R version 3.2.2 RC (2015-08-08 r68914) -- "Fire Safety" Copyright (C) 2015 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ### Regression tests for which the printed output is the issue > ### May fail, e.g. by needing Recommended packages > > pdf("reg-tests-3.pdf", encoding = "ISOLatin1.enc") > > ## str() for character & factors with NA (levels), and for Surv objects: > ff <- factor(c(2:1, NA), exclude = NULL) > str(levels(ff)) chr [1:3] "1" "2" NA > str(ff) Factor w/ 3 levels "1","2",NA: 2 1 3 > str(ordered(ff, exclude=NULL)) Ord.factor w/ 3 levels "1"<"2" if(require(survival)) { + (sa <- Surv(aml$time, aml$status)) + str(sa) + detach("package:survival") + } Loading required package: survival Surv [1:23, 1:2] 9 13 13+ 18 23 28+ 31 34 45+ 48 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:2] "time" "status" - attr(*, "type")= chr "right" > ## were different, the last one failed in 1.6.2 (at least) > > > ## lm.influence where hat[1] == 1 > if(require(MASS)) { + fit <- lm(formula = 1000/MPG.city ~ Weight + Cylinders + Type + EngineSize + DriveTrain, data = Cars93) + print(lm.influence(fit)) + ## row 57 should have hat = 1 and resid=0. + summary(influence.measures(fit)) + } Loading required package: MASS $hat 1 2 3 4 5 6 7 0.09313909 0.07134091 0.19138434 0.08101081 0.24991662 0.10448752 0.12591828 8 9 10 11 12 13 14 0.39348171 0.10008864 0.23497010 0.27831516 0.11499791 0.06684324 0.16777903 15 16 17 18 19 20 21 0.10418769 0.19438856 0.22249600 0.18531791 0.42832529 0.13160780 0.11571055 22 23 24 25 26 27 28 0.13542772 0.05989558 0.09115955 0.07274599 0.16979948 0.10059554 0.36420370 29 30 31 32 33 34 35 0.05892084 0.12226683 0.14266192 0.06389391 0.07851639 0.16317503 0.10514036 36 37 38 39 40 41 42 0.16620182 0.07407892 0.21406715 0.35800879 0.11660151 0.12115515 0.05846839 43 44 45 46 47 48 49 0.07915006 0.05841339 0.07599254 0.14272015 0.10370606 0.22461698 0.07423925 50 51 52 53 54 55 56 0.16054084 0.10007740 0.22613089 0.05679789 0.05802486 0.07274599 0.16620182 57 58 59 60 61 62 63 1.00000000 0.16034032 0.14337335 0.11805892 0.13059078 0.05892084 0.10869261 64 65 66 67 68 69 70 0.07024346 0.07721617 0.25915706 0.08887161 0.06631974 0.10330515 0.19438856 71 72 73 74 75 76 77 0.12591828 0.21538400 0.05645115 0.08933216 0.16777903 0.07190036 0.12435356 78 79 80 81 82 83 84 0.06735745 0.06233173 0.40499233 0.20574068 0.20315406 0.35602282 0.08812076 85 86 87 88 89 90 91 0.13555308 0.09482733 0.24869622 0.06728598 0.57312772 0.08142621 0.15694445 92 93 0.15864447 0.57312772 $coefficients (Intercept) Weight Cylinders4 Cylinders5 Cylinders6 1 -0.8045874665 2.867170e-04 -3.820998e-03 -0.121186522 -0.085652499 2 -0.1624020957 7.405395e-05 -3.377846e-02 -0.058135639 0.032561909 3 0.0730227014 -1.000408e-04 2.635594e-02 -0.038987943 -0.136284069 4 0.0154995743 8.165607e-06 5.679643e-03 0.005807148 0.050432610 5 2.0624972203 -5.727657e-04 4.651265e-01 1.314202802 1.689003061 6 0.1889725900 -8.408128e-05 3.552355e-02 -0.042386516 -0.038483733 7 0.0288527501 -1.891425e-05 4.221544e-03 0.012091377 0.008077810 8 -0.9237858418 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72 -0.0698149636 -0.0077907772 -5.798951e-02 -0.0404309294 0.0291804508 73 -0.2461674520 -0.1792254003 -8.946335e-03 -0.0005023396 -0.1849210773 74 0.2142676665 0.1219475973 -7.874021e-02 -0.0918428593 -0.2301439149 75 -0.0028726461 -0.0993484475 -4.198827e-02 -0.0328695117 0.0082498333 76 0.0016544356 -0.0670470508 3.751826e-02 -0.0342084452 -0.0003680573 77 -0.0256147452 0.0156400843 4.086022e-02 0.0019511571 -0.0017546993 78 0.2825081053 0.0902228220 -3.983641e-01 -0.4346234712 -0.6043441371 79 0.0159835202 -0.0629476770 1.898330e-02 0.0085089202 -0.1077243741 80 -0.8150304378 -0.7413746212 5.446384e-03 0.0101047426 0.0136845038 81 0.0006315229 0.0011374665 5.007436e-05 0.0001020962 0.0003802979 82 -0.1389697069 -0.2829444232 2.798773e-01 0.2855962270 0.2832007760 83 -0.1199642681 -0.1139380335 -3.874836e-03 -0.0029066043 0.0054537971 84 -0.4681723050 -0.3692610821 -6.915058e-02 -0.0611471408 -0.0286774399 85 0.5868282269 0.6138842050 -4.618245e-02 -0.0093232438 -0.1687348360 86 -0.0335385306 -0.0121341311 3.955944e-02 0.0530269621 -0.0049516790 87 -0.1199323644 -0.0903358174 7.132870e-02 0.0441402079 0.0305846988 88 0.3030076280 0.4897405972 2.110757e-02 0.0520618321 0.1828859889 89 -0.1751107919 -0.2847075342 -1.578805e-02 -0.1009325245 0.0816716468 90 0.0055450581 -0.2720115784 -2.803760e-01 -0.3305994053 -0.2254900027 91 1.1629492149 0.1111057651 -3.399076e-01 -0.2295981894 -0.3032814817 92 0.0465882510 -0.3782722178 -1.398749e-01 -0.2168631833 -0.2674612094 93 -0.1751107919 -0.2847075342 -1.578805e-02 -0.1009325245 0.0816716468 TypeSporty TypeVan EngineSize DriveTrainFront DriveTrainRear 1 0.0588316568 -0.1244451013 -0.0384310410 0.0770280182 0.0308309135 2 -0.0008119071 -0.0929376586 -0.0169359479 0.0241959908 -0.0339336755 3 0.1122278579 0.2291762722 0.0640824206 -0.0374592418 -0.0063680596 4 -0.0076600464 -0.0180881612 -0.0224320460 0.0058289643 -0.0019669209 5 -0.3020816254 -0.2058571702 -0.3622619462 -0.0332177591 -0.4896133013 6 0.0174438088 0.1203009860 0.0149164277 -0.0095259785 -0.0123604155 7 0.0006330938 0.0018693765 0.0107797571 -0.0019362039 -0.0134263631 8 0.0888020681 -0.0981347691 0.5308561510 -0.0605205490 0.0622791836 9 0.0084422208 0.0641063586 -0.1438112041 0.0132369251 0.1231952988 10 -0.0453797315 0.1992829759 0.2094701125 -0.0711059699 -0.5860656544 11 0.0248173290 -0.1726730349 0.0307556826 0.0513955297 -0.2418132557 12 0.1493980797 -0.0158201826 -0.0533221135 0.0282944654 0.0228550025 13 0.1994088698 0.1348657122 -0.0134379474 -0.0212986235 0.0103552709 14 0.0454481137 -0.0247086389 -0.0005370853 0.0136434594 0.0759855468 15 0.0737344548 0.0705529631 -0.0350342542 0.0366749177 0.0023334058 16 -0.0098606591 -0.6464182823 -0.1570311735 -0.3099924798 -0.2432841941 17 -0.0121432263 0.2179211370 0.2426878264 -0.2268810389 -0.3003526507 18 -0.0012645247 0.0023497958 -0.0011648194 -0.0007816983 0.0031588791 19 -0.0601680504 -0.0271883474 -0.1686877849 0.0224897952 0.0378443211 20 -0.0039938145 0.0181201352 0.0439331331 -0.0137111692 0.0000888560 21 0.1294650212 0.2558709492 -0.1869157355 -0.0403252374 0.1039524785 22 -0.0110221099 0.0377992374 0.0616023114 -0.0236300001 -0.0027554274 23 0.0036748958 -0.0109040520 0.0047056340 -0.0027760031 -0.0075815300 24 0.1217218160 -0.2001221968 0.1453646196 0.1092627610 -0.0419265979 25 -0.0473791084 -0.0661671214 0.0174623161 0.0138168014 -0.0113597498 26 0.0343625250 -0.1263789642 0.0185185704 0.0899765460 0.0628671160 27 0.0521314845 0.1121866729 0.0452354145 0.0036741989 -0.0328558430 28 -0.1357733641 0.3598998278 0.0635427842 0.1408150632 0.2289697825 29 0.0032709010 -0.0102098982 0.0072097840 -0.0046929662 -0.0097593036 30 -0.0031503060 0.0096427335 0.0191501496 -0.0072782279 0.0183269440 31 0.0124966732 -0.0309659093 -0.0029315971 0.0080704811 -0.0032291174 32 0.0842166724 -0.1426179532 -0.0047149618 0.1266243473 0.0488025203 33 -0.2007361705 -0.0700394295 0.0650345678 -0.0076553978 -0.0323613407 34 0.1985477388 0.1317049456 -0.0379303148 0.0476640032 0.2557928936 35 -0.0241684660 -0.0063797023 0.0015970165 -0.0082742198 -0.0012845847 36 -0.2213615898 0.8328871713 -0.1490208173 -0.6045260895 -0.4274997860 37 0.0832688361 0.0526955171 0.0412992657 0.0057199017 0.0750539814 38 0.0381452581 -0.0364723376 0.1718996509 -0.0083216767 -0.1711269654 39 0.0126715370 -0.1319040895 0.0157246728 -0.1238552960 -0.1433995801 40 -0.6152814178 -0.4391609078 0.1925710081 -0.1848270831 -0.1257923223 41 -0.2136693396 -0.0102756220 -0.0187378955 -0.0760535877 0.0079050473 42 0.0115250441 -0.0626784733 0.1853115582 -0.1341403576 -0.1937539975 43 0.1223234350 0.2339351557 0.0477222207 -0.0703208756 -0.0131810299 44 0.0012062101 -0.0056275300 0.0138113326 -0.0099328213 -0.0147138005 45 0.1593529911 -0.3154119193 -0.0734215883 0.2186169305 0.0863564493 46 -0.0178306744 -0.0229394143 0.0050172709 -0.0032569785 -0.0040140299 47 0.0870826598 0.5764940782 -0.0661850124 -0.0160346100 0.0058237374 48 0.0351028466 0.0519540796 0.0730561863 -0.0117874040 -0.0875600533 49 -0.0118615751 -0.1170935139 -0.0664392527 0.0351943834 -0.0256500862 50 -0.0881348868 0.0099122018 -0.1427997802 -0.0154102445 0.2834351261 51 0.0341259919 -0.1980471170 0.1301931475 0.0329557292 -0.1390726822 52 0.0198930304 0.0233944834 0.2437424291 -0.0398986993 -0.2216521963 53 0.0026357380 -0.0124859669 0.0032736381 -0.0076038099 -0.0111780184 54 -0.0052948316 0.0043390590 -0.0062558306 -0.0100922249 -0.0036764450 55 0.3245941919 0.4533108376 -0.1196342985 -0.0946588832 0.0778256270 56 0.1438282891 -0.5411631576 0.0968253310 0.3927869929 0.2777652749 57 0.0000000000 0.0000000000 0.0000000000 0.0000000000 0.0000000000 58 -0.5770947483 -0.1184054962 -0.1740228604 -0.0285545091 0.7693037902 59 0.0412280879 -0.0075198458 0.0438882586 0.0121450418 -0.1252262379 