
R Under development (unstable) (2022-03-19 r81942) -- "Unsuffered Consequences"
Copyright (C) 2022 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> ### Regression tests for which the printed output is the issue
> ### May fail.
> ### Skipped on a Unix-alike without 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"<NA: 2 1 3
> if(require(survival)) {
+     (sa <- Surv(aml$time, aml$status))
+     str(sa)
+     detach("package:survival", unload = TRUE)
+ }
Loading required package: survival
 'Surv' num [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  3.777463e-06 -2.504289e-01 -0.282474268 -0.778087752
9   0.0027522480  9.783189e-05  2.129655e-02 -0.007988299  0.014432116
10  0.9903462340 -5.169747e-04  1.630209e-01  0.294911151  0.129970424
11 -0.5182963206  1.916075e-04 -1.215727e-01 -0.226050823 -0.161523754
12 -0.6387428727  2.494952e-04 -1.008869e-01 -0.179703222 -0.143884548
13 -0.3568385025  7.917221e-05 -2.534541e-02 -0.028384831 -0.029395607
14 -0.0077820700 -4.699458e-06 -1.266873e-03  0.041173344  0.053631137
15 -0.2615585508  1.297214e-04 -5.490059e-02 -0.275540505 -0.222909574
16 -0.0195241852  2.061693e-04  6.572061e-02  0.294616782  0.074266087
17  0.0965040227 -1.534149e-04  1.527511e-02 -0.028420127 -0.112348604
18  0.0048862795 -1.111273e-06  1.332650e-03  0.001477077  0.001007189
19 -0.1837125526  1.943923e-04 -1.515756e-02 -0.122275191  0.014588114
20  0.0080448668 -3.212441e-05 -2.747115e-03  0.002147238 -0.048260808
21  0.3062320767 -7.003532e-05  1.382568e-01  0.212411378  0.364192354
22  0.0834715351 -7.391740e-05  1.122255e-02  0.030230238 -0.037744856
23 -0.0076656309  1.396550e-05 -4.020753e-02 -0.044659212 -0.050554976
24 -1.5070278126  3.723597e-04  1.647077e-02 -0.161486560 -0.278755415
25 -0.0239986724  1.966635e-05 -2.200355e-02 -0.039815491 -0.051838322
26 -0.2726502980  7.668984e-05 -7.104259e-02 -0.070135093 -0.138776999
27 -0.0237099021 -1.906594e-05 -1.449196e-02 -0.165127041 -0.182407191
28  0.5989984194 -3.350192e-04  8.982947e-02  0.072939768  0.051948750
29  0.0049978357  1.024575e-05 -4.596944e-02 -0.048846124 -0.057195412
30 -0.0420784682  4.604021e-06 -9.761996e-03 -0.018154729 -0.051613812
31 -0.1226019720  5.936642e-05 -4.859332e-02 -0.071029678 -0.070396498
32 -1.2891791826  3.470986e-04  1.870320e-01  0.029725520  0.047497829
33  0.5677503929 -2.038892e-04  6.741942e-02  0.117380014  0.074644216
34 -0.0732129453  3.754795e-05 -1.228743e-02 -0.110181891 -0.113812479
35  0.0194920033 -7.575835e-06  5.134183e-03  0.017360915  0.015396275
36  1.6888356455 -4.377455e-04  4.524306e-01  0.411034679  0.902823919
37 -0.4890085420  1.725038e-04 -1.049924e-01 -0.186804182 -0.420107392
38 -0.0082739419 -1.137356e-04 -2.907676e-02  0.013409601 -0.082572165
39 -1.0909018362  1.064695e-04  8.658188e-01  0.866130219  0.806474055
40 -0.6733060595  1.783564e-04 -1.305062e-01  0.065871030 -0.125533828
41  0.3690425668 -1.245558e-04  9.024714e-02  0.216243273  0.228933588
42  0.6432124241 -7.951017e-05 -7.008503e-01 -0.653531527 -0.847103140
43  0.2616446485 -1.975002e-04  9.041771e-02  0.187584888  0.139640344
44  0.0454749520 -4.284837e-06 -5.453546e-02 -0.051589176 -0.066116786
45 -2.2631327197  7.379870e-04  1.070445e-01 -0.204329361 -0.128262892
