0s autopkgtest [15:52:51]: starting date and time: 2025-03-15 15:52:51+0000 0s autopkgtest [15:52:51]: git checkout: 325255d2 Merge branch 'pin-any-arch' into 'ubuntu/production' 0s autopkgtest [15:52:51]: host juju-7f2275-prod-proposed-migration-environment-20; command line: /home/ubuntu/autopkgtest/runner/autopkgtest --output-dir /tmp/autopkgtest-work.3yq_oypg/out --timeout-copy=6000 --setup-commands /home/ubuntu/autopkgtest-cloud/worker-config-production/setup-canonical.sh --apt-pocket=proposed=src:glibc --apt-upgrade r-cran-rrcov --timeout-short=300 --timeout-copy=20000 --timeout-build=20000 --env=ADT_TEST_TRIGGERS=glibc/2.41-1ubuntu2 -- ssh -s /home/ubuntu/autopkgtest/ssh-setup/nova -- --flavor autopkgtest --security-groups autopkgtest-juju-7f2275-prod-proposed-migration-environment-20@bos03-arm64-9.secgroup --name adt-plucky-arm64-r-cran-rrcov-20250315-155250-juju-7f2275-prod-proposed-migration-environment-20-2a96b6f3-730b-4406-8396-bde0fee063c7 --image adt/ubuntu-plucky-arm64-server --keyname testbed-juju-7f2275-prod-proposed-migration-environment-20 --net-id=net_prod-proposed-migration -e TERM=linux -e ''"'"'http_proxy=http://squid.internal:3128'"'"'' -e ''"'"'https_proxy=http://squid.internal:3128'"'"'' -e ''"'"'no_proxy=127.0.0.1,127.0.1.1,login.ubuntu.com,localhost,localdomain,novalocal,internal,archive.ubuntu.com,ports.ubuntu.com,security.ubuntu.com,ddebs.ubuntu.com,changelogs.ubuntu.com,keyserver.ubuntu.com,launchpadlibrarian.net,launchpadcontent.net,launchpad.net,10.24.0.0/24,keystone.ps5.canonical.com,objectstorage.prodstack5.canonical.com,radosgw.ps5.canonical.com'"'"'' --mirror=http://ftpmaster.internal/ubuntu/ 170s autopkgtest [15:55:41]: testbed dpkg architecture: arm64 170s autopkgtest [15:55:41]: testbed apt version: 2.9.33 171s autopkgtest [15:55:42]: @@@@@@@@@@@@@@@@@@@@ test bed setup 171s autopkgtest [15:55:42]: testbed release detected to be: None 172s autopkgtest [15:55:43]: updating testbed package index (apt update) 173s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed InRelease [126 kB] 173s Hit:2 http://ftpmaster.internal/ubuntu plucky InRelease 173s Hit:3 http://ftpmaster.internal/ubuntu plucky-updates InRelease 173s Hit:4 http://ftpmaster.internal/ubuntu plucky-security InRelease 173s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/universe Sources [379 kB] 174s Get:6 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse Sources [15.8 kB] 174s Get:7 http://ftpmaster.internal/ubuntu plucky-proposed/main Sources [99.7 kB] 174s Get:8 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 Packages [111 kB] 174s Get:9 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 c-n-f Metadata [1856 B] 174s Get:10 http://ftpmaster.internal/ubuntu plucky-proposed/restricted arm64 c-n-f Metadata [116 B] 174s Get:11 http://ftpmaster.internal/ubuntu plucky-proposed/universe arm64 Packages [324 kB] 174s Get:12 http://ftpmaster.internal/ubuntu plucky-proposed/universe arm64 c-n-f Metadata [14.7 kB] 174s Get:13 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse arm64 Packages [4948 B] 174s Get:14 http://ftpmaster.internal/ubuntu plucky-proposed/multiverse arm64 c-n-f Metadata [268 B] 175s Fetched 1078 kB in 2s (549 kB/s) 176s Reading package lists... 177s Reading package lists... 179s Building dependency tree... 179s Reading state information... 179s Calculating upgrade... 179s Calculating upgrade... 179s The following packages will be upgraded: 179s pinentry-curses python3-jinja2 strace 179s 3 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 179s Need to get 647 kB of archives. 179s After this operation, 11.3 kB of additional disk space will be used. 179s Get:1 http://ftpmaster.internal/ubuntu plucky/main arm64 strace arm64 6.13+ds-1ubuntu1 [499 kB] 180s Get:2 http://ftpmaster.internal/ubuntu plucky/main arm64 pinentry-curses arm64 1.3.1-2ubuntu3 [39.2 kB] 180s Get:3 http://ftpmaster.internal/ubuntu plucky/main arm64 python3-jinja2 all 3.1.5-2ubuntu1 [109 kB] 181s Fetched 647 kB in 1s (597 kB/s) 181s (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 ... 117701 files and directories currently installed.) 181s Preparing to unpack .../strace_6.13+ds-1ubuntu1_arm64.deb ... 181s Unpacking strace (6.13+ds-1ubuntu1) over (6.11-0ubuntu1) ... 181s Preparing to unpack .../pinentry-curses_1.3.1-2ubuntu3_arm64.deb ... 181s Unpacking pinentry-curses (1.3.1-2ubuntu3) over (1.3.1-2ubuntu2) ... 181s Preparing to unpack .../python3-jinja2_3.1.5-2ubuntu1_all.deb ... 181s Unpacking python3-jinja2 (3.1.5-2ubuntu1) over (3.1.5-2) ... 182s Setting up pinentry-curses (1.3.1-2ubuntu3) ... 182s Setting up python3-jinja2 (3.1.5-2ubuntu1) ... 182s Setting up strace (6.13+ds-1ubuntu1) ... 182s Processing triggers for man-db (2.13.0-1) ... 183s Reading package lists... 183s Building dependency tree... 183s Reading state information... 184s Solving dependencies... 184s The following packages will be REMOVED: 184s libnsl2* libpython3.12-minimal* libpython3.12-stdlib* libpython3.12t64* 184s libunwind8* linux-headers-6.11.0-8* linux-headers-6.11.0-8-generic* 184s linux-image-6.11.0-8-generic* linux-modules-6.11.0-8-generic* 184s linux-tools-6.11.0-8* linux-tools-6.11.0-8-generic* 185s 0 upgraded, 0 newly installed, 11 to remove and 5 not upgraded. 185s After this operation, 267 MB disk space will be freed. 185s (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 ... 117701 files and directories currently installed.) 185s Removing linux-tools-6.11.0-8-generic (6.11.0-8.8) ... 185s Removing linux-tools-6.11.0-8 (6.11.0-8.8) ... 185s Removing libpython3.12t64:arm64 (3.12.9-1) ... 185s Removing libpython3.12-stdlib:arm64 (3.12.9-1) ... 185s Removing libnsl2:arm64 (1.3.0-3build3) ... 185s Removing libpython3.12-minimal:arm64 (3.12.9-1) ... 185s Removing libunwind8:arm64 (1.6.2-3.1) ... 185s Removing linux-headers-6.11.0-8-generic (6.11.0-8.8) ... 186s Removing linux-headers-6.11.0-8 (6.11.0-8.8) ... 188s Removing linux-image-6.11.0-8-generic (6.11.0-8.8) ... 188s I: /boot/vmlinuz.old is now a symlink to vmlinuz-6.14.0-10-generic 188s I: /boot/initrd.img.old is now a symlink to initrd.img-6.14.0-10-generic 188s /etc/kernel/postrm.d/initramfs-tools: 188s update-initramfs: Deleting /boot/initrd.img-6.11.0-8-generic 188s /etc/kernel/postrm.d/zz-flash-kernel: 188s flash-kernel: Kernel 6.11.0-8-generic has been removed. 188s flash-kernel: A higher version (6.14.0-10-generic) is still installed, no reflashing required. 188s /etc/kernel/postrm.d/zz-update-grub: 188s Sourcing file `/etc/default/grub' 188s Sourcing file `/etc/default/grub.d/50-cloudimg-settings.cfg' 188s Generating grub configuration file ... 189s Found linux image: /boot/vmlinuz-6.14.0-10-generic 189s Found initrd image: /boot/initrd.img-6.14.0-10-generic 189s Warning: os-prober will not be executed to detect other bootable partitions. 189s Systems on them will not be added to the GRUB boot configuration. 189s Check GRUB_DISABLE_OS_PROBER documentation entry. 189s Adding boot menu entry for UEFI Firmware Settings ... 189s done 189s Removing linux-modules-6.11.0-8-generic (6.11.0-8.8) ... 190s Processing triggers for libc-bin (2.41-1ubuntu1) ... 190s (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 ... 81650 files and directories currently installed.) 190s Purging configuration files for linux-image-6.11.0-8-generic (6.11.0-8.8) ... 190s Purging configuration files for libpython3.12-minimal:arm64 (3.12.9-1) ... 190s Purging configuration files for linux-modules-6.11.0-8-generic (6.11.0-8.8) ... 190s autopkgtest [15:56:01]: upgrading testbed (apt dist-upgrade and autopurge) 191s Reading package lists... 191s Building dependency tree... 191s Reading state information... 192s Calculating upgrade...Starting pkgProblemResolver with broken count: 0 192s Starting 2 pkgProblemResolver with broken count: 0 192s Done 193s Entering ResolveByKeep 193s 193s Calculating upgrade... 194s The following packages will be upgraded: 194s libc-bin libc-dev-bin libc6 libc6-dev locales 194s 5 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 194s Need to get 9530 kB of archives. 194s After this operation, 0 B of additional disk space will be used. 194s Get:1 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 libc6-dev arm64 2.41-1ubuntu2 [1750 kB] 196s Get:2 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 libc-dev-bin arm64 2.41-1ubuntu2 [24.0 kB] 196s Get:3 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 libc6 arm64 2.41-1ubuntu2 [2910 kB] 199s Get:4 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 libc-bin arm64 2.41-1ubuntu2 [600 kB] 200s Get:5 http://ftpmaster.internal/ubuntu plucky-proposed/main arm64 locales all 2.41-1ubuntu2 [4246 kB] 206s Preconfiguring packages ... 206s Fetched 9530 kB in 11s (839 kB/s) 206s (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 ... 81647 files and directories currently installed.) 206s Preparing to unpack .../libc6-dev_2.41-1ubuntu2_arm64.deb ... 206s Unpacking libc6-dev:arm64 (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 206s Preparing to unpack .../libc-dev-bin_2.41-1ubuntu2_arm64.deb ... 206s Unpacking libc-dev-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 206s Preparing to unpack .../libc6_2.41-1ubuntu2_arm64.deb ... 206s Unpacking libc6:arm64 (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 207s Setting up libc6:arm64 (2.41-1ubuntu2) ... 207s (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 ... 81647 files and directories currently installed.) 207s Preparing to unpack .../libc-bin_2.41-1ubuntu2_arm64.deb ... 207s Unpacking libc-bin (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 207s Setting up libc-bin (2.41-1ubuntu2) ... 207s (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 ... 81647 files and directories currently installed.) 207s Preparing to unpack .../locales_2.41-1ubuntu2_all.deb ... 207s Unpacking locales (2.41-1ubuntu2) over (2.41-1ubuntu1) ... 207s Setting up locales (2.41-1ubuntu2) ... 208s Generating locales (this might take a while)... 210s en_US.UTF-8... done 210s Generation complete. 210s Setting up libc-dev-bin (2.41-1ubuntu2) ... 210s Setting up libc6-dev:arm64 (2.41-1ubuntu2) ... 210s Processing triggers for man-db (2.13.0-1) ... 211s Processing triggers for systemd (257.3-1ubuntu3) ... 212s Reading package lists... 213s Building dependency tree... 213s Reading state information... 213s Starting pkgProblemResolver with broken count: 0 213s Starting 2 pkgProblemResolver with broken count: 0 213s Done 214s Solving dependencies... 215s 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. 215s autopkgtest [15:56:26]: rebooting testbed after setup commands that affected boot 239s autopkgtest [15:56:50]: testbed running kernel: Linux 6.14.0-10-generic #10-Ubuntu SMP PREEMPT_DYNAMIC Wed Mar 12 15:45:31 UTC 2025 241s autopkgtest [15:56:52]: @@@@@@@@@@@@@@@@@@@@ apt-source r-cran-rrcov 246s Get:1 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (dsc) [2146 B] 246s Get:2 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (tar) [1542 kB] 246s Get:3 http://ftpmaster.internal/ubuntu plucky/universe r-cran-rrcov 1.7-6-1 (diff) [3160 B] 246s gpgv: Signature made Fri Sep 6 03:10:50 2024 UTC 246s gpgv: using RSA key 73471499CC60ED9EEE805946C5BD6C8F2295D502 246s gpgv: issuer "plessy@debian.org" 246s gpgv: Can't check signature: No public key 246s dpkg-source: warning: cannot verify inline signature for ./r-cran-rrcov_1.7-6-1.dsc: no acceptable signature found 246s autopkgtest [15:56:57]: testing package r-cran-rrcov version 1.7-6-1 246s autopkgtest [15:56:57]: build not needed 249s autopkgtest [15:57:00]: test run-unit-test: preparing testbed 250s Reading package lists... 250s Building dependency tree... 250s Reading state information... 250s Starting pkgProblemResolver with broken count: 0 250s Starting 2 pkgProblemResolver with broken count: 0 250s Done 251s The following NEW packages will be installed: 251s fontconfig fontconfig-config fonts-dejavu-core fonts-dejavu-mono libblas3 251s libcairo2 libdatrie1 libdeflate0 libfontconfig1 libgfortran5 libgomp1 251s libgraphite2-3 libharfbuzz0b libice6 libjbig0 libjpeg-turbo8 libjpeg8 251s liblapack3 liblerc4 libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 251s libpaper-utils libpaper2 libpixman-1-0 libsharpyuv0 libsm6 libtcl8.6 251s libthai-data libthai0 libtiff6 libtk8.6 libwebp7 libxcb-render0 libxcb-shm0 251s libxft2 libxrender1 libxss1 libxt6t64 r-base-core r-cran-deoptimr 251s r-cran-lattice r-cran-mass r-cran-mvtnorm r-cran-pcapp r-cran-robustbase 251s r-cran-rrcov unzip x11-common xdg-utils zip 251s 0 upgraded, 51 newly installed, 0 to remove and 0 not upgraded. 251s Need to get 47.6 MB of archives. 251s After this operation, 91.2 MB of additional disk space will be used. 251s Get:1 http://ftpmaster.internal/ubuntu plucky/main arm64 fonts-dejavu-mono all 2.37-8 [502 kB] 252s Get:2 http://ftpmaster.internal/ubuntu plucky/main arm64 fonts-dejavu-core all 2.37-8 [835 kB] 253s Get:3 http://ftpmaster.internal/ubuntu plucky/main arm64 fontconfig-config arm64 2.15.0-2ubuntu1 [37.5 kB] 253s Get:4 http://ftpmaster.internal/ubuntu plucky/main arm64 libfontconfig1 arm64 2.15.0-2ubuntu1 [144 kB] 253s Get:5 http://ftpmaster.internal/ubuntu plucky/main arm64 fontconfig arm64 2.15.0-2ubuntu1 [191 kB] 253s Get:6 http://ftpmaster.internal/ubuntu plucky/main arm64 libblas3 arm64 3.12.1-2 [161 kB] 254s Get:7 http://ftpmaster.internal/ubuntu plucky/main arm64 libpixman-1-0 arm64 0.44.0-3 [197 kB] 254s Get:8 http://ftpmaster.internal/ubuntu plucky/main arm64 libxcb-render0 arm64 1.17.0-2 [16.6 kB] 254s Get:9 http://ftpmaster.internal/ubuntu plucky/main arm64 libxcb-shm0 arm64 1.17.0-2 [5884 B] 254s Get:10 http://ftpmaster.internal/ubuntu plucky/main arm64 libxrender1 arm64 1:0.9.10-1.1build1 [18.8 kB] 254s Get:11 http://ftpmaster.internal/ubuntu plucky/main arm64 libcairo2 arm64 1.18.2-2 [560 kB] 254s Get:12 http://ftpmaster.internal/ubuntu plucky/main arm64 libdatrie1 arm64 0.2.13-3build1 [19.2 kB] 254s Get:13 http://ftpmaster.internal/ubuntu plucky/main arm64 libdeflate0 arm64 1.23-1 [46.2 kB] 254s Get:14 http://ftpmaster.internal/ubuntu plucky/main arm64 libgfortran5 arm64 15-20250222-0ubuntu1 [444 kB] 255s Get:15 http://ftpmaster.internal/ubuntu plucky/main arm64 libgomp1 arm64 15-20250222-0ubuntu1 [146 kB] 255s Get:16 http://ftpmaster.internal/ubuntu plucky/main arm64 libgraphite2-3 arm64 1.3.14-2ubuntu1 [70.6 kB] 255s Get:17 http://ftpmaster.internal/ubuntu plucky/main arm64 libharfbuzz0b arm64 10.2.0-1 [490 kB] 256s Get:18 http://ftpmaster.internal/ubuntu plucky/main arm64 x11-common all 1:7.7+23ubuntu3 [21.7 kB] 256s Get:19 http://ftpmaster.internal/ubuntu plucky/main arm64 libice6 arm64 2:1.1.1-1 [42.3 kB] 256s Get:20 http://ftpmaster.internal/ubuntu plucky/main arm64 libjpeg-turbo8 arm64 2.1.5-3ubuntu2 [165 kB] 256s Get:21 http://ftpmaster.internal/ubuntu plucky/main arm64 libjpeg8 arm64 8c-2ubuntu11 [2148 B] 256s Get:22 http://ftpmaster.internal/ubuntu plucky/main arm64 liblapack3 arm64 3.12.1-2 [2307 kB] 258s Get:23 http://ftpmaster.internal/ubuntu plucky/main arm64 liblerc4 arm64 4.0.0+ds-5ubuntu1 [167 kB] 258s Get:24 http://ftpmaster.internal/ubuntu plucky/main arm64 libthai-data all 0.1.29-2build1 [158 kB] 259s Get:25 http://ftpmaster.internal/ubuntu plucky/main arm64 libthai0 arm64 0.1.29-2build1 [18.2 kB] 259s Get:26 http://ftpmaster.internal/ubuntu plucky/main arm64 libpango-1.0-0 arm64 1.56.2-1 [237 kB] 259s Get:27 http://ftpmaster.internal/ubuntu plucky/main arm64 libpangoft2-1.0-0 arm64 1.56.2-1 [49.5 kB] 259s Get:28 http://ftpmaster.internal/ubuntu plucky/main arm64 libpangocairo-1.0-0 arm64 1.56.2-1 [27.6 kB] 259s Get:29 http://ftpmaster.internal/ubuntu plucky/main arm64 libpaper2 arm64 2.2.5-0.3 [17.3 kB] 259s Get:30 http://ftpmaster.internal/ubuntu plucky/main arm64 libpaper-utils arm64 2.2.5-0.3 [15.4 kB] 259s Get:31 http://ftpmaster.internal/ubuntu plucky/main arm64 libsharpyuv0 arm64 1.5.0-0.1 [16.9 kB] 259s Get:32 http://ftpmaster.internal/ubuntu plucky/main arm64 libsm6 arm64 2:1.2.4-1 [16.4 kB] 259s Get:33 http://ftpmaster.internal/ubuntu plucky/main arm64 libtcl8.6 arm64 8.6.16+dfsg-1 [987 kB] 260s Get:34 http://ftpmaster.internal/ubuntu plucky/main arm64 libjbig0 arm64 2.1-6.1ubuntu2 [29.3 kB] 260s Get:35 http://ftpmaster.internal/ubuntu plucky/main arm64 libwebp7 arm64 1.5.0-0.1 [194 kB] 260s Get:36 http://ftpmaster.internal/ubuntu plucky/main arm64 libtiff6 arm64 4.5.1+git230720-4ubuntu4 [193 kB] 261s Get:37 http://ftpmaster.internal/ubuntu plucky/main arm64 libxft2 arm64 2.3.6-1build1 [44.1 kB] 261s Get:38 http://ftpmaster.internal/ubuntu plucky/main arm64 libxss1 arm64 1:1.2.3-1build3 [7244 B] 261s Get:39 http://ftpmaster.internal/ubuntu plucky/main arm64 libtk8.6 arm64 8.6.16-1 [776 kB] 262s Get:40 http://ftpmaster.internal/ubuntu plucky/main arm64 libxt6t64 arm64 1:1.2.1-1.2build1 [168 kB] 262s Get:41 http://ftpmaster.internal/ubuntu plucky/main arm64 zip arm64 3.0-14ubuntu2 [173 kB] 262s Get:42 http://ftpmaster.internal/ubuntu plucky/main arm64 unzip arm64 6.0-28ubuntu6 [178 kB] 262s Get:43 http://ftpmaster.internal/ubuntu plucky/main arm64 xdg-utils all 1.2.1-2ubuntu1 [66.0 kB] 262s Get:44 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-base-core arm64 4.4.3-1 [28.4 MB] 293s Get:45 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-deoptimr all 1.1-3-1-1 [76.6 kB] 293s Get:46 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-lattice arm64 0.22-6-1 [1363 kB] 295s Get:47 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-mass arm64 7.3-64-1 [1110 kB] 296s Get:48 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-mvtnorm arm64 1.3-3-1 [921 kB] 297s Get:49 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-pcapp arm64 2.0-5-1 [366 kB] 297s Get:50 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-robustbase arm64 0.99-4-1-1 [3035 kB] 300s Get:51 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-cran-rrcov arm64 1.7-6-1 [2406 kB] 303s Preconfiguring packages ... 303s Fetched 47.6 MB in 51s (927 kB/s) 303s Selecting previously unselected package fonts-dejavu-mono. 303s (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 ... 81647 files and directories currently installed.) 303s Preparing to unpack .../00-fonts-dejavu-mono_2.37-8_all.deb ... 303s Unpacking fonts-dejavu-mono (2.37-8) ... 304s Selecting previously unselected package fonts-dejavu-core. 304s Preparing to unpack .../01-fonts-dejavu-core_2.37-8_all.deb ... 304s Unpacking fonts-dejavu-core (2.37-8) ... 304s Selecting previously unselected package fontconfig-config. 304s Preparing to unpack .../02-fontconfig-config_2.15.0-2ubuntu1_arm64.deb ... 304s Unpacking fontconfig-config (2.15.0-2ubuntu1) ... 304s Selecting previously unselected package libfontconfig1:arm64. 304s Preparing to unpack .../03-libfontconfig1_2.15.0-2ubuntu1_arm64.deb ... 304s Unpacking libfontconfig1:arm64 (2.15.0-2ubuntu1) ... 304s Selecting previously unselected package fontconfig. 304s Preparing to unpack .../04-fontconfig_2.15.0-2ubuntu1_arm64.deb ... 304s Unpacking fontconfig (2.15.0-2ubuntu1) ... 304s Selecting previously unselected package libblas3:arm64. 304s Preparing to unpack .../05-libblas3_3.12.1-2_arm64.deb ... 304s Unpacking libblas3:arm64 (3.12.1-2) ... 304s Selecting previously unselected package libpixman-1-0:arm64. 304s Preparing to unpack .../06-libpixman-1-0_0.44.0-3_arm64.deb ... 304s Unpacking libpixman-1-0:arm64 (0.44.0-3) ... 304s Selecting previously unselected package libxcb-render0:arm64. 304s Preparing to unpack .../07-libxcb-render0_1.17.0-2_arm64.deb ... 304s Unpacking 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libc-bin (2.41-1ubuntu2) ... 309s Processing triggers for man-db (2.13.0-1) ... 310s Processing triggers for install-info (7.1.1-1) ... 312s autopkgtest [15:58:03]: test run-unit-test: [----------------------- 312s BEGIN TEST thubert.R 312s 312s R version 4.4.3 (2025-02-28) -- "Trophy Case" 312s Copyright (C) 2025 The R Foundation for Statistical Computing 312s Platform: aarch64-unknown-linux-gnu 312s 312s R is free software and comes with ABSOLUTELY NO WARRANTY. 312s You are welcome to redistribute it under certain conditions. 312s Type 'license()' or 'licence()' for distribution details. 312s 312s R is a collaborative project with many contributors. 312s Type 'contributors()' for more information and 312s 'citation()' on how to cite R or R packages in publications. 312s 312s Type 'demo()' for some demos, 'help()' for on-line help, or 312s 'help.start()' for an HTML browser interface to help. 312s Type 'q()' to quit R. 312s 312s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, 312s + method=c("hubert", "hubert.mcd", "locantore", "cov", "classic", 312s + "grid", "proj")) 312s + { 312s + ## Test the PcaXxx() functions on the literature datasets: 312s + ## 312s + ## Call PcaHubert() and the other functions for all regression 312s + ## data sets available in robustbase/rrcov and print: 312s + ## - execution time (if time == TRUE) 312s + ## - loadings 312s + ## - eigenvalues 312s + ## - scores 312s + ## 312s + 312s + dopca <- function(x, xname, nrep=1){ 312s + 312s + n <- dim(x)[1] 312s + p <- dim(x)[2] 312s + if(method == "hubert.mcd") 312s + pca <- PcaHubert(x, k=p) 312s + else if(method == "hubert") 312s + pca <- PcaHubert(x, mcd=FALSE) 312s + else if(method == "locantore") 312s + pca <- PcaLocantore(x) 312s + else if(method == "cov") 312s + pca <- PcaCov(x) 312s + else if(method == "classic") 312s + pca <- PcaClassic(x) 312s + else if(method == "grid") 312s + pca <- PcaGrid(x) 312s + else if(method == "proj") 312s + pca <- PcaProj(x) 312s + else 312s + stop("Undefined PCA method: ", method) 312s + 312s + 312s + e1 <- getEigenvalues(pca)[1] 312s + e2 <- getEigenvalues(pca)[2] 312s + k <- pca@k 312s + 312s + if(time){ 312s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 312s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f %10.3f\n", dim(x)[1], dim(x)[2], k, e1, e2, xtime) 312s + } 312s + else{ 312s + xres <- sprintf("%3d %3d %3d %12.6f %12.6f\n", dim(x)[1], dim(x)[2], k, e1, e2) 312s + } 312s + lpad<-lname-nchar(xname) 312s + cat(pad.right(xname, lpad), xres) 312s + 312s + if(!short){ 312s + cat("Scores: \n") 312s + print(getScores(pca)) 312s + 312s + if(full){ 312s + cat("-------------\n") 312s + show(pca) 312s + } 312s + cat("----------------------------------------------------------\n") 312s + } 312s + } 312s + 312s + stopifnot(length(nrep) == 1, nrep >= 1) 312s + method <- match.arg(method) 312s + 312s + options(digits = 5) 312s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 312s + 312s + lname <- 20 312s + 312s + ## VT::15.09.2013 - this will render the output independent 312s + ## from the version of the package 312s + suppressPackageStartupMessages(library(rrcov)) 312s + 312s + data(Animals, package = "MASS") 312s + brain <- Animals[c(1:24, 26:25, 27:28),] 312s + 312s + tmp <- sys.call() 312s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 312s + 312s + cat("Data Set n p k e1 e2\n") 312s + cat("==========================================================\n") 312s + dopca(heart[, 1:2], data(heart), nrep) 312s + dopca(starsCYG, data(starsCYG), nrep) 312s + dopca(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 312s + dopca(stack.x, data(stackloss), nrep) 312s + ## dopca(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) # differences between the architectures 312s + dopca(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 312s + ## dopca(data.matrix(subset(wood, select = -y)), data(wood), nrep) # differences between the architectures 312s + dopca(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 312s + 312s + ## dopca(brain, "Animals", nrep) 312s + dopca(milk, data(milk), nrep) 312s + dopca(bushfire, data(bushfire), nrep) 312s + cat("==========================================================\n") 312s + } 312s > 312s > dogen <- function(nrep=1, eps=0.49, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 312s + 312s + dopca <- function(x, nrep=1){ 312s + gc() 312s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 312s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 312s + xtime 312s + } 312s + 312s + set.seed(1234) 312s + 312s + ## VT::15.09.2013 - this will render the output independent 312s + ## from the version of the package 312s + suppressPackageStartupMessages(library(rrcov)) 312s + library(MASS) 312s + 312s + method <- match.arg(method) 312s + 312s + ap <- c(2, 5, 10, 20, 30) 312s + an <- c(100, 500, 1000, 10000, 50000) 312s + 312s + tottime <- 0 312s + cat(" n p Time\n") 312s + cat("=====================\n") 312s + for(i in 1:length(an)) { 312s + for(j in 1:length(ap)) { 312s + n <- an[i] 312s + p <- ap[j] 312s + if(5*p <= n){ 312s + xx <- gendata(n, p, eps) 312s + X <- xx$X 312s + ## print(dimnames(X)) 312s + tottime <- tottime + dopca(X, nrep) 312s + } 312s + } 312s + } 312s + 312s + cat("=====================\n") 312s + cat("Total time: ", tottime*nrep, "\n") 312s + } 312s > 312s > dorep <- function(x, nrep=1, method=c("hubert", "hubert.mcd", "locantore", "cov")){ 312s + 312s + method <- match.arg(method) 312s + for(i in 1:nrep) 312s + if(method == "hubert.mcd") 312s + PcaHubert(x) 312s + else if(method == "hubert") 312s + PcaHubert(x, mcd=FALSE) 312s + else if(method == "locantore") 312s + PcaLocantore(x) 312s + else if(method == "cov") 312s + PcaCov(x) 312s + else 312s + stop("Undefined PCA method: ", method) 312s + } 312s > 312s > #### gendata() #### 312s > # Generates a location contaminated multivariate 312s > # normal sample of n observations in p dimensions 312s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 312s > # where 312s > # m = (b,b,...,b) 312s > # Defaults: eps=0 and b=10 312s > # 312s > gendata <- function(n,p,eps=0,b=10){ 312s + 312s + if(missing(n) || missing(p)) 312s + stop("Please specify (n,p)") 312s + if(eps < 0 || eps >= 0.5) 312s + stop(message="eps must be in [0,0.5)") 312s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 312s + nbad <- as.integer(eps * n) 312s + xind <- vector("numeric") 312s + if(nbad > 0){ 312s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 312s + xind <- sample(n,nbad) 312s + X[xind,] <- Xbad 312s + } 312s + list(X=X, xind=xind) 312s + } 312s > 312s > pad.right <- function(z, pads) 312s + { 312s + ### Pads spaces to right of text 312s + padding <- paste(rep(" ", pads), collapse = "") 312s + paste(z, padding, sep = "") 312s + } 312s > 312s > whatis <- function(x){ 312s + if(is.data.frame(x)) 312s + cat("Type: data.frame\n") 312s + else if(is.matrix(x)) 312s + cat("Type: matrix\n") 312s + else if(is.vector(x)) 312s + cat("Type: vector\n") 312s + else 312s + cat("Type: don't know\n") 312s + } 312s > 312s > ################################################################# 312s > ## VT::27.08.2010 312s > ## bug report from Stephen Milborrow 312s > ## 312s > test.case.1 <- function() 312s + { 312s + X <- matrix(c( 312s + -0.79984, -1.00103, 0.899794, 0.00000, 312s + 0.34279, 0.52832, -1.303783, -1.17670, 312s + -0.79984, -1.00103, 0.899794, 0.00000, 312s + 0.34279, 0.52832, -1.303783, -1.17670, 312s + 0.34279, 0.52832, -1.303783, -1.17670, 312s + 1.48542, 0.66735, 0.716162, 1.17670, 312s + -0.79984, -1.00103, 0.899794, 0.00000, 312s + 1.69317, 1.91864, -0.018363, 1.76505, 312s + -1.00759, -0.16684, -0.385626, 0.58835, 312s + -0.79984, -1.00103, 0.899794, 0.00000), ncol=4, byrow=TRUE) 312s + 312s + cc1 <- PcaHubert(X, k=3) 312s + 312s + cc2 <- PcaLocantore(X, k=3) 312s + cc3 <- PcaCov(X, k=3, cov.control=CovControlSest()) 312s + 312s + cc4 <- PcaProj(X, k=2) # with k=3 will produce warnings in .distances - too small eignevalues 312s + cc5 <- PcaGrid(X, k=2) # dito 312s + 312s + list(cc1, cc2, cc3, cc4, cc5) 312s + } 312s > 312s > ################################################################# 312s > ## VT::05.08.2016 312s > ## bug report from Matthieu Lesnoff 312s > ## 312s > test.case.2 <- function() 312s + { 312s + do.test.case.2 <- function(z) 312s + { 312s + if(missing(z)) 312s + { 312s + set.seed(12345678) 312s + n <- 5 312s + z <- data.frame(v1 = rnorm(n), v2 = rnorm(n), v3 = rnorm(n)) 312s + z 312s + } 312s + 312s + fm <- PcaLocantore(z, k = 2, scale = TRUE) 312s + fm@scale 312s + apply(z, MARGIN = 2, FUN = mad) 312s + scale(z, center = fm@center, scale = fm@scale) 312s + 312s + T <- fm@scores 312s + P <- fm@loadings 312s + E <- scale(z, center = fm@center, scale = fm@scale) - T %*% t(P) 312s + d2 <- apply(E^2, MARGIN = 1, FUN = sum) 312s + ## print(sqrt(d2)); print(fm@od) 312s + print(ret <- all.equal(sqrt(d2), fm@od)) 312s + 312s + ret 312s + } 312s + do.test.case.2() 312s + do.test.case.2(phosphor) 312s + do.test.case.2(stackloss) 312s + do.test.case.2(salinity) 312s + do.test.case.2(hbk) 312s + do.test.case.2(milk) 312s + do.test.case.2(bushfire) 312s + data(rice); do.test.case.2(rice) 312s + data(un86); do.test.case.2(un86) 312s + } 312s > 312s > ## VT::15.09.2013 - this will render the output independent 312s > ## from the version of the package 312s > suppressPackageStartupMessages(library(rrcov)) 312s > 312s > dodata(method="classic") 313s 313s Call: dodata(method = "classic") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 2 812.379735 9.084962 313s Scores: 313s PC1 PC2 313s 1 2.7072 1.46576 313s 2 59.9990 -1.43041 313s 3 -3.5619 -1.54067 313s 4 -7.7696 2.52687 313s 5 14.7660 -0.95822 313s 6 -20.0489 6.91079 313s 7 1.4189 2.25961 313s 8 -34.3308 -4.23717 313s 9 -6.0487 -0.97859 313s 10 -33.0102 -3.73143 313s 11 -18.6372 0.25821 313s 12 44.5163 -0.54476 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 28.5023 3.0141 313s ---------------------------------------------------------- 313s starsCYG 47 2 2 0.331279 0.079625 313s Scores: 313s PC1 PC2 313s 1 0.2072999 0.089973 313s 2 0.6855999 0.349644 313s 3 -0.0743007 -0.061028 313s 4 0.6855999 0.349644 313s 5 0.1775161 0.015053 313s 6 0.4223986 0.211351 313s 7 -0.2926077 -0.516156 313s 8 0.2188453 0.293607 313s 9 0.5593696 0.028761 313s 10 0.0983878 0.074540 313s 11 0.8258140 -0.711176 313s 12 0.4167063 0.180244 313s 13 0.3799883 0.225541 313s 14 -0.9105236 -0.432014 313s 15 -0.7418831 -0.125322 313s 16 -0.4432862 0.048287 313s 17 -1.0503005 -0.229623 313s 18 -0.8393302 -0.007831 313s 19 -0.8126742 -0.195952 313s 20 0.9842316 -0.688729 313s 21 -0.6230699 -0.108486 313s 22 -0.7814875 -0.130933 313s 23 -0.6017038 0.025840 313s 24 -0.1857772 0.155474 313s 25 -0.0020261 0.070412 313s 26 -0.3640775 0.059510 313s 27 -0.3458392 -0.069204 313s 28 -0.1208393 0.053577 313s 29 -0.6033482 -0.176391 313s 30 1.1440521 -0.676183 313s 31 -0.5960920 -0.013765 313s 32 0.0519296 0.259855 313s 33 0.1861752 0.167779 313s 34 1.3802755 -0.632611 313s 35 -0.6542566 -0.173505 313s 36 0.5583690 0.392215 313s 37 0.0561384 0.230152 313s 38 0.1861752 0.167779 313s 39 0.1353472 0.241376 313s 40 0.5355195 0.197080 313s 41 -0.3980701 0.014294 313s 42 0.0277576 0.145332 313s 43 0.2979736 0.234120 313s 44 0.3049884 0.184614 313s 45 0.4889809 0.311684 313s 46 -0.0514512 0.134108 313s 47 -0.5224950 0.037063 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 0.57557 0.28218 313s ---------------------------------------------------------- 313s phosphor 18 2 2 220.403422 68.346121 313s Scores: 313s PC1 PC2 313s 1 4.04290 -15.3459 313s 2 -22.30489 -1.0004 313s 3 -24.52683 3.2836 313s 4 -12.54839 -6.0848 313s 5 -19.37044 2.2979 313s 6 15.20366 -19.9424 313s 7 0.44222 -3.1379 313s 8 -10.64042 3.6933 313s 9 -11.67967 5.9670 313s 10 14.26805 -7.0221 313s 11 -4.98832 1.5268 313s 12 8.74986 7.9379 313s 13 12.26290 6.0251 313s 14 6.27607 7.5768 313s 15 17.53246 3.1560 313s 16 -10.17024 -5.8994 313s 17 21.05826 5.4492 313s 18 16.39281 11.5191 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 14.8460 8.2672 313s ---------------------------------------------------------- 313s stackloss 21 3 3 99.576089 19.581136 313s Scores: 313s PC1 PC2 PC3 313s 1 20.15352 -4.359452 0.324585 313s 2 19.81554 -5.300468 0.308294 313s 3 15.45222 -1.599136 -0.203125 313s 4 2.40370 -0.145282 2.370302 313s 5 1.89538 0.070566 0.448061 313s 6 2.14954 -0.037358 1.409182 313s 7 4.43153 5.500810 2.468051 313s 8 4.43153 5.500810 2.468051 313s 9 -1.47521 1.245404 2.511773 313s 10 -5.11183 -4.802083 -2.407870 313s 11 -2.07009 3.667055 -2.261247 313s 12 -2.66223 2.833964 -3.238659 313s 13 -4.43589 -2.920053 -2.375287 313s 14 -0.46404 7.323193 -1.234961 313s 15 -9.31959 6.232579 -0.056064 313s 16 -10.33350 3.409533 -0.104938 313s 17 -14.81094 -9.872607 0.628103 313s 18 -12.44514 -3.285499 0.742143 313s 19 -11.85300 -2.452408 1.719555 313s 20 -5.73994 -2.494520 0.098250 313s 21 9.98843 1.484952 -3.614198 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 9.9788 4.4251 1.8986 313s ---------------------------------------------------------- 313s salinity 28 3 3 11.410736 7.075409 313s Scores: 313s PC1 PC2 PC3 313s 1 -0.937789 -2.40535 0.812909 313s 2 -1.752631 -2.57774 2.004437 313s 3 -6.509364 -0.78762 -1.821906 313s 4 -5.619847 -2.41333 -1.586891 313s 5 -7.268242 1.61012 1.563568 313s 6 -4.316558 -3.20411 0.029376 313s 7 -2.379545 -3.32371 0.703101 313s 8 0.013514 -3.50586 1.260502 313s 9 0.265262 -0.16736 -2.886883 313s 10 1.890755 2.43623 -0.986832 313s 11 0.804196 2.56656 0.387577 313s 12 0.935082 -1.03559 -0.074081 313s 13 1.814839 -1.61087 0.612290 313s 14 3.407535 -0.15880 2.026088 313s 15 1.731273 2.95159 -1.840286 313s 16 -6.129708 7.21368 2.632273 313s 17 -0.645124 1.06260 0.028697 313s 18 -1.307532 -2.54679 -0.280273 313s 19 0.483455 -0.55896 -3.097281 313s 20 2.053267 0.47308 -1.858703 313s 21 3.277664 -1.31002 0.453753 313s 22 4.631644 -0.78005 1.519894 313s 23 1.864403 5.32790 -0.849694 313s 24 0.623899 4.29317 0.056461 313s 25 1.301696 0.37871 -0.646220 313s 26 2.852126 -0.79527 -0.347711 313s 27 4.134051 -0.92756 0.449222 313s 28 4.781679 -0.20467 1.736616 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 3.3780 2.6600 1.4836 313s ---------------------------------------------------------- 313s hbk 75 3 3 216.162129 1.981077 313s Scores: 313s PC1 PC2 PC3 313s 1 26.2072 -0.660756 0.503340 313s 2 27.0406 -0.108506 -0.225059 313s 3 28.8351 -1.683721 0.263078 313s 4 29.9221 -0.812174 -0.674480 313s 5 29.3181 -0.909915 -0.121600 313s 6 27.5360 -0.599697 0.916574 313s 7 27.6617 -0.073753 0.676620 313s 8 26.5576 -0.882312 0.159620 313s 9 28.8726 -1.074223 -0.673462 313s 10 27.6643 -1.463829 -0.868593 313s 11 34.2019 -0.664473 -0.567265 313s 12 35.4805 -2.730949 -0.259320 313s 13 34.7544 1.325449 0.749884 313s 14 38.9522 8.171389 0.034382 313s 15 -5.5375 0.390704 1.679172 313s 16 -7.4319 0.803850 1.925633 313s 17 -8.5880 0.957577 -1.010312 313s 18 -6.6022 -0.425109 0.625148 313s 19 -6.5596 1.154721 -0.640680 313s 20 -5.2525 0.812527 1.377832 313s 21 -6.2771 0.067747 0.958907 313s 22 -6.2501 1.325491 -1.104428 313s 23 -7.2419 0.839808 0.728712 313s 24 -7.6489 1.131606 0.154897 313s 25 -9.0763 -0.670721 -0.167577 313s 26 -5.5967 0.999411 -0.810000 313s 27 -5.1460 -0.339018 1.326712 313s 28 -7.1659 -0.993461 0.125933 313s 29 -8.2104 -0.169338 -0.073569 313s 30 -6.2499 -1.689222 -0.877481 313s 31 -7.3180 -0.225795 1.687204 313s 32 -7.9446 1.473868 -0.541790 313s 33 -6.3604 1.237472 0.061800 313s 34 -8.9812 -0.710662 -0.830422 313s 35 -5.1698 -0.435484 1.102817 313s 36 -5.9995 -0.058135 -0.713550 313s 37 -5.8753 0.852882 -1.610556 313s 38 -8.4501 0.334363 0.404813 313s 39 -8.1751 -1.300317 0.633282 313s 40 -7.4495 0.672712 -0.829815 313s 41 -5.6213 -1.106765 1.395315 313s 42 -6.8571 -0.900977 -1.509937 313s 43 -7.0633 1.987372 -1.079934 313s 44 -6.3763 -1.867647 -0.251224 313s 45 -8.6456 -0.866053 0.630132 313s 46 -6.5356 -1.763526 -0.189838 313s 47 -8.2224 -1.183284 1.615150 313s 48 -5.6136 -1.100704 1.079239 313s 49 -5.9907 0.220336 1.443387 313s 50 -5.2675 0.142923 0.194023 313s 51 -7.9324 0.324710 1.113289 313s 52 -7.5544 -1.033884 1.792496 313s 53 -6.7119 -1.712257 -1.711778 313s 54 -7.4679 1.856542 0.046658 313s 55 -7.4666 1.161504 -0.725948 313s 56 -6.7110 1.574868 0.534288 313s 57 -8.2571 -0.399824 0.521995 313s 58 -5.9781 1.312567 0.926790 313s 59 -5.6960 -0.394338 -0.332938 313s 60 -6.1017 -0.797579 -1.679359 313s 61 -5.2628 0.919128 -1.436156 313s 62 -9.1245 -0.516135 -0.229065 313s 63 -7.7140 1.659145 0.068510 313s 64 -4.9886 0.173613 0.865810 313s 65 -6.6157 -1.479786 0.098390 313s 66 -7.9511 0.772770 -0.998321 313s 67 -7.1856 0.459602 0.216588 313s 68 -8.7345 -0.860784 -1.238576 313s 69 -8.5833 -0.313481 0.832074 313s 70 -5.8642 -0.142883 -0.870064 313s 71 -5.8879 0.186456 0.464467 313s 72 -7.1865 0.497156 -0.826767 313s 73 -6.8671 -0.058606 -1.335842 313s 74 -7.1398 0.727642 -1.422331 313s 75 -7.2696 -1.347832 -1.496927 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 14.70245 1.40751 0.95725 313s ---------------------------------------------------------- 313s milk 86 8 8 15.940298 2.771345 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 PC6 PC7 313s 1 6.471620 1.031110 0.469432 0.5736412 1.0294362 -0.6054039 -0.2005117 313s 2 7.439545 0.320597 0.081922 -0.6305898 0.7128977 -1.1601053 -0.1170582 313s 3 1.240654 -1.840458 0.520870 -0.1717469 0.2752079 -0.3815506 0.6004089 313s 4 5.952685 -1.856375 1.638710 0.3358626 -0.5834205 -0.0665348 -0.1580799 313s 5 -0.706973 0.261795 0.423736 0.2916399 -0.5307716 -0.3325563 -0.0062349 313s 6 2.524050 0.293380 -0.572997 0.2466367 -0.3497882 0.0386014 -0.1418131 313s 7 3.136085 -0.050202 -0.818165 -0.0451560 -0.5226337 -0.1597194 0.1669050 313s 8 3.260390 0.312365 -0.110776 0.4908006 -0.5225353 -0.1972222 -0.1068433 313s 9 -0.808914 -2.355785 1.344204 -0.4743284 -0.1394914 -0.1390080 -0.2620731 313s 10 -2.511226 -0.995321 -0.087218 -0.5950040 0.4268321 0.2561918 0.0891170 313s 11 -9.204096 -0.598364 1.587275 0.0833647 0.1865626 0.0358228 0.0920394 313s 12 -12.946774 1.951332 -0.179186 0.2560603 0.1300954 -0.1179820 -0.0999494 313s 13 -10.011603 0.726323 -2.102423 -1.3105560 0.3291550 0.0660007 -0.0794410 313s 14 -11.983644 0.768224 -0.532227 -0.5161201 -0.0817164 -0.4358934 -0.1734612 313s 15 -10.465714 -0.704271 2.035437 0.3713778 -0.0564830 -0.2696432 -0.1940091 313s 16 -2.527619 -0.286939 0.354497 0.8571223 0.1585009 0.2272835 0.4386955 313s 17 -0.514527 -2.895087 1.657181 0.2208239 0.1961109 0.1280496 -0.0182491 313s 18 -1.763931 0.854269 -0.686282 0.2848209 -0.4813608 -0.2623962 0.4757030 313s 19 -1.538419 -0.866477 1.103818 0.3874507 0.2086661 0.1267277 0.2354264 313s 20 0.732842 -1.455594 1.097358 -0.2530588 -0.0302385 0.2654274 0.6093330 313s 21 -2.530155 1.932885 -0.873095 0.6202295 -0.4153607 0.0048383 0.0067484 313s 22 -0.772646 0.675846 -0.259539 0.4844670 -0.0893266 -0.2785557 -0.0424662 313s 23 0.185417 1.413719 0.066135 1.1014470 0.0468093 0.0288637 0.2539994 313s 24 -0.280536 0.908864 0.113221 1.3370381 0.3289929 0.2588134 -0.0356289 313s 25 -3.503626 1.971233 0.203620 1.1975494 -0.3175317 0.1149685 0.0584396 313s 26 -0.639313 1.175503 0.403906 0.9082134 -0.2648165 -0.1238813 -0.0174853 313s 27 -2.923327 -0.365168 0.149478 0.8201430 -0.1544609 -0.4856934 -0.0058424 313s 28 2.505633 3.050292 -0.554424 2.1416405 -0.0378764 0.1002280 -0.3888580 313s 29 4.649504 1.054863 -0.081018 1.1454466 0.1502080 0.4967323 0.0879775 313s 30 1.049282 1.355215 -0.142701 0.7805566 -0.2059790 0.0193142 0.0815524 313s 31 1.962583 1.595396 -2.050642 0.3556747 0.1384801 0.1197984 0.1608247 313s 32 1.554846 0.095644 -1.423054 -0.3175620 0.4260008 -0.1612463 -0.0567196 313s 33 2.248977 0.010348 -0.062469 0.6388269 0.2098648 0.1330250 0.0906704 313s 34 0.993109 -0.828812 0.284059 0.3446686 0.1899096 -0.0515571 -0.2281197 313s 35 -0.335103 1.614093 -0.920661 1.2502617 0.2435013 0.1264875 0.0469238 313s 36 4.346795 1.208134 0.368889 1.1429977 -0.1362052 -0.0158169 -0.0183852 313s 37 0.992634 2.013738 -1.350619 0.8714694 0.0057776 -0.2122691 0.1760918 313s 38 2.213341 1.706516 -0.705418 1.2670281 -0.0707149 0.0670467 -0.1863588 313s 39 -1.213255 0.644062 0.163988 1.1213961 0.2945355 0.1093574 0.0019574 313s 40 3.942604 -1.704266 0.660327 0.1618506 0.4259076 0.0070193 0.3462765 313s 41 4.262054 1.687193 0.351875 0.5396477 1.0052810 -0.9331689 0.0056063 313s 42 6.865198 -1.091248 1.153585 1.1248797 0.0873276 0.2565221 0.0333265 313s 43 3.476720 0.555449 -1.030771 -0.3015720 -0.1748109 -0.1584968 0.4079902 313s 44 5.691730 -0.141240 0.565189 0.3174238 0.6478440 1.0579977 -0.5387916 313s 45 0.327134 0.152011 -0.394798 0.4998430 0.1599781 0.3159518 0.1623656 313s 46 0.280225 1.569387 -0.100397 1.2800976 0.0446645 0.0946513 0.0461599 313s 47 3.119928 -0.384834 -3.325600 -1.8865310 -0.1334744 0.1249987 -0.2561273 313s 48 0.501542 0.739816 -1.384556 -0.1244721 0.2948958 0.4836170 -0.1182802 313s 49 -1.953218 0.269986 -1.726474 -0.8510637 0.5047958 0.4860651 0.2318735 313s 50 3.706878 -2.400570 1.361047 -0.4949076 0.2180352 0.4080879 0.1156540 313s 51 -1.060358 -0.521609 -1.387412 -1.2767491 -0.0521356 0.1665452 -0.0044412 313s 52 -4.900528 0.157011 -1.015880 -0.9941168 0.2069608 0.3239762 -0.1921715 313s 53 -0.388496 0.062051 -0.643721 -0.8544141 -0.1857141 0.0063293 0.2664606 313s 54 0.109234 -0.018709 -0.242825 -0.2064701 -0.0585165 0.1720867 0.1117397 313s 55 1.176175 0.644539 -0.373694 0.0038605 -0.3436524 0.0194450 -0.0838883 313s 56 0.407259 -0.606637 0.222915 -0.3622451 -0.0737834 0.0228104 0.0297333 313s 57 -1.022756 -0.071860 0.741957 0.2273628 -0.1388444 -0.2396467 -0.2327738 313s 58 0.245419 1.167059 0.225934 0.8318795 -0.5365166 -0.0090816 -0.1680757 313s 59 -1.300617 -1.110325 -0.262740 -0.8857801 -0.0816954 -0.1186886 -0.0928322 313s 60 -1.110561 -0.832357 -0.212713 -0.4754481 -0.4105982 -0.1886992 -0.0602872 313s 61 0.381831 -1.475116 0.601047 -0.6260156 -0.1854501 -0.1749306 -0.0013904 313s 62 2.734462 -1.887861 0.813453 -0.5856987 0.2310656 0.1117041 -0.0293373 313s 63 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 313s 64 3.092464 -0.172602 0.017725 0.4874693 -0.5428206 0.0151218 -0.0683340 313s 65 0.004744 -2.712679 1.178987 -0.6677199 0.0208119 0.0621903 -0.0655693 313s 66 -2.014851 -1.060090 -0.099959 -0.7225044 -0.1947648 -0.2282902 -0.0505015 313s 67 0.621739 -1.296106 0.255632 -0.3309504 -0.0880200 0.2524306 0.1465779 313s 68 -0.271385 -1.709161 -1.100349 -2.0937671 0.2166264 0.0191278 0.0114174 313s 69 -0.326350 -0.737232 0.021639 -0.3850383 -0.4338287 0.2156624 0.1597594 313s 70 4.187093 9.708082 4.632803 -4.9751240 -0.0881576 0.2392433 0.0568049 313s 71 -1.868507 -1.600166 0.436353 -0.8078214 -0.1530893 0.0479471 -0.1999893 313s 72 2.768081 -0.556824 -0.148923 -0.3197853 -0.5524427 0.0907804 -0.0694488 313s 73 -1.441846 -2.735114 -0.294134 -1.2172969 0.0109453 -0.0562910 0.1505788 313s 74 -10.995490 0.615992 1.950966 1.1687190 0.2798335 0.2713257 0.0652135 313s 75 0.508992 -2.363945 -0.407064 -0.9522316 0.1040307 0.1088110 -0.7368484 313s 76 -1.015714 -0.307662 -1.088162 -1.0181862 -0.0440888 -0.1362208 0.0271200 313s 77 -8.028891 -0.580763 0.933638 0.4619362 0.3379832 -0.1368644 -0.0669441 313s 78 1.763308 -1.336175 -0.127809 -0.7161775 -0.1904861 -0.0900461 0.0037539 313s 79 0.208944 -0.580698 -0.626297 -0.7620610 -0.0262368 -0.2928202 0.0285908 313s 80 -3.230608 1.251352 0.195280 0.8687004 0.1812011 0.2600692 -0.1516375 313s 81 1.498160 0.669731 -0.266114 0.3772866 -0.2769688 -0.1066593 -0.1608395 313s 82 3.232051 -1.776018 0.485524 0.1170945 0.0557260 0.2219872 0.1187681 313s 83 2.999977 -0.228275 -0.467724 -0.4287672 0.0494902 -0.2337809 -0.0718159 313s 84 1.238083 0.320956 -1.806006 -1.0142266 0.2359630 -0.0857149 0.0593938 313s 85 1.276376 -2.081214 2.540850 0.3745805 -0.2596482 -0.1228412 -0.2199985 313s 86 0.930715 0.836457 -1.385153 -0.6074929 -0.2476354 0.1680713 -0.0117324 313s PC8 313s 1 9.0765e-04 313s 2 2.1811e-04 313s 3 1.1834e-03 313s 4 8.4077e-05 313s 5 9.9209e-04 313s 6 1.6277e-03 313s 7 2.4907e-04 313s 8 6.8383e-04 313s 9 -5.0924e-04 313s 10 3.1215e-04 313s 11 3.0654e-04 313s 12 -1.1951e-03 313s 13 -1.2849e-03 313s 14 -9.0801e-04 313s 15 -1.2686e-03 313s 16 -1.8441e-03 313s 17 -2.1068e-03 313s 18 -5.7816e-04 313s 19 -1.2330e-03 313s 20 3.3857e-05 313s 21 3.8623e-04 313s 22 1.3035e-04 313s 23 -3.8648e-04 313s 24 -1.7400e-04 313s 25 -3.9196e-04 313s 26 -7.6996e-04 313s 27 -4.8042e-04 313s 28 -2.0628e-04 313s 29 -4.5672e-04 313s 30 -1.4716e-04 313s 31 -4.6385e-05 313s 32 -2.0481e-04 313s 33 -3.0020e-04 313s 34 -5.8179e-05 313s 35 1.3870e-04 313s 36 -6.7177e-04 313s 37 -3.0799e-04 313s 38 6.2140e-04 313s 39 4.5912e-04 313s 40 -3.7165e-04 313s 41 -5.4362e-04 313s 42 -1.0155e-03 313s 43 1.3449e-04 313s 44 -5.4761e-04 313s 45 1.0300e-03 313s 46 1.1039e-03 313s 47 -6.4858e-04 313s 48 -7.6886e-05 313s 49 3.2590e-04 313s 50 8.6845e-05 313s 51 4.9423e-04 313s 52 9.2973e-04 313s 53 4.4342e-04 313s 54 4.9888e-04 313s 55 7.2171e-04 313s 56 -3.2133e-05 313s 57 -1.8101e-04 313s 58 -5.4969e-06 313s 59 -8.3841e-04 313s 60 5.9446e-05 313s 61 -6.5683e-05 313s 62 -3.4073e-04 313s 63 -6.5145e-04 313s 64 -6.5145e-04 313s 65 1.4986e-04 313s 66 2.8096e-04 313s 67 -6.5170e-05 313s 68 -1.3775e-04 313s 69 6.8225e-06 313s 70 -1.6290e-04 313s 71 3.9009e-04 313s 72 -1.3981e-04 313s 73 6.2613e-04 313s 74 2.6513e-03 313s 75 3.7088e-04 313s 76 9.9539e-04 313s 77 1.2979e-03 313s 78 5.6500e-04 313s 79 3.0940e-04 313s 80 8.7993e-04 313s 81 -3.1353e-04 313s 82 4.9625e-04 313s 83 -6.3951e-04 313s 84 -4.5582e-04 313s 85 5.9440e-04 313s 86 -3.6234e-04 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 3.99253025 1.66473582 1.10660264 0.96987790 0.33004256 0.29263512 0.20843280 313s [8] 0.00074024 313s ---------------------------------------------------------- 313s bushfire 38 5 5 38435.075910 1035.305774 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 313s 1 -111.9345 4.9970 -1.00881 -1.224361 3.180569 313s 2 -113.4128 7.4784 -0.79170 -0.235184 2.385812 313s 3 -105.8364 10.9615 -3.15662 -0.251662 1.017328 313s 4 -89.1684 8.7232 -6.15080 -0.075611 1.431111 313s 5 -58.7216 -1.9543 -12.70661 -0.151328 1.425570 313s 6 -35.0370 -12.8434 -17.06841 -0.525664 3.499743 313s 7 -250.2123 -49.4348 23.31261 -19.070238 0.647348 313s 8 -292.6877 -69.7708 -21.30815 13.093808 -1.288764 313s 9 -294.0765 -70.9903 -23.96326 14.940985 -0.939076 313s 10 -290.0193 -57.3747 3.51346 1.858995 0.083107 313s 11 -289.8168 -43.3207 16.08046 -1.745099 -1.506042 313s 12 -290.8645 6.2503 40.52173 -7.496479 -0.033767 313s 13 -232.6865 41.8090 37.19429 -1.280348 -0.470837 313s 14 9.8483 25.1954 -14.56970 0.538484 1.772046 313s 15 137.1924 11.8521 -37.12452 -5.130459 -0.586695 313s 16 92.9804 10.3923 -24.97267 -7.551314 -1.867125 313s 17 90.4493 10.5630 -21.92735 -5.669651 -1.001362 313s 18 78.6325 5.2211 -19.74718 -6.107880 -1.939986 313s 19 82.1178 3.6913 -21.37810 -4.259855 -1.278838 313s 20 92.9044 7.1961 -21.22900 -4.125571 -0.127089 313s 21 74.9157 10.2991 -16.60924 -5.660751 -0.406343 313s 22 66.7350 12.0460 -16.73298 -4.669080 1.333436 313s 23 -62.1981 22.7394 6.03613 -5.182356 -0.453624 313s 24 -116.5696 32.3182 12.74846 -1.465657 -0.097851 313s 25 -53.8907 22.4278 -2.18861 -2.742014 -0.990071 313s 26 -60.6384 20.2952 -3.05206 -2.953685 -0.629061 313s 27 -74.7621 28.9067 -0.65817 1.473357 -0.443957 313s 28 -50.2202 37.3457 -1.44989 5.530426 -1.073521 313s 29 -38.7483 50.2749 2.34469 10.156457 -0.416262 313s 30 -93.3887 51.7884 20.08872 8.798781 -1.620216 313s 31 35.3096 41.7158 13.46272 14.464358 -0.475973 313s 32 290.8493 3.5924 7.41501 15.244293 2.141354 313s 33 326.7236 -29.8194 15.64898 2.612061 0.064931 313s 34 322.9095 -30.6372 16.21520 1.248005 -0.711322 313s 35 328.5307 -29.9533 16.49656 1.138916 0.974792 313s 36 325.6791 -30.6990 16.83840 -0.050949 -1.211360 313s 37 323.8136 -30.7474 19.55764 -1.545150 -0.267580 313s 38 325.2991 -30.5350 20.31878 -1.928580 -0.120425 313s ------------- 313s Call: 313s PcaClassic(x = x) 313s 313s Standard deviations: 313s [1] 196.0487 32.1762 18.4819 6.9412 1.3510 313s ---------------------------------------------------------- 313s ========================================================== 313s > dodata(method="hubert.mcd") 313s 313s Call: dodata(method = "hubert.mcd") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 2 358.175786 4.590630 313s Scores: 313s PC1 PC2 313s 1 -12.2285 0.86283 313s 2 -68.9906 -7.43256 313s 3 -5.7035 -1.53793 313s 4 -1.8988 2.90891 313s 5 -24.0044 -2.68946 313s 6 9.9115 8.43321 313s 7 -11.0210 1.77484 313s 8 25.1826 -1.31573 313s 9 -3.2809 -0.74345 313s 10 23.8200 -0.93701 313s 11 9.1344 1.67701 313s 12 -53.6607 -5.08826 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 18.9255 2.1426 313s ---------------------------------------------------------- 313s starsCYG 47 2 2 0.280653 0.005921 313s Scores: 313s PC1 PC2 313s 1 -0.285731 -0.0899858 313s 2 -0.819689 0.0153191 313s 3 0.028077 -0.1501882 313s 4 -0.819689 0.0153191 313s 5 -0.234971 -0.1526225 313s 6 -0.527231 -0.0382380 313s 7 0.372118 -0.5195605 313s 8 -0.357448 0.1009508 313s 9 -0.603553 -0.2533541 313s 10 -0.177170 -0.0722541 313s 11 -0.637339 -1.0390758 313s 12 -0.512526 -0.0662337 313s 13 -0.490978 -0.0120517 313s 14 0.936868 -0.2550656 313s 15 0.684479 -0.0125787 313s 16 0.347708 0.0641382 313s 17 1.009966 -0.0202111 313s 18 0.742477 0.1286170 313s 19 0.773105 -0.0588983 313s 20 -0.795247 -1.0648673 313s 21 0.566048 -0.0319223 313s 22 0.723956 -0.0061308 313s 23 0.505616 0.0899297 313s 24 0.069956 0.0896997 313s 25 -0.080090 -0.0462652 313s 26 0.268755 0.0512425 313s 27 0.289710 -0.0770574 313s 28 0.038341 -0.0269216 313s 29 0.567463 -0.1026188 313s 30 -0.951542 -1.1005280 313s 31 0.512064 0.0504528 313s 32 -0.188059 0.1184850 313s 33 -0.288758 -0.0094200 313s 34 -1.190016 -1.1293460 313s 35 0.615197 -0.0846898 313s 36 -0.710930 0.0938781 313s 37 -0.183223 0.0888774 313s 38 -0.288758 -0.0094200 313s 39 -0.262177 0.0759816 313s 40 -0.630957 -0.0855773 313s 41 0.314679 0.0182135 313s 42 -0.130850 0.0163715 313s 43 -0.415248 0.0205825 313s 44 -0.407188 -0.0287636 313s 45 -0.620693 0.0376892 313s 46 -0.051896 0.0292672 313s 47 0.426662 0.0770340 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 0.529767 0.076946 313s ---------------------------------------------------------- 313s phosphor 18 2 2 285.985489 32.152099 313s Scores: 313s PC1 PC2 313s 1 -2.89681 -18.08811 313s 2 21.34021 -0.40854 313s 3 22.98065 4.13006 313s 4 12.33544 -6.72947 313s 5 17.99823 2.47611 313s 6 -13.35773 -24.10967 313s 7 -0.92957 -5.51314 313s 8 9.16061 2.71354 313s 9 9.89243 5.10403 313s 10 -14.12600 -11.17832 313s 11 3.84175 -0.17605 313s 12 -10.61905 4.37646 313s 13 -13.85065 2.01919 313s 14 -8.11927 4.34325 313s 15 -18.69805 -1.51673 313s 16 9.95352 -6.85784 313s 17 -22.49433 0.29387 313s 18 -18.66592 6.92359 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 16.9111 5.6703 313s ---------------------------------------------------------- 313s stackloss 21 3 3 78.703690 19.249085 313s Scores: 313s PC1 PC2 PC3 313s 1 -20.323997 10.26124 0.92041 313s 2 -19.761418 11.08797 0.92383 313s 3 -16.469919 6.43190 0.22593 313s 4 -4.171902 1.68262 2.50695 313s 5 -3.756174 1.40774 0.57004 313s 6 -3.964038 1.54518 1.53850 313s 7 -7.547376 -3.27780 2.48643 313s 8 -7.547376 -3.27780 2.48643 313s 9 -0.763294 -0.63699 2.53518 313s 10 4.214079 4.46296 -2.28315 313s 11 -0.849132 -2.97767 -2.31393 313s 12 -0.078689 -2.28838 -3.27896 313s 13 3.088921 2.80948 -2.28999 313s 14 -3.307313 -6.14718 -1.35916 313s 15 5.552354 -7.34201 -0.32057 313s 16 7.240091 -4.86180 -0.31031 313s 17 14.908334 6.84995 0.70603 313s 18 10.970281 1.06279 0.68209 313s 19 10.199838 0.37350 1.64712 313s 20 4.273564 1.99328 0.14526 313s 21 -11.992249 2.19025 -3.37391 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 8.8715 4.3874 2.1990 313s ---------------------------------------------------------- 313s salinity 28 3 3 11.651966 4.107426 313s Scores: 313s PC1 PC2 PC3 313s 1 1.68712 1.62591 0.19812128 313s 2 2.35772 2.37290 1.24965734 313s 3 6.80132 -2.14412 0.68142276 313s 4 6.41982 -0.61348 -0.31907921 313s 5 6.36697 -1.98030 4.87319903 313s 6 5.22050 1.20864 0.10252555 313s 7 3.34007 2.02950 0.00064329 313s 8 1.06220 2.89801 -0.35658064 313s 9 0.34692 -2.20572 -1.71677710 313s 10 -2.21421 -2.74842 0.76862599 313s 11 -1.40111 -2.16163 2.21124383 313s 12 -0.38242 0.32284 -0.23732191 313s 13 -1.12809 1.33152 -0.28800043 313s 14 -3.24998 1.35943 1.17514969 313s 15 -2.11006 -3.70114 0.45102357 313s 16 3.46920 -5.41242 8.56937909 313s 17 0.46682 -1.46753 1.48992481 313s 18 2.21807 0.99168 -0.61894625 313s 19 0.28525 -2.00333 -2.16450483 313s 20 -1.66639 -1.76768 -1.06946404 313s 21 -2.58106 1.23534 -0.65557612 313s 22 -4.15573 1.71244 0.08170141 313s 23 -3.07670 -4.87628 2.53200755 313s 24 -1.70808 -3.71657 2.99305849 313s 25 -1.08172 -1.05713 0.02468813 313s 26 -2.23187 0.27323 -0.85760867 313s 27 -3.50498 1.07657 -0.68503455 313s 28 -4.49819 1.43219 0.53416609 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 3.4135 2.0267 1.0764 313s ---------------------------------------------------------- 313s hbk 75 3 3 1.459908 1.201048 313s Scores: 313s PC1 PC2 PC3 313s 1 -31.105415 4.714217 10.4566165 313s 2 -31.707650 5.748724 10.7682402 313s 3 -33.366131 4.625897 12.1570167 313s 4 -34.173377 6.069657 12.4466895 313s 5 -33.780418 5.508823 11.9872893 313s 6 -32.493478 4.684595 10.5679819 313s 7 -32.592637 5.235522 10.3765493 313s 8 -31.293363 4.865797 10.9379676 313s 9 -33.160964 5.714260 12.3098920 313s 10 -31.919786 5.384537 12.3374332 313s 11 -38.231962 6.810641 13.5994385 313s 12 -39.290479 5.393906 15.2942554 313s 13 -39.418445 7.326461 11.5194898 313s 14 -43.906584 13.214819 8.3282743 313s 15 -1.906326 -0.716061 -0.8635112 313s 16 -0.263255 -0.926016 -1.9009292 313s 17 1.776489 1.072332 -0.5496140 313s 18 -0.464648 -0.702441 0.0482897 313s 19 -0.267826 1.283779 -0.2925812 313s 20 -2.122108 -0.165970 -0.8924686 313s 21 -0.937217 -0.548532 -0.4132196 313s 22 -0.423273 1.781869 -0.0323061 313s 23 -0.047532 -0.018909 -1.1259327 313s 24 0.490041 0.520202 -1.1065753 313s 25 2.143049 -0.720869 -0.0495474 313s 26 -1.094748 1.459175 0.2226246 313s 27 -2.070705 -0.898573 0.0023229 313s 28 0.294998 -0.830258 0.5929001 313s 29 1.242995 -0.300216 -0.2010507 313s 30 -0.147958 -0.439099 2.0003038 313s 31 -0.170818 -1.440946 -0.9755627 313s 32 0.958531 1.199730 -1.0129867 313s 33 -0.697307 0.874343 -0.7260649 313s 34 2.278946 -0.261106 0.4196544 313s 35 -1.962829 -0.809318 0.2033113 313s 36 -0.626631 0.600666 0.8004036 313s 37 -0.550885 1.881448 0.7382776 313s 38 1.249717 -0.336214 -0.9349845 313s 39 1.106696 -1.569418 0.1869576 313s 40 0.684034 0.939963 -0.1034965 313s 41 -1.559314 -1.551408 0.3660323 313s 42 0.538741 0.447358 1.6361099 313s 43 0.252685 2.080564 -0.7765259 313s 44 -0.217012 -1.027281 1.7015154 313s 45 1.497600 -1.349234 -0.2698932 313s 46 -0.100388 -1.026443 1.5390401 313s 47 0.811117 -2.195271 -0.5208141 313s 48 -1.462210 -1.321318 0.5600144 313s 49 -1.383976 -0.740714 -0.7348906 313s 50 -1.636773 0.215464 0.3195369 313s 51 0.530918 -0.759743 -1.2069247 313s 52 0.109566 -2.107455 -0.5315473 313s 53 0.564334 0.060847 2.3910630 313s 54 0.272234 1.122711 -1.5060028 313s 55 0.608660 1.197219 -0.5255609 313s 56 -0.565430 0.710345 -1.3708230 313s 57 1.115629 -0.888816 -0.4186014 313s 58 -1.351288 0.374815 -1.1980618 313s 59 -0.998016 0.151228 0.9007970 313s 60 -0.124017 0.764846 1.9005963 313s 61 -1.189858 1.905264 0.7721322 313s 62 2.190589 -0.579614 -0.1377914 313s 63 0.518278 0.931130 -1.4534768 313s 64 -2.124566 -0.194391 -0.0327092 313s 65 -0.154218 -1.050861 1.1309885 313s 66 1.197852 1.044147 -0.2265269 313s 67 0.114174 0.094763 -0.5168926 313s 68 2.201115 -0.032271 0.8573493 313s 69 1.307843 -1.104815 -0.7741270 313s 70 -0.691449 0.676665 1.0004603 313s 71 -1.150975 -0.050861 -0.0717068 313s 72 0.457293 0.861871 0.1026350 313s 73 0.392258 0.897451 0.9178065 313s 74 0.584658 1.450471 0.3201857 313s 75 0.972517 0.063777 1.8223995 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 1.2083 1.0959 1.0168 313s ---------------------------------------------------------- 313s milk 86 8 8 5.739740 2.405262 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 PC6 PC7 313s 1 -5.710924 -1.346213 0.01332091 -0.3709242 -0.566813 0.7529298 -1.2525433 313s 2 -6.578612 -0.440749 1.16354746 0.2870685 -0.573207 0.7368064 -1.6101427 313s 3 -0.720902 1.777381 -0.21532020 -0.3213950 0.287603 -0.4764464 -0.5638337 313s 4 -5.545889 1.621147 -0.85212883 0.4380154 0.022241 0.0718035 0.1176140 313s 5 1.323210 -0.143897 -0.78611461 0.5966857 0.043139 -0.0512545 -0.1419726 313s 6 -1.760792 -0.662792 0.46402240 0.2149752 0.130000 0.0797221 0.1916948 313s 7 -2.344198 -0.363657 0.92442296 0.3921371 0.241463 -0.2370967 0.0636268 313s 8 -2.556824 -0.680132 0.04339934 0.4635077 0.154136 0.0371259 0.0260340 313s 9 1.203234 2.712342 -1.00693092 0.1251739 0.170679 0.2231851 -0.0118196 313s 10 3.151858 1.255826 -0.01678562 -0.5087398 -0.087933 0.0115055 -0.0097828 313s 11 9.562891 1.580419 -2.65612113 -0.1748178 -0.153031 -0.0880112 -0.1648752 313s 12 13.617821 -0.999033 -1.92168237 0.0326918 -0.038488 0.0870082 -0.1809687 313s 13 10.958032 -0.097916 0.95915085 -0.2348663 0.147875 0.1219202 0.0419067 313s 14 12.675941 0.158747 -1.04153243 0.3117402 0.302036 0.1187749 -0.2310830 313s 15 10.726828 1.775339 -3.36786799 0.1285422 0.151594 0.0998947 -0.2028458 313s 16 3.042705 0.212589 -1.23921907 -0.5596596 0.277061 -0.5037073 0.0612182 313s 17 0.780071 2.990008 -1.58490147 -0.5441119 0.436485 -0.0603833 0.1016610 313s 18 2.523916 -0.923373 -0.03221722 0.3830822 0.208008 -0.5505270 -0.1252648 313s 19 1.990563 1.062648 -1.42038451 -0.3602257 -0.068006 -0.1932744 -0.1197842 313s 20 -0.243938 1.674555 -0.72225359 -0.1475652 -0.397855 -0.5385123 -0.0559660 313s 21 3.354424 -2.001060 -0.22542149 0.3346180 0.032502 -0.0953825 0.1293148 313s 22 1.477177 -0.777534 -0.35362339 0.1224412 0.203208 0.0514382 -0.2166274 313s 23 0.502055 -1.618511 -0.85013853 -0.1298862 -0.144328 -0.1941806 -0.1923681 313s 24 0.900504 -1.227820 -1.07180474 -0.5851197 0.112657 0.0467164 0.0405544 313s 25 4.161393 -1.869015 -1.54507759 0.2003123 -0.152582 -0.1382908 0.0864320 313s 26 1.277795 -1.185179 -1.13445511 0.2771556 -0.101901 0.0070037 -0.1279016 313s 27 3.447256 0.257652 -1.13407954 -0.0077859 0.853002 -0.1376443 -0.1897380 313s 28 -1.695730 -3.781876 -0.72940594 -0.0956421 0.064475 0.3665470 0.0726448 313s 29 -3.923610 -1.654818 -0.16117226 -0.4242302 -0.303749 -0.0209844 0.1723890 313s 30 -0.309616 -1.564739 -0.39909943 0.1657509 -0.178739 -0.0600221 -0.0571706 313s 31 -0.960838 -2.242733 1.50477679 -0.2957897 0.163758 -0.1034399 0.0257903 313s 32 -0.671285 -0.459839 1.39124475 -0.3669914 0.246127 0.2094780 -0.2681284 313s 33 -1.589089 -0.390812 -0.16505762 -0.3992573 0.086870 -0.0402114 -0.0399923 313s 34 -0.421868 0.636139 -0.42563447 -0.2985726 0.311365 0.2398515 -0.0540852 313s 35 1.118429 -2.116328 -0.22329747 -0.4864401 0.289927 -0.0503006 0.0101706 313s 36 -3.660291 -1.630831 -0.57876280 0.1294792 -0.260224 0.0912904 -0.1565668 313s 37 -0.087686 -2.530609 0.50076931 -0.0319873 0.194898 -0.1233526 -0.2494283 313s 38 -1.418620 -2.303011 -0.09405565 -0.0931745 0.169466 0.1581787 0.0850095 313s 39 1.815225 -0.838968 -1.10222194 -0.4897630 0.180933 0.0096330 -0.0600652 313s 40 -3.420975 1.398516 -0.17143314 -0.5852146 0.090464 -0.2066323 -0.2974177 313s 41 -3.462295 -1.795174 -0.17500650 -0.1610267 -0.595086 0.5981680 -1.5930268 313s 42 -6.401429 0.451242 -0.78723149 -0.4285618 0.055395 -0.0212476 0.0808936 313s 43 -2.583017 -0.871790 1.29937081 0.2422349 -0.190002 -0.2822972 -0.2625721 313s 44 -5.027244 -0.167503 -0.02382957 -0.8288929 -0.852207 0.7399343 0.4606076 313s 45 0.364494 -0.440380 -0.07746564 -0.4552133 0.095711 -0.1662998 0.1566706 313s 46 0.420706 -1.880819 -0.82180986 -0.1823454 -0.022661 -0.0304227 -0.0516440 313s 47 -1.932985 -0.120002 4.00934170 0.0930728 0.295428 0.2787446 0.3766231 313s 48 0.395402 -1.021393 1.07953292 -0.4599764 -0.132386 0.1895780 0.2771755 313s 49 2.886100 -0.276587 1.48851137 -0.6314648 -0.203963 -0.0891955 0.1347804 313s 50 -3.255379 2.479232 -0.37933775 -0.3651497 -0.415000 0.0045750 0.0671055 313s 51 1.939333 0.617579 1.57113225 0.0310866 -0.039226 0.0409183 0.1830694 313s 52 5.727154 0.275898 0.58814711 -0.1739820 -0.222791 0.2553797 0.1959402 313s 53 1.207873 0.131451 0.80899235 0.2872465 -0.353544 -0.1697200 -0.0987230 313s 54 0.612921 0.040062 0.17807459 -0.0053074 -0.202244 -0.0671788 0.0530276 313s 55 -0.399075 -0.727144 0.26196635 0.3657576 -0.192705 0.0903564 0.0641289 313s 56 0.240719 0.733792 -0.05030509 0.0967214 -0.186906 0.0310231 -0.0594812 313s 57 1.589641 0.289427 -1.02478822 0.2723190 -0.048378 0.2599262 -0.2040853 313s 58 0.423483 -1.262515 -0.85026016 0.4749963 -0.082647 0.0752412 0.1352259 313s 59 1.983684 1.335122 0.42593757 0.1345894 0.096456 0.1153107 -0.0385994 313s 60 1.770171 0.935428 0.14901569 0.3641973 0.274015 -0.0280119 0.0690244 313s 61 0.182845 1.706453 -0.18364654 0.2517421 -0.035773 0.0357087 -0.1363470 313s 62 -2.191617 1.966324 -0.03573689 -0.2203900 -0.235704 0.1682332 -0.1145174 313s 63 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 313s 64 -2.442239 -0.209694 -0.06681921 0.3184048 0.206772 -0.0608468 0.2425649 313s 65 0.407575 2.996346 -0.63021113 -0.1335795 0.087668 0.0627032 0.0486166 313s 66 2.660379 1.322824 0.10122110 0.2420451 0.192938 0.0344019 -0.0771918 313s 67 -0.032273 1.315299 -0.04511689 -0.1293380 -0.025923 -0.1655965 0.1887534 313s 68 1.117637 2.005809 1.97078787 -0.0429209 -0.176568 0.1634287 -0.0916254 313s 69 0.970730 0.837158 0.01621375 0.2347502 -0.071757 -0.2464626 0.2907551 313s 70 -2.688271 -5.335891 -0.64225481 4.1819517 -9.523550 2.0943027 -2.8098426 313s 71 2.428718 1.976051 -0.24749122 0.1308738 0.018276 0.1711292 0.1346284 313s 72 -2.061944 0.405943 0.50472914 0.4393739 -0.056420 -0.0031558 0.2663880 313s 73 2.029606 2.874991 0.68310320 -0.2067254 0.511537 -0.2010371 0.0805608 313s 74 11.293757 0.328931 -3.84783031 -0.4130266 -0.210499 -0.1103148 -0.0381326 313s 75 0.120896 2.287914 0.83639076 -0.2462845 0.551353 0.6629701 0.3789055 313s 76 1.859499 0.422019 1.18435547 0.1546108 0.017266 0.0470615 -0.1071011 313s 77 8.435857 1.147499 -2.19924186 -0.4156770 0.386548 0.0294075 -0.1911399 313s 78 -1.090858 1.311287 0.62897430 0.1727009 0.077341 0.0135972 -0.0096934 313s 79 0.560012 0.623617 0.83727267 0.1680787 0.087477 0.0611949 -0.2588084 313s 80 3.873817 -1.133641 -1.27469019 -0.2717298 -0.165066 0.1696232 0.0635047 313s 81 -0.758664 -0.880260 0.00057124 0.2838720 0.016243 0.1527299 -0.0150514 313s 82 -2.709588 1.464049 -0.12598126 -0.3828567 0.213647 -0.1425385 0.1552827 313s 83 -2.213670 0.059563 0.87565603 0.1255703 -0.082005 0.2189829 -0.2938264 313s 84 -0.242242 -0.483552 2.05089334 -0.0681005 -0.101578 0.1304632 -0.2218093 313s 85 -1.032129 2.375018 -2.19321259 0.2332079 -0.066379 0.1854598 -0.0873859 313s 86 0.015327 -0.948155 1.39530555 0.2701225 -0.268889 0.0578145 0.1608678 313s PC8 313s 1 2.1835e-03 313s 2 1.6801e-03 313s 3 1.6623e-03 313s 4 2.6286e-04 313s 5 9.5884e-04 313s 6 1.4430e-03 313s 7 1.8784e-04 313s 8 6.8473e-04 313s 9 -6.8490e-04 313s 10 1.1565e-04 313s 11 5.6907e-06 313s 12 -1.8395e-03 313s 13 -2.1582e-03 313s 14 -1.6294e-03 313s 15 -1.6964e-03 313s 16 -1.9664e-03 313s 17 -2.2448e-03 313s 18 -6.5884e-04 313s 19 -1.1536e-03 313s 20 2.6887e-04 313s 21 3.3199e-05 313s 22 1.1170e-04 313s 23 -1.7617e-04 313s 24 -2.1577e-04 313s 25 -6.1495e-04 313s 26 -7.2903e-04 313s 27 -6.8773e-04 313s 28 -2.0742e-04 313s 29 -2.6937e-04 313s 30 -6.7472e-05 313s 31 -1.3222e-04 313s 32 -1.6516e-04 313s 33 -1.8836e-04 313s 34 -1.1273e-04 313s 35 3.0703e-05 313s 36 -3.0311e-04 313s 37 -1.9380e-04 313s 38 5.5526e-04 313s 39 4.1987e-04 313s 40 8.4807e-05 313s 41 8.8725e-04 313s 42 -6.5647e-04 313s 43 4.3202e-04 313s 44 -5.3330e-04 313s 45 8.9161e-04 313s 46 1.1588e-03 313s 47 -1.2714e-03 313s 48 -4.0376e-04 313s 49 4.1280e-06 313s 50 3.0116e-04 313s 51 5.8510e-05 313s 52 3.3236e-04 313s 53 4.0982e-04 313s 54 4.0428e-04 313s 55 6.1600e-04 313s 56 -4.0496e-05 313s 57 -1.8342e-04 313s 58 -1.6748e-04 313s 59 -1.0894e-03 313s 60 -2.6876e-04 313s 61 -5.8951e-05 313s 62 -1.5517e-04 313s 63 -7.9933e-04 313s 64 -7.9933e-04 313s 65 2.2592e-05 313s 66 2.4984e-05 313s 67 -2.2714e-04 313s 68 -3.3991e-04 313s 69 -3.0375e-04 313s 70 3.4033e-03 313s 71 2.3288e-05 313s 72 -3.4126e-04 313s 73 2.5528e-04 313s 74 2.2760e-03 313s 75 -2.8985e-04 313s 76 7.9077e-04 313s 77 9.4636e-04 313s 78 4.9099e-04 313s 79 3.0501e-04 313s 80 6.5280e-04 313s 81 -3.6570e-04 313s 82 4.9966e-04 313s 83 -4.3245e-04 313s 84 -4.6152e-04 313s 85 7.4691e-04 313s 86 -6.1103e-04 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 2.39577535 1.55089079 0.92557331 0.33680677 0.19792033 0.17855133 0.16041702 313s [8] 0.00054179 313s ---------------------------------------------------------- 313s bushfire 38 5 5 31248.552973 358.974577 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 313s 1 155.972 1.08098 -23.31135 -1.93015 1.218941 313s 2 157.738 0.35648 -20.95658 -2.42375 0.466415 313s 3 150.667 2.12545 -16.20395 -2.00140 -0.582924 313s 4 133.892 5.25124 -15.88873 -2.78469 -0.275261 313s 5 102.462 13.00611 -21.54096 -4.69409 -0.944176 313s 6 77.694 18.75377 -28.71865 -6.44244 0.446350 313s 7 286.266 -11.36184 -98.67134 10.95233 -3.625338 313s 8 326.627 29.92767 -112.60824 -29.26330 -13.710094 313s 9 327.898 32.39553 -113.34314 -31.65905 -13.830781 313s 10 325.131 5.81628 -105.58927 -13.45695 -8.987971 313s 11 326.458 -7.84562 -94.25242 -6.11547 -8.572845 313s 12 333.171 -37.69907 -50.89207 8.98187 -1.742979 313s 13 279.789 -40.78415 -8.06209 7.65884 0.181748 313s 14 37.714 10.54231 13.46530 -1.55051 2.102662 313s 15 -90.034 34.68964 18.98186 0.69260 0.417573 313s 16 -46.492 23.65086 10.07282 4.36090 -0.748517 313s 17 -43.990 20.36443 9.61049 2.83084 -0.127983 313s 18 -32.938 19.11199 2.64850 2.92879 -1.473988 313s 19 -36.555 20.60142 2.01879 0.63832 -1.235075 313s 20 -46.837 19.89630 6.65142 0.89120 0.271108 313s 21 -28.670 15.29534 6.59311 3.29638 0.402194 313s 22 -20.331 15.06559 7.33721 2.16591 2.006327 313s 23 108.644 -7.92707 -1.45130 6.27388 0.356715 313s 24 163.697 -16.15568 0.61663 4.24231 0.464415 313s 25 100.471 -0.30739 0.87762 2.86452 -0.692735 313s 26 106.922 0.90864 -1.91436 2.54557 -0.565023 313s 27 121.966 -3.29641 4.85626 -0.47676 -0.490047 313s 28 98.650 -4.51455 16.64160 -3.08996 -0.839397 313s 29 88.795 -10.85457 30.46708 -5.37360 0.315657 313s 30 142.981 -27.89100 22.40713 -1.67126 -0.680158 313s 31 14.125 -21.60028 29.80480 -8.25272 -0.019693 313s 32 -244.044 -11.76430 24.53390 -12.52294 2.022312 313s 33 -283.842 -13.21931 -6.23565 -2.63367 -0.080728 313s 34 -280.168 -13.41903 -7.69318 -1.24571 -0.722513 313s 35 -285.666 -13.78452 -6.50318 -1.23756 1.074669 313s 36 -282.938 -13.82281 -7.63902 0.20435 -0.971673 313s 37 -281.129 -16.20408 -8.57154 1.85797 0.234486 313s 38 -282.589 -16.91969 -8.36010 2.35589 0.490630 313s ------------- 313s Call: 313s PcaHubert(x = x, k = p) 313s 313s Standard deviations: 313s [1] 176.77260 18.94662 16.21701 3.95755 0.92761 313s ---------------------------------------------------------- 313s ========================================================== 313s > dodata(method="hubert") 313s 313s Call: dodata(method = "hubert") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 1 315.227002 NA 313s Scores: 313s PC1 313s 1 13.2197 313s 2 69.9817 313s 3 6.6946 313s 4 2.8899 313s 5 24.9956 313s 6 -8.9203 313s 7 12.0121 313s 8 -24.1915 313s 9 4.2721 313s 10 -22.8289 313s 11 -8.1433 313s 12 54.6519 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 17.755 313s ---------------------------------------------------------- 313s starsCYG 47 2 1 0.308922 NA 313s Scores: 313s PC1 313s 1 0.224695 313s 2 0.758653 313s 3 -0.089113 313s 4 0.758653 313s 5 0.173934 313s 6 0.466195 313s 7 -0.433154 313s 8 0.296411 313s 9 0.542517 313s 10 0.116133 313s 11 0.576303 313s 12 0.451490 313s 13 0.429942 313s 14 -0.997904 313s 15 -0.745515 313s 16 -0.408745 313s 17 -1.071002 313s 18 -0.803514 313s 19 -0.834141 313s 20 0.734210 313s 21 -0.627085 313s 22 -0.784992 313s 23 -0.566652 313s 24 -0.130992 313s 25 0.019053 313s 26 -0.329791 313s 27 -0.350747 313s 28 -0.099378 313s 29 -0.628499 313s 30 0.890506 313s 31 -0.573100 313s 32 0.127022 313s 33 0.227721 313s 34 1.128979 313s 35 -0.676234 313s 36 0.649894 313s 37 0.122186 313s 38 0.227721 313s 39 0.201140 313s 40 0.569920 313s 41 -0.375716 313s 42 0.069814 313s 43 0.354212 313s 44 0.346152 313s 45 0.559656 313s 46 -0.009140 313s 47 -0.487699 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 0.55581 313s ---------------------------------------------------------- 313s phosphor 18 2 1 215.172048 NA 313s Scores: 313s PC1 313s 1 1.12634 313s 2 -22.10340 313s 3 -23.49216 313s 4 -13.45927 313s 5 -18.60808 313s 6 11.24086 313s 7 -0.14748 313s 8 -9.77075 313s 9 -10.37022 313s 10 12.71798 313s 11 -4.61857 313s 12 10.07037 313s 13 13.16767 313s 14 7.57254 313s 15 17.81362 313s 16 -11.08799 313s 17 21.70358 313s 18 18.24496 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 14.669 313s ---------------------------------------------------------- 313s stackloss 21 3 2 77.038636 18.859777 313s Scores: 313s PC1 PC2 313s 1 -20.334936 10.28081 313s 2 -19.772121 11.10736 313s 3 -16.461573 6.43794 313s 4 -4.258672 1.73213 313s 5 -3.773146 1.41928 313s 6 -4.015909 1.57571 313s 7 -7.635560 -3.22715 313s 8 -7.635560 -3.22715 313s 9 -0.855388 -0.58707 313s 10 4.298129 4.41664 313s 11 -0.767202 -3.02229 313s 12 0.038375 -2.35217 313s 13 3.172500 2.76354 313s 14 -3.261224 -6.17206 313s 15 5.553840 -7.34784 313s 16 7.242284 -4.86820 313s 17 14.878925 6.85989 313s 18 10.939223 1.07406 313s 19 10.133645 0.40394 313s 20 4.267234 1.99501 313s 21 -11.859921 2.12579 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 8.7772 4.3428 313s ---------------------------------------------------------- 313s salinity 28 3 2 8.001175 5.858089 313s Scores: 313s PC1 PC2 313s 1 2.858444 1.04359 313s 2 3.807704 1.55974 313s 3 6.220733 -4.32114 313s 4 6.388841 -2.83649 313s 5 6.077450 -3.70092 313s 6 5.974494 -0.67230 313s 7 4.531584 0.78322 313s 8 2.725849 2.41297 313s 9 0.100501 -2.13615 313s 10 -2.358003 -1.49718 313s 11 -1.317688 -1.15391 313s 12 0.434635 0.58230 313s 13 0.116019 1.79022 313s 14 -1.771501 2.71749 313s 15 -2.630757 -2.44003 313s 16 2.289743 -5.51829 313s 17 0.637985 -1.26452 313s 18 3.076147 0.19883 313s 19 0.097381 -1.95868 313s 20 -1.572471 -0.93003 313s 21 -1.284185 2.21858 313s 22 -2.531713 3.30313 313s 23 -3.865359 -3.01230 313s 24 -2.143461 -2.41918 313s 25 -0.714414 -0.41227 313s 26 -1.327781 1.18373 313s 27 -2.201166 2.41566 313s 28 -2.931988 3.20536 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 2.8286 2.4203 313s ---------------------------------------------------------- 313s hbk 75 3 3 1.459908 1.201048 313s Scores: 313s PC1 PC2 PC3 313s 1 31.105415 -4.714217 -10.4566165 313s 2 31.707650 -5.748724 -10.7682402 313s 3 33.366131 -4.625897 -12.1570167 313s 4 34.173377 -6.069657 -12.4466895 313s 5 33.780418 -5.508823 -11.9872893 313s 6 32.493478 -4.684595 -10.5679819 313s 7 32.592637 -5.235522 -10.3765493 313s 8 31.293363 -4.865797 -10.9379676 313s 9 33.160964 -5.714260 -12.3098920 313s 10 31.919786 -5.384537 -12.3374332 313s 11 38.231962 -6.810641 -13.5994385 313s 12 39.290479 -5.393906 -15.2942554 313s 13 39.418445 -7.326461 -11.5194898 313s 14 43.906584 -13.214819 -8.3282743 313s 15 1.906326 0.716061 0.8635112 313s 16 0.263255 0.926016 1.9009292 313s 17 -1.776489 -1.072332 0.5496140 313s 18 0.464648 0.702441 -0.0482897 313s 19 0.267826 -1.283779 0.2925812 313s 20 2.122108 0.165970 0.8924686 313s 21 0.937217 0.548532 0.4132196 313s 22 0.423273 -1.781869 0.0323061 313s 23 0.047532 0.018909 1.1259327 313s 24 -0.490041 -0.520202 1.1065753 313s 25 -2.143049 0.720869 0.0495474 313s 26 1.094748 -1.459175 -0.2226246 313s 27 2.070705 0.898573 -0.0023229 313s 28 -0.294998 0.830258 -0.5929001 313s 29 -1.242995 0.300216 0.2010507 313s 30 0.147958 0.439099 -2.0003038 313s 31 0.170818 1.440946 0.9755627 313s 32 -0.958531 -1.199730 1.0129867 313s 33 0.697307 -0.874343 0.7260649 313s 34 -2.278946 0.261106 -0.4196544 313s 35 1.962829 0.809318 -0.2033113 313s 36 0.626631 -0.600666 -0.8004036 313s 37 0.550885 -1.881448 -0.7382776 313s 38 -1.249717 0.336214 0.9349845 313s 39 -1.106696 1.569418 -0.1869576 313s 40 -0.684034 -0.939963 0.1034965 313s 41 1.559314 1.551408 -0.3660323 313s 42 -0.538741 -0.447358 -1.6361099 313s 43 -0.252685 -2.080564 0.7765259 313s 44 0.217012 1.027281 -1.7015154 313s 45 -1.497600 1.349234 0.2698932 313s 46 0.100388 1.026443 -1.5390401 313s 47 -0.811117 2.195271 0.5208141 313s 48 1.462210 1.321318 -0.5600144 313s 49 1.383976 0.740714 0.7348906 313s 50 1.636773 -0.215464 -0.3195369 313s 51 -0.530918 0.759743 1.2069247 313s 52 -0.109566 2.107455 0.5315473 313s 53 -0.564334 -0.060847 -2.3910630 313s 54 -0.272234 -1.122711 1.5060028 313s 55 -0.608660 -1.197219 0.5255609 313s 56 0.565430 -0.710345 1.3708230 313s 57 -1.115629 0.888816 0.4186014 313s 58 1.351288 -0.374815 1.1980618 313s 59 0.998016 -0.151228 -0.9007970 313s 60 0.124017 -0.764846 -1.9005963 313s 61 1.189858 -1.905264 -0.7721322 313s 62 -2.190589 0.579614 0.1377914 313s 63 -0.518278 -0.931130 1.4534768 313s 64 2.124566 0.194391 0.0327092 313s 65 0.154218 1.050861 -1.1309885 313s 66 -1.197852 -1.044147 0.2265269 313s 67 -0.114174 -0.094763 0.5168926 313s 68 -2.201115 0.032271 -0.8573493 313s 69 -1.307843 1.104815 0.7741270 313s 70 0.691449 -0.676665 -1.0004603 313s 71 1.150975 0.050861 0.0717068 313s 72 -0.457293 -0.861871 -0.1026350 313s 73 -0.392258 -0.897451 -0.9178065 313s 74 -0.584658 -1.450471 -0.3201857 313s 75 -0.972517 -0.063777 -1.8223995 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 1.2083 1.0959 1.0168 313s ---------------------------------------------------------- 313s milk 86 8 2 6.040806 2.473780 313s Scores: 313s PC1 PC2 313s 1 -5.768003 -0.9174359 313s 2 -6.664422 0.0280812 313s 3 -0.484521 1.7923710 313s 4 -5.211590 2.0747301 313s 5 1.422641 -0.3268437 313s 6 -1.810360 -0.5469828 313s 7 -2.402924 -0.1987041 313s 8 -2.553389 -0.4963662 313s 9 1.583399 2.5410448 313s 10 3.267946 0.9141367 313s 11 9.924771 0.6501301 313s 12 13.628569 -2.3009846 313s 13 10.774550 -1.1628697 313s 14 12.716376 -1.0670330 313s 15 11.176408 0.7403371 313s 16 3.209269 -0.0804317 313s 17 1.256577 2.8931153 313s 18 2.468720 -1.2008647 313s 19 2.253229 0.8379608 313s 20 0.021073 1.6394221 313s 21 3.205298 -2.3518286 313s 22 1.470733 -0.9618655 313s 23 0.475732 -1.7044535 313s 24 0.930144 -1.3288398 313s 25 4.151553 -2.2882554 313s 26 1.314488 -1.3527439 313s 27 3.613405 -0.0813605 313s 28 -1.909178 -3.6473200 313s 29 -3.987263 -1.3255834 313s 30 -0.370601 -1.5855086 313s 31 -1.273254 -2.1892809 313s 32 -0.816634 -0.4514478 313s 33 -1.553394 -0.2792004 313s 34 -0.275027 0.6359374 313s 35 0.980782 -2.2353223 313s 36 -3.678470 -1.3459182 313s 37 -0.327102 -2.5615283 313s 38 -1.563492 -2.2008288 313s 39 1.876146 -1.0292641 313s 40 -3.204182 1.6694332 313s 41 -3.561892 -1.5844770 313s 42 -6.175135 1.0123714 313s 43 -2.736601 -0.7040261 313s 44 -4.981783 0.2434304 313s 45 0.368802 -0.5011413 313s 46 0.369508 -1.9511091 313s 47 -2.306673 -0.0089446 313s 48 0.215195 -1.1000357 313s 49 2.704678 -0.5919929 313s 50 -2.930879 2.7161936 313s 51 1.846250 0.3732500 313s 52 5.661288 -0.3139157 313s 53 1.154929 -0.0575094 313s 54 0.625715 -0.0733934 313s 55 -0.453714 -0.7535924 313s 56 0.343722 0.6460318 313s 57 1.743002 0.0794685 313s 58 0.433705 -1.3500731 313s 59 2.078550 1.0860506 313s 60 1.867913 0.7162287 313s 61 0.392645 1.6184583 313s 62 -1.958732 2.0993596 313s 63 -2.383251 -0.0253919 313s 64 -2.383251 -0.0253919 313s 65 0.780239 2.9018927 313s 66 2.785329 1.0142893 313s 67 0.131210 1.2703167 313s 68 1.110073 1.8140467 313s 69 1.076878 0.6954148 313s 70 -3.260160 -5.6233069 313s 71 2.647036 1.6892084 313s 72 -2.017340 0.5353349 313s 73 2.247524 2.6406249 313s 74 11.649291 -0.7374197 313s 75 0.280544 2.2306959 313s 76 1.791213 0.1796005 313s 77 8.730344 0.3412271 313s 78 -0.987405 1.3467910 313s 79 0.560808 0.5006661 313s 80 3.897879 -1.5270179 313s 81 -0.792759 -0.8649399 313s 82 -2.493611 1.6796838 313s 83 -2.245966 0.1889555 313s 84 -0.468812 -0.5359088 313s 85 -0.538372 2.4105954 313s 86 -0.185347 -1.0176989 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 2.4578 1.5728 313s ---------------------------------------------------------- 313s bushfire 38 5 1 38435.075910 NA 313s Scores: 313s PC1 313s 1 -111.9345 313s 2 -113.4128 313s 3 -105.8364 313s 4 -89.1684 313s 5 -58.7216 313s 6 -35.0370 313s 7 -250.2123 313s 8 -292.6877 313s 9 -294.0765 313s 10 -290.0193 313s 11 -289.8168 313s 12 -290.8645 313s 13 -232.6865 313s 14 9.8483 313s 15 137.1924 313s 16 92.9804 313s 17 90.4493 313s 18 78.6325 313s 19 82.1178 313s 20 92.9044 313s 21 74.9157 313s 22 66.7350 313s 23 -62.1981 313s 24 -116.5696 313s 25 -53.8907 313s 26 -60.6384 313s 27 -74.7621 313s 28 -50.2202 313s 29 -38.7483 313s 30 -93.3887 313s 31 35.3096 313s 32 290.8493 313s 33 326.7236 313s 34 322.9095 313s 35 328.5307 313s 36 325.6791 313s 37 323.8136 313s 38 325.2991 313s ------------- 313s Call: 313s PcaHubert(x = x, mcd = FALSE) 313s 313s Standard deviations: 313s [1] 196.05 313s ---------------------------------------------------------- 313s ========================================================== 313s > 313s > dodata(method="locantore") 313s 313s Call: dodata(method = "locantore") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 2 1.835912 0.084745 313s Scores: 313s PC1 PC2 313s [1,] 7.3042 1.745289 313s [2,] 64.6474 0.164425 313s [3,] 1.1057 -1.404189 313s [4,] -3.1943 2.565728 313s [5,] 19.4154 -0.401369 313s [6,] -15.5709 6.666752 313s [7,] 5.9980 2.509372 313s [8,] -29.5933 -4.805972 313s [9,] -1.3933 -0.899323 313s [10,] -28.2845 -4.270057 313s [11,] -14.0069 0.048311 313s [12,] 49.1484 0.694598 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 1.35496 0.29111 313s ---------------------------------------------------------- 313s starsCYG 47 2 2 0.779919 0.050341 313s Scores: 313s PC1 PC2 313s [1,] 0.174291 -0.0489127 313s [2,] 0.703776 0.0769650 313s [3,] -0.136954 -0.1212071 313s [4,] 0.703776 0.0769650 313s [5,] 0.125991 -0.1134658 313s [6,] 0.413609 0.0121367 313s [7,] -0.466451 -0.5036094 313s [8,] 0.238569 0.1446547 313s [9,] 0.498194 -0.1998666 313s [10,] 0.065125 -0.0353931 313s [11,] 0.562344 -0.9836936 313s [12,] 0.399997 -0.0164068 313s [13,] 0.376370 0.0369013 313s [14,] -1.041009 -0.2611550 313s [15,] -0.798187 -0.0090880 313s [16,] -0.464636 0.0805967 313s [17,] -1.123135 -0.0293034 313s [18,] -0.861603 0.1297588 313s [19,] -0.884955 -0.0588007 313s [20,] 0.721130 -1.0033585 313s [21,] -0.679097 -0.0238366 313s [22,] -0.837884 -0.0041718 313s [23,] -0.623423 0.1002615 313s [24,] -0.188079 0.1168815 313s [25,] -0.032888 -0.0131784 313s [26,] -0.385242 0.0707643 313s [27,] -0.401220 -0.0582501 313s [28,] -0.151978 0.0015702 313s [29,] -0.677776 -0.0945350 313s [30,] 0.878688 -1.0329475 313s [31,] -0.628339 0.0605648 313s [32,] 0.068629 0.1556245 313s [33,] 0.174199 0.0317098 313s [34,] 1.118098 -1.0525206 313s [35,] -0.726168 -0.0784655 313s [36,] 0.592061 0.1512588 313s [37,] 0.064942 0.1258519 313s [38,] 0.174199 0.0317098 313s [39,] 0.144335 0.1160195 313s [40,] 0.519088 -0.0311555 313s [41,] -0.429855 0.0359837 313s [42,] 0.015412 0.0513747 313s [43,] 0.299435 0.0665821 313s [44,] 0.293289 0.0169612 313s [45,] 0.504064 0.0916219 313s [46,] -0.063981 0.0612071 313s [47,] -0.544029 0.0904291 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 0.88313 0.22437 313s ---------------------------------------------------------- 313s phosphor 18 2 2 0.933905 0.279651 313s Scores: 313s PC1 PC2 313s 1 4.5660 -15.58981 313s 2 -21.2978 -0.38905 313s 3 -23.3783 3.96546 313s 4 -11.7131 -5.79023 313s 5 -18.2569 2.81141 313s 6 15.5702 -20.54935 313s 7 1.3671 -3.27043 313s 8 -9.4859 3.92005 313s 9 -10.4501 6.22662 313s 10 15.0583 -7.60532 313s 11 -3.9078 1.56960 313s 12 10.0330 7.52732 313s 13 13.4815 5.50056 313s 14 7.5487 7.24752 313s 15 18.6543 2.46040 313s 16 -9.3301 -5.68285 313s 17 22.2533 4.63689 313s 18 17.7892 10.85633 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 0.96639 0.52882 313s ---------------------------------------------------------- 313s stackloss 21 3 3 1.137747 0.196704 313s Scores: 313s PC1 PC2 PC3 313s [1,] 19.98046 -6.20875 -3.93576 313s [2,] 19.57014 -7.11509 -4.03666 313s [3,] 15.48729 -3.14247 -3.29600 313s [4,] 3.12341 -1.38969 1.50633 313s [5,] 2.35380 -0.84492 -0.25745 313s [6,] 2.73860 -1.11731 0.62444 313s [7,] 5.58533 4.04837 2.11170 313s [8,] 5.58533 4.04837 2.11170 313s [9,] -0.56851 0.17483 2.46656 313s [10,] -5.36478 -4.80766 -2.64915 313s [11,] -1.67190 3.34943 -1.74110 313s [12,] -2.46702 2.71547 -2.72389 313s [13,] -4.54414 -2.99497 -2.44736 313s [14,] 0.35419 6.70241 -0.45563 313s [15,] -8.28612 5.93369 1.94314 313s [16,] -9.51708 3.21466 1.64046 313s [17,] -14.87676 -9.74652 1.10983 313s [18,] -12.00452 -3.40212 1.81609 313s [19,] -11.20939 -2.76816 2.79887 313s [20,] -5.42808 -2.89367 0.23748 313s [21,] 9.83969 0.74095 -5.30190 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 1.06665 0.44351 0.33935 313s ---------------------------------------------------------- 313s salinity 28 3 3 1.038873 0.621380 313s Scores: 313s PC1 PC2 PC3 313s 1 -2.7215590 -0.98924 0.3594538 313s 2 -3.6251829 -1.03361 1.4973993 313s 3 -6.0588883 4.23861 -1.1012038 313s 4 -6.2741857 2.42372 -1.4875092 313s 5 -5.7274076 5.42190 2.9332011 313s 6 -5.8431892 0.57161 -0.3385363 313s 7 -4.4051377 -0.83292 0.0851817 313s 8 -2.6155827 -2.50739 0.3386166 313s 9 -0.0426575 1.19631 -2.5025726 313s 10 2.5297488 1.65029 -0.0110335 313s 11 1.5528097 1.93255 1.4216262 313s 12 -0.3140451 -0.73269 -0.1961364 313s 13 0.0010783 -1.88658 0.1849912 313s 14 1.9554303 -2.13519 1.8471356 313s 15 2.7897250 2.40211 -0.6327944 313s 16 -1.7665706 8.69449 5.6608836 313s 17 -0.4374125 1.72696 0.7230753 313s 18 -2.9752196 -0.54118 -0.6829760 313s 19 -0.0599346 0.84127 -2.8473543 313s 20 1.6597909 0.34191 -1.4847516 313s 21 1.3857395 -2.43924 0.0039271 313s 22 2.6664754 -3.14291 1.0600254 313s 23 4.1202067 3.81886 1.0608640 313s 24 2.4163743 3.45141 1.6874099 313s 25 0.8493897 0.31424 -0.3073115 313s 26 1.4216265 -1.55310 -0.5455012 313s 27 2.3021676 -2.63392 0.0481451 313s 28 3.0877115 -2.85951 1.4378956 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 1.01925 0.78828 0.36470 313s ---------------------------------------------------------- 313s hbk 75 3 3 1.038833 0.363386 313s Scores: 313s PC1 PC2 PC3 313s 1 32.393698 -3.4318297 0.051248 313s 2 33.103072 -4.4154651 0.294662 313s 3 35.038965 -3.5996035 -0.940929 313s 4 35.955809 -4.9285404 -0.479059 313s 5 35.424918 -4.3076292 -0.366699 313s 6 33.753497 -3.2463136 0.289013 313s 7 33.817375 -3.6819421 0.684167 313s 8 32.717119 -3.7074394 -0.279567 313s 9 34.932190 -4.6939061 -0.738196 313s 10 33.737339 -4.5702346 -1.193206 313s 11 40.202273 -5.4336890 -0.229323 313s 12 41.638189 -4.5304173 -1.996311 313s 13 40.768565 -5.0531048 2.123222 313s 14 44.408749 -8.8448536 8.236462 313s 15 0.977343 1.3057899 0.938694 313s 16 -0.900390 1.6169842 1.382855 313s 17 -2.384467 -0.9835430 0.375495 313s 18 -0.143306 0.7859701 -0.237712 313s 19 -0.344479 -0.9791245 0.733869 313s 20 1.199115 0.8330752 1.216827 313s 21 0.184475 0.8630593 0.351029 313s 22 -0.100389 -1.5084406 0.718236 313s 23 -0.847925 0.4823829 0.958677 313s 24 -1.334366 -0.1021190 1.000300 313s 25 -2.669352 0.4692990 -0.811134 313s 26 0.601538 -1.1984283 0.541627 313s 27 1.373423 1.2098621 0.136249 313s 28 -0.721268 0.6164612 -0.963817 313s 29 -1.832615 0.2543279 -0.297658 313s 30 0.120086 -0.1558590 -1.976558 313s 31 -0.747437 1.7749106 0.342824 313s 32 -1.727558 -0.8325772 1.043088 313s 33 -0.073907 -0.3923823 1.083904 313s 34 -2.646454 -0.1350138 -1.101448 313s 35 1.331096 1.0443905 -0.039328 313s 36 0.281192 -0.6569943 -0.404009 313s 37 0.245349 -1.8406517 0.093656 313s 38 -2.049446 0.5320301 0.347219 313s 39 -1.645547 1.3268749 -1.068792 313s 40 -1.216874 -0.8556007 0.201262 313s 41 0.959445 1.6250030 -0.553881 313s 42 -0.603579 -0.9569812 -1.502730 313s 43 -0.946870 -1.6333180 1.324763 313s 44 0.076217 0.5018427 -1.902369 313s 45 -2.140584 1.2192726 -0.677180 313s 46 -0.081677 0.5389288 -1.785347 313s 47 -1.590461 2.1881067 -0.583771 313s 48 0.931421 1.3321181 -0.669782 313s 49 0.512639 1.2123979 0.683099 313s 50 1.095415 0.0045968 0.143109 313s 51 -1.456417 1.1186245 0.619657 313s 52 -0.917904 2.2084467 -0.366392 313s 53 -0.429654 -0.8524437 -2.326637 313s 54 -1.213858 -0.4996891 1.630709 313s 55 -1.253877 -0.9438354 0.692022 313s 56 -0.390657 -0.0427482 1.571167 313s 57 -1.797537 0.8934866 -0.281980 313s 58 0.396886 0.3227454 1.492494 313s 59 0.646360 -0.2194210 -0.562699 313s 60 0.119900 -1.2480691 -1.459763 313s 61 0.867946 -1.7843458 0.232229 313s 62 -2.733997 0.3604288 -0.692947 313s 63 -1.442683 -0.3732483 1.452800 313s 64 1.444934 0.5727959 0.434633 313s 65 -0.147284 0.7055205 -1.413940 313s 66 -1.739552 -0.9838385 0.220303 313s 67 -0.824644 0.1503195 0.411693 313s 68 -2.437638 -0.4835278 -1.392882 313s 69 -2.091970 1.1865192 -0.088483 313s 70 0.403429 -0.7855276 -0.540161 313s 71 0.507512 0.3152001 0.276885 313s 72 -0.944376 -0.8197825 0.044859 313s 73 -0.648597 -1.1160277 -0.658528 313s 74 -0.979453 -1.4589411 0.029182 313s 75 -0.982282 -0.7226425 -1.917060 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 1.01923 0.60282 0.46137 313s ---------------------------------------------------------- 313s milk 86 8 8 1.175171 0.426506 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 PC6 313s [1,] 6.1907998 0.58762698 0.686510 -0.209679 0.3321757 -1.3424985 313s [2,] 7.0503894 -0.49576086 -0.322697 -0.767415 -0.0165833 -1.4596064 313s [3,] 0.7670594 -1.83556812 0.468814 0.346810 -0.0204610 -0.2115383 313s [4,] 5.4656748 -2.29797862 1.612819 -0.378295 -0.2050232 0.3486957 313s [5,] -1.0291160 0.37303007 0.634604 -0.521527 -0.3299543 0.0859469 313s [6,] 2.2186300 0.39396818 -0.236987 -0.033975 -0.2549238 0.2541221 313s [7,] 2.7938591 -0.01152811 -0.600546 -0.098564 -0.3906602 0.3798516 313s [8,] 2.9544176 0.32646226 0.273051 -0.275073 -0.3982959 0.2377581 313s [9,] -1.3344639 -2.45440308 1.001792 -0.104783 -0.1744718 -0.0887272 313s [10,] -2.9294174 -0.79860558 -0.260533 0.375330 0.3425169 -0.2056682 313s [11,] -9.5810648 -0.09577968 1.565111 -0.112002 0.3143032 -0.3190238 313s [12,] -13.1147240 2.95665890 0.228086 -0.180867 0.0136463 -0.4604390 313s [13,] -10.2989319 1.53220781 -2.244629 0.323950 -0.0398642 -0.3463501 313s [14,] -12.2553418 1.62281167 -0.472862 -0.212983 -0.4124280 -0.4253719 313s [15,] -10.8346894 -0.09781844 2.134079 -0.272304 -0.1090226 -0.3725738 313s [16,] -2.8358474 0.28109809 0.945309 0.603249 0.1615955 0.1762086 313s [17,] -1.0353408 -2.75475311 1.677879 0.598578 0.0078965 0.0228522 313s [18,] -2.0271810 1.25894451 -0.266038 -0.168565 -0.3000200 0.2891774 313s [19,] -1.9279394 -0.68339726 1.264416 0.186749 0.3018226 -0.0869321 313s [20,] 0.2568334 -1.62632029 0.854279 -0.088175 0.5458645 0.2217019 313s [21,] -2.7017404 2.45223507 -0.243639 -0.211402 -0.2102323 0.2140100 313s [22,] -1.0386097 0.99459030 0.188462 -0.033434 -0.2857078 -0.1438517 313s [23,] -0.0198126 1.73285416 0.761979 0.005501 0.1671992 -0.0375468 313s [24,] -0.4909448 1.40982693 0.967440 0.521275 0.1625359 -0.0892501 313s [25,] -3.6632699 2.51414455 0.966410 -0.272694 0.0467958 0.1572715 313s [26,] -0.8733564 1.42247465 0.946038 -0.338985 -0.0804141 -0.0080759 313s [27,] -3.2254798 0.26912538 0.799468 0.372442 -0.6886191 -0.0553515 313s [28,] 2.4675785 3.56128696 0.813964 0.118354 -0.1677073 -0.0303774 313s [29,] 4.4177264 1.13316321 0.613509 0.261488 0.4229929 0.1780620 313s [30,] 0.8240097 1.54163297 0.398148 -0.221825 0.0309586 0.0830110 313s [31,] 1.7735990 2.00615332 -1.399933 0.469158 -0.0740282 0.0692312 313s [32,] 1.2348922 0.28918604 -1.239899 0.470999 -0.1511519 -0.3692504 313s [33,] 1.9407276 0.19123540 0.406623 0.389965 0.0994854 -0.0204286 313s [34,] 0.6225565 -0.65636700 0.565253 0.369897 -0.1612501 -0.1774611 313s [35,] -0.4869219 2.26301333 0.071825 0.588101 -0.0579092 -0.0362009 313s [36,] 4.1117242 1.16638974 0.982790 -0.266009 0.0728797 -0.0018914 313s [37,] 0.8415225 2.46677043 -0.526780 0.167456 -0.2370116 -0.0731483 313s [38,] 2.0528334 2.09648023 0.220912 0.206722 -0.1924842 0.0676382 313s [39,] -1.4493644 1.14916103 0.904194 0.455498 0.0678893 -0.1476540 313s [40,] 3.4867792 -1.82367389 0.730183 0.499859 0.2327704 -0.1518819 313s [41,] 4.0222120 1.34765470 0.580852 -0.453301 0.2482908 -1.5306566 313s [42,] 6.4789035 -1.25599522 1.644194 0.381331 0.1699942 0.1847594 313s [43,] 3.1529354 0.44884526 -0.967114 -0.220364 0.0037036 0.0802727 313s [44,] 5.3344976 -0.47975673 0.642789 0.298705 0.9983145 -0.1310548 313s [45,] 0.0325597 0.49900084 0.076948 0.486521 0.1642679 0.1392696 313s [46,] 0.1014401 1.97657735 0.733879 0.127235 0.0650844 -0.0144271 313s [47,] 2.7217685 -0.37859042 -3.696163 0.355401 -0.4123714 0.2114024 313s [48,] 0.2292225 1.01473918 -1.115726 0.434557 0.2668316 0.0103147 313s [49,] -2.2803784 0.59474034 -1.783003 0.549252 0.4660435 -0.0802352 313s [50,] 3.1560404 -2.84820361 0.913015 0.077151 0.5803961 0.0350246 313s [51,] -1.4680905 -0.43078891 -1.733657 0.074684 0.0026718 0.0819023 313s [52,] -5.2469034 0.48385240 -1.246027 0.081379 0.2380924 -0.1663831 313s [53,] -0.7670982 0.00234561 -0.923030 -0.366820 0.1582141 0.0508747 313s [54,] -0.2428655 0.04714401 -0.217187 -0.059549 0.1762969 0.0806339 313s [55,] 0.8723441 0.66109329 -0.224917 -0.360607 -0.0638127 0.1310131 313s [56,] 0.0019700 -0.67624071 0.081304 -0.182908 0.1045597 -0.0281936 313s [57,] -1.3684663 -0.00045069 0.860560 -0.350684 -0.1443970 -0.2270651 313s [58,] 0.0079047 1.36376727 0.750919 -0.437914 -0.1894910 0.2345556 313s [59,] -1.7430794 -1.06973583 -0.569381 -0.055139 -0.1582790 -0.0873605 313s [60,] -1.5171606 -0.69340281 -0.287048 -0.136559 -0.3871182 0.1606979 313s [61,] -0.0955085 -1.64221260 0.263650 -0.265665 -0.0808644 -0.0476862 313s [62,] 2.2259171 -2.22161516 0.426279 0.027834 0.2924338 -0.1784242 313s [63,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 313s [64,] 2.7573525 -0.11785122 0.391113 -0.094032 -0.3184760 0.4251268 313s [65,] -0.5520071 -2.86186682 0.746248 0.109945 0.0556927 -0.0135739 313s [66,] -2.4472964 -0.94969715 -0.329042 -0.113895 -0.2728443 -0.0523337 313s [67,] 0.1790969 -1.29190443 0.146657 0.140234 0.1534048 0.2318353 313s [68,] -0.8017055 -1.93331421 -1.968273 0.017854 0.1287513 -0.2306786 313s [69,] -0.7356418 -0.68868398 -0.075215 -0.156944 0.0302876 0.4232626 313s [70,] 3.8821693 5.16959880 0.215490 -8.985938 5.2189361 -2.8089276 313s [71,] -2.3478937 -1.60220695 0.058822 -0.111845 -0.0539018 0.0087982 313s [72,] 2.3676739 -0.70331436 -0.214457 -0.307311 -0.1582719 0.3995413 313s [73,] -1.9906385 -2.60946629 -0.730312 0.485522 -0.2391998 0.1009341 313s [74,] -11.2435515 1.44868683 2.482678 0.026711 0.4922865 -0.2822136 313s [75,] 0.0044207 -2.29768358 -0.692425 0.538923 -0.4110598 -0.0824903 313s [76,] -1.4045239 -0.22649785 -1.343257 -0.067382 -0.1322233 -0.1072330 313s [77,] -8.3637576 0.14167751 1.267616 0.384528 -0.0728561 -0.4017300 313s [78,] 1.3022939 -1.47457541 -0.394623 -0.068014 -0.1502832 0.0757414 313s [79,] -0.1950676 -0.58254701 -0.824931 -0.088174 -0.2071634 -0.1896613 313s [80,] -3.4432989 1.73593273 0.777996 0.094211 0.2377017 -0.1520088 313s [81,] 1.2167258 0.77512068 0.085803 -0.214850 -0.2201173 0.0432435 313s [82,] 2.7778798 -1.80071342 0.583878 0.465898 0.0648352 0.2148470 313s [83,] 2.6218578 -0.39825539 -0.553372 -0.145721 -0.0977092 -0.2485337 313s [84,] 0.8946018 0.33790104 -1.974267 0.091828 0.0051986 -0.2606274 313s [85,] 0.7759316 -2.34860124 2.423325 -0.384149 -0.0167182 -0.0353374 313s [86,] 0.6266756 0.87099609 -1.407948 -0.237762 0.0361644 0.1675792 313s PC7 PC8 313s [1,] -0.1014312 1.5884e-03 313s [2,] -0.3831443 1.0212e-03 313s [3,] -0.7164683 1.2035e-03 313s [4,] 0.0892864 3.5409e-04 313s [5,] -0.0943992 1.0547e-03 313s [6,] 0.1184847 1.5031e-03 313s [7,] -0.2509793 1.6850e-05 313s [8,] -0.0136880 7.0308e-04 313s [9,] 0.2238736 -1.9164e-04 313s [10,] 0.0754413 1.3614e-04 313s [11,] 0.0784380 3.5175e-04 313s [12,] 0.2033489 -1.3174e-03 313s [13,] 0.2139525 -1.7101e-03 313s [14,] 0.1209735 -9.1070e-04 313s [15,] 0.2119647 -9.2843e-04 313s [16,] -0.3011483 -2.1474e-03 313s [17,] 0.0660858 -1.9036e-03 313s [18,] -0.5199396 -9.4385e-04 313s [19,] -0.1232622 -1.2649e-03 313s [20,] -0.3900208 -2.6927e-04 313s [21,] 0.0264834 7.6074e-05 313s [22,] -0.0736288 1.7240e-04 313s [23,] -0.2156005 -5.5661e-04 313s [24,] 0.1143327 -2.5248e-04 313s [25,] 0.0481580 -6.1531e-04 313s [26,] -0.0084802 -7.5928e-04 313s [27,] -0.2173883 -3.0971e-04 313s [28,] 0.3288873 -1.8975e-04 313s [29,] 0.0788974 -7.2436e-04 313s [30,] -0.0598663 -3.0463e-04 313s [31,] -0.1511658 -4.8751e-04 313s [32,] -0.0532375 -2.5207e-04 313s [33,] -0.0635290 -3.9270e-04 313s [34,] 0.1598240 1.3024e-04 313s [35,] -0.0355175 -8.5374e-05 313s [36,] -0.0174096 -6.3294e-04 313s [37,] -0.2883141 -5.2809e-04 313s [38,] 0.1426412 5.3331e-04 313s [39,] 0.0313308 4.2738e-04 313s [40,] -0.3536195 -3.4170e-04 313s [41,] -0.3925168 1.4588e-04 313s [42,] -0.0056267 -9.1925e-04 313s [43,] -0.4447402 -1.8415e-04 313s [44,] 0.9184385 -5.9685e-04 313s [45,] -0.0340987 7.2924e-04 313s [46,] -0.0162866 9.7800e-04 313s [47,] 0.2428769 -1.1208e-03 313s [48,] 0.3026758 -4.5769e-04 313s [49,] 0.0246345 -2.6207e-04 313s [50,] 0.0857698 7.6439e-05 313s [51,] 0.1136658 1.3013e-04 313s [52,] 0.3993357 6.2796e-04 313s [53,] -0.1765161 1.1329e-04 313s [54,] 0.0016144 2.5870e-04 313s [55,] 0.1064371 5.8188e-04 313s [56,] 0.0207478 -8.7595e-05 313s [57,] 0.1560065 6.3987e-05 313s [58,] 0.1684561 -5.0193e-05 313s [59,] 0.0778732 -8.5458e-04 313s [60,] 0.0037585 1.0429e-05 313s [61,] -0.0296083 3.1526e-05 313s [62,] 0.0913974 -2.2794e-04 313s [63,] 0.0358917 -7.3721e-04 313s [64,] 0.0358917 -7.3721e-04 313s [65,] 0.1209159 2.9398e-04 313s [66,] -0.0027574 2.9380e-04 313s [67,] -0.0091059 -2.7494e-04 313s [68,] 0.0555970 -3.3016e-04 313s [69,] -0.0149255 -3.1228e-04 313s [70,] 0.9282997 4.7859e-05 313s [71,] 0.2630142 4.2617e-04 313s [72,] 0.1063248 -3.0070e-04 313s [73,] -0.1462452 4.9607e-04 313s [74,] 0.2027591 2.6399e-03 313s [75,] 0.6934350 6.0284e-04 313s [76,] -0.0430524 8.1271e-04 313s [77,] 0.0789302 1.4655e-03 313s [78,] -0.0318359 5.2799e-04 313s [79,] -0.1269568 2.9497e-04 313s [80,] 0.2903958 7.8932e-04 313s [81,] 0.0979443 -3.1531e-04 313s [82,] -0.0548155 4.2140e-04 313s [83,] -0.0371550 -5.6653e-04 313s [84,] -0.0835149 -7.0682e-04 313s [85,] 0.1864954 1.0604e-03 313s [86,] 0.1074252 -7.4859e-04 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 1.08405293 0.65307452 0.28970076 0.11162824 0.09072195 0.06659711 0.05888048 313s [8] 0.00022877 313s ---------------------------------------------------------- 313s bushfire 38 5 5 1.464779 0.043290 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 313s [1,] -69.9562 -13.0364 0.98678 1.054123 2.411188 313s [2,] -71.5209 -10.5459 0.31081 1.631208 1.663470 313s [3,] -63.9308 -7.4622 -2.43241 0.671038 0.465836 313s [4,] -47.0413 -9.6343 -3.83609 0.758349 0.683983 313s [5,] -15.9088 -20.1737 -5.55893 1.181744 -0.053563 313s [6,] 8.3484 -30.7646 -5.51541 1.877227 1.338037 313s [7,] -207.7458 -66.2492 34.48519 -5.894885 -1.051729 313s [8,] -246.4327 -97.0433 -9.57057 22.286225 -9.234869 313s [9,] -247.5984 -98.8613 -12.13406 23.948770 -9.250401 313s [10,] -245.8121 -79.2634 12.47990 13.046128 -5.125478 313s [11,] -246.8887 -62.5899 21.21764 9.111011 -5.080985 313s [12,] -251.1354 -9.2115 31.77448 0.236379 0.707528 313s [13,] -194.0239 27.1288 21.05023 0.940913 1.781359 313s [14,] 51.7182 8.5038 -11.22109 -2.132458 1.984807 313s [15,] 180.5597 -4.8151 -21.36630 -9.390663 -0.817036 313s [16,] 135.7246 -5.0756 -11.33517 -10.015567 -1.670831 313s [17,] 133.0151 -4.0344 -8.95540 -7.702087 -0.923277 313s [18,] 121.2619 -9.0627 -5.96042 -7.210971 -2.092872 313s [19,] 124.9038 -10.6649 -7.22555 -5.349553 -1.771009 313s [20,] 135.5410 -6.8146 -7.52834 -5.562769 -0.396924 313s [21,] 117.1950 -3.5643 -4.67473 -6.862117 -0.234551 313s [22,] 108.9944 -2.3344 -5.90349 -5.928299 1.455538 313s [23,] -21.4031 8.0668 6.19525 -4.784890 0.671394 313s [24,] -76.3499 16.7804 6.52545 -1.391250 1.219282 313s [25,] -12.5732 6.1109 -1.45259 -3.512072 -0.375837 313s [26,] -19.1800 3.4685 -2.02243 -3.490028 -0.169127 313s [27,] -33.6733 12.0757 -3.53322 0.048666 0.067468 313s [28,] -9.3966 21.5055 -5.91671 2.650895 -0.449672 313s [29,] 1.4123 35.8559 -5.98222 5.982362 0.613667 313s [30,] -54.2683 39.6029 7.82694 6.759994 0.035048 313s [31,] 74.8866 34.9048 10.03986 12.592158 0.149308 313s [32,] 331.4144 9.3079 27.73391 17.334531 1.015536 313s [33,] 367.6915 -19.5135 48.52753 10.213314 -1.268047 313s [34,] 363.8686 -20.4079 49.32855 8.986581 -1.930673 313s [35,] 369.4371 -19.5074 49.66761 9.001542 -0.179566 313s [36,] 366.5850 -20.2555 50.30290 7.745330 -2.259131 313s [37,] 364.5463 -19.8198 53.00407 6.757796 -1.083372 313s [38,] 365.9709 -19.3753 53.80168 6.467284 -0.854384 313s ------------- 313s Call: 313s PcaLocantore(x = x) 313s 313s Standard deviations: 313s [1] 1.210280 0.208063 0.177790 0.062694 0.014423 313s ---------------------------------------------------------- 313s ========================================================== 313s > dodata(method="cov") 313s 313s Call: dodata(method = "cov") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 2 685.776266 13.127306 313s Scores: 313s PC1 PC2 313s 1 8.18562 1.17998 313s 2 65.41185 -2.80723 313s 3 1.86039 -1.70646 313s 4 -2.26910 2.44051 313s 5 20.19603 -1.47331 313s 6 -14.46264 7.05759 313s 7 6.91264 1.99823 313s 8 -28.95436 -3.81624 313s 9 -0.61523 -1.09711 313s 10 -27.62427 -3.33575 313s 11 -13.17788 0.37931 313s 12 49.94879 -1.62675 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 26.1873 3.6232 313s ---------------------------------------------------------- 313s starsCYG 47 2 2 0.280150 0.007389 313s Scores: 313s PC1 PC2 313s 1 0.272263 -0.07964458 313s 2 0.804544 0.03382837 313s 3 -0.040587 -0.14464760 313s 4 0.804544 0.03382837 313s 5 0.222468 -0.14305159 313s 6 0.512941 -0.02420304 313s 7 -0.378928 -0.51924735 313s 8 0.341045 0.11236831 313s 9 0.592550 -0.23812462 313s 10 0.163442 -0.06357822 313s 11 0.638370 -1.02323643 313s 12 0.498667 -0.05242075 313s 13 0.476291 0.00142479 313s 14 -0.947664 -0.26343572 313s 15 -0.699020 -0.01711057 313s 16 -0.363464 0.06475681 313s 17 -1.024352 -0.02972862 313s 18 -0.759174 0.12317995 313s 19 -0.786925 -0.06478250 313s 20 0.796654 -1.04660568 313s 21 -0.580307 -0.03463751 313s 22 -0.738591 -0.01126825 313s 23 -0.521748 0.08812607 313s 24 -0.086135 0.09457052 313s 25 0.065975 -0.03907968 313s 26 -0.284322 0.05307219 313s 27 -0.303309 -0.07553370 313s 28 -0.052738 -0.02155274 313s 29 -0.580638 -0.10534741 313s 30 0.953478 -1.07986770 313s 31 -0.527590 0.04855502 313s 32 0.171408 0.12730538 313s 33 0.274054 0.00095808 313s 34 1.192364 -1.10502882 313s 35 -0.628641 -0.08815176 313s 36 0.694595 0.11071187 313s 37 0.167026 0.09762710 313s 38 0.274054 0.00095808 313s 39 0.246168 0.08594248 313s 40 0.617380 -0.06994769 313s 41 -0.329735 0.01934346 313s 42 0.115770 0.02432733 313s 43 0.400071 0.03289494 313s 44 0.392768 -0.01656886 313s 45 0.605229 0.05314718 313s 46 0.036628 0.03601196 313s 47 -0.442606 0.07644144 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 0.529292 0.085957 313s ---------------------------------------------------------- 313s phosphor 18 2 2 288.018150 22.020514 313s Scores: 313s PC1 PC2 313s 1 2.7987 -19.015683 313s 2 -20.4311 -0.032022 313s 3 -21.8198 4.589809 313s 4 -11.7869 -6.837833 313s 5 -16.9357 2.664785 313s 6 12.9132 -25.602526 313s 7 1.5249 -6.351664 313s 8 -8.0984 2.416616 313s 9 -8.6979 4.843680 313s 10 14.3903 -12.732868 313s 11 -2.9462 -0.760656 313s 12 11.7427 2.991004 313s 13 14.8400 0.459849 313s 14 9.2449 3.095095 313s 15 19.4860 -3.336883 313s 16 -9.4156 -7.096788 313s 17 23.3759 -1.737460 313s 18 19.9173 5.092467 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 16.9711 4.6926 313s ---------------------------------------------------------- 313s stackloss 21 3 3 28.153060 8.925048 313s Scores: 313s PC1 PC2 PC3 313s [1,] 10.538448 13.596944 12.84989 313s [2,] 9.674846 14.098881 12.89733 313s [3,] 8.993255 9.221043 9.94062 313s [4,] 1.744427 3.649104 0.17292 313s [5,] 0.980215 2.223126 1.34874 313s [6,] 1.362321 2.936115 0.76083 313s [7,] 6.926040 0.637480 -0.11170 313s [8,] 6.926040 0.637480 -0.11170 313s [9,] 0.046655 0.977727 -2.46930 313s [10,] -7.909092 0.926343 0.80232 313s [11,] -0.136672 -3.591094 0.37539 313s [12,] -1.382381 -3.802146 1.01074 313s [13,] -6.181887 -0.077532 0.70744 313s [14,] 3.699843 -4.885854 -0.40226 313s [15,] -2.768005 -7.507870 -6.08487 313s [16,] -5.358811 -6.002058 -5.94256 313s [17,] -17.067135 1.738055 -5.86637 313s [18,] -11.021920 -1.775507 -6.19842 313s [19,] -9.776212 -1.564455 -6.83377 313s [20,] -6.075508 0.369252 -2.08345 313s [21,] 6.301743 2.706174 8.79509 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 5.3059 2.9875 1.3020 313s ---------------------------------------------------------- 313s salinity 28 3 3 11.801732 3.961826 313s Scores: 313s PC1 PC2 PC3 313s 1 -1.59888 1.582157 0.135248 313s 2 -2.26975 2.429177 1.107832 313s 3 -6.79543 -2.034636 0.853876 313s 4 -6.36795 -0.602960 -0.267268 313s 5 -6.42044 -1.520259 5.022962 313s 6 -5.13821 1.225470 0.016977 313s 7 -3.24014 1.998671 -0.123418 313s 8 -0.93998 2.789889 -0.515656 313s 9 -0.30856 -2.424345 -1.422752 313s 10 2.20362 -2.800513 1.142127 313s 11 1.38120 -2.076832 2.515630 313s 12 0.44997 0.207439 -0.152835 313s 13 1.21669 1.193701 -0.277116 313s 14 3.31664 1.306627 1.213342 313s 15 2.08484 -3.774814 0.905400 313s 16 -3.64862 -4.677257 9.046484 313s 17 -0.46124 -1.411762 1.706719 313s 18 -2.13038 0.890401 -0.633349 313s 19 -0.23610 -2.262304 -1.885048 313s 20 1.70337 -1.970773 -0.781880 313s 21 2.67273 1.038742 -0.610945 313s 22 4.24561 1.547290 0.108927 313s 23 2.99619 -4.785343 3.094945 313s 24 1.64474 -3.564562 3.432429 313s 25 1.11703 -1.158030 0.237700 313s 26 2.30707 0.069668 -0.735809 313s 27 3.59356 0.860498 -0.611380 313s 28 4.57550 1.300407 0.589307 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 3.43536 1.99043 0.94546 313s ---------------------------------------------------------- 313s hbk 75 3 3 1.436470 1.181766 313s Scores: 313s PC1 PC2 PC3 313s 1 31.105415 -4.714217 10.4566165 313s 2 31.707650 -5.748724 10.7682402 313s 3 33.366131 -4.625897 12.1570167 313s 4 34.173377 -6.069657 12.4466895 313s 5 33.780418 -5.508823 11.9872893 313s 6 32.493478 -4.684595 10.5679819 313s 7 32.592637 -5.235522 10.3765493 313s 8 31.293363 -4.865797 10.9379676 313s 9 33.160964 -5.714260 12.3098920 313s 10 31.919786 -5.384537 12.3374332 313s 11 38.231962 -6.810641 13.5994385 313s 12 39.290479 -5.393906 15.2942554 313s 13 39.418445 -7.326461 11.5194898 313s 14 43.906584 -13.214819 8.3282743 313s 15 1.906326 0.716061 -0.8635112 313s 16 0.263255 0.926016 -1.9009292 313s 17 -1.776489 -1.072332 -0.5496140 313s 18 0.464648 0.702441 0.0482897 313s 19 0.267826 -1.283779 -0.2925812 313s 20 2.122108 0.165970 -0.8924686 313s 21 0.937217 0.548532 -0.4132196 313s 22 0.423273 -1.781869 -0.0323061 313s 23 0.047532 0.018909 -1.1259327 313s 24 -0.490041 -0.520202 -1.1065753 313s 25 -2.143049 0.720869 -0.0495474 313s 26 1.094748 -1.459175 0.2226246 313s 27 2.070705 0.898573 0.0023229 313s 28 -0.294998 0.830258 0.5929001 313s 29 -1.242995 0.300216 -0.2010507 313s 30 0.147958 0.439099 2.0003038 313s 31 0.170818 1.440946 -0.9755627 313s 32 -0.958531 -1.199730 -1.0129867 313s 33 0.697307 -0.874343 -0.7260649 313s 34 -2.278946 0.261106 0.4196544 313s 35 1.962829 0.809318 0.2033113 313s 36 0.626631 -0.600666 0.8004036 313s 37 0.550885 -1.881448 0.7382776 313s 38 -1.249717 0.336214 -0.9349845 313s 39 -1.106696 1.569418 0.1869576 313s 40 -0.684034 -0.939963 -0.1034965 313s 41 1.559314 1.551408 0.3660323 313s 42 -0.538741 -0.447358 1.6361099 313s 43 -0.252685 -2.080564 -0.7765259 313s 44 0.217012 1.027281 1.7015154 313s 45 -1.497600 1.349234 -0.2698932 313s 46 0.100388 1.026443 1.5390401 313s 47 -0.811117 2.195271 -0.5208141 313s 48 1.462210 1.321318 0.5600144 313s 49 1.383976 0.740714 -0.7348906 313s 50 1.636773 -0.215464 0.3195369 313s 51 -0.530918 0.759743 -1.2069247 313s 52 -0.109566 2.107455 -0.5315473 313s 53 -0.564334 -0.060847 2.3910630 313s 54 -0.272234 -1.122711 -1.5060028 313s 55 -0.608660 -1.197219 -0.5255609 313s 56 0.565430 -0.710345 -1.3708230 313s 57 -1.115629 0.888816 -0.4186014 313s 58 1.351288 -0.374815 -1.1980618 313s 59 0.998016 -0.151228 0.9007970 313s 60 0.124017 -0.764846 1.9005963 313s 61 1.189858 -1.905264 0.7721322 313s 62 -2.190589 0.579614 -0.1377914 313s 63 -0.518278 -0.931130 -1.4534768 313s 64 2.124566 0.194391 -0.0327092 313s 65 0.154218 1.050861 1.1309885 313s 66 -1.197852 -1.044147 -0.2265269 313s 67 -0.114174 -0.094763 -0.5168926 313s 68 -2.201115 0.032271 0.8573493 313s 69 -1.307843 1.104815 -0.7741270 313s 70 0.691449 -0.676665 1.0004603 313s 71 1.150975 0.050861 -0.0717068 313s 72 -0.457293 -0.861871 0.1026350 313s 73 -0.392258 -0.897451 0.9178065 313s 74 -0.584658 -1.450471 0.3201857 313s 75 -0.972517 -0.063777 1.8223995 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 1.1985 1.0871 1.0086 313s ---------------------------------------------------------- 313s milk 86 8 8 5.758630 2.224809 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 PC6 313s 1 5.7090867 1.388263 0.0055924 0.3510505 -0.7335114 -1.41950731 313s 2 6.5825186 0.480410 -1.1356236 -0.3250838 -0.7343177 -1.71595400 313s 3 0.7433619 -1.749281 0.2510521 0.3450575 0.2996413 -0.34585702 313s 4 5.5733255 -1.588521 0.8934908 -0.3412408 0.0087626 0.07235942 313s 5 -1.3030839 0.142394 0.8487785 -0.5847851 0.0588053 -0.08968553 313s 6 1.7708705 0.674240 -0.4153759 -0.1915734 0.1382138 0.12454293 313s 7 2.3570866 0.381017 -0.8771357 -0.3739365 0.2918453 0.13437364 313s 8 2.5700714 0.695006 0.0061108 -0.4323695 0.1643797 -0.00469369 313s 9 -1.1725766 -2.713291 1.0677483 -0.0647875 0.1183120 -0.10762785 313s 10 -3.1357225 -1.255175 0.0666017 0.5083690 -0.1096080 -0.00647493 313s 11 -9.5333894 -1.608943 2.7307809 0.1690156 -0.1682415 -0.06597478 313s 12 -13.6028505 0.941083 2.0136258 -0.1076520 -0.0475905 -0.15295614 313s 13 -10.9497471 0.048776 -0.8765307 0.1518572 0.1428294 -0.00064406 313s 14 -12.6558378 -0.219444 1.1396273 -0.3734679 0.2875578 -0.23870524 313s 15 -10.6924790 -1.818075 3.4560731 -0.1177943 0.1101199 -0.19708172 313s 16 -3.0258070 -0.203186 1.2835368 0.5799363 0.3237454 0.23168871 313s 17 -0.7498665 -2.977505 1.6310512 0.6305329 0.3994006 0.06594881 313s 18 -2.5093526 0.924459 0.0899818 -0.4026675 0.2963072 0.11324019 313s 19 -1.9689970 -1.051282 1.4659908 0.3870104 -0.0708083 -0.02148354 313s 20 0.2695886 -1.646440 0.7597630 0.1750131 -0.3418142 0.21515143 313s 21 -3.3470252 1.989939 0.2887021 -0.3599779 0.0771965 0.16867095 313s 22 -1.4659204 0.777242 0.4090149 -0.1248050 0.1916768 -0.23160291 313s 23 -0.4944476 1.634130 0.8915509 0.1222296 -0.1231015 -0.08351169 313s 24 -0.8945477 1.239223 1.1117165 0.6018455 0.0912200 -0.01204668 313s 25 -4.1499992 1.860190 1.6062973 -0.2139736 -0.1140169 0.16632426 313s 26 -1.2647012 1.188058 1.1893430 -0.2740862 -0.0971504 -0.09851714 313s 27 -3.4280131 -0.267150 1.1969552 0.0354366 0.8482718 -0.18977667 313s 28 1.6896630 3.793723 0.7706325 0.1007287 0.0317704 -0.11269816 313s 29 3.9258127 1.691428 0.1850999 0.4485202 -0.2969916 0.16594044 313s 30 0.3178322 1.577233 0.4455231 -0.1687197 -0.1587136 -0.00823174 313s 31 0.9562350 2.258138 -1.4672169 0.2675668 0.1910110 0.03177387 313s 32 0.6738452 0.470764 -1.3496896 0.3524049 0.2008218 -0.36957179 313s 33 1.5980690 0.413899 0.1999664 0.4232293 0.0768479 -0.04627841 313s 34 0.4365091 -0.626490 0.4718364 0.3392252 0.2554060 -0.19018602 313s 35 -1.1184804 2.124234 0.2650931 0.4791171 0.2927791 -0.01579964 313s 36 3.6673986 1.659798 0.6138972 -0.1092158 -0.2705583 -0.16494176 313s 37 0.0867143 2.541765 -0.4572593 0.0024263 0.2163300 -0.20116352 313s 38 1.4191839 2.315690 0.1365887 0.1028375 0.1595780 -0.02049460 313s 39 -1.8062960 0.845438 1.1469588 0.5022406 0.1603011 -0.08751261 313s 40 3.4380914 -1.358545 0.1956896 0.6314649 0.0716078 -0.21591535 313s 41 3.4608782 1.828575 0.2012565 0.1064437 -0.7454169 -1.64629924 313s 42 6.4162310 -0.402642 0.8070441 0.5146855 0.0331594 0.04373032 313s 43 2.5906567 0.897993 -1.2612252 -0.2620162 -0.1432569 -0.10279385 313s 44 5.0299750 0.203721 0.0439110 0.8775684 -0.9536011 0.15153452 313s 45 -0.3555392 0.454930 0.1173992 0.4688991 0.1137820 0.18752442 313s 46 -0.4155426 1.892410 0.8649578 0.1827426 -0.0186113 -0.04029205 313s 47 1.9328817 0.121936 -3.9578157 -0.1135807 0.2971001 0.18733657 313s 48 -0.3947656 1.028405 -1.0370498 0.4467257 -0.1445498 0.16878692 313s 49 -2.8829860 0.279064 -1.4443310 0.5889970 -0.1883118 0.16947945 313s 50 3.2797246 -2.443968 0.4100655 0.4278962 -0.4414712 0.08598366 313s 51 -1.9272930 -0.622137 -1.5136862 -0.0483369 -0.0272502 0.16006066 313s 52 -5.7161590 -0.298434 -0.5216578 0.1385780 -0.2435931 0.10628617 313s 53 -1.1933277 -0.125878 -0.7556261 -0.3129372 -0.3166453 0.03078643 313s 54 -0.5994394 -0.031069 -0.1296378 0.0061490 -0.1869578 0.09839221 313s 55 0.4104586 0.733465 -0.2088065 -0.3645266 -0.1830137 0.04705775 313s 56 -0.2227671 -0.724741 0.1007592 -0.0838897 -0.1939960 -0.04223579 313s 57 -1.5706297 -0.292436 1.0849660 -0.2559591 -0.0917278 -0.27423151 313s 58 -0.4102168 1.263831 0.9082556 -0.4592777 -0.0676902 0.11089798 313s 59 -1.9640736 -1.340173 -0.3652736 -0.1267573 0.0775692 -0.07977644 313s 60 -1.7490968 -0.941370 -0.0849901 -0.3453455 0.2858594 0.06413468 313s 61 -0.1583416 -1.699326 0.2385988 -0.2231496 -0.0513883 -0.12227279 313s 62 2.2124878 -1.942366 0.0743514 0.2627321 -0.2844018 -0.15848039 313s 63 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 313s 64 2.4578489 0.226019 0.1148050 -0.2715718 0.2322085 0.22346659 313s 65 -0.3779208 -2.987354 0.6819006 0.1942611 0.0529259 0.01315140 313s 66 -2.6385498 -1.331204 -0.0367809 -0.2327572 0.1845076 -0.08521680 313s 67 0.0526645 -1.301299 0.0912198 0.1634869 -0.0068236 0.24131589 313s 68 -1.1013065 -2.004809 -1.9168056 0.0260663 -0.2029903 -0.12625268 313s 69 -0.9495853 -0.831697 0.0389476 -0.2123483 -0.0202267 0.38463410 313s 70 2.6935893 5.369312 0.6987368 -4.5754846 -9.6833013 -2.32910628 313s 71 -2.4037611 -1.983509 0.3109848 -0.1015686 -0.0071432 0.06410351 313s 72 2.0795505 -0.392730 -0.4534128 -0.4054224 -0.0312781 0.25408988 313s 73 -2.0038405 -2.874605 -0.6269939 0.2408421 0.5184666 0.11140104 313s 74 -11.2683996 -0.361851 3.9219448 0.4045689 -0.2203308 0.05930132 313s 75 -0.1028287 -2.295813 -0.7769187 0.3071821 0.4537196 0.00522380 313s 76 -1.8466137 -0.425825 -1.1261209 -0.1760585 0.0165729 -0.10698465 313s 77 -8.4124493 -1.174820 2.2700712 0.4213953 0.3446597 -0.20636892 313s 78 1.1103236 -1.299480 -0.5787732 -0.1455945 0.0732148 -0.01806218 313s 79 -0.5451834 -0.620170 -0.7830595 -0.1746479 0.0723052 -0.26017118 313s 80 -3.8647223 1.126328 1.3299567 0.2645241 -0.1881443 0.00485531 313s 81 0.7690939 0.887363 0.0513096 -0.2730980 0.0076447 -0.07590882 313s 82 2.7287618 -1.435327 0.1602865 0.4465859 0.2129425 0.16104418 313s 83 2.2241485 -0.042822 -0.8316486 -0.1230697 -0.1193057 -0.35207561 313s 84 0.2452905 0.491732 -2.0050683 0.0286567 -0.1159415 -0.24887542 313s 85 1.0655845 -2.360746 2.2456131 -0.1479972 -0.1186670 -0.14020891 313s 86 -0.0091659 0.952208 -1.3429189 -0.2944676 -0.2433277 0.15354490 313s PC7 PC8 313s 1 -0.09778744 2.3157e-03 313s 2 0.05189698 1.8077e-03 313s 3 0.70506895 1.2838e-03 313s 4 -0.08541140 3.2781e-04 313s 5 0.11768945 8.3496e-04 313s 6 -0.17886391 1.5222e-03 313s 7 0.14143613 1.3261e-04 313s 8 -0.07724578 7.1241e-04 313s 9 -0.12298048 -7.0110e-04 313s 10 0.07569878 2.3093e-05 313s 11 0.29299858 -3.4542e-04 313s 12 0.07764899 -2.1390e-03 313s 13 -0.08945524 -2.2633e-03 313s 14 0.03597787 -1.8891e-03 313s 15 0.11780498 -2.0279e-03 313s 16 0.46501534 -2.3266e-03 313s 17 0.08603290 -2.4073e-03 313s 18 0.52605757 -9.8822e-04 313s 19 0.31007227 -1.3919e-03 313s 20 0.61582059 -2.3549e-05 313s 21 0.01199350 -6.1649e-05 313s 22 0.03654587 1.3302e-05 313s 23 0.27549986 -3.6759e-04 313s 24 -0.04155354 -2.9882e-04 313s 25 0.11473708 -7.9629e-04 313s 26 0.06673183 -8.3728e-04 313s 27 0.16937729 -9.5775e-04 313s 28 -0.41753592 -7.5544e-05 313s 29 -0.03693100 -2.2481e-04 313s 30 0.08461537 -1.3611e-04 313s 31 0.02476253 -1.4319e-04 313s 32 -0.09756048 -1.2234e-04 313s 33 0.06442434 -2.4915e-04 313s 34 -0.17828409 -9.5882e-05 313s 35 0.00881239 -7.1427e-05 313s 36 -0.01041003 -2.8489e-04 313s 37 0.15994729 -3.1472e-04 313s 38 -0.22386895 6.1384e-04 313s 39 0.03666242 2.8506e-04 313s 40 0.35883231 -8.3062e-05 313s 41 0.18521851 8.5509e-04 313s 42 0.00733985 -6.4477e-04 313s 43 0.35466617 3.2923e-04 313s 44 -0.74952524 -7.6869e-05 313s 45 0.09907237 7.9128e-04 313s 46 0.05119980 1.0606e-03 313s 47 -0.48571583 -9.3780e-04 313s 48 -0.27463442 -2.7037e-04 313s 49 0.06787536 -3.0554e-05 313s 50 0.08499400 3.1181e-04 313s 51 -0.09197457 1.1213e-04 313s 52 -0.24513244 3.9100e-04 313s 53 0.24012780 3.2068e-04 313s 54 0.07999888 3.5689e-04 313s 55 -0.09825475 6.6675e-04 313s 56 0.05133674 -7.2984e-05 313s 57 -0.10302363 -2.0693e-04 313s 58 -0.12323360 -1.6620e-04 313s 59 -0.05119989 -1.1016e-03 313s 60 0.00082131 -3.2951e-04 313s 61 0.08128272 -1.1550e-04 313s 62 -0.01789040 -1.1579e-04 313s 63 -0.07188070 -7.8367e-04 313s 64 -0.07188070 -7.8367e-04 313s 65 0.00917085 -2.6800e-05 313s 66 0.03121573 -5.3492e-05 313s 67 0.12202335 -3.0466e-04 313s 68 -0.04764366 -2.6126e-04 313s 69 0.13828337 -3.9331e-04 313s 70 0.10401069 4.2870e-03 313s 71 -0.14369640 3.7669e-05 313s 72 -0.10334451 -2.6456e-04 313s 73 0.17655402 1.0917e-04 313s 74 0.26779696 1.8685e-03 313s 75 -0.75016549 2.1079e-05 313s 76 0.01802016 7.7555e-04 313s 77 0.13081368 6.4286e-04 313s 78 0.01409131 4.9476e-04 313s 79 0.06643384 2.6590e-04 313s 80 -0.12624376 5.9801e-04 313s 81 -0.14074469 -3.2172e-04 313s 82 0.09228230 4.4064e-04 313s 83 -0.06352151 -3.6274e-04 313s 84 -0.02642452 -3.9742e-04 313s 85 -0.03502188 6.9814e-04 313s 86 -0.11749109 -5.1283e-04 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 2.39971451 1.49157920 0.93184037 0.33183258 0.19628996 0.16485446 0.12784351 313s [8] 0.00052622 313s ---------------------------------------------------------- 313s bushfire 38 5 5 11393.979994 197.523453 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 313s 1 -91.383 -16.17804 0.56195 -0.252428 1.261840 313s 2 -93.033 -13.93251 -0.67212 0.042287 0.470924 313s 3 -85.400 -10.72512 -3.09832 -1.224797 -0.504718 313s 4 -68.381 -12.12202 -3.31950 -0.676880 -0.228383 313s 5 -36.742 -21.04171 -1.98872 0.397655 -0.932613 313s 6 -12.095 -30.21719 0.59595 2.100702 0.384714 313s 7 -227.949 -71.40450 35.57308 -7.880296 -2.710415 313s 8 -262.815 -111.81228 -11.04574 2.397832 -13.646407 313s 9 -263.767 -114.13702 -13.71407 3.131736 -13.825200 313s 10 -264.312 -90.69643 9.72320 0.967173 -8.800150 313s 11 -266.681 -72.85993 16.55010 0.291092 -8.373583 313s 12 -274.050 -18.41395 20.74273 -2.464589 -1.505967 313s 13 -218.299 19.16040 7.69765 0.069012 0.054846 313s 14 29.646 10.52526 -7.50754 0.855493 1.966680 313s 15 159.575 3.86633 -6.95837 -2.753953 0.616068 313s 16 114.286 2.47164 0.62690 -3.146317 -0.501623 313s 17 111.289 3.45086 1.97182 -0.303064 -0.094416 313s 18 99.626 -1.80416 4.88197 -0.013096 -1.438397 313s 19 103.353 -3.50426 3.58993 1.578169 -1.317194 313s 20 113.769 0.84544 3.28254 2.204926 0.131167 