reg-tests-3.Rout.save 40 KB
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R version 3.2.4 RC (2016-03-02 r70281) -- "Very Secure Dishes"
Copyright (C) 2016 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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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
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.

> ### Regression tests for which the printed output is the issue
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> ### May fail, e.g. by needing Recommended packages
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> 
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> pdf("reg-tests-3.pdf", encoding = "ISOLatin1.enc")
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> 
> ## 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)
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+     detach("package:survival")
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+ }
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Loading required package: survival
 Surv [1:23, 1:2]   9   13   13+  18   23   28+  31   34   45+  48  ...
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 - 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))
+ }
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Loading required package: MASS
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$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
141
57  0.0000000000  0.000000e+00  0.000000e+00  0.000000000  0.000000000
142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
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
235
57  0.0000000000    0.0000000000  0.000000e+00  0.0000000000  0.0000000000
236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328
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
329
57  0.0000000000  0.0000000000  0.0000000000    0.0000000000   0.0000000000
330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382
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 
383
3.601231 3.523963 3.598459 3.571771 3.582319 3.600406 3.596626 3.592960 
384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441
      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   
442
57  0.00   0.00     0.00     0.00     0.00     0.00     0.00     0.00   
443 444 445 446 447 448 449 450 451 452 453 454
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
455
57  0.00     0.00      0.00      0.00     0.00     0.00     0.00      NaN
456 457 458 459 460 461
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
462 463 464 465 466 467 468 469 470 471 472 473 474
   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_*
475 476 477 478 479 480 481 482 483 484
> ## 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
> 
485 486 487
> 
> ## PR#7829 model.tables & replications
> if(require(MASS)) {
488 489
+     oats.aov <- aov(Y ~ B + V + N + V:N, data=oats[-1,])
+     model.tables(oats.aov, "means", cterms=c("N", "V:N"))
490 491 492 493 494 495 496 497
+ }
Tables of means
Grand mean
         
103.8732 

 N 
    0.0cwt 0.2cwt 0.4cwt 0.6cwt
498 499
     78.74  98.51  113.8    123
rep  17.00  18.00   18.0     18
500 501 502 503 504 505 506 507 508 509 510 511

 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
> 
512 513 514 515 516 517 518 519 520 521 522 523 524
> 
> ## 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
525 526 527 528
       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
529 530 531 532 533 534 535 536 537 538 539 540 541
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
542 543
<none>                     442.22 92.776
I(climb/dist)  1    9.7331 451.95 91.538
544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602
> ## 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.
> 
603 604 605 606 607 608
> ## 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.
> 
609 610
> 
> ## as.matrix.data.frame with coercion
611 612 613 614 615 616 617 618 619
> 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
620 621 622 623 624 625 626 627 628 629 630 631 632 633
     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
> 
634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690
> ## 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
> 
> 
691
> if(suppressMessages(require("Matrix"))) {
692 693 694
+   print(cS. <- contr.SAS(5, sparse = TRUE))
+   stopifnot(all(contr.SAS(5) == cS.),
+ 	    all(contr.helmert(5, sparse = TRUE) == contr.helmert(5)))
695 696 697 698 699
+ 
+   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'))
700
+   detach("package:Matrix")
701
+   ## failed in R <= 2.13.1
702
+ }
703
5 x 4 sparse Matrix of class "dgCMatrix"
704 705 706 707 708 709
  1 2 3 4
1 1 . . .
2 . 1 . .
3 . . 1 .
4 . . . 1
5 . . . .
710 711 712 713 714 715 716 717
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
718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
> 
> ## 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
> 
737 738 739
> ## xtabs did not exclude levels from factors
> x1 <- c('a','b','a','b','c', NA)
> x2 <- factor(x1, exclude=NULL)
740
> print(xtabs(~ x1 + x2, na.action = na.pass))
741 742 743 744 745
   x2
x1  a b c <NA>
  a 2 0 0    0
  b 0 2 0    0
  c 0 0 1    0
746
> print(xtabs(~ x1 + x2, exclude = 'c', na.action = na.pass))
747 748 749 750 751 752
      x2
x1     a b <NA>
  a    2 0    0
  b    0 2    0
  <NA> 0 0    1
> 
753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814
> 
> ## 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
> 
815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836
> 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
>