -1.4111404 344s 57 -1.306991 -1.538024 -0.1841717 344s 58 0.275991 0.491888 -1.4739863 344s 59 0.598971 0.196673 0.6208960 344s 60 -0.127953 0.485014 1.8571970 344s 61 0.140584 1.905037 0.5838465 344s 62 -2.305069 -1.617811 0.3880825 344s 63 -1.666479 0.357251 -1.1934779 344s 64 1.480143 0.248671 -0.5959984 344s 65 0.309561 -1.219790 0.9671263 344s 66 -1.986789 0.248245 0.1723620 344s 67 -0.765691 -0.269054 -0.4611368 344s 68 -2.232721 -1.090790 1.3915841 344s 69 -1.502453 -1.813763 -0.4936268 344s 70 0.170883 0.584046 0.8369571 344s 71 0.543623 0.043244 -0.3707674 344s 72 -1.168908 0.341335 0.2837393 344s 73 -0.902885 0.411872 1.0546196 344s 74 -1.425273 0.852445 0.5719123 344s 75 -0.898536 -0.555475 2.0107684 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 1.9499 1.8906 1.2797 344s ---------------------------------------------------------- 344s milk 86 8 8 8.369408 3.530461 344s Scores: 344s PC1 PC2 PC3 PC4 PC5 PC6 344s [1,] 6.337004 -0.245000 0.7704092 -4.9848e-01 -1.6599e-01 1.1763e-01 344s [2,] 7.021899 1.030349 0.2832977 -1.2673e+00 -8.7296e-01 2.0547e-01 344s [3,] 0.600831 1.686247 0.9682032 -3.2663e-02 7.4112e-02 4.7412e-01 344s [4,] 5.206465 2.665956 1.5942253 9.8285e-01 -5.4159e-01 -2.0155e-01 344s [5,] -0.955757 -0.579889 0.3206393 5.1174e-01 -6.1684e-01 -3.8990e-02 344s [6,] 2.198695 0.073770 -0.5712493 1.9440e-01 -1.0237e-01 4.1825e-02 344s [7,] 2.695361 0.644049 -0.8645373 8.1894e-02 -2.6953e-01 1.6884e-01 344s [8,] 2.945361 0.137227 -0.2071463 5.0841e-01 -4.2075e-01 5.8589e-02 344s [9,] -1.539013 1.879894 1.6952390 1.6792e-01 -2.8195e-01 5.0563e-02 344s [10,] -2.977110 0.319666 0.3515636 -5.2496e-01 4.6898e-01 8.5978e-03 344s [11,] -9.375355 -1.638105 1.9026171 4.1237e-01 1.8768e-02 -1.8546e-01 344s [12,] -12.602600 -4.715888 0.0273004 -4.7798e-02 -1.2246e-02 9.6858e-03 344s [13,] -10.114331 -2.487462 -1.6331544 -1.5139e+00 4.1903e-01 2.8313e-01 344s [14,] -11.949336 -3.190157 -0.2146943 -5.0060e-01 -2.9537e-01 3.2160e-01 344s [15,] -10.595396 -1.905517 2.3716887 7.6651e-01 -3.3531e-01 1.9933e-02 344s [16,] -2.735720 -0.748282 0.6750464 7.2415e-01 5.5304e-01 2.2283e-01 344s [17,] -1.248116 2.131195 2.2596886 6.4958e-01 3.5634e-01 2.9021e-01 344s [18,] -1.904210 -1.285804 -0.7746460 3.0198e-01 -2.7407e-01 1.7500e-01 344s [19,] -1.902313 0.095461 1.3824711 5.0369e-01 2.2193e-01 -5.5628e-02 344s [20,] 0.123220 1.399444 1.1517634 3.2546e-01 7.8261e-02 -4.0733e-01 344s [21,] -2.436023 -2.524827 -1.0197416 3.4819e-01 -1.4914e-01 -4.3669e-02 344s [22,] -0.904931 -1.114894 -0.1235807 2.0285e-01 -1.6200e-01 2.5681e-01 344s [23,] 0.220231 -1.767325 0.0482262 6.4418e-01 9.8618e-02 -5.7683e-02 344s [24,] -0.274403 -1.561826 0.3820323 7.0016e-01 5.5220e-01 1.4376e-01 344s [25,] -3.306400 -2.980247 0.0252488 9.4001e-01 -1.0841e-01 -2.5303e-01 344s [26,] -0.658015 -1.625199 0.3021005 7.2702e-01 -3.0299e-01 -1.2339e-01 344s [27,] -3.137066 -0.774218 0.5577497 6.4188e-01 -8.0125e-02 7.7819e-01 344s [28,] 2.867950 -3.099435 -0.6435415 1.0366e+00 1.5908e-01 7.6524e-02 344s [29,] 4.523097 -0.527338 -0.1032516 6.4537e-01 4.7286e-01 -2.7166e-01 344s [30,] 1.002381 -1.376693 -0.2735956 5.0522e-01 -1.2750e-01 -1.6178e-01 344s [31,] 1.894615 -1.296202 -1.9117282 -3.8032e-01 4.6473e-01 3.1085e-01 344s [32,] 1.210291 0.067230 -0.9832930 -8.5379e-01 3.2823e-01 4.9994e-01 344s [33,] 1.964118 0.022175 0.1818518 3.0464e-01 3.5596e-01 1.4985e-01 344s [34,] 0.576738 0.567851 0.6982155 1.8415e-01 1.8695e-01 3.2706e-01 344s [35,] -0.231793 -2.143909 -0.6825523 4.0681e-01 5.4492e-01 3.6259e-01 344s [36,] 4.250883 -0.719760 0.2157706 7.7167e-01 -1.9064e-01 -2.0611e-01 344s [37,] 1.077364 -2.054664 -1.3064867 1.0043e-01 8.6092e-02 3.5416e-01 344s [38,] 2.259260 -1.653588 -0.6730692 5.7300e-01 1.6930e-01 1.6986e-01 344s [39,] -1.251576 -1.451593 0.4671580 5.8957e-01 4.2672e-01 2.2495e-01 344s [40,] 3.304245 1.998193 1.0941231 1.3734e-01 3.7012e-01 2.4142e-01 344s [41,] 4.286315 -1.280951 0.5856744 -6.0980e-01 -4.3090e-01 1.9801e-01 344s [42,] 6.343820 1.801880 1.3481119 1.0355e+00 2.9802e-01 -8.4501e-04 344s [43,] 3.119491 0.214077 -1.1216236 -3.8134e-01 -1.9523e-01 -2.6706e-02 344s [44,] 5.285254 0.938072 0.7440487 1.1539e-02 8.1629e-01 -7.9286e-01 344s [45,] 0.082429 -0.416631 -0.1588203 2.3098e-01 5.1867e-01 9.4503e-02 344s [46,] 0.357862 -1.951997 -0.0731829 7.0393e-01 1.8828e-01 1.5707e-02 344s [47,] 2.428744 1.522538 -3.0467213 -1.9114e+00 2.4638e-01 3.5871e-01 344s [48,] 0.282348 -0.697287 -1.1592508 -5.4929e-01 6.2199e-01 -5.4596e-02 344s [49,] -2.266009 -0.559548 -1.3794914 -1.1300e+00 7.8872e-01 -2.0411e-02 344s [50,] 2.868649 2.860857 1.6128307 6.7382e-02 2.2344e-01 -4.1484e-01 344s [51,] -1.596061 0.546812 -1.1779327 -1.0512e+00 1.3522e-01 -9.4865e-03 344s [52,] -5.186121 -1.000829 -0.7440599 -9.6302e-01 3.0732e-01 -1.7009e-01 344s [53,] -0.800232 0.049087 -0.6946842 -5.8284e-01 -2.1277e-01 -2.7004e-01 344s [54,] -0.246388 -0.030606 -0.1814302 -1.1632e-01 5.7767e-02 -1.8637e-01 344s [55,] 0.914315 -0.428594 -0.4919557 4.5039e-02 -2.7868e-01 -2.2140e-01 344s [56,] -0.061827 0.583572 0.3263056 -1.1589e-01 -1.2973e-01 -1.6518e-01 344s [57,] -1.295979 -0.421943 0.8410805 3.0441e-01 -3.9478e-01 -4.5233e-02 344s [58,] 0.174908 -1.343854 0.0115086 8.0227e-01 -3.9364e-01 -2.2918e-01 344s [59,] -1.869684 0.840823 0.0109543 -5.5536e-01 -1.4155e-01 1.0613e-01 344s [60,] -1.614271 0.557309 -0.0690787 -9.1753e-02 -3.0975e-01 1.6192e-01 344s [61,] -0.258192 1.434984 0.7684636 -1.1998e-01 -3.4662e-01 -4.8808e-02 344s [62,] 2.000275 2.204730 1.1194067 -2.3783e-01 5.9953e-02 -1.5836e-01 344s [63,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 344s [64,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 344s [65,] -0.822201 2.427550 1.5859438 -2.6715e-16 -1.9429e-15 1.0564e-14 344s [66,] -2.545586 0.605953 0.1469837 -3.5318e-01 -2.5871e-01 1.6901e-01 344s [67,] 0.028900 1.253717 0.4474540 5.3595e-02 1.6063e-01 -1.0980e-01 344s [68,] -1.086135 1.968868 -0.7220293 -1.6576e+00 6.2061e-02 -7.0998e-04 344s [69,] -0.836638 0.660453 0.0049966 1.3663e-01 -1.0131e-01 -2.4008e-01 344s [70,] 4.843092 -6.035092 0.8250084 -3.4481e+00 -4.8538e+00 -7.8407e+00 344s [71,] -2.500038 1.146245 0.6967314 -2.4611e-01 -1.4266e-01 -8.2996e-02 344s [72,] 2.220676 1.122951 -0.2444075 1.1066e-01 -3.1540e-01 -2.1344e-01 344s [73,] -2.310518 2.354552 0.2706503 -6.4192e-01 2.0566e-01 4.5520e-01 344s [74,] -10.802799 -3.462655 2.2031446 1.1326e+00 2.8049e-01 -2.9749e-01 344s [75,] -0.301038 2.284366 0.2440764 -6.9450e-01 2.6435e-01 4.3129e-01 344s [76,] -1.477936 0.245154 -0.8869850 -8.9900e-01 -9.8013e-02 1.1983e-01 344s [77,] -8.169236 -1.599780 1.4987144 3.7767e-01 2.4726e-01 3.8246e-01 344s [78,] 1.096654 1.646072 0.0591327 -3.3138e-01 -1.7936e-01 6.2716e-02 344s [79,] -0.289199 0.625796 -0.3974294 -6.6099e-01 -2.0857e-01 2.1190e-01 344s [80,] -3.160557 -2.282579 0.3255355 4.6181e-01 2.7753e-01 -1.5673e-01 344s [81,] 1.284356 -0.548854 -0.2907281 2.4017e-01 -2.5254e-01 -1.4289e-03 344s [82,] 2.562817 2.019485 0.8249162 3.2973e-01 3.3866e-01 1.3889e-01 344s [83,] 2.538825 0.759863 -0.3142506 -5.1028e-01 -2.0539e-01 8.8979e-02 344s [84,] 0.841123 0.110035 -1.5793120 -1.2807e+00 1.2332e-01 1.6224e-01 344s [85,] 0.636271 1.793014 2.6824860 1.0329e+00 -4.8850e-01 -2.3012e-01 344s [86,] 0.633183 -0.426511 -1.4791366 -6.1314e-01 -7.0534e-02 -2.3778e-01 344s PC7 PC8 344s [1,] 1.0196e-01 -1.7180e-03 344s [2,] 2.6131e-01 -8.5191e-03 344s [3,] 6.9637e-01 -8.0573e-03 344s [4,] -1.3548e-01 -1.4969e-03 344s [5,] 3.1443e-02 -2.7307e-03 344s [6,] -2.5079e-01 3.6450e-03 344s [7,] 4.5377e-02 -2.6071e-03 344s [8,] -1.6060e-01 -2.3761e-04 344s [9,] -1.5152e-01 -4.3079e-04 344s [10,] 9.1089e-02 1.9536e-03 344s [11,] 2.5654e-01 -1.4875e-03 344s [12,] -2.3798e-03 -1.0954e-04 344s [13,] -1.3687e-01 2.8402e-03 344s [14,] -6.5248e-02 -1.5114e-03 344s [15,] 3.7695e-02 -2.7827e-03 344s [16,] 3.8131e-01 -3.7990e-03 344s [17,] 4.5661e-02 -1.4965e-03 344s [18,] 3.9910e-01 -7.2703e-03 344s [19,] 2.9353e-01 -3.3342e-03 344s [20,] 6.0915e-01 -6.0837e-03 344s [21,] -1.0079e-01 1.0179e-03 344s [22,] -2.2945e-02 -1.0515e-03 344s [23,] 2.3631e-01 -2.5558e-03 344s [24,] -7.7207e-02 3.4800e-03 344s [25,] 1.4903e-02 -3.2430e-04 344s [26,] 3.8032e-03 -2.1705e-03 344s [27,] 3.7208e-02 -3.0631e-03 344s [28,] -4.8147e-01 6.1089e-03 344s [29,] -4.0388e-02 2.8549e-03 344s [30,] 3.4318e-02 -1.0014e-03 344s [31,] -2.2872e-02 1.8706e-03 344s [32,] -8.4542e-02 1.3368e-03 344s [33,] 4.5274e-02 5.3383e-04 344s [34,] -2.0048e-01 2.4727e-03 344s [35,] -5.6482e-02 2.9923e-03 344s [36,] -2.6046e-02 -1.2910e-03 344s [37,] 9.6038e-02 -1.8897e-03 344s [38,] -2.9035e-01 4.4317e-03 344s [39,] -4.6322e-03 2.4336e-03 344s [40,] 3.8686e-01 -3.9300e-03 344s [41,] 3.7834e-01 -7.8976e-03 344s [42,] -8.2037e-04 -4.3106e-05 344s [43,] 3.3467e-01 -5.2401e-03 344s [44,] -6.2170e-01 1.2840e-02 344s [45,] 5.3557e-02 2.9156e-03 344s [46,] 5.1785e-04 2.0738e-03 344s [47,] -5.2141e-01 5.7206e-03 344s [48,] -2.7669e-01 6.7329e-03 344s [49,] 8.4319e-02 3.8528e-03 344s [50,] 1.4210e-01 1.6961e-04 344s [51,] -1.1871e-01 2.6676e-03 344s [52,] -2.5036e-01 6.4121e-03 344s [53,] 2.2399e-01 -2.8200e-03 344s [54,] 5.6532e-02 4.9304e-04 344s [55,] -1.4343e-01 1.2558e-03 344s [56,] 4.1682e-02 -9.6490e-04 344s [57,] -1.3014e-01 -6.2709e-04 344s [58,] -2.1428e-01 8.2594e-04 344s [59,] -7.9775e-02 -8.9776e-04 344s [60,] -8.6835e-02 -1.0498e-03 344s [61,] 6.2470e-02 -2.7499e-03 344s [62,] 3.3052e-02 -3.2369e-04 344s [63,] -1.7137e-01 -3.1087e-04 344s [64,] -1.7137e-01 -3.1087e-04 344s [65,] 3.5496e-14 2.5975e-12 344s [66,] -2.2016e-02 -1.2206e-03 344s [67,] 8.5160e-02 -1.4837e-04 344s [68,] -2.2535e-03 1.9054e-04 344s [69,] 5.9976e-02 -8.6961e-04 344s [70,] 1.0448e+00 -2.0167e-02 344s [71,] -1.7609e-01 1.9378e-03 344s [72,] -1.7047e-01 2.6076e-04 344s [73,] 1.1885e-01 -8.1624e-04 344s [74,] 2.0942e-01 3.3164e-03 344s [75,] -7.7528e-01 9.9316e-03 344s [76,] -4.6285e-03 2.5153e-04 344s [77,] 7.0218e-02 1.5708e-03 344s [78,] -1.4859e-02 -6.7049e-04 344s [79,] 5.1054e-02 -2.0198e-03 344s [80,] -1.5770e-01 4.9579e-03 344s [81,] -1.9411e-01 4.4401e-04 344s [82,] 6.0634e-02 8.7960e-04 344s [83,] -4.4635e-02 -1.7048e-03 344s [84,] -2.3612e-03 -2.2242e-04 344s [85,] -5.5171e-02 -1.1222e-03 344s [86,] -1.4972e-01 1.4543e-03 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 2.8929930 1.8789522 0.9946460 0.7479403 0.3744197 0.2596328 0.1421387 344s [8] 0.0025753 344s ---------------------------------------------------------- 344s bushfire 38 5 5 37473.439646 1742.633018 344s Scores: 344s PC1 PC2 PC3 PC4 PC5 344s [1,] -67.2152 -2.3010e+01 4.4179e+00 1.0892e+00 1.7536e+00 344s [2,] -69.0225 -2.1417e+01 2.5382e+00 1.1092e+00 9.3919e-01 344s [3,] -61.6651 -1.8580e+01 -6.1022e-01 -8.1124e-01 -1.6462e-01 344s [4,] -44.5883 -1.8234e+01 -3.9899e-01 -5.2145e-01 2.0050e-01 344s [5,] -12.2941 -2.2954e+01 3.5970e+00 1.1037e+00 -2.4384e-01 344s [6,] 13.0282 -2.8133e+01 8.7670e+00 3.4751e+00 1.3728e+00 344s [7,] -199.0774 -7.7956e+01 5.4935e+01 6.3134e+00 -1.9919e+00 344s [8,] -228.2849 -1.3258e+02 2.2340e+01 2.1656e+01 -1.2594e+01 344s [9,] -228.9164 -1.3560e+02 2.0463e+01 2.2625e+01 -1.2743e+01 344s [10,] -232.4703 -1.0661e+02 3.5597e+01 1.7915e+01 -7.7659e+00 344s [11,] -236.7410 -8.8072e+01 3.6632e+01 1.5095e+01 -7.4695e+00 344s [12,] -249.4091 -3.6830e+01 2.4010e+01 4.7317e+00 -1.2986e+00 344s [13,] -197.0450 4.2633e-14 4.9738e-14 1.1657e-13 -1.1369e-13 344s [14,] 50.9487 1.1397e+01 -1.1247e+01 -4.8733e+00 2.4511e+00 344s [15,] 180.7896 1.7571e+01 -8.0454e+00 -1.0582e+01 1.2714e+00 344s [16,] 135.6178 1.4189e+01 -4.9116e-01 -9.2701e+00 1.4021e-01 344s [17,] 132.5344 1.5577e+01 2.2990e-01 -6.4963e+00 7.3370e-01 344s [18,] 121.3422 1.0471e+01 4.5656e+00 -4.9831e+00 -5.2314e-01 344s [19,] 125.2722 9.0272e+00 3.7365e+00 -3.3313e+00 -2.9097e-01 344s [20,] 135.2370 1.4091e+01 2.0639e+00 -3.6800e+00 1.1733e+00 344s [21,] 116.4250 1.5147e+01 2.9085e+00 -4.8084e+00 1.2603e+00 344s [22,] 108.2925 1.4223e+01 7.7165e-01 -4.5065e+00 2.7943e+00 344s [23,] -22.8258 6.4234e+00 2.4654e+00 -3.9627e+00 7.9847e-01 344s [24,] -78.1850 4.6631e+00 -3.6818e+00 -2.7688e+00 5.8508e-01 344s [25,] -13.0417 2.7521e+00 -3.1955e+00 -4.6824e+00 -3.1085e-01 344s [26,] -19.1244 -9.5045e-01 -2.6771e+00 -4.7104e+00 -1.6172e-01 344s [27,] -34.4379 3.2761e+00 -9.2826e+00 -2.9861e+00 -3.3561e-01 344s [28,] -11.5852 1.4506e+01 -1.5649e+01 -1.6260e+00 -8.5347e-01 344s [29,] -2.9366 2.8741e+01 -2.2907e+01 3.9749e-01 3.5861e-02 344s [30,] -59.7518 2.8633e+01 -1.4710e+01 3.5226e+00 -9.9066e-01 344s [31,] 67.8017 4.7241e+01 -9.1255e+00 1.3201e+01 1.3500e-13 344s [32,] 321.9941 7.6188e+01 2.2491e+01 3.1537e+01 3.2368e+00 344s [33,] 359.5155 6.6710e+01 5.6061e+01 3.4541e+01 2.0718e+00 344s [34,] 355.8007 6.5695e+01 5.7430e+01 3.3578e+01 1.4640e+00 344s [35,] 361.1076 6.7577e+01 5.7402e+01 3.3832e+01 3.2618e+00 344s [36,] 358.3592 6.6791e+01 5.8643e+01 3.2720e+01 1.2487e+00 344s [37,] 355.9974 6.8071e+01 6.0927e+01 3.2560e+01 2.4898e+00 344s [38,] 357.2530 6.9073e+01 6.1517e+01 3.2523e+01 2.7558e+00 344s ------------- 344s Call: 344s PcaProj(x = x) 344s 344s Standard deviations: 344s [1] 193.5806 41.7449 16.7665 8.1585 1.6074 344s ---------------------------------------------------------- 344s ========================================================== 344s > ## IGNORE_RDIFF_END 344s > 344s > ## VT::14.11.2018 - commented out - on some platforms PcaHubert will choose only 1 PC 344s > ## and will show difference 344s > ## test.case.1() 344s > 344s > test.case.2() 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s [1] TRUE 344s > 344s BEGIN TEST tlda.R 344s 344s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 344s Copyright (C) 2025 The R Foundation for Statistical Computing 344s Platform: arm-unknown-linux-gnueabihf (32-bit) 344s 344s R is free software and comes with ABSOLUTELY NO WARRANTY. 344s You are welcome to redistribute it under certain conditions. 344s Type 'license()' or 'licence()' for distribution details. 344s 344s R is a collaborative project with many contributors. 344s Type 'contributors()' for more information and 344s 'citation()' on how to cite R or R packages in publications. 344s 344s Type 'demo()' for some demos, 'help()' for on-line help, or 344s 'help.start()' for an HTML browser interface to help. 344s Type 'q()' to quit R. 344s 344s > ## VT::15.09.2013 - this will render the output independent 344s > ## from the version of the package 344s > suppressPackageStartupMessages(library(rrcov)) 344s > library(MASS) 344s > 344s > ## VT::14.01.2020 344s > ## On some platforms minor differences are shown - use 344s > ## IGNORE_RDIFF_BEGIN 344s > ## IGNORE_RDIFF_END 344s > 344s > dodata <- function(method) { 344s + 344s + options(digits = 5) 344s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 344s + 344s + tmp <- sys.call() 344s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 344s + cat("===================================================\n") 344s + 344s + cat("\nData: ", "hemophilia\n") 344s + data(hemophilia) 344s + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "anorexia\n") 344s + data(anorexia) 344s + show(rlda <- Linda(Treat~., data=anorexia, method=method)) 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "Pima\n") 344s + data(Pima.tr) 344s + show(rlda <- Linda(type~., data=Pima.tr, method=method)) 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "Forest soils\n") 344s + data(soil) 344s + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) 344s + 344s + ## This will not work within the function, of course 344s + ## - comment it out 344s + ## IGNORE_RDIFF_BEGIN 344s + rlda <- Linda(F~., data=soil1983, method=method) 344s + ## show(rlda) 344s + ## IGNORE_RDIFF_END 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "Raven and Miller diabetes data\n") 344s + data(diabetes) 344s + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "iris\n") 344s + data(iris) 344s + if(method != "mcdA") 344s + { 344s + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) 344s + show(predict(rlda)) 344s + } 344s + 344s + cat("\nData: ", "crabs\n") 344s + data(crabs) 344s + show(rlda <- Linda(sp~., data=crabs, method=method)) 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "fish\n") 344s + data(fish) 344s + fish <- fish[-14,] # remove observation #14 containing missing value 344s + 344s + # The height and width are calculated as percentages 344s + # of the third length variable 344s + fish[,5] <- fish[,5]*fish[,4]/100 344s + fish[,6] <- fish[,6]*fish[,4]/100 344s + 344s + ## There is one class with only 6 observations (p=6). Normally 344s + ## Linda will fail, therefore use l1med=TRUE. 344s + ## This works only for methods mcdB and mcdC 344s + 344s + table(fish$Species) 344s + if(method != "mcdA") 344s + { 344s + ## IGNORE_RDIFF_BEGIN 344s + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) 344s + ## show(rlda) 344s + ## IGNORE_RDIFF_END 344s + show(predict(rlda)) 344s + } 344s + 344s + cat("\nData: ", "pottery\n") 344s + data(pottery) 344s + show(rlda <- Linda(origin~., data=pottery, method=method)) 344s + show(predict(rlda)) 344s + 344s + cat("\nData: ", "olitos\n") 344s + data(olitos) 344s + if(method != "mcdA") 344s + { 344s + ## IGNORE_RDIFF_BEGIN 344s + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) 344s + ## show(rlda) 344s + ## IGNORE_RDIFF_END 344s + show(predict(rlda)) 344s + } 344s + 344s + cat("===================================================\n") 344s + } 344s > 344s > 344s > ## -- now do it: 344s > dodata(method="mcdA") 344s 344s Call: dodata(method = "mcdA") 344s =================================================== 344s 344s Data: hemophilia 344s Call: 344s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 344s 344s Prior Probabilities of Groups: 344s carrier normal 344s 0.6 0.4 344s 344s Group means: 344s AHFactivity AHFantigen 344s carrier -0.30795 -0.0059911 344s normal -0.12920 -0.0603000 344s 344s Within-groups Covariance Matrix: 344s AHFactivity AHFantigen 344s AHFactivity 0.018036 0.011853 344s AHFantigen 0.011853 0.019185 344s 344s Linear Coeficients: 344s AHFactivity AHFantigen 344s carrier -28.4029 17.2368 344s normal -8.5834 2.1602 344s 344s Constants: 344s carrier normal 344s -4.8325 -1.4056 344s 344s Apparent error rate 0.1333 344s 344s Classification table 344s Predicted 344s Actual carrier normal 344s carrier 39 6 344s normal 4 26 344s 344s Confusion matrix 344s Predicted 344s Actual carrier normal 344s carrier 0.867 0.133 344s normal 0.133 0.867 344s 344s Data: anorexia 344s Call: 344s Linda(Treat ~ ., data = anorexia, method = method) 344s 344s Prior Probabilities of Groups: 344s CBT Cont FT 344s 0.40278 0.36111 0.23611 344s 344s Group means: 344s Prewt Postwt 344s CBT 82.633 82.950 344s Cont 81.558 81.108 344s FT 84.331 94.762 344s 344s Within-groups Covariance Matrix: 344s Prewt Postwt 344s Prewt 26.9291 3.3862 344s Postwt 3.3862 18.2368 344s 344s Linear Coeficients: 344s Prewt Postwt 344s CBT 2.5563 4.0738 344s Cont 2.5284 3.9780 344s FT 2.5374 4.7250 344s 344s Constants: 344s CBT Cont FT 344s -275.49 -265.45 -332.31 344s 344s Apparent error rate 0.3889 344s 344s Classification table 344s Predicted 344s Actual CBT Cont FT 344s CBT 16 5 8 344s Cont 11 15 0 344s FT 0 4 13 344s 344s Confusion matrix 344s Predicted 344s Actual CBT Cont FT 344s CBT 0.552 0.172 0.276 344s Cont 0.423 0.577 0.000 344s FT 0.000 0.235 0.765 344s 344s Data: Pima 344s Call: 344s Linda(type ~ ., data = Pima.tr, method = method) 344s 344s Prior Probabilities of Groups: 344s No Yes 344s 0.66 0.34 344s 344s Group means: 344s npreg glu bp skin bmi ped age 344s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 344s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 344s 344s Within-groups Covariance Matrix: 344s npreg glu bp skin bmi ped age 344s npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 344s glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 344s bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 344s skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 344s bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 344s ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 344s age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 344s 344s Linear Coeficients: 344s npreg glu bp skin bmi ped age 344s No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 344s Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 344s 344s Constants: 344s No Yes 344s -37.050 -51.586 344s 344s Apparent error rate 0.22 344s 344s Classification table 344s Predicted 344s Actual No Yes 344s No 107 25 344s Yes 19 49 344s 344s Confusion matrix 344s Predicted 344s Actual No Yes 344s No 0.811 0.189 344s Yes 0.279 0.721 344s 344s Data: Forest soils 344s 344s Apparent error rate 0.3103 344s 344s Classification table 344s Predicted 344s Actual 1 2 3 344s 1 7 2 2 344s 2 3 13 7 344s 3 1 3 20 344s 344s Confusion matrix 344s Predicted 344s Actual 1 2 3 344s 1 0.636 0.182 0.182 344s 2 0.130 0.565 0.304 344s 3 0.042 0.125 0.833 344s 344s Data: Raven and Miller diabetes data 345s Call: 345s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 345s 345s Prior Probabilities of Groups: 345s normal chemical overt 345s 0.52414 0.24828 0.22759 345s 345s Group means: 345s insulin glucose sspg 345s normal 163.939 345.8 99.076 345s chemical 299.448 476.9 223.621 345s overt 95.958 1026.4 343.000 345s 345s Within-groups Covariance Matrix: 345s insulin glucose sspg 345s insulin 7582.0 -1263.1 1095.8 345s glucose -1263.1 18952.4 4919.3 345s sspg 1095.8 4919.3 3351.2 345s 345s Linear Coeficients: 345s insulin glucose sspg 345s normal 0.027694 0.023859 -0.014514 345s chemical 0.040288 0.022532 0.020479 345s overt 0.017144 0.048768 0.025158 345s 345s Constants: 345s normal chemical overt 345s -6.3223 -15.0879 -31.6445 345s 345s Apparent error rate 0.1862 345s 345s Classification table 345s Predicted 345s Actual normal chemical overt 345s normal 69 7 0 345s chemical 13 23 0 345s overt 2 5 26 345s 345s Confusion matrix 345s Predicted 345s Actual normal chemical overt 345s normal 0.908 0.092 0.000 345s chemical 0.361 0.639 0.000 345s overt 0.061 0.152 0.788 345s 345s Data: iris 345s 345s Data: crabs 345s Call: 345s Linda(sp ~ ., data = crabs, method = method) 345s 345s Prior Probabilities of Groups: 345s B O 345s 0.5 0.5 345s 345s Group means: 345s sexM index FL RW CL CW BD 345s B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 345s O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 345s 345s Within-groups Covariance Matrix: 345s sexM index FL RW CL CW BD 345s sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 345s index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 345s FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 345s RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 345s CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 345s CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 345s BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 345s 345s Linear Coeficients: 345s sexM index FL RW CL CW BD 345s B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 345s O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 345s 345s Constants: 345s B O 345s -78.317 -159.259 345s 345s Apparent error rate 0 345s 345s Classification table 345s Predicted 345s Actual B O 345s B 100 0 345s O 0 100 345s 345s Confusion matrix 345s Predicted 345s Actual B O 345s B 1 0 345s O 0 1 345s 345s Data: fish 345s 345s Data: pottery 345s Call: 345s Linda(origin ~ ., data = pottery, method = method) 345s 345s Prior Probabilities of Groups: 345s Attic Eritrean 345s 0.48148 0.51852 345s 345s Group means: 345s SI AL FE MG CA TI 345s Attic 55.36 13.73 9.82 5.45 6.03 0.863 345s Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 345s 345s Within-groups Covariance Matrix: 345s SI AL FE MG CA TI 345s SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 345s AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 345s FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 345s MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 345s CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 345s TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 345s 345s Linear Coeficients: 345s SI AL FE MG CA TI 345s Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 345s Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 345s 345s Constants: 345s Attic Eritrean 345s -1578.14 -901.13 345s 345s Apparent error rate 0.1111 345s 345s Classification table 345s Predicted 345s Actual Attic Eritrean 345s Attic 12 1 345s Eritrean 2 12 345s 345s Confusion matrix 345s Predicted 345s Actual Attic Eritrean 345s Attic 0.923 0.077 345s Eritrean 0.143 0.857 345s 345s Data: olitos 345s =================================================== 345s > dodata(method="mcdB") 345s 345s Call: dodata(method = "mcdB") 345s =================================================== 345s 345s Data: hemophilia 345s Call: 345s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 345s 345s Prior Probabilities of Groups: 345s carrier normal 345s 0.6 0.4 345s 345s Group means: 345s AHFactivity AHFantigen 345s carrier -0.31456 -0.014775 345s normal -0.13582 -0.069084 345s 345s Within-groups Covariance Matrix: 345s AHFactivity AHFantigen 345s AHFactivity 0.0125319 0.0086509 345s AHFantigen 0.0086509 0.0182424 345s 345s Linear Coeficients: 345s AHFactivity AHFantigen 345s carrier -36.486 16.4923 345s normal -12.226 2.0107 345s 345s Constants: 345s carrier normal 345s -6.1276 -1.6771 345s 345s Apparent error rate 0.16 345s 345s Classification table 345s Predicted 345s Actual carrier normal 345s carrier 38 7 345s normal 5 25 345s 345s Confusion matrix 345s Predicted 345s Actual carrier normal 345s carrier 0.844 0.156 345s normal 0.167 0.833 345s 345s Data: anorexia 345s Call: 345s Linda(Treat ~ ., data = anorexia, method = method) 345s 345s Prior Probabilities of Groups: 345s CBT Cont FT 345s 0.40278 0.36111 0.23611 345s 345s Group means: 345s Prewt Postwt 345s CBT 83.254 82.381 345s Cont 82.178 80.539 345s FT 84.951 94.193 345s 345s Within-groups Covariance Matrix: 345s Prewt Postwt 345s Prewt 19.1751 8.8546 345s Postwt 8.8546 25.2326 345s 345s Linear Coeficients: 345s Prewt Postwt 345s CBT 3.3822 2.0780 345s Cont 3.3555 2.0144 345s FT 3.2299 2.5996 345s 345s Constants: 345s CBT Cont FT 345s -227.29 -220.01 -261.06 345s 345s Apparent error rate 0.4444 345s 345s Classification table 345s Predicted 345s Actual CBT Cont FT 345s CBT 16 5 8 345s Cont 12 11 3 345s FT 0 4 13 345s 345s Confusion matrix 345s Predicted 345s Actual CBT Cont FT 345s CBT 0.552 0.172 0.276 345s Cont 0.462 0.423 0.115 345s FT 0.000 0.235 0.765 345s 345s Data: Pima 345s Call: 345s Linda(type ~ ., data = Pima.tr, method = method) 345s 345s Prior Probabilities of Groups: 345s No Yes 345s 0.66 0.34 345s 345s Group means: 345s npreg glu bp skin bmi ped age 345s No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 345s Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 345s 345s Within-groups Covariance Matrix: 345s npreg glu bp skin bmi ped age 345s npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 345s glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 345s bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 345s skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 345s bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 345s ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 345s age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 345s 345s Linear Coeficients: 345s npreg glu bp skin bmi ped age 345s No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 345s Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 345s 345s Constants: 345s No Yes 345s -38.774 -53.654 345s 345s Apparent error rate 0.25 345s 345s Classification table 345s Predicted 345s Actual No Yes 345s No 104 28 345s Yes 22 46 345s 345s Confusion matrix 345s Predicted 345s Actual No Yes 345s No 0.788 0.212 345s Yes 0.324 0.676 345s 345s Data: Forest soils 345s 345s Apparent error rate 0.3448 345s 345s Classification table 345s Predicted 345s Actual 1 2 3 345s 1 4 3 4 345s 2 2 14 7 345s 3 2 2 20 345s 345s Confusion matrix 345s Predicted 345s Actual 1 2 3 345s 1 0.364 0.273 0.364 345s 2 0.087 0.609 0.304 345s 3 0.083 0.083 0.833 345s 345s Data: Raven and Miller diabetes data 345s Call: 345s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 345s 345s Prior Probabilities of Groups: 345s normal chemical overt 345s 0.52414 0.24828 0.22759 345s 345s Group means: 345s insulin glucose sspg 345s normal 152.405 346.55 99.387 345s chemical 288.244 478.80 226.226 345s overt 84.754 1028.28 345.605 345s 345s Within-groups Covariance Matrix: 345s insulin glucose sspg 345s insulin 5061.46 289.69 2071.71 345s glucose 289.69 1983.07 385.31 345s sspg 2071.71 385.31 3000.17 345s 345s Linear Coeficients: 345s insulin glucose sspg 345s normal 0.021952 0.17236 -0.0041671 345s chemical 0.034852 0.23217 0.0215200 345s overt -0.045700 0.50940 0.0813292 345s 345s Constants: 345s normal chemical overt 345s -31.976 -64.433 -275.502 345s 345s Apparent error rate 0.0966 345s 345s Classification table 345s Predicted 345s Actual normal chemical overt 345s normal 73 3 0 345s chemical 4 32 0 345s overt 0 7 26 345s 345s Confusion matrix 345s Predicted 345s Actual normal chemical overt 345s normal 0.961 0.039 0.000 345s chemical 0.111 0.889 0.000 345s overt 0.000 0.212 0.788 345s 345s Data: iris 345s Call: 345s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 345s 345s Prior Probabilities of Groups: 345s setosa versicolor virginica 345s 0.33333 0.33333 0.33333 345s 345s Group means: 345s Sepal.Length Sepal.Width Petal.Length Petal.Width 345s setosa 4.9834 3.4153 1.4532 0.22474 345s versicolor 5.8947 2.8149 4.2263 1.35024 345s virginica 6.5255 3.0017 5.4485 2.06756 345s 345s Within-groups Covariance Matrix: 345s Sepal.Length Sepal.Width Petal.Length Petal.Width 345s Sepal.Length 0.201176 0.084299 0.102984 0.037019 345s Sepal.Width 0.084299 0.108394 0.050253 0.031757 345s Petal.Length 0.102984 0.050253 0.120215 0.045016 345s Petal.Width 0.037019 0.031757 0.045016 0.032825 345s 345s Linear Coeficients: 345s Sepal.Length Sepal.Width Petal.Length Petal.Width 345s setosa 22.536 27.422168 -3.6855 -40.0445 345s versicolor 17.559 6.374082 24.1965 -18.0178 345s virginica 16.488 0.015576 29.9586 3.2926 345s 345s Constants: 345s setosa versicolor virginica 345s -96.901 -100.790 -139.937 345s 345s Apparent error rate 0.0267 345s 345s Classification table 345s Predicted 345s Actual setosa versicolor virginica 345s setosa 50 0 0 345s versicolor 0 48 2 345s virginica 0 2 48 345s 345s Confusion matrix 345s Predicted 345s Actual setosa versicolor virginica 345s setosa 1 0.00 0.00 345s versicolor 0 0.96 0.04 345s virginica 0 0.04 0.96 345s 345s Data: crabs 345s Call: 345s Linda(sp ~ ., data = crabs, method = method) 345s 345s Prior Probabilities of Groups: 345s B O 345s 0.5 0.5 345s 345s Group means: 345s sexM index FL RW CL CW BD 345s B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 345s O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 345s 345s Within-groups Covariance Matrix: 345s sexM index FL RW CL CW BD 345s sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 345s index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 345s FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 345s RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 345s CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 345s CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 345s BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 345s 345s Linear Coeficients: 345s sexM index FL RW CL CW BD 345s B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 345s O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 345s 345s Constants: 345s B O 345s -66.688 -134.375 345s 345s Apparent error rate 0 345s 345s Classification table 345s Predicted 345s Actual B O 345s B 100 0 345s O 0 100 345s 345s Confusion matrix 345s Predicted 345s Actual B O 345s B 1 0 345s O 0 1 345s 345s Data: fish 345s 345s Apparent error rate 0.0949 345s 345s Classification table 345s Predicted 345s Actual 1 2 3 4 5 6 7 345s 1 34 0 0 0 0 0 0 345s 2 0 6 0 0 0 0 0 345s 3 0 0 20 0 0 0 0 345s 4 0 0 0 11 0 0 0 345s 5 0 0 0 0 13 0 1 345s 6 0 0 0 0 0 17 0 345s 7 0 13 0 0 1 0 42 345s 345s Confusion matrix 345s Predicted 345s Actual 1 2 3 4 5 6 7 345s 1 1 0.000 0 0 0.000 0 0.000 345s 2 0 1.000 0 0 0.000 0 0.000 345s 3 0 0.000 1 0 0.000 0 0.000 345s 4 0 0.000 0 1 0.000 0 0.000 345s 5 0 0.000 0 0 0.929 0 0.071 345s 6 0 0.000 0 0 0.000 1 0.000 345s 7 0 0.232 0 0 0.018 0 0.750 345s 345s Data: pottery 345s Call: 345s Linda(origin ~ ., data = pottery, method = method) 345s 345s Prior Probabilities of Groups: 345s Attic Eritrean 345s 0.48148 0.51852 345s 345s Group means: 345s SI AL FE MG CA TI 345s Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 345s Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 345s 345s Within-groups Covariance Matrix: 345s SI AL FE MG CA TI 345s SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 345s AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 345s FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 345s MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 345s CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 345s TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 345s 345s Linear Coeficients: 345s SI AL FE MG CA TI 345s Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 345s Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 345s 345s Constants: 345s Attic Eritrean 345s -3252.6 -1961.9 345s 345s Apparent error rate 0.1111 345s 345s Classification table 345s Predicted 345s Actual Attic Eritrean 345s Attic 12 1 345s Eritrean 2 12 345s 345s Confusion matrix 345s Predicted 345s Actual Attic Eritrean 345s Attic 0.923 0.077 345s Eritrean 0.143 0.857 345s 345s Data: olitos 345s 345s Apparent error rate 0.15 345s 345s Classification table 345s Predicted 345s Actual 1 2 3 4 345s 1 44 1 4 1 345s 2 2 23 0 0 345s 3 6 1 26 1 345s 4 1 1 0 9 345s 345s Confusion matrix 345s Predicted 345s Actual 1 2 3 4 345s 1 0.880 0.020 0.080 0.020 345s 2 0.080 0.920 0.000 0.000 345s 3 0.176 0.029 0.765 0.029 345s 4 0.091 0.091 0.000 0.818 345s =================================================== 345s > dodata(method="mcdC") 345s 345s Call: dodata(method = "mcdC") 345s =================================================== 345s 345s Data: hemophilia 345s Call: 345s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 345s 345s Prior Probabilities of Groups: 345s carrier normal 345s 0.6 0.4 345s 345s Group means: 345s AHFactivity AHFantigen 345s carrier -0.32583 -0.011545 345s normal -0.12783 -0.071377 345s 345s Within-groups Covariance Matrix: 345s AHFactivity AHFantigen 345s AHFactivity 0.0120964 0.0075536 345s AHFantigen 0.0075536 0.0164883 345s 345s Linear Coeficients: 345s AHFactivity AHFantigen 345s carrier -37.117 16.30377 345s normal -11.015 0.71742 345s 345s Constants: 345s carrier normal 345s -6.4636 -1.5947 345s 345s Apparent error rate 0.16 345s 345s Classification table 345s Predicted 345s Actual carrier normal 345s carrier 38 7 345s normal 5 25 345s 345s Confusion matrix 345s Predicted 345s Actual carrier normal 345s carrier 0.844 0.156 345s normal 0.167 0.833 345s 345s Data: anorexia 345s Call: 345s Linda(Treat ~ ., data = anorexia, method = method) 345s 345s Prior Probabilities of Groups: 345s CBT Cont FT 345s 0.40278 0.36111 0.23611 345s 345s Group means: 345s Prewt Postwt 345s CBT 82.477 82.073 345s Cont 82.039 80.835 345s FT 85.242 94.750 345s 345s Within-groups Covariance Matrix: 345s Prewt Postwt 345s Prewt 19.6589 8.3891 345s Postwt 8.3891 22.8805 345s 345s Linear Coeficients: 345s Prewt Postwt 345s CBT 3.1590 2.4288 345s Cont 3.1599 2.3743 345s FT 3.0454 3.0245 345s 345s Constants: 345s CBT Cont FT 345s -230.85 -226.60 -274.53 345s 345s Apparent error rate 0.4583 345s 345s Classification table 345s Predicted 345s Actual CBT Cont FT 345s CBT 16 5 8 345s Cont 14 10 2 345s FT 0 4 13 345s 345s Confusion matrix 345s Predicted 345s Actual CBT Cont FT 345s CBT 0.552 0.172 0.276 345s Cont 0.538 0.385 0.077 345s FT 0.000 0.235 0.765 345s 345s Data: Pima 345s Call: 345s Linda(type ~ ., data = Pima.tr, method = method) 345s 345s Prior Probabilities of Groups: 345s No Yes 345s 0.66 0.34 345s 345s Group means: 345s npreg glu bp skin bmi ped age 345s No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 345s Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 345s 345s Within-groups Covariance Matrix: 345s npreg glu bp skin bmi ped age 345s npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 345s glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 345s bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 345s skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 345s bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 345s ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 345s age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 345s 345s Linear Coeficients: 345s npreg glu bp skin bmi ped age 345s No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 345s Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 345s 345s Constants: 345s No Yes 345s -41.716 -55.056 345s 345s Apparent error rate 0.235 345s 345s Classification table 345s Predicted 345s Actual No Yes 345s No 107 25 345s Yes 22 46 345s 345s Confusion matrix 345s Predicted 345s Actual No Yes 345s No 0.811 0.189 345s Yes 0.324 0.676 345s 345s Data: Forest soils 345s 345s Apparent error rate 0.3276 345s 345s Classification table 345s Predicted 345s Actual 1 2 3 345s 1 5 2 4 345s 2 2 13 8 345s 3 1 2 21 345s 345s Confusion matrix 345s Predicted 345s Actual 1 2 3 345s 1 0.455 0.182 0.364 345s 2 0.087 0.565 0.348 345s 3 0.042 0.083 0.875 345s 345s Data: Raven and Miller diabetes data 346s Call: 346s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 346s 346s Prior Probabilities of Groups: 346s normal chemical overt 346s 0.52414 0.24828 0.22759 346s 346s Group means: 346s insulin glucose sspg 346s normal 167.31 348.69 106.44 346s chemical 247.18 478.18 213.36 346s overt 101.83 932.92 322.42 346s 346s Within-groups Covariance Matrix: 346s insulin glucose sspg 346s insulin 4070.84 118.89 1701.54 346s glucose 118.89 2195.95 426.95 346s sspg 1701.54 426.95 2664.49 346s 346s Linear Coeficients: 346s insulin glucose sspg 346s normal 0.041471 0.15888 -0.011992 346s chemical 0.048103 0.21216 0.015359 346s overt -0.013579 0.41323 0.063462 346s 346s Constants: 346s normal chemical overt 346s -31.177 -59.703 -203.775 346s 346s Apparent error rate 0.0828 346s 346s Classification table 346s Predicted 346s Actual normal chemical overt 346s normal 72 4 0 346s chemical 2 34 0 346s overt 0 6 27 346s 346s Confusion matrix 346s Predicted 346s Actual normal chemical overt 346s normal 0.947 0.053 0.000 346s chemical 0.056 0.944 0.000 346s overt 0.000 0.182 0.818 346s 346s Data: iris 346s Call: 346s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 346s 346s Prior Probabilities of Groups: 346s setosa versicolor virginica 346s 0.33333 0.33333 0.33333 346s 346s Group means: 346s Sepal.Length Sepal.Width Petal.Length Petal.Width 346s setosa 5.0163 3.4510 1.4653 0.2449 346s versicolor 5.9435 2.7891 4.2543 1.3239 346s virginica 6.3867 3.0033 5.3767 2.0700 346s 