313s 21 95.186 3.50703 4.97153 0.916181 0.351658 313s 22 86.996 4.00938 2.95209 1.281788 1.920404 313s 23 -44.232 8.50898 6.30689 -1.038871 0.400078 313s 24 -99.527 13.81377 1.75130 -0.260669 0.394804 313s 25 -34.855 5.99709 -0.57224 -1.660513 -0.620158 313s 26 -41.265 2.94659 -1.04825 -2.243950 -0.440017 313s 27 -56.148 10.14428 -5.41858 0.321752 -0.608412 313s 28 -32.366 20.27795 -8.60687 3.806572 -1.267249 313s 29 -22.438 34.73585 -11.19123 8.296154 -0.511610 313s 30 -79.035 37.05713 -1.51591 9.892959 -1.618635 313s 31 49.465 39.37414 5.95714 22.874813 -1.883481 313s 32 304.825 30.19205 37.68900 45.175923 -1.293939 313s 33 341.237 7.04985 65.43451 44.553009 -3.148116 313s 34 337.467 6.16879 66.48222 43.278480 -3.688631 313s 35 342.929 7.38548 66.91291 43.941556 -1.937887 313s 36 340.143 6.70203 67.85433 42.479161 -3.873639 313s 37 337.931 7.43184 70.50828 42.333220 -2.645830 313s 38 339.281 8.07267 71.34405 42.400459 -2.392774 313s ------------- 313s Call: 313s PcaCov(x = x) 313s 313s Standard deviations: 313s [1] 106.7426 14.0543 4.9184 1.8263 1.0193 313s ---------------------------------------------------------- 313s ========================================================== 313s > dodata(method="grid") 313s 313s Call: dodata(method = "grid") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 2 516.143549 23.932102 313s Scores: 313s PC1 PC2 313s [1,] 6.4694 3.8179 313s [2,] 61.7387 19.1814 313s [3,] 1.4722 -1.0161 313s [4,] -3.8056 1.5127 313s [5,] 18.6760 5.3303 313s [6,] -16.8411 1.7900 313s [7,] 4.9962 4.1638 313s [8,] -26.8665 -13.3010 313s [9,] -1.0648 -1.2690 313s [10,] -25.7734 -12.4037 313s [11,] -13.3987 -4.0751 313s [12,] 46.7700 15.1272 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 22.719 4.892 313s ---------------------------------------------------------- 313s starsCYG 47 2 2 0.473800 0.026486 313s Scores: 313s PC1 PC2 313s [1,] 0.181489 -0.0300854 313s [2,] 0.695337 0.1492475 313s [3,] -0.120738 -0.1338110 313s [4,] 0.695337 0.1492475 313s [5,] 0.140039 -0.0992368 313s [6,] 0.413314 0.0551030 313s [7,] -0.409428 -0.5478860 313s [8,] 0.225647 0.1690378 313s [9,] 0.519123 -0.1471454 313s [10,] 0.071513 -0.0277935 313s [11,] 0.663045 -0.9203119 313s [12,] 0.402691 0.0253179 313s [13,] 0.373739 0.0759321 313s [14,] -1.005756 -0.3654219 313s [15,] -0.789968 -0.0898580 313s [16,] -0.467328 0.0334465 313s [17,] -1.111148 -0.1431778 313s [18,] -0.867242 0.0417806 313s [19,] -0.871200 -0.1481782 313s [20,] 0.823011 -0.9236455 313s [21,] -0.669994 -0.0923582 313s [22,] -0.829959 -0.0890246 313s [23,] -0.627294 0.0367802 313s [24,] -0.195929 0.0978059 313s [25,] -0.028257 -0.0157122 313s [26,] -0.387346 0.0317797 313s [27,] -0.390054 -0.0981920 313s [28,] -0.148231 -0.0132120 313s [29,] -0.661454 -0.1625514 313s [30,] 0.982767 -0.9369769 313s [31,] -0.628127 -0.0032112 313s [32,] 0.055476 0.1625819 313s [33,] 0.173158 0.0501056 313s [34,] 1.222924 -0.9319795 313s [35,] -0.711235 -0.1515118 313s [36,] 0.576613 0.2117347 313s [37,] 0.054851 0.1325884 313s [38,] 0.173158 0.0501056 313s [39,] 0.134833 0.1309216 313s [40,] 0.522665 0.0228177 313s [41,] -0.428171 -0.0073782 313s [42,] 0.013192 0.0534392 313s [43,] 0.294173 0.0975945 313s [44,] 0.293132 0.0476054 313s [45,] 0.495172 0.1434167 313s [46,] -0.066790 0.0551060 313s [47,] -0.547311 0.0351134 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 0.68833 0.16275 313s ---------------------------------------------------------- 313s phosphor 18 2 2 392.155327 50.657228 313s Scores: 313s PC1 PC2 313s 1 5.6537 -15.2305 313s 2 -21.2150 -1.8862 313s 3 -23.5966 2.3112 313s 4 -11.2742 -6.6000 313s 5 -18.4067 1.5202 313s 6 16.9795 -19.4039 313s 7 1.5964 -3.1666 313s 8 -9.7354 3.2429 313s 9 -10.8594 5.4759 313s 10 15.5585 -6.5279 313s 11 -4.0058 1.2905 313s 12 9.4815 8.2139 313s 13 13.0640 6.4346 313s 14 7.0230 7.7600 313s 15 18.4378 3.7658 313s 16 -8.9047 -6.3253 313s 17 21.8748 6.1900 313s 18 16.9843 12.0801 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 19.8029 7.1174 313s ---------------------------------------------------------- 313s stackloss 21 3 3 109.445054 16.741203 313s Scores: 313s PC1 PC2 PC3 313s [1,] 15.136434 14.82909 -2.0387704 313s [2,] 14.393636 15.46816 -1.8391595 313s [3,] 12.351209 10.12290 -2.3458098 313s [4,] 2.510036 2.07589 1.8251581 313s [5,] 1.767140 1.78527 -0.0088651 313s [6,] 2.138588 1.93058 0.9081465 313s [7,] 6.966825 -1.75851 0.6274924 313s [8,] 6.966825 -1.75851 0.6274924 313s [9,] -0.089513 -1.09062 2.2894224 313s [10,] -7.146340 2.65628 -0.8983590 313s [11,] -0.461157 -3.09532 -2.6948576 313s [12,] -1.575403 -2.60157 -3.4122582 313s [13,] -5.660744 1.37815 -1.2975809 313s [14,] 2.881484 -5.50628 -2.5762898 313s [15,] -4.917360 -9.13772 0.0676942 313s [16,] -7.145755 -7.22052 0.6665270 313s [17,] -17.173481 1.87173 4.3780920 313s [18,] -11.973894 -2.60174 2.9808153 313s [19,] -10.859648 -3.09549 3.6982160 313s [20,] -6.031899 0.15817 1.2270803 313s [21,] 8.451640 4.98077 -5.4038839 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 10.4616 4.0916 2.8271 313s ---------------------------------------------------------- 313s salinity 28 3 3 14.911546 8.034974 313s Scores: 313s PC1 PC2 PC3 313s 1 -2.72400 0.79288 0.688038 313s 2 -3.45684 0.86162 1.941690 313s 3 -5.73471 -4.79507 0.129202 313s 4 -6.17045 -3.04372 -0.352797 313s 5 -4.72453 -5.59543 4.144851 313s 6 -5.75447 -1.07062 0.579975 313s 7 -4.40759 0.47731 0.680203 313s 8 -2.76360 2.30716 0.540271 313s 9 -0.28782 -1.40644 -2.373399 313s 10 2.64361 -1.43362 -0.266957 313s 11 1.91078 -1.66975 1.312215 313s 12 -0.40661 0.68573 -0.200135 313s 13 -0.14911 1.88993 0.044001 313s 14 1.99005 2.43874 1.373229 313s 15 2.88128 -2.21263 -0.863674 313s 16 -0.12935 -8.28831 6.483875 313s 17 -0.16895 -1.68742 0.905190 313s 18 -3.08054 0.23753 -0.269165 313s 19 -0.38685 -1.08501 -2.736860 313s 20 1.45520 -0.33209 -1.686406 313s 21 1.13834 2.53553 -0.381657 313s 22 2.48522 3.42927 0.417050 313s 23 4.56487 -3.36542 0.711908 313s 24 2.94072 -3.08490 1.556939 313s 25 0.82140 -0.26895 -0.406490 313s 26 1.17794 1.61119 -0.863764 313s 27 2.02965 2.80707 -0.489050 313s 28 2.98039 3.21462 0.747622 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 3.86155 2.83460 0.95394 313s ---------------------------------------------------------- 313s hbk 75 3 3 3.714805 3.187126 313s Scores: 313s PC1 PC2 PC3 313s 1 8.423138 24.765818 19.413334 313s 2 7.823138 25.295092 20.356662 313s 3 9.023138 27.411905 20.218454 313s 4 8.223138 28.010236 21.568269 313s 5 8.623138 27.442650 21.123471 313s 6 9.123138 25.601873 20.279943 313s 7 8.823138 25.463855 20.770811 313s 8 8.223138 25.264348 19.451646 313s 9 8.023138 27.373593 20.716984 313s 10 7.623138 26.752275 19.666288 313s 11 9.323138 31.108975 24.313778 313s 12 10.323138 33.179719 23.469966 313s 13 10.323138 29.958667 26.231274 313s 14 9.323138 29.345676 34.207755 313s 15 1.723138 -0.077538 0.754886 313s 16 1.423138 -1.818609 -0.080979 313s 17 -1.676862 -1.872341 -0.686878 313s 18 0.623138 -0.077633 -0.548955 313s 19 -0.876862 -0.576068 0.716574 313s 20 1.423138 -0.016144 1.261078 313s 21 0.923138 -0.223313 0.041619 313s 22 -1.276862 -0.299937 1.038679 313s 23 0.323138 -1.327742 0.057038 313s 24 -0.376862 -1.626860 0.034051 313s 25 -0.676862 -1.550331 -2.266849 313s 26 -0.776862 0.290637 1.184359 313s 27 1.623138 0.750760 0.417361 313s 28 0.123138 -0.016334 -1.346603 313s 29 -0.476862 -1.220468 -1.338846 313s 30 -0.476862 1.387213 -1.339036 313s 31 1.423138 -1.059368 -0.824991 313s 32 -1.176862 -1.833934 0.118433 313s 33 -0.176862 -0.691099 0.908323 313s 34 -1.276862 -1.251213 -2.243862 313s 35 1.423138 0.858128 0.325317 313s 36 -0.576862 0.574335 0.102918 313s 37 -1.576862 0.413330 0.892903 313s 38 -0.176862 -1.841691 -1.085702 313s 39 0.423138 -0.752683 -2.205550 313s 40 -1.176862 -0.905930 -0.211430 313s 41 1.723138 0.819721 -0.479993 313s 42 -1.376862 0.666284 -1.093554 313s 43 -1.576862 -1.304659 1.061761 313s 44 0.123138 1.203126 -1.553772 313s 45 0.223138 -1.358581 -2.151818 313s 46 0.123138 1.003714 -1.569097 313s 47 1.323138 -1.159169 -2.136494 313s 48 1.423138 0.919427 -0.472331 313s 49 1.423138 -0.246300 0.340737 313s 50 0.423138 0.727773 0.716479 313s 51 0.623138 -1.665267 -0.771259 313s 52 1.623138 -0.798657 -1.607314 313s 53 -1.376862 1.310494 -1.645816 313s 54 -0.576862 -1.879908 0.716669 313s 55 -1.176862 -1.235698 0.164407 313s 56 0.123138 -1.296997 0.962055 313s 57 0.123138 -1.304849 -1.545920 313s 58 0.723138 -0.714086 1.207441 313s 59 -0.076862 0.881115 0.026199 313s 60 -1.376862 1.226208 -0.549050 313s 61 -1.276862 0.781504 1.322377 313s 62 -0.776862 -1.657699 -2.174806 313s 63 -0.576862 -1.956627 0.409888 313s 64 1.123138 0.712448 0.915891 313s 65 0.323138 0.689271 -1.392672 313s 66 -1.476862 -1.289430 -0.441492 313s 67 -0.076862 -0.905930 -0.211430 313s 68 -1.576862 -0.852389 -2.213213 313s 69 0.323138 -1.696011 -1.676276 313s 70 -0.676862 0.773747 0.118243 313s 71 0.523138 0.152524 0.371386 313s 72 -1.076862 -0.606812 -0.188443 313s 73 -1.376862 0.114117 -0.433924 313s 74 -1.676862 -0.522431 0.018632 313s 75 -1.376862 0.612552 -1.699453 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 1.9274 1.7853 1.6714 313s ---------------------------------------------------------- 313s milk 86 8 8 9.206694 2.910585 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 PC6 313s [1,] 6.090978 0.590424 1.1644466 -0.3835606 1.0342867 -0.4752288 313s [2,] 6.903009 -0.575027 0.8613622 -1.1221795 0.7221616 -1.3097951 313s [3,] 0.622903 -1.594239 1.2122863 -0.0555128 0.3252629 -0.2799581 313s [4,] 5.282665 -1.815742 2.2543268 0.9824543 -0.5345577 -0.7331037 313s [5,] -1.039753 0.663906 0.3353811 0.3070599 -0.3224317 -0.4056666 313s [6,] 2.247786 0.218255 -0.3382923 0.1270005 -0.0271307 -0.2035021 313s [7,] 2.784293 -0.291678 -0.4897587 0.0198481 0.0752345 -0.5986846 313s [8,] 2.942266 0.315608 0.1603961 0.3568462 -0.0647311 -0.5316127 313s [9,] -1.420086 -1.751212 1.7027572 0.0708340 -0.9226517 0.0738411 313s [10,] -2.921113 -0.727554 0.0113966 -0.3915037 -0.0772913 0.6062573 313s [11,] -9.568075 0.792291 1.0217507 0.2554182 -0.6254883 0.8899897 313s [12,] -12.885166 3.423607 -1.2579351 -0.4300397 -0.4094558 1.1727128 313s [13,] -10.038470 1.274931 -2.6913262 -1.6219658 -0.3284974 1.1228303 313s [14,] -12.044003 2.096254 -1.2859668 -0.9602250 -0.7937418 0.8264019 313s [15,] -10.798341 1.159257 1.4870766 0.3248231 -1.0787537 0.8723637 313s [16,] -2.841629 0.500846 0.4771762 0.5975365 0.3197882 0.5804087 313s [17,] -1.150691 -1.978038 2.3229313 0.5275273 -0.5339514 0.5421631 313s [18,] -1.992369 1.131288 -0.8385615 0.1156462 0.2253010 -0.3393814 313s [19,] -1.999699 -0.252876 1.2229972 0.5081648 0.0082612 0.3373454 313s [20,] 0.091385 -1.439422 1.1836134 0.6297789 0.0961407 -0.2126653 313s [21,] -2.571346 2.280701 -1.2845660 0.1463583 0.0949331 0.0902039 313s [22,] -0.990078 1.087033 -0.1638640 -0.0351472 0.0743205 -0.0040605 313s [23,] -0.010631 1.704171 0.0038808 0.5765418 0.6086460 0.0329995 313s [24,] -0.440350 1.500798 0.2769870 0.5556999 0.4751445 0.6516120 313s [25,] -3.578249 2.672783 -0.3534268 0.7398104 0.1108289 0.2704730 313s [26,] -0.854914 1.626684 0.2301131 0.5530224 0.0662862 -0.0999969 313s [27,] -3.175381 0.762609 0.5101987 0.0849002 -0.2137237 0.2729808 313s [28,] 2.599844 3.370137 -0.5174736 0.7409946 0.6853156 0.2430943 313s [29,] 4.395534 0.823611 0.1610152 0.8184845 0.7665555 0.0779724 313s [30,] 0.843794 1.438263 -0.2366601 0.4600650 0.3424806 -0.1768083 313s [31,] 1.890815 1.266935 -1.8218143 -0.3909337 0.8390127 0.1026821 313s [32,] 1.300145 -0.085976 -0.8965312 -0.8855787 0.4156780 0.1478055 313s [33,] 1.923087 0.137638 0.3487435 0.2958367 0.4245932 0.1566678 313s [34,] 0.615762 -0.390711 0.8107376 0.0295536 -0.1169590 0.2940241 313s [35,] -0.372946 2.037079 -0.7663299 0.1907237 0.6959350 0.5366205 313s [36,] 4.068134 1.129044 0.5492962 0.7640964 0.4799859 -0.4080205 313s [37,] 0.937617 2.048258 -1.2326566 -0.0942856 0.7885267 -0.1004018 313s [38,] 2.141223 1.877022 -0.5178216 0.3750868 0.4767003 0.1240656 313s [39,] -1.403505 1.327163 0.3165610 0.3989824 0.3505825 0.5915956 313s [40,] 3.337528 -1.689495 1.4737175 0.2584843 0.4308444 -0.0810597 313s [41,] 3.938506 1.384908 0.8103687 -0.5875595 1.1616535 -0.6492603 313s [42,] 6.327471 -1.061362 1.9861187 1.1016484 0.3512405 -0.1540592 313s [43,] 3.120160 -0.064108 -0.8370717 -0.2229341 0.5623447 -0.7152184 313s [44,] 5.290520 -0.669008 0.8597130 0.5518503 0.2470856 0.6454703 313s [45,] 0.058291 0.356399 -0.1896007 0.2427518 0.3705541 0.3975085 313s [46,] 0.150881 1.942057 -0.1140726 0.5656469 0.5227623 0.2151825 313s [47,] 2.870881 -1.446283 -2.8450062 -1.7292144 -0.0888429 -0.1347003 313s [48,] 0.335593 0.500884 -1.3154520 -0.3874864 0.3449038 0.5387692 313s [49,] -2.179494 -0.021237 -1.7792344 -0.8445930 0.4435338 0.6547961 313s [50,] 2.968304 -2.588546 1.8552104 0.4590101 -0.1755089 -0.0550378 313s [51,] -1.399208 -0.820296 -1.3660014 -0.8890243 -0.2344105 0.1236943 313s [52,] -5.112989 0.318983 -1.3852993 -0.8461529 -0.3467685 0.7349666 313s [53,] -0.773103 -0.267333 -0.8154896 -0.3783062 0.0113880 -0.3304648 313s [54,] -0.244565 -0.066211 -0.2541557 0.0043037 0.0390890 0.0074067 313s [55,] 0.894921 0.516411 -0.4443369 0.0708354 -0.0637890 -0.2799646 313s [56,] -0.038706 -0.588256 0.3166588 -0.0196663 -0.1793472 -0.1179341 313s [57,] -1.377469 0.428939 0.7502430 0.1458375 -0.3818977 -0.0380258 313s [58,] 0.042787 1.488605 0.0252606 0.6377516 -0.1524172 -0.1898723 313s [59,] -1.734357 -0.966494 -0.1026850 -0.5656888 -0.4831402 0.0308069 313s [60,] -1.501991 -0.544918 -0.0837127 -0.2362486 -0.5382026 -0.1351338 313s [61,] -0.175102 -1.339436 0.8403933 -0.0907428 -0.4846145 -0.2795153 313s [62,] 2.100915 -2.004702 1.3031556 -0.0041957 -0.2067776 -0.0793613 313s [63,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 313s [64,] 2.735432 -0.102018 0.3215454 0.5331904 -0.1499209 -0.3536272 313s [65,] -0.665219 -2.325594 1.6287363 0.0607163 -0.6996720 0.1353325 313s [66,] -2.439244 -0.737375 0.0187770 -0.4561269 -0.5425315 -0.0208332 313s [67,] 0.121564 -1.214385 0.4877707 0.1809998 -0.1943262 0.0662506 313s [68,] -0.804267 -2.238327 -0.8547917 -1.3449926 -0.3577254 -0.0293779 313s [69,] -0.761319 -0.676391 -0.0245494 0.2262894 -0.3396872 -0.1166505 313s [70,] 3.385399 4.360467 -0.7946150 -0.0417895 0.4474362 -4.6626174 313s [71,] -2.364955 -1.257673 0.5226907 -0.2346145 -0.7838777 0.1815821 313s [72,] 2.334511 -0.794530 0.0175620 0.1848925 -0.3437761 -0.4522442 313s [73,] -2.023440 -2.449907 0.2525041 -0.6657474 -0.5509480 0.2118442 313s [74,] -11.180192 2.456516 1.1036540 0.8711496 -0.3833194 1.3548314 313s [75,] 0.058297 -2.094811 0.3075211 -0.8052760 -0.9527729 0.5850255 313s [76,] -1.355742 -0.464355 -1.0183333 -0.8525619 -0.1577144 -0.0767323 313s [77,] -8.296881 0.945092 0.8088967 -0.0071463 -0.4527530 1.0614233 313s [78,] 1.251696 -1.460466 0.2511701 -0.2717606 -0.3158308 -0.2964813 313s [79,] -0.192380 -0.662365 -0.3671703 -0.6722658 -0.1243452 -0.2388225 313s [80,] -3.355201 1.915096 -0.1086672 0.3560062 0.0956865 0.6974817 313s [81,] 1.245305 0.736787 -0.1662155 0.1309822 -0.0122872 -0.2182528 313s [82,] 2.679561 -1.666401 1.1576691 0.3960280 -0.0059146 0.0584136 313s [83,] 2.596651 -0.556654 -0.0807307 -0.4468501 0.0964927 -0.3922894 313s [84,] 0.959377 -0.272038 -1.5879803 -1.1153057 0.3412508 -0.1281556 313s [85,] 0.602737 -1.384591 2.8844745 0.9479144 -0.7946454 -0.2014038 313s [86,] 0.698125 0.335743 -1.5248055 -0.4443037 0.0768256 -0.1999790 313s PC7 PC8 313s [1,] 0.9281777 -0.05158594 313s [2,] 0.8397946 -0.04276628 313s [3,] -0.5189230 0.04913688 313s [4,] -0.0178377 0.01578074 313s [5,] -0.0129237 0.01056305 313s [6,] -0.0764270 0.01469518 313s [7,] -0.3059779 0.04237267 313s [8,] -0.0684673 0.02289928 313s [9,] -0.2549733 -0.00832119 313s [10,] -0.0578118 -0.01894694 313s [11,] 0.0415545 -0.03474479 313s [12,] 0.0869267 -0.04485633 313s [13,] -0.2843977 -0.03100709 313s [14,] -0.3375083 -0.02155574 313s [15,] -0.1718828 -0.02996980 313s [16,] -0.4176728 0.03232381 313s [17,] -0.5923252 0.01765700 313s [18,] -0.3190679 0.04476532 313s [19,] -0.0279426 -0.00236626 313s [20,] 0.1299811 0.00586022 313s [21,] 0.0474059 0.00563264 313s [22,] -0.1240299 0.01123557 313s [23,] 0.2232631 0.00551065 313s [24,] 0.0122404 0.00060079 313s [25,] 0.2627442 -0.00824800 313s [26,] 0.2257329 -0.00440907 313s [27,] -0.8496967 0.05266701 313s [28,] 0.3473502 -0.00500580 313s [29,] 0.4172329 -0.00542705 313s [30,] 0.2773880 -0.00014648 313s [31,] -0.1224270 0.02372808 313s [32,] -0.2224748 0.00757892 313s [33,] -0.0633903 0.01236118 313s [34,] -0.2616599 0.00561781 313s [35,] -0.1671986 0.01988458 313s [36,] 0.4502086 -0.00418541 313s [37,] -0.0773232 0.02768282 313s [38,] 0.0464683 0.01134849 313s [39,] -0.0927182 0.00555823 313s [40,] -0.2162796 0.02467605 313s [41,] 0.9440753 -0.04806541 313s [42,] -0.0078920 0.02022925 313s [43,] 0.1152244 0.02074199 313s [44,] 1.0406693 -0.08815111 313s [45,] -0.1376804 0.01424369 313s [46,] 0.1673461 0.00442877 313s [47,] -0.4125225 0.01038694 313s [48,] 0.1556289 -0.02103354 313s [49,] 0.0434415 -0.01782739 313s [50,] 0.2518610 -0.02154540 313s [51,] -0.1186185 -0.00881133 313s [52,] 0.1507435 -0.04523343 313s [53,] 0.2161208 -0.00967982 313s [54,] 0.1374909 -0.00783970 313s [55,] 0.2417108 -0.00895268 313s [56,] 0.1253846 -0.01188643 313s [57,] 0.1390898 -0.01831232 313s [58,] 0.2219634 -0.00364174 313s [59,] -0.2045636 -0.00589047 313s [60,] -0.3679942 0.01673699 313s [61,] -0.0705611 -0.00273407 313s [62,] 0.1447701 -0.02026768 313s [63,] -0.1854788 0.02686899 313s [64,] -0.1854788 0.02686899 313s [65,] -0.2626650 -0.00376657 313s [66,] -0.3044266 0.00484197 313s [67,] -0.1358811 0.00605789 313s [68,] -0.0551482 -0.02379410 313s [69,] -0.0914891 0.00812122 313s [70,] 10.2524854 -0.64367029 313s [71,] -0.1326972 -0.01666774 313s [72,] 0.0051905 0.00656777 313s [73,] -0.8236843 0.03367265 313s [74,] 0.2140104 -0.04092219 313s [75,] -0.5684260 -0.00987116 313s [76,] -0.1225779 -0.00204629 313s [77,] -0.4235612 -0.00450631 313s [78,] -0.1935155 0.00973901 313s [79,] -0.1615883 0.00518643 313s [80,] 0.2915052 -0.02960159 313s [81,] 0.0908823 0.00038216 313s [82,] -0.3392789 0.02605374 313s [83,] 0.1112141 -0.00629308 313s [84,] 0.0510771 -0.00845572 313s [85,] 0.0748700 -0.01174487 313s [86,] 0.2488127 -0.01446339 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 3.034253 1.706044 1.167717 0.670864 0.536071 0.396285 0.266625 0.020768 313s ---------------------------------------------------------- 313s bushfire 38 5 5 38232.614428 1580.825276 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 313s [1,] -67.120 -23.70481 -1.06551 1.129721 1.311630 313s [2,] -69.058 -21.42113 -1.54798 0.983735 0.430774 313s [3,] -61.939 -17.23665 -3.81386 -0.635074 -0.600149 313s [4,] -44.952 -16.53458 -5.16114 0.411753 -0.390518 313s [5,] -12.644 -21.62271 -7.14146 3.519877 -1.211923 313s [6,] 12.820 -27.86930 -7.66114 7.230422 0.040330 313s [7,] -194.634 -100.67730 27.43084 -0.026242 -0.134248 313s [8,] -229.349 -129.75912 -19.46346 25.591651 -18.592601 313s [9,] -230.306 -131.28743 -22.22175 27.251157 -19.214683 313s [10,] -231.118 -115.10815 3.70208 16.303210 -10.573515 313s [11,] -234.540 -100.24984 13.67112 10.325539 -8.727961 313s [12,] -246.507 -51.03515 27.61698 -5.352226 0.514087 313s [13,] -195.712 -5.81324 20.04485 -9.226807 1.721886 313s [14,] 49.881 16.90911 -9.97400 -1.900739 2.190429 313s [15,] 179.545 23.96999 -18.71166 -2.987136 1.332713 313s [16,] 135.356 15.81282 -9.24353 -4.703584 0.971669 313s [17,] 132.350 16.65014 -7.01838 -2.428578 1.346198 313s [18,] 121.499 9.75832 -4.45699 -1.587450 0.131923 313s [19,] 125.222 9.17601 -5.88919 0.582516 -0.061642 313s [20,] 135.112 14.63812 -5.90351 0.411704 1.460488 313s [21,] 116.581 14.47390 -3.04021 -1.842579 2.005998 313s [22,] 108.223 14.62103 -4.47428 -1.196993 3.288463 313s [23,] -22.095 3.26439 6.58391 -6.164581 2.125258 313s [24,] -77.831 3.46616 6.59280 -6.373595 1.545789 313s [25,] -13.092 3.41344 -0.99296 -5.076733 0.299636 313s [26,] -19.206 -0.17007 -1.84209 -4.858675 0.347945 313s [27,] -35.022 6.54155 -3.12767 -3.556587 -0.327873 313s [28,] -12.651 20.14894 -4.61607 -2.025539 -1.214190 313s [29,] -4.404 36.39823 -3.81590 -0.633155 -0.602027 313s [30,] -60.018 30.40980 9.44610 -1.763156 -0.765133 313s [31,] 67.689 47.40087 12.70229 9.791794 -0.671751 313s [32,] 324.134 63.46147 31.52512 30.099817 2.406344 313s [33,] 364.639 38.84260 51.20467 30.648590 3.218678 313s [34,] 361.089 37.09494 52.00522 29.394356 2.861158 313s [35,] 366.403 38.88889 52.31879 29.878844 4.650618 313s [36,] 363.821 37.40859 53.10394 28.286557 2.922632 313s [37,] 361.761 37.21276 55.73012 27.648760 4.477279 313s [38,] 363.106 37.78395 56.56345 27.460078 4.845396 313s ------------- 313s Call: 313s PcaGrid(x = x) 313s 313s Standard deviations: 313s [1] 195.5316 39.7596 11.7329 7.3743 1.7656 313s ---------------------------------------------------------- 313s ========================================================== 313s > 313s > ## IGNORE_RDIFF_BEGIN 313s > dodata(method="proj") 313s 313s Call: dodata(method = "proj") 313s Data Set n p k e1 e2 313s ========================================================== 313s heart 12 2 2 512.772467 29.052346 313s Scores: 313s PC1 PC2 313s [1,] 6.7568 3.2826 313s [2,] 63.0869 14.1293 313s [3,] 1.3852 -1.1318 313s [4,] -3.6709 1.8153 313s [5,] 19.0457 3.8035 313s [6,] -16.6413 3.1452 313s [7,] 5.3163 3.7464 313s [8,] -27.8536 -11.0863 313s [9,] -1.1638 -1.1788 313s [10,] -26.6915 -10.2803 313s [11,] -13.6842 -2.9790 313s [12,] 47.8395 11.2980 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 22.644 5.390 313s ---------------------------------------------------------- 313s starsCYG 47 2 2 0.470874 0.024681 313s Scores: 313s PC1 PC2 313s [1,] 0.181333 -3.1013e-02 313s [2,] 0.696091 1.4569e-01 313s [3,] -0.121421 -1.3319e-01 313s [4,] 0.696091 1.4569e-01 313s [5,] 0.139530 -9.9951e-02 313s [6,] 0.413590 5.2989e-02 313s [7,] -0.412224 -5.4579e-01 313s [8,] 0.226508 1.6788e-01 313s [9,] 0.518364 -1.4980e-01 313s [10,] 0.071370 -2.8159e-02 313s [11,] 0.658332 -9.2369e-01 313s [12,] 0.402815 2.3259e-02 313s [13,] 0.374123 7.4020e-02 313s [14,] -1.007611 -3.6028e-01 313s [15,] -0.790417 -8.5818e-02 313s [16,] -0.467151 3.5835e-02 313s [17,] -1.111866 -1.3750e-01 313s [18,] -0.867017 4.6214e-02 313s [19,] -0.871946 -1.4372e-01 313s [20,] 0.818278 -9.2784e-01 313s [21,] -0.670457 -8.8932e-02 313s [22,] -0.830403 -8.4781e-02 313s [23,] -0.627097 3.9987e-02 313s [24,] -0.195426 9.8806e-02 313s [25,] -0.028337 -1.5568e-02 313s [26,] -0.387178 3.3760e-02 313s [27,] -0.390551 -9.6197e-02 313s [28,] -0.148297 -1.2454e-02 313s [29,] -0.662277 -1.5917e-01 313s [30,] 0.977965 -9.4199e-01 313s [31,] -0.628135 -7.3179e-16 313s [32,] 0.056306 1.6230e-01 313s [33,] 0.173412 4.9220e-02 313s [34,] 1.218143 -9.3822e-01 313s [35,] -0.712000 -1.4787e-01 313s [36,] 0.577688 2.0878e-01 313s [37,] 0.055528 1.3231e-01 313s [38,] 0.173412 4.9220e-02 313s [39,] 0.135501 1.3023e-01 313s [40,] 0.522775 2.0145e-02 313s [41,] -0.428203 -5.1892e-03 313s [42,] 0.013465 5.3371e-02 313s [43,] 0.294668 9.6089e-02 313s [44,] 0.293371 4.6106e-02 313s [45,] 0.495898 1.4088e-01 313s [46,] -0.066508 5.5447e-02 313s [47,] -0.547124 3.7911e-02 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 0.6862 0.1571 313s ---------------------------------------------------------- 313s phosphor 18 2 2 388.639033 51.954664 313s Scores: 313s PC1 PC2 313s 1 5.8164 -15.1691 313s 2 -21.1936 -2.1132 313s 3 -23.6199 2.0585 313s 4 -11.2029 -6.7203 313s 5 -18.4220 1.3231 313s 6 17.1862 -19.2211 313s 7 1.6302 -3.1493 313s 8 -9.7695 3.1385 313s 9 -10.9174 5.3594 313s 10 15.6275 -6.3610 313s 11 -4.0194 1.2476 313s 12 9.3931 8.3149 313s 13 12.9944 6.5741 313s 14 6.9396 7.8348 313s 15 18.3964 3.9629 313s 16 -8.8365 -6.4202 313s 17 21.8073 6.4237 313s 18 16.8541 12.2611 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 19.714 7.208 313s ---------------------------------------------------------- 313s stackloss 21 3 3 97.347030 38.052774 313s Scores: 313s PC1 PC2 PC3 313s [1,] 19.08066 -9.06092 -2.64544 313s [2,] 18.55152 -9.90152 -2.76118 313s [3,] 15.04269 -5.37517 -2.31373 313s [4,] 2.79667 -1.78925 1.70823 313s [5,] 2.21768 -1.17513 -0.10495 313s [6,] 2.50717 -1.48219 0.80164 313s [7,] 5.97151 3.25438 2.40268 313s [8,] 5.97151 3.25438 2.40268 313s [9,] -0.68332 0.30263 2.42495 313s [10,] -5.83478 -4.04630 -2.91819 313s [11,] -1.07253 3.51914 -1.87651 313s [12,] -1.89116 2.98559 -2.89885 313s [13,] -4.77650 -2.36509 -2.68671 313s [14,] 1.33353 6.57450 -0.50696 313s [15,] -7.45351 7.08878 1.37012 313s [16,] -9.04093 4.56697 1.02289 313s [17,] -16.15938 -7.50855 0.30909 313s [18,] -12.45541 -1.62432 1.11929 313s [19,] -11.63677 -1.09077 2.14162 313s [20,] -5.79275 -2.08680 -0.06187 313s [21,] 10.13623 -0.76824 -4.70180 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 9.8665 6.1687 3.2669 313s ---------------------------------------------------------- 313s salinity 28 3 3 12.120566 8.431549 313s Scores: 313s PC1 PC2 PC3 313s 1 -2.52547 1.45945 -1.1943e-01 313s 2 -3.32298 2.15704 8.7594e-01 313s 3 -6.64947 -3.26398 1.0135e+00 313s 4 -6.64427 -1.81382 -1.6392e-01 313s 5 -6.16898 -2.52222 5.1373e+00 313s 6 -5.87594 0.26440 -3.1956e-15 313s 7 -4.23084 1.46250 -2.8008e-01 313s 8 -2.21502 2.76478 -8.3789e-01 313s 9 -0.40186 -2.17785 -1.6702e+00 313s 10 2.27089 -1.84923 7.3391e-01 313s 11 1.37935 -1.29276 2.1418e+00 313s 12 -0.22635 0.60372 -5.0980e-01 313s 13 0.27224 1.73920 -7.0505e-01 313s 14 2.36592 2.40462 6.4320e-01 313s 15 2.37640 -2.83174 5.2669e-01 313s 16 -2.49175 -4.77664 9.0404e+00 313s 17 -0.61250 -1.11672 1.4398e+00 313s 18 -2.91853 0.63310 -8.3666e-01 313s 19 -0.39732 -2.02029 -2.1396e+00 313s 20 1.47554 -1.23407 -1.1712e+00 313s 21 1.70104 1.92401 -1.1292e+00 313s 22 3.14437 2.81928 -5.2415e-01 313s 23 3.62890 -3.51450 2.6740e+00 313s 24 2.04538 -2.63992 3.0718e+00 313s 25 0.77088 -0.54783 -1.3370e-01 313s 26 1.57254 0.89176 -1.2089e+00 313s 27 2.63610 1.97075 -1.1855e+00 313s 28 3.55112 2.67606 -6.0915e-02 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 3.4815 2.9037 1.3810 313s ---------------------------------------------------------- 313s hbk 75 3 3 3.801978 3.574192 313s Scores: 313s PC1 PC2 PC3 313s 1 28.747049 15.134042 2.3959241 313s 2 29.021724 16.318941 2.6207988 313s 3 31.271908 15.869319 3.4420860 313s 4 31.586189 17.508798 3.6246706 313s 5 31.299168 16.838093 3.2402573 313s 6 30.037754 15.591930 2.1421166 313s 7 29.888160 16.139376 1.9750096 313s 8 28.994463 15.350167 2.8226275 313s 9 30.758047 16.820526 3.7269602 313s 10 29.759314 16.079531 4.0486097 313s 11 35.301371 19.637962 3.7433562 313s 12 37.193371 18.709303 4.9915250 313s 13 35.634808 20.497713 1.4740727 313s 14 36.816439 27.523024 -2.3006796 313s 15 1.237203 -0.331072 -1.3801401 313s 16 -0.451166 -1.118847 -1.9707479 313s 17 -2.604733 0.067276 0.0130015 313s 18 0.179177 -0.804398 -0.1285240 313s 19 -0.765512 0.982349 -0.2513990 313s 20 1.236727 0.259123 -1.4210070 313s 21 0.428326 -0.503724 -0.6830690 313s 22 -0.724774 1.507943 -0.0022175 313s 23 -0.745349 -0.330094 -1.0982084 313s 24 -1.407850 -0.011831 -0.8987075 313s 25 -2.190427 -1.732051 0.4497793 313s 26 0.058631 1.444044 0.0446166 313s 27 1.680557 -0.429402 -0.6031146 313s 28 -0.315122 -1.179169 0.5822607 313s 29 -1.563355 -1.026914 0.1040012 313s 30 0.329957 -0.633156 1.8533795 313s 31 -0.110108 -1.617131 -1.0958807 313s 32 -2.035875 0.463421 -0.6346632 313s 33 -0.356033 0.740564 -0.8116369 313s 34 -2.342887 -1.340168 0.9724491 313s 35 1.607131 -0.379763 -0.3747630 313s 36 0.084455 0.486671 0.6551654 313s 37 -0.436144 1.659467 0.7145344 313s 38 -1.754819 -1.076076 -0.6037590 313s 39 -0.904375 -2.161949 0.3436723 313s 40 -1.455274 0.331839 0.1499308 313s 41 1.539788 -1.212921 -0.1715110 313s 42 -0.688338 -0.048173 1.7491184 313s 43 -1.635822 1.539067 -0.5208916 313s 44 0.511762 -1.165641 1.5020865 313s 45 -1.454500 -2.099954 0.0219268 313s 46 0.362645 -1.208389 1.3758464 313s 47 -0.615800 -2.658098 -0.4629006 313s 48 1.426278 -1.027667 0.0582638 313s 49 0.809592 -0.533893 -1.1232120 313s 50 0.996105 0.469082 -0.0988805 313s 51 -1.036368 -1.227376 -1.0843166 313s 52 -0.016464 -2.331540 -0.6477169 313s 53 -0.376625 -0.405855 2.4526088 313s 54 -1.524100 0.621590 -1.2927429 313s 55 -1.588523 0.591668 -0.2559428 313s 56 -0.592710 0.529426 -1.4111404 313s 57 -1.306991 -1.538024 -0.1841717 313s 58 0.275991 0.491888 -1.4739863 313s 59 0.598971 0.196673 0.6208960 313s 60 -0.127953 0.485014 1.8571970 313s 61 0.140584 1.905037 0.5838465 313s 62 -2.305069 -1.617811 0.3880825 313s 63 -1.666479 0.357251 -1.1934779 313s 64 1.480143 0.248671 -0.5959984 313s 65 0.309561 -1.219790 0.9671263 313s 66 -1.986789 0.248245 0.1723620 313s 67 -0.765691 -0.269054 -0.4611368 313s 68 -2.232721 -1.090790 1.3915841 313s 69 -1.502453 -1.813763 -0.4936268 313s 70 0.170883 0.584046 0.8369571 313s 71 0.543623 0.043244 -0.3707674 313s 72 -1.168908 0.341335 0.2837393 313s 73 -0.902885 0.411872 1.0546196 313s 74 -1.425273 0.852445 0.5719123 313s 75 -0.898536 -0.555475 2.0107684 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 1.9499 1.8906 1.2797 313s ---------------------------------------------------------- 313s milk 86 8 8 8.369408 3.530461 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 PC6 313s [1,] 6.337004 -0.245000 0.7704092 -4.9848e-01 -1.6599e-01 1.1763e-01 313s [2,] 7.021899 1.030349 0.2832977 -1.2673e+00 -8.7296e-01 2.0547e-01 313s [3,] 0.600831 1.686247 0.9682032 -3.2663e-02 7.4112e-02 4.7412e-01 313s [4,] 5.206465 2.665956 1.5942253 9.8285e-01 -5.4159e-01 -2.0155e-01 313s [5,] -0.955757 -0.579889 0.3206393 5.1174e-01 -6.1684e-01 -3.8990e-02 313s [6,] 2.198695 0.073770 -0.5712493 1.9440e-01 -1.0237e-01 4.1825e-02 313s [7,] 2.695361 0.644049 -0.8645373 8.1894e-02 -2.6953e-01 1.6884e-01 313s [8,] 2.945361 0.137227 -0.2071463 5.0841e-01 -4.2075e-01 5.8589e-02 313s [9,] -1.539013 1.879894 1.6952390 1.6792e-01 -2.8195e-01 5.0563e-02 313s [10,] -2.977110 0.319666 0.3515636 -5.2496e-01 4.6898e-01 8.5978e-03 313s [11,] -9.375355 -1.638105 1.9026171 4.1237e-01 1.8768e-02 -1.8546e-01 313s [12,] -12.602600 -4.715888 0.0273004 -4.7798e-02 -1.2246e-02 9.6858e-03 313s [13,] -10.114331 -2.487462 -1.6331544 -1.5139e+00 4.1903e-01 2.8313e-01 313s [14,] -11.949336 -3.190157 -0.2146943 -5.0060e-01 -2.9537e-01 3.2160e-01 313s [15,] -10.595396 -1.905517 2.3716887 7.6651e-01 -3.3531e-01 1.9933e-02 313s [16,] -2.735720 -0.748282 0.6750464 7.2415e-01 5.5304e-01 2.2283e-01 313s [17,] -1.248116 2.131195 2.2596886 6.4958e-01 3.5634e-01 2.9021e-01 313s [18,] -1.904210 -1.285804 -0.7746460 3.0198e-01 -2.7407e-01 1.7500e-01 313s [19,] -1.902313 0.095461 1.3824711 5.0369e-01 2.2193e-01 -5.5628e-02 313s [20,] 0.123220 1.399444 1.1517634 3.2546e-01 7.8261e-02 -4.0733e-01 313s [21,] -2.436023 -2.524827 -1.0197416 3.4819e-01 -1.4914e-01 -4.3669e-02 313s [22,] -0.904931 -1.114894 -0.1235807 2.0285e-01 -1.6200e-01 2.5681e-01 313s [23,] 0.220231 -1.767325 0.0482262 6.4418e-01 9.8618e-02 -5.7683e-02 313s [24,] -0.274403 -1.561826 0.3820323 7.0016e-01 5.5220e-01 1.4376e-01 313s [25,] -3.306400 -2.980247 0.0252488 9.4001e-01 -1.0841e-01 -2.5303e-01 313s [26,] -0.658015 -1.625199 0.3021005 7.2702e-01 -3.0299e-01 -1.2339e-01 313s [27,] -3.137066 -0.774218 0.5577497 6.4188e-01 -8.0125e-02 7.7819e-01 313s [28,] 2.867950 -3.099435 -0.6435415 1.0366e+00 1.5908e-01 7.6524e-02 313s [29,] 4.523097 -0.527338 -0.1032516 6.4537e-01 4.7286e-01 -2.7166e-01 313s [30,] 1.002381 -1.376693 -0.2735956 5.0522e-01 -1.2750e-01 -1.6178e-01 313s [31,] 1.894615 -1.296202 -1.9117282 -3.8032e-01 4.6473e-01 3.1085e-01 313s [32,] 1.210291 0.067230 -0.9832930 -8.5379e-01 3.2823e-01 4.9994e-01 313s [33,] 1.964118 0.022175 0.1818518 3.0464e-01 3.5596e-01 1.4985e-01 313s [34,] 0.576738 0.567851 0.6982155 1.8415e-01 1.8695e-01 3.2706e-01 313s [35,] -0.231793 -2.143909 -0.6825523 4.0681e-01 5.4492e-01 3.6259e-01 313s [36,] 4.250883 -0.719760 0.2157706 7.7167e-01 -1.9064e-01 -2.0611e-01 313s [37,] 1.077364 -2.054664 -1.3064867 1.0043e-01 8.6092e-02 3.5416e-01 313s [38,] 2.259260 -1.653588 -0.6730692 5.7300e-01 1.6930e-01 1.6986e-01 313s [39,] -1.251576 -1.451593 0.4671580 5.8957e-01 4.2672e-01 2.2495e-01 313s [40,] 3.304245 1.998193 1.0941231 1.3734e-01 3.7012e-01 2.4142e-01 313s [41,] 4.286315 -1.280951 0.5856744 -6.0980e-01 -4.3090e-01 1.9801e-01 313s [42,] 6.343820 1.801880 1.3481119 1.0355e+00 2.9802e-01 -8.4501e-04 313s [43,] 3.119491 0.214077 -1.1216236 -3.8134e-01 -1.9523e-01 -2.6706e-02 313s [44,] 5.285254 0.938072 0.7440487 1.1539e-02 8.1629e-01 -7.9286e-01 313s [45,] 0.082429 -0.416631 -0.1588203 2.3098e-01 5.1867e-01 9.4503e-02 313s [46,] 0.357862 -1.951997 -0.0731829 7.0393e-01 1.8828e-01 1.5707e-02 313s [47,] 2.428744 1.522538 -3.0467213 -1.9114e+00 2.4638e-01 3.5871e-01 313s [48,] 0.282348 -0.697287 -1.1592508 -5.4929e-01 6.2199e-01 -5.4596e-02 313s [49,] -2.266009 -0.559548 -1.3794914 -1.1300e+00 7.8872e-01 -2.0411e-02 313s [50,] 2.868649 2.860857 1.6128307 6.7382e-02 2.2344e-01 -4.1484e-01 313s [51,] -1.596061 0.546812 -1.1779327 -1.0512e+00 1.3522e-01 -9.4865e-03 313s [52,] -5.186121 -1.000829 -0.7440599 -9.6302e-01 3.0732e-01 -1.7009e-01 313s [53,] -0.800232 0.049087 -0.6946842 -5.8284e-01 -2.1277e-01 -2.7004e-01 313s [54,] -0.246388 -0.030606 -0.1814302 -1.1632e-01 5.7767e-02 -1.8637e-01 313s [55,] 0.914315 -0.428594 -0.4919557 4.5039e-02 -2.7868e-01 -2.2140e-01 313s [56,] -0.061827 0.583572 0.3263056 -1.1589e-01 -1.2973e-01 -1.6518e-01 313s [57,] -1.295979 -0.421943 0.8410805 3.0441e-01 -3.9478e-01 -4.5233e-02 313s [58,] 0.174908 -1.343854 0.0115086 8.0227e-01 -3.9364e-01 -2.2918e-01 313s [59,] -1.869684 0.840823 0.0109543 -5.5536e-01 -1.4155e-01 1.0613e-01 313s [60,] -1.614271 0.557309 -0.0690787 -9.1753e-02 -3.0975e-01 1.6192e-01 313s [61,] -0.258192 1.434984 0.7684636 -1.1998e-01 -3.4662e-01 -4.8808e-02 313s [62,] 2.000275 2.204730 1.1194067 -2.3783e-01 5.9953e-02 -1.5836e-01 313s [63,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 313s [64,] 2.694063 0.555482 -0.0340910 6.4470e-01 -2.2417e-01 1.9442e-02 313s [65,] -0.822201 2.427550 1.5859438 -3.5437e-16 2.2436e-15 -4.7251e-15 313s [66,] -2.545586 0.605953 0.1469837 -3.5318e-01 -2.5871e-01 1.6901e-01 313s [67,] 0.028900 1.253717 0.4474540 5.3595e-02 1.6063e-01 -1.0980e-01 313s [68,] -1.086135 1.968868 -0.7220293 -1.6576e+00 6.2061e-02 -7.0998e-04 313s [69,] -0.836638 0.660453 0.0049966 1.3663e-01 -1.0131e-01 -2.4008e-01 313s [70,] 4.843092 -6.035092 0.8250084 -3.4481e+00 -4.8538e+00 -7.8407e+00 313s [71,] -2.500038 1.146245 0.6967314 -2.4611e-01 -1.4266e-01 -8.2996e-02 313s [72,] 2.220676 1.122951 -0.2444075 1.1066e-01 -3.1540e-01 -2.1344e-01 313s [73,] -2.310518 2.354552 0.2706503 -6.4192e-01 2.0566e-01 4.5520e-01 313s [74,] -10.802799 -3.462655 2.2031446 1.1326e+00 2.8049e-01 -2.9749e-01 313s [75,] -0.301038 2.284366 0.2440764 -6.9450e-01 2.6435e-01 4.3129e-01 313s [76,] -1.477936 0.245154 -0.8869850 -8.9900e-01 -9.8013e-02 1.1983e-01 313s [77,] -8.169236 -1.599780 1.4987144 3.7767e-01 2.4726e-01 3.8246e-01 313s [78,] 1.096654 1.646072 0.0591327 -3.3138e-01 -1.7936e-01 6.2716e-02 313s [79,] -0.289199 0.625796 -0.3974294 -6.6099e-01 -2.0857e-01 2.1190e-01 313s [80,] -3.160557 -2.282579 0.3255355 4.6181e-01 2.7753e-01 -1.5673e-01 313s [81,] 1.284356 -0.548854 -0.2907281 2.4017e-01 -2.5254e-01 -1.4289e-03 313s [82,] 2.562817 2.019485 0.8249162 3.2973e-01 3.3866e-01 1.3889e-01 313s [83,] 2.538825 0.759863 -0.3142506 -5.1028e-01 -2.0539e-01 8.8979e-02 313s [84,] 0.841123 0.110035 -1.5793120 -1.2807e+00 1.2332e-01 1.6224e-01 313s [85,] 0.636271 1.793014 2.6824860 1.0329e+00 -4.8850e-01 -2.3012e-01 313s [86,] 0.633183 -0.426511 -1.4791366 -6.1314e-01 -7.0534e-02 -2.3778e-01 313s PC7 PC8 313s [1,] 1.0196e-01 -1.7180e-03 313s [2,] 2.6131e-01 -8.5191e-03 313s [3,] 6.9637e-01 -8.0573e-03 313s [4,] -1.3548e-01 -1.4969e-03 313s [5,] 3.1443e-02 -2.7307e-03 313s [6,] -2.5079e-01 3.6450e-03 313s [7,] 4.5377e-02 -2.6071e-03 313s [8,] -1.6060e-01 -2.3761e-04 313s [9,] -1.5152e-01 -4.3079e-04 313s [10,] 9.1089e-02 1.9536e-03 313s [11,] 2.5654e-01 -1.4875e-03 313s [12,] -2.3798e-03 -1.0954e-04 313s [13,] -1.3687e-01 2.8402e-03 313s [14,] -6.5248e-02 -1.5114e-03 313s [15,] 3.7695e-02 -2.7827e-03 313s [16,] 3.8131e-01 -3.7990e-03 313s [17,] 4.5661e-02 -1.4965e-03 313s [18,] 3.9910e-01 -7.2703e-03 313s [19,] 2.9353e-01 -3.3342e-03 313s [20,] 6.0915e-01 -6.0837e-03 313s [21,] -1.0079e-01 1.0179e-03 313s [22,] -2.2945e-02 -1.0515e-03 313s [23,] 2.3631e-01 -2.5558e-03 313s [24,] -7.7207e-02 3.4800e-03 313s [25,] 1.4903e-02 -3.2430e-04 313s [26,] 3.8032e-03 -2.1705e-03 313s [27,] 3.7208e-02 -3.0631e-03 313s [28,] -4.8147e-01 6.1089e-03 313s [29,] -4.0388e-02 2.8549e-03 313s [30,] 3.4318e-02 -1.0014e-03 313s [31,] -2.2872e-02 1.8706e-03 313s [32,] -8.4542e-02 1.3368e-03 313s [33,] 4.5274e-02 5.3383e-04 313s [34,] -2.0048e-01 2.4727e-03 313s [35,] -5.6482e-02 2.9923e-03 313s [36,] -2.6046e-02 -1.2910e-03 313s [37,] 9.6038e-02 -1.8897e-03 313s [38,] -2.9035e-01 4.4317e-03 313s [39,] -4.6322e-03 2.4336e-03 313s [40,] 3.8686e-01 -3.9300e-03 313s [41,] 3.7834e-01 -7.8976e-03 313s [42,] -8.2037e-04 -4.3106e-05 313s [43,] 3.3467e-01 -5.2401e-03 313s [44,] -6.2170e-01 1.2840e-02 313s [45,] 5.3557e-02 2.9156e-03 313s [46,] 5.1785e-04 2.0738e-03 313s [47,] -5.2141e-01 5.7206e-03 313s [48,] -2.7669e-01 6.7329e-03 313s [49,] 8.4319e-02 3.8528e-03 313s [50,] 1.4210e-01 1.6961e-04 313s [51,] -1.1871e-01 2.6676e-03 313s [52,] -2.5036e-01 6.4121e-03 313s [53,] 2.2399e-01 -2.8200e-03 313s [54,] 5.6532e-02 4.9304e-04 313s [55,] -1.4343e-01 1.2558e-03 313s [56,] 4.1682e-02 -9.6490e-04 313s [57,] -1.3014e-01 -6.2709e-04 313s [58,] -2.1428e-01 8.2594e-04 313s [59,] -7.9775e-02 -8.9776e-04 313s [60,] -8.6835e-02 -1.0498e-03 313s [61,] 6.2470e-02 -2.7499e-03 313s [62,] 3.3052e-02 -3.2369e-04 313s [63,] -1.7137e-01 -3.1087e-04 313s [64,] -1.7137e-01 -3.1087e-04 313s [65,] -1.4435e-14 -1.8299e-12 313s [66,] -2.2016e-02 -1.2206e-03 313s [67,] 8.5160e-02 -1.4837e-04 313s [68,] -2.2535e-03 1.9054e-04 313s [69,] 5.9976e-02 -8.6961e-04 313s [70,] 1.0448e+00 -2.0167e-02 313s [71,] -1.7609e-01 1.9378e-03 313s [72,] -1.7047e-01 2.6076e-04 313s [73,] 1.1885e-01 -8.1624e-04 313s [74,] 2.0942e-01 3.3164e-03 313s [75,] -7.7528e-01 9.9316e-03 313s [76,] -4.6285e-03 2.5153e-04 313s [77,] 7.0218e-02 1.5708e-03 313s [78,] -1.4859e-02 -6.7049e-04 313s [79,] 5.1054e-02 -2.0198e-03 313s [80,] -1.5770e-01 4.9579e-03 313s [81,] -1.9411e-01 4.4401e-04 313s [82,] 6.0634e-02 8.7960e-04 313s [83,] -4.4635e-02 -1.7048e-03 313s [84,] -2.3612e-03 -2.2242e-04 313s [85,] -5.5171e-02 -1.1222e-03 313s [86,] -1.4972e-01 1.4543e-03 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 2.8929930 1.8789522 0.9946460 0.7479403 0.3744197 0.2596328 0.1421387 313s [8] 0.0025753 313s ---------------------------------------------------------- 313s bushfire 38 5 5 37473.439646 1742.633018 313s Scores: 313s PC1 PC2 PC3 PC4 PC5 313s [1,] -67.2152 -2.3010e+01 4.4179e+00 1.0892e+00 1.7536e+00 313s [2,] -69.0225 -2.1417e+01 2.5382e+00 1.1092e+00 9.3919e-01 313s [3,] -61.6651 -1.8580e+01 -6.1022e-01 -8.1124e-01 -1.6462e-01 313s [4,] -44.5883 -1.8234e+01 -3.9899e-01 -5.2145e-01 2.0050e-01 313s [5,] -12.2941 -2.2954e+01 3.5970e+00 1.1037e+00 -2.4384e-01 313s [6,] 13.0282 -2.8133e+01 8.7670e+00 3.4751e+00 1.3728e+00 313s [7,] -199.0774 -7.7956e+01 5.4935e+01 6.3134e+00 -1.9919e+00 313s [8,] -228.2849 -1.3258e+02 2.2340e+01 2.1656e+01 -1.2594e+01 313s [9,] -228.9164 -1.3560e+02 2.0463e+01 2.2625e+01 -1.2743e+01 313s [10,] -232.4703 -1.0661e+02 3.5597e+01 1.7915e+01 -7.7659e+00 313s [11,] -236.7410 -8.8072e+01 3.6632e+01 1.5095e+01 -7.4695e+00 313s [12,] -249.4091 -3.6830e+01 2.4010e+01 4.7317e+00 -1.2986e+00 313s [13,] -197.0450 2.3179e-14 2.4481e-14 -1.1772e-13 -5.9580e-13 313s [14,] 50.9487 1.1397e+01 -1.1247e+01 -4.8733e+00 2.4511e+00 313s [15,] 180.7896 1.7571e+01 -8.0454e+00 -1.0582e+01 1.2714e+00 313s [16,] 135.6178 1.4189e+01 -4.9116e-01 -9.2701e+00 1.4021e-01 313s [17,] 132.5344 1.5577e+01 2.2990e-01 -6.4963e+00 7.3370e-01 313s [18,] 121.3422 1.0471e+01 4.5656e+00 -4.9831e+00 -5.2314e-01 313s [19,] 125.2722 9.0272e+00 3.7365e+00 -3.3313e+00 -2.9097e-01 313s [20,] 135.2370 1.4091e+01 2.0639e+00 -3.6800e+00 1.1733e+00 313s [21,] 116.4250 1.5147e+01 2.9085e+00 -4.8084e+00 1.2603e+00 313s [22,] 108.2925 1.4223e+01 7.7165e-01 -4.5065e+00 2.7943e+00 313s [23,] -22.8258 6.4234e+00 2.4654e+00 -3.9627e+00 7.9847e-01 313s [24,] -78.1850 4.6631e+00 -3.6818e+00 -2.7688e+00 5.8508e-01 313s [25,] -13.0417 2.7521e+00 -3.1955e+00 -4.6824e+00 -3.1085e-01 313s [26,] -19.1244 -9.5045e-01 -2.6771e+00 -4.7104e+00 -1.6172e-01 313s [27,] -34.4379 3.2761e+00 -9.2826e+00 -2.9861e+00 -3.3561e-01 313s [28,] -11.5852 1.4506e+01 -1.5649e+01 -1.6260e+00 -8.5347e-01 313s [29,] -2.9366 2.8741e+01 -2.2907e+01 3.9749e-01 3.5861e-02 313s [30,] -59.7518 2.8633e+01 -1.4710e+01 3.5226e+00 -9.9066e-01 313s [31,] 67.8017 4.7241e+01 -9.1255e+00 1.3201e+01 6.9227e-14 313s [32,] 321.9941 7.6188e+01 2.2491e+01 3.1537e+01 3.2368e+00 313s [33,] 359.5155 6.6710e+01 5.6061e+01 3.4541e+01 2.0718e+00 313s [34,] 355.8007 6.5695e+01 5.7430e+01 3.3578e+01 1.4640e+00 313s [35,] 361.1076 6.7577e+01 5.7402e+01 3.3832e+01 3.2618e+00 313s [36,] 358.3592 6.6791e+01 5.8643e+01 3.2720e+01 1.2487e+00 313s [37,] 355.9974 6.8071e+01 6.0927e+01 3.2560e+01 2.4898e+00 313s [38,] 357.2530 6.9073e+01 6.1517e+01 3.2523e+01 2.7558e+00 313s ------------- 313s Call: 313s PcaProj(x = x) 313s 313s Standard deviations: 313s [1] 193.5806 41.7449 16.7665 8.1585 1.6074 313s ---------------------------------------------------------- 313s ========================================================== 313s > ## IGNORE_RDIFF_END 313s > 313s > ## VT::14.11.2018 - commented out - on some platforms PcaHubert will choose only 1 PC 313s > ## and will show difference 313s > ## test.case.1() 313s > 313s > test.case.2() 313s [1] TRUE 313s [1] TRUE 313s [1] TRUE 314s [1] TRUE 314s [1] TRUE 314s [1] TRUE 314s [1] TRUE 314s [1] TRUE 314s [1] TRUE 314s [1] TRUE 314s > 314s BEGIN TEST tlda.R 314s 314s R version 4.4.3 (2025-02-28) -- "Trophy Case" 314s Copyright (C) 2025 The R Foundation for Statistical Computing 314s Platform: aarch64-unknown-linux-gnu 314s 314s R is free software and comes with ABSOLUTELY NO WARRANTY. 314s You are welcome to redistribute it under certain conditions. 314s Type 'license()' or 'licence()' for distribution details. 314s 314s R is a collaborative project with many contributors. 314s Type 'contributors()' for more information and 314s 'citation()' on how to cite R or R packages in publications. 314s 314s Type 'demo()' for some demos, 'help()' for on-line help, or 314s 'help.start()' for an HTML browser interface to help. 314s Type 'q()' to quit R. 314s 314s > ## VT::15.09.2013 - this will render the output independent 314s > ## from the version of the package 314s > suppressPackageStartupMessages(library(rrcov)) 314s > library(MASS) 314s > 314s > ## VT::14.01.2020 314s > ## On some platforms minor differences are shown - use 314s > ## IGNORE_RDIFF_BEGIN 314s > ## IGNORE_RDIFF_END 314s > 314s > dodata <- function(method) { 314s + 314s + options(digits = 5) 314s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 314s + 314s + tmp <- sys.call() 314s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 314s + cat("===================================================\n") 314s + 314s + cat("\nData: ", "hemophilia\n") 314s + data(hemophilia) 314s + show(rlda <- Linda(as.factor(gr)~., data=hemophilia, method=method)) 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "anorexia\n") 314s + data(anorexia) 314s + show(rlda <- Linda(Treat~., data=anorexia, method=method)) 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "Pima\n") 314s + data(Pima.tr) 314s + show(rlda <- Linda(type~., data=Pima.tr, method=method)) 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "Forest soils\n") 314s + data(soil) 314s + soil1983 <- soil[soil$D == 0, -2] # only 1983, remove column D (always 0) 314s + 314s + ## This will not work within the function, of course 314s + ## - comment it out 314s + ## IGNORE_RDIFF_BEGIN 314s + rlda <- Linda(F~., data=soil1983, method=method) 314s + ## show(rlda) 314s + ## IGNORE_RDIFF_END 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "Raven and Miller diabetes data\n") 314s + data(diabetes) 314s + show(rlda <- Linda(group~insulin+glucose+sspg, data=diabetes, method=method)) 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "iris\n") 314s + data(iris) 314s + if(method != "mcdA") 314s + { 314s + show(rlda <- Linda(Species~., data=iris, method=method, l1med=TRUE)) 314s + show(predict(rlda)) 314s + } 314s + 314s + cat("\nData: ", "crabs\n") 314s + data(crabs) 314s + show(rlda <- Linda(sp~., data=crabs, method=method)) 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "fish\n") 314s + data(fish) 314s + fish <- fish[-14,] # remove observation #14 containing missing value 314s + 314s + # The height and width are calculated as percentages 314s + # of the third length variable 314s + fish[,5] <- fish[,5]*fish[,4]/100 314s + fish[,6] <- fish[,6]*fish[,4]/100 314s + 314s + ## There is one class with only 6 observations (p=6). Normally 314s + ## Linda will fail, therefore use l1med=TRUE. 314s + ## This works only for methods mcdB and mcdC 314s + 314s + table(fish$Species) 314s + if(method != "mcdA") 314s + { 314s + ## IGNORE_RDIFF_BEGIN 314s + rlda <- Linda(Species~., data=fish, method=method, l1med=TRUE) 314s + ## show(rlda) 314s + ## IGNORE_RDIFF_END 314s + show(predict(rlda)) 314s + } 314s + 314s + cat("\nData: ", "pottery\n") 314s + data(pottery) 314s + show(rlda <- Linda(origin~., data=pottery, method=method)) 314s + show(predict(rlda)) 314s + 314s + cat("\nData: ", "olitos\n") 314s + data(olitos) 314s + if(method != "mcdA") 314s + { 314s + ## IGNORE_RDIFF_BEGIN 314s + rlda <- Linda(grp~., data=olitos, method=method, l1med=TRUE) 314s + ## show(rlda) 314s + ## IGNORE_RDIFF_END 314s + show(predict(rlda)) 314s + } 314s + 314s + cat("===================================================\n") 314s + } 314s > 314s > 314s > ## -- now do it: 314s > dodata(method="mcdA") 314s 314s Call: dodata(method = "mcdA") 314s =================================================== 314s 314s Data: hemophilia 314s Call: 314s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 314s 314s Prior Probabilities of Groups: 314s carrier normal 314s 0.6 0.4 314s 314s Group means: 314s AHFactivity AHFantigen 314s carrier -0.30795 -0.0059911 314s normal -0.12920 -0.0603000 314s 314s Within-groups Covariance Matrix: 314s AHFactivity AHFantigen 314s AHFactivity 0.018036 0.011853 314s AHFantigen 0.011853 0.019185 314s 314s Linear Coeficients: 314s AHFactivity AHFantigen 314s carrier -28.4029 17.2368 314s normal -8.5834 2.1602 314s 314s Constants: 314s carrier normal 314s -4.8325 -1.4056 314s 314s Apparent error rate 0.1333 314s 314s Classification table 314s Predicted 314s Actual carrier normal 314s carrier 39 6 314s normal 4 26 314s 314s Confusion matrix 314s Predicted 314s Actual carrier normal 314s carrier 0.867 0.133 314s normal 0.133 0.867 314s 314s Data: anorexia 314s Call: 314s Linda(Treat ~ ., data = anorexia, method = method) 314s 314s Prior Probabilities of Groups: 314s CBT Cont FT 314s 0.40278 0.36111 0.23611 314s 314s Group means: 314s Prewt Postwt 314s CBT 82.633 82.950 314s Cont 81.558 81.108 314s FT 84.331 94.762 314s 314s Within-groups Covariance Matrix: 314s Prewt Postwt 314s Prewt 26.9291 3.3862 314s Postwt 3.3862 18.2368 314s 314s Linear Coeficients: 314s Prewt Postwt 314s CBT 2.5563 4.0738 314s Cont 2.5284 3.9780 314s FT 2.5374 4.7250 314s 314s Constants: 314s CBT Cont FT 314s -275.49 -265.45 -332.31 314s 314s Apparent error rate 0.3889 314s 314s Classification table 314s Predicted 314s Actual CBT Cont FT 314s CBT 16 5 8 314s Cont 11 15 0 314s FT 0 4 13 314s 314s Confusion matrix 314s Predicted 314s Actual CBT Cont FT 314s CBT 0.552 0.172 0.276 314s Cont 0.423 0.577 0.000 314s FT 0.000 0.235 0.765 314s 314s Data: Pima 314s Call: 314s Linda(type ~ ., data = Pima.tr, method = method) 314s 314s Prior Probabilities of Groups: 314s No Yes 314s 0.66 0.34 314s 314s Group means: 314s npreg glu bp skin bmi ped age 314s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 314s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 314s 314s Within-groups Covariance Matrix: 314s npreg glu bp skin bmi ped age 314s npreg 8.51105 -5.61029 4.756672 1.52732 0.82066 -0.010070 12.382693 314s glu -5.61029 656.11894 49.855724 16.67486 23.07833 -0.352475 17.724967 314s bp 4.75667 49.85572 119.426757 29.64563 12.90698 -0.049538 21.287178 314s skin 1.52732 16.67486 29.645632 113.19900 44.15972 -0.157594 6.741105 314s bmi 0.82066 23.07833 12.906985 44.15972 35.54164 0.038640 1.481520 314s ped -0.01007 -0.35247 -0.049538 -0.15759 0.03864 0.062664 -0.069636 314s age 12.38269 17.72497 21.287178 6.74110 1.48152 -0.069636 64.887154 314s 314s Linear Coeficients: 314s npreg glu bp skin bmi ped age 314s No -0.45855 0.092789 0.45848 -0.30675 1.0075 6.2670 0.30749 314s Yes -0.22400 0.150013 0.44787 -0.26148 1.0015 8.2935 0.45187 314s 314s Constants: 314s No Yes 314s -37.050 -51.586 314s 314s Apparent error rate 0.22 314s 314s Classification table 314s Predicted 314s Actual No Yes 314s No 107 25 314s Yes 19 49 314s 314s Confusion matrix 314s Predicted 314s Actual No Yes 314s No 0.811 0.189 314s Yes 0.279 0.721 314s 314s Data: Forest soils 314s 314s Apparent error rate 0.3103 314s 314s Classification table 314s Predicted 314s Actual 1 2 3 314s 1 7 2 2 314s 2 3 13 7 314s 3 1 3 20 314s 314s Confusion matrix 314s Predicted 314s Actual 1 2 3 314s 1 0.636 0.182 0.182 314s 2 0.130 0.565 0.304 314s 3 0.042 0.125 0.833 314s 314s Data: Raven and Miller diabetes data 314s Call: 314s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 314s 314s Prior Probabilities of Groups: 314s normal chemical overt 314s 0.52414 0.24828 0.22759 314s 314s Group means: 314s insulin glucose sspg 314s normal 163.939 345.8 99.076 314s chemical 299.448 476.9 223.621 314s overt 95.958 1026.4 343.000 314s 314s Within-groups Covariance Matrix: 314s insulin glucose sspg 314s insulin 7582.0 -1263.1 1095.8 314s glucose -1263.1 18952.4 4919.3 314s sspg 1095.8 4919.3 3351.2 314s 314s Linear Coeficients: 314s insulin glucose sspg 314s normal 0.027694 0.023859 -0.014514 314s chemical 0.040288 0.022532 0.020479 314s overt 0.017144 0.048768 0.025158 314s 314s Constants: 314s normal chemical overt 314s -6.3223 -15.0879 -31.6445 314s 314s Apparent error rate 0.1862 314s 314s Classification table 314s Predicted 314s Actual normal chemical overt 314s normal 69 7 0 314s chemical 13 23 0 314s overt 2 5 26 314s 314s Confusion matrix 314s Predicted 314s Actual normal chemical overt 314s normal 0.908 0.092 0.000 314s chemical 0.361 0.639 0.000 314s overt 0.061 0.152 0.788 314s 314s Data: iris 314s 314s Data: crabs 314s Call: 314s Linda(sp ~ ., data = crabs, method = method) 314s 314s Prior Probabilities of Groups: 314s B O 314s 0.5 0.5 314s 314s Group means: 314s sexM index FL RW CL CW BD 314s B 0.34722 27.333 14.211 12.253 30.397 35.117 12.765 314s O 0.56627 25.554 17.131 13.405 34.247 38.155 15.525 314s 314s Within-groups Covariance Matrix: 314s sexM index FL RW CL CW BD 314s sexM 0.26391 0.76754 0.18606 -0.33763 0.65944 0.59857 0.28932 314s index 0.76754 191.38080 38.42685 26.32923 82.43953 91.89091 38.13688 314s FL 0.18606 38.42685 8.50147 5.68789 18.13749 20.30739 8.30920 314s RW -0.33763 26.32923 5.68789 4.95782 11.90225 13.61117 5.45814 314s CL 0.65944 82.43953 18.13749 11.90225 39.60115 44.10886 18.09504 314s CW 0.59857 91.89091 20.30739 13.61117 44.10886 49.42616 20.17554 314s BD 0.28932 38.13688 8.30920 5.45814 18.09504 20.17554 8.39525 314s 314s Linear Coeficients: 314s sexM index FL RW CL CW BD 314s B 29.104 -2.4938 10.809 15.613 0.8320 -4.2978 -0.46788 314s O 42.470 -3.9361 26.427 22.857 2.8582 -17.1526 12.31048 314s 314s Constants: 314s B O 314s -78.317 -159.259 314s 314s Apparent error rate 0 314s 314s Classification table 314s Predicted 314s Actual B O 314s B 100 0 314s O 0 100 314s 314s Confusion matrix 314s Predicted 314s Actual B O 314s B 1 0 314s O 0 1 314s 314s Data: fish 314s 314s Data: pottery 314s Call: 314s Linda(origin ~ ., data = pottery, method = method) 314s 314s Prior Probabilities of Groups: 314s Attic Eritrean 314s 0.48148 0.51852 314s 314s Group means: 314s SI AL FE MG CA TI 314s Attic 55.36 13.73 9.82 5.45 6.03 0.863 314s Eritrean 52.52 16.23 9.13 3.09 6.26 0.814 314s 314s Within-groups Covariance Matrix: 314s SI AL FE MG CA TI 314s SI 13.5941404 2.986675 -0.651132 0.173577 -0.350984 -0.0051996 314s AL 2.9866747 1.622412 0.485167 0.712400 0.077443 0.0133306 314s FE -0.6511317 0.485167 1.065427 -0.403601 -1.936552 0.0576472 314s MG 0.1735766 0.712400 -0.403601 2.814948 3.262786 -0.0427129 314s CA -0.3509837 0.077443 -1.936552 3.262786 7.720320 -0.1454065 314s TI -0.0051996 0.013331 0.057647 -0.042713 -0.145406 0.0044093 314s 314s Linear Coeficients: 314s SI AL FE MG CA TI 314s Attic 63.235 -196.99 312.92 7.28960 57.082 -1272.23 314s Eritrean 41.554 -123.49 201.47 -0.95431 43.616 -597.91 314s 314s Constants: 314s Attic Eritrean 314s -1578.14 -901.13 314s 314s Apparent error rate 0.1111 314s 314s Classification table 314s Predicted 314s Actual Attic Eritrean 314s Attic 12 1 314s Eritrean 2 12 314s 314s Confusion matrix 314s Predicted 314s Actual Attic Eritrean 314s Attic 0.923 0.077 314s Eritrean 0.143 0.857 314s 314s Data: olitos 314s =================================================== 314s > dodata(method="mcdB") 314s 314s Call: dodata(method = "mcdB") 314s =================================================== 314s 314s Data: hemophilia 314s Call: 314s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 314s 314s Prior Probabilities of Groups: 314s carrier normal 314s 0.6 0.4 314s 314s Group means: 314s AHFactivity AHFantigen 314s carrier -0.31456 -0.014775 314s normal -0.13582 -0.069084 314s 314s Within-groups Covariance Matrix: 314s AHFactivity AHFantigen 314s AHFactivity 0.0125319 0.0086509 314s AHFantigen 0.0086509 0.0182424 314s 314s Linear Coeficients: 314s AHFactivity AHFantigen 314s carrier -36.486 16.4923 314s normal -12.226 2.0107 314s 314s Constants: 314s carrier normal 314s -6.1276 -1.6771 314s 314s Apparent error rate 0.16 314s 314s Classification table 314s Predicted 314s Actual carrier normal 314s carrier 38 7 314s normal 5 25 314s 314s Confusion matrix 314s Predicted 314s Actual carrier normal 314s carrier 0.844 0.156 314s normal 0.167 0.833 314s 314s Data: anorexia 314s Call: 314s Linda(Treat ~ ., data = anorexia, method = method) 314s 314s Prior Probabilities of Groups: 314s CBT Cont FT 314s 0.40278 0.36111 0.23611 314s 314s Group means: 314s Prewt Postwt 314s CBT 83.254 82.381 314s Cont 82.178 80.539 314s FT 84.951 94.193 314s 314s Within-groups Covariance Matrix: 314s Prewt Postwt 314s Prewt 19.1751 8.8546 314s Postwt 8.8546 25.2326 314s 314s Linear Coeficients: 314s Prewt Postwt 314s CBT 3.3822 2.0780 314s Cont 3.3555 2.0144 314s FT 3.2299 2.5996 314s 314s Constants: 314s CBT Cont FT 314s -227.29 -220.01 -261.06 314s 314s Apparent error rate 0.4444 314s 314s Classification table 314s Predicted 314s Actual CBT Cont FT 314s CBT 16 5 8 314s Cont 12 11 3 314s FT 0 4 13 314s 314s Confusion matrix 314s Predicted 314s Actual CBT Cont FT 314s CBT 0.552 0.172 0.276 314s Cont 0.462 0.423 0.115 314s FT 0.000 0.235 0.765 314s 314s Data: Pima 315s Call: 315s Linda(type ~ ., data = Pima.tr, method = method) 315s 315s Prior Probabilities of Groups: 315s No Yes 315s 0.66 0.34 315s 315s Group means: 315s npreg glu bp skin bmi ped age 315s No 2.0767 109.45 67.790 26.158 30.930 0.41455 24.695 315s Yes 5.5938 145.40 74.748 33.754 34.501 0.49898 37.821 315s 315s Within-groups Covariance Matrix: 315s npreg glu bp skin bmi ped age 315s npreg 6.601330 9.54054 7.33480 3.5803 1.66539 -0.019992 10.661763 315s glu 9.540535 573.03642 60.57124 28.3698 30.28444 -0.436611 28.318034 315s bp 7.334803 60.57124 112.03792 27.7566 13.54085 -0.040510 24.692240 315s skin 3.580339 28.36976 27.75661 112.0036 47.22411 0.100399 13.408195 315s bmi 1.665393 30.28444 13.54085 47.2241 38.37753 0.175891 6.640765 315s ped -0.019992 -0.43661 -0.04051 0.1004 0.17589 0.062551 -0.070673 315s age 10.661763 28.31803 24.69224 13.4082 6.64077 -0.070673 40.492363 315s 315s Linear Coeficients: 315s npreg glu bp skin bmi ped age 315s No -1.3073 0.10851 0.48404 -0.30638 0.86002 5.9796 0.55388 315s Yes -1.3136 0.16260 0.44480 -0.25518 0.79826 8.1199 0.86269 315s 315s Constants: 315s No Yes 315s -38.774 -53.654 315s 315s Apparent error rate 0.25 315s 315s Classification table 315s Predicted 315s Actual No Yes 315s No 104 28 315s Yes 22 46 315s 315s Confusion matrix 315s Predicted 315s Actual No Yes 315s No 0.788 0.212 315s Yes 0.324 0.676 315s 315s Data: Forest soils 315s 315s Apparent error rate 0.3448 315s 315s Classification table 315s Predicted 315s Actual 1 2 3 315s 1 4 3 4 315s 2 2 14 7 315s 3 2 2 20 315s 315s Confusion matrix 315s Predicted 315s Actual 1 2 3 315s 1 0.364 0.273 0.364 315s 2 0.087 0.609 0.304 315s 3 0.083 0.083 0.833 315s 315s Data: Raven and Miller diabetes data 315s Call: 315s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 315s 315s Prior Probabilities of Groups: 315s normal chemical overt 315s 0.52414 0.24828 0.22759 315s 315s Group means: 315s insulin glucose sspg 315s normal 152.405 346.55 99.387 315s chemical 288.244 478.80 226.226 315s overt 84.754 1028.28 345.605 315s 315s Within-groups Covariance Matrix: 315s insulin glucose sspg 315s insulin 5061.46 289.69 2071.71 315s glucose 289.69 1983.07 385.31 315s sspg 2071.71 385.31 3000.17 315s 315s Linear