46 -0.0612084384  2.238252e-05 -1.234131e-02 -0.011911444 -0.017360476
47  0.8600843682 -2.923003e-04  1.777695e-01 -0.255446144 -0.114886957
48  0.1775813222 -1.196361e-04  2.413830e-02  0.077055159 -0.014203507
49 -0.1424954874  9.842541e-05 -2.377345e-02 -0.051500781  0.113004619
50  0.2613474511 -1.665525e-05  8.367041e-02  0.117064766  0.300913215
51 -0.5457545031  1.270106e-04 -1.351808e-01 -0.192894338 -0.176826828
52  0.3111798506 -2.896481e-04  2.816237e-02  0.136852358 -0.007250608
53  0.0297317368  1.296449e-05 -6.708602e-02 -0.069529496 -0.075630367
54  0.0989545407 -1.357243e-05 -3.683573e-02 -0.028339562 -0.025145841
55  0.1644148644 -1.347342e-04  1.507463e-01  0.272775865  0.355144259
56 -1.0973102505  2.844224e-04 -2.939639e-01 -0.267067176 -0.586604116
57  0.0000000000  0.000000e+00  0.000000e+00  0.000000000  0.000000000
58  1.1181936467 -1.814925e-04  1.820092e-01  0.230407448  0.281788680
59 -0.1289781075  2.595372e-05 -3.776833e-02 -0.058580136 -0.126069473
60  0.4827651281 -1.458463e-04  9.396412e-02 -0.005593267  0.099263965
61 -0.1929691299  1.326052e-04 -4.434766e-02 -0.106219857 -0.124334762
62  0.0049978357  1.024575e-05 -4.596944e-02 -0.048846124 -0.057195412
63 -0.3935296223  2.040809e-04 -7.524962e-02 -0.146355742 -0.019064641
64  0.4603333737 -1.617410e-04 -5.546499e-02  0.010390154 -0.035282319
65  0.3126987326 -1.960180e-04  1.168042e-01  0.223520593  0.218762880
66 -0.1920212451  8.908026e-05 -4.283525e-02 -0.117606580 -0.033622259
67 -0.8795466532  3.620658e-04 -1.815067e-01 -0.332837224 -0.502419092
68 -0.0331296115 -3.321728e-05  2.875172e-02  0.062289524  0.061442068
69 -0.1135318718  5.034205e-05 -2.126782e-02  0.029790905  0.026695041
70 -0.0195241852  2.061693e-04  6.572061e-02  0.294616782  0.074266087
71  0.0288527501 -1.891425e-05  4.221544e-03  0.012091377  0.008077810
72 -0.2291016407  5.487125e-05 -6.613686e-02 -0.098852165 -0.051549477
73  0.1700259290  7.628143e-06 -2.032259e-01 -0.198173180 -0.224109893
74  0.5968477257 -1.921799e-04  9.263890e-02  0.150229991  0.156628646
75 -0.0077820700 -4.699458e-06 -1.266873e-03  0.041173344  0.053631137
76 -0.0194397858  2.531489e-05 -1.458165e-07 -0.009800589 -0.035894083
77  0.0134907894 -1.033768e-05  1.558189e-03  0.006006815  0.003684406
78  0.9077181399 -1.582828e-04  7.745093e-02  0.077205972  0.141055974
79  0.2164417917 -3.971900e-05 -4.607492e-02 -0.024531503 -0.012649902
80  0.9960541035  4.247826e-06 -7.839184e-01 -0.702085425 -0.798219133
81  0.0007910652 -3.072859e-07  8.023023e-04  0.001256898  0.000758261
82 -0.6435072296 -7.492359e-05 -1.735778e-01 -0.355994454 -0.119101997
83  0.0611662403  1.159480e-05 -1.119647e-01 -0.121657673 -0.117008905
84 -0.5769304812  3.253972e-04 -3.191547e-01 -0.437477397 -0.423951235
85  1.0837148496 -4.307667e-04  2.461529e-01  0.572974426  0.542167217
86  0.0083937926 -1.990781e-06  8.287336e-04 -0.045561514 -0.037803655
87 -0.1545228365  9.611522e-05 -2.127841e-02 -0.171230178 -0.137086764
88  0.2463906019 -2.892912e-04  3.477162e-01  0.438821017  0.357674795
89 -0.6025253492  2.525728e-04 -1.370524e-01  1.172830299 -0.211660377
90 -0.2820706112  3.173532e-04 -1.101740e-01 -0.261059565 -0.118916876
91  1.4258537942 -6.533290e-04  2.630332e-01  0.890368685  1.145291551