346s Within-groups Covariance Matrix: 346s Sepal.Length Sepal.Width Petal.Length Petal.Width 346s Sepal.Length 0.186186 0.082478 0.094998 0.035445 346s Sepal.Width 0.082478 0.100137 0.049723 0.030678 346s Petal.Length 0.094998 0.049723 0.113105 0.043078 346s Petal.Width 0.035445 0.030678 0.043078 0.030885 346s 346s Linear Coeficients: 346s Sepal.Length Sepal.Width Petal.Length Petal.Width 346s setosa 23.678 30.2896 -3.1124 -44.9900 346s versicolor 20.342 4.6372 27.3265 -23.2006 346s virginica 18.377 -2.0004 31.4235 4.0906 346s 346s Constants: 346s setosa versicolor virginica 346s -104.96 -110.79 -145.49 346s 346s Apparent error rate 0.0333 346s 346s Classification table 346s Predicted 346s Actual setosa versicolor virginica 346s setosa 50 0 0 346s versicolor 0 48 2 346s virginica 0 3 47 346s 346s Confusion matrix 346s Predicted 346s Actual setosa versicolor virginica 346s setosa 1 0.00 0.00 346s versicolor 0 0.96 0.04 346s virginica 0 0.06 0.94 346s 346s Data: crabs 346s Call: 346s Linda(sp ~ ., data = crabs, method = method) 346s 346s Prior Probabilities of Groups: 346s B O 346s 0.5 0.5 346s 346s Group means: 346s sexM index FL RW CL CW BD 346s B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 346s O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 346s 346s Within-groups Covariance Matrix: 346s sexM index FL RW CL CW BD 346s sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 346s index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 346s FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 346s RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 346s CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 346s CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 346s BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 346s 346s Linear Coeficients: 346s sexM index FL RW CL CW BD 346s B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 346s O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 346s 346s Constants: 346s B O 346s -72.687 -142.365 346s 346s Apparent error rate 0 346s 346s Classification table 346s Predicted 346s Actual B O 346s B 100 0 346s O 0 100 346s 346s Confusion matrix 346s Predicted 346s Actual B O 346s B 1 0 346s O 0 1 346s 346s Data: fish 346s 346s Apparent error rate 0.0316 346s 346s Classification table 346s Predicted 346s Actual 1 2 3 4 5 6 7 346s 1 34 0 0 0 0 0 0 346s 2 0 5 0 0 1 0 0 346s 3 0 0 20 0 0 0 0 346s 4 0 0 0 11 0 0 0 346s 5 0 0 0 0 13 0 1 346s 6 0 0 0 0 0 17 0 346s 7 0 0 0 0 3 0 53 346s 346s Confusion matrix 346s Predicted 346s Actual 1 2 3 4 5 6 7 346s 1 1 0.000 0 0 0.000 0 0.000 346s 2 0 0.833 0 0 0.167 0 0.000 346s 3 0 0.000 1 0 0.000 0 0.000 346s 4 0 0.000 0 1 0.000 0 0.000 346s 5 0 0.000 0 0 0.929 0 0.071 346s 6 0 0.000 0 0 0.000 1 0.000 346s 7 0 0.000 0 0 0.054 0 0.946 346s 346s Data: pottery 346s Call: 346s Linda(origin ~ ., data = pottery, method = method) 346s 346s Prior Probabilities of Groups: 346s Attic Eritrean 346s 0.48148 0.51852 346s 346s Group means: 346s SI AL FE MG CA TI 346s Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 346s Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 346s 346s Within-groups Covariance Matrix: 346s SI AL FE MG CA TI 346s SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 346s AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 346s FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 346s MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 346s CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 346s TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 346s 346s Linear Coeficients: 346s SI AL FE MG CA TI 346s Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 346s Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 346s 346s Constants: 346s Attic Eritrean 346s -5924.2 -3802.9 346s 346s Apparent error rate 0.1111 346s 346s Classification table 346s Predicted 346s Actual Attic Eritrean 346s Attic 12 1 346s Eritrean 2 12 346s 346s Confusion matrix 346s Predicted 346s Actual Attic Eritrean 346s Attic 0.923 0.077 346s Eritrean 0.143 0.857 346s 346s Data: olitos 346s 346s Apparent error rate 0.1667 346s 346s Classification table 346s Predicted 346s Actual 1 2 3 4 346s 1 44 1 2 3 346s 2 2 22 0 1 346s 3 5 2 25 2 346s 4 1 1 0 9 346s 346s Confusion matrix 346s Predicted 346s Actual 1 2 3 4 346s 1 0.880 0.020 0.040 0.060 346s 2 0.080 0.880 0.000 0.040 346s 3 0.147 0.059 0.735 0.059 346s 4 0.091 0.091 0.000 0.818 346s =================================================== 346s > dodata(method="mrcd") 346s 346s Call: dodata(method = "mrcd") 346s =================================================== 346s 346s Data: hemophilia 346s Call: 346s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 346s 346s Prior Probabilities of Groups: 346s carrier normal 346s 0.6 0.4 346s 346s Group means: 346s AHFactivity AHFantigen 346s carrier -0.34048 -0.055943 346s normal -0.13566 -0.081191 346s 346s Within-groups Covariance Matrix: 346s AHFactivity AHFantigen 346s AHFactivity 0.0133676 0.0088055 346s AHFantigen 0.0088055 0.0221225 346s 346s Linear Coeficients: 346s AHFactivity AHFantigen 346s carrier -32.264 10.31334 346s normal -10.478 0.50044 346s 346s Constants: 346s carrier normal 346s -5.7149 -1.6067 346s 346s Apparent error rate 0.16 346s 346s Classification table 346s Predicted 346s Actual carrier normal 346s carrier 38 7 346s normal 5 25 346s 346s Confusion matrix 346s Predicted 346s Actual carrier normal 346s carrier 0.844 0.156 346s normal 0.167 0.833 346s 346s Data: anorexia 346s Call: 346s Linda(Treat ~ ., data = anorexia, method = method) 346s 346s Prior Probabilities of Groups: 346s CBT Cont FT 346s 0.40278 0.36111 0.23611 346s 346s Group means: 346s Prewt Postwt 346s CBT 83.114 84.009 346s Cont 80.327 80.125 346s FT 85.161 94.371 346s 346s Within-groups Covariance Matrix: 346s Prewt Postwt 346s Prewt 22.498 11.860 346s Postwt 11.860 20.426 346s 346s Linear Coeficients: 346s Prewt Postwt 346s CBT 2.1994 2.8357 346s Cont 2.1653 2.6654 346s FT 1.9451 3.4907 346s 346s Constants: 346s CBT Cont FT 346s -211.42 -194.77 -248.97 346s 346s Apparent error rate 0.3889 346s 346s Classification table 346s Predicted 346s Actual CBT Cont FT 346s CBT 15 6 8 346s Cont 6 16 4 346s FT 0 4 13 346s 346s Confusion matrix 346s Predicted 346s Actual CBT Cont FT 346s CBT 0.517 0.207 0.276 346s Cont 0.231 0.615 0.154 346s FT 0.000 0.235 0.765 346s 346s Data: Pima 346s Call: 346s Linda(type ~ ., data = Pima.tr, method = method) 346s 346s Prior Probabilities of Groups: 346s No Yes 346s 0.66 0.34 346s 346s Group means: 346s npreg glu bp skin bmi ped age 346s No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 346s Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 346s 346s Within-groups Covariance Matrix: 346s npreg glu bp skin bmi ped age 346s npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 346s glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 346s bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 346s skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 346s bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 346s ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 346s age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 346s 346s Linear Coeficients: 346s npreg glu bp skin bmi ped age 346s No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 346s Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 346s 346s Constants: 346s No Yes 346s -33.429 -45.434 346s 346s Apparent error rate 0.28 346s 346s Classification table 346s Predicted 346s Actual No Yes 346s No 105 27 346s Yes 29 39 346s 346s Confusion matrix 346s Predicted 346s Actual No Yes 346s No 0.795 0.205 346s Yes 0.426 0.574 346s 346s Data: Forest soils 346s 346s Apparent error rate 0.3448 346s 346s Classification table 346s Predicted 346s Actual 1 2 3 346s 1 7 2 2 346s 2 4 14 5 346s 3 3 4 17 346s 346s Confusion matrix 346s Predicted 346s Actual 1 2 3 346s 1 0.636 0.182 0.182 346s 2 0.174 0.609 0.217 346s 3 0.125 0.167 0.708 346s 346s Data: Raven and Miller diabetes data 346s Call: 346s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 346s 346s Prior Probabilities of Groups: 346s normal chemical overt 346s 0.52414 0.24828 0.22759 346s 346s Group means: 346s insulin glucose sspg 346s normal 154.014 346.07 91.606 346s chemical 248.841 451.10 221.936 346s overt 89.766 1064.16 335.100 346s 346s Within-groups Covariance Matrix: 346s insulin glucose sspg 346s insulin 4948.1 1007.61 1471.12 346s glucose 1007.6 2597.38 358.57 346s sspg 1471.1 358.57 3180.04 346s 346s Linear Coeficients: 346s insulin glucose sspg 346s normal 0.00027839 0.13121 0.013882 346s chemical 0.00148074 0.16615 0.050371 346s overt -0.10102404 0.43466 0.103100 346s 346s Constants: 346s normal chemical overt 346s -24.008 -44.642 -245.497 346s 346s Apparent error rate 0.0966 346s 346s Classification table 346s Predicted 346s Actual normal chemical overt 346s normal 71 5 0 346s chemical 2 34 0 346s overt 0 7 26 346s 346s Confusion matrix 346s Predicted 346s Actual normal chemical overt 346s normal 0.934 0.066 0.000 346s chemical 0.056 0.944 0.000 346s overt 0.000 0.212 0.788 346s 346s Data: iris 346s Call: 346s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 346s 346s Prior Probabilities of Groups: 346s setosa versicolor virginica 346s 0.33333 0.33333 0.33333 346s 346s Group means: 346s Sepal.Length Sepal.Width Petal.Length Petal.Width 346s setosa 4.9755 3.3826 1.4608 0.22053 346s versicolor 5.8868 2.7823 4.2339 1.34603 346s virginica 6.5176 2.9691 5.4560 2.06335 346s 346s Within-groups Covariance Matrix: 346s Sepal.Length Sepal.Width Petal.Length Petal.Width 346s Sepal.Length 0.238417 0.136325 0.086377 0.036955 346s Sepal.Width 0.136325 0.148452 0.067500 0.034804 346s Petal.Length 0.086377 0.067500 0.100934 0.035968 346s Petal.Width 0.036955 0.034804 0.035968 0.023856 346s 346s Linear Coeficients: 346s Sepal.Length Sepal.Width Petal.Length Petal.Width 346s setosa 17.106 15.693 7.8806 -52.031 346s versicolor 21.744 -15.813 38.0139 -11.505 346s virginica 23.032 -26.567 43.6391 23.777 346s 346s Constants: 346s setosa versicolor virginica 346s -70.214 -115.832 -180.294 346s 346s Apparent error rate 0.02 346s 346s Classification table 346s Predicted 346s Actual setosa versicolor virginica 346s setosa 50 0 0 346s versicolor 0 49 1 346s virginica 0 2 48 346s 346s Confusion matrix 346s Predicted 346s Actual setosa versicolor virginica 346s setosa 1 0.00 0.00 346s versicolor 0 0.98 0.02 346s virginica 0 0.04 0.96 346s 346s Data: crabs 346s Call: 346s Linda(sp ~ ., data = crabs, method = method) 346s 346s Prior Probabilities of Groups: 346s B O 346s 0.5 0.5 346s 346s Group means: 346s sexM index FL RW CL CW BD 346s B 0 25.5 13.270 12.138 28.102 32.624 11.816 346s O 1 25.5 16.626 12.262 33.688 37.188 15.324 346s 346s Within-groups Covariance Matrix: 346s sexM index FL RW CL CW BD 346s sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 346s index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 346s FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 346s RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 346s CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 346s CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 346s BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 346s 346s Linear Coeficients: 346s sexM index FL RW CL CW BD 346s B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 346s O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 346s 346s Constants: 346s B O 346s -2.1491e+01 -3.2776e+06 346s 346s Apparent error rate 0.5 346s 346s Classification table 346s Predicted 346s Actual B O 346s B 50 50 346s O 50 50 346s 346s Confusion matrix 346s Predicted 346s Actual B O 346s B 0.5 0.5 346s O 0.5 0.5 346s 346s Data: fish 347s 347s Apparent error rate 0.2532 347s 347s Classification table 347s Predicted 347s Actual 1 2 3 4 5 6 7 347s 1 33 0 0 1 0 0 0 347s 2 0 3 0 0 0 0 3 347s 3 0 2 5 0 0 0 13 347s 4 0 0 0 11 0 0 0 347s 5 0 0 0 0 14 0 0 347s 6 0 0 0 0 0 17 0 347s 7 0 19 0 0 2 0 35 347s 347s Confusion matrix 347s Predicted 347s Actual 1 2 3 4 5 6 7 347s 1 0.971 0.000 0.00 0.029 0.000 0 0.000 347s 2 0.000 0.500 0.00 0.000 0.000 0 0.500 347s 3 0.000 0.100 0.25 0.000 0.000 0 0.650 347s 4 0.000 0.000 0.00 1.000 0.000 0 0.000 347s 5 0.000 0.000 0.00 0.000 1.000 0 0.000 347s 6 0.000 0.000 0.00 0.000 0.000 1 0.000 347s 7 0.000 0.339 0.00 0.000 0.036 0 0.625 347s 347s Data: pottery 347s Call: 347s Linda(origin ~ ., data = pottery, method = method) 347s 347s Prior Probabilities of Groups: 347s Attic Eritrean 347s 0.48148 0.51852 347s 347s Group means: 347s SI AL FE MG CA TI 347s Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 347s Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 347s 347s Within-groups Covariance Matrix: 347s SI AL FE MG CA TI 347s SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 347s AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 347s FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 347s MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 347s CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 347s TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 347s 347s Linear Coeficients: 347s SI AL FE MG CA TI 347s Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 347s Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 347s 347s Constants: 347s Attic Eritrean 347s -4000.1 -3776.1 347s 347s Apparent error rate 0.1111 347s 347s Classification table 347s Predicted 347s Actual Attic Eritrean 347s Attic 12 1 347s Eritrean 2 12 347s 347s Confusion matrix 347s Predicted 347s Actual Attic Eritrean 347s Attic 0.923 0.077 347s Eritrean 0.143 0.857 347s 347s Data: olitos 347s 347s Apparent error rate 0.125 347s 347s Classification table 347s Predicted 347s Actual 1 2 3 4 347s 1 44 2 3 1 347s 2 1 23 1 0 347s 3 4 1 27 2 347s 4 0 0 0 11 347s 347s Confusion matrix 347s Predicted 347s Actual 1 2 3 4 347s 1 0.880 0.040 0.060 0.020 347s 2 0.040 0.920 0.040 0.000 347s 3 0.118 0.029 0.794 0.059 347s 4 0.000 0.000 0.000 1.000 347s =================================================== 347s > dodata(method="ogk") 347s 347s Call: dodata(method = "ogk") 347s =================================================== 347s 347s Data: hemophilia 347s Call: 347s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 347s 347s Prior Probabilities of Groups: 347s carrier normal 347s 0.6 0.4 347s 347s Group means: 347s AHFactivity AHFantigen 347s carrier -0.29043 -0.00052902 347s normal -0.12463 -0.06715037 347s 347s Within-groups Covariance Matrix: 347s AHFactivity AHFantigen 347s AHFactivity 0.015688 0.010544 347s AHFantigen 0.010544 0.016633 347s 347s Linear Coeficients: 347s AHFactivity AHFantigen 347s carrier -32.2203 20.3935 347s normal -9.1149 1.7409 347s 347s Constants: 347s carrier normal 347s -5.1843 -1.4259 347s 347s Apparent error rate 0.1467 347s 347s Classification table 347s Predicted 347s Actual carrier normal 347s carrier 38 7 347s normal 4 26 347s 347s Confusion matrix 347s Predicted 347s Actual carrier normal 347s carrier 0.844 0.156 347s normal 0.133 0.867 347s 347s Data: anorexia 347s Call: 347s Linda(Treat ~ ., data = anorexia, method = method) 347s 347s Prior Probabilities of Groups: 347s CBT Cont FT 347s 0.40278 0.36111 0.23611 347s 347s Group means: 347s Prewt Postwt 347s CBT 82.634 82.060 347s Cont 81.605 80.459 347s FT 85.157 93.822 347s 347s Within-groups Covariance Matrix: 347s Prewt Postwt 347s Prewt 15.8294 4.4663 347s Postwt 4.4663 19.6356 347s 347s Linear Coeficients: 347s Prewt Postwt 347s CBT 4.3183 3.1970 347s Cont 4.2734 3.1256 347s FT 4.3080 3.7983 347s 347s Constants: 347s CBT Cont FT 347s -310.50 -301.12 -363.05 347s 347s Apparent error rate 0.4583 347s 347s Classification table 347s Predicted 347s Actual CBT Cont FT 347s CBT 15 5 9 347s Cont 14 11 1 347s FT 0 4 13 347s 347s Confusion matrix 347s Predicted 347s Actual CBT Cont FT 347s CBT 0.517 0.172 0.310 347s Cont 0.538 0.423 0.038 347s FT 0.000 0.235 0.765 347s 347s Data: Pima 347s Call: 347s Linda(type ~ ., data = Pima.tr, method = method) 347s 347s Prior Probabilities of Groups: 347s No Yes 347s 0.66 0.34 347s 347s Group means: 347s npreg glu bp skin bmi ped age 347s No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 347s Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 347s 347s Within-groups Covariance Matrix: 347s npreg glu bp skin bmi ped age 347s npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 347s glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 347s bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 347s skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 347s bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 347s ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 347s age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 347s 347s Linear Coeficients: 347s npreg glu bp skin bmi ped age 347s No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 347s Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 347s 347s Constants: 347s No Yes 347s -43.449 -60.176 347s 347s Apparent error rate 0.23 347s 347s Classification table 347s Predicted 347s Actual No Yes 347s No 108 24 347s Yes 22 46 347s 347s Confusion matrix 347s Predicted 347s Actual No Yes 347s No 0.818 0.182 347s Yes 0.324 0.676 347s 347s Data: Forest soils 347s 347s Apparent error rate 0.3621 347s 347s Classification table 347s Predicted 347s Actual 1 2 3 347s 1 7 3 1 347s 2 4 13 6 347s 3 3 4 17 347s 347s Confusion matrix 347s Predicted 347s Actual 1 2 3 347s 1 0.636 0.273 0.091 347s 2 0.174 0.565 0.261 347s 3 0.125 0.167 0.708 347s 347s Data: Raven and Miller diabetes data 347s Call: 347s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 347s 347s Prior Probabilities of Groups: 347s normal chemical overt 347s 0.52414 0.24828 0.22759 347s 347s Group means: 347s insulin glucose sspg 347s normal 159.540 344.06 99.486 347s chemical 252.992 478.16 219.442 347s overt 79.635 1094.96 338.517 347s 347s Within-groups Covariance Matrix: 347s insulin glucose sspg 347s insulin 3844.877 67.238 1456.55 347s glucose 67.238 2228.396 324.21 347s sspg 1456.548 324.205 2181.73 347s 347s Linear Coeficients: 347s insulin glucose sspg 347s normal 0.040407 0.15379 -0.0042303 347s chemical 0.047858 0.20764 0.0377766 347s overt -0.026158 0.47736 0.1016873 347s 347s Constants: 347s normal chemical overt 347s -30.115 -61.233 -278.996 347s 347s Apparent error rate 0.0966 347s 347s Classification table 347s Predicted 347s Actual normal chemical overt 347s normal 71 5 0 347s chemical 2 34 0 347s overt 0 7 26 347s 347s Confusion matrix 347s Predicted 347s Actual normal chemical overt 347s normal 0.934 0.066 0.000 347s chemical 0.056 0.944 0.000 347s overt 0.000 0.212 0.788 347s 347s Data: iris 347s Call: 347s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 347s 347s Prior Probabilities of Groups: 347s setosa versicolor virginica 347s 0.33333 0.33333 0.33333 347s 347s Group means: 347s Sepal.Length Sepal.Width Petal.Length Petal.Width 347s setosa 4.9654 3.3913 1.4507 0.21639 347s versicolor 5.8767 2.7909 4.2238 1.34189 347s virginica 6.5075 2.9777 5.4459 2.05921 347s 347s Within-groups Covariance Matrix: 347s Sepal.Length Sepal.Width Petal.Length Petal.Width 347s Sepal.Length 0.180280 0.068876 0.101512 0.036096 347s Sepal.Width 0.068876 0.079556 0.047722 0.029409 347s Petal.Length 0.101512 0.047722 0.111492 0.038658 347s Petal.Width 0.036096 0.029409 0.038658 0.029965 347s 347s Linear Coeficients: 347s Sepal.Length Sepal.Width Petal.Length Petal.Width 347s setosa 28.582 46.5236 -13.859 -54.9877 347s versicolor 19.837 11.9601 20.865 -17.7704 347s virginica 16.999 1.9046 29.808 7.9189 347s 347s Constants: 347s setosa versicolor virginica 347s -134.94 -108.22 -148.56 347s 347s Apparent error rate 0.0133 347s 347s Classification table 347s Predicted 347s Actual setosa versicolor virginica 347s setosa 50 0 0 347s versicolor 0 49 1 347s virginica 0 1 49 347s 347s Confusion matrix 347s Predicted 347s Actual setosa versicolor virginica 347s setosa 1 0.00 0.00 347s versicolor 0 0.98 0.02 347s virginica 0 0.02 0.98 347s 347s Data: crabs 347s Call: 347s Linda(sp ~ ., data = crabs, method = method) 347s 347s Prior Probabilities of Groups: 347s B O 347s 0.5 0.5 347s 347s Group means: 347s sexM index FL RW CL CW BD 347s B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 347s O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 347s 347s Within-groups Covariance Matrix: 347s sexM index FL RW CL CW BD 347s sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 347s index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 347s FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 347s RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 347s CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 347s CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 347s BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 347s 347s Linear Coeficients: 347s sexM index FL RW CL CW BD 347s B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 347s O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 347s 347s Constants: 347s B O 347s -77.695 -147.287 347s 347s Apparent error rate 0 347s 347s Classification table 347s Predicted 347s Actual B O 347s B 100 0 347s O 0 100 347s 347s Confusion matrix 347s Predicted 347s Actual B O 347s B 1 0 347s O 0 1 347s 347s Data: fish 347s 347s Apparent error rate 0.0063 347s 347s Classification table 347s Predicted 347s Actual 1 2 3 4 5 6 7 347s 1 34 0 0 0 0 0 0 347s 2 0 6 0 0 0 0 0 347s 3 0 0 20 0 0 0 0 347s 4 0 0 0 11 0 0 0 347s 5 0 0 0 0 14 0 0 347s 6 0 0 0 0 0 17 0 347s 7 0 0 0 0 1 0 55 347s 347s Confusion matrix 347s Predicted 347s Actual 1 2 3 4 5 6 7 347s 1 1 0 0 0 0.000 0 0.000 347s 2 0 1 0 0 0.000 0 0.000 347s 3 0 0 1 0 0.000 0 0.000 347s 4 0 0 0 1 0.000 0 0.000 347s 5 0 0 0 0 1.000 0 0.000 347s 6 0 0 0 0 0.000 1 0.000 347s 7 0 0 0 0 0.018 0 0.982 347s 347s Data: pottery 347s Call: 347s Linda(origin ~ ., data = pottery, method = method) 347s 347s Prior Probabilities of Groups: 347s Attic Eritrean 347s 0.48148 0.51852 347s 347s Group means: 347s SI AL FE MG CA TI 347s Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 347s Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 347s 347s Within-groups Covariance Matrix: 347s SI AL FE MG CA TI 347s SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 347s AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 347s FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 347s MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 347s CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 347s TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 347s 347s Linear Coeficients: 347s SI AL FE MG CA TI 347s Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 347s Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 347s 347s Constants: 347s Attic Eritrean 347s -4346 -4139 347s 347s Apparent error rate 0.1111 347s 347s Classification table 347s Predicted 347s Actual Attic Eritrean 347s Attic 12 1 347s Eritrean 2 12 347s 347s Confusion matrix 347s Predicted 347s Actual Attic Eritrean 347s Attic 0.923 0.077 347s Eritrean 0.143 0.857 347s 347s Data: olitos 347s 347s Apparent error rate 0.1 347s 347s Classification table 347s Predicted 347s Actual 1 2 3 4 347s 1 45 2 2 1 347s 2 0 25 0 0 347s 3 4 1 27 2 347s 4 0 0 0 11 347s 347s Confusion matrix 347s Predicted 347s Actual 1 2 3 4 347s 1 0.900 0.040 0.040 0.020 347s 2 0.000 1.000 0.000 0.000 347s 3 0.118 0.029 0.794 0.059 347s 4 0.000 0.000 0.000 1.000 347s =================================================== 347s > #dodata(method="fsa") 347s > 347s BEGIN TEST tldapp.R 347s 347s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 347s Copyright (C) 2025 The R Foundation for Statistical Computing 347s Platform: arm-unknown-linux-gnueabihf (32-bit) 347s 347s R is free software and comes with ABSOLUTELY NO WARRANTY. 347s You are welcome to redistribute it under certain conditions. 347s Type 'license()' or 'licence()' for distribution details. 347s 347s R is a collaborative project with many contributors. 347s Type 'contributors()' for more information and 347s 'citation()' on how to cite R or R packages in publications. 347s 347s Type 'demo()' for some demos, 'help()' for on-line help, or 347s 'help.start()' for an HTML browser interface to help. 347s Type 'q()' to quit R. 347s 347s > ## VT::15.09.2013 - this will render the output independent 347s > ## from the version of the package 347s > suppressPackageStartupMessages(library(rrcov)) 347s > library(MASS) 347s > 347s > dodata <- function(method) { 347s + 347s + options(digits = 5) 347s + set.seed(101) 347s + 347s + tmp <- sys.call() 347s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 347s + cat("===================================================\n") 347s + 347s + data(pottery); show(lda <- LdaPP(origin~., data=pottery, method=method)); show(predict(lda)) 347s + data(hemophilia); show(lda <- LdaPP(as.factor(gr)~., data=hemophilia, method=method)); show(predict(lda)) 347s + data(anorexia); show(lda <- LdaPP(Treat~., data=anorexia, method=method)); show(predict(lda)) 347s + data(Pima.tr); show(lda <- LdaPP(type~., data=Pima.tr, method=method)); show(predict(lda)) 347s + data(crabs); show(lda <- LdaPP(sp~., data=crabs, method=method)); show(predict(lda)) 347s + 347s + cat("===================================================\n") 347s + } 347s > 347s > 347s > ## -- now do it: 347s > 347s > ## Commented out - still to slow 347s > ##dodata(method="huber") 347s > ##dodata(method="sest") 347s > 347s > ## VT::14.11.2018 - Commented out: too slow 347s > ## dodata(method="mad") 347s > ## dodata(method="class") 347s > 347s BEGIN TEST tmcd4.R 347s 347s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 347s Copyright (C) 2025 The R Foundation for Statistical Computing 347s Platform: arm-unknown-linux-gnueabihf (32-bit) 347s 347s R is free software and comes with ABSOLUTELY NO WARRANTY. 347s You are welcome to redistribute it under certain conditions. 347s Type 'license()' or 'licence()' for distribution details. 347s 347s R is a collaborative project with many contributors. 347s Type 'contributors()' for more information and 347s 'citation()' on how to cite R or R packages in publications. 347s 347s Type 'demo()' for some demos, 'help()' for on-line help, or 347s 'help.start()' for an HTML browser interface to help. 347s Type 'q()' to quit R. 347s 348s > ## Test the exact fit property of CovMcd 348s > doexactfit <- function(){ 348s + exact <-function(seed=1234){ 348s + 348s + set.seed(seed) 348s + 348s + n1 <- 45 348s + p <- 2 348s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 348s + x1[,p] <- x1[,p] + 3 348s + n2 <- 55 348s + m1 <- 0 348s + m2 <- 3 348s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 348s + x<-rbind(x1,x2) 348s + colnames(x) <- c("X1","X2") 348s + x 348s + } 348s + print(CovMcd(exact())) 348s + } 348s > 348s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 348s + ##@bdescr 348s + ## Test the function covMcd() on the literature datasets: 348s + ## 348s + ## Call CovMcd() for all regression datasets available in rrcov and print: 348s + ## - execution time (if time == TRUE) 348s + ## - objective fucntion 348s + ## - best subsample found (if short == false) 348s + ## - outliers identified (with cutoff 0.975) (if short == false) 348s + ## - estimated center and covarinance matrix if full == TRUE) 348s + ## 348s + ##@edescr 348s + ## 348s + ##@in nrep : [integer] number of repetitions to use for estimating the 348s + ## (average) execution time 348s + ##@in time : [boolean] whether to evaluate the execution time 348s + ##@in short : [boolean] whether to do short output (i.e. only the 348s + ## objective function value). If short == FALSE, 348s + ## the best subsample and the identified outliers are 348s + ## printed. See also the parameter full below 348s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 348s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 348s + 348s + doest <- function(x, xname, nrep=1){ 348s + n <- dim(x)[1] 348s + p <- dim(x)[2] 348s + if(method == "MASS"){ 348s + mcd<-cov.mcd(x) 348s + quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2) 348s + } 348s + else{ 348s + mcd <- if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 348s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 348s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 348s + else CovMcd(x, trace=FALSE) 348s + quan <- as.integer(mcd@quan) 348s + } 348s + 348s + crit <- mcd@crit 348s + 348s + if(time){ 348s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 348s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 348s + } 348s + else{ 348s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 348s + } 348s + lpad<-lname-nchar(xname) 348s + cat(pad.right(xname,lpad), xres) 348s + 348s + if(!short){ 348s + cat("Best subsample: \n") 348s + if(length(mcd@best) > 150) 348s + cat("Too long... \n") 348s + else 348s + print(mcd@best) 348s + 348s + ibad <- which(mcd@wt==0) 348s + names(ibad) <- NULL 348s + nbad <- length(ibad) 348s + cat("Outliers: ",nbad,"\n") 348s + if(nbad > 0 & nbad < 150) 348s + print(ibad) 348s + else 348s + cat("Too many to print ... \n") 348s + if(full){ 348s + cat("-------------\n") 348s + show(mcd) 348s + } 348s + cat("--------------------------------------------------------\n") 348s + } 348s + } 348s + 348s + options(digits = 5) 348s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 348s + 348s + lname <- 20 348s + 348s + ## VT::15.09.2013 - this will render the output independent 348s + ## from the version of the package 348s + suppressPackageStartupMessages(library(rrcov)) 348s + 348s + method <- match.arg(method) 348s + if(method == "MASS") 348s + library(MASS) 348s + 348s + data(Animals, package = "MASS") 348s + brain <- Animals[c(1:24, 26:25, 27:28),] 348s + 348s + data(fish) 348s + data(pottery) 348s + data(rice) 348s + data(un86) 348s + data(wages) 348s + 348s + tmp <- sys.call() 348s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 348s + 348s + cat("Data Set n p Half LOG(obj) Time\n") 348s + cat("========================================================\n") 348s + 348s + if(method=="exact") 348s + { 348s + ## only small data sets 348s + doest(heart[, 1:2], data(heart), nrep) 348s + doest(starsCYG, data(starsCYG), nrep) 348s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 348s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 348s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 348s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 348s + doest(brain, "Animals", nrep) 348s + doest(lactic, data(lactic), nrep) 348s + doest(pension, data(pension), nrep) 348s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 348s + doest(stack.x, data(stackloss), nrep) 348s + doest(pilot, data(pilot), nrep) 348s + } else 348s + { 348s + doest(heart[, 1:2], data(heart), nrep) 348s + doest(starsCYG, data(starsCYG), nrep) 348s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 348s + doest(stack.x, data(stackloss), nrep) 348s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 348s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 348s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 348s + doest(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 348s + 348s + doest(brain, "Animals", nrep) 348s + ## doest(milk, data(milk), nrep) # difference between 386 and x64 348s + doest(bushfire, data(bushfire), nrep) 348s + 348s + doest(lactic, data(lactic), nrep) 348s + doest(pension, data(pension), nrep) 348s + ## doest(pilot, data(pilot), nrep) # difference between 386 and x64 348s + 348s + if(method != "MRCD") # these two are quite slow for MRCD, especially the second one 348s + { 348s + doest(radarImage, data(radarImage), nrep) 348s + doest(NOxEmissions, data(NOxEmissions), nrep) 348s + } 348s + 348s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 348s + doest(data.matrix(subset(wagnerGrowth, select = -Period)), data(wagnerGrowth), nrep) 348s + 348s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep) 348s + doest(data.matrix(subset(pottery, select = -origin)), data(pottery), nrep) 348s + doest(rice, data(rice), nrep) 348s + doest(un86, data(un86), nrep) 348s + 348s + doest(wages, data(wages), nrep) 348s + 348s + ## from package 'datasets' 348s + doest(airquality[,1:4], data(airquality), nrep) 348s + doest(attitude, data(attitude), nrep) 348s + doest(attenu, data(attenu), nrep) 348s + doest(USJudgeRatings, data(USJudgeRatings), nrep) 348s + doest(USArrests, data(USArrests), nrep) 348s + doest(longley, data(longley), nrep) 348s + doest(Loblolly, data(Loblolly), nrep) 348s + doest(quakes[,1:4], data(quakes), nrep) 348s + } 348s + cat("========================================================\n") 348s + } 348s > 348s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMCD", "MASS")){ 348s + 348s + doest <- function(x, nrep=1){ 348s + gc() 348s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 348s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 348s + xtime 348s + } 348s + 348s + set.seed(1234) 348s + 348s + ## VT::15.09.2013 - this will render the output independent 348s + ## from the version of the package 348s + suppressPackageStartupMessages(library(rrcov)) 348s + 348s + library(MASS) 348s + method <- match.arg(method) 348s + 348s + ap <- c(2, 5, 10, 20, 30) 348s + an <- c(100, 500, 1000, 10000, 50000) 348s + 348s + tottime <- 0 348s + cat(" n p Time\n") 348s + cat("=====================\n") 348s + for(i in 1:length(an)) { 348s + for(j in 1:length(ap)) { 348s + n <- an[i] 348s + p <- ap[j] 348s + if(5*p <= n){ 348s + xx <- gendata(n, p, eps) 348s + X <- xx$X 348s + tottime <- tottime + doest(X, nrep) 348s + } 348s + } 348s + } 348s + 348s + cat("=====================\n") 348s + cat("Total time: ", tottime*nrep, "\n") 348s + } 348s > 348s > docheck <- function(n, p, eps){ 348s + xx <- gendata(n,p,eps) 348s + mcd <- CovMcd(xx$X) 348s + check(mcd, xx$xind) 348s + } 348s > 348s > check <- function(mcd, xind){ 348s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 348s + ## did not get zero weight 348s + mymatch <- xind %in% which(mcd@wt == 0) 348s + length(xind) - length(which(mymatch)) 348s + } 348s > 348s > dorep <- function(x, nrep=1, method=c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 348s + 348s + method <- match.arg(method) 348s + for(i in 1:nrep) 348s + if(method == "MASS") 348s + cov.mcd(x) 348s + else 348s + { 348s + if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 348s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 348s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 348s + else CovMcd(x, trace=FALSE) 348s + } 348s + } 348s > 348s > #### gendata() #### 348s > # Generates a location contaminated multivariate 348s > # normal sample of n observations in p dimensions 348s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 348s > # where 348s > # m = (b,b,...,b) 348s > # Defaults: eps=0 and b=10 348s > # 348s > gendata <- function(n,p,eps=0,b=10){ 348s + 348s + if(missing(n) || missing(p)) 348s + stop("Please specify (n,p)") 348s + if(eps < 0 || eps >= 0.5) 348s + stop(message="eps must be in [0,0.5)") 348s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 348s + nbad <- as.integer(eps * n) 348s + if(nbad > 0){ 348s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 348s + xind <- sample(n,nbad) 348s + X[xind,] <- Xbad 348s + } 348s + list(X=X, xind=xind) 348s + } 348s > 348s > pad.right <- function(z, pads) 348s + { 348s + ### Pads spaces to right of text 348s + padding <- paste(rep(" ", pads), collapse = "") 348s + paste(z, padding, sep = "") 348s + } 348s > 348s > whatis<-function(x){ 348s + if(is.data.frame(x)) 348s + cat("Type: data.frame\n") 348s + else if(is.matrix(x)) 348s + cat("Type: matrix\n") 348s + else if(is.vector(x)) 348s + cat("Type: vector\n") 348s + else 348s + cat("Type: don't know\n") 348s + } 348s > 348s > ## VT::15.09.2013 - this will render the output independent 348s > ## from the version of the package 348s > suppressPackageStartupMessages(library(rrcov)) 348s > 348s > dodata() 348s 348s Call: dodata() 348s Data Set n p Half LOG(obj) Time 348s ======================================================== 348s heart 12 2 7 5.678742 348s Best subsample: 348s [1] 1 3 4 5 7 9 11 348s Outliers: 0 348s Too many to print ... 