Coeficients: 315s insulin glucose sspg 315s normal 0.021952 0.17236 -0.0041671 315s chemical 0.034852 0.23217 0.0215200 315s overt -0.045700 0.50940 0.0813292 315s 315s Constants: 315s normal chemical overt 315s -31.976 -64.433 -275.502 315s 315s Apparent error rate 0.0966 315s 315s Classification table 315s Predicted 315s Actual normal chemical overt 315s normal 73 3 0 315s chemical 4 32 0 315s overt 0 7 26 315s 315s Confusion matrix 315s Predicted 315s Actual normal chemical overt 315s normal 0.961 0.039 0.000 315s chemical 0.111 0.889 0.000 315s overt 0.000 0.212 0.788 315s 315s Data: iris 315s Call: 315s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 315s 315s Prior Probabilities of Groups: 315s setosa versicolor virginica 315s 0.33333 0.33333 0.33333 315s 315s Group means: 315s Sepal.Length Sepal.Width Petal.Length Petal.Width 315s setosa 4.9834 3.4153 1.4532 0.22474 315s versicolor 5.8947 2.8149 4.2263 1.35024 315s virginica 6.5255 3.0017 5.4485 2.06756 315s 315s Within-groups Covariance Matrix: 315s Sepal.Length Sepal.Width Petal.Length Petal.Width 315s Sepal.Length 0.201176 0.084299 0.102984 0.037019 315s Sepal.Width 0.084299 0.108394 0.050253 0.031757 315s Petal.Length 0.102984 0.050253 0.120215 0.045016 315s Petal.Width 0.037019 0.031757 0.045016 0.032825 315s 315s Linear Coeficients: 315s Sepal.Length Sepal.Width Petal.Length Petal.Width 315s setosa 22.536 27.422168 -3.6855 -40.0445 315s versicolor 17.559 6.374082 24.1965 -18.0178 315s virginica 16.488 0.015576 29.9586 3.2926 315s 315s Constants: 315s setosa versicolor virginica 315s -96.901 -100.790 -139.937 315s 315s Apparent error rate 0.0267 315s 315s Classification table 315s Predicted 315s Actual setosa versicolor virginica 315s setosa 50 0 0 315s versicolor 0 48 2 315s virginica 0 2 48 315s 315s Confusion matrix 315s Predicted 315s Actual setosa versicolor virginica 315s setosa 1 0.00 0.00 315s versicolor 0 0.96 0.04 315s virginica 0 0.04 0.96 315s 315s Data: crabs 315s Call: 315s Linda(sp ~ ., data = crabs, method = method) 315s 315s Prior Probabilities of Groups: 315s B O 315s 0.5 0.5 315s 315s Group means: 315s sexM index FL RW CL CW BD 315s B 0.41060 25.420 13.947 11.922 29.783 34.404 12.470 315s O 0.60279 23.202 16.782 13.086 33.401 37.230 15.131 315s 315s Within-groups Covariance Matrix: 315s sexM index FL RW CL CW BD 315s sexM 0.27470 0.24656 0.12787 -0.34713 0.48937 0.41525 0.20253 315s index 0.24656 204.06823 42.17347 28.25816 89.28109 100.21077 40.74069 315s FL 0.12787 42.17347 9.45366 6.24808 19.97936 22.49310 9.03804 315s RW -0.34713 28.25816 6.24808 5.12921 13.01576 14.90535 5.89729 315s CL 0.48937 89.28109 19.97936 13.01576 43.06030 48.30814 19.44568 315s CW 0.41525 100.21077 22.49310 14.90535 48.30814 54.45265 21.82356 315s BD 0.20253 40.74069 9.03804 5.89729 19.44568 21.82356 8.89498 315s 315s Linear Coeficients: 315s sexM index FL RW CL CW BD 315s B 12.295 -2.3199 7.2512 9.4085 2.2846 -2.6196 -0.42557 315s O 13.138 -3.7530 21.1374 11.5680 5.0125 -13.9120 12.61928 315s 315s Constants: 315s B O 315s -66.688 -134.375 315s 315s Apparent error rate 0 315s 315s Classification table 315s Predicted 315s Actual B O 315s B 100 0 315s O 0 100 315s 315s Confusion matrix 315s Predicted 315s Actual B O 315s B 1 0 315s O 0 1 315s 315s Data: fish 315s 315s Apparent error rate 0.0949 315s 315s Classification table 315s Predicted 315s Actual 1 2 3 4 5 6 7 315s 1 34 0 0 0 0 0 0 315s 2 0 6 0 0 0 0 0 315s 3 0 0 20 0 0 0 0 315s 4 0 0 0 11 0 0 0 315s 5 0 0 0 0 13 0 1 315s 6 0 0 0 0 0 17 0 315s 7 0 13 0 0 1 0 42 315s 315s Confusion matrix 315s Predicted 315s Actual 1 2 3 4 5 6 7 315s 1 1 0.000 0 0 0.000 0 0.000 315s 2 0 1.000 0 0 0.000 0 0.000 315s 3 0 0.000 1 0 0.000 0 0.000 315s 4 0 0.000 0 1 0.000 0 0.000 315s 5 0 0.000 0 0 0.929 0 0.071 315s 6 0 0.000 0 0 0.000 1 0.000 315s 7 0 0.232 0 0 0.018 0 0.750 315s 315s Data: pottery 315s Call: 315s Linda(origin ~ ., data = pottery, method = method) 315s 315s Prior Probabilities of Groups: 315s Attic Eritrean 315s 0.48148 0.51852 315s 315s Group means: 315s SI AL FE MG CA TI 315s Attic 55.362 13.847 10.0065 5.3141 5.5371 0.87124 315s Eritrean 52.522 16.347 9.3165 2.9541 5.7671 0.82224 315s 315s Within-groups Covariance Matrix: 315s SI AL FE MG CA TI 315s SI 9.708953 2.3634831 -0.112005 0.514666 -0.591122 0.0253885 315s AL 2.363483 0.8510105 0.044491 0.485132 0.241384 0.0023349 315s FE -0.112005 0.0444910 0.247768 -0.263894 -0.503218 0.0163218 315s MG 0.514666 0.4851316 -0.263894 1.608899 1.516228 -0.0292787 315s CA -0.591122 0.2413842 -0.503218 1.516228 2.455516 -0.0531548 315s TI 0.025389 0.0023349 0.016322 -0.029279 -0.053155 0.0017412 315s 315s Linear Coeficients: 315s SI AL FE MG CA TI 315s Attic 112.705 -368.69 530.54 7.5837 149.60 -927.45 315s Eritrean 77.198 -244.65 366.95 -3.7987 116.88 -260.83 315s 315s Constants: 315s Attic Eritrean 315s -3252.6 -1961.9 315s 315s Apparent error rate 0.1111 315s 315s Classification table 315s Predicted 315s Actual Attic Eritrean 315s Attic 12 1 315s Eritrean 2 12 315s 315s Confusion matrix 315s Predicted 315s Actual Attic Eritrean 315s Attic 0.923 0.077 315s Eritrean 0.143 0.857 315s 315s Data: olitos 315s 315s Apparent error rate 0.15 315s 315s Classification table 315s Predicted 315s Actual 1 2 3 4 315s 1 44 1 4 1 315s 2 2 23 0 0 315s 3 6 1 26 1 315s 4 1 1 0 9 315s 315s Confusion matrix 315s Predicted 315s Actual 1 2 3 4 315s 1 0.880 0.020 0.080 0.020 315s 2 0.080 0.920 0.000 0.000 315s 3 0.176 0.029 0.765 0.029 315s 4 0.091 0.091 0.000 0.818 315s =================================================== 315s > dodata(method="mcdC") 315s 315s Call: dodata(method = "mcdC") 315s =================================================== 315s 315s Data: hemophilia 315s Call: 315s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 315s 315s Prior Probabilities of Groups: 315s carrier normal 315s 0.6 0.4 315s 315s Group means: 315s AHFactivity AHFantigen 315s carrier -0.32583 -0.011545 315s normal -0.12783 -0.071377 315s 315s Within-groups Covariance Matrix: 315s AHFactivity AHFantigen 315s AHFactivity 0.0120964 0.0075536 315s AHFantigen 0.0075536 0.0164883 315s 315s Linear Coeficients: 315s AHFactivity AHFantigen 315s carrier -37.117 16.30377 315s normal -11.015 0.71742 315s 315s Constants: 315s carrier normal 315s -6.4636 -1.5947 315s 315s Apparent error rate 0.16 315s 315s Classification table 315s Predicted 315s Actual carrier normal 315s carrier 38 7 315s normal 5 25 315s 315s Confusion matrix 315s Predicted 315s Actual carrier normal 315s carrier 0.844 0.156 315s normal 0.167 0.833 315s 315s Data: anorexia 315s Call: 315s Linda(Treat ~ ., data = anorexia, method = method) 315s 315s Prior Probabilities of Groups: 315s CBT Cont FT 315s 0.40278 0.36111 0.23611 315s 315s Group means: 315s Prewt Postwt 315s CBT 82.477 82.073 315s Cont 82.039 80.835 315s FT 85.242 94.750 315s 315s Within-groups Covariance Matrix: 315s Prewt Postwt 315s Prewt 19.6589 8.3891 315s Postwt 8.3891 22.8805 315s 315s Linear Coeficients: 315s Prewt Postwt 315s CBT 3.1590 2.4288 315s Cont 3.1599 2.3743 315s FT 3.0454 3.0245 315s 315s Constants: 315s CBT Cont FT 315s -230.85 -226.60 -274.53 315s 315s Apparent error rate 0.4583 315s 315s Classification table 315s Predicted 315s Actual CBT Cont FT 315s CBT 16 5 8 315s Cont 14 10 2 315s FT 0 4 13 315s 315s Confusion matrix 315s Predicted 315s Actual CBT Cont FT 315s CBT 0.552 0.172 0.276 315s Cont 0.538 0.385 0.077 315s FT 0.000 0.235 0.765 315s 315s Data: Pima 315s Call: 315s Linda(type ~ ., data = Pima.tr, method = method) 315s 315s Prior Probabilities of Groups: 315s No Yes 315s 0.66 0.34 315s 315s Group means: 315s npreg glu bp skin bmi ped age 315s No 2.3056 110.63 67.991 26.444 31.010 0.41653 25.806 315s Yes 5.0444 142.58 74.267 33.067 34.309 0.49422 35.156 315s 315s Within-groups Covariance Matrix: 315s npreg glu bp skin bmi ped age 315s npreg 6.164422 8.43753 6.879286 3.252980 1.54269 -0.020158 9.543745 315s glu 8.437528 542.79578 57.156929 26.218837 28.63494 -0.421819 23.809124 315s bp 6.879286 57.15693 106.687356 26.315526 12.86691 -0.039577 22.992973 315s skin 3.252980 26.21884 26.315526 106.552759 44.95420 0.094311 12.005740 315s bmi 1.542689 28.63494 12.866911 44.954202 36.56262 0.167258 6.112925 315s ped -0.020158 -0.42182 -0.039577 0.094311 0.16726 0.059609 -0.072712 315s age 9.543745 23.80912 22.992973 12.005740 6.11292 -0.072712 35.594886 315s 315s Linear Coeficients: 315s npreg glu bp skin bmi ped age 315s No -1.4165 0.11776 0.49336 -0.31564 0.88761 6.5013 0.67462 315s Yes -1.3784 0.17062 0.46662 -0.26771 0.83745 8.5204 0.90557 315s 315s Constants: 315s No Yes 315s -41.716 -55.056 315s 315s Apparent error rate 0.235 315s 315s Classification table 315s Predicted 315s Actual No Yes 315s No 107 25 315s Yes 22 46 315s 315s Confusion matrix 315s Predicted 315s Actual No Yes 315s No 0.811 0.189 315s Yes 0.324 0.676 315s 315s Data: Forest soils 315s 315s Apparent error rate 0.3276 315s 315s Classification table 315s Predicted 315s Actual 1 2 3 315s 1 5 2 4 315s 2 2 13 8 315s 3 1 2 21 315s 315s Confusion matrix 315s Predicted 315s Actual 1 2 3 315s 1 0.455 0.182 0.364 315s 2 0.087 0.565 0.348 315s 3 0.042 0.083 0.875 315s 315s Data: Raven and Miller diabetes data 315s Call: 315s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 315s 315s Prior Probabilities of Groups: 315s normal chemical overt 315s 0.52414 0.24828 0.22759 315s 315s Group means: 315s insulin glucose sspg 315s normal 167.31 348.69 106.44 315s chemical 247.18 478.18 213.36 315s overt 101.83 932.92 322.42 315s 315s Within-groups Covariance Matrix: 315s insulin glucose sspg 315s insulin 4070.84 118.89 1701.54 315s glucose 118.89 2195.95 426.95 315s sspg 1701.54 426.95 2664.49 315s 315s Linear Coeficients: 315s insulin glucose sspg 315s normal 0.041471 0.15888 -0.011992 315s chemical 0.048103 0.21216 0.015359 315s overt -0.013579 0.41323 0.063462 315s 315s Constants: 315s normal chemical overt 315s -31.177 -59.703 -203.775 315s 315s Apparent error rate 0.0828 315s 315s Classification table 315s Predicted 315s Actual normal chemical overt 315s normal 72 4 0 315s chemical 2 34 0 315s overt 0 6 27 315s 315s Confusion matrix 315s Predicted 315s Actual normal chemical overt 315s normal 0.947 0.053 0.000 315s chemical 0.056 0.944 0.000 315s overt 0.000 0.182 0.818 315s 315s Data: iris 315s Call: 315s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 315s 315s Prior Probabilities of Groups: 315s setosa versicolor virginica 315s 0.33333 0.33333 0.33333 315s 315s Group means: 315s Sepal.Length Sepal.Width Petal.Length Petal.Width 315s setosa 5.0163 3.4510 1.4653 0.2449 315s versicolor 5.9435 2.7891 4.2543 1.3239 315s virginica 6.3867 3.0033 5.3767 2.0700 315s 315s Within-groups Covariance Matrix: 315s Sepal.Length Sepal.Width Petal.Length Petal.Width 315s Sepal.Length 0.186186 0.082478 0.094998 0.035445 315s Sepal.Width 0.082478 0.100137 0.049723 0.030678 315s Petal.Length 0.094998 0.049723 0.113105 0.043078 315s Petal.Width 0.035445 0.030678 0.043078 0.030885 315s 315s Linear Coeficients: 315s Sepal.Length Sepal.Width Petal.Length Petal.Width 315s setosa 23.678 30.2896 -3.1124 -44.9900 315s versicolor 20.342 4.6372 27.3265 -23.2006 315s virginica 18.377 -2.0004 31.4235 4.0906 315s 315s Constants: 315s setosa versicolor virginica 315s -104.96 -110.79 -145.49 315s 315s Apparent error rate 0.0333 315s 315s Classification table 315s Predicted 315s Actual setosa versicolor virginica 315s setosa 50 0 0 315s versicolor 0 48 2 315s virginica 0 3 47 315s 315s Confusion matrix 315s Predicted 315s Actual setosa versicolor virginica 315s setosa 1 0.00 0.00 315s versicolor 0 0.96 0.04 315s virginica 0 0.06 0.94 315s 315s Data: crabs 315s Call: 315s Linda(sp ~ ., data = crabs, method = method) 315s 315s Prior Probabilities of Groups: 315s B O 315s 0.5 0.5 315s 315s Group means: 315s sexM index FL RW CL CW BD 315s B 0.50000 23.956 13.790 11.649 29.390 33.934 12.274 315s O 0.51087 24.478 16.903 13.330 33.707 37.595 15.276 315s 315s Within-groups Covariance Matrix: 315s sexM index FL RW CL CW BD 315s sexM 0.25272 0.39179 0.14054 -0.30017 0.51191 0.45114 0.21708 315s index 0.39179 192.47099 39.97343 26.56698 84.63152 94.99987 38.67917 315s FL 0.14054 39.97343 8.97950 5.91408 18.98672 21.38046 8.60313 315s RW -0.30017 26.56698 5.91408 4.81389 12.31798 14.10613 5.58933 315s CL 0.51191 84.63152 18.98672 12.31798 40.94109 45.94330 18.52367 315s CW 0.45114 94.99987 21.38046 14.10613 45.94330 51.80106 20.79704 315s BD 0.21708 38.67917 8.60313 5.58933 18.52367 20.79704 8.49355 315s 315s Linear Coeficients: 315s sexM index FL RW CL CW BD 315s B 13.993 -2.5515 9.152 9.9187 2.2321 -2.9774 -0.66797 315s O 14.362 -4.0280 23.369 12.1556 5.3672 -14.9236 12.94057 315s 315s Constants: 315s B O 315s -72.687 -142.365 315s 315s Apparent error rate 0 315s 315s Classification table 315s Predicted 315s Actual B O 315s B 100 0 315s O 0 100 315s 315s Confusion matrix 315s Predicted 315s Actual B O 315s B 1 0 315s O 0 1 315s 315s Data: fish 315s 315s Apparent error rate 0.0316 315s 315s Classification table 315s Predicted 315s Actual 1 2 3 4 5 6 7 315s 1 34 0 0 0 0 0 0 315s 2 0 5 0 0 1 0 0 315s 3 0 0 20 0 0 0 0 315s 4 0 0 0 11 0 0 0 315s 5 0 0 0 0 13 0 1 315s 6 0 0 0 0 0 17 0 315s 7 0 0 0 0 3 0 53 315s 315s Confusion matrix 315s Predicted 315s Actual 1 2 3 4 5 6 7 315s 1 1 0.000 0 0 0.000 0 0.000 315s 2 0 0.833 0 0 0.167 0 0.000 315s 3 0 0.000 1 0 0.000 0 0.000 315s 4 0 0.000 0 1 0.000 0 0.000 315s 5 0 0.000 0 0 0.929 0 0.071 315s 6 0 0.000 0 0 0.000 1 0.000 315s 7 0 0.000 0 0 0.054 0 0.946 315s 315s Data: pottery 315s Call: 315s Linda(origin ~ ., data = pottery, method = method) 315s 315s Prior Probabilities of Groups: 315s Attic Eritrean 315s 0.48148 0.51852 315s 315s Group means: 315s SI AL FE MG CA TI 315s Attic 55.450 13.738 10.0000 5.0750 5.0750 0.87375 315s Eritrean 52.444 16.444 9.3222 3.1667 6.1778 0.82000 315s 315s Within-groups Covariance Matrix: 315s SI AL FE MG CA TI 315s SI 6.565481 1.6098148 -0.075259 0.369556 -0.359407 0.0169667 315s AL 1.609815 0.5640648 0.029407 0.302056 0.112426 0.0018583 315s FE -0.075259 0.0294074 0.167704 -0.180222 -0.343704 0.0110667 315s MG 0.369556 0.3020556 -0.180222 1.031667 0.915222 -0.0192167 315s CA -0.359407 0.1124259 -0.343704 0.915222 1.447370 -0.0348167 315s TI 0.016967 0.0018583 0.011067 -0.019217 -0.034817 0.0011725 315s 315s Linear Coeficients: 315s SI AL FE MG CA TI 315s Attic 190.17 -622.48 922.21 1.5045 293.30 -990.323 315s Eritrean 135.34 -431.40 666.59 -14.3288 237.68 -44.025 315s 315s Constants: 315s Attic Eritrean 315s -5924.2 -3802.9 315s 315s Apparent error rate 0.1111 315s 315s Classification table 315s Predicted 315s Actual Attic Eritrean 315s Attic 12 1 315s Eritrean 2 12 315s 315s Confusion matrix 315s Predicted 315s Actual Attic Eritrean 315s Attic 0.923 0.077 315s Eritrean 0.143 0.857 315s 315s Data: olitos 316s 316s Apparent error rate 0.1667 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 4 316s 1 44 1 2 3 316s 2 2 22 0 1 316s 3 5 2 25 2 316s 4 1 1 0 9 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 4 316s 1 0.880 0.020 0.040 0.060 316s 2 0.080 0.880 0.000 0.040 316s 3 0.147 0.059 0.735 0.059 316s 4 0.091 0.091 0.000 0.818 316s =================================================== 316s > dodata(method="mrcd") 316s 316s Call: dodata(method = "mrcd") 316s =================================================== 316s 316s Data: hemophilia 316s Call: 316s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 316s 316s Prior Probabilities of Groups: 316s carrier normal 316s 0.6 0.4 316s 316s Group means: 316s AHFactivity AHFantigen 316s carrier -0.34048 -0.055943 316s normal -0.13566 -0.081191 316s 316s Within-groups Covariance Matrix: 316s AHFactivity AHFantigen 316s AHFactivity 0.0133676 0.0088055 316s AHFantigen 0.0088055 0.0221225 316s 316s Linear Coeficients: 316s AHFactivity AHFantigen 316s carrier -32.264 10.31334 316s normal -10.478 0.50044 316s 316s Constants: 316s carrier normal 316s -5.7149 -1.6067 316s 316s Apparent error rate 0.16 316s 316s Classification table 316s Predicted 316s Actual carrier normal 316s carrier 38 7 316s normal 5 25 316s 316s Confusion matrix 316s Predicted 316s Actual carrier normal 316s carrier 0.844 0.156 316s normal 0.167 0.833 316s 316s Data: anorexia 316s Call: 316s Linda(Treat ~ ., data = anorexia, method = method) 316s 316s Prior Probabilities of Groups: 316s CBT Cont FT 316s 0.40278 0.36111 0.23611 316s 316s Group means: 316s Prewt Postwt 316s CBT 83.114 84.009 316s Cont 80.327 80.125 316s FT 85.161 94.371 316s 316s Within-groups Covariance Matrix: 316s Prewt Postwt 316s Prewt 22.498 11.860 316s Postwt 11.860 20.426 316s 316s Linear Coeficients: 316s Prewt Postwt 316s CBT 2.1994 2.8357 316s Cont 2.1653 2.6654 316s FT 1.9451 3.4907 316s 316s Constants: 316s CBT Cont FT 316s -211.42 -194.77 -248.97 316s 316s Apparent error rate 0.3889 316s 316s Classification table 316s Predicted 316s Actual CBT Cont FT 316s CBT 15 6 8 316s Cont 6 16 4 316s FT 0 4 13 316s 316s Confusion matrix 316s Predicted 316s Actual CBT Cont FT 316s CBT 0.517 0.207 0.276 316s Cont 0.231 0.615 0.154 316s FT 0.000 0.235 0.765 316s 316s Data: Pima 316s Call: 316s Linda(type ~ ., data = Pima.tr, method = method) 316s 316s Prior Probabilities of Groups: 316s No Yes 316s 0.66 0.34 316s 316s Group means: 316s npreg glu bp skin bmi ped age 316s No 1.9925 108.32 66.240 24.856 30.310 0.37382 24.747 316s Yes 5.8855 145.88 75.715 32.541 33.915 0.39281 38.857 316s 316s Within-groups Covariance Matrix: 316s npreg glu bp skin bmi ped age 316s npreg 4.090330 7.9547 3.818380 3.35899 2.470242 0.032557 9.5929 316s glu 7.954730 770.4187 76.377665 53.32216 54.100400 -1.139087 28.5677 316s bp 3.818380 76.3777 108.201622 42.61184 18.574983 -0.089151 20.3558 316s skin 3.358992 53.3222 42.611844 146.81170 65.210794 -0.277335 15.0162 316s bmi 2.470242 54.1004 18.574983 65.21079 52.871847 0.062145 9.0741 316s ped 0.032557 -1.1391 -0.089151 -0.27733 0.062145 0.063490 0.1762 316s age 9.592948 28.5677 20.355803 15.01616 9.074109 0.176201 53.5163 316s 316s Linear Coeficients: 316s npreg glu bp skin bmi ped age 316s No -1.30832 0.065773 0.54772 -0.32738 0.70207 5.2556 0.40900 316s Yes -0.76566 0.106435 0.55777 -0.28044 0.61709 5.9199 0.54892 316s 316s Constants: 316s No Yes 316s -33.429 -45.434 316s 316s Apparent error rate 0.28 316s 316s Classification table 316s Predicted 316s Actual No Yes 316s No 105 27 316s Yes 29 39 316s 316s Confusion matrix 316s Predicted 316s Actual No Yes 316s No 0.795 0.205 316s Yes 0.426 0.574 316s 316s Data: Forest soils 316s 316s Apparent error rate 0.3448 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 316s 1 7 2 2 316s 2 4 14 5 316s 3 3 4 17 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 316s 1 0.636 0.182 0.182 316s 2 0.174 0.609 0.217 316s 3 0.125 0.167 0.708 316s 316s Data: Raven and Miller diabetes data 316s Call: 316s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 316s 316s Prior Probabilities of Groups: 316s normal chemical overt 316s 0.52414 0.24828 0.22759 316s 316s Group means: 316s insulin glucose sspg 316s normal 154.014 346.07 91.606 316s chemical 248.841 451.10 221.936 316s overt 89.766 1064.16 335.100 316s 316s Within-groups Covariance Matrix: 316s insulin glucose sspg 316s insulin 4948.1 1007.61 1471.12 316s glucose 1007.6 2597.38 358.57 316s sspg 1471.1 358.57 3180.04 316s 316s Linear Coeficients: 316s insulin glucose sspg 316s normal 0.00027839 0.13121 0.013882 316s chemical 0.00148074 0.16615 0.050371 316s overt -0.10102404 0.43466 0.103100 316s 316s Constants: 316s normal chemical overt 316s -24.008 -44.642 -245.497 316s 316s Apparent error rate 0.0966 316s 316s Classification table 316s Predicted 316s Actual normal chemical overt 316s normal 71 5 0 316s chemical 2 34 0 316s overt 0 7 26 316s 316s Confusion matrix 316s Predicted 316s Actual normal chemical overt 316s normal 0.934 0.066 0.000 316s chemical 0.056 0.944 0.000 316s overt 0.000 0.212 0.788 316s 316s Data: iris 316s Call: 316s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 316s 316s Prior Probabilities of Groups: 316s setosa versicolor virginica 316s 0.33333 0.33333 0.33333 316s 316s Group means: 316s Sepal.Length Sepal.Width Petal.Length Petal.Width 316s setosa 4.9755 3.3826 1.4608 0.22053 316s versicolor 5.8868 2.7823 4.2339 1.34603 316s virginica 6.5176 2.9691 5.4560 2.06335 316s 316s Within-groups Covariance Matrix: 316s Sepal.Length Sepal.Width Petal.Length Petal.Width 316s Sepal.Length 0.238417 0.136325 0.086377 0.036955 316s Sepal.Width 0.136325 0.148452 0.067500 0.034804 316s Petal.Length 0.086377 0.067500 0.100934 0.035968 316s Petal.Width 0.036955 0.034804 0.035968 0.023856 316s 316s Linear Coeficients: 316s Sepal.Length Sepal.Width Petal.Length Petal.Width 316s setosa 17.106 15.693 7.8806 -52.031 316s versicolor 21.744 -15.813 38.0139 -11.505 316s virginica 23.032 -26.567 43.6391 23.777 316s 316s Constants: 316s setosa versicolor virginica 316s -70.214 -115.832 -180.294 316s 316s Apparent error rate 0.02 316s 316s Classification table 316s Predicted 316s Actual setosa versicolor virginica 316s setosa 50 0 0 316s versicolor 0 49 1 316s virginica 0 2 48 316s 316s Confusion matrix 316s Predicted 316s Actual setosa versicolor virginica 316s setosa 1 0.00 0.00 316s versicolor 0 0.98 0.02 316s virginica 0 0.04 0.96 316s 316s Data: crabs 316s Call: 316s Linda(sp ~ ., data = crabs, method = method) 316s 316s Prior Probabilities of Groups: 316s B O 316s 0.5 0.5 316s 316s Group means: 316s sexM index FL RW CL CW BD 316s B 0 25.5 13.270 12.138 28.102 32.624 11.816 316s O 1 25.5 16.626 12.262 33.688 37.188 15.324 316s 316s Within-groups Covariance Matrix: 316s sexM index FL RW CL CW BD 316s sexM 1.5255e-07 0.000 0.0000 0.0000 0.000 0.000 0.000 316s index 0.0000e+00 337.501 62.8107 46.5073 137.713 154.451 63.514 316s FL 0.0000e+00 62.811 15.3164 9.8612 29.911 33.479 13.805 316s RW 0.0000e+00 46.507 9.8612 8.6949 21.878 24.604 10.092 316s CL 0.0000e+00 137.713 29.9112 21.8779 73.888 73.891 30.486 316s CW 0.0000e+00 154.451 33.4788 24.6038 73.891 92.801 34.122 316s BD 0.0000e+00 63.514 13.8053 10.0923 30.486 34.122 15.854 316s 316s Linear Coeficients: 316s sexM index FL RW CL CW BD 316s B 0 -0.64890 0.95529 2.7299 0.20747 0.28549 -0.23815 316s O 6555120 -0.83294 1.67920 1.8896 0.32330 0.23479 0.51136 316s 316s Constants: 316s B O 316s -2.1491e+01 -3.2776e+06 316s 316s Apparent error rate 0.5 316s 316s Classification table 316s Predicted 316s Actual B O 316s B 50 50 316s O 50 50 316s 316s Confusion matrix 316s Predicted 316s Actual B O 316s B 0.5 0.5 316s O 0.5 0.5 316s 316s Data: fish 316s 316s Apparent error rate 0.2532 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 4 5 6 7 316s 1 33 0 0 1 0 0 0 316s 2 0 3 0 0 0 0 3 316s 3 0 2 5 0 0 0 13 316s 4 0 0 0 11 0 0 0 316s 5 0 0 0 0 14 0 0 316s 6 0 0 0 0 0 17 0 316s 7 0 19 0 0 2 0 35 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 4 5 6 7 316s 1 0.971 0.000 0.00 0.029 0.000 0 0.000 316s 2 0.000 0.500 0.00 0.000 0.000 0 0.500 316s 3 0.000 0.100 0.25 0.000 0.000 0 0.650 316s 4 0.000 0.000 0.00 1.000 0.000 0 0.000 316s 5 0.000 0.000 0.00 0.000 1.000 0 0.000 316s 6 0.000 0.000 0.00 0.000 0.000 1 0.000 316s 7 0.000 0.339 0.00 0.000 0.036 0 0.625 316s 316s Data: pottery 316s Call: 316s Linda(origin ~ ., data = pottery, method = method) 316s 316s Prior Probabilities of Groups: 316s Attic Eritrean 316s 0.48148 0.51852 316s 316s Group means: 316s SI AL FE MG CA TI 316s Attic 55.872 13.986 10.113 5.0235 4.7316 0.88531 316s Eritrean 52.487 16.286 9.499 2.4520 5.3745 0.83959 316s 316s Within-groups Covariance Matrix: 316s SI AL FE MG CA TI 316s SI 12.795913 3.2987125 -0.35496855 0.9399999 -0.0143514 0.01132392 316s AL 3.298713 1.0829436 -0.03394751 0.2838724 0.0501000 0.00117721 316s FE -0.354969 -0.0339475 0.08078156 0.0341568 -0.0457411 0.00043177 316s MG 0.940000 0.2838724 0.03415675 0.4350013 0.1417876 0.00396576 316s CA -0.014351 0.0501000 -0.04574114 0.1417876 0.4196628 -0.00104893 316s TI 0.011324 0.0011772 0.00043177 0.0039658 -0.0010489 0.00026205 316s 316s Linear Coeficients: 316s SI AL FE MG CA TI 316s Attic 36.451 -63.784 352.90 -124.07 110.08 3826.6 316s Eritrean 29.763 -41.039 325.12 -128.32 107.36 3938.1 316s 316s Constants: 316s Attic Eritrean 316s -4000.1 -3776.1 316s 316s Apparent error rate 0.1111 316s 316s Classification table 316s Predicted 316s Actual Attic Eritrean 316s Attic 12 1 316s Eritrean 2 12 316s 316s Confusion matrix 316s Predicted 316s Actual Attic Eritrean 316s Attic 0.923 0.077 316s Eritrean 0.143 0.857 316s 316s Data: olitos 316s 316s Apparent error rate 0.125 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 4 316s 1 44 2 3 1 316s 2 1 23 1 0 316s 3 4 1 27 2 316s 4 0 0 0 11 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 4 316s 1 0.880 0.040 0.060 0.020 316s 2 0.040 0.920 0.040 0.000 316s 3 0.118 0.029 0.794 0.059 316s 4 0.000 0.000 0.000 1.000 316s =================================================== 316s > dodata(method="ogk") 316s 316s Call: dodata(method = "ogk") 316s =================================================== 316s 316s Data: hemophilia 316s Call: 316s Linda(as.factor(gr) ~ ., data = hemophilia, method = method) 316s 316s Prior Probabilities of Groups: 316s carrier normal 316s 0.6 0.4 316s 316s Group means: 316s AHFactivity AHFantigen 316s carrier -0.29043 -0.00052902 316s normal -0.12463 -0.06715037 316s 316s Within-groups Covariance Matrix: 316s AHFactivity AHFantigen 316s AHFactivity 0.015688 0.010544 316s AHFantigen 0.010544 0.016633 316s 316s Linear Coeficients: 316s AHFactivity AHFantigen 316s carrier -32.2203 20.3935 316s normal -9.1149 1.7409 316s 316s Constants: 316s carrier normal 316s -5.1843 -1.4259 316s 316s Apparent error rate 0.1467 316s 316s Classification table 316s Predicted 316s Actual carrier normal 316s carrier 38 7 316s normal 4 26 316s 316s Confusion matrix 316s Predicted 316s Actual carrier normal 316s carrier 0.844 0.156 316s normal 0.133 0.867 316s 316s Data: anorexia 316s Call: 316s Linda(Treat ~ ., data = anorexia, method = method) 316s 316s Prior Probabilities of Groups: 316s CBT Cont FT 316s 0.40278 0.36111 0.23611 316s 316s Group means: 316s Prewt Postwt 316s CBT 82.634 82.060 316s Cont 81.605 80.459 316s FT 85.157 93.822 316s 316s Within-groups Covariance Matrix: 316s Prewt Postwt 316s Prewt 15.8294 4.4663 316s Postwt 4.4663 19.6356 316s 316s Linear Coeficients: 316s Prewt Postwt 316s CBT 4.3183 3.1970 316s Cont 4.2734 3.1256 316s FT 4.3080 3.7983 316s 316s Constants: 316s CBT Cont FT 316s -310.50 -301.12 -363.05 316s 316s Apparent error rate 0.4583 316s 316s Classification table 316s Predicted 316s Actual CBT Cont FT 316s CBT 15 5 9 316s Cont 14 11 1 316s FT 0 4 13 316s 316s Confusion matrix 316s Predicted 316s Actual CBT Cont FT 316s CBT 0.517 0.172 0.310 316s Cont 0.538 0.423 0.038 316s FT 0.000 0.235 0.765 316s 316s Data: Pima 316s Call: 316s Linda(type ~ ., data = Pima.tr, method = method) 316s 316s Prior Probabilities of Groups: 316s No Yes 316s 0.66 0.34 316s 316s Group means: 316s npreg glu bp skin bmi ped age 316s No 2.4175 109.93 67.332 26.324 30.344 0.38740 26.267 316s Yes 5.1853 142.29 75.194 33.151 34.878 0.47977 37.626 316s 316s Within-groups Covariance Matrix: 316s npreg glu bp skin bmi ped age 316s npreg 7.218576 7.52457 6.96595 4.86613 0.45567 -0.054245 14.42648 316s glu 7.524571 517.38370 58.53812 31.57321 22.68396 -0.200222 22.88780 316s bp 6.965950 58.53812 101.50317 27.86784 10.89215 -0.152784 25.41819 316s skin 4.866127 31.57321 27.86784 95.16949 37.45066 -0.117375 14.60676 316s bmi 0.455675 22.68396 10.89215 37.45066 30.89491 0.043400 4.05584 316s ped -0.054245 -0.20022 -0.15278 -0.11737 0.04340 0.051268 -0.18131 316s age 14.426479 22.88780 25.41819 14.60676 4.05584 -0.181311 57.89570 316s 316s Linear Coeficients: 316s npreg glu bp skin bmi ped age 316s No -0.99043 0.12339 0.54101 -0.35335 1.0721 8.4945 0.45482 316s Yes -1.01369 0.17577 0.53898 -0.35554 1.1563 11.0474 0.63966 316s 316s Constants: 316s No Yes 316s -43.449 -60.176 316s 316s Apparent error rate 0.23 316s 316s Classification table 316s Predicted 316s Actual No Yes 316s No 108 24 316s Yes 22 46 316s 316s Confusion matrix 316s Predicted 316s Actual No Yes 316s No 0.818 0.182 316s Yes 0.324 0.676 316s 316s Data: Forest soils 316s 316s Apparent error rate 0.3621 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 316s 1 7 3 1 316s 2 4 13 6 316s 3 3 4 17 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 316s 1 0.636 0.273 0.091 316s 2 0.174 0.565 0.261 316s 3 0.125 0.167 0.708 316s 316s Data: Raven and Miller diabetes data 316s Call: 316s Linda(group ~ insulin + glucose + sspg, data = diabetes, method = method) 316s 316s Prior Probabilities of Groups: 316s normal chemical overt 316s 0.52414 0.24828 0.22759 316s 316s Group means: 316s insulin glucose sspg 316s normal 159.540 344.06 99.486 316s chemical 252.992 478.16 219.442 316s overt 79.635 1094.96 338.517 316s 316s Within-groups Covariance Matrix: 316s insulin glucose sspg 316s insulin 3844.877 67.238 1456.55 316s glucose 67.238 2228.396 324.21 316s sspg 1456.548 324.205 2181.73 316s 316s Linear Coeficients: 316s insulin glucose sspg 316s normal 0.040407 0.15379 -0.0042303 316s chemical 0.047858 0.20764 0.0377766 316s overt -0.026158 0.47736 0.1016873 316s 316s Constants: 316s normal chemical overt 316s -30.115 -61.233 -278.996 316s 316s Apparent error rate 0.0966 316s 316s Classification table 316s Predicted 316s Actual normal chemical overt 316s normal 71 5 0 316s chemical 2 34 0 316s overt 0 7 26 316s 316s Confusion matrix 316s Predicted 316s Actual normal chemical overt 316s normal 0.934 0.066 0.000 316s chemical 0.056 0.944 0.000 316s overt 0.000 0.212 0.788 316s 316s Data: iris 316s Call: 316s Linda(Species ~ ., data = iris, method = method, l1med = TRUE) 316s 316s Prior Probabilities of Groups: 316s setosa versicolor virginica 316s 0.33333 0.33333 0.33333 316s 316s Group means: 316s Sepal.Length Sepal.Width Petal.Length Petal.Width 316s setosa 4.9654 3.3913 1.4507 0.21639 316s versicolor 5.8767 2.7909 4.2238 1.34189 316s virginica 6.5075 2.9777 5.4459 2.05921 316s 316s Within-groups Covariance Matrix: 316s Sepal.Length Sepal.Width Petal.Length Petal.Width 316s Sepal.Length 0.180280 0.068876 0.101512 0.036096 316s Sepal.Width 0.068876 0.079556 0.047722 0.029409 316s Petal.Length 0.101512 0.047722 0.111492 0.038658 316s Petal.Width 0.036096 0.029409 0.038658 0.029965 316s 316s Linear Coeficients: 316s Sepal.Length Sepal.Width Petal.Length Petal.Width 316s setosa 28.582 46.5236 -13.859 -54.9877 316s versicolor 19.837 11.9601 20.865 -17.7704 316s virginica 16.999 1.9046 29.808 7.9189 316s 316s Constants: 316s setosa versicolor virginica 316s -134.94 -108.22 -148.56 316s 316s Apparent error rate 0.0133 316s 316s Classification table 316s Predicted 316s Actual setosa versicolor virginica 316s setosa 50 0 0 316s versicolor 0 49 1 316s virginica 0 1 49 316s 316s Confusion matrix 316s Predicted 316s Actual setosa versicolor virginica 316s setosa 1 0.00 0.00 316s versicolor 0 0.98 0.02 316s virginica 0 0.02 0.98 316s 316s Data: crabs 316s Call: 316s Linda(sp ~ ., data = crabs, method = method) 316s 316s Prior Probabilities of Groups: 316s B O 316s 0.5 0.5 316s 316s Group means: 316s sexM index FL RW CL CW BD 316s B 0.48948 24.060 13.801 11.738 29.491 34.062 12.329 316s O 0.56236 24.043 16.825 13.158 33.574 37.418 15.223 316s 316s Within-groups Covariance Matrix: 316s sexM index FL RW CL CW BD 316s sexM 0.24961 0.50421 0.16645 -0.28574 0.54159 0.48449 0.22563 316s index 0.50421 186.86616 38.46284 25.26749 82.29121 92.11253 37.67723 316s FL 0.16645 38.46284 8.58830 5.56769 18.33015 20.58235 8.32030 316s RW -0.28574 25.26749 5.56769 4.52038 11.70881 13.37643 5.32779 316s CL 0.54159 82.29121 18.33015 11.70881 39.78186 44.52112 18.01179 316s CW 0.48449 92.11253 20.58235 13.37643 44.52112 50.06150 20.16852 316s BD 0.22563 37.67723 8.32030 5.32779 18.01179 20.16852 8.25884 316s 316s Linear Coeficients: 316s sexM index FL RW CL CW BD 316s B 16.497 -2.5896 8.3966 11.518 1.7536 -2.5325 -0.67361 316s O 17.010 -4.0452 23.5400 13.702 4.7871 -14.8264 13.04556 316s 316s Constants: 316s B O 316s -77.695 -147.287 316s 316s Apparent error rate 0 316s 316s Classification table 316s Predicted 316s Actual B O 316s B 100 0 316s O 0 100 316s 316s Confusion matrix 316s Predicted 316s Actual B O 316s B 1 0 316s O 0 1 316s 316s Data: fish 316s 316s Apparent error rate 0.0063 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 4 5 6 7 316s 1 34 0 0 0 0 0 0 316s 2 0 6 0 0 0 0 0 316s 3 0 0 20 0 0 0 0 316s 4 0 0 0 11 0 0 0 316s 5 0 0 0 0 14 0 0 316s 6 0 0 0 0 0 17 0 316s 7 0 0 0 0 1 0 55 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 4 5 6 7 316s 1 1 0 0 0 0.000 0 0.000 316s 2 0 1 0 0 0.000 0 0.000 316s 3 0 0 1 0 0.000 0 0.000 316s 4 0 0 0 1 0.000 0 0.000 316s 5 0 0 0 0 1.000 0 0.000 316s 6 0 0 0 0 0.000 1 0.000 316s 7 0 0 0 0 0.018 0 0.982 316s 316s Data: pottery 316s Call: 316s Linda(origin ~ ., data = pottery, method = method) 316s 316s Prior Probabilities of Groups: 316s Attic Eritrean 316s 0.48148 0.51852 316s 316s Group means: 316s SI AL FE MG CA TI 316s Attic 55.381 14.088 10.1316 4.9588 4.7684 0.88444 316s Eritrean 53.559 16.251 9.1145 2.6213 5.8980 0.82501 316s 316s Within-groups Covariance Matrix: 316s SI AL FE MG CA TI 316s SI 7.878378 1.9064112 -0.545403 0.4167407 -0.11589 0.01850748 316s AL 1.906411 0.6678763 -0.037744 0.1120891 -0.10733 0.00805556 316s FE -0.545403 -0.0377438 0.213914 -0.0192356 -0.23121 0.00582800 316s MG 0.416741 0.1120891 -0.019236 0.2336721 0.17284 -0.00183128 316s CA -0.115888 -0.1073297 -0.231213 0.1728385 0.71388 -0.01895968 316s TI 0.018507 0.0080556 0.005828 -0.0018313 -0.01896 0.00081815 316s 316s Linear Coeficients: 316s SI AL FE MG CA TI 316s Attic 57.784 -107.297 319.31 -152.94 241.66 3813.6 316s Eritrean 52.523 -86.545 306.58 -165.71 242.36 3734.1 316s 316s Constants: 316s Attic Eritrean 316s -4346 -4139 316s 316s Apparent error rate 0.1111 316s 316s Classification table 316s Predicted 316s Actual Attic Eritrean 316s Attic 12 1 316s Eritrean 2 12 316s 316s Confusion matrix 316s Predicted 316s Actual Attic Eritrean 316s Attic 0.923 0.077 316s Eritrean 0.143 0.857 316s 316s Data: olitos 316s 316s Apparent error rate 0.1 316s 316s Classification table 316s Predicted 316s Actual 1 2 3 4 316s 1 45 2 2 1 316s 2 0 25 0 0 316s 3 4 1 27 2 316s 4 0 0 0 11 316s 316s Confusion matrix 316s Predicted 316s Actual 1 2 3 4 316s 1 0.900 0.040 0.040 0.020 316s 2 0.000 1.000 0.000 0.000 316s 3 0.118 0.029 0.794 0.059 316s 4 0.000 0.000 0.000 1.000 316s =================================================== 316s > #dodata(method="fsa") 316s > 316s BEGIN TEST tldapp.R 317s 317s R version 4.4.3 (2025-02-28) -- "Trophy Case" 317s Copyright (C) 2025 The R Foundation for Statistical Computing 317s Platform: aarch64-unknown-linux-gnu 317s 317s R is free software and comes with ABSOLUTELY NO WARRANTY. 317s You are welcome to redistribute it under certain conditions. 317s Type 'license()' or 'licence()' for distribution details. 317s 317s R is a collaborative project with many contributors. 317s Type 'contributors()' for more information and 317s 'citation()' on how to cite R or R packages in publications. 317s 317s Type 'demo()' for some demos, 'help()' for on-line help, or 317s 'help.start()' for an HTML browser interface to help. 317s Type 'q()' to quit R. 317s 317s > ## VT::15.09.2013 - this will render the output independent 317s > ## from the version of the package 317s > suppressPackageStartupMessages(library(rrcov)) 317s > library(MASS) 317s > 317s > dodata <- function(method) { 317s + 317s + options(digits = 5) 317s + set.seed(101) 317s + 317s + tmp <- sys.call() 317s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 317s + cat("===================================================\n") 317s + 317s + data(pottery); show(lda <- LdaPP(origin~., data=pottery, method=method)); show(predict(lda)) 317s + data(hemophilia); show(lda <- LdaPP(as.factor(gr)~., data=hemophilia, method=method)); show(predict(lda)) 317s + data(anorexia); show(lda <- LdaPP(Treat~., data=anorexia, method=method)); show(predict(lda)) 317s + data(Pima.tr); show(lda <- LdaPP(type~., data=Pima.tr, method=method)); show(predict(lda)) 317s + data(crabs); show(lda <- LdaPP(sp~., data=crabs, method=method)); show(predict(lda)) 317s + 317s + cat("===================================================\n") 317s + } 317s > 317s > 317s > ## -- now do it: 317s > 317s > ## Commented out - still to slow 317s > ##dodata(method="huber") 317s > ##dodata(method="sest") 317s > 317s > ## VT::14.11.2018 - Commented out: too slow 317s > ## dodata(method="mad") 317s > ## dodata(method="class") 317s > 317s BEGIN TEST tmcd4.R 317s 317s R version 4.4.3 (2025-02-28) -- "Trophy Case" 317s Copyright (C) 2025 The R Foundation for Statistical Computing 317s Platform: aarch64-unknown-linux-gnu 317s 317s R is free software and comes with ABSOLUTELY NO WARRANTY. 317s You are welcome to redistribute it under certain conditions. 317s Type 'license()' or 'licence()' for distribution details. 317s 317s R is a collaborative project with many contributors. 317s Type 'contributors()' for more information and 317s 'citation()' on how to cite R or R packages in publications. 317s 317s Type 'demo()' for some demos, 'help()' for on-line help, or 317s 'help.start()' for an HTML browser interface to help. 317s Type 'q()' to quit R. 317s 317s > ## Test the exact fit property of CovMcd 317s > doexactfit <- function(){ 317s + exact <-function(seed=1234){ 317s + 317s + set.seed(seed) 317s + 317s + n1 <- 45 317s + p <- 2 317s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 317s + x1[,p] <- x1[,p] + 3 317s + n2 <- 55 317s + m1 <- 0 317s + m2 <- 3 317s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 317s + x<-rbind(x1,x2) 317s + colnames(x) <- c("X1","X2") 317s + x 317s + } 317s + print(CovMcd(exact())) 317s + } 317s > 317s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 317s + ##@bdescr 317s + ## Test the function covMcd() on the literature datasets: 317s + ## 317s + ## Call CovMcd() for all regression datasets available in rrcov and print: 317s + ## - execution time (if time == TRUE) 317s + ## - objective fucntion 317s + ## - best subsample found (if short == false) 317s + ## - outliers identified (with cutoff 0.975) (if short == false) 317s + ## - estimated center and covarinance matrix if full == TRUE) 317s + ## 317s + ##@edescr 317s + ## 317s + ##@in nrep : [integer] number of repetitions to use for estimating the 317s + ## (average) execution time 317s + ##@in time : [boolean] whether to evaluate the execution time 317s + ##@in short : [boolean] whether to do short output (i.e. only the 317s + ## objective function value). If short == FALSE, 317s + ## the best subsample and the identified outliers are 317s + ## printed. See also the parameter full below 317s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 317s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 317s + 317s + doest <- function(x, xname, nrep=1){ 317s + n <- dim(x)[1] 317s + p <- dim(x)[2] 317s + if(method == "MASS"){ 317s + mcd<-cov.mcd(x) 317s + quan <- as.integer(floor((n + p + 1)/2)) #default: floor((n+p+1)/2) 317s + } 317s + else{ 317s + mcd <- if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 317s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 317s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 317s + else CovMcd(x, trace=FALSE) 317s + quan <- as.integer(mcd@quan) 317s + } 317s + 317s + crit <- mcd@crit 317s + 317s + if(time){ 317s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 317s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 317s + } 317s + else{ 317s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 317s + } 317s + lpad<-lname-nchar(xname) 317s + cat(pad.right(xname,lpad), xres) 317s + 317s + if(!short){ 317s + cat("Best subsample: \n") 317s + if(length(mcd@best) > 150) 317s + cat("Too long... \n") 317s + else 317s + print(mcd@best) 317s + 317s + ibad <- which(mcd@wt==0) 317s + names(ibad) <- NULL 317s + nbad <- length(ibad) 317s + cat("Outliers: ",nbad,"\n") 317s + if(nbad > 0 & nbad < 150) 317s + print(ibad) 317s + else 317s + cat("Too many to print ... \n") 317s + if(full){ 317s + cat("-------------\n") 317s + show(mcd) 317s + } 317s + cat("--------------------------------------------------------\n") 317s + } 317s + } 317s + 317s + options(digits = 5) 317s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 317s + 317s + lname <- 20 317s + 317s + ## VT::15.09.2013 - this will render the output independent 317s + ## from the version of the package 317s + suppressPackageStartupMessages(library(rrcov)) 317s + 317s + method <- match.arg(method) 317s + if(method == "MASS") 317s + library(MASS) 317s + 317s + data(Animals, package = "MASS") 317s + brain <- Animals[c(1:24, 26:25, 27:28),] 317s + 317s + data(fish) 317s + data(pottery) 317s + data(rice) 317s + data(un86) 317s + data(wages) 317s + 317s + tmp <- sys.call() 317s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 317s + 317s + cat("Data Set n p Half LOG(obj) Time\n") 317s + cat("========================================================\n") 317s + 317s + if(method=="exact") 317s + { 317s + ## only small data sets 317s + doest(heart[, 1:2], data(heart), nrep) 317s + doest(starsCYG, data(starsCYG), nrep) 317s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 317s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 317s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 317s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 317s + doest(brain, "Animals", nrep) 317s + doest(lactic, data(lactic), nrep) 317s + doest(pension, data(pension), nrep) 317s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 317s + doest(stack.x, data(stackloss), nrep) 317s + doest(pilot, data(pilot), nrep) 317s + } else 317s + { 317s + doest(heart[, 1:2], data(heart), nrep) 317s + doest(starsCYG, data(starsCYG), nrep) 317s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 317s + doest(stack.x, data(stackloss), nrep) 317s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 317s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 317s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 317s + doest(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 317s + 317s + doest(brain, "Animals", nrep) 317s + ## doest(milk, data(milk), nrep) # difference between 386 and x64 317s + doest(bushfire, data(bushfire), nrep) 317s + 317s + doest(lactic, data(lactic), nrep) 317s + doest(pension, data(pension), nrep) 317s + ## doest(pilot, data(pilot), nrep) # difference between 386 and x64 317s + 317s + if(method != "MRCD") # these two are quite slow for MRCD, especially the second one 317s + { 317s + doest(radarImage, data(radarImage), nrep) 317s + doest(NOxEmissions, data(NOxEmissions), nrep) 317s + } 317s + 317s + doest(data.matrix(subset(vaso, select = -Y)), data(vaso), nrep) 317s + doest(data.matrix(subset(wagnerGrowth, select = -Period)), data(wagnerGrowth), nrep) 317s + 317s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep) 317s + doest(data.matrix(subset(pottery, select = -origin)), data(pottery), nrep) 317s + doest(rice, data(rice), nrep) 317s + doest(un86, data(un86), nrep) 317s + 317s + doest(wages, data(wages), nrep) 317s + 317s + ## from package 'datasets' 317s + doest(airquality[,1:4], data(airquality), nrep) 317s + doest(attitude, data(attitude), nrep) 317s + doest(attenu, data(attenu), nrep) 317s + doest(USJudgeRatings, data(USJudgeRatings), nrep) 317s + doest(USArrests, data(USArrests), nrep) 317s + doest(longley, data(longley), nrep) 317s + doest(Loblolly, data(Loblolly), nrep) 317s + doest(quakes[,1:4], data(quakes), nrep) 317s + } 317s + cat("========================================================\n") 317s + } 317s > 317s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMCD", "MASS")){ 317s + 317s + doest <- function(x, nrep=1){ 317s + gc() 317s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 317s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 317s + xtime 317s + } 317s + 317s + set.seed(1234) 317s + 317s + ## VT::15.09.2013 - this will render the output independent 317s + ## from the version of the package 317s + suppressPackageStartupMessages(library(rrcov)) 317s + 317s + library(MASS) 317s + method <- match.arg(method) 317s + 317s + ap <- c(2, 5, 10, 20, 30) 317s + an <- c(100, 500, 1000, 10000, 50000) 317s + 317s + tottime <- 0 317s + cat(" n p Time\n") 317s + cat("=====================\n") 317s + for(i in 1:length(an)) { 317s + for(j in 1:length(ap)) { 317s + n <- an[i] 317s + p <- ap[j] 317s + if(5*p <= n){ 317s + xx <- gendata(n, p, eps) 317s + X <- xx$X 317s + tottime <- tottime + doest(X, nrep) 317s + } 317s + } 317s + } 317s + 317s + cat("=====================\n") 317s + cat("Total time: ", tottime*nrep, "\n") 317s + } 317s > 317s > docheck <- function(n, p, eps){ 317s + xx <- gendata(n,p,eps) 317s + mcd <- CovMcd(xx$X) 317s + check(mcd, xx$xind) 317s + } 317s > 317s > check <- function(mcd, xind){ 317s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 317s + ## did not get zero weight 317s + mymatch <- xind %in% which(mcd@wt == 0) 317s + length(xind) - length(which(mymatch)) 317s + } 317s > 317s > dorep <- function(x, nrep=1, method=c("FASTMCD","MASS", "deterministic", "exact", "MRCD")){ 317s + 317s + method <- match.arg(method) 317s + for(i in 1:nrep) 317s + if(method == "MASS") 317s + cov.mcd(x) 317s + else 317s + { 317s + if(method=="deterministic") CovMcd(x, nsamp="deterministic", trace=FALSE) 317s + else if(method=="exact") CovMcd(x, nsamp="exact", trace=FALSE) 317s + else if(method=="MRCD") CovMrcd(x, trace=FALSE) 317s + else CovMcd(x, trace=FALSE) 317s + } 317s + } 317s > 317s > #### gendata() #### 317s > # Generates a location contaminated multivariate 317s > # normal sample of n observations in p dimensions 317s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 317s > # where 317s > # m = (b,b,...,b) 317s > # Defaults: eps=0 and b=10 317s > # 317s > gendata <- function(n,p,eps=0,b=10){ 317s + 317s + if(missing(n) || missing(p)) 317s + stop("Please specify (n,p)") 317s + if(eps < 0 || eps >= 0.5) 317s + stop(message="eps must be in [0,0.5)") 317s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 317s + nbad <- as.integer(eps * n) 317s + if(nbad > 0){ 317s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 317s + xind <- sample(n,nbad) 317s + X[xind,] <- Xbad 317s + } 317s + list(X=X, xind=xind) 317s + } 317s > 317s > pad.right <- function(z, pads) 317s + { 317s + ### Pads spaces to right of text 317s + padding <- paste(rep(" ", pads), collapse = "") 317s + paste(z, padding, sep = "") 317s + } 317s > 317s > whatis<-function(x){ 317s + if(is.data.frame(x)) 317s + cat("Type: data.frame\n") 317s + else if(is.matrix(x)) 317s + cat("Type: matrix\n") 317s + else if(is.vector(x)) 317s + cat("Type: vector\n") 317s + else 317s + cat("Type: don't know\n") 317s + } 317s > 317s > ## VT::15.09.2013 - this will render the output independent 317s > ## from the version of the package 317s > suppressPackageStartupMessages(library(rrcov)) 317s > 317s > dodata() 318s 318s Call: dodata() 318s Data Set n p Half LOG(obj) Time 318s ======================================================== 318s heart 12 2 7 5.678742 318s Best subsample: 318s [1] 1 3 4 5 7 9 11 318s Outliers: 0 318s Too many to print ... 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s height weight 318s 38.3 33.1 318s 318s Robust Estimate of Covariance: 318s height weight 318s height 135 259 318s weight 259 564 318s -------------------------------------------------------- 318s starsCYG 47 2 25 -8.031215 318s Best subsample: 318s [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 318s Outliers: 7 318s [1] 7 9 11 14 20 30 34 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s log.Te log.light 318s 4.41 4.95 318s 318s Robust Estimate of Covariance: 318s log.Te log.light 318s log.Te 0.0132 0.0394 318s log.light 0.0394 0.2743 318s -------------------------------------------------------- 318s phosphor 18 2 10 6.878847 318s Best subsample: 318s [1] 3 5 8 9 11 12 13 14 15 17 318s Outliers: 3 318s [1] 1 6 10 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s inorg organic 318s 13.4 38.8 318s 318s Robust Estimate of Covariance: 318s inorg organic 318s inorg 129 130 318s organic 130 182 318s -------------------------------------------------------- 318s stackloss 21 3 12 5.472581 318s Best subsample: 318s [1] 4 5 6 7 8 9 10 11 12 13 14 20 318s Outliers: 9 318s [1] 1 2 3 15 16 17 18 19 21 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Air.Flow Water.Temp Acid.Conc. 318s 59.5 20.8 87.3 318s 318s Robust Estimate of Covariance: 318s Air.Flow Water.Temp Acid.Conc. 318s Air.Flow 6.29 5.85 5.74 318s Water.Temp 5.85 9.23 6.14 318s Acid.Conc. 5.74 6.14 23.25 318s -------------------------------------------------------- 318s coleman 20 5 13 1.286808 318s Best subsample: 318s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 318s Outliers: 7 318s [1] 1 6 9 10 11 15 18 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s salaryP fatherWc sstatus teacherSc motherLev 318s 2.76 48.38 6.12 25.00 6.40 318s 318s Robust Estimate of Covariance: 318s salaryP fatherWc sstatus teacherSc motherLev 318s salaryP 0.253 1.786 -0.266 0.151 0.075 318s fatherWc 1.786 1303.382 330.496 12.604 34.503 318s sstatus -0.266 330.496 119.888 3.833 10.131 318s teacherSc 0.151 12.604 3.833 0.785 0.555 318s motherLev 0.075 34.503 10.131 0.555 1.043 318s -------------------------------------------------------- 318s salinity 28 3 16 1.326364 318s Best subsample: 318s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 318s Outliers: 4 318s [1] 5 16 23 24 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s X1 X2 X3 318s 10.08 2.78 22.78 318s 318s Robust Estimate of Covariance: 318s X1 X2 X3 318s X1 10.44 1.01 -3.19 318s X2 1.01 3.83 -1.44 318s X3 -3.19 -1.44 2.39 318s -------------------------------------------------------- 318s wood 20 5 13 -36.270094 318s Best subsample: 318s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 318s Outliers: 7 318s [1] 4 6 7 8 11 16 19 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s x1 x2 x3 x4 x5 318s 0.587 0.122 0.531 0.538 0.892 318s 318s Robust Estimate of Covariance: 318s x1 x2 x3 x4 x5 318s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 318s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 318s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 318s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 318s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 318s -------------------------------------------------------- 318s hbk 75 3 39 -1.047858 318s Best subsample: 318s [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 318s [26] 55 56 58 59 61 63 64 66 67 70 71 72 73 74 318s Outliers: 14 318s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=39); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s X1 X2 X3 318s 1.54 1.78 1.69 318s 318s Robust Estimate of Covariance: 318s X1 X2 X3 318s X1 1.227 0.055 0.127 318s X2 0.055 1.249 0.153 318s X3 0.127 0.153 1.160 318s -------------------------------------------------------- 318s Animals 28 2 15 14.555543 318s Best subsample: 318s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 318s Outliers: 14 318s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s body brain 318s 18.7 64.9 318s 318s Robust Estimate of Covariance: 318s body brain 318s body 929 1576 318s brain 1576 5646 318s -------------------------------------------------------- 318s bushfire 38 5 22 18.135810 318s Best subsample: 318s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 318s Outliers: 16 318s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=22); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s V1 V2 V3 V4 V5 318s 105 147 274 218 279 318s 318s Robust Estimate of Covariance: 318s V1 V2 V3 V4 V5 318s V1 346 268 -1692 -381 -311 318s V2 268 236 -1125 -230 -194 318s V3 -1692 -1125 9993 2455 1951 318s V4 -381 -230 2455 647 505 318s V5 -311 -194 1951 505 398 318s -------------------------------------------------------- 318s lactic 20 2 11 0.359580 318s Best subsample: 318s [1] 1 2 3 4 5 7 8 9 10 11 12 318s Outliers: 4 318s [1] 17 18 19 20 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s X Y 318s 3.86 5.01 318s 318s Robust Estimate of Covariance: 318s X Y 318s X 10.6 14.6 318s Y 14.6 21.3 318s -------------------------------------------------------- 318s pension 18 2 10 16.675508 318s Best subsample: 318s [1] 1 2 3 4 5 6 8 9 11 12 318s Outliers: 5 318s [1] 14 15 16 17 18 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Income Reserves 318s 52.3 560.9 318s 318s Robust Estimate of Covariance: 318s Income Reserves 318s Income 1420 11932 318s Reserves 11932 208643 318s -------------------------------------------------------- 318s radarImage 1573 5 789 36.694425 318s Best subsample: 318s Too long... 318s Outliers: 117 318s [1] 164 237 238 242 261 262 351 450 451 462 480 481 509 516 535 318s [16] 542 572 597 620 643 654 669 697 737 802 803 804 818 832 833 318s [31] 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 1202 1223 318s [46] 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 1376 1393 318s [61] 1394 1402 1403 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1479 1487 318s [76] 1492 1504 1510 1511 1512 1517 1518 1519 1520 1521 1522 1525 1526 1527 1528 318s [91] 1530 1532 1534 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 318s [106] 1557 1558 1561 1562 1564 1565 1566 1567 1569 1570 1571 1573 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=789); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s X.coord Y.coord Band.1 Band.2 Band.3 318s 52.80 35.12 6.77 18.44 8.90 318s 318s Robust Estimate of Covariance: 318s X.coord Y.coord Band.1 Band.2 Band.3 318s X.coord 123.6 23.0 -361.9 -197.1 -22.5 318s Y.coord 23.0 400.6 34.3 -191.1 -39.1 318s Band.1 -361.9 34.3 27167.9 8178.8 473.7 318s Band.2 -197.1 -191.1 8178.8 26021.8 952.4 318s Band.3 -22.5 -39.1 473.7 952.4 4458.4 318s -------------------------------------------------------- 318s NOxEmissions 8088 4 4046 2.474539 318s Best subsample: 318s Too long... 318s Outliers: 2156 318s Too many to print ... 