-
Notifications
You must be signed in to change notification settings - Fork 204
/
Copy pathreg-tests-3.Rout.save
884 lines (852 loc) · 41 KB
/
reg-tests-3.Rout.save
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
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
235
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
329
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
383
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
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
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
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
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
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
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
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
R version 3.3.1 RC (2016-06-14 r70774) -- "Bug in Your Hair"
Copyright (C) 2016 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"<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 [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"
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 <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.
>