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s height weight 348s 38.3 33.1 348s 348s Robust Estimate of Covariance: 348s height weight 348s height 135 259 348s weight 259 564 348s -------------------------------------------------------- 348s starsCYG 47 2 25 -8.031215 348s Best subsample: 348s [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 348s Outliers: 7 348s [1] 7 9 11 14 20 30 34 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s log.Te log.light 348s 4.41 4.95 348s 348s Robust Estimate of Covariance: 348s log.Te log.light 348s log.Te 0.0132 0.0394 348s log.light 0.0394 0.2743 348s -------------------------------------------------------- 348s phosphor 18 2 10 6.878847 348s Best subsample: 348s [1] 3 5 8 9 11 12 13 14 15 17 348s Outliers: 3 348s [1] 1 6 10 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s inorg organic 348s 13.4 38.8 348s 348s Robust Estimate of Covariance: 348s inorg organic 348s inorg 129 130 348s organic 130 182 348s -------------------------------------------------------- 348s stackloss 21 3 12 5.472581 348s Best subsample: 348s [1] 4 5 6 7 8 9 10 11 12 13 14 20 348s Outliers: 9 348s [1] 1 2 3 15 16 17 18 19 21 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s Air.Flow Water.Temp Acid.Conc. 348s 59.5 20.8 87.3 348s 348s Robust Estimate of Covariance: 348s Air.Flow Water.Temp Acid.Conc. 348s Air.Flow 6.29 5.85 5.74 348s Water.Temp 5.85 9.23 6.14 348s Acid.Conc. 5.74 6.14 23.25 348s -------------------------------------------------------- 348s coleman 20 5 13 1.286808 348s Best subsample: 348s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 348s Outliers: 7 348s [1] 1 6 9 10 11 15 18 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s salaryP fatherWc sstatus teacherSc motherLev 348s 2.76 48.38 6.12 25.00 6.40 348s 348s Robust Estimate of Covariance: 348s salaryP fatherWc sstatus teacherSc motherLev 348s salaryP 0.253 1.786 -0.266 0.151 0.075 348s fatherWc 1.786 1303.382 330.496 12.604 34.503 348s sstatus -0.266 330.496 119.888 3.833 10.131 348s teacherSc 0.151 12.604 3.833 0.785 0.555 348s motherLev 0.075 34.503 10.131 0.555 1.043 348s -------------------------------------------------------- 348s salinity 28 3 16 1.326364 348s Best subsample: 348s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 348s Outliers: 4 348s [1] 5 16 23 24 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s X1 X2 X3 348s 10.08 2.78 22.78 348s 348s Robust Estimate of Covariance: 348s X1 X2 X3 348s X1 10.44 1.01 -3.19 348s X2 1.01 3.83 -1.44 348s X3 -3.19 -1.44 2.39 348s -------------------------------------------------------- 348s wood 20 5 13 -36.270094 348s Best subsample: 348s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 348s Outliers: 7 348s [1] 4 6 7 8 11 16 19 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s x1 x2 x3 x4 x5 348s 0.587 0.122 0.531 0.538 0.892 348s 348s Robust Estimate of Covariance: 348s x1 x2 x3 x4 x5 348s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 348s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 348s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 348s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 348s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 348s -------------------------------------------------------- 348s hbk 75 3 39 -1.047858 348s Best subsample: 348s [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 348s [26] 55 56 58 59 61 63 64 66 67 70 71 72 73 74 348s Outliers: 14 348s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=39); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s X1 X2 X3 348s 1.54 1.78 1.69 348s 348s Robust Estimate of Covariance: 348s X1 X2 X3 348s X1 1.227 0.055 0.127 348s X2 0.055 1.249 0.153 348s X3 0.127 0.153 1.160 348s -------------------------------------------------------- 348s Animals 28 2 15 14.555543 348s Best subsample: 348s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 348s Outliers: 14 348s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s body brain 348s 18.7 64.9 348s 348s Robust Estimate of Covariance: 348s body brain 348s body 929 1576 348s brain 1576 5646 348s -------------------------------------------------------- 348s bushfire 38 5 22 18.135810 348s Best subsample: 348s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 348s Outliers: 16 348s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=22); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s V1 V2 V3 V4 V5 348s 105 147 274 218 279 348s 348s Robust Estimate of Covariance: 348s V1 V2 V3 V4 V5 348s V1 346 268 -1692 -381 -311 348s V2 268 236 -1125 -230 -194 348s V3 -1692 -1125 9993 2455 1951 348s V4 -381 -230 2455 647 505 348s V5 -311 -194 1951 505 398 348s -------------------------------------------------------- 348s lactic 20 2 11 0.359580 348s Best subsample: 348s [1] 1 2 3 4 5 7 8 9 10 11 12 348s Outliers: 4 348s [1] 17 18 19 20 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s X Y 348s 3.86 5.01 348s 348s Robust Estimate of Covariance: 348s X Y 348s X 10.6 14.6 348s Y 14.6 21.3 348s -------------------------------------------------------- 348s pension 18 2 10 16.675508 348s Best subsample: 348s [1] 1 2 3 4 5 6 8 9 11 12 348s Outliers: 5 348s [1] 14 15 16 17 18 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s Income Reserves 348s 52.3 560.9 348s 348s Robust Estimate of Covariance: 348s Income Reserves 348s Income 1420 11932 348s Reserves 11932 208643 348s -------------------------------------------------------- 348s radarImage 1573 5 789 36.694425 348s Best subsample: 348s Too long... 348s Outliers: 117 348s [1] 164 237 238 242 261 262 351 450 451 462 480 481 509 516 535 348s [16] 542 572 597 620 643 654 669 697 737 802 803 804 818 832 833 348s [31] 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 1202 1223 348s [46] 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 1376 1393 348s [61] 1394 1402 1403 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1479 1487 348s [76] 1492 1504 1510 1511 1512 1517 1518 1519 1520 1521 1522 1525 1526 1527 1528 348s [91] 1530 1532 1534 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 348s [106] 1557 1558 1561 1562 1564 1565 1566 1567 1569 1570 1571 1573 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=789); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s X.coord Y.coord Band.1 Band.2 Band.3 348s 52.80 35.12 6.77 18.44 8.90 348s 348s Robust Estimate of Covariance: 348s X.coord Y.coord Band.1 Band.2 Band.3 348s X.coord 123.6 23.0 -361.9 -197.1 -22.5 348s Y.coord 23.0 400.6 34.3 -191.1 -39.1 348s Band.1 -361.9 34.3 27167.9 8178.8 473.7 348s Band.2 -197.1 -191.1 8178.8 26021.8 952.4 348s Band.3 -22.5 -39.1 473.7 952.4 4458.4 348s -------------------------------------------------------- 348s NOxEmissions 8088 4 4046 2.474539 348s Best subsample: 348s Too long... 348s Outliers: 2156 348s Too many to print ... 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=4046); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s julday LNOx LNOxEm sqrtWS 348s 168.19 4.73 7.91 1.37 348s 348s Robust Estimate of Covariance: 348s julday LNOx LNOxEm sqrtWS 348s julday 9180.6297 12.0306 0.7219 -10.1273 348s LNOx 12.0306 0.4721 0.1418 -0.1526 348s LNOxEm 0.7219 0.1418 0.2516 0.0438 348s sqrtWS -10.1273 -0.1526 0.0438 0.2073 348s -------------------------------------------------------- 348s vaso 39 2 21 -3.972244 348s Best subsample: 348s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 348s Outliers: 4 348s [1] 1 2 17 31 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s Volume Rate 348s 1.16 1.72 348s 348s Robust Estimate of Covariance: 348s Volume Rate 348s Volume 0.313 -0.167 348s Rate -0.167 0.728 348s -------------------------------------------------------- 348s wagnerGrowth 63 6 35 6.572208 348s Best subsample: 348s [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 348s [26] 48 51 52 53 54 55 56 57 60 62 348s Outliers: 13 348s [1] 1 8 15 21 22 28 29 33 42 43 46 50 63 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=35); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s Region PA GPA HS GHS y 348s 11.00 33.66 -2.00 2.48 0.31 7.48 348s 348s Robust Estimate of Covariance: 348s Region PA GPA HS GHS y 348s Region 35.5615 17.9337 -0.5337 -0.9545 -0.3093 -14.0090 348s PA 17.9337 27.7333 -4.9017 -1.4174 0.0343 -28.7040 348s GPA -0.5337 -4.9017 5.3410 0.2690 -0.1484 4.0006 348s HS -0.9545 -1.4174 0.2690 0.8662 -0.0454 2.9024 348s GHS -0.3093 0.0343 -0.1484 -0.0454 0.1772 0.7457 348s y -14.0090 -28.7040 4.0006 2.9024 0.7457 82.6877 348s -------------------------------------------------------- 348s fish 159 6 82 8.879005 348s Best subsample: 348s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 348s [20] 20 21 22 23 24 25 26 27 28 30 32 35 36 37 42 43 44 45 46 348s [39] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 107 109 110 111 113 348s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 348s [77] 134 135 136 137 138 139 348s Outliers: 63 348s [1] 30 39 40 41 42 62 63 64 65 66 68 69 70 73 74 75 76 77 78 348s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 348s [39] 98 99 100 101 102 103 104 105 141 143 144 145 147 148 149 150 151 152 153 348s [58] 154 155 156 157 158 159 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=82); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s Weight Length1 Length2 Length3 Height Width 348s 329.9 24.5 26.6 29.7 31.1 14.7 348s 348s Robust Estimate of Covariance: 348s Weight Length1 Length2 Length3 Height Width 348s Weight 69082.99 1477.81 1613.64 1992.62 1439.32 -62.12 348s Length1 1477.81 34.68 37.61 45.51 28.82 -1.31 348s Length2 1613.64 37.61 40.88 49.52 31.81 -1.40 348s Length3 1992.62 45.51 49.52 61.16 42.65 -2.25 348s Height 1439.32 28.82 31.81 42.65 46.74 -2.82 348s Width -62.12 -1.31 -1.40 -2.25 -2.82 1.01 348s -------------------------------------------------------- 348s pottery 27 6 17 -10.586933 348s Best subsample: 348s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 348s Outliers: 9 348s [1] 3 8 12 16 17 18 23 24 25 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=17); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s SI AL FE MG CA TI 348s 54.983 15.206 9.700 3.817 5.211 0.859 348s 348s Robust Estimate of Covariance: 348s SI AL FE MG CA TI 348s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 348s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 348s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 348s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 348s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 348s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 348s -------------------------------------------------------- 348s rice 105 6 56 -14.463986 348s Best subsample: 348s [1] 2 4 6 8 10 12 15 18 21 22 24 29 30 31 32 33 34 36 37 348s [20] 38 41 44 45 47 51 52 53 54 55 59 61 65 67 68 69 70 72 76 348s [39] 78 79 80 81 82 83 84 85 86 92 93 94 95 97 98 99 102 105 348s Outliers: 13 348s [1] 9 14 19 28 40 42 49 58 62 71 75 77 89 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=56); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s Favor Appearance Taste Stickiness 348s -0.2731 0.0600 -0.1468 0.0646 348s Toughness Overall_evaluation 348s 0.0894 -0.2192 348s 348s Robust Estimate of Covariance: 348s Favor Appearance Taste Stickiness Toughness 348s Favor 0.388 0.323 0.393 0.389 -0.195 348s Appearance 0.323 0.503 0.494 0.494 -0.270 348s Taste 0.393 0.494 0.640 0.629 -0.361 348s Stickiness 0.389 0.494 0.629 0.815 -0.486 348s Toughness -0.195 -0.270 -0.361 -0.486 0.451 348s Overall_evaluation 0.471 0.575 0.723 0.772 -0.457 348s Overall_evaluation 348s Favor 0.471 348s Appearance 0.575 348s Taste 0.723 348s Stickiness 0.772 348s Toughness -0.457 348s Overall_evaluation 0.882 348s -------------------------------------------------------- 348s un86 73 7 40 17.009322 348s Best subsample: 348s [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 348s [26] 51 52 55 56 60 61 62 63 64 65 67 70 71 72 73 348s Outliers: 30 348s [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 348s [26] 58 59 66 68 69 348s ------------- 348s 348s Call: 348s CovMcd(x = x, trace = FALSE) 348s -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5) 348s 348s Robust Estimate of Location: 348s POP MOR CAR DR GNP DEN TB 348s 20.740 71.023 6.435 0.817 1.146 56.754 0.441 348s 348s Robust Estimate of Covariance: 348s POP MOR CAR DR GNP DEN 348s POP 582.4034 224.9343 -12.6722 -1.6729 -3.3664 226.1952 348s MOR 224.9343 2351.3907 -286.9504 -32.0743 -35.5649 -527.4684 348s CAR -12.6722 -286.9504 58.1190 5.7393 6.6365 83.6180 348s DR -1.6729 -32.0743 5.7393 0.8339 0.5977 12.1938 348s GNP -3.3664 -35.5649 6.6365 0.5977 1.4175 13.0709 348s DEN 226.1952 -527.4684 83.6180 12.1938 13.0709 2041.5809 348s TB 0.4002 -1.1807 0.2701 0.0191 0.0058 -0.9346 348s TB 348s POP 0.4002 348s MOR -1.1807 348s CAR 0.2701 348s DR 0.0191 348s GNP 0.0058 348s DEN -0.9346 348s TB 0.0184 348s -------------------------------------------------------- 349s wages 39 10 19 22.994272 349s Best subsample: 349s [1] 1 2 6 7 8 9 10 11 12 13 14 15 17 18 19 25 26 27 28 349s Outliers: 9 349s [1] 4 5 6 24 28 30 32 33 34 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 349s 2153.37 2.87 1129.16 297.53 360.58 6876.58 39.48 2.36 349s RACE SCHOOL 349s 38.88 10.17 349s 349s Robust Estimate of Covariance: 349s HRS RATE ERSP ERNO NEIN ASSET 349s HRS 6.12e+03 1.73e+01 -1.67e+03 -2.06e+03 9.10e+03 2.02e+05 349s RATE 1.73e+01 2.52e-01 2.14e+01 -3.54e+00 5.85e+01 1.37e+03 349s ERSP -1.67e+03 2.14e+01 1.97e+04 7.76e+01 -1.71e+03 -1.41e+04 349s ERNO -2.06e+03 -3.54e+00 7.76e+01 2.06e+03 -2.02e+03 -4.83e+04 349s NEIN 9.10e+03 5.85e+01 -1.71e+03 -2.02e+03 2.02e+04 4.54e+05 349s ASSET 2.02e+05 1.37e+03 -1.41e+04 -4.83e+04 4.54e+05 1.03e+07 349s AGE -6.29e+01 -2.61e-01 4.83e+00 2.44e+01 -1.08e+02 -2.46e+03 349s DEP -6.17e+00 -7.05e-02 -2.13e+01 2.29e+00 -1.30e+01 -3.16e+02 349s RACE -2.17e+03 -9.46e+00 7.19e+02 5.59e+02 -3.95e+03 -8.77e+04 349s SCHOOL 7.12e+01 5.87e-01 5.39e+01 -2.14e+01 1.63e+02 3.79e+03 349s AGE DEP RACE SCHOOL 349s HRS -6.29e+01 -6.17e+00 -2.17e+03 7.12e+01 349s RATE -2.61e-01 -7.05e-02 -9.46e+00 5.87e-01 349s ERSP 4.83e+00 -2.13e+01 7.19e+02 5.39e+01 349s ERNO 2.44e+01 2.29e+00 5.59e+02 -2.14e+01 349s NEIN -1.08e+02 -1.30e+01 -3.95e+03 1.63e+02 349s ASSET -2.46e+03 -3.16e+02 -8.77e+04 3.79e+03 349s AGE 1.01e+00 7.03e-02 2.39e+01 -9.52e-01 349s DEP 7.03e-02 4.62e-02 2.72e+00 -1.94e-01 349s RACE 2.39e+01 2.72e+00 8.74e+02 -3.09e+01 349s SCHOOL -9.52e-01 -1.94e-01 -3.09e+01 1.62e+00 349s -------------------------------------------------------- 349s airquality 153 4 58 18.213499 349s Best subsample: 349s [1] 3 22 24 25 28 29 32 33 35 36 37 38 39 40 41 42 43 44 46 349s [20] 47 48 49 50 52 56 57 58 59 60 64 66 67 68 69 71 72 73 74 349s [39] 76 78 80 82 83 84 86 87 89 90 91 92 93 94 95 97 98 105 109 349s [58] 110 349s Outliers: 14 349s [1] 8 9 15 18 20 21 23 24 28 30 48 62 117 148 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=58); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s Ozone Solar.R Wind Temp 349s 43.2 192.9 9.6 80.5 349s 349s Robust Estimate of Covariance: 349s Ozone Solar.R Wind Temp 349s Ozone 959.69 771.68 -60.92 198.38 349s Solar.R 771.68 7089.72 -1.72 95.75 349s Wind -60.92 -1.72 10.71 -11.96 349s Temp 198.38 95.75 -11.96 62.78 349s -------------------------------------------------------- 349s attitude 30 7 19 24.442803 349s Best subsample: 349s [1] 2 3 4 5 7 8 10 12 15 17 19 20 22 23 25 27 28 29 30 349s Outliers: 10 349s [1] 1 6 9 13 14 16 18 21 24 26 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s rating complaints privileges learning raises critical 349s 67.1 68.0 52.4 57.6 67.2 77.4 349s advance 349s 43.4 349s 349s Robust Estimate of Covariance: 349s rating complaints privileges learning raises critical advance 349s rating 169.34 127.83 40.48 110.26 91.71 -3.59 53.84 349s complaints 127.83 156.80 52.65 110.97 96.56 7.27 76.03 349s privileges 40.48 52.65 136.91 92.38 69.00 9.53 87.98 349s learning 110.26 110.97 92.38 157.77 112.92 6.74 75.51 349s raises 91.71 96.56 69.00 112.92 112.79 4.91 70.22 349s critical -3.59 7.27 9.53 6.74 4.91 52.25 15.00 349s advance 53.84 76.03 87.98 75.51 70.22 15.00 93.11 349s -------------------------------------------------------- 349s attenu 182 5 86 6.440834 349s Best subsample: 349s [1] 68 69 70 71 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 349s [20] 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 115 116 117 118 349s [39] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 349s [58] 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 349s [77] 157 158 159 160 161 162 163 164 165 166 349s Outliers: 61 349s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 349s [20] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 349s [39] 40 45 46 47 54 55 56 57 58 59 60 61 64 65 82 97 98 100 101 349s [58] 102 103 104 105 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=86); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s event mag station dist accel 349s 18.624 5.752 67.861 22.770 0.141 349s 349s Robust Estimate of Covariance: 349s event mag station dist accel 349s event 1.64e+01 -1.22e+00 5.59e+01 9.98e+00 -8.37e-02 349s mag -1.22e+00 4.13e-01 -3.19e+00 1.35e+00 1.22e-02 349s station 5.59e+01 -3.19e+00 1.03e+03 7.00e+01 5.56e-01 349s dist 9.98e+00 1.35e+00 7.00e+01 2.21e+02 -9.24e-01 349s accel -8.37e-02 1.22e-02 5.56e-01 -9.24e-01 9.62e-03 349s -------------------------------------------------------- 349s USJudgeRatings 43 12 28 -47.889993 349s Best subsample: 349s [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 349s [26] 38 41 43 349s Outliers: 14 349s [1] 5 7 8 12 13 14 20 21 23 30 31 35 40 42 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=28); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 349s 7.40 8.19 7.80 7.96 7.74 7.82 7.74 7.73 7.57 7.63 8.25 7.94 349s 349s Robust Estimate of Covariance: 349s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL 349s CONT 0.852 -0.266 -0.422 -0.155 -0.049 -0.074 -0.117 -0.119 -0.177 349s INTG -0.266 0.397 0.537 0.406 0.340 0.325 0.404 0.409 0.430 349s DMNR -0.422 0.537 0.824 0.524 0.458 0.437 0.520 0.504 0.569 349s DILG -0.155 0.406 0.524 0.486 0.426 0.409 0.506 0.515 0.511 349s CFMG -0.049 0.340 0.458 0.426 0.427 0.403 0.466 0.476 0.478 349s DECI -0.074 0.325 0.437 0.409 0.403 0.396 0.449 0.462 0.460 349s PREP -0.117 0.404 0.520 0.506 0.466 0.449 0.552 0.565 0.551 349s FAMI -0.119 0.409 0.504 0.515 0.476 0.462 0.565 0.594 0.571 349s ORAL -0.177 0.430 0.569 0.511 0.478 0.460 0.551 0.571 0.575 349s WRIT -0.159 0.427 0.549 0.515 0.480 0.461 0.556 0.580 0.574 349s PHYS -0.184 0.269 0.362 0.308 0.298 0.307 0.335 0.358 0.369 349s RTEN -0.260 0.472 0.642 0.519 0.467 0.455 0.539 0.554 0.573 349s WRIT PHYS RTEN 349s CONT -0.159 -0.184 -0.260 349s INTG 0.427 0.269 0.472 349s DMNR 0.549 0.362 0.642 349s DILG 0.515 0.308 0.519 349s CFMG 0.480 0.298 0.467 349s DECI 0.461 0.307 0.455 349s PREP 0.556 0.335 0.539 349s FAMI 0.580 0.358 0.554 349s ORAL 0.574 0.369 0.573 349s WRIT 0.580 0.365 0.567 349s PHYS 0.365 0.300 0.378 349s RTEN 0.567 0.378 0.615 349s -------------------------------------------------------- 349s USArrests 50 4 27 15.391648 349s Best subsample: 349s [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 349s [26] 49 50 349s Outliers: 11 349s [1] 2 3 5 6 10 18 24 28 33 37 47 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s Murder Assault UrbanPop Rape 349s 6.71 145.42 65.06 17.88 349s 349s Robust Estimate of Covariance: 349s Murder Assault UrbanPop Rape 349s Murder 16.1 269.3 20.3 25.2 349s Assault 269.3 6613.0 567.8 453.7 349s UrbanPop 20.3 567.8 225.4 47.7 349s Rape 25.2 453.7 47.7 50.9 349s -------------------------------------------------------- 349s longley 16 7 12 12.747678 349s Best subsample: 349s [1] 5 6 7 8 9 10 11 12 13 14 15 16 349s Outliers: 4 349s [1] 1 2 3 4 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s GNP.deflator GNP Unemployed Armed.Forces Population 349s 106.5 430.6 328.2 295.0 120.2 349s Year Employed 349s 1956.5 66.9 349s 349s Robust Estimate of Covariance: 349s GNP.deflator GNP Unemployed Armed.Forces Population 349s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 349s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 349s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 349s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 349s Population 81.4 803.4 929.1 -327.4 63.5 349s Year 51.6 504.3 595.6 -216.7 39.7 349s Employed 34.2 344.1 323.6 -149.5 26.2 349s Year Employed 349s GNP.deflator 51.6 34.2 349s GNP 504.3 344.1 349s Unemployed 595.6 323.6 349s Armed.Forces -216.7 -149.5 349s Population 39.7 26.2 349s Year 25.1 16.7 349s Employed 16.7 12.4 349s -------------------------------------------------------- 349s Loblolly 84 3 44 4.898174 349s Best subsample: 349s [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 349s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 349s Outliers: 31 349s [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 349s [26] 72 76 77 78 83 84 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=44); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s height age Seed 349s 20.44 8.19 7.72 349s 349s Robust Estimate of Covariance: 349s height age Seed 349s height 247.8 79.5 11.9 349s age 79.5 25.7 3.0 349s Seed 11.9 3.0 17.1 349s -------------------------------------------------------- 349s quakes 1000 4 502 8.274369 349s Best subsample: 349s Too long... 349s Outliers: 265 349s Too many to print ... 349s ------------- 349s 349s Call: 349s CovMcd(x = x, trace = FALSE) 349s -> Method: Fast MCD(alpha=0.5 ==> h=502); nsamp = 500; (n,k)mini = (300,5) 349s 349s Robust Estimate of Location: 349s lat long depth mag 349s -21.31 182.48 361.35 4.54 349s 349s Robust Estimate of Covariance: 349s lat long depth mag 349s lat 1.47e+01 3.53e+00 1.34e+02 -2.52e-01 349s long 3.53e+00 4.55e+00 -3.63e+02 4.36e-02 349s depth 1.34e+02 -3.63e+02 4.84e+04 -1.29e+01 349s mag -2.52e-01 4.36e-02 -1.29e+01 1.38e-01 349s -------------------------------------------------------- 349s ======================================================== 349s > dodata(method="deterministic") 349s 349s Call: dodata(method = "deterministic") 349s Data Set n p Half LOG(obj) Time 349s ======================================================== 349s heart 12 2 7 5.678742 349s Best subsample: 349s [1] 1 3 4 5 7 9 11 349s Outliers: 0 349s Too many to print ... 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=7) 349s 349s Robust Estimate of Location: 349s height weight 349s 38.3 33.1 349s 349s Robust Estimate of Covariance: 349s height weight 349s height 135 259 349s weight 259 564 349s -------------------------------------------------------- 349s starsCYG 47 2 25 -8.028718 349s Best subsample: 349s [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 349s Outliers: 7 349s [1] 7 9 11 14 20 30 34 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=25) 349s 349s Robust Estimate of Location: 349s log.Te log.light 349s 4.41 4.95 349s 349s Robust Estimate of Covariance: 349s log.Te log.light 349s log.Te 0.0132 0.0394 349s log.light 0.0394 0.2743 349s -------------------------------------------------------- 349s phosphor 18 2 10 7.732906 349s Best subsample: 349s [1] 2 4 5 7 8 9 11 12 14 16 349s Outliers: 1 349s [1] 6 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 349s 349s Robust Estimate of Location: 349s inorg organic 349s 12.5 40.8 349s 349s Robust Estimate of Covariance: 349s inorg organic 349s inorg 124 101 349s organic 101 197 349s -------------------------------------------------------- 349s stackloss 21 3 12 6.577286 349s Best subsample: 349s [1] 4 5 6 7 8 9 11 13 16 18 19 20 349s Outliers: 2 349s [1] 1 2 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 349s 349s Robust Estimate of Location: 349s Air.Flow Water.Temp Acid.Conc. 349s 58.4 20.5 86.1 349s 349s Robust Estimate of Covariance: 349s Air.Flow Water.Temp Acid.Conc. 349s Air.Flow 56.28 13.33 26.68 349s Water.Temp 13.33 8.28 6.98 349s Acid.Conc. 26.68 6.98 37.97 349s -------------------------------------------------------- 349s coleman 20 5 13 2.149184 349s Best subsample: 349s [1] 3 4 5 7 8 12 13 14 16 17 18 19 20 349s Outliers: 2 349s [1] 6 10 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 349s 349s Robust Estimate of Location: 349s salaryP fatherWc sstatus teacherSc motherLev 349s 2.76 41.08 2.76 25.01 6.27 349s 349s Robust Estimate of Covariance: 349s salaryP fatherWc sstatus teacherSc motherLev 349s salaryP 0.391 2.956 2.146 0.447 0.110 349s fatherWc 2.956 1358.640 442.724 12.235 32.842 349s sstatus 2.146 442.724 205.590 6.464 11.382 349s teacherSc 0.447 12.235 6.464 1.179 0.510 349s motherLev 0.110 32.842 11.382 0.510 0.919 349s -------------------------------------------------------- 349s salinity 28 3 16 1.940763 349s Best subsample: 349s [1] 1 8 10 12 13 14 15 17 18 20 21 22 25 26 27 28 349s Outliers: 2 349s [1] 5 16 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=16) 349s 349s Robust Estimate of Location: 349s X1 X2 X3 349s 10.50 2.58 23.12 349s 349s Robust Estimate of Covariance: 349s X1 X2 X3 349s X1 10.90243 -0.00457 -1.46156 349s X2 -0.00457 3.85051 -1.94604 349s X3 -1.46156 -1.94604 3.21424 349s -------------------------------------------------------- 349s wood 20 5 13 -35.240819 349s Best subsample: 349s [1] 1 2 3 5 9 11 12 13 14 15 17 18 20 349s Outliers: 4 349s [1] 4 6 8 19 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 349s 349s Robust Estimate of Location: 349s x1 x2 x3 x4 x5 349s 0.582 0.125 0.530 0.534 0.888 349s 349s Robust Estimate of Covariance: 349s x1 x2 x3 x4 x5 349s x1 1.05e-02 1.81e-03 2.08e-03 -6.41e-04 -9.61e-04 349s x2 1.81e-03 5.55e-04 8.76e-04 -2.03e-04 -4.70e-05 349s x3 2.08e-03 8.76e-04 5.60e-03 -1.11e-03 -1.26e-05 349s x4 -6.41e-04 -2.03e-04 -1.11e-03 4.27e-03 2.60e-03 349s x5 -9.61e-04 -4.70e-05 -1.26e-05 2.60e-03 2.95e-03 349s -------------------------------------------------------- 349s hbk 75 3 39 -1.045501 349s Best subsample: 349s [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 349s [26] 54 55 56 58 59 63 64 66 67 70 71 72 73 74 349s Outliers: 14 349s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=39) 349s 349s Robust Estimate of Location: 349s X1 X2 X3 349s 1.54 1.78 1.69 349s 349s Robust Estimate of Covariance: 349s X1 X2 X3 349s X1 1.227 0.055 0.127 349s X2 0.055 1.249 0.153 349s X3 0.127 0.153 1.160 349s -------------------------------------------------------- 349s Animals 28 2 15 14.555543 349s Best subsample: 349s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 349s Outliers: 14 349s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=15) 349s 349s Robust Estimate of Location: 349s body brain 349s 18.7 64.9 349s 349s Robust Estimate of Covariance: 349s body brain 349s body 929 1576 349s brain 1576 5646 349s -------------------------------------------------------- 349s bushfire 38 5 22 18.135810 349s Best subsample: 349s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 349s Outliers: 16 349s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=22) 349s 349s Robust Estimate of Location: 349s V1 V2 V3 V4 V5 349s 105 147 274 218 279 349s 349s Robust Estimate of Covariance: 349s V1 V2 V3 V4 V5 349s V1 346 268 -1692 -381 -311 349s V2 268 236 -1125 -230 -194 349s V3 -1692 -1125 9993 2455 1951 349s V4 -381 -230 2455 647 505 349s V5 -311 -194 1951 505 398 349s -------------------------------------------------------- 349s lactic 20 2 11 0.359580 349s Best subsample: 349s [1] 1 2 3 4 5 7 8 9 10 11 12 349s Outliers: 4 349s [1] 17 18 19 20 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=11) 349s 349s Robust Estimate of Location: 349s X Y 349s 3.86 5.01 349s 349s Robust Estimate of Covariance: 349s X Y 349s X 10.6 14.6 349s Y 14.6 21.3 349s -------------------------------------------------------- 349s pension 18 2 10 16.675508 349s Best subsample: 349s [1] 1 2 3 4 5 6 8 9 11 12 349s Outliers: 5 349s [1] 14 15 16 17 18 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 349s 349s Robust Estimate of Location: 349s Income Reserves 349s 52.3 560.9 349s 349s Robust Estimate of Covariance: 349s Income Reserves 349s Income 1420 11932 349s Reserves 11932 208643 349s -------------------------------------------------------- 349s radarImage 1573 5 789 36.694865 349s Best subsample: 349s Too long... 349s Outliers: 114 349s [1] 164 237 238 242 261 262 351 450 451 462 463 480 481 509 516 349s [16] 535 542 572 597 620 643 654 669 679 697 737 802 803 804 818 349s [31] 832 833 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 349s [46] 1202 1223 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 349s [61] 1376 1393 1394 1402 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1504 349s [76] 1510 1511 1512 1518 1519 1520 1521 1522 1525 1526 1527 1528 1530 1532 1534 349s [91] 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1557 1558 1561 349s [106] 1562 1564 1565 1566 1567 1569 1570 1571 1573 349s ------------- 349s 349s Call: 349s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 349s -> Method: Deterministic MCD(alpha=0.5 ==> h=789) 349s 349s Robust Estimate of Location: 349s X.coord Y.coord Band.1 Band.2 Band.3 349s 52.78 35.37 7.12 18.81 9.09 349s 349s Robust Estimate of Covariance: 349s X.coord Y.coord Band.1 Band.2 Band.3 349s X.coord 123.2 21.5 -363.9 -200.1 -24.3 349s Y.coord 21.5 410.7 46.5 -177.3 -33.4 349s Band.1 -363.9 46.5 27051.1 8138.9 469.3 349s Band.2 -200.1 -177.3 8138.9 25938.0 946.2 349s Band.3 -24.3 -33.4 469.3 946.2 4470.1 349s -------------------------------------------------------- 350s NOxEmissions 8088 4 4046 2.474536 350s Best subsample: 350s Too long... 350s Outliers: 2152 350s Too many to print ... 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=4046) 350s 350s Robust Estimate of Location: 350s julday LNOx LNOxEm sqrtWS 350s 168.20 4.73 7.91 1.37 350s 350s Robust Estimate of Covariance: 350s julday LNOx LNOxEm sqrtWS 350s julday 9176.2934 12.0355 0.7022 -10.1387 350s LNOx 12.0355 0.4736 0.1430 -0.1528 350s LNOxEm 0.7022 0.1430 0.2527 0.0436 350s sqrtWS -10.1387 -0.1528 0.0436 0.2074 350s -------------------------------------------------------- 350s vaso 39 2 21 -3.972244 350s Best subsample: 350s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 350s Outliers: 4 350s [1] 1 2 17 31 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=21) 350s 350s Robust Estimate of Location: 350s Volume Rate 350s 1.16 1.72 350s 350s Robust Estimate of Covariance: 350s Volume Rate 350s Volume 0.313 -0.167 350s Rate -0.167 0.728 350s -------------------------------------------------------- 350s wagnerGrowth 63 6 35 6.511864 350s Best subsample: 350s [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 350s [26] 48 51 52 53 54 55 56 57 60 62 350s Outliers: 15 350s [1] 1 8 15 21 22 28 29 33 39 42 43 46 49 50 63 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=35) 350s 350s Robust Estimate of Location: 350s Region PA GPA HS GHS y 350s 10.91 33.65 -2.05 2.43 0.31 6.98 350s 350s Robust Estimate of Covariance: 350s Region PA GPA HS GHS y 350s Region 35.1365 17.7291 -1.4003 -0.6554 -0.4728 -14.9305 350s PA 17.7291 28.4297 -5.5245 -1.2444 -0.0452 -29.6181 350s GPA -1.4003 -5.5245 5.2170 0.3954 -0.2152 3.8252 350s HS -0.6554 -1.2444 0.3954 0.7273 -0.0107 2.1514 350s GHS -0.4728 -0.0452 -0.2152 -0.0107 0.1728 0.8440 350s y -14.9305 -29.6181 3.8252 2.1514 0.8440 79.0511 350s -------------------------------------------------------- 350s fish 159 6 82 8.880459 350s Best subsample: 350s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 350s [20] 20 21 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 350s [39] 50 51 52 53 54 55 56 57 58 59 60 106 107 108 109 110 111 112 113 350s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 350s [77] 134 135 136 137 138 139 350s Outliers: 64 350s [1] 30 39 40 41 62 63 64 65 66 68 69 70 73 74 75 76 77 78 79 350s [20] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 350s [39] 99 100 101 102 103 104 105 141 142 143 144 145 146 147 148 149 150 151 152 350s [58] 153 154 155 156 157 158 159 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=82) 350s 350s Robust Estimate of Location: 350s Weight Length1 Length2 Length3 Height Width 350s 316.3 24.1 26.3 29.3 31.0 14.7 350s 350s Robust Estimate of Covariance: 350s Weight Length1 Length2 Length3 Height Width 350s Weight 64662.19 1412.34 1541.95 1917.21 1420.83 -61.15 350s Length1 1412.34 34.14 37.04 45.07 29.25 -1.26 350s Length2 1541.95 37.04 40.26 49.04 32.21 -1.34 350s Length3 1917.21 45.07 49.04 60.82 43.03 -2.15 350s Height 1420.83 29.25 32.21 43.03 46.50 -2.66 350s Width -61.15 -1.26 -1.34 -2.15 -2.66 1.02 350s -------------------------------------------------------- 350s pottery 27 6 17 -10.586933 350s Best subsample: 350s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 350s Outliers: 9 350s [1] 3 8 12 16 17 18 23 24 25 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=17) 350s 350s Robust Estimate of Location: 350s SI AL FE MG CA TI 350s 54.983 15.206 9.700 3.817 5.211 0.859 350s 350s Robust Estimate of Covariance: 350s SI AL FE MG CA TI 350s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 350s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 350s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 350s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 350s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 350s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 350s -------------------------------------------------------- 350s rice 105 6 56 -14.423048 350s Best subsample: 350s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 350s [20] 38 44 45 47 51 52 53 55 59 60 65 66 67 70 72 74 76 78 79 350s [39] 80 81 82 83 84 85 86 90 92 93 94 95 97 98 99 100 101 105 350s Outliers: 13 350s [1] 9 19 28 40 42 43 49 58 62 64 71 75 77 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=56) 350s 350s Robust Estimate of Location: 350s Favor Appearance Taste Stickiness 350s -0.2950 0.0799 -0.1555 0.0363 350s Toughness Overall_evaluation 350s 0.0530 -0.2284 350s 350s Robust Estimate of Covariance: 350s Favor Appearance Taste Stickiness Toughness 350s Favor 0.466 0.389 0.471 0.447 -0.198 350s Appearance 0.389 0.610 0.592 0.570 -0.293 350s Taste 0.471 0.592 0.760 0.718 -0.356 350s Stickiness 0.447 0.570 0.718 0.820 -0.419 350s Toughness -0.198 -0.293 -0.356 -0.419 0.400 350s Overall_evaluation 0.557 0.669 0.838 0.846 -0.425 350s Overall_evaluation 350s Favor 0.557 350s Appearance 0.669 350s Taste 0.838 350s Stickiness 0.846 350s Toughness -0.425 350s Overall_evaluation 0.987 350s -------------------------------------------------------- 350s un86 73 7 40 17.117142 350s Best subsample: 350s [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 350s [26] 52 55 56 57 60 61 62 63 64 65 67 70 71 72 73 350s Outliers: 30 350s [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 350s [26] 58 59 66 68 69 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=40) 350s 350s Robust Estimate of Location: 350s POP MOR CAR DR GNP DEN TB 350s 17.036 68.512 6.444 0.877 1.134 64.140 0.433 350s 350s Robust Estimate of Covariance: 350s POP MOR CAR DR GNP DEN 350s POP 3.61e+02 1.95e+02 -6.28e+00 -1.91e-02 -2.07e+00 5.79e+01 350s MOR 1.95e+02 2.39e+03 -2.79e+02 -3.37e+01 -3.39e+01 -9.21e+02 350s CAR -6.28e+00 -2.79e+02 5.76e+01 5.77e+00 6.59e+00 7.81e+01 350s DR -1.91e-02 -3.37e+01 5.77e+00 9.07e-01 5.66e-01 1.69e+01 350s GNP -2.07e+00 -3.39e+01 6.59e+00 5.66e-01 1.42e+00 9.28e+00 350s DEN 5.79e+01 -9.21e+02 7.81e+01 1.69e+01 9.28e+00 3.53e+03 350s TB -6.09e-02 -9.93e-01 2.50e-01 1.98e-02 6.82e-03 -9.75e-01 350s TB 350s POP -6.09e-02 350s MOR -9.93e-01 350s CAR 2.50e-01 350s DR 1.98e-02 350s GNP 6.82e-03 350s DEN -9.75e-01 350s TB 1.64e-02 350s -------------------------------------------------------- 350s wages 39 10 19 23.119456 350s Best subsample: 350s [1] 1 2 5 6 7 9 10 11 12 13 14 15 19 21 23 25 26 27 28 350s Outliers: 9 350s [1] 4 5 9 24 25 26 28 32 34 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 350s 350s Robust Estimate of Location: 350s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 350s 2161.89 2.95 1114.21 297.68 374.00 7269.37 39.13 2.43 350s RACE SCHOOL 350s 36.13 10.39 350s 350s Robust Estimate of Covariance: 350s HRS RATE ERSP ERNO NEIN ASSET 350s HRS 3.53e+03 8.31e+00 -5.96e+03 -6.43e+02 5.15e+03 1.12e+05 350s RATE 8.31e+00 1.78e-01 8.19e+00 2.70e+00 3.90e+01 8.94e+02 350s ERSP -5.96e+03 8.19e+00 1.90e+04 1.13e+03 -4.73e+03 -9.49e+04 350s ERNO -6.43e+02 2.70e+00 1.13e+03 1.80e+03 -3.56e+02 -7.33e+03 350s NEIN 5.15e+03 3.90e+01 -4.73e+03 -3.56e+02 1.38e+04 3.00e+05 350s ASSET 1.12e+05 8.94e+02 -9.49e+04 -7.33e+03 3.00e+05 6.62e+06 350s AGE -3.33e+01 -6.55e-02 8.33e+01 1.50e+00 -3.28e+01 -7.55e+02 350s DEP 4.50e+00 -4.01e-02 -2.77e+01 1.31e+00 -8.09e+00 -1.61e+02 350s RACE -1.30e+03 -6.06e+00 1.80e+03 1.48e+02 -2.58e+03 -5.59e+04 350s SCHOOL 3.01e+01 3.58e-01 -5.57e+00 2.84e+00 9.26e+01 2.10e+03 350s AGE DEP RACE SCHOOL 350s HRS -3.33e+01 4.50e+00 -1.30e+03 3.01e+01 350s RATE -6.55e-02 -4.01e-02 -6.06e+00 3.58e-01 350s ERSP 8.33e+01 -2.77e+01 1.80e+03 -5.57e+00 350s ERNO 1.50e+00 1.31e+00 1.48e+02 2.84e+00 350s NEIN -3.28e+01 -8.09e+00 -2.58e+03 9.26e+01 350s ASSET -7.55e+02 -1.61e+02 -5.59e+04 2.10e+03 350s AGE 6.57e-01 -1.64e-01 1.13e+01 -2.67e-01 350s DEP -1.64e-01 9.20e-02 2.38e-01 -6.01e-02 350s RACE 1.13e+01 2.38e-01 5.73e+02 -1.67e+01 350s SCHOOL -2.67e-01 -6.01e-02 -1.67e+01 7.95e-01 350s -------------------------------------------------------- 350s airquality 153 4 58 18.316848 350s Best subsample: 350s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 350s [20] 47 48 49 50 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 350s [39] 81 82 84 86 87 89 90 91 92 95 97 98 100 101 105 106 108 109 110 350s [58] 111 350s Outliers: 10 350s [1] 8 9 15 18 24 30 48 62 117 148 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=58) 350s 350s Robust Estimate of Location: 350s Ozone Solar.R Wind Temp 350s 40.80 189.37 9.66 78.81 350s 350s Robust Estimate of Covariance: 350s Ozone Solar.R Wind Temp 350s Ozone 935.54 857.76 -56.30 220.48 350s Solar.R 857.76 8507.83 1.36 155.13 350s Wind -56.30 1.36 9.90 -11.61 350s Temp 220.48 155.13 -11.61 84.00 350s -------------------------------------------------------- 350s attitude 30 7 19 24.464288 350s Best subsample: 350s [1] 2 3 4 5 7 8 10 11 12 15 17 19 21 22 23 25 27 28 29 350s Outliers: 8 350s [1] 6 9 13 14 16 18 24 26 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 350s 350s Robust Estimate of Location: 350s rating complaints privileges learning raises critical 350s 64.4 65.2 51.0 55.5 65.9 77.4 350s advance 350s 43.2 350s 350s Robust Estimate of Covariance: 350s rating complaints privileges learning raises critical advance 350s rating 199.95 162.36 115.83 160.44 128.87 -13.55 66.20 350s complaints 162.36 204.84 130.33 170.66 150.19 16.28 96.66 350s privileges 115.83 130.33 181.31 152.63 106.56 4.52 91.44 350s learning 160.44 170.66 152.63 213.06 156.57 9.92 88.31 350s raises 128.87 150.19 106.56 156.57 152.05 23.10 84.00 350s critical -13.55 16.28 4.52 9.92 23.10 80.22 27.15 350s advance 66.20 96.66 91.44 88.31 84.00 27.15 95.51 350s -------------------------------------------------------- 350s attenu 182 5 86 6.593068 350s Best subsample: 350s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 350s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 350s [39] 115 116 117 119 120 121 122 124 125 126 127 128 129 130 131 132 133 134 135 350s [58] 136 137 138 139 140 141 144 145 146 147 148 149 150 151 152 153 154 155 156 350s [77] 157 158 159 160 161 162 163 164 165 166 350s Outliers: 49 350s [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 350s [20] 23 24 25 27 28 29 30 31 32 33 40 45 47 59 60 61 64 65 78 350s [39] 82 83 97 98 100 101 102 103 104 105 117 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=86) 350s 350s Robust Estimate of Location: 350s event mag station dist accel 350s 17.122 5.798 63.461 25.015 0.131 350s 350s Robust Estimate of Covariance: 350s event mag station dist accel 350s event 2.98e+01 -1.58e+00 9.49e+01 -8.36e+00 -3.59e-02 350s mag -1.58e+00 4.26e-01 -3.88e+00 3.13e+00 5.30e-03 350s station 9.49e+01 -3.88e+00 1.10e+03 2.60e+01 5.38e-01 350s dist -8.36e+00 3.13e+00 2.60e+01 2.66e+02 -9.23e-01 350s accel -3.59e-02 5.30e-03 5.38e-01 -9.23e-01 7.78e-03 350s -------------------------------------------------------- 350s USJudgeRatings 43 12 28 -47.886937 350s Best subsample: 350s [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 350s [26] 40 41 43 350s Outliers: 14 350s [1] 1 5 7 8 12 13 14 17 20 21 23 31 35 42 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=28) 350s 350s Robust Estimate of Location: 350s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 350s 7.46 8.26 7.88 8.06 7.85 7.92 7.84 7.83 7.67 7.74 8.31 8.03 350s 350s Robust Estimate of Covariance: 350s CONT INTG DMNR DILG CFMG DECI PREP FAMI 350s CONT 0.7363 -0.2916 -0.4193 -0.1943 -0.0555 -0.0690 -0.1703 -0.1727 350s INTG -0.2916 0.4179 0.5511 0.4167 0.3176 0.3102 0.4247 0.4279 350s DMNR -0.4193 0.5511 0.8141 0.5256 0.4092 0.3934 0.5294 0.5094 350s DILG -0.1943 0.4167 0.5256 0.4820 0.3904 0.3819 0.5054 0.5104 350s CFMG -0.0555 0.3176 0.4092 0.3904 0.3595 0.3368 0.4180 0.4206 350s DECI -0.0690 0.3102 0.3934 0.3819 0.3368 0.3310 0.4135 0.4194 350s PREP -0.1703 0.4247 0.5294 0.5054 0.4180 0.4135 0.5647 0.5752 350s FAMI -0.1727 0.4279 0.5094 0.5104 0.4206 0.4194 0.5752 0.6019 350s ORAL -0.2109 0.4453 0.5646 0.5054 0.4200 0.4121 0.5575 0.5735 350s WRIT -0.2033 0.4411 0.5466 0.5087 0.4222 0.4147 0.5592 0.5787 350s PHYS -0.1624 0.2578 0.3163 0.2833 0.2268 0.2362 0.3108 0.3284 350s RTEN -0.2622 0.4872 0.6324 0.5203 0.4145 0.4081 0.5488 0.5595 350s ORAL WRIT PHYS RTEN 350s CONT -0.2109 -0.2033 -0.1624 -0.2622 350s INTG 0.4453 0.4411 0.2578 0.4872 350s DMNR 0.5646 0.5466 0.3163 0.6324 350s DILG 0.5054 0.5087 0.2833 0.5203 350s CFMG 0.4200 0.4222 0.2268 0.4145 350s DECI 0.4121 0.4147 0.2362 0.4081 350s PREP 0.5575 0.5592 0.3108 0.5488 350s FAMI 0.5735 0.5787 0.3284 0.5595 350s ORAL 0.5701 0.5677 0.3283 0.5688 350s WRIT 0.5677 0.5715 0.3268 0.5645 350s PHYS 0.3283 0.3268 0.2302 0.3308 350s RTEN 0.5688 0.5645 0.3308 0.6057 350s -------------------------------------------------------- 350s USArrests 50 4 27 15.438912 350s Best subsample: 350s [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 350s [26] 49 50 350s Outliers: 7 350s [1] 2 5 6 10 24 28 33 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=27) 350s 350s Robust Estimate of Location: 350s Murder Assault UrbanPop Rape 350s 6.91 150.10 65.88 18.75 350s 350s Robust Estimate of Covariance: 350s Murder Assault UrbanPop Rape 350s Murder 17.9 285.4 17.6 25.0 350s Assault 285.4 6572.8 524.9 465.0 350s UrbanPop 17.6 524.9 211.9 50.5 350s Rape 25.0 465.0 50.5 56.4 350s -------------------------------------------------------- 350s longley 16 7 12 12.747678 350s Best subsample: 350s [1] 5 6 7 8 9 10 11 12 13 14 15 16 350s Outliers: 4 350s [1] 1 2 3 4 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 350s 350s Robust Estimate of Location: 350s GNP.deflator GNP Unemployed Armed.Forces Population 350s 106.5 430.6 328.2 295.0 120.2 350s Year Employed 350s 1956.5 66.9 350s 350s Robust Estimate of Covariance: 350s GNP.deflator GNP Unemployed Armed.Forces Population 350s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 350s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 350s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 350s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 350s Population 81.4 803.4 929.1 -327.4 63.5 350s Year 51.6 504.3 595.6 -216.7 39.7 350s Employed 34.2 344.1 323.6 -149.5 26.2 350s Year Employed 350s GNP.deflator 51.6 34.2 350s GNP 504.3 344.1 350s Unemployed 595.6 323.6 350s Armed.Forces -216.7 -149.5 350s Population 39.7 26.2 350s Year 25.1 16.7 350s Employed 16.7 12.4 350s -------------------------------------------------------- 350s Loblolly 84 3 44 4.898174 350s Best subsample: 350s [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 350s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 350s Outliers: 31 350s [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 350s [26] 72 76 77 78 83 84 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=44) 350s 350s Robust Estimate of Location: 350s height age Seed 350s 20.44 8.19 7.72 350s 350s Robust Estimate of Covariance: 350s height age Seed 350s height 247.8 79.5 11.9 350s age 79.5 25.7 3.0 350s Seed 11.9 3.0 17.1 350s -------------------------------------------------------- 350s quakes 1000 4 502 8.274209 350s Best subsample: 350s Too long... 350s Outliers: 266 350s Too many to print ... 