92  0.4060610493 -2.135821e-06  5.871214e-02  0.048538680  0.094105407
93 -0.6025253492  2.525728e-04 -1.370524e-01 -1.878927238 -0.211660377
      Cylinders8 Cylindersrotary     TypeLarge   TypeMidsize     TypeSmall
1  -0.1124657876   -0.0651605878 -5.802887e-02 -0.0555471473  0.2528314269
2   0.0053388461    0.0047782307 -6.569650e-02  0.0350485943  0.0252041164
3  -0.1679122402    0.0274285066  2.008834e-01  0.1844628894  0.0674315814
4   0.0638568606   -0.0003503783 -9.564934e-03  0.0149006180 -0.0087430921
5   2.5938169745    0.8582948229 -2.399208e-01 -0.5792472372 -0.5285037464
6  -0.0288706903    0.0299679804  1.045061e-01  0.1382939842 -0.0401298599
7  -0.0312731330    0.0246836012  5.564636e-02  0.0035711688 -0.0041697238
8  -1.8340782811    0.0716558396  2.304687e-01  0.0032843291  0.3376172236
9   0.1847255512   -0.2306585282  1.114501e-01 -0.0803119855 -0.0298407737
10  1.1851421958    0.8654297998  4.409688e-01  0.0708134508 -0.2042104727
11  0.3843948456    0.1406061718 -3.010002e-01  0.0273414412  0.0837774246
12 -0.1597129689   -0.1864801897  6.450603e-02  0.0597885728  0.2076322416
13 -0.0586771076   -0.0646598581  1.706543e-01  0.1735883622  0.2361560351
14 -0.0028726461   -0.0993484475 -4.198827e-02 -0.0328695117  0.0082498333
15 -0.2068476225   -0.1445082313  1.225231e-01  0.1673204751  0.0312950406
16  0.2059222894   -0.1200505921  8.218720e-02  0.0002209106  0.0173235673
17 -0.3504899068    0.3446336843 -9.851809e-02  0.0158568288  0.0633581211
18  0.0120965218   -0.0020492335  6.525536e-03  0.0002012250 -0.0008929206
19 -0.0539420147   -0.1374351833  1.576257e-01  0.0201581755 -0.0041475049
20 -0.0070837751    0.0317471863 -1.531881e-01  0.0051020166  0.0109305108
21  0.5602808822   -0.0939068584  2.916889e-01  0.2079842274  0.0719036134
22  0.0126364571    0.0642138666 -1.824736e-01  0.0127807493  0.0009786882
23 -0.0590906405   -0.0349751702 -5.567345e-03 -0.0025732617 -0.0225313251
24 -0.5271125509    0.1602771709 -1.660813e-01 -0.0765130701  0.5564898293
25 -0.0681411476    0.0031024272 -6.907299e-02 -0.0610995775 -0.0429840790
26 -0.1735454433   -0.0706705707 -1.739837e-02 -0.0112946369  0.0452224232
27 -0.2193360665    0.0008241219  1.092126e-01  0.1738553921  0.0047428510
28  0.0830814596    0.1950801461  1.354514e-01  0.1170474131 -0.1299135184
29 -0.0675817916   -0.0375435877 -5.699613e-03 -0.0018561352 -0.0297646003
30  0.0331484828   -0.0113220048 -1.952427e-01 -0.0016275866  0.0158334658
31 -0.0814880252   -0.0547203065 -1.397635e-02 -0.0112820160  0.0077466973
32 -0.0305111798    0.1574388970 -9.111875e-02 -0.0694657561  0.5501978390
33  0.0753789673    0.1710615320 -1.462954e-01 -0.1345579924 -0.2507910840
34 -0.1921551895   -0.4405196059  1.130618e-01  0.0550822506  0.0142852838
35  0.0139101694    0.0258169696 -7.341174e-03 -0.0040191802 -0.0014323289
36  1.1438837591    0.4210260247  1.057460e-01  0.0605827133 -0.2795146003
37 -0.4363174667   -0.2224017015  7.276605e-02 -0.1577658535  0.1089681503
38 -0.5706613637    0.2470506317 -2.448941e-01  0.0200009992  0.0295593111
39  0.7628832511    0.8929585543 -2.839854e-02 -0.0150058474  0.0623044167
40 -0.4269483338    0.6476956209 -3.978519e-01 -0.2049708931  0.2326834589
41  0.2669874618    0.2216701222 -2.713083e-02 -0.0189484276 -0.0596156237