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=4046); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s julday LNOx LNOxEm sqrtWS 318s 168.19 4.73 7.91 1.37 318s 318s Robust Estimate of Covariance: 318s julday LNOx LNOxEm sqrtWS 318s julday 9180.6297 12.0306 0.7219 -10.1273 318s LNOx 12.0306 0.4721 0.1418 -0.1526 318s LNOxEm 0.7219 0.1418 0.2516 0.0438 318s sqrtWS -10.1273 -0.1526 0.0438 0.2073 318s -------------------------------------------------------- 318s vaso 39 2 21 -3.972244 318s Best subsample: 318s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 318s Outliers: 4 318s [1] 1 2 17 31 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Volume Rate 318s 1.16 1.72 318s 318s Robust Estimate of Covariance: 318s Volume Rate 318s Volume 0.313 -0.167 318s Rate -0.167 0.728 318s -------------------------------------------------------- 318s wagnerGrowth 63 6 35 6.572208 318s Best subsample: 318s [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 318s [26] 48 51 52 53 54 55 56 57 60 62 318s Outliers: 13 318s [1] 1 8 15 21 22 28 29 33 42 43 46 50 63 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=35); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Region PA GPA HS GHS y 318s 11.00 33.66 -2.00 2.48 0.31 7.48 318s 318s Robust Estimate of Covariance: 318s Region PA GPA HS GHS y 318s Region 35.5615 17.9337 -0.5337 -0.9545 -0.3093 -14.0090 318s PA 17.9337 27.7333 -4.9017 -1.4174 0.0343 -28.7040 318s GPA -0.5337 -4.9017 5.3410 0.2690 -0.1484 4.0006 318s HS -0.9545 -1.4174 0.2690 0.8662 -0.0454 2.9024 318s GHS -0.3093 0.0343 -0.1484 -0.0454 0.1772 0.7457 318s y -14.0090 -28.7040 4.0006 2.9024 0.7457 82.6877 318s -------------------------------------------------------- 318s fish 159 6 82 8.879005 318s Best subsample: 318s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 318s [20] 20 21 22 23 24 25 26 27 28 30 32 35 36 37 42 43 44 45 46 318s [39] 47 48 49 50 51 52 53 54 55 56 57 58 59 60 107 109 110 111 113 318s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 318s [77] 134 135 136 137 138 139 318s Outliers: 63 318s [1] 30 39 40 41 42 62 63 64 65 66 68 69 70 73 74 75 76 77 78 318s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 318s [39] 98 99 100 101 102 103 104 105 141 143 144 145 147 148 149 150 151 152 153 318s [58] 154 155 156 157 158 159 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=82); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Weight Length1 Length2 Length3 Height Width 318s 329.9 24.5 26.6 29.7 31.1 14.7 318s 318s Robust Estimate of Covariance: 318s Weight Length1 Length2 Length3 Height Width 318s Weight 69082.99 1477.81 1613.64 1992.62 1439.32 -62.12 318s Length1 1477.81 34.68 37.61 45.51 28.82 -1.31 318s Length2 1613.64 37.61 40.88 49.52 31.81 -1.40 318s Length3 1992.62 45.51 49.52 61.16 42.65 -2.25 318s Height 1439.32 28.82 31.81 42.65 46.74 -2.82 318s Width -62.12 -1.31 -1.40 -2.25 -2.82 1.01 318s -------------------------------------------------------- 318s pottery 27 6 17 -10.586933 318s Best subsample: 318s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 318s Outliers: 9 318s [1] 3 8 12 16 17 18 23 24 25 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=17); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s SI AL FE MG CA TI 318s 54.983 15.206 9.700 3.817 5.211 0.859 318s 318s Robust Estimate of Covariance: 318s SI AL FE MG CA TI 318s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 318s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 318s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 318s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 318s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 318s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 318s -------------------------------------------------------- 318s rice 105 6 56 -14.463986 318s Best subsample: 318s [1] 2 4 6 8 10 12 15 18 21 22 24 29 30 31 32 33 34 36 37 318s [20] 38 41 44 45 47 51 52 53 54 55 59 61 65 67 68 69 70 72 76 318s [39] 78 79 80 81 82 83 84 85 86 92 93 94 95 97 98 99 102 105 318s Outliers: 13 318s [1] 9 14 19 28 40 42 49 58 62 71 75 77 89 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=56); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Favor Appearance Taste Stickiness 318s -0.2731 0.0600 -0.1468 0.0646 318s Toughness Overall_evaluation 318s 0.0894 -0.2192 318s 318s Robust Estimate of Covariance: 318s Favor Appearance Taste Stickiness Toughness 318s Favor 0.388 0.323 0.393 0.389 -0.195 318s Appearance 0.323 0.503 0.494 0.494 -0.270 318s Taste 0.393 0.494 0.640 0.629 -0.361 318s Stickiness 0.389 0.494 0.629 0.815 -0.486 318s Toughness -0.195 -0.270 -0.361 -0.486 0.451 318s Overall_evaluation 0.471 0.575 0.723 0.772 -0.457 318s Overall_evaluation 318s Favor 0.471 318s Appearance 0.575 318s Taste 0.723 318s Stickiness 0.772 318s Toughness -0.457 318s Overall_evaluation 0.882 318s -------------------------------------------------------- 318s un86 73 7 40 17.009322 318s Best subsample: 318s [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 318s [26] 51 52 55 56 60 61 62 63 64 65 67 70 71 72 73 318s Outliers: 30 318s [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 318s [26] 58 59 66 68 69 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=40); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s POP MOR CAR DR GNP DEN TB 318s 20.740 71.023 6.435 0.817 1.146 56.754 0.441 318s 318s Robust Estimate of Covariance: 318s POP MOR CAR DR GNP DEN 318s POP 582.4034 224.9343 -12.6722 -1.6729 -3.3664 226.1952 318s MOR 224.9343 2351.3907 -286.9504 -32.0743 -35.5649 -527.4684 318s CAR -12.6722 -286.9504 58.1190 5.7393 6.6365 83.6180 318s DR -1.6729 -32.0743 5.7393 0.8339 0.5977 12.1938 318s GNP -3.3664 -35.5649 6.6365 0.5977 1.4175 13.0709 318s DEN 226.1952 -527.4684 83.6180 12.1938 13.0709 2041.5809 318s TB 0.4002 -1.1807 0.2701 0.0191 0.0058 -0.9346 318s TB 318s POP 0.4002 318s MOR -1.1807 318s CAR 0.2701 318s DR 0.0191 318s GNP 0.0058 318s DEN -0.9346 318s TB 0.0184 318s -------------------------------------------------------- 318s wages 39 10 19 22.994272 318s Best subsample: 318s [1] 1 2 6 7 8 9 10 11 12 13 14 15 17 18 19 25 26 27 28 318s Outliers: 9 318s [1] 4 5 6 24 28 30 32 33 34 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 318s 2153.37 2.87 1129.16 297.53 360.58 6876.58 39.48 2.36 318s RACE SCHOOL 318s 38.88 10.17 318s 318s Robust Estimate of Covariance: 318s HRS RATE ERSP ERNO NEIN ASSET 318s HRS 6.12e+03 1.73e+01 -1.67e+03 -2.06e+03 9.10e+03 2.02e+05 318s RATE 1.73e+01 2.52e-01 2.14e+01 -3.54e+00 5.85e+01 1.37e+03 318s ERSP -1.67e+03 2.14e+01 1.97e+04 7.76e+01 -1.71e+03 -1.41e+04 318s ERNO -2.06e+03 -3.54e+00 7.76e+01 2.06e+03 -2.02e+03 -4.83e+04 318s NEIN 9.10e+03 5.85e+01 -1.71e+03 -2.02e+03 2.02e+04 4.54e+05 318s ASSET 2.02e+05 1.37e+03 -1.41e+04 -4.83e+04 4.54e+05 1.03e+07 318s AGE -6.29e+01 -2.61e-01 4.83e+00 2.44e+01 -1.08e+02 -2.46e+03 318s DEP -6.17e+00 -7.05e-02 -2.13e+01 2.29e+00 -1.30e+01 -3.16e+02 318s RACE -2.17e+03 -9.46e+00 7.19e+02 5.59e+02 -3.95e+03 -8.77e+04 318s SCHOOL 7.12e+01 5.87e-01 5.39e+01 -2.14e+01 1.63e+02 3.79e+03 318s AGE DEP RACE SCHOOL 318s HRS -6.29e+01 -6.17e+00 -2.17e+03 7.12e+01 318s RATE -2.61e-01 -7.05e-02 -9.46e+00 5.87e-01 318s ERSP 4.83e+00 -2.13e+01 7.19e+02 5.39e+01 318s ERNO 2.44e+01 2.29e+00 5.59e+02 -2.14e+01 318s NEIN -1.08e+02 -1.30e+01 -3.95e+03 1.63e+02 318s ASSET -2.46e+03 -3.16e+02 -8.77e+04 3.79e+03 318s AGE 1.01e+00 7.03e-02 2.39e+01 -9.52e-01 318s DEP 7.03e-02 4.62e-02 2.72e+00 -1.94e-01 318s RACE 2.39e+01 2.72e+00 8.74e+02 -3.09e+01 318s SCHOOL -9.52e-01 -1.94e-01 -3.09e+01 1.62e+00 318s -------------------------------------------------------- 318s airquality 153 4 58 18.213499 318s Best subsample: 318s [1] 3 22 24 25 28 29 32 33 35 36 37 38 39 40 41 42 43 44 46 318s [20] 47 48 49 50 52 56 57 58 59 60 64 66 67 68 69 71 72 73 74 318s [39] 76 78 80 82 83 84 86 87 89 90 91 92 93 94 95 97 98 105 109 318s [58] 110 318s Outliers: 14 318s [1] 8 9 15 18 20 21 23 24 28 30 48 62 117 148 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=58); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Ozone Solar.R Wind Temp 318s 43.2 192.9 9.6 80.5 318s 318s Robust Estimate of Covariance: 318s Ozone Solar.R Wind Temp 318s Ozone 959.69 771.68 -60.92 198.38 318s Solar.R 771.68 7089.72 -1.72 95.75 318s Wind -60.92 -1.72 10.71 -11.96 318s Temp 198.38 95.75 -11.96 62.78 318s -------------------------------------------------------- 318s attitude 30 7 19 24.442803 318s Best subsample: 318s [1] 2 3 4 5 7 8 10 12 15 17 19 20 22 23 25 27 28 29 30 318s Outliers: 10 318s [1] 1 6 9 13 14 16 18 21 24 26 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=19); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s rating complaints privileges learning raises critical 318s 67.1 68.0 52.4 57.6 67.2 77.4 318s advance 318s 43.4 318s 318s Robust Estimate of Covariance: 318s rating complaints privileges learning raises critical advance 318s rating 169.34 127.83 40.48 110.26 91.71 -3.59 53.84 318s complaints 127.83 156.80 52.65 110.97 96.56 7.27 76.03 318s privileges 40.48 52.65 136.91 92.38 69.00 9.53 87.98 318s learning 110.26 110.97 92.38 157.77 112.92 6.74 75.51 318s raises 91.71 96.56 69.00 112.92 112.79 4.91 70.22 318s critical -3.59 7.27 9.53 6.74 4.91 52.25 15.00 318s advance 53.84 76.03 87.98 75.51 70.22 15.00 93.11 318s -------------------------------------------------------- 318s attenu 182 5 86 6.440834 318s Best subsample: 318s [1] 68 69 70 71 72 73 74 75 76 77 79 82 83 84 85 86 87 88 89 318s [20] 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 115 116 117 118 318s [39] 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 318s [58] 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 318s [77] 157 158 159 160 161 162 163 164 165 166 318s Outliers: 61 318s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 318s [20] 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 36 37 38 39 318s [39] 40 45 46 47 54 55 56 57 58 59 60 61 64 65 82 97 98 100 101 318s [58] 102 103 104 105 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=86); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s event mag station dist accel 318s 18.624 5.752 67.861 22.770 0.141 318s 318s Robust Estimate of Covariance: 318s event mag station dist accel 318s event 1.64e+01 -1.22e+00 5.59e+01 9.98e+00 -8.37e-02 318s mag -1.22e+00 4.13e-01 -3.19e+00 1.35e+00 1.22e-02 318s station 5.59e+01 -3.19e+00 1.03e+03 7.00e+01 5.56e-01 318s dist 9.98e+00 1.35e+00 7.00e+01 2.21e+02 -9.24e-01 318s accel -8.37e-02 1.22e-02 5.56e-01 -9.24e-01 9.62e-03 318s -------------------------------------------------------- 318s USJudgeRatings 43 12 28 -47.889993 318s Best subsample: 318s [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 318s [26] 38 41 43 318s Outliers: 14 318s [1] 5 7 8 12 13 14 20 21 23 30 31 35 40 42 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=28); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 318s 7.40 8.19 7.80 7.96 7.74 7.82 7.74 7.73 7.57 7.63 8.25 7.94 318s 318s Robust Estimate of Covariance: 318s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL 318s CONT 0.852 -0.266 -0.422 -0.155 -0.049 -0.074 -0.117 -0.119 -0.177 318s INTG -0.266 0.397 0.537 0.406 0.340 0.325 0.404 0.409 0.430 318s DMNR -0.422 0.537 0.824 0.524 0.458 0.437 0.520 0.504 0.569 318s DILG -0.155 0.406 0.524 0.486 0.426 0.409 0.506 0.515 0.511 318s CFMG -0.049 0.340 0.458 0.426 0.427 0.403 0.466 0.476 0.478 318s DECI -0.074 0.325 0.437 0.409 0.403 0.396 0.449 0.462 0.460 318s PREP -0.117 0.404 0.520 0.506 0.466 0.449 0.552 0.565 0.551 318s FAMI -0.119 0.409 0.504 0.515 0.476 0.462 0.565 0.594 0.571 318s ORAL -0.177 0.430 0.569 0.511 0.478 0.460 0.551 0.571 0.575 318s WRIT -0.159 0.427 0.549 0.515 0.480 0.461 0.556 0.580 0.574 318s PHYS -0.184 0.269 0.362 0.308 0.298 0.307 0.335 0.358 0.369 318s RTEN -0.260 0.472 0.642 0.519 0.467 0.455 0.539 0.554 0.573 318s WRIT PHYS RTEN 318s CONT -0.159 -0.184 -0.260 318s INTG 0.427 0.269 0.472 318s DMNR 0.549 0.362 0.642 318s DILG 0.515 0.308 0.519 318s CFMG 0.480 0.298 0.467 318s DECI 0.461 0.307 0.455 318s PREP 0.556 0.335 0.539 318s FAMI 0.580 0.358 0.554 318s ORAL 0.574 0.369 0.573 318s WRIT 0.580 0.365 0.567 318s PHYS 0.365 0.300 0.378 318s RTEN 0.567 0.378 0.615 318s -------------------------------------------------------- 318s USArrests 50 4 27 15.391648 318s Best subsample: 318s [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 318s [26] 49 50 318s Outliers: 11 318s [1] 2 3 5 6 10 18 24 28 33 37 47 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=27); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s Murder Assault UrbanPop Rape 318s 6.71 145.42 65.06 17.88 318s 318s Robust Estimate of Covariance: 318s Murder Assault UrbanPop Rape 318s Murder 16.1 269.3 20.3 25.2 318s Assault 269.3 6613.0 567.8 453.7 318s UrbanPop 20.3 567.8 225.4 47.7 318s Rape 25.2 453.7 47.7 50.9 318s -------------------------------------------------------- 318s longley 16 7 12 12.747678 318s Best subsample: 318s [1] 5 6 7 8 9 10 11 12 13 14 15 16 318s Outliers: 4 318s [1] 1 2 3 4 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s GNP.deflator GNP Unemployed Armed.Forces Population 318s 106.5 430.6 328.2 295.0 120.2 318s Year Employed 318s 1956.5 66.9 318s 318s Robust Estimate of Covariance: 318s GNP.deflator GNP Unemployed Armed.Forces Population 318s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 318s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 318s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 318s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 318s Population 81.4 803.4 929.1 -327.4 63.5 318s Year 51.6 504.3 595.6 -216.7 39.7 318s Employed 34.2 344.1 323.6 -149.5 26.2 318s Year Employed 318s GNP.deflator 51.6 34.2 318s GNP 504.3 344.1 318s Unemployed 595.6 323.6 318s Armed.Forces -216.7 -149.5 318s Population 39.7 26.2 318s Year 25.1 16.7 318s Employed 16.7 12.4 318s -------------------------------------------------------- 318s Loblolly 84 3 44 4.898174 318s Best subsample: 318s [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 318s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 318s Outliers: 31 318s [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 318s [26] 72 76 77 78 83 84 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=44); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s height age Seed 318s 20.44 8.19 7.72 318s 318s Robust Estimate of Covariance: 318s height age Seed 318s height 247.8 79.5 11.9 318s age 79.5 25.7 3.0 318s Seed 11.9 3.0 17.1 318s -------------------------------------------------------- 318s quakes 1000 4 502 8.274369 318s Best subsample: 318s Too long... 318s Outliers: 265 318s Too many to print ... 318s ------------- 318s 318s Call: 318s CovMcd(x = x, trace = FALSE) 318s -> Method: Fast MCD(alpha=0.5 ==> h=502); nsamp = 500; (n,k)mini = (300,5) 318s 318s Robust Estimate of Location: 318s lat long depth mag 318s -21.31 182.48 361.35 4.54 318s 318s Robust Estimate of Covariance: 318s lat long depth mag 318s lat 1.47e+01 3.53e+00 1.34e+02 -2.52e-01 318s long 3.53e+00 4.55e+00 -3.63e+02 4.36e-02 318s depth 1.34e+02 -3.63e+02 4.84e+04 -1.29e+01 318s mag -2.52e-01 4.36e-02 -1.29e+01 1.38e-01 318s -------------------------------------------------------- 318s ======================================================== 318s > dodata(method="deterministic") 318s 318s Call: dodata(method = "deterministic") 318s Data Set n p Half LOG(obj) Time 318s ======================================================== 318s heart 12 2 7 5.678742 318s Best subsample: 318s [1] 1 3 4 5 7 9 11 318s Outliers: 0 318s Too many to print ... 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=7) 318s 318s Robust Estimate of Location: 318s height weight 318s 38.3 33.1 318s 318s Robust Estimate of Covariance: 318s height weight 318s height 135 259 318s weight 259 564 318s -------------------------------------------------------- 318s starsCYG 47 2 25 -8.028718 318s Best subsample: 318s [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 318s Outliers: 7 318s [1] 7 9 11 14 20 30 34 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=25) 318s 318s Robust Estimate of Location: 318s log.Te log.light 318s 4.41 4.95 318s 318s Robust Estimate of Covariance: 318s log.Te log.light 318s log.Te 0.0132 0.0394 318s log.light 0.0394 0.2743 318s -------------------------------------------------------- 318s phosphor 18 2 10 7.732906 318s Best subsample: 318s [1] 2 4 5 7 8 9 11 12 14 16 318s Outliers: 1 318s [1] 6 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 318s 318s Robust Estimate of Location: 318s inorg organic 318s 12.5 40.8 318s 318s Robust Estimate of Covariance: 318s inorg organic 318s inorg 124 101 318s organic 101 197 318s -------------------------------------------------------- 318s stackloss 21 3 12 6.577286 318s Best subsample: 318s [1] 4 5 6 7 8 9 11 13 16 18 19 20 318s Outliers: 2 318s [1] 1 2 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 318s 318s Robust Estimate of Location: 318s Air.Flow Water.Temp Acid.Conc. 318s 58.4 20.5 86.1 318s 318s Robust Estimate of Covariance: 318s Air.Flow Water.Temp Acid.Conc. 318s Air.Flow 56.28 13.33 26.68 318s Water.Temp 13.33 8.28 6.98 318s Acid.Conc. 26.68 6.98 37.97 318s -------------------------------------------------------- 318s coleman 20 5 13 2.149184 318s Best subsample: 318s [1] 3 4 5 7 8 12 13 14 16 17 18 19 20 318s Outliers: 2 318s [1] 6 10 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 318s 318s Robust Estimate of Location: 318s salaryP fatherWc sstatus teacherSc motherLev 318s 2.76 41.08 2.76 25.01 6.27 318s 318s Robust Estimate of Covariance: 318s salaryP fatherWc sstatus teacherSc motherLev 318s salaryP 0.391 2.956 2.146 0.447 0.110 318s fatherWc 2.956 1358.640 442.724 12.235 32.842 318s sstatus 2.146 442.724 205.590 6.464 11.382 318s teacherSc 0.447 12.235 6.464 1.179 0.510 318s motherLev 0.110 32.842 11.382 0.510 0.919 318s -------------------------------------------------------- 318s salinity 28 3 16 1.940763 318s Best subsample: 318s [1] 1 8 10 12 13 14 15 17 18 20 21 22 25 26 27 28 318s Outliers: 2 318s [1] 5 16 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=16) 318s 318s Robust Estimate of Location: 318s X1 X2 X3 318s 10.50 2.58 23.12 318s 318s Robust Estimate of Covariance: 318s X1 X2 X3 318s X1 10.90243 -0.00457 -1.46156 318s X2 -0.00457 3.85051 -1.94604 318s X3 -1.46156 -1.94604 3.21424 318s -------------------------------------------------------- 318s wood 20 5 13 -35.240819 318s Best subsample: 318s [1] 1 2 3 5 9 11 12 13 14 15 17 18 20 318s Outliers: 4 318s [1] 4 6 8 19 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=13) 318s 318s Robust Estimate of Location: 318s x1 x2 x3 x4 x5 318s 0.582 0.125 0.530 0.534 0.888 318s 318s Robust Estimate of Covariance: 318s x1 x2 x3 x4 x5 318s x1 1.05e-02 1.81e-03 2.08e-03 -6.41e-04 -9.61e-04 318s x2 1.81e-03 5.55e-04 8.76e-04 -2.03e-04 -4.70e-05 318s x3 2.08e-03 8.76e-04 5.60e-03 -1.11e-03 -1.26e-05 318s x4 -6.41e-04 -2.03e-04 -1.11e-03 4.27e-03 2.60e-03 318s x5 -9.61e-04 -4.70e-05 -1.26e-05 2.60e-03 2.95e-03 318s -------------------------------------------------------- 318s hbk 75 3 39 -1.045501 318s Best subsample: 318s [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 318s [26] 54 55 56 58 59 63 64 66 67 70 71 72 73 74 318s Outliers: 14 318s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=39) 318s 318s Robust Estimate of Location: 318s X1 X2 X3 318s 1.54 1.78 1.69 318s 318s Robust Estimate of Covariance: 318s X1 X2 X3 318s X1 1.227 0.055 0.127 318s X2 0.055 1.249 0.153 318s X3 0.127 0.153 1.160 318s -------------------------------------------------------- 318s Animals 28 2 15 14.555543 318s Best subsample: 318s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 318s Outliers: 14 318s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=15) 318s 318s Robust Estimate of Location: 318s body brain 318s 18.7 64.9 318s 318s Robust Estimate of Covariance: 318s body brain 318s body 929 1576 318s brain 1576 5646 318s -------------------------------------------------------- 318s bushfire 38 5 22 18.135810 318s Best subsample: 318s [1] 1 2 3 4 5 6 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 318s Outliers: 16 318s [1] 7 8 9 10 11 12 29 30 31 32 33 34 35 36 37 38 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=22) 318s 318s Robust Estimate of Location: 318s V1 V2 V3 V4 V5 318s 105 147 274 218 279 318s 318s Robust Estimate of Covariance: 318s V1 V2 V3 V4 V5 318s V1 346 268 -1692 -381 -311 318s V2 268 236 -1125 -230 -194 318s V3 -1692 -1125 9993 2455 1951 318s V4 -381 -230 2455 647 505 318s V5 -311 -194 1951 505 398 318s -------------------------------------------------------- 318s lactic 20 2 11 0.359580 318s Best subsample: 318s [1] 1 2 3 4 5 7 8 9 10 11 12 318s Outliers: 4 318s [1] 17 18 19 20 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=11) 318s 318s Robust Estimate of Location: 318s X Y 318s 3.86 5.01 318s 318s Robust Estimate of Covariance: 318s X Y 318s X 10.6 14.6 318s Y 14.6 21.3 318s -------------------------------------------------------- 318s pension 18 2 10 16.675508 318s Best subsample: 318s [1] 1 2 3 4 5 6 8 9 11 12 318s Outliers: 5 318s [1] 14 15 16 17 18 318s ------------- 318s 318s Call: 318s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 318s -> Method: Deterministic MCD(alpha=0.5 ==> h=10) 318s 318s Robust Estimate of Location: 318s Income Reserves 318s 52.3 560.9 318s 318s Robust Estimate of Covariance: 318s Income Reserves 318s Income 1420 11932 318s Reserves 11932 208643 318s -------------------------------------------------------- 319s radarImage 1573 5 789 36.694865 319s Best subsample: 319s Too long... 319s Outliers: 114 319s [1] 164 237 238 242 261 262 351 450 451 462 463 480 481 509 516 319s [16] 535 542 572 597 620 643 654 669 679 697 737 802 803 804 818 319s [31] 832 833 834 862 863 864 892 900 939 989 1029 1064 1123 1132 1145 319s [46] 1202 1223 1224 1232 1233 1249 1250 1258 1259 1267 1303 1347 1357 1368 1375 319s [61] 1376 1393 1394 1402 1411 1417 1419 1420 1428 1436 1443 1444 1453 1470 1504 319s [76] 1510 1511 1512 1518 1519 1520 1521 1522 1525 1526 1527 1528 1530 1532 1534 319s [91] 1543 1544 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1557 1558 1561 319s [106] 1562 1564 1565 1566 1567 1569 1570 1571 1573 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=789) 319s 319s Robust Estimate of Location: 319s X.coord Y.coord Band.1 Band.2 Band.3 319s 52.78 35.37 7.12 18.81 9.09 319s 319s Robust Estimate of Covariance: 319s X.coord Y.coord Band.1 Band.2 Band.3 319s X.coord 123.2 21.5 -363.9 -200.1 -24.3 319s Y.coord 21.5 410.7 46.5 -177.3 -33.4 319s Band.1 -363.9 46.5 27051.1 8138.9 469.3 319s Band.2 -200.1 -177.3 8138.9 25938.0 946.2 319s Band.3 -24.3 -33.4 469.3 946.2 4470.1 319s -------------------------------------------------------- 319s NOxEmissions 8088 4 4046 2.474536 319s Best subsample: 319s Too long... 319s Outliers: 2152 319s Too many to print ... 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=4046) 319s 319s Robust Estimate of Location: 319s julday LNOx LNOxEm sqrtWS 319s 168.20 4.73 7.91 1.37 319s 319s Robust Estimate of Covariance: 319s julday LNOx LNOxEm sqrtWS 319s julday 9176.2934 12.0355 0.7022 -10.1387 319s LNOx 12.0355 0.4736 0.1430 -0.1528 319s LNOxEm 0.7022 0.1430 0.2527 0.0436 319s sqrtWS -10.1387 -0.1528 0.0436 0.2074 319s -------------------------------------------------------- 319s vaso 39 2 21 -3.972244 319s Best subsample: 319s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 319s Outliers: 4 319s [1] 1 2 17 31 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=21) 319s 319s Robust Estimate of Location: 319s Volume Rate 319s 1.16 1.72 319s 319s Robust Estimate of Covariance: 319s Volume Rate 319s Volume 0.313 -0.167 319s Rate -0.167 0.728 319s -------------------------------------------------------- 319s wagnerGrowth 63 6 35 6.511864 319s Best subsample: 319s [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 319s [26] 48 51 52 53 54 55 56 57 60 62 319s Outliers: 15 319s [1] 1 8 15 21 22 28 29 33 39 42 43 46 49 50 63 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=35) 319s 319s Robust Estimate of Location: 319s Region PA GPA HS GHS y 319s 10.91 33.65 -2.05 2.43 0.31 6.98 319s 319s Robust Estimate of Covariance: 319s Region PA GPA HS GHS y 319s Region 35.1365 17.7291 -1.4003 -0.6554 -0.4728 -14.9305 319s PA 17.7291 28.4297 -5.5245 -1.2444 -0.0452 -29.6181 319s GPA -1.4003 -5.5245 5.2170 0.3954 -0.2152 3.8252 319s HS -0.6554 -1.2444 0.3954 0.7273 -0.0107 2.1514 319s GHS -0.4728 -0.0452 -0.2152 -0.0107 0.1728 0.8440 319s y -14.9305 -29.6181 3.8252 2.1514 0.8440 79.0511 319s -------------------------------------------------------- 319s fish 159 6 82 8.880459 319s Best subsample: 319s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 319s [20] 20 21 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 319s [39] 50 51 52 53 54 55 56 57 58 59 60 106 107 108 109 110 111 112 113 319s [58] 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 319s [77] 134 135 136 137 138 139 319s Outliers: 64 319s [1] 30 39 40 41 62 63 64 65 66 68 69 70 73 74 75 76 77 78 79 319s [20] 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 319s [39] 99 100 101 102 103 104 105 141 142 143 144 145 146 147 148 149 150 151 152 319s [58] 153 154 155 156 157 158 159 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=82) 319s 319s Robust Estimate of Location: 319s Weight Length1 Length2 Length3 Height Width 319s 316.3 24.1 26.3 29.3 31.0 14.7 319s 319s Robust Estimate of Covariance: 319s Weight Length1 Length2 Length3 Height Width 319s Weight 64662.19 1412.34 1541.95 1917.21 1420.83 -61.15 319s Length1 1412.34 34.14 37.04 45.07 29.25 -1.26 319s Length2 1541.95 37.04 40.26 49.04 32.21 -1.34 319s Length3 1917.21 45.07 49.04 60.82 43.03 -2.15 319s Height 1420.83 29.25 32.21 43.03 46.50 -2.66 319s Width -61.15 -1.26 -1.34 -2.15 -2.66 1.02 319s -------------------------------------------------------- 319s pottery 27 6 17 -10.586933 319s Best subsample: 319s [1] 1 2 4 5 6 9 10 11 13 14 15 19 20 21 22 26 27 319s Outliers: 9 319s [1] 3 8 12 16 17 18 23 24 25 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=17) 319s 319s Robust Estimate of Location: 319s SI AL FE MG CA TI 319s 54.983 15.206 9.700 3.817 5.211 0.859 319s 319s Robust Estimate of Covariance: 319s SI AL FE MG CA TI 319s SI 20.58227 2.28743 -0.02039 2.12648 -1.80227 0.08821 319s AL 2.28743 4.03605 -0.63021 -2.49966 0.20842 -0.02038 319s FE -0.02039 -0.63021 0.27803 0.53382 -0.35125 0.01427 319s MG 2.12648 -2.49966 0.53382 2.79561 -0.15786 0.02847 319s CA -1.80227 0.20842 -0.35125 -0.15786 1.23240 -0.03465 319s TI 0.08821 -0.02038 0.01427 0.02847 -0.03465 0.00175 319s -------------------------------------------------------- 319s rice 105 6 56 -14.423048 319s Best subsample: 319s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 319s [20] 38 44 45 47 51 52 53 55 59 60 65 66 67 70 72 74 76 78 79 319s [39] 80 81 82 83 84 85 86 90 92 93 94 95 97 98 99 100 101 105 319s Outliers: 13 319s [1] 9 19 28 40 42 43 49 58 62 64 71 75 77 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=56) 319s 319s Robust Estimate of Location: 319s Favor Appearance Taste Stickiness 319s -0.2950 0.0799 -0.1555 0.0363 319s Toughness Overall_evaluation 319s 0.0530 -0.2284 319s 319s Robust Estimate of Covariance: 319s Favor Appearance Taste Stickiness Toughness 319s Favor 0.466 0.389 0.471 0.447 -0.198 319s Appearance 0.389 0.610 0.592 0.570 -0.293 319s Taste 0.471 0.592 0.760 0.718 -0.356 319s Stickiness 0.447 0.570 0.718 0.820 -0.419 319s Toughness -0.198 -0.293 -0.356 -0.419 0.400 319s Overall_evaluation 0.557 0.669 0.838 0.846 -0.425 319s Overall_evaluation 319s Favor 0.557 319s Appearance 0.669 319s Taste 0.838 319s Stickiness 0.846 319s Toughness -0.425 319s Overall_evaluation 0.987 319s -------------------------------------------------------- 319s un86 73 7 40 17.117142 319s Best subsample: 319s [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 319s [26] 52 55 56 57 60 61 62 63 64 65 67 70 71 72 73 319s Outliers: 30 319s [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 319s [26] 58 59 66 68 69 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=40) 319s 319s Robust Estimate of Location: 319s POP MOR CAR DR GNP DEN TB 319s 17.036 68.512 6.444 0.877 1.134 64.140 0.433 319s 319s Robust Estimate of Covariance: 319s POP MOR CAR DR GNP DEN 319s POP 3.61e+02 1.95e+02 -6.28e+00 -1.91e-02 -2.07e+00 5.79e+01 319s MOR 1.95e+02 2.39e+03 -2.79e+02 -3.37e+01 -3.39e+01 -9.21e+02 319s CAR -6.28e+00 -2.79e+02 5.76e+01 5.77e+00 6.59e+00 7.81e+01 319s DR -1.91e-02 -3.37e+01 5.77e+00 9.07e-01 5.66e-01 1.69e+01 319s GNP -2.07e+00 -3.39e+01 6.59e+00 5.66e-01 1.42e+00 9.28e+00 319s DEN 5.79e+01 -9.21e+02 7.81e+01 1.69e+01 9.28e+00 3.53e+03 319s TB -6.09e-02 -9.93e-01 2.50e-01 1.98e-02 6.82e-03 -9.75e-01 319s TB 319s POP -6.09e-02 319s MOR -9.93e-01 319s CAR 2.50e-01 319s DR 1.98e-02 319s GNP 6.82e-03 319s DEN -9.75e-01 319s TB 1.64e-02 319s -------------------------------------------------------- 319s wages 39 10 19 23.119456 319s Best subsample: 319s [1] 1 2 5 6 7 9 10 11 12 13 14 15 19 21 23 25 26 27 28 319s Outliers: 9 319s [1] 4 5 9 24 25 26 28 32 34 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 319s 319s Robust Estimate of Location: 319s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 319s 2161.89 2.95 1114.21 297.68 374.00 7269.37 39.13 2.43 319s RACE SCHOOL 319s 36.13 10.39 319s 319s Robust Estimate of Covariance: 319s HRS RATE ERSP ERNO NEIN ASSET 319s HRS 3.53e+03 8.31e+00 -5.96e+03 -6.43e+02 5.15e+03 1.12e+05 319s RATE 8.31e+00 1.78e-01 8.19e+00 2.70e+00 3.90e+01 8.94e+02 319s ERSP -5.96e+03 8.19e+00 1.90e+04 1.13e+03 -4.73e+03 -9.49e+04 319s ERNO -6.43e+02 2.70e+00 1.13e+03 1.80e+03 -3.56e+02 -7.33e+03 319s NEIN 5.15e+03 3.90e+01 -4.73e+03 -3.56e+02 1.38e+04 3.00e+05 319s ASSET 1.12e+05 8.94e+02 -9.49e+04 -7.33e+03 3.00e+05 6.62e+06 319s AGE -3.33e+01 -6.55e-02 8.33e+01 1.50e+00 -3.28e+01 -7.55e+02 319s DEP 4.50e+00 -4.01e-02 -2.77e+01 1.31e+00 -8.09e+00 -1.61e+02 319s RACE -1.30e+03 -6.06e+00 1.80e+03 1.48e+02 -2.58e+03 -5.59e+04 319s SCHOOL 3.01e+01 3.58e-01 -5.57e+00 2.84e+00 9.26e+01 2.10e+03 319s AGE DEP RACE SCHOOL 319s HRS -3.33e+01 4.50e+00 -1.30e+03 3.01e+01 319s RATE -6.55e-02 -4.01e-02 -6.06e+00 3.58e-01 319s ERSP 8.33e+01 -2.77e+01 1.80e+03 -5.57e+00 319s ERNO 1.50e+00 1.31e+00 1.48e+02 2.84e+00 319s NEIN -3.28e+01 -8.09e+00 -2.58e+03 9.26e+01 319s ASSET -7.55e+02 -1.61e+02 -5.59e+04 2.10e+03 319s AGE 6.57e-01 -1.64e-01 1.13e+01 -2.67e-01 319s DEP -1.64e-01 9.20e-02 2.38e-01 -6.01e-02 319s RACE 1.13e+01 2.38e-01 5.73e+02 -1.67e+01 319s SCHOOL -2.67e-01 -6.01e-02 -1.67e+01 7.95e-01 319s -------------------------------------------------------- 319s airquality 153 4 58 18.316848 319s Best subsample: 319s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 319s [20] 47 48 49 50 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 319s [39] 81 82 84 86 87 89 90 91 92 95 97 98 100 101 105 106 108 109 110 319s [58] 111 319s Outliers: 10 319s [1] 8 9 15 18 24 30 48 62 117 148 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=58) 319s 319s Robust Estimate of Location: 319s Ozone Solar.R Wind Temp 319s 40.80 189.37 9.66 78.81 319s 319s Robust Estimate of Covariance: 319s Ozone Solar.R Wind Temp 319s Ozone 935.54 857.76 -56.30 220.48 319s Solar.R 857.76 8507.83 1.36 155.13 319s Wind -56.30 1.36 9.90 -11.61 319s Temp 220.48 155.13 -11.61 84.00 319s -------------------------------------------------------- 319s attitude 30 7 19 24.464288 319s Best subsample: 319s [1] 2 3 4 5 7 8 10 11 12 15 17 19 21 22 23 25 27 28 29 319s Outliers: 8 319s [1] 6 9 13 14 16 18 24 26 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=19) 319s 319s Robust Estimate of Location: 319s rating complaints privileges learning raises critical 319s 64.4 65.2 51.0 55.5 65.9 77.4 319s advance 319s 43.2 319s 319s Robust Estimate of Covariance: 319s rating complaints privileges learning raises critical advance 319s rating 199.95 162.36 115.83 160.44 128.87 -13.55 66.20 319s complaints 162.36 204.84 130.33 170.66 150.19 16.28 96.66 319s privileges 115.83 130.33 181.31 152.63 106.56 4.52 91.44 319s learning 160.44 170.66 152.63 213.06 156.57 9.92 88.31 319s raises 128.87 150.19 106.56 156.57 152.05 23.10 84.00 319s critical -13.55 16.28 4.52 9.92 23.10 80.22 27.15 319s advance 66.20 96.66 91.44 88.31 84.00 27.15 95.51 319s -------------------------------------------------------- 319s attenu 182 5 86 6.593068 319s Best subsample: 319s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 319s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 319s [39] 115 116 117 119 120 121 122 124 125 126 127 128 129 130 131 132 133 134 135 319s [58] 136 137 138 139 140 141 144 145 146 147 148 149 150 151 152 153 154 155 156 319s [77] 157 158 159 160 161 162 163 164 165 166 319s Outliers: 49 319s [1] 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 19 20 21 22 319s [20] 23 24 25 27 28 29 30 31 32 33 40 45 47 59 60 61 64 65 78 319s [39] 82 83 97 98 100 101 102 103 104 105 117 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=86) 319s 319s Robust Estimate of Location: 319s event mag station dist accel 319s 17.122 5.798 63.461 25.015 0.131 319s 319s Robust Estimate of Covariance: 319s event mag station dist accel 319s event 2.98e+01 -1.58e+00 9.49e+01 -8.36e+00 -3.59e-02 319s mag -1.58e+00 4.26e-01 -3.88e+00 3.13e+00 5.30e-03 319s station 9.49e+01 -3.88e+00 1.10e+03 2.60e+01 5.38e-01 319s dist -8.36e+00 3.13e+00 2.60e+01 2.66e+02 -9.23e-01 319s accel -3.59e-02 5.30e-03 5.38e-01 -9.23e-01 7.78e-03 319s -------------------------------------------------------- 319s USJudgeRatings 43 12 28 -47.886937 319s Best subsample: 319s [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 319s [26] 40 41 43 319s Outliers: 14 319s [1] 1 5 7 8 12 13 14 17 20 21 23 31 35 42 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=28) 319s 319s Robust Estimate of Location: 319s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 319s 7.46 8.26 7.88 8.06 7.85 7.92 7.84 7.83 7.67 7.74 8.31 8.03 319s 319s Robust Estimate of Covariance: 319s CONT INTG DMNR DILG CFMG DECI PREP FAMI 319s CONT 0.7363 -0.2916 -0.4193 -0.1943 -0.0555 -0.0690 -0.1703 -0.1727 319s INTG -0.2916 0.4179 0.5511 0.4167 0.3176 0.3102 0.4247 0.4279 319s DMNR -0.4193 0.5511 0.8141 0.5256 0.4092 0.3934 0.5294 0.5094 319s DILG -0.1943 0.4167 0.5256 0.4820 0.3904 0.3819 0.5054 0.5104 319s CFMG -0.0555 0.3176 0.4092 0.3904 0.3595 0.3368 0.4180 0.4206 319s DECI -0.0690 0.3102 0.3934 0.3819 0.3368 0.3310 0.4135 0.4194 319s PREP -0.1703 0.4247 0.5294 0.5054 0.4180 0.4135 0.5647 0.5752 319s FAMI -0.1727 0.4279 0.5094 0.5104 0.4206 0.4194 0.5752 0.6019 319s ORAL -0.2109 0.4453 0.5646 0.5054 0.4200 0.4121 0.5575 0.5735 319s WRIT -0.2033 0.4411 0.5466 0.5087 0.4222 0.4147 0.5592 0.5787 319s PHYS -0.1624 0.2578 0.3163 0.2833 0.2268 0.2362 0.3108 0.3284 319s RTEN -0.2622 0.4872 0.6324 0.5203 0.4145 0.4081 0.5488 0.5595 319s ORAL WRIT PHYS RTEN 319s CONT -0.2109 -0.2033 -0.1624 -0.2622 319s INTG 0.4453 0.4411 0.2578 0.4872 319s DMNR 0.5646 0.5466 0.3163 0.6324 319s DILG 0.5054 0.5087 0.2833 0.5203 319s CFMG 0.4200 0.4222 0.2268 0.4145 319s DECI 0.4121 0.4147 0.2362 0.4081 319s PREP 0.5575 0.5592 0.3108 0.5488 319s FAMI 0.5735 0.5787 0.3284 0.5595 319s ORAL 0.5701 0.5677 0.3283 0.5688 319s WRIT 0.5677 0.5715 0.3268 0.5645 319s PHYS 0.3283 0.3268 0.2302 0.3308 319s RTEN 0.5688 0.5645 0.3308 0.6057 319s -------------------------------------------------------- 319s USArrests 50 4 27 15.438912 319s Best subsample: 319s [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 319s [26] 49 50 319s Outliers: 7 319s [1] 2 5 6 10 24 28 33 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=27) 319s 319s Robust Estimate of Location: 319s Murder Assault UrbanPop Rape 319s 6.91 150.10 65.88 18.75 319s 319s Robust Estimate of Covariance: 319s Murder Assault UrbanPop Rape 319s Murder 17.9 285.4 17.6 25.0 319s Assault 285.4 6572.8 524.9 465.0 319s UrbanPop 17.6 524.9 211.9 50.5 319s Rape 25.0 465.0 50.5 56.4 319s -------------------------------------------------------- 319s longley 16 7 12 12.747678 319s Best subsample: 319s [1] 5 6 7 8 9 10 11 12 13 14 15 16 319s Outliers: 4 319s [1] 1 2 3 4 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=12) 319s 319s Robust Estimate of Location: 319s GNP.deflator GNP Unemployed Armed.Forces Population 319s 106.5 430.6 328.2 295.0 120.2 319s Year Employed 319s 1956.5 66.9 319s 319s Robust Estimate of Covariance: 319s GNP.deflator GNP Unemployed Armed.Forces Population 319s GNP.deflator 108.5 1039.9 1231.9 -465.6 81.4 319s GNP 1039.9 10300.0 11161.6 -4277.6 803.4 319s Unemployed 1231.9 11161.6 19799.4 -5805.6 929.1 319s Armed.Forces -465.6 -4277.6 -5805.6 2805.5 -327.4 319s Population 81.4 803.4 929.1 -327.4 63.5 319s Year 51.6 504.3 595.6 -216.7 39.7 319s Employed 34.2 344.1 323.6 -149.5 26.2 319s Year Employed 319s GNP.deflator 51.6 34.2 319s GNP 504.3 344.1 319s Unemployed 595.6 323.6 319s Armed.Forces -216.7 -149.5 319s Population 39.7 26.2 319s Year 25.1 16.7 319s Employed 16.7 12.4 319s -------------------------------------------------------- 319s Loblolly 84 3 44 4.898174 319s Best subsample: 319s [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 319s [26] 46 49 50 51 55 56 58 61 62 64 67 68 69 73 74 75 79 80 81 319s Outliers: 31 319s [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 319s [26] 72 76 77 78 83 84 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=44) 319s 319s Robust Estimate of Location: 319s height age Seed 319s 20.44 8.19 7.72 319s 319s Robust Estimate of Covariance: 319s height age Seed 319s height 247.8 79.5 11.9 319s age 79.5 25.7 3.0 319s Seed 11.9 3.0 17.1 319s -------------------------------------------------------- 319s quakes 1000 4 502 8.274209 319s Best subsample: 319s Too long... 319s Outliers: 266 319s Too many to print ... 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "deterministic", trace = FALSE) 319s -> Method: Deterministic MCD(alpha=0.5 ==> h=502) 319s 319s Robust Estimate of Location: 319s lat long depth mag 319s -21.34 182.47 360.58 4.54 319s 319s Robust Estimate of Covariance: 319s lat long depth mag 319s lat 1.50e+01 3.58e+00 1.37e+02 -2.66e-01 319s long 3.58e+00 4.55e+00 -3.61e+02 4.64e-02 319s depth 1.37e+02 -3.61e+02 4.84e+04 -1.36e+01 319s mag -2.66e-01 4.64e-02 -1.36e+01 1.34e-01 319s -------------------------------------------------------- 319s ======================================================== 319s > dodata(method="exact") 319s 319s Call: dodata(method = "exact") 319s Data Set n p Half LOG(obj) Time 319s ======================================================== 319s heart 12 2 7 5.678742 319s Best subsample: 319s [1] 1 3 4 5 7 9 11 319s Outliers: 0 319s Too many to print ... 319s ------------- 319s 319s Call: 319s CovMcd(x = x, nsamp = "exact", trace = FALSE) 319s -> Method: Fast MCD(alpha=0.5 ==> h=7); nsamp = exact; (n,k)mini = (300,5) 319s 319s Robust Estimate of Location: 319s height weight 319s 38.3 33.1 319s 319s Robust Estimate of Covariance: 319s height weight 319s height 135 259 319s weight 259 564 319s -------------------------------------------------------- 320s starsCYG 47 2 25 -8.031215 320s Best subsample: 320s [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 320s Outliers: 7 320s [1] 7 9 11 14 20 30 34 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=25); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s log.Te log.light 320s 4.41 4.95 320s 320s Robust Estimate of Covariance: 320s log.Te log.light 320s log.Te 0.0132 0.0394 320s log.light 0.0394 0.2743 320s -------------------------------------------------------- 320s phosphor 18 2 10 6.878847 320s Best subsample: 320s [1] 3 5 8 9 11 12 13 14 15 17 320s Outliers: 3 320s [1] 1 6 10 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s inorg organic 320s 13.4 38.8 320s 320s Robust Estimate of Covariance: 320s inorg organic 320s inorg 129 130 320s organic 130 182 320s -------------------------------------------------------- 320s coleman 20 5 13 1.286808 320s Best subsample: 320s [1] 2 3 4 5 7 8 12 13 14 16 17 19 20 320s Outliers: 7 320s [1] 1 6 9 10 11 15 18 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s salaryP fatherWc sstatus teacherSc motherLev 320s 2.76 48.38 6.12 25.00 6.40 320s 320s Robust Estimate of Covariance: 320s salaryP fatherWc sstatus teacherSc motherLev 320s salaryP 0.253 1.786 -0.266 0.151 0.075 320s fatherWc 1.786 1303.382 330.496 12.604 34.503 320s sstatus -0.266 330.496 119.888 3.833 10.131 320s teacherSc 0.151 12.604 3.833 0.785 0.555 320s motherLev 0.075 34.503 10.131 0.555 1.043 320s -------------------------------------------------------- 320s salinity 28 3 16 1.326364 320s Best subsample: 320s [1] 1 2 6 7 8 12 13 14 18 20 21 22 25 26 27 28 320s Outliers: 4 320s [1] 5 16 23 24 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=16); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s X1 X2 X3 320s 10.08 2.78 22.78 320s 320s Robust Estimate of Covariance: 320s X1 X2 X3 320s X1 10.44 1.01 -3.19 320s X2 1.01 3.83 -1.44 320s X3 -3.19 -1.44 2.39 320s -------------------------------------------------------- 320s wood 20 5 13 -36.270094 320s Best subsample: 320s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 320s Outliers: 7 320s [1] 4 6 7 8 11 16 19 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=13); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s x1 x2 x3 x4 x5 320s 0.587 0.122 0.531 0.538 0.892 320s 320s Robust Estimate of Covariance: 320s x1 x2 x3 x4 x5 320s x1 1.00e-02 1.88e-03 3.15e-03 -5.86e-04 -1.63e-03 320s x2 1.88e-03 4.85e-04 1.27e-03 -5.20e-05 2.36e-05 320s x3 3.15e-03 1.27e-03 6.63e-03 -8.71e-04 3.52e-04 320s x4 -5.86e-04 -5.20e-05 -8.71e-04 2.85e-03 1.83e-03 320s x5 -1.63e-03 2.36e-05 3.52e-04 1.83e-03 2.77e-03 320s -------------------------------------------------------- 320s Animals 28 2 15 14.555543 320s Best subsample: 320s [1] 1 3 4 5 10 11 17 18 19 20 21 22 23 26 27 320s Outliers: 14 320s [1] 2 6 7 8 9 12 13 14 15 16 23 24 25 28 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=15); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s body brain 320s 18.7 64.9 320s 320s Robust Estimate of Covariance: 320s body brain 320s body 929 1576 320s brain 1576 5646 320s -------------------------------------------------------- 320s lactic 20 2 11 0.359580 320s Best subsample: 320s [1] 1 2 3 4 5 7 8 9 10 11 12 320s Outliers: 4 320s [1] 17 18 19 20 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s X Y 320s 3.86 5.01 320s 320s Robust Estimate of Covariance: 320s X Y 320s X 10.6 14.6 320s Y 14.6 21.3 320s -------------------------------------------------------- 320s pension 18 2 10 16.675508 320s Best subsample: 320s [1] 1 2 3 4 5 6 8 9 11 12 320s Outliers: 5 320s [1] 14 15 16 17 18 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=10); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s Income Reserves 320s 52.3 560.9 320s 320s Robust Estimate of Covariance: 320s Income Reserves 320s Income 1420 11932 320s Reserves 11932 208643 320s -------------------------------------------------------- 320s vaso 39 2 21 -3.972244 320s Best subsample: 320s [1] 3 4 8 14 18 19 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 320s Outliers: 4 320s [1] 1 2 17 31 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=21); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s Volume Rate 320s 1.16 1.72 320s 320s Robust Estimate of Covariance: 320s Volume Rate 320s Volume 0.313 -0.167 320s Rate -0.167 0.728 320s -------------------------------------------------------- 320s stackloss 21 3 12 5.472581 320s Best subsample: 320s [1] 4 5 6 7 8 9 10 11 12 13 14 20 320s Outliers: 9 320s [1] 1 2 3 15 16 17 18 19 21 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=12); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s Air.Flow Water.Temp Acid.Conc. 320s 59.5 20.8 87.3 320s 320s Robust Estimate of Covariance: 320s Air.Flow Water.Temp Acid.Conc. 320s Air.Flow 6.29 5.85 5.74 320s Water.Temp 5.85 9.23 6.14 320s Acid.Conc. 5.74 6.14 23.25 320s -------------------------------------------------------- 320s pilot 20 2 11 6.487287 320s Best subsample: 320s [1] 2 3 6 7 9 12 15 16 17 18 20 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMcd(x = x, nsamp = "exact", trace = FALSE) 320s -> Method: Fast MCD(alpha=0.5 ==> h=11); nsamp = exact; (n,k)mini = (300,5) 320s 320s Robust Estimate of Location: 320s X Y 320s 101.1 67.7 320s 320s Robust Estimate of Covariance: 320s X Y 320s X 3344 1070 320s Y 1070 343 320s -------------------------------------------------------- 320s ======================================================== 320s > dodata(method="MRCD") 320s 320s Call: dodata(method = "MRCD") 320s Data Set n p Half LOG(obj) Time 320s ======================================================== 320s heart 12 2 6 7.446266 320s Best subsample: 320s [1] 1 3 4 7 9 11 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=6) 320s 320s Robust Estimate of Location: 320s height weight 320s 38.8 33.0 320s 320s Robust Estimate of Covariance: 320s height weight 320s height 47.4 75.2 320s weight 75.2 155.4 320s -------------------------------------------------------- 320s starsCYG 47 2 24 -5.862050 320s Best subsample: 320s [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 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=24) 320s 320s Robust Estimate of Location: 320s log.Te log.light 320s 4.44 5.05 320s 320s Robust Estimate of Covariance: 320s log.Te log.light 320s log.Te 0.00867 0.02686 320s log.light 0.02686 0.41127 320s -------------------------------------------------------- 320s phosphor 18 2 9 9.954788 320s Best subsample: 320s [1] 4 7 8 9 11 12 13 14 16 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 320s 320s Robust Estimate of Location: 320s inorg organic 320s 12.5 39.0 320s 320s Robust Estimate of Covariance: 320s inorg organic 320s inorg 236 140 320s organic 140 172 320s -------------------------------------------------------- 320s stackloss 21 3 11 7.991165 320s Best subsample: 320s [1] 4 5 6 7 8 9 10 13 18 19 20 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=11) 320s 320s Robust Estimate of Location: 320s Air.Flow Water.Temp Acid.Conc. 320s 58.2 21.4 85.2 320s 320s Robust Estimate of Covariance: 320s Air.Flow Water.Temp Acid.Conc. 320s Air.Flow 49.8 17.2 42.7 320s Water.Temp 17.2 13.8 25.2 320s Acid.Conc. 42.7 25.2 58.2 320s -------------------------------------------------------- 320s coleman 20 5 10 5.212156 320s Best subsample: 320s [1] 3 4 5 7 8 9 14 16 19 20 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 320s 320s Robust Estimate of Location: 320s salaryP fatherWc sstatus teacherSc motherLev 320s 2.78 59.44 9.28 25.41 6.70 320s 320s Robust Estimate of Covariance: 320s salaryP fatherWc sstatus teacherSc motherLev 320s salaryP 0.1582 -0.2826 0.4112 0.1754 0.0153 320s fatherWc -0.2826 902.9210 201.5815 -2.1236 18.8736 320s sstatus 0.4112 201.5815 65.4580 -0.3876 4.7794 320s teacherSc 0.1754 -2.1236 -0.3876 0.7233 -0.0322 320s motherLev 0.0153 18.8736 4.7794 -0.0322 0.5417 320s -------------------------------------------------------- 320s salinity 28 3 14 3.586919 320s Best subsample: 320s [1] 1 7 8 12 13 14 18 20 21 22 25 26 27 28 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 320s 320s Robust Estimate of Location: 320s X1 X2 X3 320s 10.95 3.71 21.99 320s 320s Robust Estimate of Covariance: 320s X1 X2 X3 320s X1 14.153 0.718 -3.359 320s X2 0.718 3.565 -0.722 320s X3 -3.359 -0.722 1.607 320s -------------------------------------------------------- 320s wood 20 5 10 -33.100492 320s Best subsample: 320s [1] 1 2 3 5 11 14 15 17 18 20 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 320s 320s Robust Estimate of Location: 320s x1 x2 x3 x4 x5 320s 0.572 0.120 0.504 0.545 0.899 320s 320s Robust Estimate of Covariance: 320s x1 x2 x3 x4 x5 320s x1 0.007543 0.001720 0.000412 -0.001230 -0.001222 320s x2 0.001720 0.000568 0.000355 -0.000533 -0.000132 320s x3 0.000412 0.000355 0.002478 0.000190 0.000811 320s x4 -0.001230 -0.000533 0.000190 0.002327 0.000967 320s x5 -0.001222 -0.000132 0.000811 0.000967 0.001894 320s -------------------------------------------------------- 320s hbk 75 3 38 1.539545 320s Best subsample: 320s [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 320s [26] 55 56 58 59 63 64 66 67 70 71 72 73 74 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=38) 320s 320s Robust Estimate of Location: 320s X1 X2 X3 320s 1.60 2.37 1.64 320s 320s Robust Estimate of Covariance: 320s X1 X2 X3 320s X1 2.810 0.124 1.248 320s X2 0.124 1.017 0.208 320s X3 1.248 0.208 2.218 320s -------------------------------------------------------- 320s Animals 28 2 14 16.278395 320s Best subsample: 320s [1] 1 3 4 5 10 11 18 19 20 21 22 23 26 27 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 320s 320s Robust Estimate of Location: 320s body brain 320s 19.5 56.8 320s 320s Robust Estimate of Covariance: 320s body brain 320s body 2802 5179 320s brain 5179 13761 320s -------------------------------------------------------- 320s bushfire 38 5 19 28.483413 320s Best subsample: 320s [1] 1 2 3 4 5 14 15 16 17 18 19 20 21 22 23 24 25 26 27 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=19) 320s 320s Robust Estimate of Location: 320s V1 V2 V3 V4 V5 320s 103 145 287 221 281 320s 320s Robust Estimate of Covariance: 320s V1 V2 V3 V4 V5 320s V1 366 249 -1993 -503 -396 320s V2 249 252 -1223 -291 -233 320s V3 -1993 -1223 14246 3479 2718 320s V4 -503 -291 3479 1083 748 320s V5 -396 -233 2718 748 660 320s -------------------------------------------------------- 320s lactic 20 2 10 2.593141 320s Best subsample: 320s [1] 1 2 3 4 5 7 8 9 10 11 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=10) 320s 320s Robust Estimate of Location: 320s X Y 320s 2.60 3.63 320s 320s Robust Estimate of Covariance: 320s X Y 320s X 8.13 13.54 320s Y 13.54 24.17 320s -------------------------------------------------------- 320s pension 18 2 9 18.931204 320s Best subsample: 320s [1] 2 3 4 5 6 8 9 11 12 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=9) 320s 320s Robust Estimate of Location: 320s Income Reserves 320s 45.7 466.9 320s 320s Robust Estimate of Covariance: 320s Income Reserves 320s Income 2127 23960 320s Reserves 23960 348275 320s -------------------------------------------------------- 320s vaso 39 2 20 -1.864710 320s Best subsample: 320s [1] 3 4 8 14 18 20 21 22 23 24 25 26 27 28 33 34 35 37 38 39 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=20) 320s 320s Robust Estimate of Location: 320s Volume Rate 320s 1.14 1.77 320s 320s Robust Estimate of Covariance: 320s Volume Rate 320s Volume 0.44943 -0.00465 320s Rate -0.00465 0.34480 320s -------------------------------------------------------- 320s wagnerGrowth 63 6 32 9.287760 320s Best subsample: 320s [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 320s [26] 53 54 55 56 57 60 62 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=32) 320s 320s Robust Estimate of Location: 320s Region PA GPA HS GHS y 320s 10.719 33.816 -2.144 2.487 0.293 4.918 320s 320s Robust Estimate of Covariance: 320s Region PA GPA HS GHS y 320s Region 56.7128 17.4919 -2.9710 -0.6491 -0.4545 -10.4287 320s PA 17.4919 29.9968 -7.6846 -1.3141 0.5418 -35.6434 320s GPA -2.9710 -7.6846 6.3238 1.1257 -0.4757 12.4707 320s HS -0.6491 -1.3141 1.1257 1.1330 -0.0915 3.3617 320s GHS -0.4545 0.5418 -0.4757 -0.0915 0.1468 -1.1228 320s y -10.4287 -35.6434 12.4707 3.3617 -1.1228 67.4215 320s -------------------------------------------------------- 320s fish 159 6 79 22.142828 320s Best subsample: 320s [1] 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 19 20 21 320s [20] 22 23 24 25 26 27 35 36 37 42 43 44 45 46 47 48 49 50 51 320s [39] 52 53 54 55 56 57 58 59 60 71 105 106 107 109 110 111 113 114 115 320s [58] 116 117 118 119 120 122 123 124 125 126 127 128 129 130 131 132 134 135 136 320s [77] 137 138 139 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=79) 320s 320s Robust Estimate of Location: 320s Weight Length1 Length2 Length3 Height Width 320s 291.7 23.8 25.9 28.9 30.4 14.7 320s 320s Robust Estimate of Covariance: 320s Weight Length1 Length2 Length3 Height Width 320s Weight 77155.07 1567.55 1713.74 2213.16 1912.62 -103.97 320s Length1 1567.55 45.66 41.57 52.14 38.66 -2.39 320s Length2 1713.74 41.57 54.26 56.77 42.72 -2.55 320s Length3 2213.16 52.14 56.77 82.57 58.84 -3.65 320s Height 1912.62 38.66 42.72 58.84 70.51 -3.80 320s Width -103.97 -2.39 -2.55 -3.65 -3.80 1.19 320s -------------------------------------------------------- 320s pottery 27 6 14 -6.897459 320s Best subsample: 320s [1] 1 2 4 5 6 10 11 13 14 15 19 21 22 26 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 320s 320s Robust Estimate of Location: 320s SI AL FE MG CA TI 320s 54.39 14.93 9.78 3.82 5.11 0.86 320s 320s Robust Estimate of Covariance: 320s SI AL FE MG CA TI 320s SI 17.47469 -0.16656 0.39943 4.48192 -0.71153 0.06515 320s AL -0.16656 3.93154 -0.35738 -2.29899 0.14770 -0.02050 320s FE 0.39943 -0.35738 0.20434 0.37562 -0.22460 0.00943 320s MG 4.48192 -2.29899 0.37562 2.82339 -0.16027 0.02943 320s CA -0.71153 0.14770 -0.22460 -0.16027 0.88443 -0.01711 320s TI 0.06515 -0.02050 0.00943 0.02943 -0.01711 0.00114 320s -------------------------------------------------------- 320s rice 105 6 53 -8.916472 320s Best subsample: 320s [1] 4 6 8 10 13 15 16 17 18 25 27 29 30 31 32 33 34 36 37 320s [20] 38 44 45 47 51 52 53 54 55 59 60 65 67 70 72 76 79 80 81 320s [39] 82 83 84 85 86 90 92 93 94 95 97 98 99 101 105 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=53) 320s 320s Robust Estimate of Location: 320s Favor Appearance Taste Stickiness 320s -0.1741 0.0774 -0.0472 0.1868 320s Toughness Overall_evaluation 320s -0.0346 -0.0683 320s 320s Robust Estimate of Covariance: 320s Favor Appearance Taste Stickiness Toughness 320s Favor 0.402 0.306 0.378 0.364 -0.134 320s Appearance 0.306 0.508 0.474 0.407 -0.146 320s Taste 0.378 0.474 0.708 0.611 -0.258 320s Stickiness 0.364 0.407 0.611 0.795 -0.320 320s Toughness -0.134 -0.146 -0.258 -0.320 0.302 320s Overall_evaluation 0.453 0.536 0.746 0.745 -0.327 320s Overall_evaluation 320s Favor 0.453 320s Appearance 0.536 320s Taste 0.746 320s Stickiness 0.745 320s Toughness -0.327 320s Overall_evaluation 0.963 320s -------------------------------------------------------- 320s un86 73 7 37 19.832993 320s Best subsample: 320s [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 320s [26] 56 57 60 62 63 64 65 67 70 71 72 73 320s Outliers: 0 320s Too many to print ... 320s ------------- 320s 320s Call: 320s CovMrcd(x = x, trace = FALSE) 320s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=37) 320s 320s Robust Estimate of Location: 320s POP MOR CAR DR GNP DEN TB 320s 14.462 66.892 6.670 0.858 1.251 55.518 0.429 320s 320s Robust Estimate of Covariance: 320s POP MOR CAR DR GNP DEN 320s POP 3.00e+02 1.58e+02 9.83e+00 2.74e+00 5.51e-01 6.87e+01 320s MOR 1.58e+02 2.96e+03 -4.24e+02 -4.72e+01 -5.40e+01 -1.01e+03 320s CAR 9.83e+00 -4.24e+02 9.12e+01 8.71e+00 1.13e+01 1.96e+02 320s DR 2.74e+00 -4.72e+01 8.71e+00 1.25e+00 1.03e+00 2.74e+01 320s GNP 5.51e-01 -5.40e+01 1.13e+01 1.03e+00 2.31e+00 2.36e+01 320s DEN 6.87e+01 -1.01e+03 1.96e+02 2.74e+01 2.36e+01 3.12e+03 320s TB 2.04e-02 -1.81e+00 3.42e-01 2.57e-02 2.09e-02 -6.88e-01 320s TB 320s POP 2.04e-02 320s MOR -1.81e+00 320s CAR 3.42e-01 320s DR 2.57e-02 320s GNP 2.09e-02 320s DEN -6.88e-01 320s TB 2.59e-02 320s -------------------------------------------------------- 321s wages 39 10 14 35.698016 321s Best subsample: 321s [1] 1 2 5 6 9 10 11 13 15 19 23 25 26 28 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=14) 321s 321s Robust Estimate of Location: 321s HRS RATE ERSP ERNO NEIN ASSET AGE DEP 321s 2167.71 2.96 1113.50 300.43 382.29 7438.00 39.06 2.41 321s RACE SCHOOL 321s 33.00 10.45 321s 321s Robust Estimate of Covariance: 321s HRS RATE ERSP ERNO NEIN ASSET 321s HRS 1.97e+03 -4.14e-01 -4.71e+03 -6.58e+02 1.81e+03 3.84e+04 321s RATE -4.14e-01 1.14e-01 1.79e+01 3.08e+00 1.40e+01 3.57e+02 321s ERSP -4.71e+03 1.79e+01 1.87e+04 2.33e+03 -2.06e+03 -3.57e+04 321s ERNO -6.58e+02 3.08e+00 2.33e+03 5.36e+02 -3.42e+02 -5.56e+03 321s NEIN 1.81e+03 1.40e+01 -2.06e+03 -3.42e+02 5.77e+03 1.10e+05 321s ASSET 3.84e+04 3.57e+02 -3.57e+04 -5.56e+03 1.10e+05 2.86e+06 321s AGE -1.83e+01 1.09e-02 6.69e+01 8.78e+00 -5.07e+00 -1.51e+02 321s DEP 4.82e+00 -3.14e-02 -2.52e+01 -2.96e+00 -5.33e+00 -1.03e+02 321s RACE -5.67e+02 -1.33e+00 1.21e+03 1.81e+02 -9.13e+02 -1.96e+04 321s SCHOOL 5.33e+00 1.87e-01 1.86e+01 3.12e+00 3.20e+01 7.89e+02 321s AGE DEP RACE SCHOOL 321s HRS -1.83e+01 4.82e+00 -5.67e+02 5.33e+00 321s RATE 1.09e-02 -3.14e-02 -1.33e+00 1.87e-01 321s ERSP 6.69e+01 -2.52e+01 1.21e+03 1.86e+01 321s ERNO 8.78e+00 -2.96e+00 1.81e+02 3.12e+00 321s NEIN -5.07e+00 -5.33e+00 -9.13e+02 3.20e+01 321s ASSET -1.51e+02 -1.03e+02 -1.96e+04 7.89e+02 321s AGE 5.71e-01 -1.56e-01 4.58e+00 -5.00e-02 321s DEP -1.56e-01 8.08e-02 -3.02e-01 -4.47e-02 321s RACE 4.58e+00 -3.02e-01 2.36e+02 -4.54e+00 321s SCHOOL -5.00e-02 -4.47e-02 -4.54e+00 4.23e-01 321s -------------------------------------------------------- 321s airquality 153 4 56 21.136376 321s Best subsample: 321s [1] 2 3 8 10 24 25 28 32 33 35 36 37 38 39 40 41 42 43 46 321s [20] 47 48 49 52 54 56 57 58 59 60 66 67 69 71 72 73 76 78 81 321s [39] 82 84 86 87 89 90 91 92 96 97 98 100 101 105 106 109 110 111 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=56) 321s 321s Robust Estimate of Location: 321s Ozone Solar.R Wind Temp 321s 41.84 197.21 8.93 80.39 321s 321s Robust Estimate of Covariance: 321s Ozone Solar.R Wind Temp 321s Ozone 1480.7 1562.8 -99.9 347.3 321s Solar.R 1562.8 11401.2 -35.2 276.8 321s Wind -99.9 -35.2 11.4 -23.5 321s Temp 347.3 276.8 -23.5 107.7 321s -------------------------------------------------------- 321s attitude 30 7 15 27.040805 321s Best subsample: 321s [1] 2 3 4 5 7 8 10 12 15 19 22 23 25 27 28 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=15) 321s 321s Robust Estimate of Location: 321s rating complaints privileges learning raises critical 321s 65.8 66.5 50.1 56.1 66.7 78.1 321s advance 321s 41.7 321s 321s Robust Estimate of Covariance: 321s rating complaints privileges learning raises critical advance 321s rating 138.77 80.02 59.22 107.33 95.83 -1.24 54.36 321s complaints 80.02 97.23 50.59 99.50 79.15 -2.71 42.81 321s privileges 59.22 50.59 84.92 90.03 60.88 22.39 44.93 321s learning 107.33 99.50 90.03 187.67 128.71 15.48 63.67 321s raises 95.83 79.15 60.88 128.71 123.94 -1.46 49.98 321s critical -1.24 -2.71 22.39 15.48 -1.46 61.23 12.88 321s advance 54.36 42.81 44.93 63.67 49.98 12.88 48.61 321s -------------------------------------------------------- 321s attenu 182 5 83 9.710111 321s Best subsample: 321s [1] 41 42 43 44 48 49 51 68 70 72 73 74 75 76 77 82 83 84 85 321s [20] 86 87 88 89 90 91 92 101 102 103 104 106 107 109 110 111 112 113 114 321s [39] 115 116 117 121 122 124 125 126 127 128 129 130 131 132 133 134 135 136 137 321s [58] 138 139 140 141 144 145 146 147 148 149 150 151 152 153 155 156 157 158 159 321s [77] 160 161 162 163 164 165 166 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=83) 321s 321s Robust Estimate of Location: 321s event mag station dist accel 321s 18.940 5.741 67.988 23.365 0.124 321s 321s Robust Estimate of Covariance: 321s event mag station dist accel 321s event 2.86e+01 -2.31e+00 1.02e+02 2.68e+01 -1.99e-01 321s mag -2.31e+00 6.17e-01 -7.03e+00 4.67e-01 2.59e-02 321s station 1.02e+02 -7.03e+00 1.66e+03 1.62e+02 7.96e-02 321s dist 2.68e+01 4.67e-01 1.62e+02 3.61e+02 -1.23e+00 321s accel -1.99e-01 2.59e-02 7.96e-02 -1.23e+00 9.42e-03 321s -------------------------------------------------------- 321s USJudgeRatings 43 12 22 -23.463708 321s Best subsample: 321s [1] 2 3 4 6 9 11 15 16 18 19 24 25 26 27 28 29 32 33 34 36 37 38 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=22) 321s 321s Robust Estimate of Location: 321s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 321s 7.24 8.42 8.10 8.19 7.95 8.00 7.96 7.96 7.81 7.89 8.40 8.20 321s 321s Robust Estimate of Covariance: 321s CONT INTG DMNR DILG CFMG DECI PREP 321s CONT 0.61805 -0.05601 -0.09540 0.00694 0.09853 0.06261 0.03939 321s INTG -0.05601 0.23560 0.27537 0.20758 0.16603 0.17281 0.21128 321s DMNR -0.09540 0.27537 0.55349 0.28872 0.24014 0.24293 0.28886 321s DILG 0.00694 0.20758 0.28872 0.34099 0.23502 0.23917 0.29672 321s CFMG 0.09853 0.16603 0.24014 0.23502 0.31649 0.23291 0.27651 321s DECI 0.06261 0.17281 0.24293 0.23917 0.23291 0.30681 0.27737 321s PREP 0.03939 0.21128 0.28886 0.29672 0.27651 0.27737 0.42020 321s FAMI 0.04588 0.20388 0.26072 0.29037 0.27179 0.27737 0.34857 321s ORAL 0.03000 0.21379 0.29606 0.28764 0.27338 0.27424 0.33503 321s WRIT 0.03261 0.20258 0.26931 0.27962 0.26382 0.26610 0.32677 321s PHYS -0.04485 0.13598 0.17659 0.16834 0.14554 0.16467 0.18948 321s RTEN 0.01543 0.22654 0.32117 0.27307 0.23826 0.24669 0.29450 321s FAMI ORAL WRIT PHYS RTEN 321s CONT 0.04588 0.03000 0.03261 -0.04485 0.01543 321s INTG 0.20388 0.21379 0.20258 0.13598 0.22654 321s DMNR 0.26072 0.29606 0.26931 0.17659 0.32117 321s DILG 0.29037 0.28764 0.27962 0.16834 0.27307 321s CFMG 0.27179 0.27338 0.26382 0.14554 0.23826 321s DECI 0.27737 0.27424 0.26610 0.16467 0.24669 321s PREP 0.34857 0.33503 0.32677 0.18948 0.29450 321s FAMI 0.47232 0.33762 0.33420 0.19759 0.29015 321s ORAL 0.33762 0.40361 0.32208 0.19794 0.29544 321s WRIT 0.33420 0.32208 0.38733 0.19276 0.28184 321s PHYS 0.19759 0.19794 0.19276 0.20284 0.18097 321s RTEN 0.29015 0.29544 0.28184 0.18097 0.36877 321s -------------------------------------------------------- 321s USArrests 50 4 25 17.834643 321s Best subsample: 321s [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 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=25) 321s 321s Robust Estimate of Location: 321s Murder Assault UrbanPop Rape 321s 5.38 121.68 63.80 16.33 321s 321s Robust Estimate of Covariance: 321s Murder Assault UrbanPop Rape 321s Murder 17.8 316.3 48.5 31.1 321s Assault 316.3 6863.0 1040.0 548.9 321s UrbanPop 48.5 1040.0 424.8 93.6 321s Rape 31.1 548.9 93.6 63.8 321s -------------------------------------------------------- 321s longley 16 7 8 31.147844 321s Best subsample: 321s [1] 5 6 7 9 10 11 13 14 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=8) 321s 321s Robust Estimate of Location: 321s GNP.deflator GNP Unemployed Armed.Forces Population 321s 104.3 410.8 278.8 300.1 118.2 321s Year Employed 321s 1955.4 66.5 321s 321s Robust Estimate of Covariance: 321s GNP.deflator GNP Unemployed Armed.Forces Population 321s GNP.deflator 85.0 652.3 784.4 -370.7 48.7 321s GNP 652.3 7502.9 7328.6 -3414.2 453.9 321s Unemployed 784.4 7328.6 10760.3 -4646.7 548.1 321s Armed.Forces -370.7 -3414.2 -4646.7 2824.3 -253.9 321s Population 48.7 453.9 548.1 -253.9 40.2 321s Year 33.5 312.7 378.8 -176.1 23.4 321s Employed 23.9 224.8 263.6 -128.3 16.8 321s Year Employed 321s GNP.deflator 33.5 23.9 321s GNP 312.7 224.8 321s Unemployed 378.8 263.6 321s Armed.Forces -176.1 -128.3 321s Population 23.4 16.8 321s Year 18.9 11.7 321s Employed 11.7 10.3 321s -------------------------------------------------------- 321s Loblolly 84 3 42 11.163448 321s Best subsample: 321s [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 321s [26] 53 54 57 58 59 63 64 65 66 70 71 76 77 81 82 83 84 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=42) 321s 321s Robust Estimate of Location: 321s height age Seed 321s 44.20 17.26 6.76 321s 321s Robust Estimate of Covariance: 321s height age Seed 321s height 326.74 139.18 3.50 321s age 139.18 68.48 -2.72 321s Seed 3.50 -2.72 25.43 321s -------------------------------------------------------- 321s quakes 1000 4 500 11.802478 321s Best subsample: 321s Too long... 321s Outliers: 0 321s Too many to print ... 