60 0.3533541160 0.2760694598 -0.1036620689 0.1001975219 0.0703124944 61 0.0813382295 -0.0073739718 -0.0680618001 0.0531536577 -0.2332865147 62 0.0032709010 -0.0102098982 0.0072097840 -0.0046929662 -0.0097593036 63 0.0180571278 -0.1370722536 -0.0731027259 0.0500357603 -0.0039945662 64 -0.0280219408 0.0596353403 0.0485330769 -0.0523499513 -0.0380625252 65 0.1445564205 0.2773146947 -0.0044527086 -0.0739571632 0.0145722623 66 0.0147554787 0.0493296285 -0.0390174812 0.0705967740 0.0634638159 67 0.1109162662 -0.0476703926 -0.0101172134 0.0463473545 0.0838855959 68 0.1228600624 0.1417644316 -0.0078297996 -0.0320462976 0.0093214403 69 -0.0117468402 -0.0753433644 -0.0087753330 0.0054190284 0.0076171833 70 -0.0098606591 -0.6464182823 -0.1570311735 -0.3099924798 -0.2432841941 71 0.0006330938 0.0018693765 0.0107797571 -0.0019362039 -0.0134263631 72 -0.0675185509 0.0151499809 -0.0006299133 0.1444639061 0.1463776010 73 0.0024427576 -0.0249870886 0.0183259078 -0.0314868930 -0.0391504393 74 -0.1684498617 -0.0211344382 0.0051450670 -0.0108665177 -0.0006733384 75 0.0454481137 -0.0247086389 -0.0005370853 0.0136434594 0.0759855468 76 0.0087561990 0.0294347022 -0.0243866906 0.0001777584 0.0361467380 77 0.0011372964 -0.0003003688 0.0071919929 -0.0007018973 -0.0096901840 78 -0.4998716808 -0.3342094594 -0.0381922269 0.0632770184 0.0098092574 79 -0.0142230621 0.0179025413 -0.0175009156 -0.0191510694 -0.0015333501 80 -0.0247650968 -0.1469466395 0.0130804686 -0.2728976699 -0.2592164502 81 -0.0001483293 -0.0004719821 0.0001778419 -0.0011917350 -0.0011218042 82 0.3679140434 0.7422456267 0.0018768183 0.7508948030 0.7730015531 83 0.0043841507 0.0024787823 0.0002320259 0.0183666140 0.0145190633 84 0.0640691059 -0.1711824885 -0.0312673044 0.0349340607 -0.0192550330 85 -0.4756750417 0.0452676883 0.0298336291 -0.1959168864 -0.0136381614 86 0.0135669048 0.0363260497 -0.0012178976 0.0026468538 -0.0024799878 87 0.0376963721 0.1229412256 -0.0225447495 -0.0488081493 -0.0417831082 88 -0.0360445039 0.1399780291 0.1137882315 -0.0178410448 -0.0105132722 89 0.0609817970 0.1720567027 -0.0724262979 0.1875627215 0.1892070773 90 -0.2273870236 -0.3709143259 -0.1506696521 0.1197628934 0.0586023325 91 0.4164006880 -0.1004183249 0.0323442452 0.1231599812 -0.1040204060 92 -0.2996222296 -0.1090340253 -0.1173564279 0.0035664196 0.4305799574 93 0.0609817970 0.1720567027 -0.0724262979 0.1875627215 0.1892070773 $sigma 1 2 3 4 5 6 7 8 3.591432 3.594883 3.598562 3.600558 3.522694 3.598166 3.600799 3.582363 9 10 11 12 13 14 15 16 3.590729 3.528259 3.589350 3.596037 3.584177 3.600028 3.593696 3.580547 17 18 19 20 21 22 23 24 3.590040 3.601220 3.599441 3.598460 3.584656 3.597078 3.600671 3.553725 25 26 27 28 29 30 31 32 3.599688 3.599193 3.595214 3.598433 3.600406 3.596419 3.601044 3.518649 33 34 35 36 37 38 39 40 3.587115 3.592663 3.601139 3.501762 3.560763 3.588001 3.592963 3.514670 41 42 43 44 45 46 47 48 3.595497 3.334292 3.589110 3.599718 3.471882 3.601133 3.521852 3.594122 