42 -1.0313086539   -0.4717065449 -5.947438e-02  0.0164415576 -0.6187804909
43  0.1137733941    0.1389371633  1.866498e-01  0.1973976445  0.1051395744
44 -0.0802417862   -0.0375174905 -4.849177e-03  0.0009183418 -0.0468179606
45 -0.2250554681   -0.0236009451 -1.620885e-01 -0.1443281442  0.7996413905
46 -0.0298791385    0.0117333041 -1.717830e-02 -0.0101575259  0.0149621883
47  0.0567064443   -0.0207407914  5.592524e-01  0.6766291378 -0.2163209845
48 -0.4128397633    0.1262494802  1.555317e-01 -0.0336491273 -0.0274472024
49  0.1227716704   -0.0185633712 -7.120767e-02  0.0510945769  0.0093727142
50  0.2753274562   -0.2227910135  1.638844e-02  0.0838038858 -0.0528014684
51 -0.3972662514    0.1137542901 -1.850157e-01  0.0563752358  0.1303601054
52 -0.6043582748    0.4122452714 -2.755341e-01  0.0529148176 -0.0181328373
53 -0.0823840936   -0.0629773704 -4.430653e-03 -0.0021608372 -0.0531359038
54 -0.0150792891   -0.0425585103  6.777594e-03  0.0030583353 -0.0591176911
55  0.4668348880   -0.0212547234  4.732189e-01  0.4185931034  0.2944838529
56 -0.7432312183   -0.2735589895 -6.870778e-02 -0.0393632338  0.1816128390
57  0.0000000000    0.0000000000  0.000000e+00  0.0000000000  0.0000000000
58  0.1945880055   -0.5587521455 -2.266734e-01 -0.3613221042 -0.5509923750
59 -0.0900361434    0.0807855154 -8.877433e-04 -0.0437027380  0.0203231292
60  0.2757970992   -0.3494459094  2.397384e-01  0.1279587545 -0.1512732820
61  0.1520532649    0.0495057257  6.424741e-02 -0.1224729549 -0.0152320811
62 -0.0675817916   -0.0375435877 -5.699613e-03 -0.0018561352 -0.0297646003
63 -0.0124737319   -0.1163136988 -6.666660e-02  0.0045716898  0.0573650532
64 -0.0495762572    0.0108982197  2.072756e-02  0.0311491661 -0.1744779053
65  0.2450971786    0.1014330960  2.438442e-01  0.2322886433  0.1145616875
66 -0.0137450945   -0.0933626778 -1.455145e-02 -0.0219051447  0.0210761620
67 -0.5159902331   -0.3502832221  3.148977e-02 -0.1793660556  0.1719234355
68  0.0601331294    0.0072908696  1.448763e-01  0.1417048477  0.1267967674
69  0.0205395611   -0.0166707782 -6.624376e-02 -0.0877281099  0.0244232730
70  0.2059222894   -0.1200505921  8.218720e-02  0.0002209106  0.0173235673
71 -0.0312731330    0.0246836012  5.564636e-02  0.0035711688 -0.0041697238
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
<none>              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
<none>     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
<none>                     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
<none>      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
<none>    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", unload = TRUE)
+ }
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"
   x2
x1  a b
  a 2 .
  b . 2
2 x 2 sparse Matrix of class "dgCMatrix"
   x3
x1  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 <NA>
  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 <NA>
  a    2 0    0
  b    0 2    0
  <NA> 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
> 
> 
> ## Invalid UTF-8 strings
> x <- c("Jetz", "no", "chli", "z\xc3\xbcrit\xc3\xbc\xc3\xbctsch:",
+        "(noch", "ein", "bi\xc3\x9fchen", "Z\xc3\xbc", "deutsch)",
+        "\xfa\xb4\xbf\xbf\x9f")
> lapply(x, utf8ToInt)
[[1]]
[1]  74 101 116 122