321s ------------- 321s 321s Call: 321s CovMrcd(x = x, trace = FALSE) 321s -> Method: Minimum Regularized Covariance Determinant MRCD(alpha=0.5 ==> h=500) 321s 321s Robust Estimate of Location: 321s lat long depth mag 321s -20.59 182.13 432.46 4.42 321s 321s Robust Estimate of Covariance: 321s lat long depth mag 321s lat 15.841 5.702 -106.720 -0.441 321s long 5.702 7.426 -577.189 -0.136 321s depth -106.720 -577.189 66701.479 3.992 321s mag -0.441 -0.136 3.992 0.144 321s -------------------------------------------------------- 321s ======================================================== 321s > ##doexactfit() 321s > 321s BEGIN TEST tmest4.R 321s 321s R version 4.4.3 (2025-02-28) -- "Trophy Case" 321s Copyright (C) 2025 The R Foundation for Statistical Computing 321s Platform: aarch64-unknown-linux-gnu 321s 321s R is free software and comes with ABSOLUTELY NO WARRANTY. 321s You are welcome to redistribute it under certain conditions. 321s Type 'license()' or 'licence()' for distribution details. 321s 321s R is a collaborative project with many contributors. 321s Type 'contributors()' for more information and 321s 'citation()' on how to cite R or R packages in publications. 321s 321s Type 'demo()' for some demos, 'help()' for on-line help, or 321s 'help.start()' for an HTML browser interface to help. 321s Type 'q()' to quit R. 321s 321s > ## VT::15.09.2013 - this will render the output independent 321s > ## from the version of the package 321s > suppressPackageStartupMessages(library(rrcov)) 322s > 322s > library(MASS) 322s > dodata <- function(nrep = 1, time = FALSE, full = TRUE) { 322s + domest <- function(x, xname, nrep = 1) { 322s + n <- dim(x)[1] 322s + p <- dim(x)[2] 322s + mm <- CovMest(x) 322s + crit <- log(mm@crit) 322s + ## c1 <- mm@psi@c1 322s + ## M <- mm$psi@M 322s + 322s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 322s + lpad <- lname-nchar(xname) 322s + cat(pad.right(xname,lpad), xres) 322s + 322s + dist <- getDistance(mm) 322s + quantiel <- qchisq(0.975, p) 322s + ibad <- which(dist >= quantiel) 322s + names(ibad) <- NULL 322s + nbad <- length(ibad) 322s + cat("Outliers: ",nbad,"\n") 322s + if(nbad > 0) 322s + print(ibad) 322s + cat("-------------\n") 322s + show(mm) 322s + cat("--------------------------------------------------------\n") 322s + } 322s + 322s + options(digits = 5) 322s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 322s + 322s + lname <- 20 322s + 322s + data(heart) 322s + data(starsCYG) 322s + data(phosphor) 322s + data(stackloss) 322s + data(coleman) 322s + data(salinity) 322s + data(wood) 322s + data(hbk) 322s + 322s + data(Animals, package = "MASS") 322s + brain <- Animals[c(1:24, 26:25, 27:28),] 322s + data(milk) 322s + data(bushfire) 322s + 322s + tmp <- sys.call() 322s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 322s + 322s + cat("Data Set n p c1 M LOG(det) Time\n") 322s + cat("======================================================================\n") 322s + domest(heart[, 1:2], data(heart), nrep) 322s + domest(starsCYG, data(starsCYG), nrep) 322s + domest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 322s + domest(stack.x, data(stackloss), nrep) 322s + domest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 322s + domest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 322s + domest(data.matrix(subset(wood, select = -y)), data(wood), nrep) 322s + domest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 322s + 322s + 322s + domest(brain, "Animals", nrep) 322s + domest(milk, data(milk), nrep) 322s + domest(bushfire, data(bushfire), nrep) 322s + cat("======================================================================\n") 322s + } 322s > 322s > # generate contaminated data using the function gendata with different 322s > # number of outliers and check if the M-estimate breaks - i.e. the 322s > # largest eigenvalue is larger than e.g. 5. 322s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 322s > # outliers grater than 20. 322s > dogen <- function(){ 322s + eig <- vector("numeric",26) 322s + for(i in 0:25) { 322s + gg <- gendata(eps=i) 322s + mm <- CovMest(gg$x, t0=gg$tgood, S0=gg$sgood, arp=0.001) 322s + eig[i+1] <- ev <- getEvals(mm)[1] 322s + # cat(i, ev, "\n") 322s + 322s + stopifnot(ev < 5 || i > 20) 322s + } 322s + # plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 322s + } 322s > 322s > # 322s > # generate data 50x10 as multivariate normal N(0,I) and add 322s > # eps % outliers by adding d=5.0 to each component. 322s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 322s > # - if eps >= 1, it is the number of outliers 322s > # - use the center and cov of the good data as good start 322s > # - use the center and the cov of all data as a bad start 322s > # If using a good start, the M-estimate must iterate to 322s > # the good solution: the largest eigenvalue is less then e.g. 5 322s > # 322s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 322s + 322s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 322s + stop("eps is out of range") 322s + 322s + library(MASS) 322s + 322s + x <- mvrnorm(n, rep(0,p), diag(p)) 322s + bad <- vector("numeric") 322s + nbad = if(eps < 1) eps*n else eps 322s + if(nbad > 0){ 322s + bad <- sample(n, nbad) 322s + x[bad,] <- x[bad,] + d 322s + } 322s + cov1 <- cov.wt(x) 322s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 322s + 322s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 322s + } 322s > 322s > pad.right <- function(z, pads) 322s + { 322s + ## Pads spaces to right of text 322s + padding <- paste(rep(" ", pads), collapse = "") 322s + paste(z, padding, sep = "") 322s + } 322s > 322s > 322s > ## -- now do it: 322s > dodata() 322s 322s Call: dodata() 322s Data Set n p c1 M LOG(det) Time 322s ====================================================================== 322s heart 12 2 7.160341 322s Outliers: 3 322s [1] 2 6 12 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s height weight 322s 34.9 27.0 322s 322s Robust Estimate of Covariance: 322s height weight 322s height 102 155 322s weight 155 250 322s -------------------------------------------------------- 322s starsCYG 47 2 -5.994588 322s Outliers: 7 322s [1] 7 9 11 14 20 30 34 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s log.Te log.light 322s 4.42 4.95 322s 322s Robust Estimate of Covariance: 322s log.Te log.light 322s log.Te 0.0169 0.0587 322s log.light 0.0587 0.3523 322s -------------------------------------------------------- 322s phosphor 18 2 8.867522 322s Outliers: 3 322s [1] 1 6 10 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s inorg organic 322s 15.4 39.1 322s 322s Robust Estimate of Covariance: 322s inorg organic 322s inorg 169 213 322s organic 213 308 322s -------------------------------------------------------- 322s stackloss 21 3 7.241400 322s Outliers: 9 322s [1] 1 2 3 15 16 17 18 19 21 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s Air.Flow Water.Temp Acid.Conc. 322s 59.5 20.8 87.3 322s 322s Robust Estimate of Covariance: 322s Air.Flow Water.Temp Acid.Conc. 322s Air.Flow 9.34 8.69 8.52 322s Water.Temp 8.69 13.72 9.13 322s Acid.Conc. 8.52 9.13 34.54 322s -------------------------------------------------------- 322s coleman 20 5 2.574752 322s Outliers: 7 322s [1] 2 6 9 10 12 13 15 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s salaryP fatherWc sstatus teacherSc motherLev 322s 2.82 48.44 5.30 25.19 6.51 322s 322s Robust Estimate of Covariance: 322s salaryP fatherWc sstatus teacherSc motherLev 322s salaryP 0.2850 0.1045 1.7585 0.3074 0.0355 322s fatherWc 0.1045 824.8305 260.7062 3.7507 17.7959 322s sstatus 1.7585 260.7062 105.6135 4.1140 5.7714 322s teacherSc 0.3074 3.7507 4.1140 0.6753 0.1563 322s motherLev 0.0355 17.7959 5.7714 0.1563 0.4147 322s -------------------------------------------------------- 322s salinity 28 3 3.875096 322s Outliers: 9 322s [1] 3 5 10 11 15 16 17 23 24 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s X1 X2 X3 322s 10.02 3.21 22.36 322s 322s Robust Estimate of Covariance: 322s X1 X2 X3 322s X1 15.353 1.990 -5.075 322s X2 1.990 5.210 -0.769 322s X3 -5.075 -0.769 2.314 322s -------------------------------------------------------- 322s wood 20 5 -35.156305 322s Outliers: 7 322s [1] 4 6 7 8 11 16 19 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s x1 x2 x3 x4 x5 322s 0.587 0.122 0.531 0.538 0.892 322s 322s Robust Estimate of Covariance: 322s x1 x2 x3 x4 x5 322s x1 6.45e-03 1.21e-03 2.03e-03 -3.77e-04 -1.05e-03 322s x2 1.21e-03 3.12e-04 8.16e-04 -3.34e-05 1.52e-05 322s x3 2.03e-03 8.16e-04 4.27e-03 -5.60e-04 2.27e-04 322s x4 -3.77e-04 -3.34e-05 -5.60e-04 1.83e-03 1.18e-03 322s x5 -1.05e-03 1.52e-05 2.27e-04 1.18e-03 1.78e-03 322s -------------------------------------------------------- 322s hbk 75 3 1.432485 322s Outliers: 14 322s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s X1 X2 X3 322s 1.54 1.78 1.69 322s 322s Robust Estimate of Covariance: 322s X1 X2 X3 322s X1 1.6485 0.0739 0.1709 322s X2 0.0739 1.6780 0.2049 322s X3 0.1709 0.2049 1.5584 322s -------------------------------------------------------- 322s Animals 28 2 18.194822 322s Outliers: 10 322s [1] 2 6 7 9 12 14 15 16 25 28 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s body brain 322s 18.7 64.9 322s 322s Robust Estimate of Covariance: 322s body brain 322s body 4993 8466 322s brain 8466 30335 322s -------------------------------------------------------- 322s milk 86 8 -25.041802 322s Outliers: 20 322s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s X1 X2 X3 X4 X5 X6 X7 X8 322s 1.03 35.88 33.04 26.11 25.09 25.02 123.12 14.39 322s 322s Robust Estimate of Covariance: 322s X1 X2 X3 X4 X5 X6 X7 322s X1 4.89e-07 9.64e-05 1.83e-04 1.76e-04 1.57e-04 1.48e-04 6.53e-04 322s X2 9.64e-05 2.05e+00 3.38e-01 2.37e-01 1.70e-01 2.71e-01 1.91e+00 322s X3 1.83e-04 3.38e-01 1.16e+00 8.56e-01 8.48e-01 8.31e-01 8.85e-01 322s X4 1.76e-04 2.37e-01 8.56e-01 6.83e-01 6.55e-01 6.40e-01 6.91e-01 322s X5 1.57e-04 1.70e-01 8.48e-01 6.55e-01 6.93e-01 6.52e-01 6.90e-01 322s X6 1.48e-04 2.71e-01 8.31e-01 6.40e-01 6.52e-01 6.61e-01 6.95e-01 322s X7 6.53e-04 1.91e+00 8.85e-01 6.91e-01 6.90e-01 6.95e-01 4.40e+00 322s X8 5.56e-06 2.60e-01 1.98e-01 1.29e-01 1.12e-01 1.19e-01 4.12e-01 322s X8 322s X1 5.56e-06 322s X2 2.60e-01 322s X3 1.98e-01 322s X4 1.29e-01 322s X5 1.12e-01 322s X6 1.19e-01 322s X7 4.12e-01 322s X8 1.65e-01 322s -------------------------------------------------------- 322s bushfire 38 5 23.457490 322s Outliers: 15 322s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 322s ------------- 322s 322s Call: 322s CovMest(x = x) 322s -> Method: M-Estimates 322s 322s Robust Estimate of Location: 322s V1 V2 V3 V4 V5 322s 107 147 263 215 277 322s 322s Robust Estimate of Covariance: 322s V1 V2 V3 V4 V5 322s V1 775 560 -4179 -925 -759 322s V2 560 478 -2494 -510 -431 322s V3 -4179 -2494 27433 6441 5196 322s V4 -925 -510 6441 1607 1276 322s V5 -759 -431 5196 1276 1020 322s -------------------------------------------------------- 322s ====================================================================== 322s > dogen() 322s > #cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 322s > 322s BEGIN TEST tmve4.R 322s 322s R version 4.4.3 (2025-02-28) -- "Trophy Case" 322s Copyright (C) 2025 The R Foundation for Statistical Computing 322s Platform: aarch64-unknown-linux-gnu 322s 322s R is free software and comes with ABSOLUTELY NO WARRANTY. 322s You are welcome to redistribute it under certain conditions. 322s Type 'license()' or 'licence()' for distribution details. 322s 322s R is a collaborative project with many contributors. 322s Type 'contributors()' for more information and 322s 'citation()' on how to cite R or R packages in publications. 322s 322s Type 'demo()' for some demos, 'help()' for on-line help, or 322s 'help.start()' for an HTML browser interface to help. 322s Type 'q()' to quit R. 322s 322s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMVE","MASS")){ 322s + ##@bdescr 322s + ## Test the function covMve() on the literature datasets: 322s + ## 322s + ## Call covMve() for all regression datasets available in rrco/robustbasev and print: 322s + ## - execution time (if time == TRUE) 322s + ## - objective fucntion 322s + ## - best subsample found (if short == false) 322s + ## - outliers identified (with cutoff 0.975) (if short == false) 322s + ## - estimated center and covarinance matrix if full == TRUE) 322s + ## 322s + ##@edescr 322s + ## 322s + ##@in nrep : [integer] number of repetitions to use for estimating the 322s + ## (average) execution time 322s + ##@in time : [boolean] whether to evaluate the execution time 322s + ##@in short : [boolean] whether to do short output (i.e. only the 322s + ## objective function value). If short == FALSE, 322s + ## the best subsample and the identified outliers are 322s + ## printed. See also the parameter full below 322s + ##@in full : [boolean] whether to print the estimated cente and covariance matrix 322s + ##@in method : [character] select a method: one of (FASTMCD, MASS) 322s + 322s + domve <- function(x, xname, nrep=1){ 322s + n <- dim(x)[1] 322s + p <- dim(x)[2] 322s + alpha <- 0.5 322s + h <- h.alpha.n(alpha, n, p) 322s + if(method == "MASS"){ 322s + mve <- cov.mve(x, quantile.used=h) 322s + quan <- h #default: floor((n+p+1)/2) 322s + crit <- mve$crit 322s + best <- mve$best 322s + mah <- mahalanobis(x, mve$center, mve$cov) 322s + quantiel <- qchisq(0.975, p) 322s + wt <- as.numeric(mah < quantiel) 322s + } 322s + else{ 322s + mve <- CovMve(x, trace=FALSE) 322s + quan <- as.integer(mve@quan) 322s + crit <- log(mve@crit) 322s + best <- mve@best 322s + wt <- mve@wt 322s + } 322s + 322s + 322s + if(time){ 322s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 322s + xres <- sprintf("%3d %3d %3d %12.6f %10.3f\n", dim(x)[1], dim(x)[2], quan, crit, xtime) 322s + } 322s + else{ 322s + xres <- sprintf("%3d %3d %3d %12.6f\n", dim(x)[1], dim(x)[2], quan, crit) 322s + } 322s + 322s + lpad<-lname-nchar(xname) 322s + cat(pad.right(xname,lpad), xres) 322s + 322s + if(!short){ 322s + cat("Best subsample: \n") 322s + print(best) 322s + 322s + ibad <- which(wt == 0) 322s + names(ibad) <- NULL 322s + nbad <- length(ibad) 322s + cat("Outliers: ", nbad, "\n") 322s + if(nbad > 0) 322s + print(ibad) 322s + if(full){ 322s + cat("-------------\n") 322s + show(mve) 322s + } 322s + cat("--------------------------------------------------------\n") 322s + } 322s + } 322s + 322s + options(digits = 5) 322s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 322s + 322s + lname <- 20 322s + 322s + ## VT::15.09.2013 - this will render the output independent 322s + ## from the version of the package 322s + suppressPackageStartupMessages(library(rrcov)) 322s + 322s + method <- match.arg(method) 322s + if(method == "MASS") 322s + library(MASS) 322s + 322s + 322s + data(heart) 322s + data(starsCYG) 322s + data(phosphor) 322s + data(stackloss) 322s + data(coleman) 322s + data(salinity) 322s + data(wood) 322s + 322s + data(hbk) 322s + 322s + data(Animals, package = "MASS") 322s + brain <- Animals[c(1:24, 26:25, 27:28),] 322s + data(milk) 322s + data(bushfire) 322s + 322s + tmp <- sys.call() 322s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 322s + 322s + cat("Data Set n p Half LOG(obj) Time\n") 322s + cat("========================================================\n") 322s + domve(heart[, 1:2], data(heart), nrep) 322s + domve(starsCYG, data(starsCYG), nrep) 322s + domve(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 322s + domve(stack.x, data(stackloss), nrep) 322s + domve(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 322s + domve(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 322s + domve(data.matrix(subset(wood, select = -y)), data(wood), nrep) 322s + domve(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 322s + 322s + domve(brain, "Animals", nrep) 322s + domve(milk, data(milk), nrep) 322s + domve(bushfire, data(bushfire), nrep) 322s + cat("========================================================\n") 322s + } 322s > 322s > dogen <- function(nrep=1, eps=0.49, method=c("FASTMVE", "MASS")){ 322s + 322s + domve <- function(x, nrep=1){ 322s + gc() 322s + xtime <- system.time(dorep(x, nrep, method))[1]/nrep 322s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 322s + xtime 322s + } 322s + 322s + set.seed(1234) 322s + 322s + ## VT::15.09.2013 - this will render the output independent 322s + ## from the version of the package 322s + suppressPackageStartupMessages(library(rrcov)) 322s + library(MASS) 322s + 322s + method <- match.arg(method) 322s + 322s + ap <- c(2, 5, 10, 20, 30) 322s + an <- c(100, 500, 1000, 10000, 50000) 322s + 322s + tottime <- 0 322s + cat(" n p Time\n") 322s + cat("=====================\n") 322s + for(i in 1:length(an)) { 322s + for(j in 1:length(ap)) { 322s + n <- an[i] 322s + p <- ap[j] 322s + if(5*p <= n){ 322s + xx <- gendata(n, p, eps) 322s + X <- xx$X 322s + tottime <- tottime + domve(X, nrep) 322s + } 322s + } 322s + } 322s + 322s + cat("=====================\n") 322s + cat("Total time: ", tottime*nrep, "\n") 322s + } 322s > 322s > docheck <- function(n, p, eps){ 322s + xx <- gendata(n,p,eps) 322s + mve <- CovMve(xx$X) 322s + check(mve, xx$xind) 322s + } 322s > 322s > check <- function(mcd, xind){ 322s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 322s + ## did not get zero weight 322s + mymatch <- xind %in% which(mcd@wt == 0) 322s + length(xind) - length(which(mymatch)) 322s + } 322s > 322s > dorep <- function(x, nrep=1, method=c("FASTMVE","MASS")){ 322s + 322s + method <- match.arg(method) 322s + for(i in 1:nrep) 322s + if(method == "MASS") 322s + cov.mve(x) 322s + else 322s + CovMve(x) 322s + } 322s > 322s > #### gendata() #### 322s > # Generates a location contaminated multivariate 322s > # normal sample of n observations in p dimensions 322s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 322s > # where 322s > # m = (b,b,...,b) 322s > # Defaults: eps=0 and b=10 322s > # 322s > gendata <- function(n,p,eps=0,b=10){ 322s + 322s + if(missing(n) || missing(p)) 322s + stop("Please specify (n,p)") 322s + if(eps < 0 || eps >= 0.5) 322s + stop(message="eps must be in [0,0.5)") 322s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 322s + nbad <- as.integer(eps * n) 322s + if(nbad > 0){ 322s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 322s + xind <- sample(n,nbad) 322s + X[xind,] <- Xbad 322s + } 322s + list(X=X, xind=xind) 322s + } 322s > 322s > pad.right <- function(z, pads) 322s + { 322s + ### Pads spaces to right of text 322s + padding <- paste(rep(" ", pads), collapse = "") 322s + paste(z, padding, sep = "") 322s + } 322s > 322s > whatis<-function(x){ 322s + if(is.data.frame(x)) 322s + cat("Type: data.frame\n") 322s + else if(is.matrix(x)) 322s + cat("Type: matrix\n") 322s + else if(is.vector(x)) 322s + cat("Type: vector\n") 322s + else 322s + cat("Type: don't know\n") 322s + } 322s > 322s > ## VT::15.09.2013 - this will render the output independent 322s > ## from the version of the package 322s > suppressPackageStartupMessages(library(rrcov)) 322s > 322s > dodata() 323s 323s Call: dodata() 323s Data Set n p Half LOG(obj) Time 323s ======================================================== 323s heart 12 2 7 3.827606 323s Best subsample: 323s [1] 1 4 7 8 9 10 11 323s Outliers: 3 323s [1] 2 6 12 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s height weight 323s 34.9 27.0 323s 323s Robust Estimate of Covariance: 323s height weight 323s height 142 217 323s weight 217 350 323s -------------------------------------------------------- 323s starsCYG 47 2 25 -2.742997 323s Best subsample: 323s [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 323s Outliers: 7 323s [1] 7 9 11 14 20 30 34 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s log.Te log.light 323s 4.41 4.93 323s 323s Robust Estimate of Covariance: 323s log.Te log.light 323s log.Te 0.0173 0.0578 323s log.light 0.0578 0.3615 323s -------------------------------------------------------- 323s phosphor 18 2 10 4.443101 323s Best subsample: 323s [1] 3 5 8 9 11 12 13 14 15 17 323s Outliers: 3 323s [1] 1 6 10 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s inorg organic 323s 15.2 39.4 323s 323s Robust Estimate of Covariance: 323s inorg organic 323s inorg 188 230 323s organic 230 339 323s -------------------------------------------------------- 323s stackloss 21 3 12 3.327582 323s Best subsample: 323s [1] 4 5 6 7 8 9 10 11 12 13 14 20 323s Outliers: 3 323s [1] 1 2 3 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s Air.Flow Water.Temp Acid.Conc. 323s 56.7 20.2 85.5 323s 323s Robust Estimate of Covariance: 323s Air.Flow Water.Temp Acid.Conc. 323s Air.Flow 34.31 11.07 23.54 323s Water.Temp 11.07 9.23 7.85 323s Acid.Conc. 23.54 7.85 47.35 323s -------------------------------------------------------- 323s coleman 20 5 13 2.065143 323s Best subsample: 323s [1] 1 3 4 5 7 8 11 14 16 17 18 19 20 323s Outliers: 5 323s [1] 2 6 9 10 13 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s salaryP fatherWc sstatus teacherSc motherLev 323s 2.79 44.26 3.59 25.08 6.38 323s 323s Robust Estimate of Covariance: 323s salaryP fatherWc sstatus teacherSc motherLev 323s salaryP 0.2920 1.1188 2.0421 0.3487 0.0748 323s fatherWc 1.1188 996.7540 338.6587 7.1673 23.1783 323s sstatus 2.0421 338.6587 148.2501 4.4894 7.8135 323s teacherSc 0.3487 7.1673 4.4894 0.9082 0.3204 323s motherLev 0.0748 23.1783 7.8135 0.3204 0.6024 323s -------------------------------------------------------- 323s salinity 28 3 16 2.002555 323s Best subsample: 323s [1] 1 7 8 9 12 13 14 18 19 20 21 22 25 26 27 28 323s Outliers: 5 323s [1] 5 11 16 23 24 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s X1 X2 X3 323s 10.2 3.1 22.4 323s 323s Robust Estimate of Covariance: 323s X1 X2 X3 323s X1 14.387 1.153 -4.072 323s X2 1.153 5.005 -0.954 323s X3 -4.072 -0.954 2.222 323s -------------------------------------------------------- 323s wood 20 5 13 -5.471407 323s Best subsample: 323s [1] 1 2 3 5 9 10 12 13 14 15 17 18 20 323s Outliers: 5 323s [1] 4 6 8 11 19 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s x1 x2 x3 x4 x5 323s 0.576 0.123 0.531 0.538 0.889 323s 323s Robust Estimate of Covariance: 323s x1 x2 x3 x4 x5 323s x1 7.45e-03 1.11e-03 1.83e-03 -2.90e-05 -5.65e-04 323s x2 1.11e-03 3.11e-04 7.68e-04 3.37e-05 3.85e-05 323s x3 1.83e-03 7.68e-04 4.30e-03 -9.96e-04 -6.27e-05 323s x4 -2.90e-05 3.37e-05 -9.96e-04 3.02e-03 1.91e-03 323s x5 -5.65e-04 3.85e-05 -6.27e-05 1.91e-03 2.25e-03 323s -------------------------------------------------------- 323s hbk 75 3 39 1.096831 323s Best subsample: 323s [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 323s [26] 55 56 58 59 64 65 66 67 70 71 72 73 74 75 323s Outliers: 14 323s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s X1 X2 X3 323s 1.48 1.86 1.73 323s 323s Robust Estimate of Covariance: 323s X1 X2 X3 323s X1 1.695 0.230 0.265 323s X2 0.230 1.679 0.119 323s X3 0.265 0.119 1.683 323s -------------------------------------------------------- 323s Animals 28 2 15 8.945423 323s Best subsample: 323s [1] 1 3 4 5 10 11 17 18 21 22 23 24 26 27 28 323s Outliers: 9 323s [1] 2 6 7 9 12 14 15 16 25 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s body brain 323s 48.3 127.3 323s 323s Robust Estimate of Covariance: 323s body brain 323s body 10767 16872 323s brain 16872 46918 323s -------------------------------------------------------- 323s milk 86 8 47 -1.160085 323s Best subsample: 323s [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 323s [26] 46 54 56 57 59 60 61 62 63 64 65 66 67 69 72 76 78 79 81 82 83 85 323s Outliers: 18 323s [1] 1 2 3 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s X1 X2 X3 X4 X5 X6 X7 X8 323s 1.03 35.91 33.02 26.08 25.06 24.99 122.93 14.38 323s 323s Robust Estimate of Covariance: 323s X1 X2 X3 X4 X5 X6 X7 323s X1 6.00e-07 1.51e-04 3.34e-04 3.09e-04 2.82e-04 2.77e-04 1.09e-03 323s X2 1.51e-04 2.03e+00 3.83e-01 3.04e-01 2.20e-01 3.51e-01 2.18e+00 323s X3 3.34e-04 3.83e-01 1.58e+00 1.21e+00 1.18e+00 1.20e+00 1.60e+00 323s X4 3.09e-04 3.04e-01 1.21e+00 9.82e-01 9.39e-01 9.53e-01 1.36e+00 323s X5 2.82e-04 2.20e-01 1.18e+00 9.39e-01 9.67e-01 9.52e-01 1.34e+00 323s X6 2.77e-04 3.51e-01 1.20e+00 9.53e-01 9.52e-01 9.92e-01 1.38e+00 323s X7 1.09e-03 2.18e+00 1.60e+00 1.36e+00 1.34e+00 1.38e+00 6.73e+00 323s X8 3.33e-05 2.92e-01 2.65e-01 1.83e-01 1.65e-01 1.76e-01 5.64e-01 323s X8 323s X1 3.33e-05 323s X2 2.92e-01 323s X3 2.65e-01 323s X4 1.83e-01 323s X5 1.65e-01 323s X6 1.76e-01 323s X7 5.64e-01 323s X8 1.80e-01 323s -------------------------------------------------------- 323s bushfire 38 5 22 5.644315 323s Best subsample: 323s [1] 1 2 3 4 5 6 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 323s Outliers: 15 323s [1] 7 8 9 10 11 29 30 31 32 33 34 35 36 37 38 323s ------------- 323s 323s Call: 323s CovMve(x = x, trace = FALSE) 323s -> Method: Minimum volume ellipsoid estimator 323s 323s Robust Estimate of Location: 323s V1 V2 V3 V4 V5 323s 107 147 263 215 277 323s 323s Robust Estimate of Covariance: 323s V1 V2 V3 V4 V5 323s V1 519 375 -2799 -619 -509 323s V2 375 320 -1671 -342 -289 323s V3 -2799 -1671 18373 4314 3480 323s V4 -619 -342 4314 1076 854 323s V5 -509 -289 3480 854 683 323s -------------------------------------------------------- 323s ======================================================== 323s > 323s BEGIN TEST togk4.R 323s 323s R version 4.4.3 (2025-02-28) -- "Trophy Case" 323s Copyright (C) 2025 The R Foundation for Statistical Computing 323s Platform: aarch64-unknown-linux-gnu 323s 323s R is free software and comes with ABSOLUTELY NO WARRANTY. 323s You are welcome to redistribute it under certain conditions. 323s Type 'license()' or 'licence()' for distribution details. 323s 323s R is a collaborative project with many contributors. 323s Type 'contributors()' for more information and 323s 'citation()' on how to cite R or R packages in publications. 323s 323s Type 'demo()' for some demos, 'help()' for on-line help, or 323s 'help.start()' for an HTML browser interface to help. 323s Type 'q()' to quit R. 323s 323s > ## VT::15.09.2013 - this will render the output independent 323s > ## from the version of the package 323s > suppressPackageStartupMessages(library(rrcov)) 323s > 323s > ## VT::14.01.2020 323s > ## On some platforms minor differences are shown - use 323s > ## IGNORE_RDIFF_BEGIN 323s > ## IGNORE_RDIFF_END 323s > 323s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE, method = c("FASTMCD","MASS")){ 323s + domcd <- function(x, xname, nrep=1){ 323s + n <- dim(x)[1] 323s + p <- dim(x)[2] 323s + 323s + mcd<-CovOgk(x) 323s + 323s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 323s + 323s + lpad<-lname-nchar(xname) 323s + cat(pad.right(xname,lpad), xres) 323s + 323s + dist <- getDistance(mcd) 323s + quantiel <- qchisq(0.975, p) 323s + ibad <- which(dist >= quantiel) 323s + names(ibad) <- NULL 323s + nbad <- length(ibad) 323s + cat("Outliers: ",nbad,"\n") 323s + if(nbad > 0) 323s + print(ibad) 323s + cat("-------------\n") 323s + show(mcd) 323s + cat("--------------------------------------------------------\n") 323s + } 323s + 323s + lname <- 20 323s + 323s + ## VT::15.09.2013 - this will render the output independent 323s + ## from the version of the package 323s + suppressPackageStartupMessages(library(rrcov)) 323s + 323s + method <- match.arg(method) 323s + 323s + data(heart) 323s + data(starsCYG) 323s + data(phosphor) 323s + data(stackloss) 323s + data(coleman) 323s + data(salinity) 323s + data(wood) 323s + 323s + data(hbk) 323s + 323s + data(Animals, package = "MASS") 323s + brain <- Animals[c(1:24, 26:25, 27:28),] 323s + data(milk) 323s + data(bushfire) 323s + 323s + tmp <- sys.call() 323s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 323s + 323s + cat("Data Set n p Half LOG(obj) Time\n") 323s + cat("========================================================\n") 323s + domcd(heart[, 1:2], data(heart), nrep) 323s + ## This will not work within the function, of course 323s + ## - comment it out 323s + ## IGNORE_RDIFF_BEGIN 323s + ## domcd(starsCYG,data(starsCYG), nrep) 323s + ## IGNORE_RDIFF_END 323s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 323s + domcd(stack.x,data(stackloss), nrep) 323s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 323s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 323s + ## IGNORE_RDIFF_BEGIN 323s + ## domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 323s + ## IGNORE_RDIFF_END 323s + domcd(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep) 323s + 323s + domcd(brain, "Animals", nrep) 323s + domcd(milk, data(milk), nrep) 323s + domcd(bushfire, data(bushfire), nrep) 323s + cat("========================================================\n") 323s + } 323s > 323s > pad.right <- function(z, pads) 323s + { 323s + ### Pads spaces to right of text 323s + padding <- paste(rep(" ", pads), collapse = "") 323s + paste(z, padding, sep = "") 323s + } 323s > 323s > dodata() 323s 323s Call: dodata() 323s Data Set n p Half LOG(obj) Time 323s ======================================================== 323s heart 12 2 323s Outliers: 5 323s [1] 2 6 8 10 12 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s height weight 323s 39.76 35.71 323s 323s Robust Estimate of Covariance: 323s height weight 323s height 15.88 32.07 323s weight 32.07 78.28 323s -------------------------------------------------------- 323s phosphor 18 2 323s Outliers: 2 323s [1] 1 6 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s inorg organic 323s 13.31 40.00 323s 323s Robust Estimate of Covariance: 323s inorg organic 323s inorg 92.82 93.24 323s organic 93.24 152.62 323s -------------------------------------------------------- 323s stackloss 21 3 323s Outliers: 2 323s [1] 1 2 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s Air.Flow Water.Temp Acid.Conc. 323s 57.72 20.50 85.78 323s 323s Robust Estimate of Covariance: 323s Air.Flow Water.Temp Acid.Conc. 323s Air.Flow 38.423 11.306 18.605 323s Water.Temp 11.306 6.806 5.889 323s Acid.Conc. 18.605 5.889 29.840 323s -------------------------------------------------------- 323s coleman 20 5 323s Outliers: 3 323s [1] 1 6 10 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s salaryP fatherWc sstatus teacherSc motherLev 323s 2.723 43.202 2.912 25.010 6.290 323s 323s Robust Estimate of Covariance: 323s salaryP fatherWc sstatus teacherSc motherLev 323s salaryP 0.12867 2.80048 0.92026 0.15118 0.06413 323s fatherWc 2.80048 678.72549 227.36415 9.30826 16.15102 323s sstatus 0.92026 227.36415 101.39094 3.38013 5.63283 323s teacherSc 0.15118 9.30826 3.38013 0.57112 0.27701 323s motherLev 0.06413 16.15102 5.63283 0.27701 0.44801 323s -------------------------------------------------------- 323s salinity 28 3 323s Outliers: 3 323s [1] 3 5 16 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s X1 X2 X3 323s 10.74 2.68 22.99 323s 323s Robust Estimate of Covariance: 323s X1 X2 X3 323s X1 8.1047 -0.6365 -0.4720 323s X2 -0.6365 3.0976 -1.3520 323s X3 -0.4720 -1.3520 2.3648 323s -------------------------------------------------------- 323s hbk 75 3 323s Outliers: 14 323s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s X1 X2 X3 323s 1.538 1.780 1.687 323s 323s Robust Estimate of Covariance: 323s X1 X2 X3 323s X1 1.11350 0.04992 0.11541 323s X2 0.04992 1.13338 0.13843 323s X3 0.11541 0.13843 1.05261 323s -------------------------------------------------------- 323s Animals 28 2 323s Outliers: 12 323s [1] 2 6 7 9 12 14 15 16 17 24 25 28 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s body brain 323s 39.65 105.83 323s 323s Robust Estimate of Covariance: 323s body brain 323s body 3981 7558 323s brain 7558 16594 323s -------------------------------------------------------- 323s milk 86 8 323s Outliers: 22 323s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 41 44 47 50 70 74 75 77 85 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s X1 X2 X3 X4 X5 X6 X7 X8 323s 1.03 35.80 33.10 26.15 25.13 25.06 123.06 14.39 323s 323s Robust Estimate of Covariance: 323s X1 X2 X3 X4 X5 X6 X7 323s X1 4.074e-07 5.255e-05 1.564e-04 1.506e-04 1.340e-04 1.234e-04 5.308e-04 323s X2 5.255e-05 1.464e+00 3.425e-01 2.465e-01 1.847e-01 2.484e-01 1.459e+00 323s X3 1.564e-04 3.425e-01 1.070e+00 7.834e-01 7.665e-01 7.808e-01 7.632e-01 323s X4 1.506e-04 2.465e-01 7.834e-01 6.178e-01 5.868e-01 5.959e-01 5.923e-01 323s X5 1.340e-04 1.847e-01 7.665e-01 5.868e-01 6.124e-01 5.967e-01 5.868e-01 323s X6 1.234e-04 2.484e-01 7.808e-01 5.959e-01 5.967e-01 6.253e-01 5.819e-01 323s X7 5.308e-04 1.459e+00 7.632e-01 5.923e-01 5.868e-01 5.819e-01 3.535e+00 323s X8 1.990e-07 1.851e-01 1.861e-01 1.210e-01 1.041e-01 1.116e-01 3.046e-01 323s X8 323s X1 1.990e-07 323s X2 1.851e-01 323s X3 1.861e-01 323s X4 1.210e-01 323s X5 1.041e-01 323s X6 1.116e-01 323s X7 3.046e-01 323s X8 1.292e-01 323s -------------------------------------------------------- 323s bushfire 38 5 323s Outliers: 17 323s [1] 7 8 9 10 11 12 28 29 30 31 32 33 34 35 36 37 38 323s ------------- 323s 323s Call: 323s CovOgk(x = x) 323s -> Method: Orthogonalized Gnanadesikan-Kettenring Estimator 323s 323s Robust Estimate of Location: 323s V1 V2 V3 V4 V5 323s 104.5 146.0 275.6 217.8 279.3 323s 323s Robust Estimate of Covariance: 323s V1 V2 V3 V4 V5 323s V1 266.8 203.2 -1380.7 -311.1 -252.2 323s V2 203.2 178.4 -910.9 -185.9 -155.9 323s V3 -1380.7 -910.9 8279.7 2035.5 1615.4 323s V4 -311.1 -185.9 2035.5 536.5 418.6 323s V5 -252.2 -155.9 1615.4 418.6 329.2 323s -------------------------------------------------------- 323s ======================================================== 323s > 323s BEGIN TEST tqda.R 323s 323s R version 4.4.3 (2025-02-28) -- "Trophy Case" 323s Copyright (C) 2025 The R Foundation for Statistical Computing 323s Platform: aarch64-unknown-linux-gnu 323s 323s R is free software and comes with ABSOLUTELY NO WARRANTY. 323s You are welcome to redistribute it under certain conditions. 323s Type 'license()' or 'licence()' for distribution details. 323s 323s R is a collaborative project with many contributors. 323s Type 'contributors()' for more information and 323s 'citation()' on how to cite R or R packages in publications. 323s 323s Type 'demo()' for some demos, 'help()' for on-line help, or 323s 'help.start()' for an HTML browser interface to help. 323s Type 'q()' to quit R. 323s 324s > ## VT::15.09.2013 - this will render the output independent 324s > ## from the version of the package 324s > suppressPackageStartupMessages(library(rrcov)) 324s > 324s > dodata <- function(method) { 324s + 324s + options(digits = 5) 324s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 324s + 324s + tmp <- sys.call() 324s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 324s + cat("===================================================\n") 324s + 324s + data(hemophilia); show(QdaCov(as.factor(gr)~., data=hemophilia, method=method)) 324s + data(anorexia, package="MASS"); show(QdaCov(Treat~., data=anorexia, method=method)) 324s + data(Pima.tr, package="MASS"); show(QdaCov(type~., data=Pima.tr, method=method)) 324s + data(iris); # show(QdaCov(Species~., data=iris, method=method)) 324s + data(crabs, package="MASS"); # show(QdaCov(sp~., data=crabs, method=method)) 324s + 324s + show(QdaClassic(as.factor(gr)~., data=hemophilia)) 324s + show(QdaClassic(Treat~., data=anorexia)) 324s + show(QdaClassic(type~., data=Pima.tr)) 324s + show(QdaClassic(Species~., data=iris)) 324s + ## show(QdaClassic(sp~., data=crabs)) 324s + cat("===================================================\n") 324s + } 324s > 324s > 324s > ## -- now do it: 324s > dodata(method="mcd") 324s 324s Call: dodata(method = "mcd") 324s =================================================== 324s Call: 324s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.30795 -0.0059911 324s normal -0.12920 -0.0603000 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.023784 0.015376 324s AHFantigen 0.015376 0.024035 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.0057546 0.0042606 324s AHFantigen 0.0042606 0.0084914 324s Call: 324s QdaCov(Treat ~ ., data = anorexia, method = method) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.633 82.950 324s Cont 81.558 81.108 324s FT 84.331 94.762 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 9.8671 8.6611 324s Postwt 8.6611 11.8966 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 32.5705 -4.3705 324s Postwt -4.3705 22.5079 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 33.056 10.814 324s Postwt 10.814 14.265 324s Call: 324s QdaCov(type ~ ., data = Pima.tr, method = method) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 1.8602 107.69 67.344 25.29 30.642 0.40777 24.667 324s Yes 5.3167 145.85 74.283 31.80 34.095 0.49533 37.883 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 2.221983 -0.18658 1.86507 -0.44427 0.1725348 -0.0683616 2.63439 324s glu -0.186582 471.88789 45.28021 8.95404 30.6551510 -0.6359899 3.50218 324s bp 1.865066 45.28021 110.09787 26.11192 14.4739180 -0.2104074 13.23392 324s skin -0.444272 8.95404 26.11192 118.30521 52.3115719 -0.2995751 8.65861 324s bmi 0.172535 30.65515 14.47392 52.31157 43.3140415 0.0079866 6.75720 324s ped -0.068362 -0.63599 -0.21041 -0.29958 0.0079866 0.0587710 -0.18683 324s age 2.634387 3.50218 13.23392 8.65861 6.7572019 -0.1868284 12.09493 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 17.875215 -13.740021 9.03580 4.498580 1.787458 0.079504 26.92283 324s glu -13.740021 917.719003 55.30399 27.976265 10.755113 0.092673 38.94970 324s bp 9.035798 55.303991 129.97953 34.130200 10.104275 0.198342 32.95351 324s skin 4.498580 27.976265 34.13020 101.842647 30.297210 0.064739 3.59427 324s bmi 1.787458 10.755113 10.10428 30.297210 22.529467 0.084369 -6.64317 324s ped 0.079504 0.092673 0.19834 0.064739 0.084369 0.066667 0.11199 324s age 26.922828 38.949697 32.95351 3.594266 -6.643165 0.111992 143.69752 324s Call: 324s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.30795 -0.0059911 324s normal -0.13487 -0.0778567 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.023784 0.015376 324s AHFantigen 0.015376 0.024035 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.020897 0.015515 324s AHFantigen 0.015515 0.017920 324s Call: 324s QdaClassic(Treat ~ ., data = anorexia) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.690 85.697 324s Cont 81.558 81.108 324s FT 83.229 90.494 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 23.479 19.910 324s Postwt 19.910 69.755 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 32.5705 -4.3705 324s Postwt -4.3705 22.5079 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 25.167 22.883 324s Postwt 22.883 71.827 324s Call: 324s QdaClassic(type ~ ., data = Pima.tr) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 324s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 324s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 324s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 324s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 324s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 324s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 324s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 324s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 324s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 324s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 324s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 324s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 324s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 324s Call: 324s QdaClassic(Species ~ ., data = iris) 324s 324s Prior Probabilities of Groups: 324s setosa versicolor virginica 324s 0.33333 0.33333 0.33333 324s 324s Group means: 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s setosa 5.006 3.428 1.462 0.246 324s versicolor 5.936 2.770 4.260 1.326 324s virginica 6.588 2.974 5.552 2.026 324s 324s Group: setosa 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 324s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 324s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 324s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 324s 324s Group: versicolor 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.266433 0.085184 0.182898 0.055780 324s Sepal.Width 0.085184 0.098469 0.082653 0.041204 324s Petal.Length 0.182898 0.082653 0.220816 0.073102 324s Petal.Width 0.055780 0.041204 0.073102 0.039106 324s 324s Group: virginica 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.404343 0.093763 0.303290 0.049094 324s Sepal.Width 0.093763 0.104004 0.071380 0.047629 324s Petal.Length 0.303290 0.071380 0.304588 0.048824 324s Petal.Width 0.049094 0.047629 0.048824 0.075433 324s =================================================== 324s > dodata(method="m") 324s 324s Call: dodata(method = "m") 324s =================================================== 324s Call: 324s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.29810 -0.0028222 324s normal -0.13081 -0.0675283 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.026018 0.017653 324s AHFantigen 0.017653 0.030128 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.0081933 0.0065737 324s AHFantigen 0.0065737 0.0118565 324s Call: 324s QdaCov(Treat ~ ., data = anorexia, method = method) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.436 82.631 324s Cont 81.559 80.272 324s FT 85.120 94.657 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 23.630 25.128 324s Postwt 25.128 38.142 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 35.8824 -8.2405 324s Postwt -8.2405 23.7416 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 33.805 18.206 324s Postwt 18.206 24.639 324s Call: 324s QdaCov(type ~ ., data = Pima.tr, method = method) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.5225 111.26 68.081 26.640 30.801 0.40452 26.306 324s Yes 5.0702 144.32 75.088 31.982 34.267 0.47004 37.140 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 5.74219 14.47051 6.63766 4.98559 0.826570 -0.128106 10.71303 324s glu 14.47051 591.08717 68.81219 44.73311 40.658393 -0.545716 38.01918 324s bp 6.63766 68.81219 121.02716 30.46466 16.789801 -0.320065 25.29371 324s skin 4.98559 44.73311 30.46466 136.52176 56.604475 -0.250711 19.73259 324s bmi 0.82657 40.65839 16.78980 56.60447 47.859747 0.046358 6.94523 324s ped -0.12811 -0.54572 -0.32006 -0.25071 0.046358 0.061485 -0.34653 324s age 10.71303 38.01918 25.29371 19.73259 6.945227 -0.346527 35.66101 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.98861 -1.2430 10.48556 9.05947 2.425316 0.162453 30.149872 324s glu -1.24304 867.1076 46.43838 25.92297 5.517382 1.044360 31.152650 324s bp 10.48556 46.4384 130.12536 17.21407 6.343942 -0.235121 41.091494 324s skin 9.05947 25.9230 17.21407 85.96314 26.089017 0.170061 14.562516 324s bmi 2.42532 5.5174 6.34394 26.08902 22.051976 0.097786 -5.297971 324s ped 0.16245 1.0444 -0.23512 0.17006 0.097786 0.057112 0.055286 324s age 30.14987 31.1527 41.09149 14.56252 -5.297971 0.055286 137.440921 324s Call: 324s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.30795 -0.0059911 324s normal -0.13487 -0.0778567 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.023784 0.015376 324s AHFantigen 0.015376 0.024035 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.020897 0.015515 324s AHFantigen 0.015515 0.017920 324s Call: 324s QdaClassic(Treat ~ ., data = anorexia) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.690 85.697 324s Cont 81.558 81.108 324s FT 83.229 90.494 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 23.479 19.910 324s Postwt 19.910 69.755 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 32.5705 -4.3705 324s Postwt -4.3705 22.5079 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 25.167 22.883 324s Postwt 22.883 71.827 324s Call: 324s QdaClassic(type ~ ., data = Pima.tr) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 324s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 324s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 324s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 324s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 324s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 324s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 324s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 324s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 324s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 324s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 324s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 324s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 324s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 324s Call: 324s QdaClassic(Species ~ ., data = iris) 324s 324s Prior Probabilities of Groups: 324s setosa versicolor virginica 324s 0.33333 0.33333 0.33333 324s 324s Group means: 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s setosa 5.006 3.428 1.462 0.246 324s versicolor 5.936 2.770 4.260 1.326 324s virginica 6.588 2.974 5.552 2.026 324s 324s Group: setosa 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 324s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 324s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 324s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 324s 324s Group: versicolor 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.266433 0.085184 0.182898 0.055780 324s Sepal.Width 0.085184 0.098469 0.082653 0.041204 324s Petal.Length 0.182898 0.082653 0.220816 0.073102 324s Petal.Width 0.055780 0.041204 0.073102 0.039106 324s 324s Group: virginica 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.404343 0.093763 0.303290 0.049094 324s Sepal.Width 0.093763 0.104004 0.071380 0.047629 324s Petal.Length 0.303290 0.071380 0.304588 0.048824 324s Petal.Width 0.049094 0.047629 0.048824 0.075433 324s =================================================== 324s > dodata(method="ogk") 324s 324s Call: dodata(method = "ogk") 324s =================================================== 324s Call: 324s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.29324 0.00033953 324s normal -0.12744 -0.06628182 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.019260 0.013026 324s AHFantigen 0.013026 0.021889 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.0049651 0.0039707 324s AHFantigen 0.0039707 0.0066084 324s Call: 324s QdaCov(Treat ~ ., data = anorexia, method = method) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.587 82.709 324s Cont 81.558 81.108 324s FT 85.110 94.470 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 10.452 15.115 324s Postwt 15.115 37.085 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 31.3178 -4.2024 324s Postwt -4.2024 21.6422 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 5.5309 1.4813 324s Postwt 1.4813 7.5501 324s Call: 324s QdaCov(type ~ ., data = Pima.tr, method = method) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.4286 110.35 67.495 25.905 30.275 0.39587 26.248 324s Yes 5.1964 142.71 75.357 32.732 34.809 0.48823 37.607 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 3.97823 8.70612 4.58776 4.16463 0.250612 -0.117238 8.21769 324s glu 8.70612 448.91392 51.71120 38.66213 21.816345 -0.296524 24.29370 324s bp 4.58776 51.71120 99.41188 24.27574 10.491311 -0.290753 20.02975 324s skin 4.16463 38.66213 24.27574 98.61950 41.682404 -0.335213 16.60454 324s bmi 0.25061 21.81634 10.49131 41.68240 35.237101 -0.019774 5.12042 324s ped -0.11724 -0.29652 -0.29075 -0.33521 -0.019774 0.051431 -0.36275 324s age 8.21769 24.29370 20.02975 16.60454 5.120417 -0.362748 31.32916 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.26499 6.30612 3.01913 3.76690 0.94825 0.12076 22.64860 324s glu 6.30612 688.16837 22.22704 12.81633 3.55791 0.68833 32.28061 324s bp 3.01913 22.22704 103.97959 9.95281 2.09860 0.45672 31.17602 324s skin 3.76690 12.81633 9.95281 67.51754 19.51489 0.59831 -2.35523 324s bmi 0.94825 3.55791 2.09860 19.51489 17.20331 0.24347 -6.88221 324s ped 0.12076 0.68833 0.45672 0.59831 0.24347 0.05933 0.43798 324s age 22.64860 32.28061 31.17602 -2.35523 -6.88221 0.43798 111.16709 324s Call: 324s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.30795 -0.0059911 324s normal -0.13487 -0.0778567 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.023784 0.015376 324s AHFantigen 0.015376 0.024035 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.020897 0.015515 324s AHFantigen 0.015515 0.017920 324s Call: 324s QdaClassic(Treat ~ ., data = anorexia) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.690 85.697 324s Cont 81.558 81.108 324s FT 83.229 90.494 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 23.479 19.910 324s Postwt 19.910 69.755 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 32.5705 -4.3705 324s Postwt -4.3705 22.5079 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 25.167 22.883 324s Postwt 22.883 71.827 324s Call: 324s QdaClassic(type ~ ., data = Pima.tr) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 324s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 324s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 324s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 324s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 324s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 324s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 324s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 324s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 324s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 324s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 324s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 324s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 324s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 324s Call: 324s QdaClassic(Species ~ ., data = iris) 324s 324s Prior Probabilities of Groups: 324s setosa versicolor virginica 324s 0.33333 0.33333 0.33333 324s 324s Group means: 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s setosa 5.006 3.428 1.462 0.246 324s versicolor 5.936 2.770 4.260 1.326 324s virginica 6.588 2.974 5.552 2.026 324s 324s Group: setosa 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 324s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 324s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 324s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 324s 324s Group: versicolor 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.266433 0.085184 0.182898 0.055780 324s Sepal.Width 0.085184 0.098469 0.082653 0.041204 324s Petal.Length 0.182898 0.082653 0.220816 0.073102 324s Petal.Width 0.055780 0.041204 0.073102 0.039106 324s 324s Group: virginica 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.404343 0.093763 0.303290 0.049094 324s Sepal.Width 0.093763 0.104004 0.071380 0.047629 324s Petal.Length 0.303290 0.071380 0.304588 0.048824 324s Petal.Width 0.049094 0.047629 0.048824 0.075433 324s =================================================== 324s > dodata(method="sde") 324s 324s Call: dodata(method = "sde") 324s =================================================== 324s Call: 324s QdaCov(as.factor(gr) ~ ., data = hemophilia, method = method) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.29834 -0.0032286 324s normal -0.12944 -0.0676930 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.025398 0.017810 324s AHFantigen 0.017810 0.030639 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.0083435 0.0067686 324s AHFantigen 0.0067686 0.0119565 324s Call: 324s QdaCov(Treat ~ ., data = anorexia, method = method) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.949 83.323 324s Cont 81.484 80.840 324s FT 84.596 93.835 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 22.283 17.084 324s Postwt 17.084 25.308 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 37.1864 -8.8896 324s Postwt -8.8896 31.1930 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 20.7108 3.1531 324s Postwt 3.1531 25.7046 324s Call: 324s QdaCov(type ~ ., data = Pima.tr, method = method) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.2567 109.91 67.538 25.484 30.355 0.38618 25.628 324s Yes 5.2216 141.64 75.048 32.349 34.387 0.47742 37.634 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 4.396832 10.20629 5.43346 4.38313 7.9891e-01 -0.09389257 7.45638 324s glu 10.206286 601.12211 56.62047 49.67071 3.3829e+01 -0.31896741 31.64508 324s bp 5.433462 56.62047 120.38052 34.38984 1.4817e+01 -0.21784446 26.44853 324s skin 4.383134 49.67071 34.38984 136.94931 6.1576e+01 -0.47532490 17.74141 324s bmi 0.798908 33.82928 14.81668 61.57578 5.1441e+01 0.00061983 8.56856 324s ped -0.093893 -0.31897 -0.21784 -0.47532 6.1983e-04 0.06012655 -0.26872 324s age 7.456376 31.64508 26.44853 17.74141 8.5686e+00 -0.26872005 29.93856 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.91978 7.7491 7.24229 10.46802 5.40627 0.320434 25.88314 324s glu 7.74907 856.4955 58.59554 29.65331 11.44745 1.388745 38.24430 324s bp 7.24229 58.5955 89.66288 21.36597 6.46859 0.764486 36.30462 324s skin 10.46802 29.6533 21.36597 86.79253 26.22071 0.620654 5.28665 324s bmi 5.40627 11.4475 6.46859 26.22071 20.12351 0.211701 0.71583 324s ped 0.32043 1.3887 0.76449 0.62065 0.21170 0.062727 0.93743 324s age 25.88314 38.2443 36.30462 5.28665 0.71583 0.937430 136.24335 324s Call: 324s QdaClassic(as.factor(gr) ~ ., data = hemophilia) 324s 324s Prior Probabilities of Groups: 324s carrier normal 324s 0.6 0.4 324s 324s Group means: 324s AHFactivity AHFantigen 324s carrier -0.30795 -0.0059911 324s normal -0.13487 -0.0778567 324s 324s Group: carrier 324s AHFactivity AHFantigen 324s AHFactivity 0.023784 0.015376 324s AHFantigen 0.015376 0.024035 324s 324s Group: normal 324s AHFactivity AHFantigen 324s AHFactivity 0.020897 0.015515 324s AHFantigen 0.015515 0.017920 324s Call: 324s QdaClassic(Treat ~ ., data = anorexia) 324s 324s Prior Probabilities of Groups: 324s CBT Cont FT 324s 0.40278 0.36111 0.23611 324s 324s Group means: 324s Prewt Postwt 324s CBT 82.690 85.697 324s Cont 81.558 81.108 324s FT 83.229 90.494 324s 324s Group: CBT 324s Prewt Postwt 324s Prewt 23.479 19.910 324s Postwt 19.910 69.755 324s 324s Group: Cont 324s Prewt Postwt 324s Prewt 32.5705 -4.3705 324s Postwt -4.3705 22.5079 324s 324s Group: FT 324s Prewt Postwt 324s Prewt 25.167 22.883 324s Postwt 22.883 71.827 324s Call: 324s QdaClassic(type ~ ., data = Pima.tr) 324s 324s Prior Probabilities of Groups: 324s No Yes 324s 0.66 0.34 324s 324s Group means: 324s npreg glu bp skin bmi ped age 324s No 2.9167 113.11 69.545 27.205 31.074 0.41548 29.235 324s Yes 4.8382 145.06 74.588 33.118 34.709 0.54866 37.691 324s 324s Group: No 324s npreg glu bp skin bmi ped age 324s npreg 7.878499 10.77226 8.190840 2.910305 -0.035751 -0.207341 16.82888 324s glu 10.772265 709.56118 81.430257 13.237682 19.037867 -0.518609 59.01307 324s bp 8.190840 81.43026 122.845246 33.879944 16.612630 -0.077183 46.78695 324s skin 2.910305 13.23768 33.879944 119.446391 50.125920 0.074282 18.47068 324s bmi -0.035751 19.03787 16.612630 50.125920 40.722996 0.145242 6.99999 324s ped -0.207341 -0.51861 -0.077183 0.074282 0.145242 0.071388 -0.53814 324s age 16.828880 59.01307 46.786954 18.470680 6.999988 -0.538138 91.08183 324s 324s Group: Yes 324s npreg glu bp skin bmi ped age 324s npreg 15.77941 -8.199298 6.42493 -0.51800 -1.03288 -0.133011 21.93437 324s glu -8.19930 907.250219 23.71115 87.51536 9.98156 -0.082159 58.12291 324s bp 6.42493 23.711150 134.18613 19.70588 5.15891 -0.795470 26.30378 324s skin -0.51800 87.515364 19.70588 151.32924 28.28551 0.347951 26.67867 324s bmi -1.03288 9.981563 5.15891 28.28551 23.14529 0.457694 -7.91216 324s ped -0.13301 -0.082159 -0.79547 0.34795 0.45769 0.128883 -0.41737 324s age 21.93437 58.122915 26.30378 26.67867 -7.91216 -0.417375 131.79873 324s Call: 324s QdaClassic(Species ~ ., data = iris) 324s 324s Prior Probabilities of Groups: 324s setosa versicolor virginica 324s 0.33333 0.33333 0.33333 324s 324s Group means: 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s setosa 5.006 3.428 1.462 0.246 324s versicolor 5.936 2.770 4.260 1.326 324s virginica 6.588 2.974 5.552 2.026 324s 324s Group: setosa 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.124249 0.099216 0.0163551 0.0103306 324s Sepal.Width 0.099216 0.143690 0.0116980 0.0092980 324s Petal.Length 0.016355 0.011698 0.0301592 0.0060694 324s Petal.Width 0.010331 0.009298 0.0060694 0.0111061 324s 324s Group: versicolor 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.266433 0.085184 0.182898 0.055780 324s Sepal.Width 0.085184 0.098469 0.082653 0.041204 324s Petal.Length 0.182898 0.082653 0.220816 0.073102 324s Petal.Width 0.055780 0.041204 0.073102 0.039106 324s 324s Group: virginica 324s Sepal.Length Sepal.Width Petal.Length Petal.Width 324s Sepal.Length 0.404343 0.093763 0.303290 0.049094 324s Sepal.Width 0.093763 0.104004 0.071380 0.047629 324s Petal.Length 0.303290 0.071380 0.304588 0.048824 324s Petal.Width 0.049094 0.047629 0.048824 0.075433 324s =================================================== 324s > 324s BEGIN TEST tsde.R 324s 324s R version 4.4.3 (2025-02-28) -- "Trophy Case" 324s Copyright (C) 2025 The R Foundation for Statistical Computing 324s Platform: aarch64-unknown-linux-gnu 324s 324s R is free software and comes with ABSOLUTELY NO WARRANTY. 