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 350s -> Method: Deterministic MCD(alpha=0.5 ==> h=502) 350s 350s Robust Estimate of Location: 350s lat long depth mag 350s -21.34 182.47 360.58 4.54 350s 350s Robust Estimate of Covariance: 350s lat long depth mag 350s lat 1.50e+01 3.58e+00 1.37e+02 -2.66e-01 350s long 3.58e+00 4.55e+00 -3.61e+02 4.64e-02 350s depth 1.37e+02 -3.61e+02 4.84e+04 -1.36e+01 350s mag -2.66e-01 4.64e-02 -1.36e+01 1.34e-01 350s -------------------------------------------------------- 350s ======================================================== 350s > dodata(method="exact") 350s 350s Call: dodata(method = "exact") 350s Data Set n p Half LOG(obj) Time 350s ======================================================== 350s heart 12 2 7 5.678742 350s Best subsample: 350s [1] 1 3 4 5 7 9 11 350s Outliers: 0 350s Too many to print ... 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "exact", trace = FALSE) 350s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = exact; (n,k)mini = (300,5) 350s 350s Robust Estimate of Location: 350s height weight 350s 38.3 33.1 350s 350s Robust Estimate of Covariance: 350s height weight 350s height 135 259 350s weight 259 564 350s -------------------------------------------------------- 350s starsCYG 47 2 25 -8.031215 350s Best subsample: 350s [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 350s Outliers: 7 350s [1] 7 9 11 14 20 30 34 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "exact", trace = FALSE) 350s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = exact; (n,k)mini = (300,5) 350s 350s Robust Estimate of Location: 350s log.Te log.light 350s 4.41 4.95 350s 350s Robust Estimate of Covariance: 350s log.Te log.light 350s log.Te 0.0132 0.0394 350s log.light 0.0394 0.2743 350s -------------------------------------------------------- 350s phosphor 18 2 10 6.878847 350s Best subsample: 350s [1] 3 5 8 9 11 12 13 14 15 17 350s Outliers: 3 350s [1] 1 6 10 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "exact", trace = FALSE) 350s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 350s 350s Robust Estimate of Location: 350s inorg organic 350s 13.4 38.8 350s 350s Robust Estimate of Covariance: 350s inorg organic 350s inorg 129 130 350s organic 130 182 350s -------------------------------------------------------- 350s coleman 20 5 13 1.286808 350s Best subsample: 350s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 350s Outliers: 7 350s [1] 1 6 9 10 11 15 18 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "exact", trace = FALSE) 350s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 350s 350s Robust Estimate of Location: 350s salaryP fatherWc sstatus teacherSc motherLev 350s 2.76 48.38 6.12 25.00 6.40 350s 350s Robust Estimate of Covariance: 350s salaryP fatherWc sstatus teacherSc motherLev 350s salaryP 0.253 1.786 -0.266 0.151 0.075 350s fatherWc 1.786 1303.382 330.496 12.604 34.503 350s sstatus -0.266 330.496 119.888 3.833 10.131 350s teacherSc 0.151 12.604 3.833 0.785 0.555 350s motherLev 0.075 34.503 10.131 0.555 1.043 350s -------------------------------------------------------- 350s salinity 28 3 16 1.326364 350s Best subsample: 350s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 350s Outliers: 4 350s [1] 5 16 23 24 350s ------------- 350s 350s Call: 350s CovMcd(x = x, nsamp = "exact", trace = FALSE) 350s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = exact; (n,k)mini = (300,5) 350s 350s Robust Estimate of Location: 350s X1 X2 X3 350s 10.08 2.78 22.78 350s 350s Robust Estimate of Covariance: 350s X1 X2 X3 350s X1 10.44 1.01 -3.19 350s X2 1.01 3.83 -1.44 350s X3 -3.19 -1.44 2.39 350s -------------------------------------------------------- 351s wood 20 5 13 -36.270094 351s Best subsample: 351s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 351s Outliers: 7 351s [1] 4 6 7 8 11 16 19 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s x1 x2 x3 x4 x5 351s 0.587 0.122 0.531 0.538 0.892 351s 351s Robust Estimate of Covariance: 351s x1 x2 x3 x4 x5 351s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 351s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 351s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 351s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 351s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 351s -------------------------------------------------------- 351s Animals 28 2 15 14.555543 351s Best subsample: 351s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 351s Outliers: 14 351s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s body brain 351s 18.7 64.9 351s 351s Robust Estimate of Covariance: 351s body brain 351s body 929 1576 351s brain 1576 5646 351s -------------------------------------------------------- 351s lactic 20 2 11 0.359580 351s Best subsample: 351s [1] 1 2 3 4 5 7 8 9 10 11 12 351s Outliers: 4 351s [1] 17 18 19 20 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s X Y 351s 3.86 5.01 351s 351s Robust Estimate of Covariance: 351s X Y 351s X 10.6 14.6 351s Y 14.6 21.3 351s -------------------------------------------------------- 351s pension 18 2 10 16.675508 351s Best subsample: 351s [1] 1 2 3 4 5 6 8 9 11 12 351s Outliers: 5 351s [1] 14 15 16 17 18 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s Income Reserves 351s 52.3 560.9 351s 351s Robust Estimate of Covariance: 351s Income Reserves 351s Income 1420 11932 351s Reserves 11932 208643 351s -------------------------------------------------------- 351s vaso 39 2 21 -3.972244 351s Best subsample: 351s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 351s Outliers: 4 351s [1] 1 2 17 31 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s Volume Rate 351s 1.16 1.72 351s 351s Robust Estimate of Covariance: 351s Volume Rate 351s Volume 0.313 -0.167 351s Rate -0.167 0.728 351s -------------------------------------------------------- 351s stackloss 21 3 12 5.472581 351s Best subsample: 351s [1] 4 5 6 7 8 9 10 11 12 13 14 20 351s Outliers: 9 351s [1] 1 2 3 15 16 17 18 19 21 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s Air.Flow Water.Temp Acid.Conc. 351s 59.5 20.8 87.3 351s 351s Robust Estimate of Covariance: 351s Air.Flow Water.Temp Acid.Conc. 351s Air.Flow 6.29 5.85 5.74 351s Water.Temp 5.85 9.23 6.14 351s Acid.Conc. 5.74 6.14 23.25 351s -------------------------------------------------------- 351s pilot 20 2 11 6.487287 351s Best subsample: 351s [1] 2 3 6 7 9 12 15 16 17 18 20 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMcd(x = x, nsamp = "exact", trace = FALSE) 351s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 351s 351s Robust Estimate of Location: 351s X Y 351s 101.1 67.7 351s 351s Robust Estimate of Covariance: 351s X Y 351s X 3344 1070 351s Y 1070 343 351s -------------------------------------------------------- 351s ======================================================== 351s > dodata(method="MRCD") 351s 351s Call: dodata(method = "MRCD") 351s Data Set n p Half LOG(obj) Time 351s ======================================================== 351s heart 12 2 6 7.446266 351s Best subsample: 351s [1] 1 3 4 7 9 11 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=6) 351s 351s Robust Estimate of Location: 351s height weight 351s 38.8 33.0 351s 351s Robust Estimate of Covariance: 351s height weight 351s height 47.4 75.2 351s weight 75.2 155.4 351s -------------------------------------------------------- 351s starsCYG 47 2 24 -5.862050 351s Best subsample: 351s [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 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=24) 351s 351s Robust Estimate of Location: 351s log.Te log.light 351s 4.44 5.05 351s 351s Robust Estimate of Covariance: 351s log.Te log.light 351s log.Te 0.00867 0.02686 351s log.light 0.02686 0.41127 351s -------------------------------------------------------- 351s phosphor 18 2 9 9.954788 351s Best subsample: 351s [1] 4 7 8 9 11 12 13 14 16 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 351s 351s Robust Estimate of Location: 351s inorg organic 351s 12.5 39.0 351s 351s Robust Estimate of Covariance: 351s inorg organic 351s inorg 236 140 351s organic 140 172 351s -------------------------------------------------------- 351s stackloss 21 3 11 7.991165 351s Best subsample: 351s [1] 4 5 6 7 8 9 10 13 18 19 20 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=11) 351s 351s Robust Estimate of Location: 351s Air.Flow Water.Temp Acid.Conc. 351s 58.2 21.4 85.2 351s 351s Robust Estimate of Covariance: 351s Air.Flow Water.Temp Acid.Conc. 351s Air.Flow 49.8 17.2 42.7 351s Water.Temp 17.2 13.8 25.2 351s Acid.Conc. 42.7 25.2 58.2 351s -------------------------------------------------------- 351s coleman 20 5 10 5.212156 351s Best subsample: 351s [1] 3 4 5 7 8 9 14 16 19 20 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 351s 351s Robust Estimate of Location: 351s salaryP fatherWc sstatus teacherSc motherLev 351s 2.78 59.44 9.28 25.41 6.70 351s 351s Robust Estimate of Covariance: 351s salaryP fatherWc sstatus teacherSc motherLev 351s salaryP 0.1582 -0.2826 0.4112 0.1754 0.0153 351s fatherWc -0.2826 902.9210 201.5815 -2.1236 18.8736 351s sstatus 0.4112 201.5815 65.4580 -0.3876 4.7794 351s teacherSc 0.1754 -2.1236 -0.3876 0.7233 -0.0322 351s motherLev 0.0153 18.8736 4.7794 -0.0322 0.5417 351s -------------------------------------------------------- 351s salinity 28 3 14 3.586919 351s Best subsample: 351s [1] 1 7 8 12 13 14 18 20 21 22 25 26 27 28 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 351s 351s Robust Estimate of Location: 351s X1 X2 X3 351s 10.95 3.71 21.99 351s 351s Robust Estimate of Covariance: 351s X1 X2 X3 351s X1 14.153 0.718 -3.359 351s X2 0.718 3.565 -0.722 351s X3 -3.359 -0.722 1.607 351s -------------------------------------------------------- 351s wood 20 5 10 -33.100492 351s Best subsample: 351s [1] 1 2 3 5 11 14 15 17 18 20 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 351s 351s Robust Estimate of Location: 351s x1 x2 x3 x4 x5 351s 0.572 0.120 0.504 0.545 0.899 351s 351s Robust Estimate of Covariance: 351s x1 x2 x3 x4 x5 351s x1 0.007543 0.001720 0.000412 -0.001230 -0.001222 351s x2 0.001720 0.000568 0.000355 -0.000533 -0.000132 351s x3 0.000412 0.000355 0.002478 0.000190 0.000811 351s x4 -0.001230 -0.000533 0.000190 0.002327 0.000967 351s x5 -0.001222 -0.000132 0.000811 0.000967 0.001894 351s -------------------------------------------------------- 351s hbk 75 3 38 1.539545 351s Best subsample: 351s [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 351s [26] 55 56 58 59 63 64 66 67 70 71 72 73 74 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=38) 351s 351s Robust Estimate of Location: 351s X1 X2 X3 351s 1.60 2.37 1.64 351s 351s Robust Estimate of Covariance: 351s X1 X2 X3 351s X1 2.810 0.124 1.248 351s X2 0.124 1.017 0.208 351s X3 1.248 0.208 2.218 351s -------------------------------------------------------- 351s Animals 28 2 14 16.278395 351s Best subsample: 351s [1] 1 3 4 5 10 11 18 19 20 21 22 23 26 27 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 351s 351s Robust Estimate of Location: 351s body brain 351s 19.5 56.8 351s 351s Robust Estimate of Covariance: 351s body brain 351s body 2802 5179 351s brain 5179 13761 351s -------------------------------------------------------- 351s bushfire 38 5 19 28.483413 351s Best subsample: 351s [1] 1 2 3 4 5 14 15 16 17 18 19 20 21 22 23 24 25 26 27 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=19) 351s 351s Robust Estimate of Location: 351s V1 V2 V3 V4 V5 351s 103 145 287 221 281 351s 351s Robust Estimate of Covariance: 351s V1 V2 V3 V4 V5 351s V1 366 249 -1993 -503 -396 351s V2 249 252 -1223 -291 -233 351s V3 -1993 -1223 14246 3479 2718 351s V4 -503 -291 3479 1083 748 351s V5 -396 -233 2718 748 660 351s -------------------------------------------------------- 351s lactic 20 2 10 2.593141 351s Best subsample: 351s [1] 1 2 3 4 5 7 8 9 10 11 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 351s 351s Robust Estimate of Location: 351s X Y 351s 2.60 3.63 351s 351s Robust Estimate of Covariance: 351s X Y 351s X 8.13 13.54 351s Y 13.54 24.17 351s -------------------------------------------------------- 351s pension 18 2 9 18.931204 351s Best subsample: 351s [1] 2 3 4 5 6 8 9 11 12 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 351s 351s Robust Estimate of Location: 351s Income Reserves 351s 45.7 466.9 351s 351s Robust Estimate of Covariance: 351s Income Reserves 351s Income 2127 23960 351s Reserves 23960 348275 351s -------------------------------------------------------- 351s vaso 39 2 20 -1.864710 351s Best subsample: 351s [1] 3 4 8 14 18 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=20) 351s 351s Robust Estimate of Location: 351s Volume Rate 351s 1.14 1.77 351s 351s Robust Estimate of Covariance: 351s Volume Rate 351s Volume 0.44943 -0.00465 351s Rate -0.00465 0.34480 351s -------------------------------------------------------- 351s wagnerGrowth 63 6 32 9.287760 351s Best subsample: 351s [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 351s [26] 53 54 55 56 57 60 62 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=32) 351s 351s Robust Estimate of Location: 351s Region PA GPA HS GHS y 351s 10.719 33.816 -2.144 2.487 0.293 4.918 351s 351s Robust Estimate of Covariance: 351s Region PA GPA HS GHS y 351s Region 56.7128 17.4919 -2.9710 -0.6491 -0.4545 -10.4287 351s PA 17.4919 29.9968 -7.6846 -1.3141 0.5418 -35.6434 351s GPA -2.9710 -7.6846 6.3238 1.1257 -0.4757 12.4707 351s HS -0.6491 -1.3141 1.1257 1.1330 -0.0915 3.3617 351s GHS -0.4545 0.5418 -0.4757 -0.0915 0.1468 -1.1228 351s y -10.4287 -35.6434 12.4707 3.3617 -1.1228 67.4215 351s -------------------------------------------------------- 351s fish 159 6 79 22.142828 351s Best subsample: 351s [1] 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 351s [20] 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 50 51 351s [39] 52 53 54 55 56 57 58 59 60 71 105 106 107 109 110 111 113 114 115 351s [58] 116 117 118 119 120 122 123 124 125 126 127 128 129 130 131 132 134 135 136 351s [77] 137 138 139 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=79) 351s 351s Robust Estimate of Location: 351s Weight Length1 Length2 Length3 Height Width 351s 291.7 23.8 25.9 28.9 30.4 14.7 351s 351s Robust Estimate of Covariance: 351s Weight Length1 Length2 Length3 Height Width 351s Weight 77155.07 1567.55 1713.74 2213.16 1912.62 -103.97 351s Length1 1567.55 45.66 41.57 52.14 38.66 -2.39 351s Length2 1713.74 41.57 54.26 56.77 42.72 -2.55 351s Length3 2213.16 52.14 56.77 82.57 58.84 -3.65 351s Height 1912.62 38.66 42.72 58.84 70.51 -3.80 351s Width -103.97 -2.39 -2.55 -3.65 -3.80 1.19 351s -------------------------------------------------------- 351s pottery 27 6 14 -6.897459 351s Best subsample: 351s [1] 1 2 4 5 6 10 11 13 14 15 19 21 22 26 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 351s 351s Robust Estimate of Location: 351s SI AL FE MG CA TI 351s 54.39 14.93 9.78 3.82 5.11 0.86 351s 351s Robust Estimate of Covariance: 351s SI AL FE MG CA TI 351s SI 17.47469 -0.16656 0.39943 4.48192 -0.71153 0.06515 351s AL -0.16656 3.93154 -0.35738 -2.29899 0.14770 -0.02050 351s FE 0.39943 -0.35738 0.20434 0.37562 -0.22460 0.00943 351s MG 4.48192 -2.29899 0.37562 2.82339 -0.16027 0.02943 351s CA -0.71153 0.14770 -0.22460 -0.16027 0.88443 -0.01711 351s TI 0.06515 -0.02050 0.00943 0.02943 -0.01711 0.00114 351s -------------------------------------------------------- 351s rice 105 6 53 -8.916472 351s Best subsample: 351s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 351s [20] 38 44 45 47 51 52 53 54 55 59 60 65 67 70 72 76 79 80 81 351s [39] 82 83 84 85 86 90 92 93 94 95 97 98 99 101 105 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=53) 351s 351s Robust Estimate of Location: 351s Favor Appearance Taste Stickiness 351s -0.1741 0.0774 -0.0472 0.1868 351s Toughness Overall_evaluation 351s -0.0346 -0.0683 351s 351s Robust Estimate of Covariance: 351s Favor Appearance Taste Stickiness Toughness 351s Favor 0.402 0.306 0.378 0.364 -0.134 351s Appearance 0.306 0.508 0.474 0.407 -0.146 351s Taste 0.378 0.474 0.708 0.611 -0.258 351s Stickiness 0.364 0.407 0.611 0.795 -0.320 351s Toughness -0.134 -0.146 -0.258 -0.320 0.302 351s Overall_evaluation 0.453 0.536 0.746 0.745 -0.327 351s Overall_evaluation 351s Favor 0.453 351s Appearance 0.536 351s Taste 0.746 351s Stickiness 0.745 351s Toughness -0.327 351s Overall_evaluation 0.963 351s -------------------------------------------------------- 351s un86 73 7 37 19.832993 351s Best subsample: 351s [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 351s [26] 56 57 60 62 63 64 65 67 70 71 72 73 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=37) 351s 351s Robust Estimate of Location: 351s POP MOR CAR DR GNP DEN TB 351s 14.462 66.892 6.670 0.858 1.251 55.518 0.429 351s 351s Robust Estimate of Covariance: 351s POP MOR CAR DR GNP DEN 351s POP 3.00e+02 1.58e+02 9.83e+00 2.74e+00 5.51e-01 6.87e+01 351s MOR 1.58e+02 2.96e+03 -4.24e+02 -4.72e+01 -5.40e+01 -1.01e+03 351s CAR 9.83e+00 -4.24e+02 9.12e+01 8.71e+00 1.13e+01 1.96e+02 351s DR 2.74e+00 -4.72e+01 8.71e+00 1.25e+00 1.03e+00 2.74e+01 351s GNP 5.51e-01 -5.40e+01 1.13e+01 1.03e+00 2.31e+00 2.36e+01 351s DEN 6.87e+01 -1.01e+03 1.96e+02 2.74e+01 2.36e+01 3.12e+03 351s TB 2.04e-02 -1.81e+00 3.42e-01 2.57e-02 2.09e-02 -6.88e-01 351s TB 351s POP 2.04e-02 351s MOR -1.81e+00 351s CAR 3.42e-01 351s DR 2.57e-02 351s GNP 2.09e-02 351s DEN -6.88e-01 351s TB 2.59e-02 351s -------------------------------------------------------- 351s wages 39 10 14 35.698016 351s Best subsample: 351s [1] 1 2 5 6 9 10 11 13 15 19 23 25 26 28 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 351s 351s Robust Estimate of Location: 351s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 351s 2167.71 2.96 1113.50 300.43 382.29 7438.00 39.06 2.41 351s RACE SCHOOL 351s 33.00 10.45 351s 351s Robust Estimate of Covariance: 351s HRS RATE ERSP ERNO NEIN ASSET 351s HRS 1.97e+03 -4.14e-01 -4.71e+03 -6.58e+02 1.81e+03 3.84e+04 351s RATE -4.14e-01 1.14e-01 1.79e+01 3.08e+00 1.40e+01 3.57e+02 351s ERSP -4.71e+03 1.79e+01 1.87e+04 2.33e+03 -2.06e+03 -3.57e+04 351s ERNO -6.58e+02 3.08e+00 2.33e+03 5.36e+02 -3.42e+02 -5.56e+03 351s NEIN 1.81e+03 1.40e+01 -2.06e+03 -3.42e+02 5.77e+03 1.10e+05 351s ASSET 3.84e+04 3.57e+02 -3.57e+04 -5.56e+03 1.10e+05 2.86e+06 351s AGE -1.83e+01 1.09e-02 6.69e+01 8.78e+00 -5.07e+00 -1.51e+02 351s DEP 4.82e+00 -3.14e-02 -2.52e+01 -2.96e+00 -5.33e+00 -1.03e+02 351s RACE -5.67e+02 -1.33e+00 1.21e+03 1.81e+02 -9.13e+02 -1.96e+04 351s SCHOOL 5.33e+00 1.87e-01 1.86e+01 3.12e+00 3.20e+01 7.89e+02 351s AGE DEP RACE SCHOOL 351s HRS -1.83e+01 4.82e+00 -5.67e+02 5.33e+00 351s RATE 1.09e-02 -3.14e-02 -1.33e+00 1.87e-01 351s ERSP 6.69e+01 -2.52e+01 1.21e+03 1.86e+01 351s ERNO 8.78e+00 -2.96e+00 1.81e+02 3.12e+00 351s NEIN -5.07e+00 -5.33e+00 -9.13e+02 3.20e+01 351s ASSET -1.51e+02 -1.03e+02 -1.96e+04 7.89e+02 351s AGE 5.71e-01 -1.56e-01 4.58e+00 -5.00e-02 351s DEP -1.56e-01 8.08e-02 -3.02e-01 -4.47e-02 351s RACE 4.58e+00 -3.02e-01 2.36e+02 -4.54e+00 351s SCHOOL -5.00e-02 -4.47e-02 -4.54e+00 4.23e-01 351s -------------------------------------------------------- 351s airquality 153 4 56 21.136376 351s Best subsample: 351s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 351s [20] 47 48 49 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 81 351s [39] 82 84 86 87 89 90 91 92 96 97 98 100 101 105 106 109 110 111 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=56) 351s 351s Robust Estimate of Location: 351s Ozone Solar.R Wind Temp 351s 41.84 197.21 8.93 80.39 351s 351s Robust Estimate of Covariance: 351s Ozone Solar.R Wind Temp 351s Ozone 1480.7 1562.8 -99.9 347.3 351s Solar.R 1562.8 11401.2 -35.2 276.8 351s Wind -99.9 -35.2 11.4 -23.5 351s Temp 347.3 276.8 -23.5 107.7 351s -------------------------------------------------------- 351s attitude 30 7 15 27.040805 351s Best subsample: 351s [1] 2 3 4 5 7 8 10 12 15 19 22 23 25 27 28 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=15) 351s 351s Robust Estimate of Location: 351s rating complaints privileges learning raises critical 351s 65.8 66.5 50.1 56.1 66.7 78.1 351s advance 351s 41.7 351s 351s Robust Estimate of Covariance: 351s rating complaints privileges learning raises critical advance 351s rating 138.77 80.02 59.22 107.33 95.83 -1.24 54.36 351s complaints 80.02 97.23 50.59 99.50 79.15 -2.71 42.81 351s privileges 59.22 50.59 84.92 90.03 60.88 22.39 44.93 351s learning 107.33 99.50 90.03 187.67 128.71 15.48 63.67 351s raises 95.83 79.15 60.88 128.71 123.94 -1.46 49.98 351s critical -1.24 -2.71 22.39 15.48 -1.46 61.23 12.88 351s advance 54.36 42.81 44.93 63.67 49.98 12.88 48.61 351s -------------------------------------------------------- 351s attenu 182 5 83 9.710111 351s Best subsample: 351s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 351s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 351s [39] 115 116 117 121 122 124 125 126 127 128 129 130 131 132 133 134 135 136 137 351s [58] 138 139 140 141 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 351s [77] 160 161 162 163 164 165 166 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=83) 351s 351s Robust Estimate of Location: 351s event mag station dist accel 351s 18.940 5.741 67.988 23.365 0.124 351s 351s Robust Estimate of Covariance: 351s event mag station dist accel 351s event 2.86e+01 -2.31e+00 1.02e+02 2.68e+01 -1.99e-01 351s mag -2.31e+00 6.17e-01 -7.03e+00 4.67e-01 2.59e-02 351s station 1.02e+02 -7.03e+00 1.66e+03 1.62e+02 7.96e-02 351s dist 2.68e+01 4.67e-01 1.62e+02 3.61e+02 -1.23e+00 351s accel -1.99e-01 2.59e-02 7.96e-02 -1.23e+00 9.42e-03 351s -------------------------------------------------------- 351s USJudgeRatings 43 12 22 -23.463708 351s Best subsample: 351s [1] 2 3 4 6 9 11 15 16 18 19 24 25 26 27 28 29 32 33 34 36 37 38 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=22) 351s 351s Robust Estimate of Location: 351s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 351s 7.24 8.42 8.10 8.19 7.95 8.00 7.96 7.96 7.81 7.89 8.40 8.20 351s 351s Robust Estimate of Covariance: 351s CONT INTG DMNR DILG CFMG DECI PREP 351s CONT 0.61805 -0.05601 -0.09540 0.00694 0.09853 0.06261 0.03939 351s INTG -0.05601 0.23560 0.27537 0.20758 0.16603 0.17281 0.21128 351s DMNR -0.09540 0.27537 0.55349 0.28872 0.24014 0.24293 0.28886 351s DILG 0.00694 0.20758 0.28872 0.34099 0.23502 0.23917 0.29672 351s CFMG 0.09853 0.16603 0.24014 0.23502 0.31649 0.23291 0.27651 351s DECI 0.06261 0.17281 0.24293 0.23917 0.23291 0.30681 0.27737 351s PREP 0.03939 0.21128 0.28886 0.29672 0.27651 0.27737 0.42020 351s FAMI 0.04588 0.20388 0.26072 0.29037 0.27179 0.27737 0.34857 351s ORAL 0.03000 0.21379 0.29606 0.28764 0.27338 0.27424 0.33503 351s WRIT 0.03261 0.20258 0.26931 0.27962 0.26382 0.26610 0.32677 351s PHYS -0.04485 0.13598 0.17659 0.16834 0.14554 0.16467 0.18948 351s RTEN 0.01543 0.22654 0.32117 0.27307 0.23826 0.24669 0.29450 351s FAMI ORAL WRIT PHYS RTEN 351s CONT 0.04588 0.03000 0.03261 -0.04485 0.01543 351s INTG 0.20388 0.21379 0.20258 0.13598 0.22654 351s DMNR 0.26072 0.29606 0.26931 0.17659 0.32117 351s DILG 0.29037 0.28764 0.27962 0.16834 0.27307 351s CFMG 0.27179 0.27338 0.26382 0.14554 0.23826 351s DECI 0.27737 0.27424 0.26610 0.16467 0.24669 351s PREP 0.34857 0.33503 0.32677 0.18948 0.29450 351s FAMI 0.47232 0.33762 0.33420 0.19759 0.29015 351s ORAL 0.33762 0.40361 0.32208 0.19794 0.29544 351s WRIT 0.33420 0.32208 0.38733 0.19276 0.28184 351s PHYS 0.19759 0.19794 0.19276 0.20284 0.18097 351s RTEN 0.29015 0.29544 0.28184 0.18097 0.36877 351s -------------------------------------------------------- 351s USArrests 50 4 25 17.834643 351s Best subsample: 351s [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 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=25) 351s 351s Robust Estimate of Location: 351s Murder Assault UrbanPop Rape 351s 5.38 121.68 63.80 16.33 351s 351s Robust Estimate of Covariance: 351s Murder Assault UrbanPop Rape 351s Murder 17.8 316.3 48.5 31.1 351s Assault 316.3 6863.0 1040.0 548.9 351s UrbanPop 48.5 1040.0 424.8 93.6 351s Rape 31.1 548.9 93.6 63.8 351s -------------------------------------------------------- 351s longley 16 7 8 31.147844 351s Best subsample: 351s [1] 5 6 7 9 10 11 13 14 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=8) 351s 351s Robust Estimate of Location: 351s GNP.deflator GNP Unemployed Armed.Forces Population 351s 104.3 410.8 278.8 300.1 118.2 351s Year Employed 351s 1955.4 66.5 351s 351s Robust Estimate of Covariance: 351s GNP.deflator GNP Unemployed Armed.Forces Population 351s GNP.deflator 85.0 652.3 784.4 -370.7 48.7 351s GNP 652.3 7502.9 7328.6 -3414.2 453.9 351s Unemployed 784.4 7328.6 10760.3 -4646.7 548.1 351s Armed.Forces -370.7 -3414.2 -4646.7 2824.3 -253.9 351s Population 48.7 453.9 548.1 -253.9 40.2 351s Year 33.5 312.7 378.8 -176.1 23.4 351s Employed 23.9 224.8 263.6 -128.3 16.8 351s Year Employed 351s GNP.deflator 33.5 23.9 351s GNP 312.7 224.8 351s Unemployed 378.8 263.6 351s Armed.Forces -176.1 -128.3 351s Population 23.4 16.8 351s Year 18.9 11.7 351s Employed 11.7 10.3 351s -------------------------------------------------------- 351s Loblolly 84 3 42 11.163448 351s Best subsample: 351s [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 351s [26] 53 54 57 58 59 63 64 65 66 70 71 76 77 81 82 83 84 351s Outliers: 0 351s Too many to print ... 351s ------------- 351s 351s Call: 351s CovMrcd(x = x, trace = FALSE) 351s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=42) 351s 351s Robust Estimate of Location: 351s height age Seed 351s 44.20 17.26 6.76 351s 351s Robust Estimate of Covariance: 351s height age Seed 351s height 326.74 139.18 3.50 351s age 139.18 68.48 -2.72 351s Seed 3.50 -2.72 25.43 351s -------------------------------------------------------- 352s quakes 1000 4 500 11.802478 352s Best subsample: 352s Too long... 352s Outliers: 0 352s Too many to print ... 352s ------------- 352s 352s Call: 352s CovMrcd(x = x, trace = FALSE) 352s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=500) 352s 352s Robust Estimate of Location: 352s lat long depth mag 352s -20.59 182.13 432.46 4.42 352s 352s Robust Estimate of Covariance: 352s lat long depth mag 352s lat 15.841 5.702 -106.720 -0.441 352s long 5.702 7.426 -577.189 -0.136 352s depth -106.720 -577.189 66701.479 3.992 352s mag -0.441 -0.136 3.992 0.144 352s -------------------------------------------------------- 352s ======================================================== 352s > ##doexactfit() 352s > 352s BEGIN TEST tmest4.R 352s 352s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 352s Copyright (C) 2025 The R Foundation for Statistical Computing 352s Platform: arm-unknown-linux-gnueabihf (32-bit) 352s 352s R is free software and comes with ABSOLUTELY NO WARRANTY. 352s You are welcome to redistribute it under certain conditions. 352s Type 'license()' or 'licence()' for distribution details. 352s 352s R is a collaborative project with many contributors. 352s Type 'contributors()' for more information and 352s 'citation()' on how to cite R or R packages in publications. 352s 352s Type 'demo()' for some demos, 'help()' for on-line help, or 352s 'help.start()' for an HTML browser interface to help. 352s Type 'q()' to quit R. 352s 352s > ## VT::15.09.2013 - this will render the output independent 352s > ## from the version of the package 352s > suppressPackageStartupMessages(library(rrcov)) 352s > 352s > library(MASS) 352s > dodata <- function(nrep = 1, time = FALSE, full = TRUE) { 352s + domest <- function(x, xname, nrep = 1) { 352s + n <- dim(x)[1] 352s + p <- dim(x)[2] 352s + mm <- CovMest(x) 352s + crit <- log(mm@crit) 352s + ## c1 <- mm@psi@c1 352s + ## M <- mm$psi@M 352s + 352s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 352s + lpad <- lname-nchar(xname) 352s + cat(pad.right(xname,lpad), xres) 352s + 352s + dist <- getDistance(mm) 352s + quantiel <- qchisq(0.975, p) 352s + ibad <- which(dist >= quantiel) 352s + names(ibad) <- NULL 352s + nbad <- length(ibad) 352s + cat("Outliers: ",nbad,"\n") 352s + if(nbad > 0) 352s + print(ibad) 352s + cat("-------------\n") 352s + show(mm) 352s + cat("--------------------------------------------------------\n") 352s + } 352s + 352s + options(digits = 5) 352s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 352s + 352s + lname <- 20 352s + 352s + data(heart) 352s + data(starsCYG) 352s + data(phosphor) 352s + data(stackloss) 352s + data(coleman) 352s + data(salinity) 352s + data(wood) 352s + data(hbk) 352s + 352s + data(Animals, package = "MASS") 352s + brain <- Animals[c(1:24, 26:25, 27:28),] 352s + data(milk) 352s + data(bushfire) 352s + 352s + tmp <- sys.call() 352s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 352s + 352s + cat("Data Set n p c1 M LOG(det) Time\n") 352s + cat("======================================================================\n") 352s + domest(heart[, 1:2], data(heart), nrep) 352s + domest(starsCYG, data(starsCYG), nrep) 352s + domest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 352s + domest(stack.x, data(stackloss), nrep) 352s + domest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 352s + domest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 352s + domest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 352s + domest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 352s + 352s + 352s + domest(brain, "Animals", nrep) 352s + domest(milk, data(milk), nrep) 352s + domest(bushfire, data(bushfire), nrep) 352s + cat("======================================================================\n") 352s + } 352s > 352s > # generate contaminated data using the function gendata with different 352s > # number of outliers and check if the M-estimate breaks - i.e. the 352s > # largest eigenvalue is larger than e.g. 5. 352s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 352s > # outliers grater than 20. 352s > dogen <- function(){ 352s + eig <- vector("numeric",26) 352s + for(i in 0:25) { 352s + gg <- gendata(eps=i) 352s + mm <- CovMest(gg$x, t0=gg$tgood, S0=gg$sgood, arp=0.001) 352s + eig[i+1] <- ev <- getEvals(mm)[1] 352s + # cat(i, ev, "\n") 352s + 352s + stopifnot(ev < 5 || i > 20) 352s + } 352s + # plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 352s + } 352s > 352s > # 352s > # generate data 50x10 as multivariate normal N(0,I) and add 352s > # eps % outliers by adding d=5.0 to each component. 352s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 352s > # - if eps >= 1, it is the number of outliers 352s > # - use the center and cov of the good data as good start 352s > # - use the center and the cov of all data as a bad start 352s > # If using a good start, the M-estimate must iterate to 352s > # the good solution: the largest eigenvalue is less then e.g. 5 352s > # 352s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 352s + 352s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 352s + stop("eps is out of range") 352s + 352s + library(MASS) 352s + 352s + x <- mvrnorm(n, rep(0,p), diag(p)) 352s + bad <- vector("numeric") 352s + nbad = if(eps < 1) eps*n else eps 352s + if(nbad > 0){ 352s + bad <- sample(n, nbad) 352s + x[bad,] <- x[bad,] + d 352s + } 352s + cov1 <- cov.wt(x) 352s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 352s + 352s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 352s + } 352s > 352s > pad.right <- function(z, pads) 352s + { 352s + ## Pads spaces to right of text 352s + padding <- paste(rep(" ", pads), collapse = "") 352s + paste(z, padding, sep = "") 352s + } 352s > 352s > 352s > ## -- now do it: 352s > dodata() 352s 352s Call: dodata() 352s Data Set n p c1 M LOG(det) Time 352s ====================================================================== 352s heart 12 2 7.160341 352s Outliers: 3 352s [1] 2 6 12 352s ------------- 352s 352s Call: 352s CovMest(x = x) 352s -> Method: M-Estimates 352s 352s Robust Estimate of Location: 352s height weight 352s 34.9 27.0 352s 352s Robust Estimate of Covariance: 352s height weight 352s height 102 155 352s weight 155 250 352s -------------------------------------------------------- 352s starsCYG 47 2 -5.994588 352s Outliers: 7 352s [1] 7 9 11 14 20 30 34 352s ------------- 352s 352s Call: 352s CovMest(x = x) 352s -> Method: M-Estimates 352s 352s Robust Estimate of Location: 352s log.Te log.light 352s 4.42 4.95 352s 352s Robust Estimate of Covariance: 352s log.Te log.light 352s log.Te 0.0169 0.0587 352s log.light 0.0587 0.3523 352s -------------------------------------------------------- 353s phosphor 18 2 8.867522 353s Outliers: 3 353s [1] 1 6 10 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s inorg organic 353s 15.4 39.1 353s 353s Robust Estimate of Covariance: 353s inorg organic 353s inorg 169 213 353s organic 213 308 353s -------------------------------------------------------- 353s stackloss 21 3 7.241400 353s Outliers: 9 353s [1] 1 2 3 15 16 17 18 19 21 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s Air.Flow Water.Temp Acid.Conc. 353s 59.5 20.8 87.3 353s 353s Robust Estimate of Covariance: 353s Air.Flow Water.Temp Acid.Conc. 353s Air.Flow 9.34 8.69 8.52 353s Water.Temp 8.69 13.72 9.13 353s Acid.Conc. 8.52 9.13 34.54 353s -------------------------------------------------------- 353s coleman 20 5 2.574752 353s Outliers: 7 353s [1] 2 6 9 10 12 13 15 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s salaryP fatherWc sstatus teacherSc motherLev 353s 2.82 48.44 5.30 25.19 6.51 353s 353s Robust Estimate of Covariance: 353s salaryP fatherWc sstatus teacherSc motherLev 353s salaryP 0.2850 0.1045 1.7585 0.3074 0.0355 353s fatherWc 0.1045 824.8305 260.7062 3.7507 17.7959 353s sstatus 1.7585 260.7062 105.6135 4.1140 5.7714 353s teacherSc 0.3074 3.7507 4.1140 0.6753 0.1563 353s motherLev 0.0355 17.7959 5.7714 0.1563 0.4147 353s -------------------------------------------------------- 353s salinity 28 3 3.875096 353s Outliers: 9 353s [1] 3 5 10 11 15 16 17 23 24 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s X1 X2 X3 353s 10.02 3.21 22.36 353s 353s Robust Estimate of Covariance: 353s X1 X2 X3 353s X1 15.353 1.990 -5.075 353s X2 1.990 5.210 -0.769 353s X3 -5.075 -0.769 2.314 353s -------------------------------------------------------- 353s wood 20 5 -35.156305 353s Outliers: 7 353s [1] 4 6 7 8 11 16 19 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s x1 x2 x3 x4 x5 353s 0.587 0.122 0.531 0.538 0.892 353s 353s Robust Estimate of Covariance: 353s x1 x2 x3 x4 x5 353s x1 6.45e-03 1.21e-03 2.03e-03 -3.77e-04 -1.05e-03 353s x2 1.21e-03 3.12e-04 8.16e-04 -3.34e-05 1.52e-05 353s x3 2.03e-03 8.16e-04 4.27e-03 -5.60e-04 2.27e-04 353s x4 -3.77e-04 -3.34e-05 -5.60e-04 1.83e-03 1.18e-03 353s x5 -1.05e-03 1.52e-05 2.27e-04 1.18e-03 1.78e-03 353s -------------------------------------------------------- 353s hbk 75 3 1.432485 353s Outliers: 14 353s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s X1 X2 X3 353s 1.54 1.78 1.69 353s 353s Robust Estimate of Covariance: 353s X1 X2 X3 353s X1 1.6485 0.0739 0.1709 353s X2 0.0739 1.6780 0.2049 353s X3 0.1709 0.2049 1.5584 353s -------------------------------------------------------- 353s Animals 28 2 18.194822 353s Outliers: 10 353s [1] 2 6 7 9 12 14 15 16 25 28 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s body brain 353s 18.7 64.9 353s 353s Robust Estimate of Covariance: 353s body brain 353s body 4993 8466 353s brain 8466 30335 353s -------------------------------------------------------- 353s milk 86 8 -25.041802 353s Outliers: 20 353s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s X1 X2 X3 X4 X5 X6 X7 X8 353s 1.03 35.88 33.04 26.11 25.09 25.02 123.12 14.39 353s 353s Robust Estimate of Covariance: 353s X1 X2 X3 X4 X5 X6 X7 353s X1 4.89e-07 9.64e-05 1.83e-04 1.76e-04 1.57e-04 1.48e-04 6.53e-04 353s X2 9.64e-05 2.05e+00 3.38e-01 2.37e-01 1.70e-01 2.71e-01 1.91e+00 353s X3 1.83e-04 3.38e-01 1.16e+00 8.56e-01 8.48e-01 8.31e-01 8.85e-01 353s X4 1.76e-04 2.37e-01 8.56e-01 6.83e-01 6.55e-01 6.40e-01 6.91e-01 353s X5 1.57e-04 1.70e-01 8.48e-01 6.55e-01 6.93e-01 6.52e-01 6.90e-01 353s X6 1.48e-04 2.71e-01 8.31e-01 6.40e-01 6.52e-01 6.61e-01 6.95e-01 353s X7 6.53e-04 1.91e+00 8.85e-01 6.91e-01 6.90e-01 6.95e-01 4.40e+00 353s X8 5.56e-06 2.60e-01 1.98e-01 1.29e-01 1.12e-01 1.19e-01 4.12e-01 353s X8 353s X1 5.56e-06 353s X2 2.60e-01 353s X3 1.98e-01 353s X4 1.29e-01 353s X5 1.12e-01 353s X6 1.19e-01 353s X7 4.12e-01 353s X8 1.65e-01 353s -------------------------------------------------------- 353s bushfire 38 5 23.457490 353s Outliers: 15 353s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 353s ------------- 353s 353s Call: 353s CovMest(x = x) 353s -> Method: M-Estimates 353s 353s Robust Estimate of Location: 353s V1 V2 V3 V4 V5 353s 107 147 263 215 277 353s 353s Robust Estimate of Covariance: 353s V1 V2 V3 V4 V5 353s V1 775 560 -4179 -925 -759 353s V2 560 478 -2494 -510 -431 353s V3 -4179 -2494 27433 6441 5196 353s V4 -925 -510 6441 1607 1276 353s V5 -759 -431 5196 1276 1020 353s -------------------------------------------------------- 353s ====================================================================== 353s > dogen() 353s > #cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 353s > 353s BEGIN TEST tmve4.R 353s 353s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 353s Copyright (C) 2025 The R Foundation for Statistical Computing 353s Platform: arm-unknown-linux-gnueabihf (32-bit) 353s 353s R is free software and comes with ABSOLUTELY NO WARRANTY. 