49 50 51 52 53 54 55 56 3.588734 3.589710 3.583459 3.581755 3.599157 3.599895 3.528075 3.559578 57 58 59 60 61 62 63 64 3.601231 3.523963 3.598459 3.571771 3.582319 3.600406 3.596626 3.592960 65 66 67 68 69 70 71 72 3.583325 3.600828 3.569987 3.592482 3.599979 3.580547 3.600799 3.599768 73 74 75 76 77 78 79 80 3.579302 3.593475 3.600028 3.598895 3.600991 3.495965 3.597794 3.594229 81 82 83 84 85 86 87 88 3.601231 3.563077 3.601124 3.585986 3.575773 3.600658 3.599786 3.563954 89 90 91 92 93 3.594056 3.566216 3.484273 3.578056 3.594056 $wt.res 1 2 3 4 5 2.218439708 1.807326787 -1.093991760 0.585986462 -5.684589270 6 7 8 9 10 1.233522386 0.457950438 2.515990067 -2.287803728 5.535960625 11 12 13 14 15 2.178905651 -1.596117514 -2.967297860 0.745175851 1.933882824 16 17 18 19 20 -3.035594591 2.195009903 0.072527577 -0.753360641 -1.155059621 21 22 23 24 25 -2.847828869 -1.410703414 -0.540252474 4.877197398 0.890715640 26 27 28 29 30 -0.968642103 1.731771561 -0.993218102 -0.656454198 -1.529950693 31 32 33 34 35 -0.298424469 6.510112647 2.683267748 1.992954455 -0.214079423 36 37 38 39 40 6.735053083 -4.545792145 -2.399183444 -1.714795692 -6.472930954 41 42 43 44 45 -1.671169308 -11.585332803 -2.485888781 -0.888857647 8.068071025 46 47 48 49 50 -0.216046323 6.246829383 -1.747619081 2.530823032 2.314106628 51 52 53 54 55 2.974532942 -2.887230686 -1.041442666 -0.835536301 -6.102291353 56 57 58 59 60 -4.376058028 0.000000000 5.966190371 -1.147443467 3.788198306 61 62 63 64 65 -3.015807719 -0.656454198 1.508247858 -2.064017840 -3.023462268 66 67 68 69 70 0.407243898 -3.964783523 -2.127186213 -0.789242889 -3.035594591 71 72 73 74 75 0.457950438 -0.797900840 -3.382338495 1.978150290 0.745175851 76 77 78 79 80 -1.096455031 0.341748714 7.324729228 -1.336726492 1.519313995 81 82 83 84 85 0.005901292 -4.095330927 0.195481697 -2.773676266 -3.487375439 86 87 88 89 90 0.536312040 0.775871729 4.379791779 1.302710701 4.213228761 91 92 93 7.334576566 3.283113507 -1.302710701 Potentially influential observations of lm(formula = 1000/MPG.city ~ Weight + Cylinders + Type + EngineSize + DriveTrain, data = Cars93) : dfb.1_ dfb.Wght dfb.Cyl4 dfb.Cyl5 dfb.Cyl6 dfb.Cyl8 dfb.Cyln dfb.TypL 8 -0.16 0.00 -0.10 -0.07 -0.24 -0.44 0.01 0.12 19 -0.03 0.09 -0.01 -0.03 0.00 -0.01 -0.03 0.08 28 0.11 -0.15 0.04 0.02 0.02 0.02 0.04 0.07 39 -0.19 0.05 0.34 0.21 0.25 0.18 0.18 -0.01 42 0.12 -0.04 -0.30 -0.17 -0.28 -0.26 -0.11 -0.03 57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 66 -0.03 0.04 -0.02 -0.03 -0.01 0.00 -0.02 -0.01 80 0.18 0.00 -0.31 -0.17 -0.24 -0.19 -0.15 0.00 83 0.01 0.01 -0.04 -0.03 -0.04 -0.03 -0.02 0.00 87 -0.03 0.04 -0.01 -0.04 -0.04 -0.03 -0.02 0.04 89 -0.11 0.11 -0.05 0.28 -0.06 -0.04 -0.06 -0.01 93 -0.11 0.11 -0.05 -0.45 -0.06 -0.04 -0.06 -0.01 dfb.TypM dfb.TypSm dfb.TypSp dfb.TypV dfb.EngS dfb.DrTF dfb.DrTR dffit 8 0.00 0.21 0.06 -0.04 0.47 -0.04 0.03 0.73 19 0.01 0.00 -0.04 -0.01 -0.15 0.01 0.02 -0.24 28 0.08 -0.08 -0.09 0.16 0.06 0.09 