324s You are welcome to redistribute it under certain conditions. 324s Type 'license()' or 'licence()' for distribution details. 324s 324s R is a collaborative project with many contributors. 324s Type 'contributors()' for more information and 324s 'citation()' on how to cite R or R packages in publications. 324s 324s Type 'demo()' for some demos, 'help()' for on-line help, or 324s 'help.start()' for an HTML browser interface to help. 324s Type 'q()' to quit R. 324s 325s > ## Test for singularity 325s > doexact <- function(){ 325s + exact <-function(){ 325s + n1 <- 45 325s + p <- 2 325s + x1 <- matrix(rnorm(p*n1),nrow=n1, ncol=p) 325s + x1[,p] <- x1[,p] + 3 325s + ## library(MASS) 325s + ## x1 <- mvrnorm(n=n1, mu=c(0,3), Sigma=diag(1,nrow=p)) 325s + 325s + n2 <- 55 325s + m1 <- 0 325s + m2 <- 3 325s + x2 <- cbind(rnorm(n2),rep(m2,n2)) 325s + x<-rbind(x1,x2) 325s + colnames(x) <- c("X1","X2") 325s + x 325s + } 325s + print(CovSde(exact())) 325s + } 325s > 325s > dodata <- function(nrep=1, time=FALSE, short=FALSE, full=TRUE){ 325s + 325s + domcd <- function(x, xname, nrep=1){ 325s + n <- dim(x)[1] 325s + p <- dim(x)[2] 325s + 325s + mcd<-CovSde(x) 325s + 325s + if(time){ 325s + xtime <- system.time(dorep(x, nrep))[1]/nrep 325s + xres <- sprintf("%3d %3d %3d\n", dim(x)[1], dim(x)[2], xtime) 325s + } 325s + else{ 325s + xres <- sprintf("%3d %3d\n", dim(x)[1], dim(x)[2]) 325s + } 325s + lpad<-lname-nchar(xname) 325s + cat(pad.right(xname,lpad), xres) 325s + 325s + if(!short){ 325s + 325s + ibad <- which(mcd@wt==0) 325s + names(ibad) <- NULL 325s + nbad <- length(ibad) 325s + cat("Outliers: ",nbad,"\n") 325s + if(nbad > 0) 325s + print(ibad) 325s + if(full){ 325s + cat("-------------\n") 325s + show(mcd) 325s + } 325s + cat("--------------------------------------------------------\n") 325s + } 325s + } 325s + 325s + options(digits = 5) 325s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 325s + 325s + lname <- 20 325s + 325s + ## VT::15.09.2013 - this will render the output independent 325s + ## from the version of the package 325s + suppressPackageStartupMessages(library(rrcov)) 325s + 325s + data(heart) 325s + data(starsCYG) 325s + data(phosphor) 325s + data(stackloss) 325s + data(coleman) 325s + data(salinity) 325s + data(wood) 325s + 325s + data(hbk) 325s + 325s + data(Animals, package = "MASS") 325s + brain <- Animals[c(1:24, 26:25, 27:28),] 325s + data(milk) 325s + data(bushfire) 325s + 325s + tmp <- sys.call() 325s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 325s + 325s + cat("Data Set n p Half LOG(obj) Time\n") 325s + cat("========================================================\n") 325s + domcd(heart[, 1:2], data(heart), nrep) 325s + domcd(starsCYG, data(starsCYG), nrep) 325s + domcd(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep) 325s + domcd(stack.x, data(stackloss), nrep) 325s + domcd(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep) 325s + domcd(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep) 325s + domcd(data.matrix(subset(wood, select = -y)), data(wood), nrep) 325s + domcd(data.matrix(subset(hbk, select = -Y)),data(hbk), nrep) 325s + 325s + domcd(brain, "Animals", nrep) 325s + domcd(milk, data(milk), nrep) 325s + domcd(bushfire, data(bushfire), nrep) 325s + ## VT::19.07.2010: test the univariate SDE 325s + for(i in 1:ncol(bushfire)) 325s + domcd(bushfire[i], data(bushfire), nrep) 325s + cat("========================================================\n") 325s + } 325s > 325s > dogen <- function(nrep=1, eps=0.49){ 325s + 325s + library(MASS) 325s + domcd <- function(x, nrep=1){ 325s + gc() 325s + xtime <- system.time(dorep(x, nrep))[1]/nrep 325s + cat(sprintf("%6d %3d %10.2f\n", dim(x)[1], dim(x)[2], xtime)) 325s + xtime 325s + } 325s + 325s + set.seed(1234) 325s + 325s + ## VT::15.09.2013 - this will render the output independent 325s + ## from the version of the package 325s + suppressPackageStartupMessages(library(rrcov)) 325s + 325s + ap <- c(2, 5, 10, 20, 30) 325s + an <- c(100, 500, 1000, 10000, 50000) 325s + 325s + tottime <- 0 325s + cat(" n p Time\n") 325s + cat("=====================\n") 325s + for(i in 1:length(an)) { 325s + for(j in 1:length(ap)) { 325s + n <- an[i] 325s + p <- ap[j] 325s + if(5*p <= n){ 325s + xx <- gendata(n, p, eps) 325s + X <- xx$X 325s + tottime <- tottime + domcd(X, nrep) 325s + } 325s + } 325s + } 325s + 325s + cat("=====================\n") 325s + cat("Total time: ", tottime*nrep, "\n") 325s + } 325s > 325s > docheck <- function(n, p, eps){ 325s + xx <- gendata(n,p,eps) 325s + mcd <- CovSde(xx$X) 325s + check(mcd, xx$xind) 325s + } 325s > 325s > check <- function(mcd, xind){ 325s + ## check if mcd is robust w.r.t xind, i.e. check how many of xind 325s + ## did not get zero weight 325s + mymatch <- xind %in% which(mcd@wt == 0) 325s + length(xind) - length(which(mymatch)) 325s + } 325s > 325s > dorep <- function(x, nrep=1){ 325s + 325s + for(i in 1:nrep) 325s + CovSde(x) 325s + } 325s > 325s > #### gendata() #### 325s > # Generates a location contaminated multivariate 325s > # normal sample of n observations in p dimensions 325s > # (1-eps)*Np(0,Ip) + eps*Np(m,Ip) 325s > # where 325s > # m = (b,b,...,b) 325s > # Defaults: eps=0 and b=10 325s > # 325s > gendata <- function(n,p,eps=0,b=10){ 325s + 325s + if(missing(n) || missing(p)) 325s + stop("Please specify (n,p)") 325s + if(eps < 0 || eps >= 0.5) 325s + stop(message="eps must be in [0,0.5)") 325s + X <- mvrnorm(n,rep(0,p),diag(1,nrow=p,ncol=p)) 325s + nbad <- as.integer(eps * n) 325s + if(nbad > 0){ 325s + Xbad <- mvrnorm(nbad,rep(b,p),diag(1,nrow=p,ncol=p)) 325s + xind <- sample(n,nbad) 325s + X[xind,] <- Xbad 325s + } 325s + list(X=X, xind=xind) 325s + } 325s > 325s > pad.right <- function(z, pads) 325s + { 325s + ### Pads spaces to right of text 325s + padding <- paste(rep(" ", pads), collapse = "") 325s + paste(z, padding, sep = "") 325s + } 325s > 325s > whatis<-function(x){ 325s + if(is.data.frame(x)) 325s + cat("Type: data.frame\n") 325s + else if(is.matrix(x)) 325s + cat("Type: matrix\n") 325s + else if(is.vector(x)) 325s + cat("Type: vector\n") 325s + else 325s + cat("Type: don't know\n") 325s + } 325s > 325s > ## VT::15.09.2013 - this will render the output independent 325s > ## from the version of the package 325s > suppressPackageStartupMessages(library(rrcov)) 325s > 325s > dodata() 325s 325s Call: dodata() 325s Data Set n p Half LOG(obj) Time 325s ======================================================== 325s heart 12 2 325s Outliers: 5 325s [1] 2 6 8 10 12 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s height weight 325s 39.8 35.7 325s 325s Robust Estimate of Covariance: 325s height weight 325s height 38.2 77.1 325s weight 77.1 188.1 325s -------------------------------------------------------- 325s starsCYG 47 2 325s Outliers: 7 325s [1] 7 9 11 14 20 30 34 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s log.Te log.light 325s 4.42 4.96 325s 325s Robust Estimate of Covariance: 325s log.Te log.light 325s log.Te 0.0163 0.0522 325s log.light 0.0522 0.3243 325s -------------------------------------------------------- 325s phosphor 18 2 325s Outliers: 2 325s [1] 1 6 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s inorg organic 325s 13.3 39.7 325s 325s Robust Estimate of Covariance: 325s inorg organic 325s inorg 133 134 325s organic 134 204 325s -------------------------------------------------------- 325s stackloss 21 3 325s Outliers: 6 325s [1] 1 2 3 15 17 21 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s Air.Flow Water.Temp Acid.Conc. 325s 57.8 20.7 86.4 325s 325s Robust Estimate of Covariance: 325s Air.Flow Water.Temp Acid.Conc. 325s Air.Flow 39.7 15.6 25.0 325s Water.Temp 15.6 13.0 11.9 325s Acid.Conc. 25.0 11.9 40.3 325s -------------------------------------------------------- 325s coleman 20 5 325s Outliers: 8 325s [1] 1 2 6 10 11 12 15 18 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s salaryP fatherWc sstatus teacherSc motherLev 325s 2.78 58.64 9.09 25.37 6.69 325s 325s Robust Estimate of Covariance: 325s salaryP fatherWc sstatus teacherSc motherLev 325s salaryP 0.2556 -1.0144 0.6599 0.2673 0.0339 325s fatherWc -1.0144 1615.9192 382.7846 -4.8287 36.0227 325s sstatus 0.6599 382.7846 108.1781 -0.7904 9.1027 325s teacherSc 0.2673 -4.8287 -0.7904 0.9346 -0.0239 325s motherLev 0.0339 36.0227 9.1027 -0.0239 0.9155 325s -------------------------------------------------------- 325s salinity 28 3 325s Outliers: 9 325s [1] 3 4 5 9 11 16 19 23 24 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s X1 X2 X3 325s 10.84 3.35 22.48 325s 325s Robust Estimate of Covariance: 325s X1 X2 X3 325s X1 10.75 -1.62 -2.05 325s X2 -1.62 4.21 -1.43 325s X3 -2.05 -1.43 2.63 325s -------------------------------------------------------- 325s wood 20 5 325s Outliers: 11 325s [1] 4 6 7 8 9 10 12 13 16 19 20 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s x1 x2 x3 x4 x5 325s 0.573 0.119 0.517 0.549 0.904 325s 325s Robust Estimate of Covariance: 325s x1 x2 x3 x4 x5 325s x1 0.025185 0.004279 -0.001262 -0.000382 -0.001907 325s x2 0.004279 0.001011 0.000197 -0.000117 0.000247 325s x3 -0.001262 0.000197 0.003042 0.002034 0.001773 325s x4 -0.000382 -0.000117 0.002034 0.007943 0.001137 325s x5 -0.001907 0.000247 0.001773 0.001137 0.005392 325s -------------------------------------------------------- 325s hbk 75 3 325s Outliers: 15 325s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 53 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s X1 X2 X3 325s 1.59 1.79 1.67 325s 325s Robust Estimate of Covariance: 325s X1 X2 X3 325s X1 1.6354 0.0793 0.2284 325s X2 0.0793 1.6461 0.3186 325s X3 0.2284 0.3186 1.5673 325s -------------------------------------------------------- 325s Animals 28 2 325s Outliers: 13 325s [1] 2 6 7 8 9 12 13 14 15 16 24 25 28 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s body brain 325s 18.7 64.9 325s 325s Robust Estimate of Covariance: 325s body brain 325s body 4702 7973 325s brain 7973 28571 325s -------------------------------------------------------- 325s milk 86 8 325s Outliers: 21 325s [1] 1 2 3 6 11 12 13 14 15 16 17 18 20 27 41 44 47 70 74 75 77 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s X1 X2 X3 X4 X5 X6 X7 X8 325s 1.03 35.90 33.04 26.11 25.10 25.02 123.06 14.37 325s 325s Robust Estimate of Covariance: 325s X1 X2 X3 X4 X5 X6 X7 325s X1 4.73e-07 6.57e-05 1.79e-04 1.71e-04 1.62e-04 1.42e-04 6.85e-04 325s X2 6.57e-05 1.57e+00 1.36e-01 9.28e-02 4.18e-02 1.30e-01 1.58e+00 325s X3 1.79e-04 1.36e-01 1.12e+00 8.20e-01 8.27e-01 8.00e-01 6.66e-01 325s X4 1.71e-04 9.28e-02 8.20e-01 6.57e-01 6.41e-01 6.18e-01 5.47e-01 325s X5 1.62e-04 4.18e-02 8.27e-01 6.41e-01 6.93e-01 6.44e-01 5.71e-01 325s X6 1.42e-04 1.30e-01 8.00e-01 6.18e-01 6.44e-01 6.44e-01 5.55e-01 325s X7 6.85e-04 1.58e+00 6.66e-01 5.47e-01 5.71e-01 5.55e-01 4.17e+00 325s X8 1.40e-05 2.33e-01 1.74e-01 1.06e-01 9.44e-02 9.86e-02 3.54e-01 325s X8 325s X1 1.40e-05 325s X2 2.33e-01 325s X3 1.74e-01 325s X4 1.06e-01 325s X5 9.44e-02 325s X6 9.86e-02 325s X7 3.54e-01 325s X8 1.57e-01 325s -------------------------------------------------------- 325s bushfire 38 5 325s Outliers: 23 325s [1] 1 5 6 7 8 9 10 11 12 13 15 16 28 29 30 31 32 33 34 35 36 37 38 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s V1 V2 V3 V4 V5 325s 105 148 287 223 283 325s 325s Robust Estimate of Covariance: 325s V1 V2 V3 V4 V5 325s V1 1964 1712 -10230 -2504 -2066 325s V2 1712 1526 -8732 -2145 -1763 325s V3 -10230 -8732 56327 13803 11472 325s V4 -2504 -2145 13803 3509 2894 325s V5 -2066 -1763 11472 2894 2404 325s -------------------------------------------------------- 325s bushfire 38 1 325s Outliers: 2 325s [1] 13 30 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s V1 325s 98.5 325s 325s Robust Estimate of Covariance: 325s V1 325s V1 431 325s -------------------------------------------------------- 325s bushfire 38 1 325s Outliers: 6 325s [1] 33 34 35 36 37 38 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s V2 325s 141 325s 325s Robust Estimate of Covariance: 325s V2 325s V2 528 325s -------------------------------------------------------- 325s bushfire 38 1 325s Outliers: 0 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s V3 325s 238 325s 325s Robust Estimate of Covariance: 325s V3 325s V3 37148 325s -------------------------------------------------------- 325s bushfire 38 1 325s Outliers: 9 325s [1] 8 9 32 33 34 35 36 37 38 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s V4 325s 210 325s 325s Robust Estimate of Covariance: 325s V4 325s V4 2543 325s -------------------------------------------------------- 325s bushfire 38 1 325s Outliers: 9 325s [1] 8 9 32 33 34 35 36 37 38 325s ------------- 325s 325s Call: 325s CovSde(x = x) 325s -> Method: Stahel-Donoho estimator 325s 325s Robust Estimate of Location: 325s V5 325s 273 325s 325s Robust Estimate of Covariance: 325s V5 325s V5 1575 325s -------------------------------------------------------- 325s ======================================================== 325s > ##doexact() 325s > 325s BEGIN TEST tsest.R 325s 325s R version 4.4.3 (2025-02-28) -- "Trophy Case" 325s Copyright (C) 2025 The R Foundation for Statistical Computing 325s Platform: aarch64-unknown-linux-gnu 325s 325s R is free software and comes with ABSOLUTELY NO WARRANTY. 325s You are welcome to redistribute it under certain conditions. 325s Type 'license()' or 'licence()' for distribution details. 325s 325s R is a collaborative project with many contributors. 325s Type 'contributors()' for more information and 325s 'citation()' on how to cite R or R packages in publications. 325s 325s Type 'demo()' for some demos, 'help()' for on-line help, or 325s 'help.start()' for an HTML browser interface to help. 325s Type 'q()' to quit R. 325s 325s > ## VT::15.09.2013 - this will render the output independent 325s > ## from the version of the package 325s > suppressPackageStartupMessages(library(rrcov)) 325s > 325s > library(MASS) 325s > 325s > dodata <- function(nrep = 1, time = FALSE, full = TRUE, method) { 325s + doest <- function(x, xname, nrep = 1, method=c("sfast", "surreal", "bisquare", "rocke", "suser", "MM", "sdet")) { 325s + 325s + method <- match.arg(method) 325s + 325s + lname <- 20 325s + n <- dim(x)[1] 325s + p <- dim(x)[2] 325s + 325s + mm <- if(method == "MM") CovMMest(x) else CovSest(x, method=method) 325s + 325s + crit <- log(mm@crit) 325s + 325s + xres <- sprintf("%3d %3d %12.6f\n", dim(x)[1], dim(x)[2], crit) 325s + lpad <- lname-nchar(xname) 325s + cat(pad.right(xname,lpad), xres) 325s + 325s + dist <- getDistance(mm) 325s + quantiel <- qchisq(0.975, p) 325s + ibad <- which(dist >= quantiel) 325s + names(ibad) <- NULL 325s + nbad <- length(ibad) 325s + cat("Outliers: ",nbad,"\n") 325s + if(nbad > 0) 325s + print(ibad) 325s + cat("-------------\n") 325s + show(mm) 325s + cat("--------------------------------------------------------\n") 325s + } 325s + 325s + options(digits = 5) 325s + set.seed(101) # <<-- sub-sampling algorithm now based on R's RNG and seed 325s + 325s + data(heart) 325s + data(starsCYG) 325s + data(phosphor) 325s + data(stackloss) 325s + data(coleman) 325s + data(salinity) 325s + data(wood) 325s + data(hbk) 325s + 325s + data(Animals, package = "MASS") 325s + brain <- Animals[c(1:24, 26:25, 27:28),] 325s + data(milk) 325s + data(bushfire) 325s + ### 325s + data(rice) 325s + data(hemophilia) 325s + data(fish) 325s + 325s + tmp <- sys.call() 325s + cat("\nCall: ", deparse(substitute(tmp)),"\n") 325s + 325s + cat("Data Set n p LOG(det) Time\n") 325s + cat("===================================================\n") 325s + doest(heart[, 1:2], data(heart), nrep, method=method) 325s + doest(starsCYG, data(starsCYG), nrep, method=method) 325s + doest(data.matrix(subset(phosphor, select = -plant)), data(phosphor), nrep, method=method) 325s + doest(stack.x, data(stackloss), nrep, method=method) 325s + doest(data.matrix(subset(coleman, select = -Y)), data(coleman), nrep, method=method) 325s + doest(data.matrix(subset(salinity, select = -Y)), data(salinity), nrep, method=method) 325s + doest(data.matrix(subset(wood, select = -y)), data(wood), nrep, method=method) 325s + doest(data.matrix(subset(hbk, select = -Y)), data(hbk), nrep, method=method) 325s + 325s + 325s + doest(brain, "Animals", nrep, method=method) 325s + doest(milk, data(milk), nrep, method=method) 325s + doest(bushfire, data(bushfire), nrep, method=method) 325s + 325s + doest(data.matrix(subset(rice, select = -Overall_evaluation)), data(rice), nrep, method=method) 325s + doest(data.matrix(subset(hemophilia, select = -gr)), data(hemophilia), nrep, method=method) 325s + doest(data.matrix(subset(fish, select = -Species)), data(fish), nrep, method=method) 325s + 325s + ## from package 'datasets' 325s + doest(airquality[,1:4], data(airquality), nrep, method=method) 325s + doest(attitude, data(attitude), nrep, method=method) 325s + doest(attenu, data(attenu), nrep, method=method) 325s + doest(USJudgeRatings, data(USJudgeRatings), nrep, method=method) 325s + doest(USArrests, data(USArrests), nrep, method=method) 325s + doest(longley, data(longley), nrep, method=method) 325s + doest(Loblolly, data(Loblolly), nrep, method=method) 325s + doest(quakes[,1:4], data(quakes), nrep, method=method) 325s + 325s + cat("===================================================\n") 325s + } 325s > 325s > # generate contaminated data using the function gendata with different 325s > # number of outliers and check if the M-estimate breaks - i.e. the 325s > # largest eigenvalue is larger than e.g. 5. 325s > # For n=50 and p=10 and d=5 the M-estimate can break for number of 325s > # outliers grater than 20. 325s > dogen <- function(){ 325s + eig <- vector("numeric",26) 325s + for(i in 0:25) { 325s + gg <- gendata(eps=i) 325s + mm <- CovSRocke(gg$x, t0=gg$tgood, S0=gg$sgood) 325s + eig[i+1] <- ev <- getEvals(mm)[1] 325s + cat(i, ev, "\n") 325s + 325s + ## stopifnot(ev < 5 || i > 20) 325s + } 325s + plot(0:25, eig, type="l", xlab="Number of outliers", ylab="Largest Eigenvalue") 325s + } 325s > 325s > # 325s > # generate data 50x10 as multivariate normal N(0,I) and add 325s > # eps % outliers by adding d=5.0 to each component. 325s > # - if eps <0 and eps <=0.5, the number of outliers is eps*n 325s > # - if eps >= 1, it is the number of outliers 325s > # - use the center and cov of the good data as good start 325s > # - use the center and the cov of all data as a bad start 325s > # If using a good start, the M-estimate must iterate to 325s > # the good solution: the largest eigenvalue is less then e.g. 5 325s > # 325s > gendata <- function(n=50, p=10, eps=0, d=5.0){ 325s + 325s + if(eps < 0 || eps > 0.5 && eps < 1.0 || eps > 0.5*n) 325s + stop("eps is out of range") 325s + 325s + library(MASS) 325s + 325s + x <- mvrnorm(n, rep(0,p), diag(p)) 325s + bad <- vector("numeric") 325s + nbad = if(eps < 1) eps*n else eps 325s + if(nbad > 0){ 325s + bad <- sample(n, nbad) 325s + x[bad,] <- x[bad,] + d 325s + } 325s + cov1 <- cov.wt(x) 325s + cov2 <- if(nbad <= 0) cov1 else cov.wt(x[-bad,]) 325s + 325s + list(x=x, bad=sort(bad), tgood=cov2$center, sgood=cov2$cov, tbad=cov1$center, sbad=cov1$cov) 325s + } 325s > 325s > pad.right <- function(z, pads) 325s + { 325s + ## Pads spaces to right of text 325s + padding <- paste(rep(" ", pads), collapse = "") 325s + paste(z, padding, sep = "") 325s + } 325s > 325s > 325s > ## -- now do it: 325s > dodata(method="sfast") 326s 326s Call: dodata(method = "sfast") 326s Data Set n p LOG(det) Time 326s =================================================== 326s heart 12 2 2.017701 326s Outliers: 3 326s [1] 2 6 12 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 36.1 29.5 326s 326s Robust Estimate of Covariance: 326s height weight 326s height 129 210 326s weight 210 365 326s -------------------------------------------------------- 326s starsCYG 47 2 -1.450032 326s Outliers: 7 326s [1] 7 9 11 14 20 30 34 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 4.42 4.97 326s 326s Robust Estimate of Covariance: 326s log.Te log.light 326s log.Te 0.0176 0.0617 326s log.light 0.0617 0.3880 326s -------------------------------------------------------- 326s phosphor 18 2 2.320721 326s Outliers: 2 326s [1] 1 6 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 14.1 38.8 326s 326s Robust Estimate of Covariance: 326s inorg organic 326s inorg 174 190 326s organic 190 268 326s -------------------------------------------------------- 326s stackloss 21 3 1.470031 326s Outliers: 3 326s [1] 1 2 3 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 57.5 20.5 86.0 326s 326s Robust Estimate of Covariance: 326s Air.Flow Water.Temp Acid.Conc. 326s Air.Flow 38.94 11.66 22.89 326s Water.Temp 11.66 9.96 7.81 326s Acid.Conc. 22.89 7.81 40.48 326s -------------------------------------------------------- 326s coleman 20 5 0.491419 326s Outliers: 2 326s [1] 6 10 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 2.77 45.58 4.13 25.13 6.39 326s 326s Robust Estimate of Covariance: 326s salaryP fatherWc sstatus teacherSc motherLev 326s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 326s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 326s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 326s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 326s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 326s -------------------------------------------------------- 326s salinity 28 3 0.734619 326s Outliers: 4 326s [1] 5 16 23 24 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 10.31 3.07 22.60 326s 326s Robust Estimate of Covariance: 326s X1 X2 X3 326s X1 13.200 0.784 -3.611 326s X2 0.784 4.441 -1.658 326s X3 -3.611 -1.658 2.877 326s -------------------------------------------------------- 326s wood 20 5 -3.202636 326s Outliers: 2 326s [1] 7 9 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 0.551 0.135 0.496 0.511 0.916 326s 326s Robust Estimate of Covariance: 326s x1 x2 x3 x4 x5 326s x1 0.011361 -0.000791 0.005473 0.004204 -0.004747 326s x2 -0.000791 0.000701 -0.000534 -0.001452 0.000864 326s x3 0.005473 -0.000534 0.004905 0.002960 -0.001914 326s x4 0.004204 -0.001452 0.002960 0.005154 -0.002187 326s x5 -0.004747 0.000864 -0.001914 -0.002187 0.003214 326s -------------------------------------------------------- 326s hbk 75 3 0.283145 326s Outliers: 14 326s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 1.53 1.83 1.66 326s 326s Robust Estimate of Covariance: 326s X1 X2 X3 326s X1 1.8091 0.0479 0.2446 326s X2 0.0479 1.8190 0.2513 326s X3 0.2446 0.2513 1.7288 326s -------------------------------------------------------- 326s Animals 28 2 4.685129 326s Outliers: 10 326s [1] 2 6 7 9 12 14 15 16 24 25 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 30.8 84.2 326s 326s Robust Estimate of Covariance: 326s body brain 326s body 14806 28767 326s brain 28767 65195 326s -------------------------------------------------------- 326s milk 86 8 -1.437863 326s Outliers: 15 326s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 326s 326s Robust Estimate of Covariance: 326s X1 X2 X3 X4 X5 X6 X7 326s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 326s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 326s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 326s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 326s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 326s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 326s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 326s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 326s X8 326s X1 7.45e-05 326s X2 3.33e-01 326s X3 2.82e-01 326s X4 2.01e-01 326s X5 1.80e-01 326s X6 1.91e-01 326s X7 6.38e-01 326s X8 2.01e-01 326s -------------------------------------------------------- 326s bushfire 38 5 2.443148 326s Outliers: 13 326s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 108 149 266 216 278 326s 326s Robust Estimate of Covariance: 326s V1 V2 V3 V4 V5 326s V1 911 688 -3961 -856 -707 326s V2 688 587 -2493 -492 -420 326s V3 -3961 -2493 24146 5765 4627 326s V4 -856 -492 5765 1477 1164 326s V5 -707 -420 4627 1164 925 326s -------------------------------------------------------- 326s rice 105 5 -0.724874 326s Outliers: 7 326s [1] 9 40 42 49 57 58 71 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 326s 326s Robust Estimate of Covariance: 326s Favor Appearance Taste Stickiness Toughness 326s Favor 0.423 0.345 0.427 0.405 -0.202 326s Appearance 0.345 0.592 0.570 0.549 -0.316 326s Taste 0.427 0.570 0.739 0.706 -0.393 326s Stickiness 0.405 0.549 0.706 0.876 -0.497 326s Toughness -0.202 -0.316 -0.393 -0.497 0.467 326s -------------------------------------------------------- 326s hemophilia 75 2 -1.868949 326s Outliers: 2 326s [1] 11 36 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] -0.2126 -0.0357 326s 326s Robust Estimate of Covariance: 326s AHFactivity AHFantigen 326s AHFactivity 0.0317 0.0112 326s AHFantigen 0.0112 0.0218 326s -------------------------------------------------------- 326s fish 159 6 1.285876 326s Outliers: 21 326s [1] 61 62 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 326s [20] 103 142 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 358.3 24.7 26.9 29.7 30.0 14.7 326s 326s Robust Estimate of Covariance: 326s Weight Length1 Length2 Length3 Height Width 326s Weight 1.33e+05 3.09e+03 3.34e+03 3.78e+03 1.72e+03 2.24e+02 326s Length1 3.09e+03 7.92e+01 8.54e+01 9.55e+01 4.04e+01 7.43e+00 326s Length2 3.34e+03 8.54e+01 9.22e+01 1.03e+02 4.49e+01 8.07e+00 326s Length3 3.78e+03 9.55e+01 1.03e+02 1.18e+02 5.92e+01 7.65e+00 326s Height 1.72e+03 4.04e+01 4.49e+01 5.92e+01 7.12e+01 8.51e-01 326s Width 2.24e+02 7.43e+00 8.07e+00 7.65e+00 8.51e-01 3.57e+00 326s -------------------------------------------------------- 326s airquality 153 4 2.684374 326s Outliers: 7 326s [1] 7 14 23 30 34 77 107 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 39.34 192.12 9.67 78.71 326s 326s Robust Estimate of Covariance: 326s Ozone Solar.R Wind Temp 326s Ozone 973.104 894.011 -61.856 243.560 326s Solar.R 894.011 9677.269 0.388 179.429 326s Wind -61.856 0.388 11.287 -14.310 326s Temp 243.560 179.429 -14.310 96.714 326s -------------------------------------------------------- 326s attitude 30 7 2.091968 326s Outliers: 4 326s [1] 14 16 18 24 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 326s 326s Robust Estimate of Covariance: 326s rating complaints privileges learning raises critical advance 326s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 326s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 326s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 326s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 326s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 326s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 326s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 326s -------------------------------------------------------- 326s attenu 182 5 1.148032 326s Outliers: 31 326s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 326s [20] 29 30 31 32 64 65 80 94 95 96 97 100 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 16.432 5.849 60.297 27.144 0.134 326s 326s Robust Estimate of Covariance: 326s event mag station dist accel 326s event 54.9236 -3.0733 181.0954 -49.4194 -0.0628 326s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 326s station 181.0954 -8.4388 1689.7161 -114.6319 0.7285 326s dist -49.4194 6.7388 -114.6319 597.3606 -1.7988 326s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 326s -------------------------------------------------------- 326s USJudgeRatings 43 12 -1.683847 326s Outliers: 7 326s [1] 5 7 12 13 14 23 31 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [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 326s 326s Robust Estimate of Covariance: 326s CONT INTG DMNR DILG CFMG DECI PREP FAMI 326s CONT 0.8710 -0.3019 -0.4682 -0.1893 -0.0569 -0.0992 -0.1771 -0.1975 326s INTG -0.3019 0.6401 0.8598 0.6955 0.5732 0.5439 0.7091 0.7084 326s DMNR -0.4682 0.8598 1.2412 0.9107 0.7668 0.7305 0.9292 0.9158 326s DILG -0.1893 0.6955 0.9107 0.8554 0.7408 0.7036 0.8865 0.8791 326s CFMG -0.0569 0.5732 0.7668 0.7408 0.6994 0.6545 0.7788 0.7721 326s DECI -0.0992 0.5439 0.7305 0.7036 0.6545 0.6342 0.7492 0.7511 326s PREP -0.1771 0.7091 0.9292 0.8865 0.7788 0.7492 0.9541 0.9556 326s FAMI -0.1975 0.7084 0.9158 0.8791 0.7721 0.7511 0.9556 0.9785 326s ORAL -0.2444 0.7453 0.9939 0.8917 0.7842 0.7551 0.9554 0.9680 326s WRIT -0.2344 0.7319 0.9649 0.8853 0.7781 0.7511 0.9498 0.9668 326s PHYS -0.1983 0.4676 0.6263 0.5629 0.5073 0.5039 0.5990 0.6140 326s RTEN -0.3152 0.8000 1.0798 0.9234 0.7952 0.7663 0.9637 0.9693 326s ORAL WRIT PHYS RTEN 326s CONT -0.2444 -0.2344 -0.1983 -0.3152 326s INTG 0.7453 0.7319 0.4676 0.8000 326s DMNR 0.9939 0.9649 0.6263 1.0798 326s DILG 0.8917 0.8853 0.5629 0.9234 326s CFMG 0.7842 0.7781 0.5073 0.7952 326s DECI 0.7551 0.7511 0.5039 0.7663 326s PREP 0.9554 0.9498 0.5990 0.9637 326s FAMI 0.9680 0.9668 0.6140 0.9693 326s ORAL 0.9853 0.9744 0.6280 1.0032 326s WRIT 0.9744 0.9711 0.6184 0.9870 326s PHYS 0.6280 0.6184 0.4716 0.6520 326s RTEN 1.0032 0.9870 0.6520 1.0622 326s -------------------------------------------------------- 326s USArrests 50 4 2.411726 326s Outliers: 4 326s [1] 2 28 33 39 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 7.05 150.66 64.66 19.37 326s 326s Robust Estimate of Covariance: 326s Murder Assault UrbanPop Rape 326s Murder 23.8 380.8 19.2 29.7 326s Assault 380.8 8436.2 605.6 645.3 326s UrbanPop 19.2 605.6 246.5 78.8 326s Rape 29.7 645.3 78.8 77.3 326s -------------------------------------------------------- 326s longley 16 7 1.038316 326s Outliers: 5 326s [1] 1 2 3 4 5 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 107.6 440.8 339.7 292.5 121.0 1957.1 67.2 326s 326s Robust Estimate of Covariance: 326s GNP.deflator GNP Unemployed Armed.Forces Population 326s GNP.deflator 100.6 954.7 1147.1 -507.6 74.2 326s GNP 954.7 9430.9 10115.8 -4616.5 730.1 326s Unemployed 1147.1 10115.8 19838.3 -6376.9 850.6 326s Armed.Forces -507.6 -4616.5 -6376.9 3240.2 -351.3 326s Population 74.2 730.1 850.6 -351.3 57.5 326s Year 46.4 450.8 539.5 -233.0 35.3 326s Employed 30.8 310.5 274.0 -160.8 23.3 326s Year Employed 326s GNP.deflator 46.4 30.8 326s GNP 450.8 310.5 326s Unemployed 539.5 274.0 326s Armed.Forces -233.0 -160.8 326s Population 35.3 23.3 326s Year 21.9 14.6 326s Employed 14.6 11.2 326s -------------------------------------------------------- 326s Loblolly 84 3 1.481317 326s Outliers: 14 326s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] 24.22 9.65 7.50 326s 326s Robust Estimate of Covariance: 326s height age Seed 326s height 525.08 179.21 14.27 326s age 179.21 61.85 2.94 326s Seed 14.27 2.94 25.86 326s -------------------------------------------------------- 326s quakes 1000 4 1.576855 326s Outliers: 223 326s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 326s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 326s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 326s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 326s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 326s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 326s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 326s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 326s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 326s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 326s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 326s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 326s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 326s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 326s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: S-FAST 326s 326s Robust Estimate of Location: 326s [1] -21.54 182.35 369.21 4.54 326s 326s Robust Estimate of Covariance: 326s lat long depth mag 326s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 326s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 326s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 326s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 326s -------------------------------------------------------- 326s =================================================== 326s > dodata(method="sdet") 326s 326s Call: dodata(method = "sdet") 326s Data Set n p LOG(det) Time 326s =================================================== 326s heart 12 2 2.017701 326s Outliers: 3 326s [1] 2 6 12 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: DET-S 326s 326s Robust Estimate of Location: 326s [1] 36.1 29.5 326s 326s Robust Estimate of Covariance: 326s height weight 326s height 129 210 326s weight 210 365 326s -------------------------------------------------------- 326s starsCYG 47 2 -1.450032 326s Outliers: 7 326s [1] 7 9 11 14 20 30 34 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: DET-S 326s 326s Robust Estimate of Location: 326s [1] 4.42 4.97 326s 326s Robust Estimate of Covariance: 326s log.Te log.light 326s log.Te 0.0176 0.0617 326s log.light 0.0617 0.3880 326s -------------------------------------------------------- 326s phosphor 18 2 2.320721 326s Outliers: 2 326s [1] 1 6 326s ------------- 326s 326s Call: 326s CovSest(x = x, method = method) 326s -> Method: S-estimates: DET-S 326s 326s Robust Estimate of Location: 326s [1] 14.1 38.8 326s 326s Robust Estimate of Covariance: 326s inorg organic 326s inorg 174 190 326s organic 190 268 326s -------------------------------------------------------- 327s stackloss 21 3 1.470031 327s Outliers: 3 327s [1] 1 2 3 327s ------------- 327s 327s Call: 327s CovSest(x = x, method = method) 327s -> Method: S-estimates: DET-S 327s 327s Robust Estimate of Location: 327s [1] 57.5 20.5 86.0 327s 327s Robust Estimate of Covariance: 327s Air.Flow Water.Temp Acid.Conc. 327s Air.Flow 38.94 11.66 22.89 327s Water.Temp 11.66 9.96 7.81 327s Acid.Conc. 22.89 7.81 40.48 327s -------------------------------------------------------- 327s coleman 20 5 0.491419 327s Outliers: 2 327s [1] 6 10 327s ------------- 327s 327s Call: 327s CovSest(x = x, method = method) 327s -> Method: S-estimates: DET-S 327s 327s Robust Estimate of Location: 327s [1] 2.77 45.58 4.13 25.13 6.39 327s 327s Robust Estimate of Covariance: 327s salaryP fatherWc sstatus teacherSc motherLev 327s salaryP 0.2209 1.9568 1.4389 0.2638 0.0674 327s fatherWc 1.9568 940.7409 307.8297 8.3290 21.9143 327s sstatus 1.4389 307.8297 134.0540 4.1808 7.4799 327s teacherSc 0.2638 8.3290 4.1808 0.7604 0.2917 327s motherLev 0.0674 21.9143 7.4799 0.2917 0.5817 327s -------------------------------------------------------- 327s salinity 28 3 0.734619 327s Outliers: 4 327s [1] 5 16 23 24 327s ------------- 327s 327s Call: 327s CovSest(x = x, method = method) 327s -> Method: S-estimates: DET-S 327s 327s Robust Estimate of Location: 327s [1] 10.31 3.07 22.60 327s 327s Robust Estimate of Covariance: 327s X1 X2 X3 327s X1 13.200 0.784 -3.611 327s X2 0.784 4.441 -1.658 327s X3 -3.611 -1.658 2.877 327s -------------------------------------------------------- 327s wood 20 5 -3.220754 327s Outliers: 4 327s [1] 4 6 8 19 327s ------------- 327s 327s Call: 327s CovSest(x = x, method = method) 327s -> Method: S-estimates: DET-S 327s 327s Robust Estimate of Location: 327s [1] 0.580 0.123 0.530 0.538 0.890 327s 327s Robust Estimate of Covariance: 327s x1 x2 x3 x4 x5 327s x1 8.16e-03 1.39e-03 1.97e-03 -2.82e-04 -7.61e-04 327s x2 1.39e-03 4.00e-04 8.14e-04 -8.51e-05 -5.07e-06 327s x3 1.97e-03 8.14e-04 4.74e-03 -9.59e-04 2.06e-05 327s x4 -2.82e-04 -8.51e-05 -9.59e-04 3.09e-03 1.87e-03 327s x5 -7.61e-04 -5.07e-06 2.06e-05 1.87e-03 2.28e-03 327s -------------------------------------------------------- 327s hbk 75 3 0.283145 327s Outliers: 14 327s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 327s ------------- 327s 327s Call: 327s CovSest(x = x, method = method) 327s -> Method: S-estimates: DET-S 327s 327s Robust Estimate of Location: 327s [1] 1.53 1.83 1.66 327s 327s Robust Estimate of Covariance: 327s X1 X2 X3 327s X1 1.8091 0.0479 0.2446 327s X2 0.0479 1.8190 0.2513 327s X3 0.2446 0.2513 1.7288 327s -------------------------------------------------------- 327s Animals 28 2 4.685129 327s Outliers: 10 327s [1] 2 6 7 9 12 14 15 16 24 25 327s ------------- 327s 327s Call: 327s CovSest(x = x, method = method) 327s -> Method: S-estimates: DET-S 327s 327s Robust Estimate of Location: 327s [1] 30.8 84.2 327s 327s Robust Estimate of Covariance: 327s body brain 327s body 14806 28767 327s brain 28767 65194 327s -------------------------------------------------------- 328s milk 86 8 -1.437863 328s Outliers: 15 328s [1] 1 2 3 12 13 14 15 16 17 41 44 47 70 74 75 328s ------------- 328s 328s Call: 328s CovSest(x = x, method = method) 328s -> Method: S-estimates: DET-S 328s 328s Robust Estimate of Location: 328s [1] 1.03 35.81 32.97 26.04 25.02 24.94 122.81 14.36 328s 328s Robust Estimate of Covariance: 328s X1 X2 X3 X4 X5 X6 X7 328s X1 8.30e-07 2.53e-04 4.43e-04 4.02e-04 3.92e-04 3.96e-04 1.44e-03 328s X2 2.53e-04 2.24e+00 4.77e-01 3.63e-01 2.91e-01 3.94e-01 2.44e+00 328s X3 4.43e-04 4.77e-01 1.58e+00 1.20e+00 1.18e+00 1.19e+00 1.65e+00 328s X4 4.02e-04 3.63e-01 1.20e+00 9.74e-01 9.37e-01 9.39e-01 1.39e+00 328s X5 3.92e-04 2.91e-01 1.18e+00 9.37e-01 9.78e-01 9.44e-01 1.37e+00 328s X6 3.96e-04 3.94e-01 1.19e+00 9.39e-01 9.44e-01 9.82e-01 1.41e+00 328s X7 1.44e-03 2.44e+00 1.65e+00 1.39e+00 1.37e+00 1.41e+00 6.96e+00 328s X8 7.45e-05 3.33e-01 2.82e-01 2.01e-01 1.80e-01 1.91e-01 6.38e-01 328s X8 328s X1 7.45e-05 328s X2 3.33e-01 328s X3 2.82e-01 328s X4 2.01e-01 328s X5 1.80e-01 328s X6 1.91e-01 328s X7 6.38e-01 328s X8 2.01e-01 328s -------------------------------------------------------- 328s bushfire 38 5 2.443148 328s Outliers: 13 328s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 328s ------------- 328s 328s Call: 328s CovSest(x = x, method = method) 328s -> Method: S-estimates: DET-S 328s 328s Robust Estimate of Location: 328s [1] 108 149 266 216 278 328s 328s Robust Estimate of Covariance: 328s V1 V2 V3 V4 V5 328s V1 911 688 -3961 -856 -707 328s V2 688 587 -2493 -492 -420 328s V3 -3961 -2493 24146 5765 4627 328s V4 -856 -492 5765 1477 1164 328s V5 -707 -420 4627 1164 925 328s -------------------------------------------------------- 328s rice 105 5 -0.724874 328s Outliers: 7 328s [1] 9 40 42 49 57 58 71 328s ------------- 328s 328s Call: 328s CovSest(x = x, method = method) 328s -> Method: S-estimates: DET-S 328s 328s Robust Estimate of Location: 328s [1] -0.2472 0.1211 -0.1207 0.0715 0.0640 328s 328s Robust Estimate of Covariance: 328s Favor Appearance Taste Stickiness Toughness 328s Favor 0.423 0.345 0.427 0.405 -0.202 328s Appearance 0.345 0.592 0.570 0.549 -0.316 328s Taste 0.427 0.570 0.739 0.706 -0.393 328s Stickiness 0.405 0.549 0.706 0.876 -0.497 328s Toughness -0.202 -0.316 -0.393 -0.497 0.467 328s -------------------------------------------------------- 328s hemophilia 75 2 -1.868949 328s Outliers: 2 328s [1] 11 36 328s ------------- 328s 328s Call: 328s CovSest(x = x, method = method) 328s -> Method: S-estimates: DET-S 328s 328s Robust Estimate of Location: 328s [1] -0.2126 -0.0357 328s 328s Robust Estimate of Covariance: 328s AHFactivity AHFantigen 328s AHFactivity 0.0317 0.0112 328s AHFantigen 0.0112 0.0218 328s -------------------------------------------------------- 329s fish 159 6 1.267294 329s Outliers: 33 329s [1] 61 72 73 74 75 76 77 78 79 80 81 82 83 85 86 87 88 89 90 329s [20] 91 92 93 94 95 96 97 98 99 100 101 102 103 142 329s ------------- 329s 329s Call: 329s CovSest(x = x, method = method) 329s -> Method: S-estimates: DET-S 329s 329s Robust Estimate of Location: 329s [1] 381.2 25.6 27.8 30.8 31.0 14.9 329s 329s Robust Estimate of Covariance: 329s Weight Length1 Length2 Length3 Height Width 329s Weight 148372.04 3260.48 3508.71 3976.93 1507.43 127.94 329s Length1 3260.48 77.00 82.52 92.18 27.56 3.29 329s Length2 3508.71 82.52 88.57 99.20 30.83 3.43 329s Length3 3976.93 92.18 99.20 113.97 45.50 2.21 329s Height 1507.43 27.56 30.83 45.50 70.54 -4.95 329s Width 127.94 3.29 3.43 2.21 -4.95 2.28 329s -------------------------------------------------------- 329s airquality 153 4 2.684374 329s Outliers: 7 329s [1] 7 14 23 30 34 77 107 329s ------------- 329s 329s Call: 329s CovSest(x = x, method = method) 329s -> Method: S-estimates: DET-S 329s 329s Robust Estimate of Location: 329s [1] 39.34 192.12 9.67 78.71 329s 329s Robust Estimate of Covariance: 329s Ozone Solar.R Wind Temp 329s Ozone 973.104 894.011 -61.856 243.560 329s Solar.R 894.011 9677.269 0.388 179.429 329s Wind -61.856 0.388 11.287 -14.310 329s Temp 243.560 179.429 -14.310 96.714 329s -------------------------------------------------------- 329s attitude 30 7 2.091968 329s Outliers: 4 329s [1] 14 16 18 24 329s ------------- 329s 329s Call: 329s CovSest(x = x, method = method) 329s -> Method: S-estimates: DET-S 329s 329s Robust Estimate of Location: 329s [1] 65.7 66.8 51.9 56.1 66.4 76.7 43.0 329s 329s Robust Estimate of Covariance: 329s rating complaints privileges learning raises critical advance 329s rating 170.59 136.40 77.41 125.46 99.72 8.01 49.52 329s complaints 136.40 170.94 94.62 136.73 120.76 23.52 78.52 329s privileges 77.41 94.62 150.49 112.77 87.92 6.43 72.33 329s learning 125.46 136.73 112.77 173.77 131.46 25.81 81.38 329s raises 99.72 120.76 87.92 131.46 136.76 29.50 91.70 329s critical 8.01 23.52 6.43 25.81 29.50 84.75 30.59 329s advance 49.52 78.52 72.33 81.38 91.70 30.59 116.28 329s -------------------------------------------------------- 329s attenu 182 5 1.148032 330s Outliers: 31 330s [1] 2 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 28 330s [20] 29 30 31 32 64 65 80 94 95 96 97 100 330s ------------- 330s 330s Call: 330s CovSest(x = x, method = method) 330s -> Method: S-estimates: DET-S 330s 330s Robust Estimate of Location: 330s [1] 16.432 5.849 60.297 27.144 0.134 330s 330s Robust Estimate of Covariance: 330s event mag station dist accel 330s event 54.9236 -3.0733 181.0954 -49.4195 -0.0628 330s mag -3.0733 0.6530 -8.4388 6.7388 0.0161 330s station 181.0954 -8.4388 1689.7161 -114.6321 0.7285 330s dist -49.4195 6.7388 -114.6321 597.3609 -1.7988 330s accel -0.0628 0.0161 0.7285 -1.7988 0.0152 330s -------------------------------------------------------- 330s USJudgeRatings 43 12 -1.683847 330s Outliers: 7 330s [1] 5 7 12 13 14 23 31 330s ------------- 330s 330s Call: 330s CovSest(x = x, method = method) 330s -> Method: S-estimates: DET-S 330s 330s Robust Estimate of Location: 330s [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 330s 330s Robust Estimate of Covariance: 330s CONT INTG DMNR DILG CFMG DECI PREP FAMI 330s CONT 0.8715 -0.3020 -0.4683 -0.1894 -0.0569 -0.0993 -0.1772 -0.1976 330s INTG -0.3020 0.6403 0.8600 0.6956 0.5733 0.5440 0.7093 0.7086 330s DMNR -0.4683 0.8600 1.2416 0.9109 0.7669 0.7307 0.9295 0.9161 330s DILG -0.1894 0.6956 0.9109 0.8555 0.7410 0.7037 0.8867 0.8793 330s CFMG -0.0569 0.5733 0.7669 0.7410 0.6995 0.6546 0.7789 0.7723 330s DECI -0.0993 0.5440 0.7307 0.7037 0.6546 0.6343 0.7493 0.7513 330s PREP -0.1772 0.7093 0.9295 0.8867 0.7789 0.7493 0.9543 0.9559 330s FAMI -0.1976 0.7086 0.9161 0.8793 0.7723 0.7513 0.9559 0.9788 330s ORAL -0.2445 0.7456 0.9942 0.8919 0.7844 0.7553 0.9557 0.9683 330s WRIT -0.2345 0.7321 0.9652 0.8856 0.7783 0.7513 0.9501 0.9671 330s PHYS -0.1986 0.4676 0.6264 0.5628 0.5072 0.5038 0.5990 0.6140 330s RTEN -0.3154 0.8002 1.0801 0.9236 0.7954 0.7665 0.9639 0.9695 330s ORAL WRIT PHYS RTEN 330s CONT -0.2445 -0.2345 -0.1986 -0.3154 330s INTG 0.7456 0.7321 0.4676 0.8002 330s DMNR 0.9942 0.9652 0.6264 1.0801 330s DILG 0.8919 0.8856 0.5628 0.9236 330s CFMG 0.7844 0.7783 0.5072 0.7954 330s DECI 0.7553 0.7513 0.5038 0.7665 330s PREP 0.9557 0.9501 0.5990 0.9639 330s FAMI 0.9683 0.9671 0.6140 0.9695 330s ORAL 0.9856 0.9748 0.6281 1.0035 330s WRIT 0.9748 0.9714 0.6184 0.9873 330s PHYS 0.6281 0.6184 0.4713 0.6520 330s RTEN 1.0035 0.9873 0.6520 1.0624 330s -------------------------------------------------------- 330s USArrests 50 4 2.411726 330s Outliers: 4 330s [1] 2 28 33 39 330s ------------- 330s 330s Call: 330s CovSest(x = x, method = method) 330s -> Method: S-estimates: DET-S 330s 330s Robust Estimate of Location: 330s [1] 7.05 150.66 64.66 19.37 330s 330s Robust Estimate of Covariance: 330s Murder Assault UrbanPop Rape 330s Murder 23.8 380.8 19.2 29.7 330s Assault 380.8 8436.2 605.6 645.3 330s UrbanPop 19.2 605.6 246.5 78.8 330s Rape 29.7 645.3 78.8 77.3 330s -------------------------------------------------------- 330s longley 16 7 1.143113 330s Outliers: 4 330s [1] 1 2 3 4 330s ------------- 330s 330s Call: 330s CovSest(x = x, method = method) 330s -> Method: S-estimates: DET-S 330s 330s Robust Estimate of Location: 330s [1] 107 435 334 293 120 1957 67 330s 330s Robust Estimate of Covariance: 330s GNP.deflator GNP Unemployed Armed.Forces Population 330s GNP.deflator 89.2 850.1 1007.4 -404.4 66.2 330s GNP 850.1 8384.4 9020.8 -3692.0 650.5 330s Unemployed 1007.4 9020.8 16585.4 -4990.7 752.5 330s Armed.Forces -404.4 -3692.0 -4990.7 2474.2 -280.9 330s Population 66.2 650.5 752.5 -280.9 51.2 330s Year 41.9 407.6 481.9 -186.4 31.9 330s Employed 27.9 279.7 255.6 -128.8 21.1 330s Year Employed 330s GNP.deflator 41.9 27.9 330s GNP 407.6 279.7 330s Unemployed 481.9 255.6 330s Armed.Forces -186.4 -128.8 330s Population 31.9 21.1 330s Year 20.2 13.4 330s Employed 13.4 10.1 330s -------------------------------------------------------- 331s Loblolly 84 3 1.481317 331s Outliers: 14 331s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: DET-S 331s 331s Robust Estimate of Location: 331s [1] 24.22 9.65 7.50 331s 331s Robust Estimate of Covariance: 331s height age Seed 331s height 525.08 179.21 14.27 331s age 179.21 61.85 2.94 331s Seed 14.27 2.94 25.86 331s -------------------------------------------------------- 331s quakes 1000 4 1.576855 331s Outliers: 223 331s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 331s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 331s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 331s [46] 163 170 192 205 222 226 230 239 243 250 251 252 254 258 263 331s [61] 267 268 271 283 292 300 301 305 311 312 318 320 321 325 328 331s [76] 330 334 352 357 360 365 381 382 384 389 400 402 408 413 416 331s [91] 417 419 426 429 437 441 443 453 456 467 474 477 490 492 496 331s [106] 504 507 508 509 517 524 527 528 531 532 534 536 538 539 541 331s [121] 542 543 544 545 546 547 552 553 560 571 581 583 587 593 594 331s [136] 596 597 605 612 613 618 620 625 629 638 642 647 649 653 655 331s [151] 656 672 675 681 686 699 701 702 712 714 716 721 725 726 735 331s [166] 744 754 756 759 765 766 769 779 781 782 785 787 797 804 813 331s [181] 825 827 837 840 844 852 853 857 860 865 866 869 870 872 873 331s [196] 883 884 887 888 890 891 893 908 909 912 915 916 921 927 930 331s [211] 952 962 963 969 974 980 982 986 987 988 992 997 1000 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: DET-S 331s 331s Robust Estimate of Location: 331s [1] -21.54 182.35 369.21 4.54 331s 331s Robust Estimate of Covariance: 331s lat long depth mag 331s lat 2.81e+01 6.19e+00 3.27e+02 -4.56e-01 331s long 6.19e+00 7.54e+00 -5.95e+02 9.56e-02 331s depth 3.27e+02 -5.95e+02 8.36e+04 -2.70e+01 331s mag -4.56e-01 9.56e-02 -2.70e+01 2.35e-01 331s -------------------------------------------------------- 331s =================================================== 331s > ##dodata(method="suser") 331s > ##dodata(method="surreal") 331s > dodata(method="bisquare") 331s 331s Call: dodata(method = "bisquare") 331s Data Set n p LOG(det) Time 331s =================================================== 331s heart 12 2 7.721793 331s Outliers: 3 331s [1] 2 6 12 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s height weight 331s 36.1 29.4 331s 331s Robust Estimate of Covariance: 331s height weight 331s height 109 177 331s weight 177 307 331s -------------------------------------------------------- 331s starsCYG 47 2 -5.942108 331s Outliers: 7 331s [1] 7 9 11 14 20 30 34 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s log.Te log.light 331s 4.42 4.97 331s 331s Robust Estimate of Covariance: 331s log.Te log.light 331s log.Te 0.0164 0.0574 331s log.light 0.0574 0.3613 331s -------------------------------------------------------- 331s phosphor 18 2 9.269096 331s Outliers: 2 331s [1] 1 6 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s inorg organic 331s 14.1 38.7 331s 331s Robust Estimate of Covariance: 331s inorg organic 331s inorg 173 189 331s organic 189 268 331s -------------------------------------------------------- 331s stackloss 21 3 8.411100 331s Outliers: 3 331s [1] 1 2 3 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s Air.Flow Water.Temp Acid.Conc. 331s 57.5 20.5 86.0 331s 331s Robust Estimate of Covariance: 331s Air.Flow Water.Temp Acid.Conc. 331s Air.Flow 33.82 10.17 20.02 331s Water.Temp 10.17 8.70 6.84 331s Acid.Conc. 20.02 6.84 35.51 331s -------------------------------------------------------- 331s coleman 20 5 4.722046 331s Outliers: 2 331s [1] 6 10 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s salaryP fatherWc sstatus teacherSc motherLev 331s 2.77 45.59 4.14 25.13 6.39 331s 331s Robust Estimate of Covariance: 331s salaryP fatherWc sstatus teacherSc motherLev 331s salaryP 0.2135 1.8732 1.3883 0.2547 0.0648 331s fatherWc 1.8732 905.6704 296.1916 7.9820 21.0848 331s sstatus 1.3883 296.1916 128.9536 4.0196 7.1917 331s teacherSc 0.2547 7.9820 4.0196 0.7321 0.2799 331s motherLev 0.0648 21.0848 7.1917 0.2799 0.5592 331s -------------------------------------------------------- 331s salinity 28 3 4.169963 331s Outliers: 4 331s [1] 5 16 23 24 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s X1 X2 X3 331s 10.30 3.07 22.59 331s 331s Robust Estimate of Covariance: 331s X1 X2 X3 331s X1 12.234 0.748 -3.369 331s X2 0.748 4.115 -1.524 331s X3 -3.369 -1.524 2.655 331s -------------------------------------------------------- 331s wood 20 5 -33.862485 331s Outliers: 5 331s [1] 4 6 8 11 19 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s x1 x2 x3 x4 x5 331s 0.580 0.123 0.530 0.538 0.890 331s 331s Robust Estimate of Covariance: 331s x1 x2 x3 x4 x5 331s x1 5.88e-03 9.96e-04 1.43e-03 -1.96e-04 -5.46e-04 331s x2 9.96e-04 2.86e-04 5.89e-04 -5.78e-05 -2.24e-06 331s x3 1.43e-03 5.89e-04 3.42e-03 -6.95e-04 1.43e-05 331s x4 -1.96e-04 -5.78e-05 -6.95e-04 2.23e-03 1.35e-03 331s x5 -5.46e-04 -2.24e-06 1.43e-05 1.35e-03 1.65e-03 331s -------------------------------------------------------- 331s hbk 75 3 1.472421 331s Outliers: 14 331s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s X1 X2 X3 331s 1.53 1.83 1.66 331s 331s Robust Estimate of Covariance: 331s X1 X2 X3 331s X1 1.6775 0.0447 0.2268 331s X2 0.0447 1.6865 0.2325 331s X3 0.2268 0.2325 1.6032 331s -------------------------------------------------------- 331s Animals 28 2 18.528307 331s Outliers: 11 331s [1] 2 6 7 9 12 14 15 16 24 25 28 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s body brain 331s 30.7 84.1 331s 331s Robust Estimate of Covariance: 331s body brain 331s body 13278 25795 331s brain 25795 58499 331s -------------------------------------------------------- 331s milk 86 8 -24.816943 331s Outliers: 19 331s [1] 1 2 3 11 12 13 14 15 16 17 20 27 41 44 47 70 74 75 77 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s X1 X2 X3 X4 X5 X6 X7 X8 331s 1.03 35.81 32.96 26.04 25.02 24.94 122.79 14.35 331s 331s Robust Estimate of Covariance: 331s X1 X2 X3 X4 X5 X6 X7 331s X1 6.80e-07 2.20e-04 3.70e-04 3.35e-04 3.27e-04 3.30e-04 1.21e-03 331s X2 2.20e-04 1.80e+00 3.96e-01 3.03e-01 2.45e-01 3.27e-01 2.00e+00 331s X3 3.70e-04 3.96e-01 1.27e+00 9.68e-01 9.49e-01 9.56e-01 1.37e+00 331s X4 3.35e-04 3.03e-01 9.68e-01 7.86e-01 7.55e-01 7.57e-01 1.15e+00 331s X5 3.27e-04 2.45e-01 9.49e-01 7.55e-01 7.88e-01 7.61e-01 1.14e+00 331s X6 3.30e-04 3.27e-01 9.56e-01 7.57e-01 7.61e-01 7.90e-01 1.17e+00 331s X7 1.21e-03 2.00e+00 1.37e+00 1.15e+00 1.14e+00 1.17e+00 5.71e+00 331s X8 6.57e-05 2.71e-01 2.30e-01 1.64e-01 1.48e-01 1.57e-01 5.27e-01 331s X8 331s X1 6.57e-05 331s X2 2.71e-01 331s X3 2.30e-01 331s X4 1.64e-01 331s X5 1.48e-01 331s X6 1.57e-01 331s X7 5.27e-01 331s X8 1.62e-01 331s -------------------------------------------------------- 331s bushfire 38 5 21.704243 331s Outliers: 13 331s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s V1 V2 V3 V4 V5 331s 108 149 266 216 278 331s 331s Robust Estimate of Covariance: 331s V1 V2 V3 V4 V5 331s V1 528 398 -2298 -497 -410 331s V2 398 340 -1445 -285 -244 331s V3 -2298 -1445 14026 3348 2687 331s V4 -497 -285 3348 857 676 331s V5 -410 -244 2687 676 537 331s -------------------------------------------------------- 331s rice 105 5 -7.346939 331s Outliers: 8 331s [1] 9 14 40 42 49 57 58 71 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s Favor Appearance Taste Stickiness Toughness 331s -0.2480 0.1203 -0.1213 0.0710 0.0644 331s 331s Robust Estimate of Covariance: 331s Favor Appearance Taste Stickiness Toughness 331s Favor 0.415 0.338 0.419 0.398 -0.198 331s Appearance 0.338 0.580 0.559 0.539 -0.310 331s Taste 0.419 0.559 0.725 0.693 -0.386 331s Stickiness 0.398 0.539 0.693 0.859 -0.487 331s Toughness -0.198 -0.310 -0.386 -0.487 0.457 331s -------------------------------------------------------- 331s hemophilia 75 2 -7.465173 331s Outliers: 2 331s [1] 11 36 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s AHFactivity AHFantigen 331s -0.2128 -0.0366 331s 331s Robust Estimate of Covariance: 331s AHFactivity AHFantigen 331s AHFactivity 0.0321 0.0115 331s AHFantigen 0.0115 0.0220 331s -------------------------------------------------------- 331s fish 159 6 13.465134 331s Outliers: 35 331s [1] 38 61 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 331s [20] 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 142 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s Weight Length1 Length2 Length3 Height Width 331s 381.4 25.6 27.8 30.8 31.0 14.9 331s 331s Robust Estimate of Covariance: 331s Weight Length1 Length2 Length3 Height Width 331s Weight 111094.92 2440.81 2626.59 2976.92 1129.78 95.85 331s Length1 2440.81 57.63 61.75 68.98 20.67 2.46 331s Length2 2626.59 61.75 66.28 74.24 23.13 2.57 331s Length3 2976.92 68.98 74.24 85.29 34.11 1.65 331s Height 1129.78 20.67 23.13 34.11 52.75 -3.70 331s Width 95.85 2.46 2.57 1.65 -3.70 1.71 331s -------------------------------------------------------- 331s airquality 153 4 21.282926 331s Outliers: 8 331s [1] 7 11 14 23 30 34 77 107 331s ------------- 331s 331s Call: 331s CovSest(x = x, method = method) 331s -> Method: S-estimates: bisquare 331s 331s Robust Estimate of Location: 331s Ozone Solar.R Wind Temp 331s 39.40 192.29 9.66 78.74 331s 331s Robust Estimate of Covariance: 331s Ozone Solar.R Wind Temp 331s Ozone 930.566 849.644 -59.157 232.459 331s Solar.R 849.644 9207.569 0.594 168.122 331s Wind -59.157 0.594 10.783 -13.645 331s Temp 232.459 168.122 -13.645 92.048 331s -------------------------------------------------------- 332s attitude 30 7 28.084183 332s Outliers: 6 332s [1] 6 9 14 16 18 24 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s rating complaints privileges learning raises critical 332s 65.7 66.8 51.9 56.1 66.4 76.7 332s advance 332s 43.0 332s 332s Robust Estimate of Covariance: 332s rating complaints privileges learning raises critical advance 332s rating 143.88 114.95 64.97 105.69 83.95 6.96 41.78 332s complaints 114.95 143.84 79.28 115.00 101.48 19.69 66.13 332s privileges 64.97 79.28 126.38 94.70 73.87 5.37 61.07 332s learning 105.69 115.00 94.70 146.14 110.50 21.67 68.49 332s raises 83.95 101.48 73.87 110.50 115.01 24.91 77.16 332s critical 6.96 19.69 5.37 21.67 24.91 71.74 25.88 332s advance 41.78 66.13 61.07 68.49 77.16 25.88 97.71 332s -------------------------------------------------------- 332s attenu 182 5 10.109049 332s