353s You are welcome to redistribute it under certain conditions. 353s Type 'license()' or 'licence()' for distribution details. 353s 353s R is a collaborative project with many contributors. 353s Type 'contributors()' for more information and 353s 'citation()' on how to cite R or R packages in publications. 353s 353s Type 'demo()' for some demos, 'help()' for on-line help, or 353s 'help.start()' for an HTML browser interface to help. 353s Type 'q()' to quit R. 353s 353s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMVE","MASS")){ 353s + ##@bdescr 353s + ## Test the function covMve() on the literature datasets: 353s + ## 353s + ## Call covMve() for all regression datasets available in rrco/robustbasev and print: 353s + ## - execution time (if time == TRUE) 353s + ## - objective fucntion 353s + ## - best subsample found (if short == false) 353s + ## - outliers identified (with cutoff 0.975) (if short == false) 353s + ## - estimated center and covarinance matrix if full == TRUE) 353s + ## 353s + ##@edescr 353s + ## 353s + ##@in nrep : [integer] number of repetitions to use for estimating the 353s + ## (average) execution time 353s + ##@in time : [boolean] whether to evaluate the execution time 353s + ##@in short : [boolean] whether to do short output (i.e. only the 353s + ## objective function value). If short == FALSE, 353s + ## the best subsample and the identified outliers are 353s + ## printed. See also the parameter full below 353s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 353s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 353s + 353s + domve <- function(x, xname, nrep=1){ 353s + n <- dim(x)[1] 353s + p <- dim(x)[2] 353s + alpha <- 0.5 353s + h <- h.alpha.n(alpha, n, p) 353s + if(method == "MASS"){ 353s + mve <- cov.mve(x, quantile.used=h) 353s + quan <- h #default: floor((n+p+1)/2) 353s + crit <- mve$crit 353s + best <- mve$best 353s + mah <- mahalanobis(x, mve$center, mve$cov) 353s + quantiel <- qchisq(0.975, p) 353s + wt <- as.numeric(mah < quantiel) 353s + } 353s + else{ 353s + mve <- CovMve(x, trace=FALSE) 353s + quan <- as.integer(mve@quan) 353s + crit <- log(mve@crit) 353s + best <- mve@best 353s + wt <- mve@wt 353s + } 353s + 353s + 353s + if(time){ 353s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 353s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 353s + } 353s + else{ 353s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 353s + } 353s + 353s + lpad<-lname-nchar(xname) 353s + cat(pad.right(xname,lpad), xres) 353s + 353s + if(!short){ 353s + cat("Best subsample: \n") 353s + print(best) 353s + 353s + ibad <- which(wt == 0) 353s + names(ibad) <- NULL 353s + nbad <- length(ibad) 353s + cat("Outliers: ", nbad, "\n") 353s + if(nbad > 0) 353s + print(ibad) 353s + if(full){ 353s + cat("-------------\n") 353s + show(mve) 353s + } 353s + cat("--------------------------------------------------------\n") 353s + } 353s + } 353s + 353s + options(digits = 5) 353s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 353s + 353s + lname <- 20 353s + 353s + ## VT::15.09.2013 - this will render the output independent 353s + ## from the version of the package 353s + suppressPackageStartupMessages(library(rrcov)) 353s + 353s + method <- match.arg(method) 353s + if(method == "MASS") 353s + library(MASS) 353s + 353s + 353s + data(heart) 353s + data(starsCYG) 353s + data(phosphor) 353s + data(stackloss) 353s + data(coleman) 353s + data(salinity) 353s + data(wood) 353s + 353s + data(hbk) 353s + 353s + data(Animals, package = "MASS") 353s + brain <- Animals[c(1:24, 26:25, 27:28),] 353s + data(milk) 353s + data(bushfire) 353s + 353s + tmp <- sys.call() 353s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 353s + 353s + cat("Data Set n p Half LOG(obj) Time\n") 353s + cat("========================================================\n") 353s + domve(heart[, 1:2], data(heart), nrep) 353s + domve(starsCYG, data(starsCYG), nrep) 353s + domve(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 353s + domve(stack.x, data(stackloss), nrep) 353s + domve(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 353s + domve(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 353s + domve(data.matrix(subset(wood, select = -y)), data(wood), nrep) 353s + domve(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 353s + 353s + domve(brain, "Animals", nrep) 353s + domve(milk, data(milk), nrep) 353s + domve(bushfire, data(bushfire), nrep) 353s + cat("========================================================\n") 353s + } 353s > 353s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMVE", "MASS")){ 353s + 353s + domve <- function(x, nrep=1){ 353s + gc() 353s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 353s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 353s + xtime 353s + } 353s + 353s + set.seed(1234) 353s + 353s + ## VT::15.09.2013 - this will render the output independent 353s + ## from the version of the package 353s + suppressPackageStartupMessages(library(rrcov)) 353s + library(MASS) 353s + 353s + method <- match.arg(method) 353s + 353s + ap <- c(2, 5, 10, 20, 30) 353s + an <- c(100, 500, 1000, 10000, 50000) 353s + 353s + tottime <- 0 353s + cat(" n p Time\n") 353s + cat("=====================\n") 353s + for(i in 1:length(an)) { 353s + for(j in 1:length(ap)) { 353s + n <- an[i] 353s + p <- ap[j] 353s + if(5*p <= n){ 353s + xx <- gendata(n, p, eps) 353s + X <- xx$X 353s + tottime <- tottime + domve(X, nrep) 353s + } 353s + } 353s + } 353s + 353s + cat("=====================\n") 353s + cat("Total time: ", tottime*nrep, "\n") 353s + } 353s > 353s > docheck <- function(n, p, eps){ 353s + xx <- gendata(n,p,eps) 353s + mve <- CovMve(xx$X) 353s + check(mve, xx$xind) 353s + } 353s > 353s > check <- function(mcd, xind){ 353s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 353s + ## did not get zero weight 353s + mymatch <- xind %in% which(mcd@wt == 0) 353s + length(xind) - length(which(mymatch)) 353s + } 353s > 353s > dorep <- function(x, nrep=1, method=c("FASTMVE","MASS")){ 353s + 353s + method <- match.arg(method) 353s + for(i in 1:nrep) 353s + if(method == "MASS") 353s + cov.mve(x) 353s + else 353s + CovMve(x) 353s + } 353s > 353s > #### gendata() #### 353s > # Generates a location contaminated multivariate 353s > # normal sample of n observations in p dimensions 353s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 353s > # where 353s > # m = (b,b,...,b) 353s > # Defaults: eps=0 and b=10 353s > # 353s > gendata <- function(n,p,eps=0,b=10){ 353s + 353s + if(missing(n) || missing(p)) 353s + stop("Please specify (n,p)") 353s + if(eps < 0 || eps >= 0.5) 353s + stop(message="eps must be in [0,0.5)") 353s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 353s + nbad <- as.integer(eps * n) 353s + if(nbad > 0){ 353s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 353s + xind <- sample(n,nbad) 353s + X[xind,] <- Xbad 353s + } 353s + list(X=X, xind=xind) 353s + } 353s > 353s > pad.right <- function(z, pads) 353s + { 353s + ### Pads spaces to right of text 353s + padding <- paste(rep(" ", pads), collapse = "") 353s + paste(z, padding, sep = "") 353s + } 353s > 353s > whatis<-function(x){ 353s + if(is.data.frame(x)) 353s + cat("Type: data.frame\n") 353s + else if(is.matrix(x)) 353s + cat("Type: matrix\n") 353s + else if(is.vector(x)) 353s + cat("Type: vector\n") 353s + else 353s + cat("Type: don't know\n") 353s + } 353s > 353s > ## VT::15.09.2013 - this will render the output independent 353s > ## from the version of the package 353s > suppressPackageStartupMessages(library(rrcov)) 353s > 353s > dodata() 353s 353s Call: dodata() 353s Data Set n p Half LOG(obj) Time 353s ======================================================== 353s heart 12 2 7 3.827606 353s Best subsample: 353s [1] 1 4 7 8 9 10 11 353s Outliers: 3 353s [1] 2 6 12 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s height weight 353s 34.9 27.0 353s 353s Robust Estimate of Covariance: 353s height weight 353s height 142 217 353s weight 217 350 353s -------------------------------------------------------- 353s starsCYG 47 2 25 -2.742997 353s Best subsample: 353s [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 353s Outliers: 7 353s [1] 7 9 11 14 20 30 34 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s log.Te log.light 353s 4.41 4.93 353s 353s Robust Estimate of Covariance: 353s log.Te log.light 353s log.Te 0.0173 0.0578 353s log.light 0.0578 0.3615 353s -------------------------------------------------------- 353s phosphor 18 2 10 4.443101 353s Best subsample: 353s [1] 3 5 8 9 11 12 13 14 15 17 353s Outliers: 3 353s [1] 1 6 10 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s inorg organic 353s 15.2 39.4 353s 353s Robust Estimate of Covariance: 353s inorg organic 353s inorg 188 230 353s organic 230 339 353s -------------------------------------------------------- 353s stackloss 21 3 12 3.327582 353s Best subsample: 353s [1] 4 5 6 7 8 9 10 11 12 13 14 20 353s Outliers: 3 353s [1] 1 2 3 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s Air.Flow Water.Temp Acid.Conc. 353s 56.7 20.2 85.5 353s 353s Robust Estimate of Covariance: 353s Air.Flow Water.Temp Acid.Conc. 353s Air.Flow 34.31 11.07 23.54 353s Water.Temp 11.07 9.23 7.85 353s Acid.Conc. 23.54 7.85 47.35 353s -------------------------------------------------------- 353s coleman 20 5 13 2.065143 353s Best subsample: 353s [1] 1 3 4 5 7 8 11 14 16 17 18 19 20 353s Outliers: 5 353s [1] 2 6 9 10 13 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s salaryP fatherWc sstatus teacherSc motherLev 353s 2.79 44.26 3.59 25.08 6.38 353s 353s Robust Estimate of Covariance: 353s salaryP fatherWc sstatus teacherSc motherLev 353s salaryP 0.2920 1.1188 2.0421 0.3487 0.0748 353s fatherWc 1.1188 996.7540 338.6587 7.1673 23.1783 353s sstatus 2.0421 338.6587 148.2501 4.4894 7.8135 353s teacherSc 0.3487 7.1673 4.4894 0.9082 0.3204 353s motherLev 0.0748 23.1783 7.8135 0.3204 0.6024 353s -------------------------------------------------------- 353s salinity 28 3 16 2.002555 353s Best subsample: 353s [1] 1 7 8 9 12 13 14 18 19 20 21 22 25 26 27 28 353s Outliers: 5 353s [1] 5 11 16 23 24 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s X1 X2 X3 353s 10.2 3.1 22.4 353s 353s Robust Estimate of Covariance: 353s X1 X2 X3 353s X1 14.387 1.153 -4.072 353s X2 1.153 5.005 -0.954 353s X3 -4.072 -0.954 2.222 353s -------------------------------------------------------- 353s wood 20 5 13 -5.471407 353s Best subsample: 353s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 353s Outliers: 5 353s [1] 4 6 8 11 19 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s x1 x2 x3 x4 x5 353s 0.576 0.123 0.531 0.538 0.889 353s 353s Robust Estimate of Covariance: 353s x1 x2 x3 x4 x5 353s x1 7.45e-03 1.11e-03 1.83e-03 -2.90e-05 -5.65e-04 353s x2 1.11e-03 3.11e-04 7.68e-04 3.37e-05 3.85e-05 353s x3 1.83e-03 7.68e-04 4.30e-03 -9.96e-04 -6.27e-05 353s x4 -2.90e-05 3.37e-05 -9.96e-04 3.02e-03 1.91e-03 353s x5 -5.65e-04 3.85e-05 -6.27e-05 1.91e-03 2.25e-03 353s -------------------------------------------------------- 353s hbk 75 3 39 1.096831 353s Best subsample: 353s [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 353s [26] 55 56 58 59 64 65 66 67 70 71 72 73 74 75 353s Outliers: 14 353s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s X1 X2 X3 353s 1.48 1.86 1.73 353s 353s Robust Estimate of Covariance: 353s X1 X2 X3 353s X1 1.695 0.230 0.265 353s X2 0.230 1.679 0.119 353s X3 0.265 0.119 1.683 353s -------------------------------------------------------- 353s Animals 28 2 15 8.945423 353s Best subsample: 353s [1] 1 3 4 5 10 11 17 18 21 22 23 24 26 27 28 353s Outliers: 9 353s [1] 2 6 7 9 12 14 15 16 25 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s body brain 353s 48.3 127.3 353s 353s Robust Estimate of Covariance: 353s body brain 353s body 10767 16872 353s brain 16872 46918 353s -------------------------------------------------------- 353s milk 86 8 47 -1.160085 353s Best subsample: 353s [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 353s [26] 46 54 56 57 59 60 61 62 63 64 65 66 67 69 72 76 78 79 81 82 83 85 353s Outliers: 18 353s [1] 1 2 3 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 353s ------------- 353s 353s Call: 353s CovMve(x = x, trace = FALSE) 353s -> Method: Minimum volume ellipsoid estimator 353s 353s Robust Estimate of Location: 353s X1 X2 X3 X4 X5 X6 X7 X8 353s 1.03 35.91 33.02 26.08 25.06 24.99 122.93 14.38 353s 353s Robust Estimate of Covariance: 353s X1 X2 X3 X4 X5 X6 X7 353s X1 6.00e-07 1.51e-04 3.34e-04 3.09e-04 2.82e-04 2.77e-04 1.09e-03 353s X2 1.51e-04 2.03e+00 3.83e-01 3.04e-01 2.20e-01 3.51e-01 2.18e+00 353s X3 3.34e-04 3.83e-01 1.58e+00 1.21e+00 1.18e+00 1.20e+00 1.60e+00 353s X4 3.09e-04 3.04e-01 1.21e+00 9.82e-01 9.39e-01 9.53e-01 1.36e+00 353s X5 2.82e-04 2.20e-01 1.18e+00 9.39e-01 9.67e-01 9.52e-01 1.34e+00 353s X6 2.77e-04 3.51e-01 1.20e+00 9.53e-01 9.52e-01 9.92e-01 1.38e+00 353s X7 1.09e-03 2.18e+00 1.60e+00 1.36e+00 1.34e+00 1.38e+00 6.73e+00 353s X8 3.33e-05 2.92e-01 2.65e-01 1.83e-01 1.65e-01 1.76e-01 5.64e-01 353s X8 353s X1 3.33e-05 353s X2 2.92e-01 353s X3 2.65e-01 353s X4 1.83e-01 353s X5 1.65e-01 353s X6 1.76e-01 353s X7 5.64e-01 353s X8 1.80e-01 353s -------------------------------------------------------- 354s bushfire 38 5 22 5.644315 354s Best subsample: 354s [1] 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 354s Outliers: 15 354s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 354s ------------- 354s 354s Call: 354s CovMve(x = x, trace = FALSE) 354s -> Method: Minimum volume ellipsoid estimator 354s 354s Robust Estimate of Location: 354s V1 V2 V3 V4 V5 354s 107 147 263 215 277 354s 354s Robust Estimate of Covariance: 354s V1 V2 V3 V4 V5 354s V1 519 375 -2799 -619 -509 354s V2 375 320 -1671 -342 -289 354s V3 -2799 -1671 18373 4314 3480 354s V4 -619 -342 4314 1076 854 354s V5 -509 -289 3480 854 683 354s -------------------------------------------------------- 354s ======================================================== 354s > 354s BEGIN TEST togk4.R 354s 354s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 354s Copyright (C) 2025 The R Foundation for Statistical Computing 354s Platform: arm-unknown-linux-gnueabihf (32-bit) 354s 354s R is free software and comes with ABSOLUTELY NO WARRANTY. 354s You are welcome to redistribute it under certain conditions. 354s Type 'license()' or 'licence()' for distribution details. 354s 354s R is a collaborative project with many contributors. 354s Type 'contributors()' for more information and 354s 'citation()' on how to cite R or R packages in publications. 354s 354s Type 'demo()' for some demos, 'help()' for on-line help, or 354s 'help.start()' for an HTML browser interface to help. 354s Type 'q()' to quit R. 354s 354s > ## VT::15.09.2013 - this will render the output independent 354s > ## from the version of the package 354s > suppressPackageStartupMessages(library(rrcov)) 354s > 354s > ## VT::14.01.2020 354s > ## On some platforms minor differences are shown - use 354s > ## IGNORE_RDIFF_BEGIN 354s > ## IGNORE_RDIFF_END 354s > 354s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS")){ 354s + domcd <- function(x, xname, nrep=1){ 354s + n <- dim(x)[1] 354s + p <- dim(x)[2] 354s + 354s + mcd<-CovOgk(x) 354s + 354s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 354s + 354s + lpad<-lname-nchar(xname) 354s + cat(pad.right(xname,lpad), xres) 354s + 354s + dist <- getDistance(mcd) 354s + quantiel <- qchisq(0.975, p) 354s + ibad <- which(dist >= quantiel) 354s + names(ibad) <- NULL 354s + nbad <- length(ibad) 354s + cat("Outliers: ",nbad,"\n") 354s + if(nbad > 0) 354s + print(ibad) 354s + cat("-------------\n") 354s + show(mcd) 354s + cat("--------------------------------------------------------\n") 354s + } 354s + 354s + lname <- 20 354s + 354s + ## VT::15.09.2013 - this will render the output independent 354s + ## from the version of the package 354s + suppressPackageStartupMessages(library(rrcov)) 354s + 354s + method <- match.arg(method) 354s + 354s + data(heart) 354s + data(starsCYG) 354s + data(phosphor) 354s + data(stackloss) 354s + data(coleman) 354s + data(salinity) 354s + data(wood) 354s + 354s + data(hbk) 354s + 354s + data(Animals, package = "MASS") 354s + brain <- Animals[c(1:24, 26:25, 27:28),] 354s + data(milk) 354s + data(bushfire) 354s + 354s + tmp <- sys.call() 354s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 354s + 354s + cat("Data Set n p Half LOG(obj) Time\n") 354s + cat("========================================================\n") 354s + domcd(heart[, 1:2], data(heart), nrep) 354s + ## This will not work within the function, of course 354s + ## - comment it out 354s + ## IGNORE_RDIFF_BEGIN 354s + ## domcd(starsCYG,data(starsCYG), nrep) 354s + ## IGNORE_RDIFF_END 354s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 354s + domcd(stack.x,data(stackloss), nrep) 354s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 354s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 354s + ## IGNORE_RDIFF_BEGIN 354s + ## domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 354s + ## IGNORE_RDIFF_END 354s + domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 354s + 354s + domcd(brain, "Animals", nrep) 354s + domcd(milk, data(milk), nrep) 354s + domcd(bushfire, data(bushfire), nrep) 354s + cat("========================================================\n") 354s + } 354s > 354s > pad.right <- function(z, pads) 354s + { 354s + ### Pads spaces to right of text 354s + padding <- paste(rep(" ", pads), collapse = "") 354s + paste(z, padding, sep = "") 354s + } 354s > 354s > dodata() 354s 354s Call: dodata() 354s Data Set n p Half LOG(obj) Time 354s ======================================================== 354s heart 12 2 354s Outliers: 5 354s [1] 2 6 8 10 12 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s height weight 354s 39.76 35.71 354s 354s Robust Estimate of Covariance: 354s height weight 354s height 15.88 32.07 354s weight 32.07 78.28 354s -------------------------------------------------------- 354s phosphor 18 2 354s Outliers: 2 354s [1] 1 6 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s inorg organic 354s 13.31 40.00 354s 354s Robust Estimate of Covariance: 354s inorg organic 354s inorg 92.82 93.24 354s organic 93.24 152.62 354s -------------------------------------------------------- 354s stackloss 21 3 354s Outliers: 2 354s [1] 1 2 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s Air.Flow Water.Temp Acid.Conc. 354s 57.72 20.50 85.78 354s 354s Robust Estimate of Covariance: 354s Air.Flow Water.Temp Acid.Conc. 354s Air.Flow 38.423 11.306 18.605 354s Water.Temp 11.306 6.806 5.889 354s Acid.Conc. 18.605 5.889 29.840 354s -------------------------------------------------------- 354s coleman 20 5 354s Outliers: 3 354s [1] 1 6 10 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s salaryP fatherWc sstatus teacherSc motherLev 354s 2.723 43.202 2.912 25.010 6.290 354s 354s Robust Estimate of Covariance: 354s salaryP fatherWc sstatus teacherSc motherLev 354s salaryP 0.12867 2.80048 0.92026 0.15118 0.06413 354s fatherWc 2.80048 678.72549 227.36415 9.30826 16.15102 354s sstatus 0.92026 227.36415 101.39094 3.38013 5.63283 354s teacherSc 0.15118 9.30826 3.38013 0.57112 0.27701 354s motherLev 0.06413 16.15102 5.63283 0.27701 0.44801 354s -------------------------------------------------------- 354s salinity 28 3 354s Outliers: 3 354s [1] 3 5 16 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s X1 X2 X3 354s 10.74 2.68 22.99 354s 354s Robust Estimate of Covariance: 354s X1 X2 X3 354s X1 8.1047 -0.6365 -0.4720 354s X2 -0.6365 3.0976 -1.3520 354s X3 -0.4720 -1.3520 2.3648 354s -------------------------------------------------------- 354s hbk 75 3 354s Outliers: 14 354s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s X1 X2 X3 354s 1.538 1.780 1.687 354s 354s Robust Estimate of Covariance: 354s X1 X2 X3 354s X1 1.11350 0.04992 0.11541 354s X2 0.04992 1.13338 0.13843 354s X3 0.11541 0.13843 1.05261 354s -------------------------------------------------------- 354s Animals 28 2 354s Outliers: 12 354s [1] 2 6 7 9 12 14 15 16 17 24 25 28 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s body brain 354s 39.65 105.83 354s 354s Robust Estimate of Covariance: 354s body brain 354s body 3981 7558 354s brain 7558 16594 354s -------------------------------------------------------- 354s milk 86 8 354s Outliers: 22 354s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 50 70 74 75 77 85 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s X1 X2 X3 X4 X5 X6 X7 X8 354s 1.03 35.80 33.10 26.15 25.13 25.06 123.06 14.39 354s 354s Robust Estimate of Covariance: 354s X1 X2 X3 X4 X5 X6 X7 354s X1 4.074e-07 5.255e-05 1.564e-04 1.506e-04 1.340e-04 1.234e-04 5.308e-04 354s X2 5.255e-05 1.464e+00 3.425e-01 2.465e-01 1.847e-01 2.484e-01 1.459e+00 354s X3 1.564e-04 3.425e-01 1.070e+00 7.834e-01 7.665e-01 7.808e-01 7.632e-01 354s X4 1.506e-04 2.465e-01 7.834e-01 6.178e-01 5.868e-01 5.959e-01 5.923e-01 354s X5 1.340e-04 1.847e-01 7.665e-01 5.868e-01 6.124e-01 5.967e-01 5.868e-01 354s X6 1.234e-04 2.484e-01 7.808e-01 5.959e-01 5.967e-01 6.253e-01 5.819e-01 354s X7 5.308e-04 1.459e+00 7.632e-01 5.923e-01 5.868e-01 5.819e-01 3.535e+00 354s X8 1.990e-07 1.851e-01 1.861e-01 1.210e-01 1.041e-01 1.116e-01 3.046e-01 354s X8 354s X1 1.990e-07 354s X2 1.851e-01 354s X3 1.861e-01 354s X4 1.210e-01 354s X5 1.041e-01 354s X6 1.116e-01 354s X7 3.046e-01 354s X8 1.292e-01 354s -------------------------------------------------------- 354s bushfire 38 5 354s Outliers: 17 354s [1] 7 8 9 10 11 12 28 29 30 31 32 33 34 35 36 37 38 354s ------------- 354s 354s Call: 354s CovOgk(x = x) 354s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 354s 354s Robust Estimate of Location: 354s V1 V2 V3 V4 V5 354s 104.5 146.0 275.6 217.8 279.3 354s 354s Robust Estimate of Covariance: 354s V1 V2 V3 V4 V5 354s V1 266.8 203.2 -1380.7 -311.1 -252.2 354s V2 203.2 178.4 -910.9 -185.9 -155.9 354s V3 -1380.7 -910.9 8279.7 2035.5 1615.4 354s V4 -311.1 -185.9 2035.5 536.5 418.6 354s V5 -252.2 -155.9 1615.4 418.6 329.2 354s -------------------------------------------------------- 354s ======================================================== 354s > 354s BEGIN TEST tqda.R 354s 354s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 354s Copyright (C) 2025 The R Foundation for Statistical Computing 354s Platform: arm-unknown-linux-gnueabihf (32-bit) 354s 354s R is free software and comes with ABSOLUTELY NO WARRANTY. 354s You are welcome to redistribute it under certain conditions. 354s Type 'license()' or 'licence()' for distribution details. 354s 354s R is a collaborative project with many contributors. 354s Type 'contributors()' for more information and 354s 'citation()' on how to cite R or R packages in publications. 354s 354s Type 'demo()' for some demos, 'help()' for on-line help, or 354s 'help.start()' for an HTML browser interface to help. 354s Type 'q()' to quit R. 354s 354s > ## VT::15.09.2013 - this will render the output independent 354s > ## from the version of the package 354s > suppressPackageStartupMessages(library(rrcov)) 355s > 355s > dodata <- function(method) { 355s + 355s + options(digits = 5) 355s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 355s + 355s + tmp <- sys.call() 355s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 355s + cat("===================================================\n") 355s + 355s + data(hemophilia); show(QdaCov(as.factor(gr)~., data=hemophilia, method=method)) 355s + data(anorexia, package="MASS"); show(QdaCov(Treat~., data=anorexia, method=method)) 355s + data(Pima.tr, package="MASS"); show(QdaCov(type~., data=Pima.tr, method=method)) 355s + data(iris); # show(QdaCov(Species~., data=iris, method=method)) 355s + data(crabs, package="MASS"); # show(QdaCov(sp~., data=crabs, method=method)) 355s + 355s + show(QdaClassic(as.factor(gr)~., data=hemophilia)) 355s + show(QdaClassic(Treat~., data=anorexia)) 355s + show(QdaClassic(type~., data=Pima.tr)) 355s + show(QdaClassic(Species~., data=iris)) 355s + ## show(QdaClassic(sp~., data=crabs)) 355s + cat("===================================================\n") 355s + } 355s > 355s > 355s > ## -- now do it: 355s > dodata(method="mcd") 355s 355s Call: dodata(method = "mcd") 355s =================================================== 355s Call: 355s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.30795 -0.0059911 355s normal -0.12920 -0.0603000 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.023784 0.015376 355s AHFantigen 0.015376 0.024035 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.0057546 0.0042606 355s AHFantigen 0.0042606 0.0084914 355s Call: 355s QdaCov(Treat ~ ., data = anorexia, method = method) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.633 82.950 355s Cont 81.558 81.108 355s FT 84.331 94.762 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 9.8671 8.6611 355s Postwt 8.6611 11.8966 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 32.5705 -4.3705 355s Postwt -4.3705 22.5079 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 33.056 10.814 355s Postwt 10.814 14.265 355s Call: 355s QdaCov(type ~ ., data = Pima.tr, method = method) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 355s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 2.221983 -0.18658 1.86507 -0.44427 0.1725348 -0.0683616 2.63439 355s glu -0.186582 471.88789 45.28021 8.95404 30.6551510 -0.6359899 3.50218 355s bp 1.865066 45.28021 110.09787 26.11192 14.4739180 -0.2104074 13.23392 355s skin -0.444272 8.95404 26.11192 118.30521 52.3115719 -0.2995751 8.65861 355s bmi 0.172535 30.65515 14.47392 52.31157 43.3140415 0.0079866 6.75720 355s ped -0.068362 -0.63599 -0.21041 -0.29958 0.0079866 0.0587710 -0.18683 355s age 2.634387 3.50218 13.23392 8.65861 6.7572019 -0.1868284 12.09493 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 17.875215 -13.740021 9.03580 4.498580 1.787458 0.079504 26.92283 355s glu -13.740021 917.719003 55.30399 27.976265 10.755113 0.092673 38.94970 355s bp 9.035798 55.303991 129.97953 34.130200 10.104275 0.198342 32.95351 355s skin 4.498580 27.976265 34.13020 101.842647 30.297210 0.064739 3.59427 355s bmi 1.787458 10.755113 10.10428 30.297210 22.529467 0.084369 -6.64317 355s ped 0.079504 0.092673 0.19834 0.064739 0.084369 0.066667 0.11199 355s age 26.922828 38.949697 32.95351 3.594266 -6.643165 0.111992 143.69752 355s Call: 355s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.30795 -0.0059911 355s normal -0.13487 -0.0778567 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.023784 0.015376 355s AHFantigen 0.015376 0.024035 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.020897 0.015515 355s AHFantigen 0.015515 0.017920 355s Call: 355s QdaClassic(Treat ~ ., data = anorexia) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.690 85.697 355s Cont 81.558 81.108 355s FT 83.229 90.494 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 23.479 19.910 355s Postwt 19.910 69.755 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 32.5705 -4.3705 355s Postwt -4.3705 22.5079 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 25.167 22.883 355s Postwt 22.883 71.827 355s Call: 355s QdaClassic(type ~ ., data = Pima.tr) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 355s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 355s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 355s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 355s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 355s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 355s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 355s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 355s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 355s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 355s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 355s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 355s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 355s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 355s Call: 355s QdaClassic(Species ~ ., data = iris) 355s 355s Prior Probabilities of Groups: 355s setosa versicolor virginica 355s 0.33333 0.33333 0.33333 355s 355s Group means: 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s setosa 5.006 3.428 1.462 0.246 355s versicolor 5.936 2.770 4.260 1.326 355s virginica 6.588 2.974 5.552 2.026 355s 355s Group: setosa 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 355s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 355s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 355s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 355s 355s Group: versicolor 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.266433 0.085184 0.182898 0.055780 355s Sepal.Width 0.085184 0.098469 0.082653 0.041204 355s Petal.Length 0.182898 0.082653 0.220816 0.073102 355s Petal.Width 0.055780 0.041204 0.073102 0.039106 355s 355s Group: virginica 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.404343 0.093763 0.303290 0.049094 355s Sepal.Width 0.093763 0.104004 0.071380 0.047629 355s Petal.Length 0.303290 0.071380 0.304588 0.048824 355s Petal.Width 0.049094 0.047629 0.048824 0.075433 355s =================================================== 355s > dodata(method="m") 355s 355s Call: dodata(method = "m") 355s =================================================== 355s Call: 355s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.29810 -0.0028222 355s normal -0.13081 -0.0675283 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.026018 0.017653 355s AHFantigen 0.017653 0.030128 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.0081933 0.0065737 355s AHFantigen 0.0065737 0.0118565 355s Call: 355s QdaCov(Treat ~ ., data = anorexia, method = method) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.436 82.631 355s Cont 81.559 80.272 355s FT 85.120 94.657 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 23.630 25.128 355s Postwt 25.128 38.142 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 35.8824 -8.2405 355s Postwt -8.2405 23.7416 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 33.805 18.206 355s Postwt 18.206 24.639 355s Call: 355s QdaCov(type ~ ., data = Pima.tr, method = method) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.5225 111.26 68.081 26.640 30.801 0.40452 26.306 355s Yes 5.0702 144.32 75.088 31.982 34.267 0.47004 37.140 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 5.74219 14.47051 6.63766 4.98559 0.826570 -0.128106 10.71303 355s glu 14.47051 591.08717 68.81219 44.73311 40.658393 -0.545716 38.01918 355s bp 6.63766 68.81219 121.02716 30.46466 16.789801 -0.320065 25.29371 355s skin 4.98559 44.73311 30.46466 136.52176 56.604475 -0.250711 19.73259 355s bmi 0.82657 40.65839 16.78980 56.60447 47.859747 0.046358 6.94523 355s ped -0.12811 -0.54572 -0.32006 -0.25071 0.046358 0.061485 -0.34653 355s age 10.71303 38.01918 25.29371 19.73259 6.945227 -0.346527 35.66101 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.98861 -1.2430 10.48556 9.05947 2.425316 0.162453 30.149872 355s glu -1.24304 867.1076 46.43838 25.92297 5.517382 1.044360 31.152650 355s bp 10.48556 46.4384 130.12536 17.21407 6.343942 -0.235121 41.091494 355s skin 9.05947 25.9230 17.21407 85.96314 26.089017 0.170061 14.562516 355s bmi 2.42532 5.5174 6.34394 26.08902 22.051976 0.097786 -5.297971 355s ped 0.16245 1.0444 -0.23512 0.17006 0.097786 0.057112 0.055286 355s age 30.14987 31.1527 41.09149 14.56252 -5.297971 0.055286 137.440921 355s Call: 355s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.30795 -0.0059911 355s normal -0.13487 -0.0778567 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.023784 0.015376 355s AHFantigen 0.015376 0.024035 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.020897 0.015515 355s AHFantigen 0.015515 0.017920 355s Call: 355s QdaClassic(Treat ~ ., data = anorexia) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.690 85.697 355s Cont 81.558 81.108 355s FT 83.229 90.494 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 23.479 19.910 355s Postwt 19.910 69.755 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 32.5705 -4.3705 355s Postwt -4.3705 22.5079 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 25.167 22.883 355s Postwt 22.883 71.827 355s Call: 355s QdaClassic(type ~ ., data = Pima.tr) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 355s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 355s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 355s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 355s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 355s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 355s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 355s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 355s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 355s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 355s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 355s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 355s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 355s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 355s Call: 355s QdaClassic(Species ~ ., data = iris) 355s 355s Prior Probabilities of Groups: 355s setosa versicolor virginica 355s 0.33333 0.33333 0.33333 355s 355s Group means: 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s setosa 5.006 3.428 1.462 0.246 355s versicolor 5.936 2.770 4.260 1.326 355s virginica 6.588 2.974 5.552 2.026 355s 355s Group: setosa 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 355s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 355s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 355s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 355s 355s Group: versicolor 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.266433 0.085184 0.182898 0.055780 355s Sepal.Width 0.085184 0.098469 0.082653 0.041204 355s Petal.Length 0.182898 0.082653 0.220816 0.073102 355s Petal.Width 0.055780 0.041204 0.073102 0.039106 355s 355s Group: virginica 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.404343 0.093763 0.303290 0.049094 355s Sepal.Width 0.093763 0.104004 0.071380 0.047629 355s Petal.Length 0.303290 0.071380 0.304588 0.048824 355s Petal.Width 0.049094 0.047629 0.048824 0.075433 355s =================================================== 355s > dodata(method="ogk") 355s 355s Call: dodata(method = "ogk") 355s =================================================== 355s Call: 355s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.29324 0.00033953 355s normal -0.12744 -0.06628182 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.019260 0.013026 355s AHFantigen 0.013026 0.021889 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.0049651 0.0039707 355s AHFantigen 0.0039707 0.0066084 355s Call: 355s QdaCov(Treat ~ ., data = anorexia, method = method) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.587 82.709 355s Cont 81.558 81.108 355s FT 85.110 94.470 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 10.452 15.115 355s Postwt 15.115 37.085 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 31.3178 -4.2024 355s Postwt -4.2024 21.6422 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 5.5309 1.4813 355s Postwt 1.4813 7.5501 355s Call: 355s QdaCov(type ~ ., data = Pima.tr, method = method) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.4286 110.35 67.495 25.905 30.275 0.39587 26.248 355s Yes 5.1964 142.71 75.357 32.732 34.809 0.48823 37.607 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 3.97823 8.70612 4.58776 4.16463 0.250612 -0.117238 8.21769 355s glu 8.70612 448.91392 51.71120 38.66213 21.816345 -0.296524 24.29370 355s bp 4.58776 51.71120 99.41188 24.27574 10.491311 -0.290753 20.02975 355s skin 4.16463 38.66213 24.27574 98.61950 41.682404 -0.335213 16.60454 355s bmi 0.25061 21.81634 10.49131 41.68240 35.237101 -0.019774 5.12042 355s ped -0.11724 -0.29652 -0.29075 -0.33521 -0.019774 0.051431 -0.36275 355s age 8.21769 24.29370 20.02975 16.60454 5.120417 -0.362748 31.32916 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.26499 6.30612 3.01913 3.76690 0.94825 0.12076 22.64860 355s glu 6.30612 688.16837 22.22704 12.81633 3.55791 0.68833 32.28061 355s bp 3.01913 22.22704 103.97959 9.95281 2.09860 0.45672 31.17602 355s skin 3.76690 12.81633 9.95281 67.51754 19.51489 0.59831 -2.35523 355s bmi 0.94825 3.55791 2.09860 19.51489 17.20331 0.24347 -6.88221 355s ped 0.12076 0.68833 0.45672 0.59831 0.24347 0.05933 0.43798 355s age 22.64860 32.28061 31.17602 -2.35523 -6.88221 0.43798 111.16709 355s Call: 355s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.30795 -0.0059911 355s normal -0.13487 -0.0778567 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.023784 0.015376 355s AHFantigen 0.015376 0.024035 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.020897 0.015515 355s AHFantigen 0.015515 0.017920 355s Call: 355s QdaClassic(Treat ~ ., data = anorexia) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.690 85.697 355s Cont 81.558 81.108 355s FT 83.229 90.494 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 23.479 19.910 355s Postwt 19.910 69.755 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 32.5705 -4.3705 355s Postwt -4.3705 22.5079 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 25.167 22.883 355s Postwt 22.883 71.827 355s Call: 355s QdaClassic(type ~ ., data = Pima.tr) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 355s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 355s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 355s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 355s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 355s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 355s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 355s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 355s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 355s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 355s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 355s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 355s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 355s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 355s Call: 355s QdaClassic(Species ~ ., data = iris) 355s 355s Prior Probabilities of Groups: 355s setosa versicolor virginica 355s 0.33333 0.33333 0.33333 355s 355s Group means: 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s setosa 5.006 3.428 1.462 0.246 355s versicolor 5.936 2.770 4.260 1.326 355s virginica 6.588 2.974 5.552 2.026 355s 355s Group: setosa 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 355s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 