0.12 -0.26 39 -0.01 0.04 0.01 -0.06 0.01 -0.08 -0.08 -0.44 42 0.01 -0.42 0.01 -0.03 0.18 -0.10 -0.11 -0.89 57 0.00 0.00 0.00 0.00 0.00 0.00 0.00 NaN 66 -0.02 0.01 0.01 0.02 -0.03 0.05 0.03 0.08 80 0.01 0.01 -0.02 -0.07 0.01 -0.18 -0.14 0.45 83 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.05 87 0.03 0.02 0.03 0.06 -0.02 -0.03 -0.02 0.14 89 -0.07 0.05 0.04 0.08 -0.06 0.12 0.10 0.64 93 -0.07 0.05 0.04 0.08 -0.06 0.12 0.10 -0.64 cov.r cook.d hat 8 1.71_* 0.04 0.39 19 2.09_* 0.00 0.43 28 1.86_* 0.00 0.36 39 1.76_* 0.01 0.36 42 0.13_* 0.05 0.06 57 NaN NaN 1.00_* 66 1.63_* 0.00 0.26 80 1.92_* 0.01 0.40 83 1.88_* 0.00 0.36 87 1.60_* 0.00 0.25 89 2.68_* 0.03 0.57_* 93 2.68_* 0.03 0.57_* > ## only last two cols in row 57 should be influential > > > ## PR#6640 Zero weights in plot.lm > if(require(MASS)) { + fm1 <- lm(time~dist, data=hills, weights=c(0,0,rep(1,33))) + plot(fm1) + } > ## gave warnings in 1.8.1 > > > ## PR#7829 model.tables & replications > if(require(MASS)) { + oats.aov <- aov(Y ~ B + V + N + V:N, data=oats[-1,]) + model.tables(oats.aov, "means", cterms=c("N", "V:N")) + } Tables of means Grand mean 103.8732 N 0.0cwt 0.2cwt 0.4cwt 0.6cwt 78.74 98.51 113.8 123 rep 17.00 18.00 18.0 18 V:N N V 0.0cwt 0.2cwt 0.4cwt 0.6cwt Golden.rain 79.53 98.03 114.20 124.37 rep 6.00 6.00 6.00 6.00 Marvellous 86.20 108.03 116.70 126.37 rep 6.00 6.00 6.00 6.00 Victory 69.77 89.20 110.37 118.03 rep 5.00 6.00 6.00 6.00 > ## wrong printed output in 2.1.0 > > > ## drop1 on weighted lm() fits > if(require(MASS)) { + hills.lm <- lm(time ~ 0 + dist + climb, data=hills, weights=1/dist^2) + print(drop1(hills.lm)) + print(stats:::drop1.default(hills.lm)) + hills.lm2 <- lm(time/dist ~ 1 + I(climb/dist), data=hills) + drop1(hills.lm2) + } Single term deletions Model: time ~ 0 + dist + climb Df Sum of Sq RSS AIC 442.22 92.776 dist 1 330.92 773.14 110.329 climb 1 9.73 451.95 91.538 Single term deletions Model: time ~ 0 + dist + climb Df AIC 92.776 dist 1 110.329 climb 1 91.538 Single term deletions Model: time/dist ~ 1 + I(climb/dist) Df Sum of Sq RSS AIC 442.22 92.776 I(climb/dist) 1 9.7331 451.95 91.538 > ## quoted unweighted RSS etc in 2.2.1 > > > ## tests of ISO C99 compliance (Windows fails without a workaround) > sprintf("%g", 123456789) [1] "1.23457e+08" > sprintf("%8g", 123456789) [1] "1.23457e+08" > sprintf("%9.7g", 123456789) [1] "1.234568e+08" > sprintf("%10.9g", 123456789) [1] " 123456789" > sprintf("%g", 12345.6789) [1] "12345.7" > sprintf("%10.9g", 12345.6789) [1] "12345.6789" > sprintf("%10.7g", 12345.6789) [1] " 12345.68" > sprintf("%.7g", 12345.6789) [1] "12345.68" > sprintf("%.5g", 12345.6789) [1] "12346" > sprintf("%.4g", 12345.6789) [1] "1.235e+04" > sprintf("%9.4g", 12345.6789) [1] "1.235e+04" > sprintf("%10.4g", 12345.6789) [1] " 1.235e+04" > ## Windows used e+008 etc prior to 2.3.0 > > > ## weighted glm() fits > if(require(MASS)) { + hills.glm <- glm(time ~ 0 + dist + climb, data=hills, weights=1/dist^2) + print(AIC(hills.glm)) + print(extractAIC(hills.glm)) + print(drop1(hills.glm)) + stats:::drop1.default(hills.glm) + } [1] 322.2318 [1] 2.0000 322.2318 Single term deletions Model: time ~ 0 + dist + climb Df Deviance AIC 442.22 322.23 dist 1 773.14 339.78 climb 1 451.95 320.99 Single term deletions Model: time ~ 0 + dist + climb Df AIC 322.23 dist 1 339.78 climb 1 320.99 > ## wrong AIC() and drop1 prior to 2.3.0. > > ## calculating no of signif digits > print(1.001, digits=16) [1] 1.001 > ## 2.4.1 gave 1.001000000000000 > ## 2.5.0 errs on the side of caution. > > > ## as.matrix.data.frame with coercion > if(require("survival")) { + soa <- Surv(1:5, c(0, 0, 1, 0, 1)) + df.soa <- data.frame(soa) + print(as.matrix(df.soa)) # numeric result + df.soac <- data.frame(soa, letters[1:5]) + print(as.matrix(df.soac)) # character result + detach("package:survival") + } Loading required package: survival soa.time soa.status [1,] 1 0 [2,] 2 0 [3,] 3 1 [4,] 4 0 [5,] 5 1 soa letters.1.5. [1,] "1+" "a" [2,] "2+" "b" [3,] "3 " "c" [4,] "4+" "d" [5,] "5 " "e" > ## failed in 2.8.1 > > ## wish of PR#13505 > npk.aov <- aov(yield ~ block + N * P + K, npk) > foo <- proj(npk.aov) > cbind(npk, foo) block N P K yield (Intercept) block N P K 1 1 0 1 1 49.5 54.875 -0.850 -2.808333 -0.5916667 -1.991667 2 1 1 1 0 62.8 54.875 -0.850 2.808333 -0.5916667 1.991667 3 1 0 0 0 46.8 54.875 -0.850 -2.808333 0.5916667 1.991667 4 1 1 0 1 57.0 54.875 -0.850 2.808333 0.5916667 -1.991667 5 2 1 0 0 59.8 54.875 2.575 2.808333 0.5916667 1.991667 6 2 1 1 1 58.5 54.875 2.575 2.808333 -0.5916667 -1.991667 7 2 0 0 1 55.5 54.875 2.575 -2.808333 0.5916667 -1.991667 8 2 0 1 0 56.0 54.875 2.575 -2.808333 -0.5916667 1.991667 9 3 0 1 0 62.8 54.875 5.900 -2.808333 -0.5916667 1.991667 10 3 1 1 1 55.8 54.875 5.900 2.808333 -0.5916667 -1.991667 11 3 1 0 0 69.5 54.875 5.900 2.808333 0.5916667 1.991667 12 3 0 0 1 55.0 54.875 5.900 -2.808333 0.5916667 -1.991667 13 4 1 0 0 62.0 54.875 -4.750 2.808333 0.5916667 1.991667 14 4 1 1 1 48.8 54.875 -4.750 2.808333 -0.5916667 -1.991667 15 4 0 0 1 45.5 54.875 -4.750 -2.808333 0.5916667 -1.991667 16 4 0 1 0 44.2 54.875 -4.750 -2.808333 -0.5916667 1.991667 17 5 1 1 0 52.0 54.875 -4.350 2.808333 -0.5916667 1.991667 18 5 0 0 0 51.5 54.875 -4.350 -2.808333 0.5916667 1.991667 19 5 1 0 1 49.8 54.875 -4.350 2.808333 0.5916667 -1.991667 20 5 0 1 1 48.8 54.875 -4.350 -2.808333 -0.5916667 -1.991667 21 6 1 0 1 57.2 54.875 1.475 2.808333 0.5916667 -1.991667 22 6 1 1 0 59.0 54.875 1.475 2.808333 -0.5916667 1.991667 23 6 0 1 1 53.2 54.875 1.475 -2.808333 -0.5916667 -1.991667 24 6 0 0 0 56.0 54.875 1.475 -2.808333 0.5916667 1.991667 N:P Residuals 1 0.9416667 -0.0750000 2 -0.9416667 5.5083333 3 -0.9416667 -6.0583333 4 0.9416667 0.6250000 5 0.9416667 -3.9833333 6 -0.9416667 1.7666667 7 -0.9416667 3.2000000 8 0.9416667 -0.9833333 9 0.9416667 2.4916667 10 -0.9416667 -4.2583333 11 0.9416667 2.3916667 12 -0.9416667 -0.6250000 13 0.9416667 5.5416667 14 -0.9416667 -0.6083333 