Outliers: 35 332s [1] 2 4 5 6 7 8 9 10 11 15 16 19 20 21 22 23 24 25 27 332s [20] 28 29 30 31 32 64 65 80 93 94 95 96 97 98 99 100 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s event mag station dist accel 332s 16.418 5.850 60.243 27.307 0.134 332s 332s Robust Estimate of Covariance: 332s event mag station dist accel 332s event 41.9000 -2.3543 137.8110 -39.0321 -0.0447 332s mag -2.3543 0.4978 -6.4461 5.2644 0.0118 332s station 137.8110 -6.4461 1283.9675 -90.1657 0.5554 332s dist -39.0321 5.2644 -90.1657 462.3898 -1.3672 332s accel -0.0447 0.0118 0.5554 -1.3672 0.0114 332s -------------------------------------------------------- 332s USJudgeRatings 43 12 -43.367499 332s Outliers: 10 332s [1] 5 7 8 12 13 14 20 23 31 35 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 332s 7.43 8.16 7.75 7.89 7.69 7.76 7.68 7.67 7.52 7.59 8.19 7.87 332s 332s Robust Estimate of Covariance: 332s CONT INTG DMNR DILG CFMG DECI PREP FAMI 332s CONT 0.6895 -0.2399 -0.3728 -0.1514 -0.0461 -0.0801 -0.1419 -0.1577 332s INTG -0.2399 0.5021 0.6746 0.5446 0.4479 0.4254 0.5564 0.5558 332s DMNR -0.3728 0.6746 0.9753 0.7128 0.5992 0.5715 0.7289 0.7181 332s DILG -0.1514 0.5446 0.7128 0.6691 0.5789 0.5501 0.6949 0.6892 332s CFMG -0.0461 0.4479 0.5992 0.5789 0.5468 0.5118 0.6100 0.6049 332s DECI -0.0801 0.4254 0.5715 0.5501 0.5118 0.4965 0.5872 0.5890 332s PREP -0.1419 0.5564 0.7289 0.6949 0.6100 0.5872 0.7497 0.7511 332s FAMI -0.1577 0.5558 0.7181 0.6892 0.6049 0.5890 0.7511 0.7696 332s ORAL -0.1950 0.5848 0.7798 0.6990 0.6143 0.5921 0.7508 0.7610 332s WRIT -0.1866 0.5747 0.7575 0.6946 0.6101 0.5895 0.7470 0.7607 332s PHYS -0.1620 0.3640 0.4878 0.4361 0.3927 0.3910 0.4655 0.4779 332s RTEN -0.2522 0.6268 0.8462 0.7220 0.6210 0.5991 0.7553 0.7599 332s ORAL WRIT PHYS RTEN 332s CONT -0.1950 -0.1866 -0.1620 -0.2522 332s INTG 0.5848 0.5747 0.3640 0.6268 332s DMNR 0.7798 0.7575 0.4878 0.8462 332s DILG 0.6990 0.6946 0.4361 0.7220 332s CFMG 0.6143 0.6101 0.3927 0.6210 332s DECI 0.5921 0.5895 0.3910 0.5991 332s PREP 0.7508 0.7470 0.4655 0.7553 332s FAMI 0.7610 0.7607 0.4779 0.7599 332s ORAL 0.7745 0.7665 0.4893 0.7866 332s WRIT 0.7665 0.7645 0.4823 0.7745 332s PHYS 0.4893 0.4823 0.3620 0.5062 332s RTEN 0.7866 0.7745 0.5062 0.8313 332s -------------------------------------------------------- 332s USArrests 50 4 19.266763 332s Outliers: 4 332s [1] 2 28 33 39 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s Murder Assault UrbanPop Rape 332s 7.04 150.55 64.64 19.34 332s 332s Robust Estimate of Covariance: 332s Murder Assault UrbanPop Rape 332s Murder 23.7 378.9 19.1 29.5 332s Assault 378.9 8388.2 601.3 639.7 332s UrbanPop 19.1 601.3 245.3 77.9 332s Rape 29.5 639.7 77.9 76.3 332s -------------------------------------------------------- 332s longley 16 7 13.789499 332s Outliers: 4 332s [1] 1 2 3 4 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s GNP.deflator GNP Unemployed Armed.Forces Population 332s 107 435 333 293 120 332s Year Employed 332s 1957 67 332s 332s Robust Estimate of Covariance: 332s GNP.deflator GNP Unemployed Armed.Forces Population 332s GNP.deflator 65.05 619.75 734.33 -294.02 48.27 332s GNP 619.75 6112.14 6578.12 -2684.52 474.26 332s Unemployed 734.33 6578.12 12075.90 -3627.79 548.58 332s Armed.Forces -294.02 -2684.52 -3627.79 1797.05 -204.25 332s Population 48.27 474.26 548.58 -204.25 37.36 332s Year 30.58 297.29 351.44 -135.53 23.29 332s Employed 20.36 203.96 186.62 -93.64 15.42 332s Year Employed 332s GNP.deflator 30.58 20.36 332s GNP 297.29 203.96 332s Unemployed 351.44 186.62 332s Armed.Forces -135.53 -93.64 332s Population 23.29 15.42 332s Year 14.70 9.80 332s Employed 9.80 7.36 332s -------------------------------------------------------- 332s Loblolly 84 3 8.518440 332s Outliers: 14 332s [1] 6 12 18 24 30 36 42 48 54 60 66 72 78 84 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s height age Seed 332s 24.14 9.62 7.51 332s 332s Robust Estimate of Covariance: 332s height age Seed 332s height 464.64 158.43 12.83 332s age 158.43 54.62 2.67 332s Seed 12.83 2.67 22.98 332s -------------------------------------------------------- 332s quakes 1000 4 11.611413 332s Outliers: 234 332s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 332s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 332s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 332s [46] 163 166 170 174 192 205 222 226 230 239 243 250 251 252 254 332s [61] 258 263 267 268 271 283 292 297 300 301 305 311 312 318 320 332s [76] 321 325 328 330 331 334 352 357 360 365 368 376 381 382 384 332s [91] 389 399 400 402 408 413 416 417 418 419 426 429 437 441 443 332s [106] 453 456 467 474 477 490 492 496 504 507 508 509 517 524 527 332s [121] 528 531 532 534 536 538 539 541 542 543 544 545 546 547 552 332s [136] 553 558 560 570 571 581 583 587 593 594 596 597 605 612 613 332s [151] 618 620 625 629 638 642 647 649 653 655 656 672 675 681 686 332s [166] 699 701 702 712 714 716 721 725 726 735 744 753 754 756 759 332s [181] 765 766 769 779 781 782 785 787 797 804 813 825 827 837 840 332s [196] 844 852 853 857 860 865 866 869 870 872 873 883 884 887 888 332s [211] 890 891 893 908 909 912 915 916 921 927 930 952 962 963 969 332s [226] 974 980 982 986 987 988 992 997 1000 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: bisquare 332s 332s Robust Estimate of Location: 332s lat long depth mag 332s -21.54 182.35 369.29 4.54 332s 332s Robust Estimate of Covariance: 332s lat long depth mag 332s lat 2.18e+01 4.82e+00 2.53e+02 -3.54e-01 332s long 4.82e+00 5.87e+00 -4.63e+02 7.45e-02 332s depth 2.53e+02 -4.63e+02 6.51e+04 -2.10e+01 332s mag -3.54e-01 7.45e-02 -2.10e+01 1.83e-01 332s -------------------------------------------------------- 332s =================================================== 332s > dodata(method="rocke") 332s 332s Call: dodata(method = "rocke") 332s Data Set n p LOG(det) Time 332s =================================================== 332s heart 12 2 7.285196 332s Outliers: 3 332s [1] 2 6 12 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s height weight 332s 34.3 26.1 332s 332s Robust Estimate of Covariance: 332s height weight 332s height 105 159 332s weight 159 256 332s -------------------------------------------------------- 332s starsCYG 47 2 -5.929361 332s Outliers: 7 332s [1] 7 9 11 14 20 30 34 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s log.Te log.light 332s 4.42 4.93 332s 332s Robust Estimate of Covariance: 332s log.Te log.light 332s log.Te 0.0193 0.0709 332s log.light 0.0709 0.3987 332s -------------------------------------------------------- 332s phosphor 18 2 8.907518 332s Outliers: 3 332s [1] 1 6 10 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s inorg organic 332s 15.8 39.4 332s 332s Robust Estimate of Covariance: 332s inorg organic 332s inorg 196 252 332s organic 252 360 332s -------------------------------------------------------- 332s stackloss 21 3 8.143313 332s Outliers: 4 332s [1] 1 2 3 21 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s Air.Flow Water.Temp Acid.Conc. 332s 56.8 20.2 86.4 332s 332s Robust Estimate of Covariance: 332s Air.Flow Water.Temp Acid.Conc. 332s Air.Flow 29.26 9.62 14.78 332s Water.Temp 9.62 8.54 6.25 332s Acid.Conc. 14.78 6.25 29.70 332s -------------------------------------------------------- 332s coleman 20 5 4.001659 332s Outliers: 5 332s [1] 2 6 9 10 13 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s salaryP fatherWc sstatus teacherSc motherLev 332s 2.81 40.27 2.11 25.01 6.27 332s 332s Robust Estimate of Covariance: 332s salaryP fatherWc sstatus teacherSc motherLev 332s salaryP 0.2850 1.1473 2.0254 0.3536 0.0737 332s fatherWc 1.1473 798.0714 278.0145 6.4590 18.6357 332s sstatus 2.0254 278.0145 128.7601 4.0666 6.3845 332s teacherSc 0.3536 6.4590 4.0666 0.8749 0.2980 332s motherLev 0.0737 18.6357 6.3845 0.2980 0.4948 332s -------------------------------------------------------- 332s salinity 28 3 3.455146 332s Outliers: 9 332s [1] 3 5 10 11 15 16 17 23 24 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s X1 X2 X3 332s 9.89 3.10 22.46 332s 332s Robust Estimate of Covariance: 332s X1 X2 X3 332s X1 12.710 1.868 -4.135 332s X2 1.868 4.710 -0.663 332s X3 -4.135 -0.663 1.907 332s -------------------------------------------------------- 332s wood 20 5 -35.020244 332s Outliers: 7 332s [1] 4 6 7 8 11 16 19 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s x1 x2 x3 x4 x5 332s 0.588 0.123 0.534 0.535 0.891 332s 332s Robust Estimate of Covariance: 332s x1 x2 x3 x4 x5 332s x1 6.60e-03 1.25e-03 2.16e-03 -3.73e-04 -1.10e-03 332s x2 1.25e-03 3.30e-04 8.91e-04 -1.23e-05 2.62e-05 332s x3 2.16e-03 8.91e-04 4.55e-03 -4.90e-04 1.93e-04 332s x4 -3.73e-04 -1.23e-05 -4.90e-04 2.01e-03 1.36e-03 332s x5 -1.10e-03 2.62e-05 1.93e-04 1.36e-03 1.95e-03 332s -------------------------------------------------------- 332s hbk 75 3 1.413303 332s Outliers: 14 332s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s X1 X2 X3 332s 1.56 1.77 1.68 332s 332s Robust Estimate of Covariance: 332s X1 X2 X3 332s X1 1.6483 0.0825 0.2133 332s X2 0.0825 1.6928 0.2334 332s X3 0.2133 0.2334 1.5334 332s -------------------------------------------------------- 332s Animals 28 2 17.787210 332s Outliers: 11 332s [1] 2 6 7 9 12 14 15 16 24 25 28 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s body brain 332s 60.6 150.2 332s 332s Robust Estimate of Covariance: 332s body brain 332s body 10670 19646 332s brain 19646 41147 332s -------------------------------------------------------- 332s milk 86 8 -25.169970 332s Outliers: 22 332s [1] 1 2 3 11 12 13 14 15 16 17 18 20 27 28 41 44 47 70 73 74 75 77 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s X1 X2 X3 X4 X5 X6 X7 X8 332s 1.03 35.87 33.14 26.19 25.17 25.11 123.16 14.41 332s 332s Robust Estimate of Covariance: 332s X1 X2 X3 X4 X5 X6 X7 332s X1 4.47e-07 1.77e-04 1.94e-04 1.79e-04 1.60e-04 1.45e-04 6.45e-04 332s X2 1.77e-04 2.36e+00 4.03e-01 3.08e-01 2.08e-01 3.45e-01 2.18e+00 332s X3 1.94e-04 4.03e-01 1.13e+00 8.31e-01 8.08e-01 7.79e-01 9.83e-01 332s X4 1.79e-04 3.08e-01 8.31e-01 6.62e-01 6.22e-01 5.95e-01 7.82e-01 332s X5 1.60e-04 2.08e-01 8.08e-01 6.22e-01 6.51e-01 5.93e-01 7.60e-01 332s X6 1.45e-04 3.45e-01 7.79e-01 5.95e-01 5.93e-01 5.88e-01 7.81e-01 332s X7 6.45e-04 2.18e+00 9.83e-01 7.82e-01 7.60e-01 7.81e-01 4.81e+00 332s X8 2.47e-05 2.57e-01 2.00e-01 1.37e-01 1.13e-01 1.28e-01 4.38e-01 332s X8 332s X1 2.47e-05 332s X2 2.57e-01 332s X3 2.00e-01 332s X4 1.37e-01 332s X5 1.13e-01 332s X6 1.28e-01 332s X7 4.38e-01 332s X8 1.61e-01 332s -------------------------------------------------------- 332s bushfire 38 5 21.641566 332s Outliers: 13 332s [1] 7 8 9 10 11 31 32 33 34 35 36 37 38 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s V1 V2 V3 V4 V5 332s 111 150 256 214 276 332s 332s Robust Estimate of Covariance: 332s V1 V2 V3 V4 V5 332s V1 554 408 -2321 -464 -393 332s V2 408 343 -1361 -244 -215 332s V3 -2321 -1361 14690 3277 2684 332s V4 -464 -244 3277 783 629 332s V5 -393 -215 2684 629 509 332s -------------------------------------------------------- 332s rice 105 5 -7.208835 332s Outliers: 8 332s [1] 9 14 40 42 49 57 58 71 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s Favor Appearance Taste Stickiness Toughness 332s -0.21721 0.20948 -0.04581 0.15355 -0.00254 332s 332s Robust Estimate of Covariance: 332s Favor Appearance Taste Stickiness Toughness 332s Favor 0.432 0.337 0.417 0.382 -0.201 332s Appearance 0.337 0.591 0.553 0.510 -0.295 332s Taste 0.417 0.553 0.735 0.683 -0.385 332s Stickiness 0.382 0.510 0.683 0.834 -0.462 332s Toughness -0.201 -0.295 -0.385 -0.462 0.408 332s -------------------------------------------------------- 332s hemophilia 75 2 -7.453807 332s Outliers: 2 332s [1] 46 53 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s AHFactivity AHFantigen 332s -0.2276 -0.0637 332s 332s Robust Estimate of Covariance: 332s AHFactivity AHFantigen 332s AHFactivity 0.0405 0.0221 332s AHFantigen 0.0221 0.0263 332s -------------------------------------------------------- 332s fish 159 6 13.110263 332s Outliers: 47 332s [1] 38 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 332s [20] 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 332s [39] 98 99 100 101 102 103 104 140 142 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s Weight Length1 Length2 Length3 Height Width 332s 452.1 27.2 29.5 32.6 30.8 15.0 332s 332s Robust Estimate of Covariance: 332s Weight Length1 Length2 Length3 Height Width 332s Weight 132559.85 2817.97 3035.69 3369.07 1231.68 112.19 332s Length1 2817.97 64.16 68.74 75.36 22.52 2.37 332s Length2 3035.69 68.74 73.77 81.12 25.57 2.47 332s Length3 3369.07 75.36 81.12 91.65 37.39 1.40 332s Height 1231.68 22.52 25.57 37.39 50.91 -3.92 332s Width 112.19 2.37 2.47 1.40 -3.92 1.87 332s -------------------------------------------------------- 332s airquality 153 4 21.181656 332s Outliers: 13 332s [1] 6 7 11 14 17 20 23 30 34 53 63 77 107 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s Ozone Solar.R Wind Temp 332s 40.21 198.33 9.76 79.35 332s 332s Robust Estimate of Covariance: 332s Ozone Solar.R Wind Temp 332s Ozone 885.7 581.1 -57.3 226.4 332s Solar.R 581.1 8870.9 26.2 -15.1 332s Wind -57.3 26.2 11.8 -13.4 332s Temp 226.4 -15.1 -13.4 89.4 332s -------------------------------------------------------- 332s attitude 30 7 27.836398 332s Outliers: 8 332s [1] 1 9 13 14 17 18 24 26 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s rating complaints privileges learning raises critical 332s 64.0 65.4 50.5 54.9 63.1 72.6 332s advance 332s 40.5 332s 332s Robust Estimate of Covariance: 332s rating complaints privileges learning raises critical advance 332s rating 180.10 153.16 42.04 128.90 90.25 18.75 39.81 332s complaints 153.16 192.38 58.32 142.48 94.29 8.13 45.33 332s privileges 42.04 58.32 113.65 82.31 69.53 23.13 61.96 332s learning 128.90 142.48 82.31 156.99 101.74 13.22 49.64 332s raises 90.25 94.29 69.53 101.74 110.85 47.84 55.76 332s critical 18.75 8.13 23.13 13.22 47.84 123.00 36.97 332s advance 39.81 45.33 61.96 49.64 55.76 36.97 53.59 332s -------------------------------------------------------- 332s attenu 182 5 9.726797 332s Outliers: 44 332s [1] 1 2 4 5 6 7 8 9 10 11 13 15 16 19 20 21 22 23 24 332s [20] 25 27 28 29 30 31 32 40 45 60 61 64 65 78 80 81 93 94 95 332s [39] 96 97 98 99 100 108 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s event mag station dist accel 332s 16.39 5.82 60.89 27.97 0.12 332s 332s Robust Estimate of Covariance: 332s event mag station dist accel 332s event 4.20e+01 -1.97e+00 1.44e+02 -3.50e+01 4.05e-02 332s mag -1.97e+00 5.05e-01 -4.78e+00 4.63e+00 4.19e-03 332s station 1.44e+02 -4.78e+00 1.47e+03 -5.74e+01 7.88e-01 332s dist -3.50e+01 4.63e+00 -5.74e+01 3.99e+02 -1.18e+00 332s accel 4.05e-02 4.19e-03 7.88e-01 -1.18e+00 7.71e-03 332s -------------------------------------------------------- 332s USJudgeRatings 43 12 -46.356873 332s Outliers: 15 332s [1] 1 5 7 8 12 13 14 17 20 21 23 30 31 35 42 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 332s 7.56 8.12 7.70 7.91 7.74 7.82 7.66 7.66 7.50 7.58 8.22 7.86 332s 332s Robust Estimate of Covariance: 332s CONT INTG DMNR DILG CFMG DECI PREP 332s CONT 0.63426 -0.20121 -0.31858 -0.09578 0.00521 -0.00436 -0.07140 332s INTG -0.20121 0.28326 0.37540 0.27103 0.20362 0.19838 0.25706 332s DMNR -0.31858 0.37540 0.58265 0.33615 0.25649 0.24804 0.31696 332s DILG -0.09578 0.27103 0.33615 0.32588 0.27022 0.26302 0.32236 332s CFMG 0.00521 0.20362 0.25649 0.27022 0.25929 0.24217 0.27784 332s DECI -0.00436 0.19838 0.24804 0.26302 0.24217 0.23830 0.27284 332s PREP -0.07140 0.25706 0.31696 0.32236 0.27784 0.27284 0.35071 332s FAMI -0.07118 0.25858 0.29511 0.32582 0.27863 0.27657 0.35941 332s ORAL -0.11149 0.27055 0.33919 0.31768 0.27339 0.26739 0.34200 332s WRIT -0.10050 0.26857 0.32570 0.32327 0.27860 0.27201 0.34399 332s PHYS -0.09693 0.15339 0.18416 0.17089 0.13837 0.14895 0.18472 332s RTEN -0.15643 0.31793 0.40884 0.33863 0.27073 0.26854 0.34049 332s FAMI ORAL WRIT PHYS RTEN 332s CONT -0.07118 -0.11149 -0.10050 -0.09693 -0.15643 332s INTG 0.25858 0.27055 0.26857 0.15339 0.31793 332s DMNR 0.29511 0.33919 0.32570 0.18416 0.40884 332s DILG 0.32582 0.31768 0.32327 0.17089 0.33863 332s CFMG 0.27863 0.27339 0.27860 0.13837 0.27073 332s DECI 0.27657 0.26739 0.27201 0.14895 0.26854 332s PREP 0.35941 0.34200 0.34399 0.18472 0.34049 332s FAMI 0.38378 0.35617 0.36094 0.19998 0.35048 332s ORAL 0.35617 0.34918 0.34808 0.19759 0.35217 332s WRIT 0.36094 0.34808 0.35242 0.19666 0.35090 332s PHYS 0.19998 0.19759 0.19666 0.14770 0.20304 332s RTEN 0.35048 0.35217 0.35090 0.20304 0.39451 332s -------------------------------------------------------- 332s USArrests 50 4 19.206310 332s Outliers: 4 332s [1] 2 28 33 39 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s Murder Assault UrbanPop Rape 332s 7.55 160.94 65.10 19.97 332s 332s Robust Estimate of Covariance: 332s Murder Assault UrbanPop Rape 332s Murder 25.6 409.5 23.4 32.1 332s Assault 409.5 8530.9 676.9 669.4 332s UrbanPop 23.4 676.9 269.9 76.6 332s Rape 32.1 669.4 76.6 76.6 332s -------------------------------------------------------- 332s longley 16 7 13.387132 332s Outliers: 4 332s [1] 1 2 3 4 332s ------------- 332s 332s Call: 332s CovSest(x = x, method = method) 332s -> Method: S-estimates: Rocke type 332s 332s Robust Estimate of Location: 332s GNP.deflator GNP Unemployed Armed.Forces Population 332s 105.5 422.4 318.3 299.7 119.5 332s Year Employed 332s 1956.1 66.5 332s 332s Robust Estimate of Covariance: 332s GNP.deflator GNP Unemployed Armed.Forces Population 332s GNP.deflator 59.97 582.66 694.99 -237.75 46.12 332s GNP 582.66 5849.82 6383.68 -2207.26 461.15 332s Unemployed 694.99 6383.68 11155.03 -3104.18 534.25 332s Armed.Forces -237.75 -2207.26 -3104.18 1429.11 -171.28 332s Population 46.12 461.15 534.25 -171.28 36.79 332s Year 29.01 287.48 340.95 -112.61 22.85 332s Employed 18.99 193.66 186.31 -76.88 14.94 332s Year Employed 332s GNP.deflator 29.01 18.99 332s GNP 287.48 193.66 332s Unemployed 340.95 186.31 332s Armed.Forces -112.61 -76.88 332s Population 22.85 14.94 332s Year 14.36 9.45 332s Employed 9.45 6.90 332s -------------------------------------------------------- 333s Loblolly 84 3 7.757906 333s Outliers: 27 333s [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 333s [26] 83 84 333s ------------- 333s 333s Call: 333s CovSest(x = x, method = method) 333s -> Method: S-estimates: Rocke type 333s 333s Robust Estimate of Location: 333s height age Seed 333s 21.72 8.60 7.58 333s 333s Robust Estimate of Covariance: 333s height age Seed 333s height 316.590 102.273 5.939 333s age 102.273 33.465 -0.121 333s Seed 5.939 -0.121 27.203 333s -------------------------------------------------------- 333s quakes 1000 4 11.473431 333s Outliers: 237 333s [1] 7 12 15 17 22 25 27 28 32 37 40 41 45 48 53 333s [16] 63 64 73 78 87 91 92 94 99 108 110 117 118 119 120 333s [31] 121 122 126 133 136 141 143 145 148 152 154 155 157 159 160 333s [46] 163 166 170 174 176 192 205 222 226 230 239 243 244 250 251 333s [61] 252 254 258 263 267 268 271 283 292 297 300 301 305 311 312 333s [76] 318 320 321 325 328 330 331 334 352 357 360 365 368 376 381 333s [91] 382 384 389 399 400 402 408 410 413 416 417 418 419 426 429 333s [106] 437 441 443 453 456 467 474 477 490 492 496 504 507 508 509 333s [121] 517 524 527 528 531 532 534 536 538 539 541 542 543 544 545 333s [136] 546 547 552 553 558 560 570 571 581 583 587 593 594 596 597 333s [151] 605 612 613 618 620 625 629 638 642 647 649 653 655 656 672 333s [166] 675 681 686 699 701 702 712 714 716 721 725 726 735 744 753 333s [181] 754 756 759 765 766 769 779 781 782 785 787 797 804 813 825 333s [196] 827 837 840 844 852 853 857 860 865 866 869 870 872 873 883 333s [211] 884 887 888 890 891 893 908 909 912 915 916 921 927 930 952 333s [226] 962 963 969 974 980 982 986 987 988 992 997 1000 333s ------------- 333s 333s Call: 333s CovSest(x = x, method = method) 333s -> Method: S-estimates: Rocke type 333s 333s Robust Estimate of Location: 333s lat long depth mag 333s -21.45 182.54 351.18 4.55 333s 333s Robust Estimate of Covariance: 333s lat long depth mag 333s lat 2.10e+01 4.66e+00 2.45e+02 -3.38e-01 333s long 4.66e+00 5.88e+00 -4.63e+02 9.36e-02 333s depth 2.45e+02 -4.63e+02 6.38e+04 -2.02e+01 333s mag -3.38e-01 9.36e-02 -2.02e+01 1.78e-01 333s -------------------------------------------------------- 333s =================================================== 333s > dodata(method="MM") 333s 333s Call: dodata(method = "MM") 333s Data Set n p LOG(det) Time 333s =================================================== 333s heart 12 2 2.017701 333s Outliers: 1 333s [1] 6 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s height weight 333s 40.0 37.7 333s 333s Robust Estimate of Covariance: 333s height weight 333s height 99.2 205.7 333s weight 205.7 458.9 333s -------------------------------------------------------- 333s starsCYG 47 2 -1.450032 333s Outliers: 7 333s [1] 7 9 11 14 20 30 34 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s log.Te log.light 333s 4.41 4.94 333s 333s Robust Estimate of Covariance: 333s log.Te log.light 333s log.Te 0.0180 0.0526 333s log.light 0.0526 0.3217 333s -------------------------------------------------------- 333s phosphor 18 2 2.320721 333s Outliers: 1 333s [1] 6 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s inorg organic 333s 12.3 41.4 333s 333s Robust Estimate of Covariance: 333s inorg organic 333s inorg 94.2 67.2 333s organic 67.2 162.1 333s -------------------------------------------------------- 333s stackloss 21 3 1.470031 333s Outliers: 0 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s Air.Flow Water.Temp Acid.Conc. 333s 60.2 21.0 86.4 333s 333s Robust Estimate of Covariance: 333s Air.Flow Water.Temp Acid.Conc. 333s Air.Flow 81.13 21.99 23.15 333s Water.Temp 21.99 10.01 6.43 333s Acid.Conc. 23.15 6.43 27.22 333s -------------------------------------------------------- 333s coleman 20 5 0.491419 333s Outliers: 1 333s [1] 10 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s salaryP fatherWc sstatus teacherSc motherLev 333s 2.74 43.14 3.65 25.07 6.32 333s 333s Robust Estimate of Covariance: 333s salaryP fatherWc sstatus teacherSc motherLev 333s salaryP 0.1878 2.0635 1.0433 0.2721 0.0582 333s fatherWc 2.0635 670.2232 211.0609 4.3625 15.6083 333s sstatus 1.0433 211.0609 92.8743 2.6532 5.1816 333s teacherSc 0.2721 4.3625 2.6532 1.2757 0.1613 333s motherLev 0.0582 15.6083 5.1816 0.1613 0.4192 333s -------------------------------------------------------- 333s salinity 28 3 0.734619 333s Outliers: 2 333s [1] 5 16 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s X1 X2 X3 333s 10.46 2.66 23.15 333s 333s Robust Estimate of Covariance: 333s X1 X2 X3 333s X1 10.079 -0.024 -1.899 333s X2 -0.024 3.466 -1.817 333s X3 -1.899 -1.817 3.665 333s -------------------------------------------------------- 333s wood 20 5 -3.202636 333s Outliers: 0 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s x1 x2 x3 x4 x5 333s 0.550 0.133 0.506 0.511 0.909 333s 333s Robust Estimate of Covariance: 333s x1 x2 x3 x4 x5 333s x1 0.008454 -0.000377 0.003720 0.002874 -0.003065 333s x2 -0.000377 0.000516 -0.000399 -0.000933 0.000645 333s x3 0.003720 -0.000399 0.004186 0.001720 -0.001714 333s x4 0.002874 -0.000933 0.001720 0.003993 -0.001028 333s x5 -0.003065 0.000645 -0.001714 -0.001028 0.002744 333s -------------------------------------------------------- 333s hbk 75 3 0.283145 333s Outliers: 14 333s [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s X1 X2 X3 333s 1.54 1.79 1.68 333s 333s Robust Estimate of Covariance: 333s X1 X2 X3 333s X1 1.8016 0.0739 0.2000 333s X2 0.0739 1.8301 0.2295 333s X3 0.2000 0.2295 1.7101 333s -------------------------------------------------------- 333s Animals 28 2 4.685129 333s Outliers: 10 333s [1] 2 6 7 9 12 14 15 16 24 25 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s body brain 333s 82 148 333s 333s Robust Estimate of Covariance: 333s body brain 333s body 21050 24534 333s brain 24534 35135 333s -------------------------------------------------------- 333s milk 86 8 -1.437863 333s Outliers: 12 333s [1] 1 2 3 12 13 17 41 44 47 70 74 75 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s X1 X2 X3 X4 X5 X6 X7 X8 333s 1.03 35.73 32.87 25.96 24.94 24.85 122.55 14.33 333s 333s Robust Estimate of Covariance: 333s X1 X2 X3 X4 X5 X6 X7 333s X1 1.08e-06 5.36e-04 6.80e-04 5.96e-04 5.87e-04 5.91e-04 2.22e-03 333s X2 5.36e-04 2.42e+00 7.07e-01 5.51e-01 4.89e-01 5.70e-01 3.08e+00 333s X3 6.80e-04 7.07e-01 1.64e+00 1.28e+00 1.25e+00 1.26e+00 2.38e+00 333s X4 5.96e-04 5.51e-01 1.28e+00 1.05e+00 1.01e+00 1.02e+00 2.01e+00 333s X5 5.87e-04 4.89e-01 1.25e+00 1.01e+00 1.05e+00 1.02e+00 1.96e+00 333s X6 5.91e-04 5.70e-01 1.26e+00 1.02e+00 1.02e+00 1.05e+00 2.01e+00 333s X7 2.22e-03 3.08e+00 2.38e+00 2.01e+00 1.96e+00 2.01e+00 9.22e+00 333s X8 1.68e-04 4.13e-01 3.37e-01 2.53e-01 2.34e-01 2.43e-01 8.81e-01 333s X8 333s X1 1.68e-04 333s X2 4.13e-01 333s X3 3.37e-01 333s X4 2.53e-01 333s X5 2.34e-01 333s X6 2.43e-01 333s X7 8.81e-01 333s X8 2.11e-01 333s -------------------------------------------------------- 333s bushfire 38 5 2.443148 333s Outliers: 12 333s [1] 8 9 10 11 31 32 33 34 35 36 37 38 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s V1 V2 V3 V4 V5 333s 109 149 258 215 276 333s 333s Robust Estimate of Covariance: 333s V1 V2 V3 V4 V5 333s V1 708 538 -2705 -558 -464 333s V2 538 497 -1376 -248 -216 333s V3 -2705 -1376 20521 4833 3914 333s V4 -558 -248 4833 1217 969 333s V5 -464 -216 3914 969 778 333s -------------------------------------------------------- 333s rice 105 5 -0.724874 333s Outliers: 5 333s [1] 9 42 49 58 71 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s Favor Appearance Taste Stickiness Toughness 333s -0.2653 0.0969 -0.1371 0.0483 0.0731 333s 333s Robust Estimate of Covariance: 333s Favor Appearance Taste Stickiness Toughness 333s Favor 0.421 0.349 0.427 0.405 -0.191 333s Appearance 0.349 0.605 0.565 0.553 -0.316 333s Taste 0.427 0.565 0.725 0.701 -0.378 333s Stickiness 0.405 0.553 0.701 0.868 -0.484 333s Toughness -0.191 -0.316 -0.378 -0.484 0.464 333s -------------------------------------------------------- 333s hemophilia 75 2 -1.868949 333s Outliers: 2 333s [1] 11 36 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s AHFactivity AHFantigen 333s -0.2342 -0.0333 333s 333s Robust Estimate of Covariance: 333s AHFactivity AHFantigen 333s AHFactivity 0.0309 0.0122 333s AHFantigen 0.0122 0.0231 333s -------------------------------------------------------- 333s fish 159 6 1.285876 333s Outliers: 20 333s [1] 61 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 333s [20] 142 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s Weight Length1 Length2 Length3 Height Width 333s 352.7 24.3 26.4 29.2 29.7 14.6 333s 333s Robust Estimate of Covariance: 333s Weight Length1 Length2 Length3 Height Width 333s Weight 1.20e+05 2.89e+03 3.12e+03 3.51e+03 1.49e+03 2.83e+02 333s Length1 2.89e+03 7.73e+01 8.35e+01 9.28e+01 3.73e+01 9.26e+00 333s Length2 3.12e+03 8.35e+01 9.04e+01 1.01e+02 4.16e+01 1.01e+01 333s Length3 3.51e+03 9.28e+01 1.01e+02 1.14e+02 5.37e+01 1.01e+01 333s Height 1.49e+03 3.73e+01 4.16e+01 5.37e+01 6.75e+01 3.22e+00 333s Width 2.83e+02 9.26e+00 1.01e+01 1.01e+01 3.22e+00 4.18e+00 333s -------------------------------------------------------- 333s airquality 153 4 2.684374 333s Outliers: 6 333s [1] 7 14 23 30 34 77 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s Ozone Solar.R Wind Temp 333s 40.35 186.21 9.86 78.09 333s 333s Robust Estimate of Covariance: 333s Ozone Solar.R Wind Temp 333s Ozone 951.0 959.9 -62.5 224.6 333s Solar.R 959.9 8629.9 -28.1 244.9 333s Wind -62.5 -28.1 11.6 -15.8 333s Temp 224.6 244.9 -15.8 93.1 333s -------------------------------------------------------- 333s attitude 30 7 2.091968 333s Outliers: 4 333s [1] 14 16 18 24 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s rating complaints privileges learning raises critical 333s 65.0 66.5 52.4 56.2 65.3 75.6 333s advance 333s 42.7 333s 333s Robust Estimate of Covariance: 333s rating complaints privileges learning raises critical advance 333s rating 143.5 123.4 62.4 92.5 79.2 17.7 28.2 333s complaints 123.4 159.8 83.9 99.7 96.0 27.3 44.0 333s privileges 62.4 83.9 133.5 78.6 62.0 13.4 46.4 333s learning 92.5 99.7 78.6 136.0 90.9 18.9 62.6 333s raises 79.2 96.0 62.0 90.9 107.6 34.6 63.3 333s critical 17.7 27.3 13.4 18.9 34.6 84.9 25.9 333s advance 28.2 44.0 46.4 62.6 63.3 25.9 94.4 333s -------------------------------------------------------- 333s attenu 182 5 1.148032 333s Outliers: 21 333s [1] 2 7 8 9 10 11 15 16 24 25 28 29 30 31 32 64 65 94 95 333s [20] 96 100 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s event mag station dist accel 333s 15.36 5.95 58.11 33.56 0.14 333s 333s Robust Estimate of Covariance: 333s event mag station dist accel 333s event 4.88e+01 -2.74e+00 1.53e+02 -1.14e+02 5.95e-02 333s mag -2.74e+00 5.32e-01 -6.29e+00 1.10e+01 9.37e-03 333s station 1.53e+02 -6.29e+00 1.29e+03 -2.95e+02 1.04e+00 333s dist -1.14e+02 1.10e+01 -2.95e+02 1.13e+03 -2.41e+00 333s accel 5.95e-02 9.37e-03 1.04e+00 -2.41e+00 1.70e-02 333s -------------------------------------------------------- 333s USJudgeRatings 43 12 -1.683847 333s Outliers: 7 333s [1] 5 7 12 13 14 23 31 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s CONT INTG DMNR DILG CFMG DECI PREP FAMI ORAL WRIT PHYS RTEN 333s 7.45 8.15 7.74 7.87 7.67 7.74 7.65 7.65 7.50 7.57 8.17 7.85 333s 333s Robust Estimate of Covariance: 333s CONT INTG DMNR DILG CFMG DECI PREP FAMI 333s CONT 0.9403 -0.2500 -0.3953 -0.1418 -0.0176 -0.0620 -0.1304 -0.1517 333s INTG -0.2500 0.6314 0.8479 0.6889 0.5697 0.5386 0.7007 0.6985 333s DMNR -0.3953 0.8479 1.2186 0.9027 0.7613 0.7232 0.9191 0.9055 333s DILG -0.1418 0.6889 0.9027 0.8474 0.7344 0.6949 0.8751 0.8655 333s CFMG -0.0176 0.5697 0.7613 0.7344 0.6904 0.6442 0.7683 0.7594 333s DECI -0.0620 0.5386 0.7232 0.6949 0.6442 0.6219 0.7362 0.7360 333s PREP -0.1304 0.7007 0.9191 0.8751 0.7683 0.7362 0.9370 0.9357 333s FAMI -0.1517 0.6985 0.9055 0.8655 0.7594 0.7360 0.9357 0.9547 333s ORAL -0.1866 0.7375 0.9841 0.8816 0.7747 0.7433 0.9400 0.9496 333s WRIT -0.1881 0.7208 0.9516 0.8711 0.7646 0.7357 0.9302 0.9439 333s PHYS -0.1407 0.4673 0.6261 0.5661 0.5105 0.5039 0.5996 0.6112 333s RTEN -0.2494 0.7921 1.0688 0.9167 0.7902 0.7585 0.9533 0.9561 333s ORAL WRIT PHYS RTEN 333s CONT -0.1866 -0.1881 -0.1407 -0.2494 333s INTG 0.7375 0.7208 0.4673 0.7921 333s DMNR 0.9841 0.9516 0.6261 1.0688 333s DILG 0.8816 0.8711 0.5661 0.9167 333s CFMG 0.7747 0.7646 0.5105 0.7902 333s DECI 0.7433 0.7357 0.5039 0.7585 333s PREP 0.9400 0.9302 0.5996 0.9533 333s FAMI 0.9496 0.9439 0.6112 0.9561 333s ORAL 0.9712 0.9558 0.6271 0.9933 333s WRIT 0.9558 0.9483 0.6135 0.9725 333s PHYS 0.6271 0.6135 0.4816 0.6549 333s RTEN 0.9933 0.9725 0.6549 1.0540 333s -------------------------------------------------------- 333s USArrests 50 4 2.411726 333s Outliers: 3 333s [1] 2 33 39 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s Murder Assault UrbanPop Rape 333s 7.52 163.86 65.66 20.64 333s 333s Robust Estimate of Covariance: 333s Murder Assault UrbanPop Rape 333s Murder 19.05 295.96 8.32 23.40 333s Assault 295.96 6905.03 396.53 523.49 333s UrbanPop 8.32 396.53 202.98 62.81 333s Rape 23.40 523.49 62.81 79.10 333s -------------------------------------------------------- 333s longley 16 7 1.038316 333s Outliers: 5 333s [1] 1 2 3 4 5 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s GNP.deflator GNP Unemployed Armed.Forces Population 333s 107.5 440.4 339.4 293.0 120.9 333s Year Employed 333s 1957.0 67.2 333s 333s Robust Estimate of Covariance: 333s GNP.deflator GNP Unemployed Armed.Forces Population 333s GNP.deflator 100.4 953.8 1140.8 -501.8 74.3 333s GNP 953.8 9434.3 10084.3 -4573.8 731.3 333s Unemployed 1140.8 10084.3 19644.6 -6296.3 848.4 333s Armed.Forces -501.8 -4573.8 -6296.3 3192.3 -348.5 333s Population 74.3 731.3 848.4 -348.5 57.7 333s Year 46.3 450.7 537.0 -230.7 35.3 333s Employed 30.8 310.2 273.8 -159.4 23.3 333s Year Employed 333s GNP.deflator 46.3 30.8 333s GNP 450.7 310.2 333s Unemployed 537.0 273.8 333s Armed.Forces -230.7 -159.4 333s Population 35.3 23.3 333s Year 21.9 14.6 333s Employed 14.6 11.2 333s -------------------------------------------------------- 333s Loblolly 84 3 1.481317 333s Outliers: 0 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s height age Seed 333s 31.93 12.79 7.48 333s 333s Robust Estimate of Covariance: 333s height age Seed 333s height 440.644 165.652 6.958 333s age 165.652 63.500 0.681 333s Seed 6.958 0.681 16.564 333s -------------------------------------------------------- 333s quakes 1000 4 1.576855 333s Outliers: 218 333s [1] 7 12 15 17 22 27 32 37 40 41 45 48 53 63 64 333s [16] 73 78 87 91 92 94 99 108 110 117 118 119 120 121 122 333s [31] 126 133 136 141 143 145 148 152 154 155 157 159 160 163 170 333s [46] 192 205 222 226 230 239 243 250 251 252 254 258 263 267 268 333s [61] 271 283 292 300 301 305 311 312 318 320 321 325 328 330 334 333s [76] 352 357 360 365 381 382 384 389 400 402 408 413 416 417 419 333s [91] 429 437 441 443 453 456 467 474 477 490 492 496 504 507 508 333s [106] 509 517 524 527 528 531 532 534 536 538 539 541 542 543 544 333s [121] 545 546 547 552 553 560 571 581 583 587 593 594 596 597 605 333s [136] 612 613 618 620 625 629 638 642 647 649 653 655 656 672 675 333s [151] 681 686 699 701 702 712 714 716 721 725 726 735 744 754 756 333s [166] 759 765 766 769 779 781 782 785 787 797 804 813 825 827 837 333s [181] 840 844 852 853 857 860 865 866 869 870 872 873 883 884 887 333s [196] 888 890 891 893 908 909 912 915 916 921 927 930 962 963 969 333s [211] 974 980 982 986 987 988 997 1000 333s ------------- 333s 333s Call: 333s CovMMest(x = x) 333s -> Method: MM-estimates 333s 333s Robust Estimate of Location: 333s lat long depth mag 333s -21.74 182.37 356.37 4.56 333s 333s Robust Estimate of Covariance: 333s lat long depth mag 333s lat 2.97e+01 6.53e+00 3.46e+02 -4.66e-01 333s long 6.53e+00 6.92e+00 -5.05e+02 5.62e-02 333s depth 3.46e+02 -5.05e+02 7.39e+04 -2.51e+01 333s mag -4.66e-01 5.62e-02 -2.51e+01 2.32e-01 333s -------------------------------------------------------- 333s =================================================== 333s > ##dogen() 333s > ##cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' 333s > 334s autopkgtest [15:58:25]: test run-unit-test: -----------------------] 334s autopkgtest [15:58:25]: test run-unit-test: - - - - - - - - - - results - - - - - - - - - - 334s run-unit-test PASS 334s autopkgtest [15:58:25]: test pkg-r-autopkgtest: preparing testbed 335s Reading package lists... 335s Building dependency tree... 335s Reading state information... 335s Starting pkgProblemResolver with broken count: 0 335s Starting 2 pkgProblemResolver with broken count: 0 335s Done 336s The following NEW packages will be installed: 336s build-essential cpp cpp-14 cpp-14-aarch64-linux-gnu cpp-aarch64-linux-gnu 336s dctrl-tools g++ g++-14 g++-14-aarch64-linux-gnu g++-aarch64-linux-gnu gcc 336s gcc-14 gcc-14-aarch64-linux-gnu gcc-aarch64-linux-gnu gfortran gfortran-14 336s gfortran-14-aarch64-linux-gnu gfortran-aarch64-linux-gnu icu-devtools 336s libasan8 libblas-dev libbz2-dev libcc1-0 libdeflate-dev libgcc-14-dev 336s libgfortran-14-dev libhwasan0 libicu-dev libisl23 libitm1 libjpeg-dev 336s libjpeg-turbo8-dev libjpeg8-dev liblapack-dev liblsan0 liblzma-dev libmpc3 336s libncurses-dev libpcre2-16-0 libpcre2-32-0 libpcre2-dev libpcre2-posix3 336s libpkgconf3 libpng-dev libreadline-dev libstdc++-14-dev libtirpc-dev 336s libtsan2 libubsan1 pkg-r-autopkgtest pkgconf pkgconf-bin r-base-dev 336s zlib1g-dev 336s 0 upgraded, 54 newly installed, 0 to remove and 0 not upgraded. 336s Need to get 92.7 MB of archives. 336s After this operation, 334 MB of additional disk space will be used. 336s Get:1 http://ftpmaster.internal/ubuntu plucky/main arm64 libisl23 arm64 0.27-1 [676 kB] 337s Get:2 http://ftpmaster.internal/ubuntu plucky/main arm64 libmpc3 arm64 1.3.1-1build2 [56.8 kB] 337s Get:3 http://ftpmaster.internal/ubuntu plucky/main arm64 cpp-14-aarch64-linux-gnu arm64 14.2.0-17ubuntu3 [10.6 MB] 349s Get:4 http://ftpmaster.internal/ubuntu plucky/main arm64 cpp-14 arm64 14.2.0-17ubuntu3 [1028 B] 349s Get:5 http://ftpmaster.internal/ubuntu plucky/main arm64 cpp-aarch64-linux-gnu arm64 4:14.2.0-1ubuntu1 [5558 B] 349s Get:6 http://ftpmaster.internal/ubuntu plucky/main arm64 cpp arm64 4:14.2.0-1ubuntu1 [22.4 kB] 349s Get:7 http://ftpmaster.internal/ubuntu plucky/main arm64 libcc1-0 arm64 15-20250222-0ubuntu1 [44.2 kB] 349s Get:8 http://ftpmaster.internal/ubuntu plucky/main arm64 libitm1 arm64 15-20250222-0ubuntu1 [28.0 kB] 349s Get:9 http://ftpmaster.internal/ubuntu plucky/main arm64 libasan8 arm64 15-20250222-0ubuntu1 [2924 kB] 352s Get:10 http://ftpmaster.internal/ubuntu plucky/main arm64 liblsan0 arm64 15-20250222-0ubuntu1 [1319 kB] 354s Get:11 http://ftpmaster.internal/ubuntu plucky/main arm64 libtsan2 arm64 15-20250222-0ubuntu1 [2694 kB] 357s Get:12 http://ftpmaster.internal/ubuntu plucky/main arm64 libubsan1 arm64 15-20250222-0ubuntu1 [1178 kB] 358s Get:13 http://ftpmaster.internal/ubuntu plucky/main arm64 libhwasan0 arm64 15-20250222-0ubuntu1 [1642 kB] 359s Get:14 http://ftpmaster.internal/ubuntu plucky/main arm64 libgcc-14-dev arm64 14.2.0-17ubuntu3 [2593 kB] 362s Get:15 http://ftpmaster.internal/ubuntu plucky/main arm64 gcc-14-aarch64-linux-gnu arm64 14.2.0-17ubuntu3 [20.9 MB] 385s Get:16 http://ftpmaster.internal/ubuntu plucky/main arm64 gcc-14 arm64 14.2.0-17ubuntu3 [526 kB] 385s Get:17 http://ftpmaster.internal/ubuntu plucky/main arm64 gcc-aarch64-linux-gnu arm64 4:14.2.0-1ubuntu1 [1200 B] 385s Get:18 http://ftpmaster.internal/ubuntu plucky/main arm64 gcc arm64 4:14.2.0-1ubuntu1 [4998 B] 385s Get:19 http://ftpmaster.internal/ubuntu plucky/main arm64 libstdc++-14-dev arm64 14.2.0-17ubuntu3 [2499 kB] 388s Get:20 http://ftpmaster.internal/ubuntu plucky/main arm64 g++-14-aarch64-linux-gnu arm64 14.2.0-17ubuntu3 [12.1 MB] 400s Get:21 http://ftpmaster.internal/ubuntu plucky/main arm64 g++-14 arm64 14.2.0-17ubuntu3 [21.8 kB] 400s Get:22 http://ftpmaster.internal/ubuntu plucky/main arm64 g++-aarch64-linux-gnu arm64 4:14.2.0-1ubuntu1 [956 B] 400s Get:23 http://ftpmaster.internal/ubuntu plucky/main arm64 g++ arm64 4:14.2.0-1ubuntu1 [1080 B] 400s Get:24 http://ftpmaster.internal/ubuntu plucky/main arm64 build-essential arm64 12.10ubuntu1 [4932 B] 400s Get:25 http://ftpmaster.internal/ubuntu plucky/main arm64 dctrl-tools arm64 2.24-3build3 [103 kB] 400s Get:26 http://ftpmaster.internal/ubuntu plucky/main arm64 libgfortran-14-dev arm64 14.2.0-17ubuntu3 [498 kB] 401s Get:27 http://ftpmaster.internal/ubuntu plucky/main arm64 gfortran-14-aarch64-linux-gnu arm64 14.2.0-17ubuntu3 [11.4 MB] 413s Get:28 http://ftpmaster.internal/ubuntu plucky/main arm64 gfortran-14 arm64 14.2.0-17ubuntu3 [13.6 kB] 413s Get:29 http://ftpmaster.internal/ubuntu plucky/main arm64 gfortran-aarch64-linux-gnu arm64 4:14.2.0-1ubuntu1 [1022 B] 413s Get:30 http://ftpmaster.internal/ubuntu plucky/main arm64 gfortran arm64 4:14.2.0-1ubuntu1 [1166 B] 413s Get:31 http://ftpmaster.internal/ubuntu plucky/main arm64 icu-devtools arm64 76.1-1ubuntu2 [213 kB] 413s Get:32 http://ftpmaster.internal/ubuntu plucky/main arm64 libblas-dev arm64 3.12.1-2 [126 kB] 414s Get:33 http://ftpmaster.internal/ubuntu plucky/main arm64 libbz2-dev arm64 1.0.8-6 [36.1 kB] 414s Get:34 http://ftpmaster.internal/ubuntu plucky/main arm64 libdeflate-dev arm64 1.23-1 [53.7 kB] 414s Get:35 http://ftpmaster.internal/ubuntu plucky/main arm64 libicu-dev arm64 76.1-1ubuntu2 [12.2 MB] 427s Get:36 http://ftpmaster.internal/ubuntu plucky/main arm64 libjpeg-turbo8-dev arm64 2.1.5-3ubuntu2 [306 kB] 427s Get:37 http://ftpmaster.internal/ubuntu plucky/main arm64 libjpeg8-dev arm64 8c-2ubuntu11 [1484 B] 427s Get:38 http://ftpmaster.internal/ubuntu plucky/main arm64 libjpeg-dev arm64 8c-2ubuntu11 [1482 B] 427s Get:39 http://ftpmaster.internal/ubuntu plucky/main arm64 liblapack-dev arm64 3.12.1-2 [4439 kB] 433s Get:40 http://ftpmaster.internal/ubuntu plucky/main arm64 libncurses-dev arm64 6.5+20250216-2 [389 kB] 433s Get:41 http://ftpmaster.internal/ubuntu plucky/main arm64 libpcre2-16-0 arm64 10.45-1 [222 kB] 434s Get:42 http://ftpmaster.internal/ubuntu plucky/main arm64 libpcre2-32-0 arm64 10.45-1 [210 kB] 434s Get:43 http://ftpmaster.internal/ubuntu plucky/main arm64 libpcre2-posix3 arm64 10.45-1 [7084 B] 434s Get:44 http://ftpmaster.internal/ubuntu plucky/main arm64 libpcre2-dev arm64 10.45-1 [768 kB] 435s Get:45 http://ftpmaster.internal/ubuntu plucky/main arm64 libpkgconf3 arm64 1.8.1-4 [31.4 kB] 435s Get:46 http://ftpmaster.internal/ubuntu plucky/main arm64 zlib1g-dev arm64 1:1.3.dfsg+really1.3.1-1ubuntu1 [894 kB] 436s Get:47 http://ftpmaster.internal/ubuntu plucky/main arm64 libpng-dev arm64 1.6.47-1 [269 kB] 436s Get:48 http://ftpmaster.internal/ubuntu plucky/main arm64 libreadline-dev arm64 8.2-6 [179 kB] 436s Get:49 http://ftpmaster.internal/ubuntu plucky/main arm64 liblzma-dev arm64 5.6.4-1 [180 kB] 436s Get:50 http://ftpmaster.internal/ubuntu plucky/main arm64 pkgconf-bin arm64 1.8.1-4 [20.9 kB] 436s Get:51 http://ftpmaster.internal/ubuntu plucky/main arm64 pkgconf arm64 1.8.1-4 [16.7 kB] 436s Get:52 http://ftpmaster.internal/ubuntu plucky/main arm64 libtirpc-dev arm64 1.3.4+ds-1.3 [201 kB] 437s Get:53 http://ftpmaster.internal/ubuntu plucky/universe arm64 r-base-dev all 4.4.3-1 [4176 B] 437s Get:54 http://ftpmaster.internal/ubuntu plucky/universe arm64 pkg-r-autopkgtest all 20231212ubuntu1 [6448 B] 437s Fetched 92.7 MB in 1min 41s (922 kB/s) 437s Selecting previously unselected package libisl23:arm64. 437s (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 ... 84252 files and directories currently installed.) 437s Preparing to unpack .../00-libisl23_0.27-1_arm64.deb ... 437s Unpacking libisl23:arm64 (0.27-1) ... 437s Selecting previously unselected package libmpc3:arm64. 437s Preparing to unpack .../01-libmpc3_1.3.1-1build2_arm64.deb ... 437s Unpacking libmpc3:arm64 (1.3.1-1build2) ... 437s Selecting previously unselected package cpp-14-aarch64-linux-gnu. 437s Preparing to unpack .../02-cpp-14-aarch64-linux-gnu_14.2.0-17ubuntu3_arm64.deb ... 437s Unpacking cpp-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 438s Selecting previously unselected package cpp-14. 438s Preparing to unpack .../03-cpp-14_14.2.0-17ubuntu3_arm64.deb ... 438s Unpacking cpp-14 (14.2.0-17ubuntu3) ... 438s Selecting previously unselected package cpp-aarch64-linux-gnu. 438s Preparing to unpack .../04-cpp-aarch64-linux-gnu_4%3a14.2.0-1ubuntu1_arm64.deb ... 438s Unpacking cpp-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 438s Selecting previously unselected package cpp. 438s Preparing to unpack .../05-cpp_4%3a14.2.0-1ubuntu1_arm64.deb ... 438s Unpacking cpp (4:14.2.0-1ubuntu1) ... 438s Selecting previously unselected package libcc1-0:arm64. 438s Preparing to unpack .../06-libcc1-0_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking libcc1-0:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package libitm1:arm64. 438s Preparing to unpack .../07-libitm1_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking libitm1:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package libasan8:arm64. 438s Preparing to unpack .../08-libasan8_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking libasan8:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package liblsan0:arm64. 438s Preparing to unpack .../09-liblsan0_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking liblsan0:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package libtsan2:arm64. 438s Preparing to unpack .../10-libtsan2_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking libtsan2:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package libubsan1:arm64. 438s Preparing to unpack .../11-libubsan1_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking libubsan1:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package libhwasan0:arm64. 438s Preparing to unpack .../12-libhwasan0_15-20250222-0ubuntu1_arm64.deb ... 438s Unpacking libhwasan0:arm64 (15-20250222-0ubuntu1) ... 438s Selecting previously unselected package libgcc-14-dev:arm64. 438s Preparing to unpack .../13-libgcc-14-dev_14.2.0-17ubuntu3_arm64.deb ... 438s Unpacking libgcc-14-dev:arm64 (14.2.0-17ubuntu3) ... 438s Selecting previously unselected package gcc-14-aarch64-linux-gnu. 438s Preparing to unpack .../14-gcc-14-aarch64-linux-gnu_14.2.0-17ubuntu3_arm64.deb ... 438s Unpacking gcc-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 439s Selecting previously unselected package gcc-14. 439s Preparing to unpack .../15-gcc-14_14.2.0-17ubuntu3_arm64.deb ... 439s Unpacking gcc-14 (14.2.0-17ubuntu3) ... 439s Selecting previously unselected package gcc-aarch64-linux-gnu. 439s Preparing to unpack .../16-gcc-aarch64-linux-gnu_4%3a14.2.0-1ubuntu1_arm64.deb ... 439s Unpacking gcc-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 439s Selecting previously unselected package gcc. 439s Preparing to unpack .../17-gcc_4%3a14.2.0-1ubuntu1_arm64.deb ... 439s Unpacking gcc (4:14.2.0-1ubuntu1) ... 439s Selecting previously unselected package libstdc++-14-dev:arm64. 439s Preparing to unpack .../18-libstdc++-14-dev_14.2.0-17ubuntu3_arm64.deb ... 439s Unpacking libstdc++-14-dev:arm64 (14.2.0-17ubuntu3) ... 439s Selecting previously unselected package g++-14-aarch64-linux-gnu. 439s Preparing to unpack .../19-g++-14-aarch64-linux-gnu_14.2.0-17ubuntu3_arm64.deb ... 439s Unpacking g++-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 439s Selecting previously unselected package g++-14. 439s Preparing to unpack .../20-g++-14_14.2.0-17ubuntu3_arm64.deb ... 439s Unpacking g++-14 (14.2.0-17ubuntu3) ... 439s Selecting previously unselected package g++-aarch64-linux-gnu. 439s Preparing to unpack .../21-g++-aarch64-linux-gnu_4%3a14.2.0-1ubuntu1_arm64.deb ... 439s Unpacking g++-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 439s Selecting previously unselected package g++. 439s Preparing to unpack .../22-g++_4%3a14.2.0-1ubuntu1_arm64.deb ... 439s Unpacking g++ (4:14.2.0-1ubuntu1) ... 439s Selecting previously unselected package build-essential. 439s Preparing to unpack .../23-build-essential_12.10ubuntu1_arm64.deb ... 439s Unpacking build-essential (12.10ubuntu1) ... 439s Selecting previously unselected package dctrl-tools. 439s Preparing to unpack .../24-dctrl-tools_2.24-3build3_arm64.deb ... 439s Unpacking dctrl-tools (2.24-3build3) ... 439s Selecting previously unselected package libgfortran-14-dev:arm64. 439s Preparing to unpack .../25-libgfortran-14-dev_14.2.0-17ubuntu3_arm64.deb ... 439s Unpacking libgfortran-14-dev:arm64 (14.2.0-17ubuntu3) ... 440s Selecting previously unselected package gfortran-14-aarch64-linux-gnu. 440s Preparing to unpack .../26-gfortran-14-aarch64-linux-gnu_14.2.0-17ubuntu3_arm64.deb ... 440s Unpacking gfortran-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 440s Selecting previously unselected package gfortran-14. 440s Preparing to unpack .../27-gfortran-14_14.2.0-17ubuntu3_arm64.deb ... 440s Unpacking gfortran-14 (14.2.0-17ubuntu3) ... 440s Selecting previously unselected package gfortran-aarch64-linux-gnu. 440s Preparing to unpack .../28-gfortran-aarch64-linux-gnu_4%3a14.2.0-1ubuntu1_arm64.deb ... 440s Unpacking gfortran-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 440s Selecting previously unselected package gfortran. 440s Preparing to unpack .../29-gfortran_4%3a14.2.0-1ubuntu1_arm64.deb ... 440s Unpacking gfortran (4:14.2.0-1ubuntu1) ... 440s Selecting previously unselected package icu-devtools. 440s Preparing to unpack .../30-icu-devtools_76.1-1ubuntu2_arm64.deb ... 440s Unpacking icu-devtools (76.1-1ubuntu2) ... 440s Selecting previously unselected package libblas-dev:arm64. 440s Preparing to unpack .../31-libblas-dev_3.12.1-2_arm64.deb ... 440s Unpacking libblas-dev:arm64 (3.12.1-2) ... 440s Selecting previously unselected package libbz2-dev:arm64. 440s Preparing to unpack .../32-libbz2-dev_1.0.8-6_arm64.deb ... 440s Unpacking libbz2-dev:arm64 (1.0.8-6) ... 440s Selecting previously unselected package libdeflate-dev:arm64. 440s Preparing to unpack .../33-libdeflate-dev_1.23-1_arm64.deb ... 440s Unpacking libdeflate-dev:arm64 (1.23-1) ... 440s Selecting previously unselected package libicu-dev:arm64. 440s Preparing to unpack .../34-libicu-dev_76.1-1ubuntu2_arm64.deb ... 440s Unpacking libicu-dev:arm64 (76.1-1ubuntu2) ... 440s Selecting previously unselected package libjpeg-turbo8-dev:arm64. 440s Preparing to unpack .../35-libjpeg-turbo8-dev_2.1.5-3ubuntu2_arm64.deb ... 440s Unpacking libjpeg-turbo8-dev:arm64 (2.1.5-3ubuntu2) ... 440s Selecting previously unselected package libjpeg8-dev:arm64. 440s Preparing to unpack .../36-libjpeg8-dev_8c-2ubuntu11_arm64.deb ... 440s Unpacking libjpeg8-dev:arm64 (8c-2ubuntu11) ... 440s Selecting previously unselected package libjpeg-dev:arm64. 440s Preparing to unpack .../37-libjpeg-dev_8c-2ubuntu11_arm64.deb ... 440s Unpacking libjpeg-dev:arm64 (8c-2ubuntu11) ... 440s Selecting previously unselected package liblapack-dev:arm64. 441s Preparing to unpack .../38-liblapack-dev_3.12.1-2_arm64.deb ... 441s Unpacking liblapack-dev:arm64 (3.12.1-2) ... 441s Selecting previously unselected package libncurses-dev:arm64. 441s Preparing to unpack .../39-libncurses-dev_6.5+20250216-2_arm64.deb ... 441s Unpacking libncurses-dev:arm64 (6.5+20250216-2) ... 441s Selecting previously unselected package libpcre2-16-0:arm64. 441s Preparing to unpack .../40-libpcre2-16-0_10.45-1_arm64.deb ... 441s Unpacking libpcre2-16-0:arm64 (10.45-1) ... 441s Selecting previously unselected package libpcre2-32-0:arm64. 441s Preparing to unpack .../41-libpcre2-32-0_10.45-1_arm64.deb ... 441s Unpacking libpcre2-32-0:arm64 (10.45-1) ... 441s Selecting previously unselected package libpcre2-posix3:arm64. 441s Preparing to unpack .../42-libpcre2-posix3_10.45-1_arm64.deb ... 441s Unpacking libpcre2-posix3:arm64 (10.45-1) ... 441s Selecting previously unselected package libpcre2-dev:arm64. 441s Preparing to unpack .../43-libpcre2-dev_10.45-1_arm64.deb ... 441s Unpacking libpcre2-dev:arm64 (10.45-1) ... 441s Selecting previously unselected package libpkgconf3:arm64. 441s Preparing to unpack .../44-libpkgconf3_1.8.1-4_arm64.deb ... 441s Unpacking libpkgconf3:arm64 (1.8.1-4) ... 441s Selecting previously unselected package zlib1g-dev:arm64. 441s Preparing to unpack .../45-zlib1g-dev_1%3a1.3.dfsg+really1.3.1-1ubuntu1_arm64.deb ... 441s Unpacking zlib1g-dev:arm64 (1:1.3.dfsg+really1.3.1-1ubuntu1) ... 441s Selecting previously unselected package libpng-dev:arm64. 441s Preparing to unpack .../46-libpng-dev_1.6.47-1_arm64.deb ... 441s Unpacking libpng-dev:arm64 (1.6.47-1) ... 441s Selecting previously unselected package libreadline-dev:arm64. 441s Preparing to unpack .../47-libreadline-dev_8.2-6_arm64.deb ... 441s Unpacking libreadline-dev:arm64 (8.2-6) ... 441s Selecting previously unselected package liblzma-dev:arm64. 441s Preparing to unpack .../48-liblzma-dev_5.6.4-1_arm64.deb ... 441s Unpacking liblzma-dev:arm64 (5.6.4-1) ... 441s Selecting previously unselected package pkgconf-bin. 441s Preparing to unpack .../49-pkgconf-bin_1.8.1-4_arm64.deb ... 441s Unpacking pkgconf-bin (1.8.1-4) ... 441s Selecting previously unselected package pkgconf:arm64. 441s Preparing to unpack .../50-pkgconf_1.8.1-4_arm64.deb ... 441s Unpacking pkgconf:arm64 (1.8.1-4) ... 441s Selecting previously unselected package libtirpc-dev:arm64. 441s Preparing to unpack .../51-libtirpc-dev_1.3.4+ds-1.3_arm64.deb ... 441s Unpacking libtirpc-dev:arm64 (1.3.4+ds-1.3) ... 441s Selecting previously unselected package r-base-dev. 441s Preparing to unpack .../52-r-base-dev_4.4.3-1_all.deb ... 441s Unpacking r-base-dev (4.4.3-1) ... 441s Selecting previously unselected package pkg-r-autopkgtest. 441s Preparing to unpack .../53-pkg-r-autopkgtest_20231212ubuntu1_all.deb ... 441s Unpacking pkg-r-autopkgtest (20231212ubuntu1) ... 441s Setting up libjpeg-turbo8-dev:arm64 (2.1.5-3ubuntu2) ... 441s Setting up libncurses-dev:arm64 (6.5+20250216-2) ... 441s Setting up libreadline-dev:arm64 (8.2-6) ... 441s Setting up libpcre2-16-0:arm64 (10.45-1) ... 441s Setting up libpcre2-32-0:arm64 (10.45-1) ... 441s Setting up libtirpc-dev:arm64 (1.3.4+ds-1.3) ... 441s Setting up libpkgconf3:arm64 (1.8.1-4) ... 441s Setting up libmpc3:arm64 (1.3.1-1build2) ... 441s Setting up icu-devtools (76.1-1ubuntu2) ... 441s Setting up pkgconf-bin (1.8.1-4) ... 441s Setting up liblzma-dev:arm64 (5.6.4-1) ... 441s Setting up libubsan1:arm64 (15-20250222-0ubuntu1) ... 441s Setting up zlib1g-dev:arm64 (1:1.3.dfsg+really1.3.1-1ubuntu1) ... 441s Setting up libpcre2-posix3:arm64 (10.45-1) ... 441s Setting up libhwasan0:arm64 (15-20250222-0ubuntu1) ... 441s Setting up libasan8:arm64 (15-20250222-0ubuntu1) ... 441s Setting up libtsan2:arm64 (15-20250222-0ubuntu1) ... 441s Setting up libjpeg8-dev:arm64 (8c-2ubuntu11) ... 441s Setting up libisl23:arm64 (0.27-1) ... 441s Setting up libdeflate-dev:arm64 (1.23-1) ... 441s Setting up libicu-dev:arm64 (76.1-1ubuntu2) ... 441s Setting up libcc1-0:arm64 (15-20250222-0ubuntu1) ... 441s Setting up liblsan0:arm64 (15-20250222-0ubuntu1) ... 441s Setting up libblas-dev:arm64 (3.12.1-2) ... 441s update-alternatives: using /usr/lib/aarch64-linux-gnu/blas/libblas.so to provide /usr/lib/aarch64-linux-gnu/libblas.so (libblas.so-aarch64-linux-gnu) in auto mode 441s Setting up dctrl-tools (2.24-3build3) ... 441s Setting up libitm1:arm64 (15-20250222-0ubuntu1) ... 441s Setting up libbz2-dev:arm64 (1.0.8-6) ... 441s Setting up libpcre2-dev:arm64 (10.45-1) ... 441s Setting up libpng-dev:arm64 (1.6.47-1) ... 441s Setting up libjpeg-dev:arm64 (8c-2ubuntu11) ... 441s Setting up pkgconf:arm64 (1.8.1-4) ... 441s Setting up liblapack-dev:arm64 (3.12.1-2) ... 441s update-alternatives: using /usr/lib/aarch64-linux-gnu/lapack/liblapack.so to provide /usr/lib/aarch64-linux-gnu/liblapack.so (liblapack.so-aarch64-linux-gnu) in auto mode 441s Setting up cpp-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 441s Setting up libgcc-14-dev:arm64 (14.2.0-17ubuntu3) ... 441s Setting up libstdc++-14-dev:arm64 (14.2.0-17ubuntu3) ... 441s Setting up libgfortran-14-dev:arm64 (14.2.0-17ubuntu3) ... 441s Setting up cpp-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 441s Setting up cpp-14 (14.2.0-17ubuntu3) ... 441s Setting up cpp (4:14.2.0-1ubuntu1) ... 441s Setting up gcc-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 441s Setting up gcc-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 441s Setting up g++-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 441s Setting up gcc-14 (14.2.0-17ubuntu3) ... 441s Setting up gfortran-14-aarch64-linux-gnu (14.2.0-17ubuntu3) ... 441s Setting up g++-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 441s Setting up gfortran-aarch64-linux-gnu (4:14.2.0-1ubuntu1) ... 441s Setting up g++-14 (14.2.0-17ubuntu3) ... 441s Setting up gfortran-14 (14.2.0-17ubuntu3) ... 441s Setting up gcc (4:14.2.0-1ubuntu1) ... 441s Setting up g++ (4:14.2.0-1ubuntu1) ... 441s update-alternatives: using /usr/bin/g++ to provide /usr/bin/c++ (c++) in auto mode 441s Setting up build-essential (12.10ubuntu1) ... 441s Setting up gfortran (4:14.2.0-1ubuntu1) ... 441s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f95 (f95) in auto mode 441s 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 441s update-alternatives: using /usr/bin/gfortran to provide /usr/bin/f77 (f77) in auto mode 441s 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 441s Setting up r-base-dev (4.4.3-1) ... 441s Setting up pkg-r-autopkgtest (20231212ubuntu1) ... 441s Processing triggers for libc-bin (2.41-1ubuntu2) ... 441s Processing triggers for man-db (2.13.0-1) ... 442s Processing triggers for install-info (7.1.1-1) ... 445s autopkgtest [16:00:16]: test pkg-r-autopkgtest: /usr/share/dh-r/pkg-r-autopkgtest 445s autopkgtest [16:00:16]: test pkg-r-autopkgtest: [----------------------- 446s Test: Try to load the R library rrcov 446s 446s R version 4.4.3 (2025-02-28) -- "Trophy Case" 446s Copyright (C) 2025 The R Foundation for Statistical Computing 446s Platform: aarch64-unknown-linux-gnu 446s 446s R is free software and comes with ABSOLUTELY NO WARRANTY. 446s You are welcome to redistribute it under certain conditions. 446s Type 'license()' or 'licence()' for distribution details. 446s 446s R is a collaborative project with many contributors. 446s Type 'contributors()' for more information and 446s 'citation()' on how to cite R or R packages in publications. 446s 446s Type 'demo()' for some demos, 'help()' for on-line help, or 446s 'help.start()' for an HTML browser interface to help. 446s Type 'q()' to quit R. 446s 446s Loading required package: robustbase 446s > library('rrcov') 446s Scalable Robust Estimators with High Breakdown Point (version 1.7-6) 446s 446s > 446s > 446s Other tests are currently unsupported! 446s They will be progressively added. 446s autopkgtest [16:00:17]: test pkg-r-autopkgtest: -----------------------] 447s autopkgtest [16:00:18]: test pkg-r-autopkgtest: - - - - - - - - - - results - - - - - - - - - - 447s pkg-r-autopkgtest PASS 448s autopkgtest [16:00:19]: @@@@@@@@@@@@@@@@@@@@ summary 448s run-unit-test PASS 448s pkg-r-autopkgtest PASS 474s nova [W] Using flock in prodstack6-arm64 474s Creating nova instance adt-plucky-arm64-r-cran-rrcov-20250315-155250-juju-7f2275-prod-proposed-migration-environment-20-2a96b6f3-730b-4406-8396-bde0fee063c7 from image adt/ubuntu-plucky-arm64-server-20250315.img (UUID bd6e766c-b51f-4b53-86d6-23aa4d18f524)... 474s nova [W] Timed out waiting for 3bfb2e99-2661-42cc-8fa9-b33e3406b5ff to get deleted.