355s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 355s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 355s 355s Group: versicolor 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.266433 0.085184 0.182898 0.055780 355s Sepal.Width 0.085184 0.098469 0.082653 0.041204 355s Petal.Length 0.182898 0.082653 0.220816 0.073102 355s Petal.Width 0.055780 0.041204 0.073102 0.039106 355s 355s Group: virginica 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.404343 0.093763 0.303290 0.049094 355s Sepal.Width 0.093763 0.104004 0.071380 0.047629 355s Petal.Length 0.303290 0.071380 0.304588 0.048824 355s Petal.Width 0.049094 0.047629 0.048824 0.075433 355s =================================================== 355s > dodata(method="sde") 355s 355s Call: dodata(method = "sde") 355s =================================================== 355s Call: 355s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.29834 -0.0032286 355s normal -0.12944 -0.0676930 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.025398 0.017810 355s AHFantigen 0.017810 0.030639 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.0083435 0.0067686 355s AHFantigen 0.0067686 0.0119565 355s Call: 355s QdaCov(Treat ~ ., data = anorexia, method = method) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.949 83.323 355s Cont 81.484 80.840 355s FT 84.596 93.835 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 22.283 17.084 355s Postwt 17.084 25.308 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 37.1864 -8.8896 355s Postwt -8.8896 31.1930 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 20.7108 3.1531 355s Postwt 3.1531 25.7046 355s Call: 355s QdaCov(type ~ ., data = Pima.tr, method = method) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.2567 109.91 67.538 25.484 30.355 0.38618 25.628 355s Yes 5.2216 141.64 75.048 32.349 34.387 0.47742 37.634 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 4.396832 10.20629 5.43346 4.38313 7.9891e-01 -0.09389257 7.45638 355s glu 10.206286 601.12211 56.62047 49.67071 3.3829e+01 -0.31896741 31.64508 355s bp 5.433462 56.62047 120.38052 34.38984 1.4817e+01 -0.21784446 26.44853 355s skin 4.383134 49.67071 34.38984 136.94931 6.1576e+01 -0.47532490 17.74141 355s bmi 0.798908 33.82928 14.81668 61.57578 5.1441e+01 0.00061983 8.56856 355s ped -0.093893 -0.31897 -0.21784 -0.47532 6.1983e-04 0.06012655 -0.26872 355s age 7.456376 31.64508 26.44853 17.74141 8.5686e+00 -0.26872005 29.93856 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.91978 7.7491 7.24229 10.46802 5.40627 0.320434 25.88314 355s glu 7.74907 856.4955 58.59554 29.65331 11.44745 1.388745 38.24430 355s bp 7.24229 58.5955 89.66288 21.36597 6.46859 0.764486 36.30462 355s skin 10.46802 29.6533 21.36597 86.79253 26.22071 0.620654 5.28665 355s bmi 5.40627 11.4475 6.46859 26.22071 20.12351 0.211701 0.71583 355s ped 0.32043 1.3887 0.76449 0.62065 0.21170 0.062727 0.93743 355s age 25.88314 38.2443 36.30462 5.28665 0.71583 0.937430 136.24335 355s Call: 355s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 355s 355s Prior Probabilities of Groups: 355s carrier normal 355s 0.6 0.4 355s 355s Group means: 355s AHFactivity AHFantigen 355s carrier -0.30795 -0.0059911 355s normal -0.13487 -0.0778567 355s 355s Group: carrier 355s AHFactivity AHFantigen 355s AHFactivity 0.023784 0.015376 355s AHFantigen 0.015376 0.024035 355s 355s Group: normal 355s AHFactivity AHFantigen 355s AHFactivity 0.020897 0.015515 355s AHFantigen 0.015515 0.017920 355s Call: 355s QdaClassic(Treat ~ ., data = anorexia) 355s 355s Prior Probabilities of Groups: 355s CBT Cont FT 355s 0.40278 0.36111 0.23611 355s 355s Group means: 355s Prewt Postwt 355s CBT 82.690 85.697 355s Cont 81.558 81.108 355s FT 83.229 90.494 355s 355s Group: CBT 355s Prewt Postwt 355s Prewt 23.479 19.910 355s Postwt 19.910 69.755 355s 355s Group: Cont 355s Prewt Postwt 355s Prewt 32.5705 -4.3705 355s Postwt -4.3705 22.5079 355s 355s Group: FT 355s Prewt Postwt 355s Prewt 25.167 22.883 355s Postwt 22.883 71.827 355s Call: 355s QdaClassic(type ~ ., data = Pima.tr) 355s 355s Prior Probabilities of Groups: 355s No Yes 355s 0.66 0.34 355s 355s Group means: 355s npreg glu bp skin bmi ped age 355s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 355s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 355s 355s Group: No 355s npreg glu bp skin bmi ped age 355s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 355s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 355s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 355s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 355s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 355s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 355s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 355s 355s Group: Yes 355s npreg glu bp skin bmi ped age 355s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 355s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 355s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 355s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 355s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 355s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 355s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 355s Call: 355s QdaClassic(Species ~ ., data = iris) 355s 355s Prior Probabilities of Groups: 355s setosa versicolor virginica 355s 0.33333 0.33333 0.33333 355s 355s Group means: 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s setosa 5.006 3.428 1.462 0.246 355s versicolor 5.936 2.770 4.260 1.326 355s virginica 6.588 2.974 5.552 2.026 355s 355s Group: setosa 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 355s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 355s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 355s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 355s 355s Group: versicolor 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.266433 0.085184 0.182898 0.055780 355s Sepal.Width 0.085184 0.098469 0.082653 0.041204 355s Petal.Length 0.182898 0.082653 0.220816 0.073102 355s Petal.Width 0.055780 0.041204 0.073102 0.039106 355s 355s Group: virginica 355s Sepal.Length Sepal.Width Petal.Length Petal.Width 355s Sepal.Length 0.404343 0.093763 0.303290 0.049094 355s Sepal.Width 0.093763 0.104004 0.071380 0.047629 355s Petal.Length 0.303290 0.071380 0.304588 0.048824 355s Petal.Width 0.049094 0.047629 0.048824 0.075433 355s =================================================== 355s > 355s BEGIN TEST tsde.R 355s 355s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 355s Copyright (C) 2025 The R Foundation for Statistical Computing 355s Platform: arm-unknown-linux-gnueabihf (32-bit) 355s 355s R is free software and comes with ABSOLUTELY NO WARRANTY. 355s You are welcome to redistribute it under certain conditions. 355s Type 'license()' or 'licence()' for distribution details. 355s 355s R is a collaborative project with many contributors. 355s Type 'contributors()' for more information and 355s 'citation()' on how to cite R or R packages in publications. 355s 355s Type 'demo()' for some demos, 'help()' for on-line help, or 355s 'help.start()' for an HTML browser interface to help. 355s Type 'q()' to quit R. 355s 355s > ## Test for singularity 355s > doexact <- function(){ 355s + exact <-function(){ 355s + n1 <- 45 355s + p <- 2 355s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 355s + x1[,p] <- x1[,p] + 3 355s + ## library(MASS) 355s + ## x1 <- mvrnorm(n=n1, mu=c(0,3), Sigma=diag(1,nrow=p)) 355s + 355s + n2 <- 55 355s + m1 <- 0 355s + m2 <- 3 355s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 355s + x<-rbind(x1,x2) 355s + colnames(x) <- c("X1","X2") 355s + x 355s + } 355s + print(CovSde(exact())) 355s + } 355s > 355s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE){ 355s + 355s + domcd <- function(x, xname, nrep=1){ 355s + n <- dim(x)[1] 355s + p <- dim(x)[2] 355s + 355s + mcd<-CovSde(x) 355s + 355s + if(time){ 355s + xtime <- system.time(dorep(x, nrep))[1]/nrep 355s + xres <- sprintf("%3d %3d %3d\n", dim(x)[1], dim(x)[2], xtime) 355s + } 355s + else{ 355s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 355s + } 355s + lpad<-lname-nchar(xname) 355s + cat(pad.right(xname,lpad), xres) 355s + 355s + if(!short){ 355s + 355s + ibad <- which(mcd@wt==0) 355s + names(ibad) <- NULL 355s + nbad <- length(ibad) 355s + cat("Outliers: ",nbad,"\n") 355s + if(nbad > 0) 355s + print(ibad) 355s + if(full){ 355s + cat("-------------\n") 355s + show(mcd) 355s + } 355s + cat("--------------------------------------------------------\n") 355s + } 355s + } 355s + 355s + options(digits = 5) 355s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 355s + 355s + lname <- 20 355s + 355s + ## VT::15.09.2013 - this will render the output independent 355s + ## from the version of the package 355s + suppressPackageStartupMessages(library(rrcov)) 355s + 355s + data(heart) 355s + data(starsCYG) 355s + data(phosphor) 355s + data(stackloss) 355s + data(coleman) 355s + data(salinity) 355s + data(wood) 355s + 355s + data(hbk) 355s + 355s + data(Animals, package = "MASS") 355s + brain <- Animals[c(1:24, 26:25, 27:28),] 355s + data(milk) 355s + data(bushfire) 355s + 355s + tmp <- sys.call() 355s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 355s + 355s + cat("Data Set n p Half LOG(obj) Time\n") 355s + cat("========================================================\n") 355s + domcd(heart[, 1:2], data(heart), nrep) 355s + domcd(starsCYG, data(starsCYG), nrep) 355s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 355s + domcd(stack.x, data(stackloss), nrep) 355s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 355s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 355s + domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 355s + domcd(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 355s + 355s + domcd(brain, "Animals", nrep) 355s + domcd(milk, data(milk), nrep) 355s + domcd(bushfire, data(bushfire), nrep) 355s + ## VT::19.07.2010: test the univariate SDE 355s + for(i in 1:ncol(bushfire)) 355s + domcd(bushfire[i], data(bushfire), nrep) 355s + cat("========================================================\n") 355s + } 355s > 355s > dogen <- function(nrep=1, eps=0.49){ 355s + 355s + library(MASS) 355s + domcd <- function(x, nrep=1){ 355s + gc() 355s + xtime <- system.time(dorep(x, nrep))[1]/nrep 355s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 355s + xtime 355s + } 355s + 355s + set.seed(1234) 355s + 355s + ## VT::15.09.2013 - this will render the output independent 355s + ## from the version of the package 355s + suppressPackageStartupMessages(library(rrcov)) 355s + 355s + ap <- c(2, 5, 10, 20, 30) 355s + an <- c(100, 500, 1000, 10000, 50000) 355s + 355s + tottime <- 0 355s + cat(" n p Time\n") 355s + cat("=====================\n") 355s + for(i in 1:length(an)) { 355s + for(j in 1:length(ap)) { 355s + n <- an[i] 355s + p <- ap[j] 355s + if(5*p <= n){ 355s + xx <- gendata(n, p, eps) 355s + X <- xx$X 355s + tottime <- tottime + domcd(X, nrep) 355s + } 355s + } 355s + } 355s + 355s + cat("=====================\n") 355s + cat("Total time: ", tottime*nrep, "\n") 355s + } 355s > 355s > docheck <- function(n, p, eps){ 355s + xx <- gendata(n,p,eps) 355s + mcd <- CovSde(xx$X) 355s + check(mcd, xx$xind) 355s + } 355s > 355s > check <- function(mcd, xind){ 355s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 355s + ## did not get zero weight 355s + mymatch <- xind %in% which(mcd@wt == 0) 355s + length(xind) - length(which(mymatch)) 355s + } 355s > 355s > dorep <- function(x, nrep=1){ 355s + 355s + for(i in 1:nrep) 355s + CovSde(x) 355s + } 355s > 355s > #### gendata() #### 355s > # Generates a location contaminated multivariate 355s > # normal sample of n observations in p dimensions 355s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 355s > # where 355s > # m = (b,b,...,b) 355s > # Defaults: eps=0 and b=10 355s > # 355s > gendata <- function(n,p,eps=0,b=10){ 355s + 355s + if(missing(n) || missing(p)) 355s + stop("Please specify (n,p)") 355s + if(eps < 0 || eps >= 0.5) 355s + stop(message="eps must be in [0,0.5)") 355s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 355s + nbad <- as.integer(eps * n) 355s + if(nbad > 0){ 355s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 355s + xind <- sample(n,nbad) 355s + X[xind,] <- Xbad 355s + } 355s + list(X=X, xind=xind) 355s + } 355s > 355s > pad.right <- function(z, pads) 355s + { 355s + ### Pads spaces to right of text 355s + padding <- paste(rep(" ", pads), collapse = "") 355s + paste(z, padding, sep = "") 355s + } 355s > 355s > whatis<-function(x){ 355s + if(is.data.frame(x)) 355s + cat("Type: data.frame\n") 355s + else if(is.matrix(x)) 355s + cat("Type: matrix\n") 355s + else if(is.vector(x)) 355s + cat("Type: vector\n") 355s + else 355s + cat("Type: don't know\n") 355s + } 355s > 355s > ## VT::15.09.2013 - this will render the output independent 355s > ## from the version of the package 355s > suppressPackageStartupMessages(library(rrcov)) 356s > 356s > dodata() 356s 356s Call: dodata() 356s Data Set n p Half LOG(obj) Time 356s ======================================================== 356s heart 12 2 356s Outliers: 5 356s [1] 2 6 8 10 12 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s height weight 356s 39.8 35.7 356s 356s Robust Estimate of Covariance: 356s height weight 356s height 38.2 77.1 356s weight 77.1 188.1 356s -------------------------------------------------------- 356s starsCYG 47 2 356s Outliers: 7 356s [1] 7 9 11 14 20 30 34 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s log.Te log.light 356s 4.42 4.96 356s 356s Robust Estimate of Covariance: 356s log.Te log.light 356s log.Te 0.0163 0.0522 356s log.light 0.0522 0.3243 356s -------------------------------------------------------- 356s phosphor 18 2 356s Outliers: 2 356s [1] 1 6 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s inorg organic 356s 13.3 39.7 356s 356s Robust Estimate of Covariance: 356s inorg organic 356s inorg 133 134 356s organic 134 204 356s -------------------------------------------------------- 356s stackloss 21 3 356s Outliers: 6 356s [1] 1 2 3 15 17 21 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s Air.Flow Water.Temp Acid.Conc. 356s 57.8 20.7 86.4 356s 356s Robust Estimate of Covariance: 356s Air.Flow Water.Temp Acid.Conc. 356s Air.Flow 39.7 15.6 25.0 356s Water.Temp 15.6 13.0 11.9 356s Acid.Conc. 25.0 11.9 40.3 356s -------------------------------------------------------- 356s coleman 20 5 356s Outliers: 8 356s [1] 1 2 6 10 11 12 15 18 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s salaryP fatherWc sstatus teacherSc motherLev 356s 2.78 58.64 9.09 25.37 6.69 356s 356s Robust Estimate of Covariance: 356s salaryP fatherWc sstatus teacherSc motherLev 356s salaryP 0.2556 -1.0144 0.6599 0.2673 0.0339 356s fatherWc -1.0144 1615.9192 382.7846 -4.8287 36.0227 356s sstatus 0.6599 382.7846 108.1781 -0.7904 9.1027 356s teacherSc 0.2673 -4.8287 -0.7904 0.9346 -0.0239 356s motherLev 0.0339 36.0227 9.1027 -0.0239 0.9155 356s -------------------------------------------------------- 356s salinity 28 3 356s Outliers: 9 356s [1] 3 4 5 9 11 16 19 23 24 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s X1 X2 X3 356s 10.84 3.35 22.48 356s 356s Robust Estimate of Covariance: 356s X1 X2 X3 356s X1 10.75 -1.62 -2.05 356s X2 -1.62 4.21 -1.43 356s X3 -2.05 -1.43 2.63 356s -------------------------------------------------------- 356s wood 20 5 356s Outliers: 11 356s [1] 4 6 7 8 9 10 12 13 16 19 20 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s x1 x2 x3 x4 x5 356s 0.573 0.119 0.517 0.549 0.904 356s 356s Robust Estimate of Covariance: 356s x1 x2 x3 x4 x5 356s x1 0.025185 0.004279 -0.001262 -0.000382 -0.001907 356s x2 0.004279 0.001011 0.000197 -0.000117 0.000247 356s x3 -0.001262 0.000197 0.003042 0.002034 0.001773 356s x4 -0.000382 -0.000117 0.002034 0.007943 0.001137 356s x5 -0.001907 0.000247 0.001773 0.001137 0.005392 356s -------------------------------------------------------- 356s hbk 75 3 356s Outliers: 15 356s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 53 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s X1 X2 X3 356s 1.59 1.79 1.67 356s 356s Robust Estimate of Covariance: 356s X1 X2 X3 356s X1 1.6354 0.0793 0.2284 356s X2 0.0793 1.6461 0.3186 356s X3 0.2284 0.3186 1.5673 356s -------------------------------------------------------- 356s Animals 28 2 356s Outliers: 13 356s [1] 2 6 7 8 9 12 13 14 15 16 24 25 28 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s body brain 356s 18.7 64.9 356s 356s Robust Estimate of Covariance: 356s body brain 356s body 4702 7973 356s brain 7973 28571 356s -------------------------------------------------------- 356s milk 86 8 356s Outliers: 21 356s [1] 1 2 3 6 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s X1 X2 X3 X4 X5 X6 X7 X8 356s 1.03 35.90 33.04 26.11 25.10 25.02 123.06 14.37 356s 356s Robust Estimate of Covariance: 356s X1 X2 X3 X4 X5 X6 X7 356s X1 4.73e-07 6.57e-05 1.79e-04 1.71e-04 1.62e-04 1.42e-04 6.85e-04 356s X2 6.57e-05 1.57e+00 1.36e-01 9.28e-02 4.18e-02 1.30e-01 1.58e+00 356s X3 1.79e-04 1.36e-01 1.12e+00 8.20e-01 8.27e-01 8.00e-01 6.66e-01 356s X4 1.71e-04 9.28e-02 8.20e-01 6.57e-01 6.41e-01 6.18e-01 5.47e-01 356s X5 1.62e-04 4.18e-02 8.27e-01 6.41e-01 6.93e-01 6.44e-01 5.71e-01 356s X6 1.42e-04 1.30e-01 8.00e-01 6.18e-01 6.44e-01 6.44e-01 5.55e-01 356s X7 6.85e-04 1.58e+00 6.66e-01 5.47e-01 5.71e-01 5.55e-01 4.17e+00 356s X8 1.40e-05 2.33e-01 1.74e-01 1.06e-01 9.44e-02 9.86e-02 3.54e-01 356s X8 356s X1 1.40e-05 356s X2 2.33e-01 356s X3 1.74e-01 356s X4 1.06e-01 356s X5 9.44e-02 356s X6 9.86e-02 356s X7 3.54e-01 356s X8 1.57e-01 356s -------------------------------------------------------- 356s bushfire 38 5 356s Outliers: 23 356s [1] 1 5 6 7 8 9 10 11 12 13 15 16 28 29 30 31 32 33 34 35 36 37 38 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s V1 V2 V3 V4 V5 356s 105 148 287 223 283 356s 356s Robust Estimate of Covariance: 356s V1 V2 V3 V4 V5 356s V1 1964 1712 -10230 -2504 -2066 356s V2 1712 1526 -8732 -2145 -1763 356s V3 -10230 -8732 56327 13803 11472 356s V4 -2504 -2145 13803 3509 2894 356s V5 -2066 -1763 11472 2894 2404 356s -------------------------------------------------------- 356s bushfire 38 1 356s Outliers: 2 356s [1] 13 30 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s V1 356s 98.5 356s 356s Robust Estimate of Covariance: 356s V1 356s V1 431 356s -------------------------------------------------------- 356s bushfire 38 1 356s Outliers: 6 356s [1] 33 34 35 36 37 38 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s V2 356s 141 356s 356s Robust Estimate of Covariance: 356s V2 356s V2 528 356s -------------------------------------------------------- 356s bushfire 38 1 356s Outliers: 0 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s V3 356s 238 356s 356s Robust Estimate of Covariance: 356s V3 356s V3 37148 356s -------------------------------------------------------- 356s bushfire 38 1 356s Outliers: 9 356s [1] 8 9 32 33 34 35 36 37 38 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s V4 356s 210 356s 356s Robust Estimate of Covariance: 356s V4 356s V4 2543 356s -------------------------------------------------------- 356s bushfire 38 1 356s Outliers: 9 356s [1] 8 9 32 33 34 35 36 37 38 356s ------------- 356s 356s Call: 356s CovSde(x = x) 356s -> Method: Stahel-Donoho estimator 356s 356s Robust Estimate of Location: 356s V5 356s 273 356s 356s Robust Estimate of Covariance: 356s V5 356s V5 1575 356s -------------------------------------------------------- 356s ======================================================== 356s > ##doexact() 356s > 356s BEGIN TEST tsest.R 356s 356s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 356s Copyright (C) 2025 The R Foundation for Statistical Computing 356s Platform: arm-unknown-linux-gnueabihf (32-bit) 356s 356s R is free software and comes with ABSOLUTELY NO WARRANTY. 356s You are welcome to redistribute it under certain conditions. 356s Type 'license()' or 'licence()' for distribution details. 356s 356s R is a collaborative project with many contributors. 356s Type 'contributors()' for more information and 356s 'citation()' on how to cite R or R packages in publications. 356s 356s Type 'demo()' for some demos, 'help()' for on-line help, or 356s 'help.start()' for an HTML browser interface to help. 356s Type 'q()' to quit R. 356s 356s > ## VT::15.09.2013 - this will render the output independent 356s > ## from the version of the package 356s > suppressPackageStartupMessages(library(rrcov)) 356s > 356s > library(MASS) 356s > 356s > dodata <- function(nrep = 1, time = FALSE, full = TRUE, method) { 356s + doest <- function(x, xname, nrep = 1, method=c("sfast", "surreal", "bisquare", "rocke", "suser", "MM", "sdet")) { 356s + 356s + method <- match.arg(method) 356s + 356s + lname <- 20 356s + n <- dim(x)[1] 356s + p <- dim(x)[2] 356s + 356s + mm <- if(method == "MM") CovMMest(x) else CovSest(x, method=method) 356s + 356s + crit <- log(mm@crit) 356s + 356s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 356s + lpad <- lname-nchar(xname) 356s + cat(pad.right(xname,lpad), xres) 356s + 356s + dist <- getDistance(mm) 356s + quantiel <- qchisq(0.975, p) 356s + ibad <- which(dist >= quantiel) 356s + names(ibad) <- NULL 356s + nbad <- length(ibad) 356s + cat("Outliers: ",nbad,"\n") 356s + if(nbad > 0) 356s + print(ibad) 356s + cat("-------------\n") 356s + show(mm) 356s + cat("--------------------------------------------------------\n") 356s + } 356s + 356s + options(digits = 5) 356s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 356s + 356s + data(heart) 356s + data(starsCYG) 356s + data(phosphor) 356s + data(stackloss) 356s + data(coleman) 356s + data(salinity) 356s + data(wood) 356s + data(hbk) 356s + 356s + data(Animals, package = "MASS") 356s + brain <- Animals[c(1:24, 26:25, 27:28),] 356s + data(milk) 356s + data(bushfire) 356s + ### 356s + data(rice) 356s + data(hemophilia) 356s + data(fish) 356s + 356s + tmp <- sys.call() 356s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 356s + 356s + cat("Data Set n p LOG(det) Time\n") 356s + cat("===================================================\n") 356s + doest(heart[, 1:2], data(heart), nrep, method=method) 356s + doest(starsCYG, data(starsCYG), nrep, method=method) 356s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep, method=method) 356s + doest(stack.x, data(stackloss), nrep, method=method) 356s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep, method=method) 356s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep, method=method) 356s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep, method=method) 356s + doest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep, method=method) 356s + 356s + 356s + doest(brain, "Animals", nrep, method=method) 356s + doest(milk, data(milk), nrep, method=method) 356s + doest(bushfire, data(bushfire), nrep, method=method) 356s + 356s + doest(data.matrix(subset(rice, select = -Overall_evaluation)), data(rice), nrep, method=method) 356s + doest(data.matrix(subset(hemophilia, select = -gr)), data(hemophilia), nrep, method=method) 356s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep, method=method) 356s + 356s + ## from package 'datasets' 356s + doest(airquality[,1:4], data(airquality), nrep, method=method) 356s + doest(attitude, data(attitude), nrep, method=method) 356s + doest(attenu, data(attenu), nrep, method=method) 356s + doest(USJudgeRatings, data(USJudgeRatings), nrep, method=method) 356s + doest(USArrests, data(USArrests), nrep, method=method) 356s + doest(longley, data(longley), nrep, method=method) 356s + doest(Loblolly, data(Loblolly), nrep, method=method) 356s + doest(quakes[,1:4], data(quakes), nrep, method=method) 356s + 356s + cat("===================================================\n") 356s + } 356s > 356s > # generate contaminated data using the function gendata with different 356s > # number of outliers and check if the M-estimate breaks - i.e. the 356s > # largest eigenvalue is larger than e.g. 5. 356s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 356s > # outliers grater than 20. 356s > dogen <- function(){ 356s + eig <- vector("numeric",26) 356s + for(i in 0:25) { 356s + gg <- gendata(eps=i) 356s + mm <- CovSRocke(gg$x, t0=gg$tgood, S0=gg$sgood) 356s + eig[i+1] <- ev <- getEvals(mm)[1] 356s + cat(i, ev, "\n") 356s + 356s + ## stopifnot(ev < 5 || i > 20) 356s + } 356s + plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 356s + } 356s > 356s > # 356s > # generate data 50x10 as multivariate normal N(0,I) and add 356s > # eps % outliers by adding d=5.0 to each component. 356s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 356s > # - if eps >= 1, it is the number of outliers 356s > # - use the center and cov of the good data as good start 356s > # - use the center and the cov of all data as a bad start 356s > # If using a good start, the M-estimate must iterate to 356s > # the good solution: the largest eigenvalue is less then e.g. 5 356s > # 356s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 356s + 356s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 356s + stop("eps is out of range") 356s + 356s + library(MASS) 356s + 356s + x <- mvrnorm(n, rep(0,p), diag(p)) 356s + bad <- vector("numeric") 356s + nbad = if(eps < 1) eps*n else eps 356s + if(nbad > 0){ 356s + bad <- sample(n, nbad) 356s + x[bad,] <- x[bad,] + d 356s + } 356s + cov1 <- cov.wt(x) 356s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 356s + 356s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 356s + } 356s > 356s > pad.right <- function(z, pads) 356s + { 356s + ## Pads spaces to right of text 356s + padding <- paste(rep(" ", pads), collapse = "") 356s + paste(z, padding, sep = "") 356s + } 356s > 356s > 356s > ## -- now do it: 356s > dodata(method="sfast") 357s 357s Call: dodata(method = "sfast") 357s Data Set n p LOG(det) Time 357s =================================================== 357s heart 12 2 2.017701 357s Outliers: 3 357s [1] 2 6 12 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 36.1 29.5 357s 357s Robust Estimate of Covariance: 357s height weight 357s height 129 210 357s weight 210 365 357s -------------------------------------------------------- 357s starsCYG 47 2 -1.450032 357s Outliers: 7 357s [1] 7 9 11 14 20 30 34 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 4.42 4.97 357s 357s Robust Estimate of Covariance: 357s log.Te log.light 357s log.Te 0.0176 0.0617 357s log.light 0.0617 0.3880 357s -------------------------------------------------------- 357s phosphor 18 2 2.320721 357s Outliers: 2 357s [1] 1 6 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 14.1 38.8 357s 357s Robust Estimate of Covariance: 357s inorg organic 357s inorg 174 190 357s organic 190 268 357s -------------------------------------------------------- 357s stackloss 21 3 1.470031 357s Outliers: 3 357s [1] 1 2 3 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 57.5 20.5 86.0 357s 357s Robust Estimate of Covariance: 357s Air.Flow Water.Temp Acid.Conc. 357s Air.Flow 38.94 11.66 22.89 357s Water.Temp 11.66 9.96 7.81 357s Acid.Conc. 22.89 7.81 40.48 357s -------------------------------------------------------- 357s coleman 20 5 0.491419 357s Outliers: 2 357s [1] 6 10 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 2.77 45.58 4.13 25.13 6.39 357s 357s Robust Estimate of Covariance: 357s salaryP fatherWc sstatus teacherSc motherLev 357s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 357s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 357s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 357s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 357s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 357s -------------------------------------------------------- 357s salinity 28 3 0.734619 357s Outliers: 4 357s [1] 5 16 23 24 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 10.31 3.07 22.60 357s 357s Robust Estimate of Covariance: 357s X1 X2 X3 357s X1 13.200 0.784 -3.611 357s X2 0.784 4.441 -1.658 357s X3 -3.611 -1.658 2.877 357s -------------------------------------------------------- 357s wood 20 5 -3.202636 357s Outliers: 2 357s [1] 7 9 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 0.551 0.135 0.496 0.511 0.916 357s 357s Robust Estimate of Covariance: 357s x1 x2 x3 x4 x5 357s x1 0.011361 -0.000791 0.005473 0.004204 -0.004747 357s x2 -0.000791 0.000701 -0.000534 -0.001452 0.000864 357s x3 0.005473 -0.000534 0.004905 0.002960 -0.001914 357s x4 0.004204 -0.001452 0.002960 0.005154 -0.002187 357s x5 -0.004747 0.000864 -0.001914 -0.002187 0.003214 357s -------------------------------------------------------- 357s hbk 75 3 0.283145 357s Outliers: 14 357s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 1.53 1.83 1.66 357s 357s Robust Estimate of Covariance: 357s X1 X2 X3 357s X1 1.8091 0.0479 0.2446 357s X2 0.0479 1.8190 0.2513 357s X3 0.2446 0.2513 1.7288 357s -------------------------------------------------------- 357s Animals 28 2 4.685129 357s Outliers: 10 357s [1] 2 6 7 9 12 14 15 16 24 25 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 30.8 84.2 357s 357s Robust Estimate of Covariance: 357s body brain 357s body 14806 28767 357s brain 28767 65195 357s -------------------------------------------------------- 357s milk 86 8 -1.437863 357s Outliers: 15 357s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 357s 357s Robust Estimate of Covariance: 357s X1 X2 X3 X4 X5 X6 X7 357s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 357s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 357s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 357s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 357s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 357s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 357s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 357s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 357s X8 357s X1 7.45e-05 357s X2 3.33e-01 357s X3 2.82e-01 357s X4 2.01e-01 357s X5 1.80e-01 357s X6 1.91e-01 357s X7 6.38e-01 357s X8 2.01e-01 357s -------------------------------------------------------- 357s bushfire 38 5 2.443148 357s Outliers: 13 357s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 108 149 266 216 278 357s 357s Robust Estimate of Covariance: 357s V1 V2 V3 V4 V5 357s V1 911 688 -3961 -856 -707 357s V2 688 587 -2493 -492 -420 357s V3 -3961 -2493 24146 5765 4627 357s V4 -856 -492 5765 1477 1164 357s V5 -707 -420 4627 1164 925 357s -------------------------------------------------------- 357s rice 105 5 -0.724874 357s Outliers: 7 357s [1] 9 40 42 49 57 58 71 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 357s 357s Robust Estimate of Covariance: 357s Favor Appearance Taste Stickiness Toughness 357s Favor 0.423 0.345 0.427 0.405 -0.202 357s Appearance 0.345 0.592 0.570 0.549 -0.316 357s Taste 0.427 0.570 0.739 0.706 -0.393 357s Stickiness 0.405 0.549 0.706 0.876 -0.497 357s Toughness -0.202 -0.316 -0.393 -0.497 0.467 357s -------------------------------------------------------- 357s hemophilia 75 2 -1.868949 357s Outliers: 2 357s [1] 11 36 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] -0.2126 -0.0357 357s 357s Robust Estimate of Covariance: 357s AHFactivity AHFantigen 357s AHFactivity 0.0317 0.0112 357s AHFantigen 0.0112 0.0218 357s -------------------------------------------------------- 357s fish 159 6 1.285876 357s Outliers: 21 357s [1] 61 62 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 357s [20] 103 142 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 358.3 24.7 26.9 29.7 30.0 14.7 357s 357s Robust Estimate of Covariance: 357s Weight Length1 Length2 Length3 Height Width 357s Weight 1.33e+05 3.09e+03 3.34e+03 3.78e+03 1.72e+03 2.24e+02 357s Length1 3.09e+03 7.92e+01 8.54e+01 9.55e+01 4.04e+01 7.43e+00 357s Length2 3.34e+03 8.54e+01 9.22e+01 1.03e+02 4.49e+01 8.07e+00 357s Length3 3.78e+03 9.55e+01 1.03e+02 1.18e+02 5.92e+01 7.65e+00 357s Height 1.72e+03 4.04e+01 4.49e+01 5.92e+01 7.12e+01 8.51e-01 357s Width 2.24e+02 7.43e+00 8.07e+00 7.65e+00 8.51e-01 3.57e+00 357s -------------------------------------------------------- 357s airquality 153 4 2.684374 357s Outliers: 7 357s [1] 7 14 23 30 34 77 107 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 39.34 192.12 9.67 78.71 357s 357s Robust Estimate of Covariance: 357s Ozone Solar.R Wind Temp 357s Ozone 973.104 894.011 -61.856 243.560 357s Solar.R 894.011 9677.269 0.388 179.429 357s Wind -61.856 0.388 11.287 -14.310 357s Temp 243.560 179.429 -14.310 96.714 357s -------------------------------------------------------- 357s attitude 30 7 2.091968 357s Outliers: 4 357s [1] 14 16 18 24 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 357s 357s Robust Estimate of Covariance: 357s rating complaints privileges learning raises critical advance 357s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 357s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 357s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 357s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 357s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 357s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 357s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 357s -------------------------------------------------------- 357s attenu 182 5 1.148032 357s Outliers: 31 357s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 357s [20] 29 30 31 32 64 65 80 94 95 96 97 100 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 16.432 5.849 60.297 27.144 0.134 357s 357s Robust Estimate of Covariance: 357s event mag station dist accel 357s event 54.9236 -3.0733 181.0954 -49.4194 -0.0628 357s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 357s station 181.0954 -8.4388 1689.7161 -114.6319 0.7285 357s dist -49.4194 6.7388 -114.6319 597.3606 -1.7988 357s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 357s -------------------------------------------------------- 357s USJudgeRatings 43 12 -1.683847 357s Outliers: 7 357s [1] 5 7 12 13 14 23 31 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [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 357s 357s Robust Estimate of Covariance: 357s CONT INTG DMNR DILG CFMG DECI PREP FAMI 357s CONT 0.8710 -0.3019 -0.4682 -0.1893 -0.0569 -0.0992 -0.1771 -0.1975 357s INTG -0.3019 0.6401 0.8598 0.6955 0.5732 0.5439 0.7091 0.7084 357s DMNR -0.4682 0.8598 1.2412 0.9107 0.7668 0.7305 0.9292 0.9158 357s DILG -0.1893 0.6955 0.9107 0.8554 0.7408 0.7036 0.8865 0.8791 357s CFMG -0.0569 0.5732 0.7668 0.7408 0.6994 0.6545 0.7788 0.7721 357s DECI -0.0992 0.5439 0.7305 0.7036 0.6545 0.6342 0.7492 0.7511 357s PREP -0.1771 0.7091 0.9292 0.8865 0.7788 0.7492 0.9541 0.9556 357s FAMI -0.1975 0.7084 0.9158 0.8791 0.7721 0.7511 0.9556 0.9785 357s ORAL -0.2444 0.7453 0.9939 0.8917 0.7842 0.7551 0.9554 0.9680 357s WRIT -0.2344 0.7319 0.9649 0.8853 0.7781 0.7511 0.9498 0.9668 357s PHYS -0.1983 0.4676 0.6263 0.5629 0.5073 0.5039 0.5990 0.6140 357s RTEN -0.3152 0.8000 1.0798 0.9234 0.7952 0.7663 0.9637 0.9693 357s ORAL WRIT PHYS RTEN 357s CONT -0.2444 -0.2344 -0.1983 -0.3152 357s INTG 0.7453 0.7319 0.4676 0.8000 357s DMNR 0.9939 0.9649 0.6263 1.0798 357s DILG 0.8917 0.8853 0.5629 0.9234 357s CFMG 0.7842 0.7781 0.5073 0.7952 357s DECI 0.7551 0.7511 0.5039 0.7663 357s PREP 0.9554 0.9498 0.5990 0.9637 357s FAMI 0.9680 0.9668 0.6140 0.9693 357s ORAL 0.9853 0.9744 0.6280 1.0032 357s WRIT 0.9744 0.9711 0.6184 0.9870 357s PHYS 0.6280 0.6184 0.4716 0.6520 357s RTEN 1.0032 0.9870 0.6520 1.0622 357s -------------------------------------------------------- 357s USArrests 50 4 2.411726 357s Outliers: 4 357s [1] 2 28 33 39 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 7.05 150.66 64.66 19.37 357s 357s Robust Estimate of Covariance: 357s Murder Assault UrbanPop Rape 357s Murder 23.8 380.8 19.2 29.7 357s Assault 380.8 8436.2 605.6 645.3 357s UrbanPop 19.2 605.6 246.5 78.8 357s Rape 29.7 645.3 78.8 77.3 357s -------------------------------------------------------- 357s longley 16 7 1.038316 357s Outliers: 5 357s [1] 1 2 3 4 5 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 107.6 440.8 339.7 292.5 121.0 1957.1 67.2 357s 357s Robust Estimate of Covariance: 357s GNP.deflator GNP Unemployed Armed.Forces Population 357s GNP.deflator 100.6 954.7 1147.1 -507.6 74.2 357s GNP 954.7 9430.9 10115.8 -4616.5 730.1 357s Unemployed 1147.1 10115.8 19838.3 -6376.9 850.6 357s Armed.Forces -507.6 -4616.5 -6376.9 3240.2 -351.3 357s Population 74.2 730.1 850.6 -351.3 57.5 357s Year 46.4 450.8 539.5 -233.0 35.3 357s Employed 30.8 310.5 274.0 -160.8 23.3 357s Year Employed 357s GNP.deflator 46.4 30.8 357s GNP 450.8 310.5 357s Unemployed 539.5 274.0 357s Armed.Forces -233.0 -160.8 357s Population 35.3 23.3 357s Year 21.9 14.6 357s Employed 14.6 11.2 357s -------------------------------------------------------- 357s Loblolly 84 3 1.481317 357s Outliers: 14 357s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] 24.22 9.65 7.50 357s 357s Robust Estimate of Covariance: 357s height age Seed 357s height 525.08 179.21 14.27 357s age 179.21 61.85 2.94 357s Seed 14.27 2.94 25.86 357s -------------------------------------------------------- 357s quakes 1000 4 1.576855 357s Outliers: 223 357s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 357s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 357s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 357s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 357s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 357s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 357s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 357s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 357s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 357s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 357s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 357s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 357s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 357s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 357s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: S-FAST 357s 357s Robust Estimate of Location: 357s [1] -21.54 182.35 369.21 4.54 357s 357s Robust Estimate of Covariance: 357s lat long depth mag 357s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 357s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 357s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 357s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 357s -------------------------------------------------------- 357s =================================================== 357s > dodata(method="sdet") 357s 357s Call: dodata(method = "sdet") 357s Data Set n p LOG(det) Time 357s =================================================== 357s heart 12 2 2.017701 357s Outliers: 3 357s [1] 2 6 12 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: DET-S 357s 357s Robust Estimate of Location: 357s [1] 36.1 29.5 357s 357s Robust Estimate of Covariance: 357s height weight 357s height 129 210 357s weight 210 365 357s -------------------------------------------------------- 357s starsCYG 47 2 -1.450032 357s Outliers: 7 357s [1] 7 9 11 14 20 30 34 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: DET-S 357s 357s Robust Estimate of Location: 357s [1] 4.42 4.97 357s 357s Robust Estimate of Covariance: 357s log.Te log.light 357s log.Te 0.0176 0.0617 357s log.light 0.0617 0.3880 357s -------------------------------------------------------- 357s phosphor 18 2 2.320721 357s Outliers: 2 357s [1] 1 6 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: DET-S 357s 357s Robust Estimate of Location: 357s [1] 14.1 38.8 357s 357s Robust Estimate of Covariance: 357s inorg organic 357s inorg 174 190 357s organic 190 268 357s -------------------------------------------------------- 357s stackloss 21 3 1.470031 357s Outliers: 3 357s [1] 1 2 3 357s ------------- 357s 357s Call: 357s CovSest(x = x, method = method) 357s -> Method: S-estimates: DET-S 357s 357s Robust Estimate of Location: 357s [1] 57.5 20.5 86.0 357s 357s Robust Estimate of Covariance: 357s Air.Flow Water.Temp Acid.Conc. 357s Air.Flow 38.94 11.66 22.89 357s Water.Temp 11.66 9.96 7.81 357s Acid.Conc. 22.89 7.81 40.48 357s -------------------------------------------------------- 358s coleman 20 5 0.491419 358s Outliers: 2 358s [1] 6 10 358s ------------- 358s 358s Call: 358s CovSest(x = x, method = method) 358s -> Method: S-estimates: DET-S 358s 358s Robust Estimate of Location: 358s [1] 2.77 45.58 4.13 25.13 6.39 358s 358s Robust Estimate of Covariance: 358s salaryP fatherWc sstatus teacherSc motherLev 358s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 358s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 358s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 358s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 358s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 358s -------------------------------------------------------- 358s salinity 28 3 0.734619 358s Outliers: 4 358s [1] 5 16 23 24 358s ------------- 358s 358s Call: 358s CovSest(x = x, method = method) 358s -> Method: S-estimates: DET-S 358s 358s Robust Estimate of Location: 358s [1] 10.31 3.07 22.60 358s 358s Robust Estimate of Covariance: 358s X1 X2 X3 358s X1 13.200 0.784 -3.611 358s X2 0.784 4.441 -1.658 358s X3 -3.611 -1.658 2.877 358s -------------------------------------------------------- 358s wood 20 5 -3.220754 358s Outliers: 4 358s [1] 4 6 8 19 358s ------------- 358s 358s Call: 358s CovSest(x = x, method = method) 358s -> Method: S-estimates: DET-S 358s 358s Robust Estimate of Location: 358s [1] 0.580 0.123 0.530 0.538 0.890 358s 358s Robust Estimate of Covariance: 358s x1 x2 x3 x4 x5 358s x1 8.16e-03 1.39e-03 1.97e-03 -2.82e-04 -7.61e-04 358s x2 1.39e-03 4.00e-04 8.14e-04 -8.51e-05 -5.07e-06 358s x3 1.97e-03 8.14e-04 4.74e-03 -9.59e-04 2.06e-05 358s x4 -2.82e-04 -8.51e-05 -9.59e-04 3.09e-03 1.87e-03 358s x5 -7.61e-04 -5.07e-06 2.06e-05 1.87e-03 2.28e-03 358s -------------------------------------------------------- 358s hbk 75 3 0.283145 358s Outliers: 14 358s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 358s ------------- 358s 358s Call: 358s CovSest(x = x, method = method) 358s -> Method: S-estimates: DET-S 358s 358s Robust Estimate of Location: 358s [1] 1.53 1.83 1.66 358s 358s Robust Estimate of Covariance: 358s X1 X2 X3 358s X1 1.8091 0.0479 0.2446 358s X2 0.0479 1.8190 0.2513 358s X3 0.2446 0.2513 1.7288 358s -------------------------------------------------------- 358s Animals 28 2 4.685129 358s Outliers: 10 358s [1] 2 6 7 9 12 14 15 16 24 25 358s ------------- 358s 358s Call: 358s CovSest(x = x, method = method) 358s -> Method: S-estimates: DET-S 358s 358s Robust Estimate of Location: 358s [1] 30.8 84.2 358s 358s Robust Estimate of Covariance: 358s body brain 358s body 14806 28767 358s brain 28767 65194 358s -------------------------------------------------------- 359s milk 86 8 -1.437863 359s Outliers: 15 359s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 359s ------------- 359s 359s Call: 359s CovSest(x = x, method = method) 359s -> Method: S-estimates: DET-S 359s 359s Robust Estimate of Location: 359s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 359s 359s Robust Estimate of Covariance: 359s X1 X2 X3 X4 X5 X6 X7 359s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 359s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 359s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 359s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 359s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 359s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 359s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 359s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 359s X8 359s X1 7.45e-05 359s X2 3.33e-01 359s X3 2.82e-01 359s X4 2.01e-01 359s X5 1.80e-01 359s X6 1.91e-01 359s X7 6.38e-01 359s X8 2.01e-01 359s -------------------------------------------------------- 359s bushfire 38 5 2.443148 359s Outliers: 13 359s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 359s ------------- 359s 359s Call: 359s CovSest(x = x, method = method) 359s -> Method: S-estimates: DET-S 359s 359s Robust Estimate of Location: 359s [1] 108 149 266 216 278 359s 359s Robust Estimate of Covariance: 359s V1 V2 V3 V4 V5 359s V1 911 688 -3961 -856 -707 359s V2 688 587 -2493 -492 -420 359s V3 -3961 -2493 24146 5765 4627 359s V4 -856 -492 5765 1477 1164 359s V5 -707 -420 4627 1164 925 359s -------------------------------------------------------- 359s rice 105 5 -0.724874 359s Outliers: 7 359s [1] 9 40 42 49 57 58 71 359s ------------- 359s 359s Call: 359s CovSest(x = x, method = method) 359s -> Method: S-estimates: DET-S 359s 359s Robust Estimate of Location: 359s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 359s 359s Robust Estimate of Covariance: 359s Favor Appearance Taste Stickiness Toughness 359s Favor 0.423 0.345 0.427 0.405 -0.202 359s Appearance 0.345 0.592 0.570 0.549 -0.316 359s Taste 0.427 0.570 0.739 0.706 -0.393 359s Stickiness 0.405 0.549 0.706 0.876 -0.497 359s Toughness -0.202 -0.316 -0.393 -0.497 0.467 359s -------------------------------------------------------- 359s hemophilia 75 2 -1.868949 359s Outliers: 2 359s [1] 11 36 359s ------------- 359s 359s Call: 359s CovSest(x = x, method = method) 359s -> Method: S-estimates: DET-S 359s 359s Robust Estimate of Location: 359s [1] -0.2126 -0.0357 359s 359s Robust Estimate of Covariance: 359s AHFactivity AHFantigen 359s AHFactivity 0.0317 0.0112 359s AHFantigen 0.0112 0.0218 359s -------------------------------------------------------- 360s fish 159 6 1.267294 360s Outliers: 33 360s [1] 61 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 360s [20] 91 92 93 94 95 96 97 98 99 100 101 102 103 142 360s ------------- 360s 360s Call: 360s CovSest(x = x, method = method) 360s -> Method: S-estimates: DET-S 360s 360s Robust Estimate of Location: 360s [1] 381.2 25.6 27.8 30.8 31.0 14.9 360s 360s Robust Estimate of Covariance: 360s Weight Length1 Length2 Length3 Height Width 360s Weight 148372.04 3260.48 3508.71 3976.93 1507.43 127.94 360s Length1 3260.48 77.00 82.52 92.18 27.56 3.29 360s Length2 3508.71 82.52 88.57 99.20 30.83 3.43 360s Length3 3976.93 92.18 99.20 113.97 45.50 2.21 360s Height 1507.43 27.56 30.83 45.50 70.54 -4.95 360s Width 127.94 3.29 3.43 2.21 -4.95 2.28 360s -------------------------------------------------------- 360s airquality 153 4 2.684374 360s Outliers: 7 360s [1] 7 14 23 30 34 77 107 360s ------------- 360s 360s Call: 360s CovSest(x = x, method = method) 360s -> Method: S-estimates: DET-S 360s 360s Robust Estimate of Location: 360s [1] 39.34 192.12 9.67 78.71 360s 360s Robust Estimate of Covariance: 360s Ozone Solar.R Wind Temp 360s Ozone 973.104 894.011 -61.856 243.560 360s Solar.R 894.011 9677.269 0.388 179.429 360s Wind -61.856 0.388 11.287 -14.310 360s Temp 243.560 179.429 -14.310 96.714 360s -------------------------------------------------------- 360s attitude 30 7 2.091968 360s Outliers: 4 360s [1] 14 16 18 24 360s ------------- 360s 360s Call: 360s CovSest(x = x, method = method) 360s -> Method: S-estimates: DET-S 360s 360s Robust Estimate of Location: 360s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 360s 360s Robust Estimate of Covariance: 360s rating complaints privileges learning raises critical advance 360s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 360s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 360s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 360s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 360s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 360s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 360s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 360s -------------------------------------------------------- 361s attenu 182 5 1.148032 361s Outliers: 31 361s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 361s [20] 29 30 31 32 64 65 80 94 95 96 97 100 361s ------------- 361s 361s Call: 361s CovSest(x = x, method = method) 361s -> Method: S-estimates: DET-S 361s 361s Robust Estimate of Location: 361s [1] 16.432 5.849 60.297 27.144 0.134 361s 361s Robust Estimate of Covariance: 361s event mag station dist accel 361s event 54.9236 -3.0733 181.0954 -49.4195 -0.0628 361s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 361s station 181.0954 -8.4388 1689.7161 -114.6321 0.7285 361s dist -49.4195 6.7388 -114.6321 597.3609 -1.7988 361s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 361s -------------------------------------------------------- 361s USJudgeRatings 43 12 -1.683847 361s Outliers: 7 361s [1] 5 7 12 13 14 23 31 361s ------------- 361s 361s Call: 361s CovSest(x = x, method = method) 361s -> Method: S-estimates: DET-S 361s 361s Robust Estimate of Location: 361s [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 361s 361s Robust Estimate of Covariance: 361s CONT INTG DMNR DILG CFMG DECI PREP FAMI 361s CONT 0.8715 -0.3020 -0.4683 -0.1894 -0.0569 -0.0993 -0.1772 -0.1976 361s INTG -0.3020 0.6403 0.8600 0.6956 0.5733 0.5440 0.7093 0.7086 361s DMNR -0.4683 0.8600 1.2416 0.9109 0.7669 0.7307 0.9295 0.9161 361s DILG -0.1894 0.6956 0.9109 0.8555 0.7410 0.7037 0.8867 0.8793 361s CFMG -0.0569 0.5733 0.7669 0.7410 0.6995 0.6546 0.7789 0.7723 361s DECI -0.0993 0.5440 0.7307 0.7037 0.6546 0.6343 0.7493 0.7513 361s PREP -0.1772 0.7093 0.9295 0.8867 0.7789 0.7493 0.9543 0.9559 361s FAMI -0.1976 0.7086 0.9161 0.8793 0.7723 0.7513 0.9559 0.9788 361s ORAL -0.2445 0.7456 0.9942 0.8919 0.7844 0.7553 0.9557 0.9683 361s WRIT -0.2345 0.7321 0.9652 0.8856 0.7783 0.7513 0.9501 0.9671 361s PHYS -0.1986 0.4676 0.6264 0.5628 0.5072 0.5038 0.5990 0.6140 361s RTEN -0.3154 0.8002 1.0801 0.9236 0.7954 0.7665 0.9639 0.9695 361s ORAL WRIT PHYS RTEN 361s CONT -0.2445 -0.2345 -0.1986 -0.3154 361s INTG 0.7456 0.7321 0.4676 0.8002 361s DMNR 0.9942 0.9652 0.6264 1.0801 361s DILG 0.8919 0.8856 0.5628 0.9236 361s CFMG 0.7844 0.7783 0.5072 0.7954 361s DECI 0.7553 0.7513 0.5038 0.7665 361s PREP 0.9557 0.9501 0.5990 0.9639 361s FAMI 0.9683 0.9671 0.6140 0.9695 361s ORAL 0.9856 0.9748 0.6281 1.0035 361s WRIT 0.9748 0.9714 0.6184 0.9873 361s PHYS 0.6281 0.6184 0.4713 0.6520 361s RTEN 1.0035 0.9873 0.6520 1.0624 361s -------------------------------------------------------- 361s USArrests 50 4 2.411726 361s Outliers: 4 361s [1] 2 28 33 39 361s ------------- 361s 361s Call: 361s CovSest(x = x, method = method) 361s -> Method: S-estimates: DET-S 361s 361s Robust Estimate of Location: 361s [1] 7.05 150.66 64.66 19.37 361s 361s Robust Estimate of Covariance: 361s Murder Assault UrbanPop Rape 361s Murder 23.8 380.8 19.2 29.7 361s Assault 380.8 8436.2 605.6 645.3 361s UrbanPop 19.2 605.6 246.5 78.8 361s Rape 29.7 645.3 78.8 77.3 361s -------------------------------------------------------- 362s longley 16 7 1.143113 362s Outliers: 4 362s [1] 1 2 3 4 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: DET-S 362s 362s Robust Estimate of Location: 362s [1] 107 435 334 293 120 1957 67 362s 362s Robust Estimate of Covariance: 362s GNP.deflator GNP Unemployed Armed.Forces Population 362s GNP.deflator 89.2 850.1 1007.4 -404.4 66.2 362s GNP 850.1 8384.4 9020.8 -3692.0 650.5 362s Unemployed 1007.4 9020.8 16585.4 -4990.7 752.5 362s Armed.Forces -404.4 -3692.0 -4990.7 2474.2 -280.9 362s Population 66.2 650.5 752.5 -280.9 51.2 362s Year 41.9 407.6 481.9 -186.4 31.9 362s Employed 27.9 279.7 255.6 -128.8 21.1 362s Year Employed 362s GNP.deflator 41.9 27.9 362s GNP 407.6 279.7 362s Unemployed 481.9 255.6 362s Armed.Forces -186.4 -128.8 362s Population 31.9 21.1 362s Year 20.2 13.4 362s Employed 13.4 10.1 362s -------------------------------------------------------- 362s Loblolly 84 3 1.481317 362s Outliers: 14 362s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: DET-S 362s 362s Robust Estimate of Location: 362s [1] 24.22 9.65 7.50 362s 362s Robust Estimate of Covariance: 362s height age Seed 362s height 525.08 179.21 14.27 362s age 179.21 61.85 2.94 362s Seed 14.27 2.94 25.86 362s -------------------------------------------------------- 362s quakes 1000 4 1.576855 362s Outliers: 223 362s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 362s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 362s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 362s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 362s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 362s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 362s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 362s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 362s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 362s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 362s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 362s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 362s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 362s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 362s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: DET-S 362s 362s Robust Estimate of Location: 362s [1] -21.54 182.35 369.21 4.54 362s 362s Robust Estimate of Covariance: 362s lat long depth mag 362s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 362s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 362s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 362s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 362s -------------------------------------------------------- 362s =================================================== 362s > ##dodata(method="suser") 362s > ##dodata(method="surreal") 362s > dodata(method="bisquare") 362s 362s Call: dodata(method = "bisquare") 362s Data Set n p LOG(det) Time 362s =================================================== 362s heart 12 2 7.721793 362s Outliers: 3 362s [1] 2 6 12 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s height weight 362s 36.1 29.4 362s 362s Robust Estimate of Covariance: 362s height weight 362s height 109 177 362s weight 177 307 362s -------------------------------------------------------- 362s starsCYG 47 2 -5.942108 362s Outliers: 7 362s [1] 7 9 11 14 20 30 34 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s log.Te log.light 362s 4.42 4.97 362s 362s Robust Estimate of Covariance: 362s log.Te log.light 362s log.Te 0.0164 0.0574 362s log.light 0.0574 0.3613 362s -------------------------------------------------------- 362s phosphor 18 2 9.269096 362s Outliers: 2 362s [1] 1 6 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s inorg organic 362s 14.1 38.7 362s 362s Robust Estimate of Covariance: 362s inorg organic 362s inorg 173 189 362s organic 189 268 362s -------------------------------------------------------- 362s stackloss 21 3 8.411100 362s Outliers: 3 362s [1] 1 2 3 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s Air.Flow Water.Temp Acid.Conc. 362s 57.5 20.5 86.0 362s 362s Robust Estimate of Covariance: 362s Air.Flow Water.Temp Acid.Conc. 362s Air.Flow 33.82 10.17 20.02 362s Water.Temp 10.17 8.70 6.84 362s Acid.Conc. 20.02 6.84 35.51 362s -------------------------------------------------------- 362s coleman 20 5 4.722046 362s Outliers: 2 362s [1] 6 10 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s salaryP fatherWc sstatus teacherSc motherLev 362s 2.77 45.59 4.14 25.13 6.39 362s 362s Robust Estimate of Covariance: 362s salaryP fatherWc sstatus teacherSc motherLev 362s salaryP 0.2135 1.8732 1.3883 0.2547 0.0648 362s fatherWc 1.8732 905.6704 296.1916 7.9820 21.0848 362s sstatus 1.3883 296.1916 128.9536 4.0196 7.1917 362s teacherSc 0.2547 7.9820 4.0196 0.7321 0.2799 362s motherLev 0.0648 21.0848 7.1917 0.2799 0.5592 362s -------------------------------------------------------- 362s salinity 28 3 4.169963 362s Outliers: 4 362s [1] 5 16 23 24 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s X1 X2 X3 362s 10.30 3.07 22.59 362s 362s Robust Estimate of Covariance: 362s X1 X2 X3 362s X1 12.234 0.748 -3.369 362s X2 0.748 4.115 -1.524 362s X3 -3.369 -1.524 2.655 362s -------------------------------------------------------- 362s wood 20 5 -33.862485 362s Outliers: 5 362s [1] 4 6 8 11 19 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s x1 x2 x3 x4 x5 362s 0.580 0.123 0.530 0.538 0.890 362s 362s Robust Estimate of Covariance: 362s x1 x2 x3 x4 x5 362s x1 5.88e-03 9.96e-04 1.43e-03 -1.96e-04 -5.46e-04 362s x2 9.96e-04 2.86e-04 5.89e-04 -5.78e-05 -2.24e-06 362s x3 1.43e-03 5.89e-04 3.42e-03 -6.95e-04 1.43e-05 362s x4 -1.96e-04 -5.78e-05 -6.95e-04 2.23e-03 1.35e-03 362s x5 -5.46e-04 -2.24e-06 1.43e-05 1.35e-03 1.65e-03 362s -------------------------------------------------------- 362s hbk 75 3 1.472421 362s Outliers: 14 362s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s X1 X2 X3 362s 1.53 1.83 1.66 362s 362s Robust Estimate of Covariance: 362s X1 X2 X3 362s X1 1.6775 0.0447 0.2268 362s X2 0.0447 1.6865 0.2325 362s X3 0.2268 0.2325 1.6032 362s -------------------------------------------------------- 362s Animals 28 2 18.528307 362s Outliers: 11 362s [1] 2 6 7 9 12 14 15 16 24 25 28 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s body brain 362s 30.7 84.1 362s 362s Robust Estimate of Covariance: 362s body brain 362s body 13278 25795 362s brain 25795 58499 362s -------------------------------------------------------- 362s milk 86 8 -24.816943 362s Outliers: 19 362s [1] 1 2 3 11 12 13 14 15 16 17 20 27 41 44 47 70 74 75 77 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s X1 X2 X3 X4 X5 X6 X7 X8 362s 1.03 35.81 32.96 26.04 25.02 24.94 122.79 14.35 362s 362s Robust Estimate of Covariance: 362s X1 X2 X3 X4 X5 X6 X7 362s X1 6.80e-07 2.20e-04 3.70e-04 3.35e-04 3.27e-04 3.30e-04 1.21e-03 362s X2 2.20e-04 1.80e+00 3.96e-01 3.03e-01 2.45e-01 3.27e-01 2.00e+00 362s X3 3.70e-04 3.96e-01 1.27e+00 9.68e-01 9.49e-01 9.56e-01 1.37e+00 362s X4 3.35e-04 3.03e-01 9.68e-01 7.86e-01 7.55e-01 7.57e-01 1.15e+00 362s X5 3.27e-04 2.45e-01 9.49e-01 7.55e-01 7.88e-01 7.61e-01 1.14e+00 362s X6 3.30e-04 3.27e-01 9.56e-01 7.57e-01 7.61e-01 7.90e-01 1.17e+00 362s X7 1.21e-03 2.00e+00 1.37e+00 1.15e+00 1.14e+00 1.17e+00 5.71e+00 362s X8 6.57e-05 2.71e-01 2.30e-01 1.64e-01 1.48e-01 1.57e-01 5.27e-01 362s X8 362s X1 6.57e-05 362s X2 2.71e-01 362s X3 2.30e-01 362s X4 1.64e-01 362s X5 1.48e-01 362s X6 1.57e-01 362s X7 5.27e-01 362s X8 1.62e-01 362s -------------------------------------------------------- 362s bushfire 38 5 21.704243 362s Outliers: 13 362s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s V1 V2 V3 V4 V5 362s 108 149 266 216 278 362s 362s Robust Estimate of Covariance: 362s V1 V2 V3 V4 V5 362s V1 528 398 -2298 -497 -410 362s V2 398 340 -1445 -285 -244 362s V3 -2298 -1445 14026 3348 2687 362s V4 -497 -285 3348 857 676 362s V5 -410 -244 2687 676 537 362s -------------------------------------------------------- 362s rice 105 5 -7.346939 362s Outliers: 8 362s [1] 9 14 40 42 49 57 58 71 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s Favor Appearance Taste Stickiness Toughness 362s -0.2480 0.1203 -0.1213 0.0710 0.0644 362s 362s Robust Estimate of Covariance: 362s Favor Appearance Taste Stickiness Toughness 362s Favor 0.415 0.338 0.419 0.398 -0.198 362s Appearance 0.338 0.580 0.559 0.539 -0.310 362s Taste 0.419 0.559 0.725 0.693 -0.386 362s Stickiness 0.398 0.539 0.693 0.859 -0.487 362s Toughness -0.198 -0.310 -0.386 -0.487 0.457 362s -------------------------------------------------------- 362s hemophilia 75 2 -7.465173 362s Outliers: 2 362s [1] 11 36 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s AHFactivity AHFantigen 362s -0.2128 -0.0366 362s 362s Robust Estimate of Covariance: 362s AHFactivity AHFantigen 362s AHFactivity 0.0321 0.0115 362s AHFantigen 0.0115 0.0220 362s -------------------------------------------------------- 362s fish 159 6 13.465134 362s Outliers: 35 362s [1] 38 61 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 362s [20] 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 142 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s Weight Length1 Length2 Length3 Height Width 362s 381.4 25.6 27.8 30.8 31.0 14.9 362s 362s Robust Estimate of Covariance: 362s Weight Length1 Length2 Length3 Height Width 362s Weight 111094.92 2440.81 2626.59 2976.92 1129.78 95.85 362s Length1 2440.81 57.63 61.75 68.98 20.67 2.46 362s Length2 2626.59 61.75 66.28 74.24 23.13 2.57 362s Length3 2976.92 68.98 74.24 85.29 34.11 1.65 362s Height 1129.78 20.67 23.13 34.11 52.75 -3.70 362s Width 95.85 2.46 2.57 1.65 -3.70 1.71 362s -------------------------------------------------------- 362s airquality 153 4 21.282926 362s Outliers: 8 362s [1] 7 11 14 23 30 34 77 107 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s Ozone Solar.R Wind Temp 362s 39.40 192.29 9.66 78.74 362s 362s Robust Estimate of Covariance: 362s Ozone Solar.R Wind Temp 362s Ozone 930.566 849.644 -59.157 232.459 362s Solar.R 849.644 9207.569 0.594 168.122 362s Wind -59.157 0.594 10.783 -13.645 362s Temp 232.459 168.122 -13.645 92.048 362s -------------------------------------------------------- 362s attitude 30 7 28.084183 362s Outliers: 6 362s [1] 6 9 14 16 18 24 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s rating complaints privileges learning raises critical 362s 65.7 66.8 51.9 56.1 66.4 76.7 362s advance 362s 43.0 362s 362s Robust Estimate of Covariance: 362s rating complaints privileges learning raises critical advance 362s rating 143.88 114.95 64.97 105.69 83.95 6.96 41.78 362s complaints 114.95 143.84 79.28 115.00 101.48 19.69 66.13 362s privileges 64.97 79.28 126.38 94.70 73.87 5.37 61.07 362s learning 105.69 115.00 94.70 146.14 110.50 21.67 68.49 362s raises 83.95 101.48 73.87 110.50 115.01 24.91 77.16 362s critical 6.96 19.69 5.37 21.67 24.91 71.74 25.88 362s advance 41.78 66.13 61.07 68.49 77.16 25.88 97.71 362s -------------------------------------------------------- 362s attenu 182 5 10.109049 362s Outliers: 35 362s [1] 2 4 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 362s [20] 28 29 30 31 32 64 65 80 93 94 95 96 97 98 99 100 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s event mag station dist accel 362s 16.418 5.850 60.243 27.307 0.134 362s 362s Robust Estimate of Covariance: 362s event mag station dist accel 362s event 41.9000 -2.3543 137.8110 -39.0321 -0.0447 362s mag -2.3543 0.4978 -6.4461 5.2644 0.0118 362s station 137.8110 -6.4461 1283.9675 -90.1657 0.5554 362s dist -39.0321 5.2644 -90.1657 462.3898 -1.3672 362s accel -0.0447 0.0118 0.5554 -1.3672 0.0114 362s -------------------------------------------------------- 362s USJudgeRatings 43 12 -43.367499 362s Outliers: 10 362s [1] 5 7 8 12 13 14 20 23 31 35 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 362s 7.43 8.16 7.75 7.89 7.69 7.76 7.68 7.67 7.52 7.59 8.19 7.87 362s 362s Robust Estimate of Covariance: 362s CONT INTG DMNR DILG CFMG DECI PREP FAMI 362s CONT 0.6895 -0.2399 -0.3728 -0.1514 -0.0461 -0.0801 -0.1419 -0.1577 362s INTG -0.2399 0.5021 0.6746 0.5446 0.4479 0.4254 0.5564 0.5558 362s DMNR -0.3728 0.6746 0.9753 0.7128 0.5992 0.5715 0.7289 0.7181 362s DILG -0.1514 0.5446 0.7128 0.6691 0.5789 0.5501 0.6949 0.6892 362s CFMG -0.0461 0.4479 0.5992 0.5789 0.5468 0.5118 0.6100 0.6049 362s DECI -0.0801 0.4254 0.5715 0.5501 0.5118 0.4965 0.5872 0.5890 362s PREP -0.1419 0.5564 0.7289 0.6949 0.6100 0.5872 0.7497 0.7511 362s FAMI -0.1577 0.5558 0.7181 0.6892 0.6049 0.5890 0.7511 0.7696 362s ORAL -0.1950 0.5848 0.7798 0.6990 0.6143 0.5921 0.7508 0.7610 362s WRIT -0.1866 0.5747 0.7575 0.6946 0.6101 0.5895 0.7470 0.7607 362s PHYS -0.1620 0.3640 0.4878 0.4361 0.3927 0.3910 0.4655 0.4779 362s RTEN -0.2522 0.6268 0.8462 0.7220 0.6210 0.5991 0.7553 0.7599 362s ORAL WRIT PHYS RTEN 362s CONT -0.1950 -0.1866 -0.1620 -0.2522 362s INTG 0.5848 0.5747 0.3640 0.6268 362s DMNR 0.7798 0.7575 0.4878 0.8462 362s DILG 0.6990 0.6946 0.4361 0.7220 362s CFMG 0.6143 0.6101 0.3927 0.6210 362s DECI 0.5921 0.5895 0.3910 0.5991 362s PREP 0.7508 0.7470 0.4655 0.7553 362s FAMI 0.7610 0.7607 0.4779 0.7599 362s ORAL 0.7745 0.7665 0.4893 0.7866 362s WRIT 0.7665 0.7645 0.4823 0.7745 362s PHYS 0.4893 0.4823 0.3620 0.5062 362s RTEN 0.7866 0.7745 0.5062 0.8313 362s -------------------------------------------------------- 362s USArrests 50 4 19.266763 362s Outliers: 4 362s [1] 2 28 33 39 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s Murder Assault UrbanPop Rape 362s 7.04 150.55 64.64 19.34 362s 362s Robust Estimate of Covariance: 362s Murder Assault UrbanPop Rape 362s Murder 23.7 378.9 19.1 29.5 362s Assault 378.9 8388.2 601.3 639.7 362s UrbanPop 19.1 601.3 245.3 77.9 362s Rape 29.5 639.7 77.9 76.3 362s -------------------------------------------------------- 362s longley 16 7 13.789499 362s Outliers: 4 362s [1] 1 2 3 4 362s ------------- 362s 362s Call: 362s CovSest(x = x, method = method) 362s -> Method: S-estimates: bisquare 362s 362s Robust Estimate of Location: 362s GNP.deflator GNP Unemployed Armed.Forces Population 362s 107 435 333 293 120 362s Year Employed 362s 1957 67 362s 362s Robust Estimate of Covariance: 362s GNP.deflator GNP Unemployed Armed.Forces Population 362s GNP.deflator 65.05 619.75 734.33 -294.02 48.27 362s GNP 619.75 6112.14 6578.12 -2684.52 474.26 362s Unemployed 734.33 6578.12 12075.90 -3627.79 548.58 362s Armed.Forces -294.02 -2684.52 -3627.79 1797.05 -204.25 362s Population 48.27 474.26 548.58 -204.25 37.36 362s Year 30.58 297.29 351.44 -135.53 23.29 362s Employed 20.36 203.96 186.62 -93.64 15.42 362s Year Employed 362s GNP.deflator 30.58 20.36 362s GNP 297.29 203.96 362s Unemployed 351.44 186.62 362s Armed.Forces -135.53 -93.64 362s Population 23.29 15.42 362s Year 14.70 9.80 362s Employed 9.80 7.36 362s -------------------------------------------------------- 363s Loblolly 84 3 8.518440 363s Outliers: 14 363s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: bisquare 363s 363s Robust Estimate of Location: 363s height age Seed 363s 24.14 9.62 7.51 363s 363s Robust Estimate of Covariance: 363s height age Seed 363s height 464.64 158.43 12.83 363s age 158.43 54.62 2.67 363s Seed 12.83 2.67 22.98 363s -------------------------------------------------------- 363s quakes 1000 4 11.611413 363s Outliers: 234 363s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 363s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 363s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 363s [46] 163 166 170 174 192 205 222 226 230 239 243 250 251 252 254 363s [61] 258 263 267 268 271 283 292 297 300 301 305 311 312 318 320 363s [76] 321 325 328 330 331 334 352 357 360 365 368 376 381 382 384 363s [91] 389 399 400 402 408 413 416 417 418 419 426 429 437 441 443 363s [106] 453 456 467 474 477 490 492 496 504 507 508 509 517 524 527 363s [121] 528 531 532 534 536 538 539 541 542 543 544 545 546 547 552 363s [136] 553 558 560 570 571 581 583 587 593 594 596 597 605 612 613 363s [151] 618 620 625 629 638 642 647 649 653 655 656 672 675 681 686 363s [166] 699 701 702 712 714 716 721 725 726 735 744 753 754 756 759 363s [181] 765 766 769 779 781 782 785 787 797 804 813 825 827 837 840 363s [196] 844 852 853 857 860 865 866 869 870 872 873 883 884 887 888 363s [211] 890 891 893 908 909 912 915 916 921 927 930 952 962 963 969 363s [226] 974 980 982 986 987 988 992 997 1000 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: bisquare 363s 363s Robust Estimate of Location: 363s lat long depth mag 363s -21.54 182.35 369.29 4.54 363s 363s Robust Estimate of Covariance: 363s lat long depth mag 363s lat 2.18e+01 4.82e+00 2.53e+02 -3.54e-01 363s long 4.82e+00 5.87e+00 -4.63e+02 7.45e-02 363s depth 2.53e+02 -4.63e+02 6.51e+04 -2.10e+01 363s mag -3.54e-01 7.45e-02 -2.10e+01 1.83e-01 363s -------------------------------------------------------- 363s =================================================== 363s > dodata(method="rocke") 363s 363s Call: dodata(method = "rocke") 363s Data Set n p LOG(det) Time 363s =================================================== 363s heart 12 2 7.285196 363s Outliers: 3 363s [1] 2 6 12 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s height weight 363s 34.3 26.1 363s 363s Robust Estimate of Covariance: 363s height weight 363s height 105 159 363s weight 159 256 363s -------------------------------------------------------- 363s starsCYG 47 2 -5.929361 363s Outliers: 7 363s [1] 7 9 11 14 20 30 34 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s log.Te log.light 363s 4.42 4.93 363s 363s Robust Estimate of Covariance: 363s log.Te log.light 363s log.Te 0.0193 0.0709 363s log.light 0.0709 0.3987 363s -------------------------------------------------------- 363s phosphor 18 2 8.907518 363s Outliers: 3 363s [1] 1 6 10 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s inorg organic 363s 15.8 39.4 363s 363s Robust Estimate of Covariance: 363s inorg organic 363s inorg 196 252 363s organic 252 360 363s -------------------------------------------------------- 363s stackloss 21 3 8.143313 363s Outliers: 4 363s [1] 1 2 3 21 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s Air.Flow Water.Temp Acid.Conc. 363s 56.8 20.2 86.4 363s 363s Robust Estimate of Covariance: 363s Air.Flow Water.Temp Acid.Conc. 363s Air.Flow 29.26 9.62 14.78 363s Water.Temp 9.62 8.54 6.25 363s Acid.Conc. 14.78 6.25 29.70 363s -------------------------------------------------------- 363s coleman 20 5 4.001659 363s Outliers: 5 363s [1] 2 6 9 10 13 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s salaryP fatherWc sstatus teacherSc motherLev 363s 2.81 40.27 2.11 25.01 6.27 363s 363s Robust Estimate of Covariance: 363s salaryP fatherWc sstatus teacherSc motherLev 363s salaryP 0.2850 1.1473 2.0254 0.3536 0.0737 363s fatherWc 1.1473 798.0714 278.0145 6.4590 18.6357 363s sstatus 2.0254 278.0145 128.7601 4.0666 6.3845 363s teacherSc 0.3536 6.4590 4.0666 0.8749 0.2980 363s motherLev 0.0737 18.6357 6.3845 0.2980 0.4948 363s -------------------------------------------------------- 363s salinity 28 3 3.455146 363s Outliers: 9 363s [1] 3 5 10 11 15 16 17 23 24 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s X1 X2 X3 363s 9.89 3.10 22.46 363s 363s Robust Estimate of Covariance: 363s X1 X2 X3 363s X1 12.710 1.868 -4.135 363s X2 1.868 4.710 -0.663 363s X3 -4.135 -0.663 1.907 363s -------------------------------------------------------- 363s wood 20 5 -35.020244 363s Outliers: 7 363s [1] 4 6 7 8 11 16 19 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s x1 x2 x3 x4 x5 363s 0.588 0.123 0.534 0.535 0.891 363s 363s Robust Estimate of Covariance: 363s x1 x2 x3 x4 x5 363s x1 6.60e-03 1.25e-03 2.16e-03 -3.73e-04 -1.10e-03 363s x2 1.25e-03 3.30e-04 8.91e-04 -1.23e-05 2.62e-05 363s x3 2.16e-03 8.91e-04 4.55e-03 -4.90e-04 1.93e-04 363s x4 -3.73e-04 -1.23e-05 -4.90e-04 2.01e-03 1.36e-03 363s x5 -1.10e-03 2.62e-05 1.93e-04 1.36e-03 1.95e-03 363s -------------------------------------------------------- 363s hbk 75 3 1.413303 363s Outliers: 14 363s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s X1 X2 X3 363s 1.56 1.77 1.68 363s 363s Robust Estimate of Covariance: 363s X1 X2 X3 363s X1 1.6483 0.0825 0.2133 363s X2 0.0825 1.6928 0.2334 363s X3 0.2133 0.2334 1.5334 363s -------------------------------------------------------- 363s Animals 28 2 17.787210 363s Outliers: 11 363s [1] 2 6 7 9 12 14 15 16 24 25 28 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s body brain 363s 60.6 150.2 363s 363s Robust Estimate of Covariance: 363s body brain 363s body 10670 19646 363s brain 19646 41147 363s -------------------------------------------------------- 363s milk 86 8 -25.169970 363s Outliers: 22 363s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 28 41 44 47 70 73 74 75 77 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s X1 X2 X3 X4 X5 X6 X7 X8 363s 1.03 35.87 33.14 26.19 25.17 25.11 123.16 14.41 363s 363s Robust Estimate of Covariance: 363s X1 X2 X3 X4 X5 X6 X7 363s X1 4.47e-07 1.77e-04 1.94e-04 1.79e-04 1.60e-04 1.45e-04 6.45e-04 363s X2 1.77e-04 2.36e+00 4.03e-01 3.08e-01 2.08e-01 3.45e-01 2.18e+00 363s X3 1.94e-04 4.03e-01 1.13e+00 8.31e-01 8.08e-01 7.79e-01 9.83e-01 363s X4 1.79e-04 3.08e-01 8.31e-01 6.62e-01 6.22e-01 5.95e-01 7.82e-01 363s X5 1.60e-04 2.08e-01 8.08e-01 6.22e-01 6.51e-01 5.93e-01 7.60e-01 363s X6 1.45e-04 3.45e-01 7.79e-01 5.95e-01 5.93e-01 5.88e-01 7.81e-01 363s X7 6.45e-04 2.18e+00 9.83e-01 7.82e-01 7.60e-01 7.81e-01 4.81e+00 363s X8 2.47e-05 2.57e-01 2.00e-01 1.37e-01 1.13e-01 1.28e-01 4.38e-01 363s X8 363s X1 2.47e-05 363s X2 2.57e-01 363s X3 2.00e-01 363s X4 1.37e-01 363s X5 1.13e-01 363s X6 1.28e-01 363s X7 4.38e-01 363s X8 1.61e-01 363s -------------------------------------------------------- 363s bushfire 38 5 21.641566 363s Outliers: 13 363s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s V1 V2 V3 V4 V5 363s 111 150 256 214 276 363s 363s Robust Estimate of Covariance: 363s V1 V2 V3 V4 V5 363s V1 554 408 -2321 -464 -393 363s V2 408 343 -1361 -244 -215 363s V3 -2321 -1361 14690 3277 2684 363s V4 -464 -244 3277 783 629 363s V5 -393 -215 2684 629 509 363s -------------------------------------------------------- 363s rice 105 5 -7.208835 363s Outliers: 8 363s [1] 9 14 40 42 49 57 58 71 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s Favor Appearance Taste Stickiness Toughness 363s -0.21721 0.20948 -0.04581 0.15355 -0.00254 363s 363s Robust Estimate of Covariance: 363s Favor Appearance Taste Stickiness Toughness 363s Favor 0.432 0.337 0.417 0.382 -0.201 363s Appearance 0.337 0.591 0.553 0.510 -0.295 363s Taste 0.417 0.553 0.735 0.683 -0.385 363s Stickiness 0.382 0.510 0.683 0.834 -0.462 363s Toughness -0.201 -0.295 -0.385 -0.462 0.408 363s -------------------------------------------------------- 363s hemophilia 75 2 -7.453807 363s Outliers: 2 363s [1] 46 53 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s AHFactivity AHFantigen 363s -0.2276 -0.0637 363s 363s Robust Estimate of Covariance: 363s AHFactivity AHFantigen 363s AHFactivity 0.0405 0.0221 363s AHFantigen 0.0221 0.0263 363s -------------------------------------------------------- 363s fish 159 6 13.110263 363s Outliers: 47 363s [1] 38 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 363s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 363s [39] 98 99 100 101 102 103 104 140 142 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s Weight Length1 Length2 Length3 Height Width 363s 452.1 27.2 29.5 32.6 30.8 15.0 363s 363s Robust Estimate of Covariance: 363s Weight Length1 Length2 Length3 Height Width 363s Weight 132559.85 2817.97 3035.69 3369.07 1231.68 112.19 363s Length1 2817.97 64.16 68.74 75.36 22.52 2.37 363s Length2 3035.69 68.74 73.77 81.12 25.57 2.47 363s Length3 3369.07 75.36 81.12 91.65 37.39 1.40 363s Height 1231.68 22.52 25.57 37.39 50.91 -3.92 363s Width 112.19 2.37 2.47 1.40 -3.92 1.87 363s -------------------------------------------------------- 363s airquality 153 4 21.181656 363s Outliers: 13 363s [1] 6 7 11 14 17 20 23 30 34 53 63 77 107 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s Ozone Solar.R Wind Temp 363s 40.21 198.33 9.76 79.35 363s 363s Robust Estimate of Covariance: 363s Ozone Solar.R Wind Temp 363s Ozone 885.7 581.1 -57.3 226.4 363s Solar.R 581.1 8870.9 26.2 -15.1 363s Wind -57.3 26.2 11.8 -13.4 363s Temp 226.4 -15.1 -13.4 89.4 363s -------------------------------------------------------- 363s attitude 30 7 27.836398 363s Outliers: 8 363s [1] 1 9 13 14 17 18 24 26 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s rating complaints privileges learning raises critical 363s 64.0 65.4 50.5 54.9 63.1 72.6 363s advance 363s 40.5 363s 363s Robust Estimate of Covariance: 363s rating complaints privileges learning raises critical advance 363s rating 180.10 153.16 42.04 128.90 90.25 18.75 39.81 363s complaints 153.16 192.38 58.32 142.48 94.29 8.13 45.33 363s privileges 42.04 58.32 113.65 82.31 69.53 23.13 61.96 363s learning 128.90 142.48 82.31 156.99 101.74 13.22 49.64 363s raises 90.25 94.29 69.53 101.74 110.85 47.84 55.76 363s critical 18.75 8.13 23.13 13.22 47.84 123.00 36.97 363s advance 39.81 45.33 61.96 49.64 55.76 36.97 53.59 363s -------------------------------------------------------- 363s attenu 182 5 9.726797 363s Outliers: 44 363s [1] 1 2 4 5 6 7 8 9 10 11 13 15 16 19 20 21 22 23 24 363s [20] 25 27 28 29 30 31 32 40 45 60 61 64 65 78 80 81 93 94 95 363s [39] 96 97 98 99 100 108 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s event mag station dist accel 363s 16.39 5.82 60.89 27.97 0.12 363s 363s Robust Estimate of Covariance: 363s event mag station dist accel 363s event 4.20e+01 -1.97e+00 1.44e+02 -3.50e+01 4.05e-02 363s mag -1.97e+00 5.05e-01 -4.78e+00 4.63e+00 4.19e-03 363s station 1.44e+02 -4.78e+00 1.47e+03 -5.74e+01 7.88e-01 363s dist -3.50e+01 4.63e+00 -5.74e+01 3.99e+02 -1.18e+00 363s accel 4.05e-02 4.19e-03 7.88e-01 -1.18e+00 7.71e-03 363s -------------------------------------------------------- 363s USJudgeRatings 43 12 -46.356873 363s Outliers: 15 363s [1] 1 5 7 8 12 13 14 17 20 21 23 30 31 35 42 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 363s 7.56 8.12 7.70 7.91 7.74 7.82 7.66 7.66 7.50 7.58 8.22 7.86 363s 363s Robust Estimate of Covariance: 363s CONT INTG DMNR DILG CFMG DECI PREP 363s CONT 0.63426 -0.20121 -0.31858 -0.09578 0.00521 -0.00436 -0.07140 363s INTG -0.20121 0.28326 0.37540 0.27103 0.20362 0.19838 0.25706 363s DMNR -0.31858 0.37540 0.58265 0.33615 0.25649 0.24804 0.31696 363s DILG -0.09578 0.27103 0.33615 0.32588 0.27022 0.26302 0.32236 363s CFMG 0.00521 0.20362 0.25649 0.27022 0.25929 0.24217 0.27784 363s DECI -0.00436 0.19838 0.24804 0.26302 0.24217 0.23830 0.27284 363s PREP -0.07140 0.25706 0.31696 0.32236 0.27784 0.27284 0.35071 363s FAMI -0.07118 0.25858 0.29511 0.32582 0.27863 0.27657 0.35941 363s ORAL -0.11149 0.27055 0.33919 0.31768 0.27339 0.26739 0.34200 363s WRIT -0.10050 0.26857 0.32570 0.32327 0.27860 0.27201 0.34399 363s PHYS -0.09693 0.15339 0.18416 0.17089 0.13837 0.14895 0.18472 363s RTEN -0.15643 0.31793 0.40884 0.33863 0.27073 0.26854 0.34049 363s FAMI ORAL WRIT PHYS RTEN 363s CONT -0.07118 -0.11149 -0.10050 -0.09693 -0.15643 363s INTG 0.25858 0.27055 0.26857 0.15339 0.31793 363s DMNR 0.29511 0.33919 0.32570 0.18416 0.40884 363s DILG 0.32582 0.31768 0.32327 0.17089 0.33863 363s CFMG 0.27863 0.27339 0.27860 0.13837 0.27073 363s DECI 0.27657 0.26739 0.27201 0.14895 0.26854 363s PREP 0.35941 0.34200 0.34399 0.18472 0.34049 363s FAMI 0.38378 0.35617 0.36094 0.19998 0.35048 363s ORAL 0.35617 0.34918 0.34808 0.19759 0.35217 363s WRIT 0.36094 0.34808 0.35242 0.19666 0.35090 363s PHYS 0.19998 0.19759 0.19666 0.14770 0.20304 363s RTEN 0.35048 0.35217 0.35090 0.20304 0.39451 363s -------------------------------------------------------- 363s USArrests 50 4 19.206310 363s Outliers: 4 363s [1] 2 28 33 39 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s Murder Assault UrbanPop Rape 363s 7.55 160.94 65.10 19.97 363s 363s Robust Estimate of Covariance: 363s Murder Assault UrbanPop Rape 363s Murder 25.6 409.5 23.4 32.1 363s Assault 409.5 8530.9 676.9 669.4 363s UrbanPop 23.4 676.9 269.9 76.6 363s Rape 32.1 669.4 76.6 76.6 363s -------------------------------------------------------- 363s longley 16 7 13.387132 363s Outliers: 4 363s [1] 1 2 3 4 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s GNP.deflator GNP Unemployed Armed.Forces Population 363s 105.5 422.4 318.3 299.7 119.5 363s Year Employed 363s 1956.1 66.5 363s 363s Robust Estimate of Covariance: 363s GNP.deflator GNP Unemployed Armed.Forces Population 363s GNP.deflator 59.97 582.66 694.99 -237.75 46.12 363s GNP 582.66 5849.82 6383.68 -2207.26 461.15 363s Unemployed 694.99 6383.68 11155.03 -3104.18 534.25 363s Armed.Forces -237.75 -2207.26 -3104.18 1429.11 -171.28 363s Population 46.12 461.15 534.25 -171.28 36.79 363s Year 29.01 287.48 340.95 -112.61 22.85 363s Employed 18.99 193.66 186.31 -76.88 14.94 363s Year Employed 363s GNP.deflator 29.01 18.99 363s GNP 287.48 193.66 363s Unemployed 340.95 186.31 363s Armed.Forces -112.61 -76.88 363s Population 22.85 14.94 363s Year 14.36 9.45 363s Employed 9.45 6.90 363s -------------------------------------------------------- 363s Loblolly 84 3 7.757906 363s Outliers: 27 363s [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 363s [26] 83 84 363s ------------- 363s 363s Call: 363s CovSest(x = x, method = method) 363s -> Method: S-estimates: Rocke type 363s 363s Robust Estimate of Location: 363s height age Seed 363s 21.72 8.60 7.58 363s 363s Robust Estimate of Covariance: 363s height age Seed 363s height 316.590 102.273 5.939 363s age 102.273 33.465 -0.121 363s Seed 5.939 -0.121 27.203 363s -------------------------------------------------------- 364s quakes 1000 4 11.473431 364s Outliers: 237 364s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 364s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 364s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 364s [46] 163 166 170 174 176 192 205 222 226 230 239 243 244 250 251 364s [61] 252 254 258 263 267 268 271 283 292 297 300 301 305 311 312 364s [76] 318 320 321 325 328 330 331 334 352 357 360 365 368 376 381 364s [91] 382 384 389 399 400 402 408 410 413 416 417 418 419 426 429 364s [106] 437 441 443 453 456 467 474 477 490 492 496 504 507 508 509 364s [121] 517 524 527 528 531 532 534 536 538 539 541 542 543 544 545 364s [136] 546 547 552 553 558 560 570 571 581 583 587 593 594 596 597 364s [151] 605 612 613 618 620 625 629 638 642 647 649 653 655 656 672 364s [166] 675 681 686 699 701 702 712 714 716 721 725 726 735 744 753 364s [181] 754 756 759 765 766 769 779 781 782 785 787 797 804 813 825 364s [196] 827 837 840 844 852 853 857 860 865 866 869 870 872 873 883 364s [211] 884 887 888 890 891 893 908 909 912 915 916 921 927 930 952 364s [226] 962 963 969 974 980 982 986 987 988 992 997 1000 364s ------------- 364s 364s Call: 364s CovSest(x = x, method = method) 364s -> Method: S-estimates: Rocke type 364s 364s Robust Estimate of Location: 364s lat long depth mag 364s -21.45 182.54 351.18 4.55 364s 364s Robust Estimate of Covariance: 364s lat long depth mag 364s lat 2.10e+01 4.66e+00 2.45e+02 -3.38e-01 364s long 4.66e+00 5.88e+00 -4.63e+02 9.36e-02 364s depth 2.45e+02 -4.63e+02 6.38e+04 -2.02e+01 364s mag -3.38e-01 9.36e-02 -2.02e+01 1.78e-01 364s -------------------------------------------------------- 364s =================================================== 364s > dodata(method="MM") 364s 364s Call: dodata(method = "MM") 364s Data Set n p LOG(det) Time 364s =================================================== 364s heart 12 2 2.017701 364s Outliers: 1 364s [1] 6 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s height weight 364s 40.0 37.7 364s 364s Robust Estimate of Covariance: 364s height weight 364s height 99.2 205.7 364s weight 205.7 458.9 364s -------------------------------------------------------- 364s starsCYG 47 2 -1.450032 364s Outliers: 7 364s [1] 7 9 11 14 20 30 34 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s log.Te log.light 364s 4.41 4.94 364s 364s Robust Estimate of Covariance: 364s log.Te log.light 364s log.Te 0.0180 0.0526 364s log.light 0.0526 0.3217 364s -------------------------------------------------------- 364s phosphor 18 2 2.320721 364s Outliers: 1 364s [1] 6 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s inorg organic 364s 12.3 41.4 364s 364s Robust Estimate of Covariance: 364s inorg organic 364s inorg 94.2 67.2 364s organic 67.2 162.1 364s -------------------------------------------------------- 364s stackloss 21 3 1.470031 364s Outliers: 0 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s Air.Flow Water.Temp Acid.Conc. 364s 60.2 21.0 86.4 364s 364s Robust Estimate of Covariance: 364s Air.Flow Water.Temp Acid.Conc. 364s Air.Flow 81.13 21.99 23.15 364s Water.Temp 21.99 10.01 6.43 364s Acid.Conc. 23.15 6.43 27.22 364s -------------------------------------------------------- 364s coleman 20 5 0.491419 364s Outliers: 1 364s [1] 10 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s salaryP fatherWc sstatus teacherSc motherLev 364s 2.74 43.14 3.65 25.07 6.32 364s 364s Robust Estimate of Covariance: 364s salaryP fatherWc sstatus teacherSc motherLev 364s salaryP 0.1878 2.0635 1.0433 0.2721 0.0582 364s fatherWc 2.0635 670.2232 211.0609 4.3625 15.6083 364s sstatus 1.0433 211.0609 92.8743 2.6532 5.1816 364s teacherSc 0.2721 4.3625 2.6532 1.2757 0.1613 364s motherLev 0.0582 15.6083 5.1816 0.1613 0.4192 364s -------------------------------------------------------- 364s salinity 28 3 0.734619 364s Outliers: 2 364s [1] 5 16 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s X1 X2 X3 364s 10.46 2.66 23.15 364s 364s Robust Estimate of Covariance: 364s X1 X2 X3 364s X1 10.079 -0.024 -1.899 364s X2 -0.024 3.466 -1.817 364s X3 -1.899 -1.817 3.665 364s -------------------------------------------------------- 364s wood 20 5 -3.202636 364s Outliers: 0 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s x1 x2 x3 x4 x5 364s 0.550 0.133 0.506 0.511 0.909 364s 364s Robust Estimate of Covariance: 364s x1 x2 x3 x4 x5 364s x1 0.008454 -0.000377 0.003720 0.002874 -0.003065 364s x2 -0.000377 0.000516 -0.000399 -0.000933 0.000645 364s x3 0.003720 -0.000399 0.004186 0.001720 -0.001714 364s x4 0.002874 -0.000933 0.001720 0.003993 -0.001028 364s x5 -0.003065 0.000645 -0.001714 -0.001028 0.002744 364s -------------------------------------------------------- 364s hbk 75 3 0.283145 364s Outliers: 14 364s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s X1 X2 X3 364s 1.54 1.79 1.68 364s 364s Robust Estimate of Covariance: 364s X1 X2 X3 364s X1 1.8016 0.0739 0.2000 364s X2 0.0739 1.8301 0.2295 364s X3 0.2000 0.2295 1.7101 364s -------------------------------------------------------- 364s Animals 28 2 4.685129 364s Outliers: 10 364s [1] 2 6 7 9 12 14 15 16 24 25 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s body brain 364s 82 148 364s 364s Robust Estimate of Covariance: 364s body brain 364s body 21050 24534 364s brain 24534 35135 364s -------------------------------------------------------- 364s milk 86 8 -1.437863 364s Outliers: 12 364s [1] 1 2 3 12 13 17 41 44 47 70 74 75 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s X1 X2 X3 X4 X5 X6 X7 X8 364s 1.03 35.73 32.87 25.96 24.94 24.85 122.55 14.33 364s 364s Robust Estimate of Covariance: 364s X1 X2 X3 X4 X5 X6 X7 364s X1 1.08e-06 5.36e-04 6.80e-04 5.96e-04 5.87e-04 5.91e-04 2.22e-03 364s X2 5.36e-04 2.42e+00 7.07e-01 5.51e-01 4.89e-01 5.70e-01 3.08e+00 364s X3 6.80e-04 7.07e-01 1.64e+00 1.28e+00 1.25e+00 1.26e+00 2.38e+00 364s X4 5.96e-04 5.51e-01 1.28e+00 1.05e+00 1.01e+00 1.02e+00 2.01e+00 364s X5 5.87e-04 4.89e-01 1.25e+00 1.01e+00 1.05e+00 1.02e+00 1.96e+00 364s X6 5.91e-04 5.70e-01 1.26e+00 1.02e+00 1.02e+00 1.05e+00 2.01e+00 364s X7 2.22e-03 3.08e+00 2.38e+00 2.01e+00 1.96e+00 2.01e+00 9.22e+00 364s X8 1.68e-04 4.13e-01 3.37e-01 2.53e-01 2.34e-01 2.43e-01 8.81e-01 364s X8 364s X1 1.68e-04 364s X2 4.13e-01 364s X3 3.37e-01 364s X4 2.53e-01 364s X5 2.34e-01 364s X6 2.43e-01 364s X7 8.81e-01 364s X8 2.11e-01 364s -------------------------------------------------------- 364s bushfire 38 5 2.443148 364s Outliers: 12 364s [1] 8 9 10 11 31 32 33 34 35 36 37 38 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s V1 V2 V3 V4 V5 364s 109 149 258 215 276 364s 364s Robust Estimate of Covariance: 364s V1 V2 V3 V4 V5 364s V1 708 538 -2705 -558 -464 364s V2 538 497 -1376 -248 -216 364s V3 -2705 -1376 20521 4833 3914 364s V4 -558 -248 4833 1217 969 364s V5 -464 -216 3914 969 778 364s -------------------------------------------------------- 364s rice 105 5 -0.724874 364s Outliers: 5 364s [1] 9 42 49 58 71 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s Favor Appearance Taste Stickiness Toughness 364s -0.2653 0.0969 -0.1371 0.0483 0.0731 364s 364s Robust Estimate of Covariance: 364s Favor Appearance Taste Stickiness Toughness 364s Favor 0.421 0.349 0.427 0.405 -0.191 364s Appearance 0.349 0.605 0.565 0.553 -0.316 364s Taste 0.427 0.565 0.725 0.701 -0.378 364s Stickiness 0.405 0.553 0.701 0.868 -0.484 364s Toughness -0.191 -0.316 -0.378 -0.484 0.464 364s -------------------------------------------------------- 364s hemophilia 75 2 -1.868949 364s Outliers: 2 364s [1] 11 36 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s AHFactivity AHFantigen 364s -0.2342 -0.0333 364s 364s Robust Estimate of Covariance: 364s AHFactivity AHFantigen 364s AHFactivity 0.0309 0.0122 364s AHFantigen 0.0122 0.0231 364s -------------------------------------------------------- 364s fish 159 6 1.285876 364s Outliers: 20 364s [1] 61 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 364s [20] 142 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s Weight Length1 Length2 Length3 Height Width 364s 352.7 24.3 26.4 29.2 29.7 14.6 364s 364s Robust Estimate of Covariance: 364s Weight Length1 Length2 Length3 Height Width 364s Weight 1.20e+05 2.89e+03 3.12e+03 3.51e+03 1.49e+03 2.83e+02 364s Length1 2.89e+03 7.73e+01 8.35e+01 9.28e+01 3.73e+01 9.26e+00 364s Length2 3.12e+03 8.35e+01 9.04e+01 1.01e+02 4.16e+01 1.01e+01 364s Length3 3.51e+03 9.28e+01 1.01e+02 1.14e+02 5.37e+01 1.01e+01 364s Height 1.49e+03 3.73e+01 4.16e+01 5.37e+01 6.75e+01 3.22e+00 364s Width 2.83e+02 9.26e+00 1.01e+01 1.01e+01 3.22e+00 4.18e+00 364s -------------------------------------------------------- 364s airquality 153 4 2.684374 364s Outliers: 6 364s [1] 7 14 23 30 34 77 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s Ozone Solar.R Wind Temp 364s 40.35 186.21 9.86 78.09 364s 364s Robust Estimate of Covariance: 364s Ozone Solar.R Wind Temp 364s Ozone 951.0 959.9 -62.5 224.6 364s Solar.R 959.9 8629.9 -28.1 244.9 364s Wind -62.5 -28.1 11.6 -15.8 364s Temp 224.6 244.9 -15.8 93.1 364s -------------------------------------------------------- 364s attitude 30 7 2.091968 364s Outliers: 4 364s [1] 14 16 18 24 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s rating complaints privileges learning raises critical 364s 65.0 66.5 52.4 56.2 65.3 75.6 364s advance 364s 42.7 364s 364s Robust Estimate of Covariance: 364s rating complaints privileges learning raises critical advance 364s rating 143.5 123.4 62.4 92.5 79.2 17.7 28.2 364s complaints 123.4 159.8 83.9 99.7 96.0 27.3 44.0 364s privileges 62.4 83.9 133.5 78.6 62.0 13.4 46.4 364s learning 92.5 99.7 78.6 136.0 90.9 18.9 62.6 364s raises 79.2 96.0 62.0 90.9 107.6 34.6 63.3 364s critical 17.7 27.3 13.4 18.9 34.6 84.9 25.9 364s advance 28.2 44.0 46.4 62.6 63.3 25.9 94.4 364s -------------------------------------------------------- 364s attenu 182 5 1.148032 364s Outliers: 21 364s [1] 2 7 8 9 10 11 15 16 24 25 28 29 30 31 32 64 65 94 95 364s [20] 96 100 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s event mag station dist accel 364s 15.36 5.95 58.11 33.56 0.14 364s 364s Robust Estimate of Covariance: 364s event mag station dist accel 364s event 4.88e+01 -2.74e+00 1.53e+02 -1.14e+02 5.95e-02 364s mag -2.74e+00 5.32e-01 -6.29e+00 1.10e+01 9.37e-03 364s station 1.53e+02 -6.29e+00 1.29e+03 -2.95e+02 1.04e+00 364s dist -1.14e+02 1.10e+01 -2.95e+02 1.13e+03 -2.41e+00 364s accel 5.95e-02 9.37e-03 1.04e+00 -2.41e+00 1.70e-02 364s -------------------------------------------------------- 364s USJudgeRatings 43 12 -1.683847 364s Outliers: 7 364s [1] 5 7 12 13 14 23 31 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 364s 7.45 8.15 7.74 7.87 7.67 7.74 7.65 7.65 7.50 7.57 8.17 7.85 364s 364s Robust Estimate of Covariance: 364s CONT INTG DMNR DILG CFMG DECI PREP FAMI 364s CONT 0.9403 -0.2500 -0.3953 -0.1418 -0.0176 -0.0620 -0.1304 -0.1517 364s INTG -0.2500 0.6314 0.8479 0.6889 0.5697 0.5386 0.7007 0.6985 364s DMNR -0.3953 0.8479 1.2186 0.9027 0.7613 0.7232 0.9191 0.9055 364s DILG -0.1418 0.6889 0.9027 0.8474 0.7344 0.6949 0.8751 0.8655 364s CFMG -0.0176 0.5697 0.7613 0.7344 0.6904 0.6442 0.7683 0.7594 364s DECI -0.0620 0.5386 0.7232 0.6949 0.6442 0.6219 0.7362 0.7360 364s PREP -0.1304 0.7007 0.9191 0.8751 0.7683 0.7362 0.9370 0.9357 364s FAMI -0.1517 0.6985 0.9055 0.8655 0.7594 0.7360 0.9357 0.9547 364s ORAL -0.1866 0.7375 0.9841 0.8816 0.7747 0.7433 0.9400 0.9496 364s WRIT -0.1881 0.7208 0.9516 0.8711 0.7646 0.7357 0.9302 0.9439 364s PHYS -0.1407 0.4673 0.6261 0.5661 0.5105 0.5039 0.5996 0.6112 364s RTEN -0.2494 0.7921 1.0688 0.9167 0.7902 0.7585 0.9533 0.9561 364s ORAL WRIT PHYS RTEN 364s CONT -0.1866 -0.1881 -0.1407 -0.2494 364s INTG 0.7375 0.7208 0.4673 0.7921 364s DMNR 0.9841 0.9516 0.6261 1.0688 364s DILG 0.8816 0.8711 0.5661 0.9167 364s CFMG 0.7747 0.7646 0.5105 0.7902 364s DECI 0.7433 0.7357 0.5039 0.7585 364s PREP 0.9400 0.9302 0.5996 0.9533 364s FAMI 0.9496 0.9439 0.6112 0.9561 364s ORAL 0.9712 0.9558 0.6271 0.9933 364s WRIT 0.9558 0.9483 0.6135 0.9725 364s PHYS 0.6271 0.6135 0.4816 0.6549 364s RTEN 0.9933 0.9725 0.6549 1.0540 364s -------------------------------------------------------- 364s USArrests 50 4 2.411726 364s Outliers: 3 364s [1] 2 33 39 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s Murder Assault UrbanPop Rape 364s 7.52 163.86 65.66 20.64 364s 364s Robust Estimate of Covariance: 364s Murder Assault UrbanPop Rape 364s Murder 19.05 295.96 8.32 23.40 364s Assault 295.96 6905.03 396.53 523.49 364s UrbanPop 8.32 396.53 202.98 62.81 364s Rape 23.40 523.49 62.81 79.10 364s -------------------------------------------------------- 364s longley 16 7 1.038316 364s Outliers: 5 364s [1] 1 2 3 4 5 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s GNP.deflator GNP Unemployed Armed.Forces Population 364s 107.5 440.4 339.4 293.0 120.9 364s Year Employed 364s 1957.0 67.2 364s 364s Robust Estimate of Covariance: 364s GNP.deflator GNP Unemployed Armed.Forces Population 364s GNP.deflator 100.4 953.8 1140.8 -501.8 74.3 364s GNP 953.8 9434.3 10084.3 -4573.8 731.3 364s Unemployed 1140.8 10084.3 19644.6 -6296.3 848.4 364s Armed.Forces -501.8 -4573.8 -6296.3 3192.3 -348.5 364s Population 74.3 731.3 848.4 -348.5 57.7 364s Year 46.3 450.7 537.0 -230.7 35.3 364s Employed 30.8 310.2 273.8 -159.4 23.3 364s Year Employed 364s GNP.deflator 46.3 30.8 364s GNP 450.7 310.2 364s Unemployed 537.0 273.8 364s Armed.Forces -230.7 -159.4 364s Population 35.3 23.3 364s Year 21.9 14.6 364s Employed 14.6 11.2 364s -------------------------------------------------------- 364s Loblolly 84 3 1.481317 364s Outliers: 0 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s height age Seed 364s 31.93 12.79 7.48 364s 364s Robust Estimate of Covariance: 364s height age Seed 364s height 440.644 165.652 6.958 364s age 165.652 63.500 0.681 364s Seed 6.958 0.681 16.564 364s -------------------------------------------------------- 364s quakes 1000 4 1.576855 364s Outliers: 218 364s [1] 7 12 15 17 22 27 32 37 40 41 45 48 53 63 64 364s [16] 73 78 87 91 92 94 99 108 110 117 118 119 120 121 122 364s [31] 126 133 136 141 143 145 148 152 154 155 157 159 160 163 170 364s [46] 192 205 222 226 230 239 243 250 251 252 254 258 263 267 268 364s [61] 271 283 292 300 301 305 311 312 318 320 321 325 328 330 334 364s [76] 352 357 360 365 381 382 384 389 400 402 408 413 416 417 419 364s [91] 429 437 441 443 453 456 467 474 477 490 492 496 504 507 508 364s [106] 509 517 524 527 528 531 532 534 536 538 539 541 542 543 544 364s [121] 545 546 547 552 553 560 571 581 583 587 593 594 596 597 605 364s [136] 612 613 618 620 625 629 638 642 647 649 653 655 656 672 675 364s [151] 681 686 699 701 702 712 714 716 721 725 726 735 744 754 756 364s [166] 759 765 766 769 779 781 782 785 787 797 804 813 825 827 837 364s [181] 840 844 852 853 857 860 865 866 869 870 872 873 883 884 887 364s [196] 888 890 891 893 908 909 912 915 916 921 927 930 962 963 969 364s [211] 974 980 982 986 987 988 997 1000 364s ------------- 364s 364s Call: 364s CovMMest(x = x) 364s -> Method: MM-estimates 364s 364s Robust Estimate of Location: 364s lat long depth mag 364s -21.74 182.37 356.37 4.56 364s 364s Robust Estimate of Covariance: 364s lat long depth mag 364s lat 2.97e+01 6.53e+00 3.46e+02 -4.66e-01 364s long 6.53e+00 6.92e+00 -5.05e+02 5.62e-02 364s depth 3.46e+02 -5.05e+02 7.39e+04 -2.51e+01 364s mag -4.66e-01 5.62e-02 -2.51e+01 2.32e-01 364s -------------------------------------------------------- 364s =================================================== 364s > ##dogen() 364s > ##cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 364s > 365s autopkgtest [23:08:25]: test run-unit-test: -----------------------] 369s autopkgtest [23:08:29]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 369s run-unit-test PASS 373s autopkgtest [23:08:33]: test pkg-r-autopkgtest: preparing testbed 375s Reading package lists... 375s Building dependency tree... 375s Reading state information... 375s Solving dependencies... 376s The following NEW packages will be installed: 376s build-essential cpp cpp-15 cpp-15-arm-linux-gnueabihf 376s cpp-arm-linux-gnueabihf dctrl-tools g++ g++-15 g++-15-arm-linux-gnueabihf 376s g++-arm-linux-gnueabihf gcc gcc-15 gcc-15-arm-linux-gnueabihf 376s gcc-arm-linux-gnueabihf gfortran gfortran-15 gfortran-15-arm-linux-gnueabihf 376s gfortran-arm-linux-gnueabihf icu-devtools libasan8 libblas-dev libbz2-dev 376s libc-dev-bin libc6-dev libcc1-0 libcrypt-dev libdeflate-dev libgcc-15-dev 376s libgfortran-15-dev libicu-dev libisl23 libjpeg-dev libjpeg-turbo8-dev 376s libjpeg8-dev liblapack-dev liblzma-dev libmpc3 libncurses-dev libpcre2-16-0 376s libpcre2-32-0 libpcre2-dev libpcre2-posix3 libpkgconf3 libpng-dev 376s libreadline-dev libstdc++-15-dev libtirpc-dev libubsan1 libzstd-dev 376s linux-libc-dev pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev rpcsvc-proto 376s zlib1g-dev 376s 0 upgraded, 56 newly installed, 0 to remove and 0 not upgraded. 376s Need to get 83.1 MB of archives. 376s After this operation, 253 MB of additional disk space will be used. 376s Get:1 http://ftpmaster.internal/ubuntu resolute/main armhf libc-dev-bin armhf 2.42-2ubuntu4 [21.8 kB] 376s Get:2 http://ftpmaster.internal/ubuntu resolute/main armhf linux-libc-dev armhf 6.19.0-3.3 [1804 kB] 380s Get:3 http://ftpmaster.internal/ubuntu resolute/main armhf libcrypt-dev armhf 1:4.5.1-1 [128 kB] 381s Get:4 http://ftpmaster.internal/ubuntu resolute/main armhf rpcsvc-proto armhf 1.4.3-1build1 [62.6 kB] 381s Get:5 http://ftpmaster.internal/ubuntu resolute/main armhf libc6-dev armhf 2.42-2ubuntu4 [1416 kB] 384s Get:6 http://ftpmaster.internal/ubuntu resolute/main armhf libisl23 armhf 0.27-1build1 [553 kB] 385s Get:7 http://ftpmaster.internal/ubuntu resolute/main armhf libmpc3 armhf 1.3.1-2 [47.0 kB] 385s Get:8 http://ftpmaster.internal/ubuntu resolute/main armhf cpp-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [10.1 MB] 403s Get:9 http://ftpmaster.internal/ubuntu resolute/main armhf cpp-15 armhf 15.2.0-12ubuntu1 [1032 B] 403s Get:10 http://ftpmaster.internal/ubuntu resolute/main armhf cpp-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [5756 B] 403s Get:11 http://ftpmaster.internal/ubuntu resolute/main armhf cpp armhf 4:15.2.0-4ubuntu1 [22.4 kB] 403s Get:12 http://ftpmaster.internal/ubuntu resolute/main armhf libcc1-0 armhf 15.2.0-12ubuntu1 [43.5 kB] 403s Get:13 http://ftpmaster.internal/ubuntu resolute/main armhf libasan8 armhf 15.2.0-12ubuntu1 [2949 kB] 408s Get:14 http://ftpmaster.internal/ubuntu resolute/main armhf libubsan1 armhf 15.2.0-12ubuntu1 [1187 kB] 410s Get:15 http://ftpmaster.internal/ubuntu resolute/main armhf libgcc-15-dev armhf 15.2.0-12ubuntu1 [898 kB] 412s Get:16 http://ftpmaster.internal/ubuntu resolute/main armhf gcc-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [19.5 MB] 444s Get:17 http://ftpmaster.internal/ubuntu resolute/main armhf gcc-15 armhf 15.2.0-12ubuntu1 [499 kB] 445s Get:18 http://ftpmaster.internal/ubuntu resolute/main armhf gcc-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [1220 B] 445s Get:19 http://ftpmaster.internal/ubuntu resolute/main armhf gcc armhf 4:15.2.0-4ubuntu1 [5022 B] 445s Get:20 http://ftpmaster.internal/ubuntu resolute/main armhf libstdc++-15-dev armhf 15.2.0-12ubuntu1 [2638 kB] 448s Get:21 http://ftpmaster.internal/ubuntu resolute/main armhf g++-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [11.4 MB] 464s Get:22 http://ftpmaster.internal/ubuntu resolute/main armhf g++-15 armhf 15.2.0-12ubuntu1 [25.3 kB] 464s Get:23 http://ftpmaster.internal/ubuntu resolute/main armhf g++-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [968 B] 464s Get:24 http://ftpmaster.internal/ubuntu resolute/main armhf g++ armhf 4:15.2.0-4ubuntu1 [1086 B] 464s Get:25 http://ftpmaster.internal/ubuntu resolute/main armhf build-essential armhf 12.12ubuntu2 [5256 B] 464s Get:26 http://ftpmaster.internal/ubuntu resolute/main armhf dctrl-tools armhf 2.24-3build4 [95.0 kB] 464s Get:27 http://ftpmaster.internal/ubuntu resolute/main armhf libgfortran-15-dev armhf 15.2.0-12ubuntu1 [380 kB] 465s Get:28 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran-15-arm-linux-gnueabihf armhf 15.2.0-12ubuntu1 [10.7 MB] 478s Get:29 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran-15 armhf 15.2.0-12ubuntu1 [18.1 kB] 478s Get:30 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran-arm-linux-gnueabihf armhf 4:15.2.0-4ubuntu1 [1020 B] 478s Get:31 http://ftpmaster.internal/ubuntu resolute/main armhf gfortran armhf 4:15.2.0-4ubuntu1 [1162 B] 479s Get:32 http://ftpmaster.internal/ubuntu resolute/main armhf icu-devtools armhf 78.2-1ubuntu1 [203 kB] 479s Get:33 http://ftpmaster.internal/ubuntu resolute/main armhf libblas-dev armhf 3.12.1-7ubuntu1 [141 kB] 479s Get:34 http://ftpmaster.internal/ubuntu resolute/main armhf libbz2-dev armhf 1.0.8-6build2 [31.2 kB] 479s Get:35 http://ftpmaster.internal/ubuntu resolute/main armhf libdeflate-dev armhf 1.23-2build1 [45.1 kB] 479s Get:36 http://ftpmaster.internal/ubuntu resolute/main armhf libicu-dev armhf 78.2-1ubuntu1 [12.2 MB] 494s Get:37 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg-turbo8-dev armhf 2.1.5-4ubuntu3 [265 kB] 494s Get:38 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg8-dev armhf 8c-2ubuntu11 [1484 B] 494s Get:39 http://ftpmaster.internal/ubuntu resolute/main armhf libjpeg-dev armhf 8c-2ubuntu11 [1482 B] 494s Get:40 http://ftpmaster.internal/ubuntu resolute/main armhf liblapack-dev armhf 3.12.1-7ubuntu1 [2207 kB] 497s Get:41 http://ftpmaster.internal/ubuntu resolute/main armhf libncurses-dev armhf 6.6+20251231-1 [348 kB] 497s Get:42 http://ftpmaster.internal/ubuntu resolute/main armhf libpcre2-16-0 armhf 10.46-1 [206 kB] 497s Get:43 http://ftpmaster.internal/ubuntu resolute/main armhf libpcre2-32-0 armhf 10.46-1 [197 kB] 498s Get:44 http://ftpmaster.internal/ubuntu resolute/main armhf libpcre2-posix3 armhf 10.46-1 [6286 B] 498s Get:45 http://ftpmaster.internal/ubuntu resolute/main armhf libpcre2-dev armhf 10.46-1 [744 kB] 498s Get:46 http://ftpmaster.internal/ubuntu resolute/main armhf libpkgconf3 armhf 1.8.1-4build1 [26.6 kB] 498s Get:47 http://ftpmaster.internal/ubuntu resolute/main armhf zlib1g-dev armhf 1:1.3.dfsg+really1.3.1-1ubuntu2 [881 kB] 499s Get:48 http://ftpmaster.internal/ubuntu resolute/main armhf libpng-dev armhf 1.6.54-1 [252 kB] 500s Get:49 http://ftpmaster.internal/ubuntu resolute/main armhf libreadline-dev armhf 8.3-3 [165 kB] 500s Get:50 http://ftpmaster.internal/ubuntu resolute/main armhf libzstd-dev armhf 1.5.7+dfsg-3 [342 kB] 500s Get:51 http://ftpmaster.internal/ubuntu resolute/main armhf liblzma-dev armhf 5.8.2-2 [167 kB] 500s Get:52 http://ftpmaster.internal/ubuntu resolute/main armhf pkgconf-bin armhf 1.8.1-4build1 [21.4 kB] 500s Get:53 http://ftpmaster.internal/ubuntu resolute/main armhf pkgconf armhf 1.8.1-4build1 [16.8 kB] 500s Get:54 http://ftpmaster.internal/ubuntu resolute/main armhf libtirpc-dev armhf 1.3.6+ds-1 [184 kB] 501s Get:55 http://ftpmaster.internal/ubuntu resolute/universe armhf r-base-dev all 4.5.2-1ubuntu2 [1880 B] 501s Get:56 http://ftpmaster.internal/ubuntu resolute/universe armhf pkg-r-autopkgtest all 20250812 [6158 B] 501s Fetched 83.1 MB in 2min 5s (666 kB/s) 501s Selecting previously unselected package libc-dev-bin. 501s (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 ... 71320 files and directories currently installed.) 501s Preparing to unpack .../00-libc-dev-bin_2.42-2ubuntu4_armhf.deb ... 501s Unpacking libc-dev-bin (2.42-2ubuntu4) ... 501s Selecting previously unselected package linux-libc-dev:armhf. 501s Preparing to unpack .../01-linux-libc-dev_6.19.0-3.3_armhf.deb ... 501s Unpacking linux-libc-dev:armhf (6.19.0-3.3) ... 501s Selecting previously unselected package libcrypt-dev:armhf. 501s Preparing to unpack .../02-libcrypt-dev_1%3a4.5.1-1_armhf.deb ... 501s Unpacking libcrypt-dev:armhf (1:4.5.1-1) ... 501s Selecting previously unselected package rpcsvc-proto. 501s Preparing to unpack .../03-rpcsvc-proto_1.4.3-1build1_armhf.deb ... 501s Unpacking rpcsvc-proto (1.4.3-1build1) ... 501s Selecting previously unselected package libc6-dev:armhf. 501s Preparing to unpack .../04-libc6-dev_2.42-2ubuntu4_armhf.deb ... 501s Unpacking libc6-dev:armhf (2.42-2ubuntu4) ... 502s Selecting previously unselected package libisl23:armhf. 502s Preparing to unpack .../05-libisl23_0.27-1build1_armhf.deb ... 502s Unpacking libisl23:armhf (0.27-1build1) ... 502s Selecting previously unselected package libmpc3:armhf. 502s Preparing to unpack .../06-libmpc3_1.3.1-2_armhf.deb ... 502s Unpacking libmpc3:armhf (1.3.1-2) ... 502s Selecting previously unselected package cpp-15-arm-linux-gnueabihf. 502s Preparing to unpack .../07-cpp-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking cpp-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 502s Selecting previously unselected package cpp-15. 502s Preparing to unpack .../08-cpp-15_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking cpp-15 (15.2.0-12ubuntu1) ... 502s Selecting previously unselected package cpp-arm-linux-gnueabihf. 502s Preparing to unpack .../09-cpp-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ... 502s Unpacking cpp-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 502s Selecting previously unselected package cpp. 502s Preparing to unpack .../10-cpp_4%3a15.2.0-4ubuntu1_armhf.deb ... 502s Unpacking cpp (4:15.2.0-4ubuntu1) ... 502s Selecting previously unselected package libcc1-0:armhf. 502s Preparing to unpack .../11-libcc1-0_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking libcc1-0:armhf (15.2.0-12ubuntu1) ... 502s Selecting previously unselected package libasan8:armhf. 502s Preparing to unpack .../12-libasan8_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking libasan8:armhf (15.2.0-12ubuntu1) ... 502s Selecting previously unselected package libubsan1:armhf. 502s Preparing to unpack .../13-libubsan1_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking libubsan1:armhf (15.2.0-12ubuntu1) ... 502s Selecting previously unselected package libgcc-15-dev:armhf. 502s Preparing to unpack .../14-libgcc-15-dev_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking libgcc-15-dev:armhf (15.2.0-12ubuntu1) ... 502s Selecting previously unselected package gcc-15-arm-linux-gnueabihf. 502s Preparing to unpack .../15-gcc-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ... 502s Unpacking gcc-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 503s Selecting previously unselected package gcc-15. 503s Preparing to unpack .../16-gcc-15_15.2.0-12ubuntu1_armhf.deb ... 503s Unpacking gcc-15 (15.2.0-12ubuntu1) ... 503s Selecting previously unselected package gcc-arm-linux-gnueabihf. 503s Preparing to unpack .../17-gcc-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ... 503s Unpacking gcc-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 503s 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Selecting previously unselected package g++. 503s Preparing to unpack .../23-g++_4%3a15.2.0-4ubuntu1_armhf.deb ... 503s Unpacking g++ (4:15.2.0-4ubuntu1) ... 503s Selecting previously unselected package build-essential. 503s Preparing to unpack .../24-build-essential_12.12ubuntu2_armhf.deb ... 503s Unpacking build-essential (12.12ubuntu2) ... 503s Selecting previously unselected package dctrl-tools. 503s Preparing to unpack .../25-dctrl-tools_2.24-3build4_armhf.deb ... 503s Unpacking dctrl-tools (2.24-3build4) ... 503s Selecting previously unselected package libgfortran-15-dev:armhf. 503s Preparing to unpack .../26-libgfortran-15-dev_15.2.0-12ubuntu1_armhf.deb ... 503s Unpacking libgfortran-15-dev:armhf (15.2.0-12ubuntu1) ... 503s Selecting previously unselected package gfortran-15-arm-linux-gnueabihf. 503s Preparing to unpack .../27-gfortran-15-arm-linux-gnueabihf_15.2.0-12ubuntu1_armhf.deb ... 503s Unpacking gfortran-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 504s Selecting previously unselected package gfortran-15. 504s Preparing to unpack .../28-gfortran-15_15.2.0-12ubuntu1_armhf.deb ... 504s Unpacking gfortran-15 (15.2.0-12ubuntu1) ... 504s Selecting previously unselected package gfortran-arm-linux-gnueabihf. 504s Preparing to unpack .../29-gfortran-arm-linux-gnueabihf_4%3a15.2.0-4ubuntu1_armhf.deb ... 504s Unpacking gfortran-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 504s Selecting previously unselected package gfortran. 504s Preparing to unpack .../30-gfortran_4%3a15.2.0-4ubuntu1_armhf.deb ... 504s Unpacking gfortran (4:15.2.0-4ubuntu1) ... 504s Selecting previously unselected package icu-devtools. 504s Preparing to unpack .../31-icu-devtools_78.2-1ubuntu1_armhf.deb ... 504s Unpacking icu-devtools (78.2-1ubuntu1) ... 504s Selecting previously unselected package libblas-dev:armhf. 504s Preparing to unpack .../32-libblas-dev_3.12.1-7ubuntu1_armhf.deb ... 504s Unpacking libblas-dev:armhf (3.12.1-7ubuntu1) ... 504s Selecting previously unselected package libbz2-dev:armhf. 504s Preparing to unpack .../33-libbz2-dev_1.0.8-6build2_armhf.deb ... 504s Unpacking libbz2-dev:armhf (1.0.8-6build2) ... 504s Selecting previously unselected package libdeflate-dev:armhf. 504s Preparing to unpack .../34-libdeflate-dev_1.23-2build1_armhf.deb ... 504s Unpacking libdeflate-dev:armhf (1.23-2build1) ... 504s Selecting previously unselected package libicu-dev:armhf. 504s Preparing to unpack .../35-libicu-dev_78.2-1ubuntu1_armhf.deb ... 504s Unpacking libicu-dev:armhf (78.2-1ubuntu1) ... 504s Selecting previously unselected package libjpeg-turbo8-dev:armhf. 504s Preparing to unpack .../36-libjpeg-turbo8-dev_2.1.5-4ubuntu3_armhf.deb ... 504s Unpacking libjpeg-turbo8-dev:armhf (2.1.5-4ubuntu3) ... 504s Selecting previously unselected package libjpeg8-dev:armhf. 504s Preparing to unpack .../37-libjpeg8-dev_8c-2ubuntu11_armhf.deb ... 504s Unpacking libjpeg8-dev:armhf (8c-2ubuntu11) ... 504s Selecting previously unselected package libjpeg-dev:armhf. 504s Preparing to unpack .../38-libjpeg-dev_8c-2ubuntu11_armhf.deb ... 504s Unpacking libjpeg-dev:armhf (8c-2ubuntu11) ... 504s Selecting previously unselected package liblapack-dev:armhf. 504s Preparing to unpack .../39-liblapack-dev_3.12.1-7ubuntu1_armhf.deb ... 504s Unpacking liblapack-dev:armhf (3.12.1-7ubuntu1) ... 504s Selecting previously unselected package libncurses-dev:armhf. 504s Preparing to unpack .../40-libncurses-dev_6.6+20251231-1_armhf.deb ... 504s Unpacking libncurses-dev:armhf (6.6+20251231-1) ... 505s Selecting previously unselected package libpcre2-16-0:armhf. 505s Preparing to unpack .../41-libpcre2-16-0_10.46-1_armhf.deb ... 505s Unpacking libpcre2-16-0:armhf (10.46-1) ... 505s Selecting previously unselected package libpcre2-32-0:armhf. 505s Preparing to unpack .../42-libpcre2-32-0_10.46-1_armhf.deb ... 505s Unpacking libpcre2-32-0:armhf (10.46-1) ... 505s Selecting previously unselected package libpcre2-posix3:armhf. 505s Preparing to unpack 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505s Unpacking libreadline-dev:armhf (8.3-3) ... 505s Selecting previously unselected package libzstd-dev:armhf. 505s Preparing to unpack .../49-libzstd-dev_1.5.7+dfsg-3_armhf.deb ... 505s Unpacking libzstd-dev:armhf (1.5.7+dfsg-3) ... 505s Selecting previously unselected package liblzma-dev:armhf. 505s Preparing to unpack .../50-liblzma-dev_5.8.2-2_armhf.deb ... 505s Unpacking liblzma-dev:armhf (5.8.2-2) ... 505s Selecting previously unselected package pkgconf-bin. 505s Preparing to unpack .../51-pkgconf-bin_1.8.1-4build1_armhf.deb ... 505s Unpacking pkgconf-bin (1.8.1-4build1) ... 505s Selecting previously unselected package pkgconf:armhf. 505s Preparing to unpack .../52-pkgconf_1.8.1-4build1_armhf.deb ... 505s Unpacking pkgconf:armhf (1.8.1-4build1) ... 505s Selecting previously unselected package libtirpc-dev:armhf. 505s Preparing to unpack .../53-libtirpc-dev_1.3.6+ds-1_armhf.deb ... 505s Unpacking libtirpc-dev:armhf (1.3.6+ds-1) ... 505s Selecting previously unselected package r-base-dev. 505s Preparing to unpack .../54-r-base-dev_4.5.2-1ubuntu2_all.deb ... 505s Unpacking r-base-dev (4.5.2-1ubuntu2) ... 505s Selecting previously unselected package pkg-r-autopkgtest. 505s Preparing to unpack .../55-pkg-r-autopkgtest_20250812_all.deb ... 505s Unpacking pkg-r-autopkgtest (20250812) ... 505s Setting up libzstd-dev:armhf (1.5.7+dfsg-3) ... 505s Setting up linux-libc-dev:armhf (6.19.0-3.3) ... 505s Setting up libpcre2-16-0:armhf (10.46-1) ... 505s Setting up libpcre2-32-0:armhf (10.46-1) ... 505s Setting up libtirpc-dev:armhf (1.3.6+ds-1) ... 505s Setting up libpkgconf3:armhf (1.8.1-4build1) ... 505s Setting up rpcsvc-proto (1.4.3-1build1) ... 505s Setting up libmpc3:armhf (1.3.1-2) ... 505s Setting up icu-devtools (78.2-1ubuntu1) ... 505s Setting up pkgconf-bin (1.8.1-4build1) ... 505s Setting up liblzma-dev:armhf (5.8.2-2) ... 505s Setting up libubsan1:armhf (15.2.0-12ubuntu1) ... 505s Setting up libpcre2-posix3:armhf (10.46-1) ... 505s Setting up libcrypt-dev:armhf (1:4.5.1-1) ... 505s Setting up libasan8:armhf (15.2.0-12ubuntu1) ... 505s Setting up libisl23:armhf (0.27-1build1) ... 505s Setting up libc-dev-bin (2.42-2ubuntu4) ... 505s Setting up libdeflate-dev:armhf (1.23-2build1) ... 505s Setting up cpp-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 505s Setting up libcc1-0:armhf (15.2.0-12ubuntu1) ... 505s Setting up cpp-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 505s Setting up libblas-dev:armhf (3.12.1-7ubuntu1) ... 505s 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 505s Setting up dctrl-tools (2.24-3build4) ... 505s Setting up libgcc-15-dev:armhf (15.2.0-12ubuntu1) ... 505s Setting up gcc-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 505s Setting up libgfortran-15-dev:armhf (15.2.0-12ubuntu1) ... 505s Setting up pkgconf:armhf (1.8.1-4build1) ... 505s Setting up liblapack-dev:armhf (3.12.1-7ubuntu1) ... 505s 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 505s Setting up cpp-15 (15.2.0-12ubuntu1) ... 505s Setting up cpp (4:15.2.0-4ubuntu1) ... 505s Setting up libc6-dev:armhf (2.42-2ubuntu4) ... 505s Setting up libicu-dev:armhf (78.2-1ubuntu1) ... 505s Setting up libbz2-dev:armhf (1.0.8-6build2) ... 505s Setting up gcc-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 505s Setting up libjpeg-turbo8-dev:armhf (2.1.5-4ubuntu3) ... 505s Setting up libncurses-dev:armhf (6.6+20251231-1) ... 505s Setting up gfortran-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 505s Setting up libpcre2-dev:armhf (10.46-1) ... 505s Setting up libreadline-dev:armhf (8.3-3) ... 505s Setting up gfortran-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 505s Setting up gcc-15 (15.2.0-12ubuntu1) ... 505s Setting up libstdc++-15-dev:armhf (15.2.0-12ubuntu1) ... 505s Setting up zlib1g-dev:armhf (1:1.3.dfsg+really1.3.1-1ubuntu2) ... 505s Setting up libjpeg8-dev:armhf (8c-2ubuntu11) ... 505s Setting up gfortran-15 (15.2.0-12ubuntu1) ... 505s Setting up libpng-dev:armhf (1.6.54-1) ... 505s Setting up libjpeg-dev:armhf (8c-2ubuntu11) ... 505s Setting up g++-15-arm-linux-gnueabihf (15.2.0-12ubuntu1) ... 505s Setting up gcc (4:15.2.0-4ubuntu1) ... 505s Setting up g++-15 (15.2.0-12ubuntu1) ... 505s Setting up g++-arm-linux-gnueabihf (4:15.2.0-4ubuntu1) ... 505s Setting up gfortran (4:15.2.0-4ubuntu1) ... 505s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 505s 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 505s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 505s 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 505s Setting up g++ (4:15.2.0-4ubuntu1) ... 505s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 505s Setting up build-essential (12.12ubuntu2) ... 505s Setting up r-base-dev (4.5.2-1ubuntu2) ... 505s Setting up pkg-r-autopkgtest (20250812) ... 505s Processing triggers for libc-bin (2.42-2ubuntu4) ... 505s Processing triggers for man-db (2.13.1-1build1) ... 506s Processing triggers for install-info (7.2-5) ... 514s autopkgtest [23:10:54]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 514s autopkgtest [23:10:54]: test pkg-r-autopkgtest: [----------------------- 517s Test: Try to load the R library rrcov 517s 517s R version 4.5.2 (2025-10-31) -- "[Not] Part in a Rumble" 517s Copyright (C) 2025 The R Foundation for Statistical Computing 517s Platform: arm-unknown-linux-gnueabihf (32-bit) 517s 517s R is free software and comes with ABSOLUTELY NO WARRANTY. 517s You are welcome to redistribute it under certain conditions. 517s Type 'license()' or 'licence()' for distribution details. 517s 517s R is a collaborative project with many contributors. 517s Type 'contributors()' for more information and 517s 'citation()' on how to cite R or R packages in publications. 517s 517s Type 'demo()' for some demos, 'help()' for on-line help, or 517s 'help.start()' for an HTML browser interface to help. 517s Type 'q()' to quit R. 517s 517s > library('rrcov') 517s Loading required package: robustbase 517s Scalable Robust Estimators with High Breakdown Point (version 1.7-6) 517s 517s > 517s autopkgtest [23:10:57]: test pkg-r-autopkgtest: -----------------------] 521s autopkgtest [23:11:01]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 521s pkg-r-autopkgtest PASS 525s autopkgtest [23:11:05]: @@@@@@@@@@@@@@@@@@@@ summary 525s run-unit-test PASS 525s pkg-r-autopkgtest PASS