15 -0.9416667 0.5250000 16 0.9416667 -5.4583333 17 -0.9416667 -1.7916667 18 -0.9416667 2.1416667 19 0.9416667 -3.0750000 20 0.9416667 2.7250000 21 0.9416667 -1.5000000 22 -0.9416667 -0.6166667 23 0.9416667 1.3000000 24 -0.9416667 0.8166667 > ## failed in R < 2.10.0 > > > if(suppressMessages(require("Matrix"))) { + print(cS. <- contr.SAS(5, sparse = TRUE)) + stopifnot(all(contr.SAS(5) == cS.), + all(contr.helmert(5, sparse = TRUE) == contr.helmert(5))) + + x1 <- x2 <- c('a','b','a','b','c') + x3 <- x2; x3[4:5] <- x2[5:4] + print(xtabs(~ x1 + x2, sparse= TRUE, exclude = 'c')) + print(xtabs(~ x1 + x3, sparse= TRUE, exclude = 'c')) + detach("package:Matrix") + ## failed in R <= 2.13.1 + } 5 x 4 sparse Matrix of class "dgCMatrix" 1 2 3 4 1 1 . . . 2 . 1 . . 3 . . 1 . 4 . . . 1 5 . . . . 2 x 2 sparse Matrix of class "dgCMatrix" a b a 2 . b . 2 2 x 2 sparse Matrix of class "dgCMatrix" a b a 2 . b . 1 > > ## regression tests for dimnames (broken on 2009-07-31) > contr.sum(4) [,1] [,2] [,3] 1 1 0 0 2 0 1 0 3 0 0 1 4 -1 -1 -1 > contr.helmert(4) [,1] [,2] [,3] 1 -1 -1 -1 2 1 -1 -1 3 0 2 -1 4 0 0 3 > contr.sum(2) # needed drop=FALSE at one point. [,1] 1 1 2 -1 > > ## xtabs did not exclude levels from factors > x1 <- c('a','b','a','b','c', NA) > x2 <- factor(x1, exclude=NULL) > print(xtabs(~ x1 + x2, na.action = na.pass)) x2 x1 a b c a 2 0 0 0 b 0 2 0 0 c 0 0 1 0 > print(xtabs(~ x1 + x2, exclude = 'c', na.action = na.pass)) x2 x1 a b a 2 0 0 b 0 2 0 0 0 1 > > > ## median should work by default for a suitable S4 class. > ## adapted from adaptsmoFMRI > if(suppressMessages(require("Matrix"))) { + x <- matrix(c(1,2,3,4)) + print(median(x)) + print(median(as(x, "dgeMatrix"))) + detach("package:Matrix") + } [1] 2.5 [1] 2.5 > > ## Various arguments were not duplicated: PR#15352 to 15354 > x <- 5 > y <- 2 > f <- function (y) x > numericDeriv(f(y),"y") [1] 5 attr(,"gradient") [,1] [1,] 0 > x [1] 5 > > a<-list(1,2) > b<-rep.int(a,c(2,2)) > b[[1]][1]<-9 > a[[1]] [1] 1 > > a <- numeric(1) > x <- mget("a",as.environment(1)) > x $a [1] 0 > a[1] <- 9 > x $a [1] 0 > > > ## needs MASS installed > ## PR#2586 labelling in alias() > if(require("MASS")) { + Y <- c(0,1,2) + X1 <- c(0,1,0) + X2 <- c(0,1,0) + X3 <- c(0,0,1) + print(res <- alias(lm(Y ~ X1 + X2 + X3))) + stopifnot(identical(rownames(res[[2]]), "X2")) + } Model : Y ~ X1 + X2 + X3 Complete : (Intercept) X1 X3 X2 0 1 0 > ## the error was in lm.(w)fit > > if(require("Matrix")) { + m1 <- m2 <- m <- matrix(1:12, 3,4) + dimnames(m2) <- list(LETTERS[1:3], + letters[1:4]) + dimnames(m1) <- list(NULL,letters[1:4]) + M <- Matrix(m) + M1 <- Matrix(m1) + M2 <- Matrix(m2) + ## Now, with a new ideal cbind(), rbind(): + print(cbind(M, M1)) + stopifnot(identical(cbind (M, M1), + cbind2(M, M1))) + rm(M,M1,M2) + detach("package:Matrix", unload=TRUE) + }##{Matrix} Loading required package: Matrix 3 x 8 Matrix of class "dgeMatrix" a b c d [1,] 1 4 7 10 1 4 7 10 [2,] 2 5 8 11 2 5 8 11 [3,] 3 6 9 12 3 6 9 12 >