[[2]]
[1] 110 111

[[3]]
[1]  99 104 108 105

[[4]]
 [1] 122 252 114 105 116 252 252 116 115  99 104  58

[[5]]
[1]  40 110 111  99 104

[[6]]
[1] 101 105 110

[[7]]
[1]  98 105 223  99 104 101 110

[[8]]
[1]  90 252

[[9]]
[1] 100 101 117 116 115  99 104  41

[[10]]
[1] NA

> Encoding(x) <- "UTF-8"
> nchar(x, "b")
 [1]  4  2  4 15  5  3  8  3  8  5
> try(nchar(x, "c"))
Error in nchar(x, "c") : invalid multibyte string, element 10
> try(nchar(x, "w"))
Error in nchar(x, "w") : invalid multibyte string, element 10
> nchar(x, "c", allowNA = TRUE)
 [1]  4  2  4 12  5  3  7  2  8 NA
> nchar(x, "w", allowNA = TRUE)
 [1]  4  2  4 12  5  3  7  2  8 NA
> ## Results differed by platform, but some gave incorrect results on string 10.
> 
> 
> ## str() on large strings
> if (l10n_info()$"UTF-8" || l10n_info()$"Latin-1") {
+   cc <- "J\xf6reskog" # valid in "latin-1"; invalid multibyte string in UTF-8
+   .tmp <- capture.output(
+   str(cc) # failed in some R-devel versions
+   )
+   stopifnot(grepl("chr \"J.*reskog\"", .tmp))
+ 
+   print(nchar(L <- strrep(paste(LETTERS, collapse="."), 100000), type="b")) # 5.1 M
+   print(str(L))
+ }
> 
> if(require("Matrix", .Library)) {
+     M <- Matrix(diag(1:10), sparse=TRUE) # a "dsCMatrix"
+     setClass("TestM", slots = c(M='numeric'))
+     setMethod("+", c("TestM","TestM"), function(e1,e2) {
+         e1@M + e2@M
+     })
+     M+M # works the first time
+     M+M # was error   "object '.Generic' not found"
+     ##
+     as.Matrix <- function(x) `dimnames<-`(as.matrix(x), list(NULL,NULL))
+     stopifnot(exprs = {
+         identical(pmin(2,M), pmin(2, as.matrix(M)))
+         identical(as.matrix(pmax(M, 7)),
+                   pmax(as.Matrix(M), 7))
+     })
+     rm(M)
+ 
+     ## show(<S4 generic>) from base
+     show(chol2inv) # last line now has .. showMethods(chol2inv) ..
+ 
+     detach("package:Matrix", unload=TRUE)
+ }##{Matrix}
Loading required package: Matrix
<sparse>[ <logic> ] : .M.sub.i.logical() maybe inefficient
standardGeneric for "chol2inv" defined from package "base"

function (x, ...) 
standardGeneric("chol2inv")
<bytecode: 0x635abd0>
<environment: 0x63542d8>
Methods may be defined for arguments: x
Use  showMethods(chol2inv)  for currently available ones.
>