R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(3111
+ ,5140
+ ,17153
+ ,2.5
+ ,766
+ ,332
+ ,2.4
+ ,3995
+ ,4749
+ ,15579
+ ,1.8
+ ,294
+ ,369
+ ,2.4
+ ,5245
+ ,3635
+ ,16755
+ ,7.3
+ ,235
+ ,384
+ ,2.4
+ ,5588
+ ,4305
+ ,16585
+ ,9.9
+ ,462
+ ,373
+ ,2.1
+ ,10681
+ ,5805
+ ,16572
+ ,13.2
+ ,919
+ ,378
+ ,2
+ ,10516
+ ,4260
+ ,16325
+ ,17.8
+ ,346
+ ,426
+ ,2
+ ,7496
+ ,3869
+ ,17913
+ ,18.8
+ ,298
+ ,423
+ ,2.1
+ ,9935
+ ,7325
+ ,17572
+ ,19.3
+ ,92
+ ,397
+ ,2.1
+ ,10249
+ ,9280
+ ,17338
+ ,13.9
+ ,516
+ ,422
+ ,2
+ ,6271
+ ,6222
+ ,17087
+ ,7.5
+ ,843
+ ,409
+ ,2
+ ,3616
+ ,3272
+ ,15864
+ ,8
+ ,395
+ ,430
+ ,2
+ ,3724
+ ,7598
+ ,15554
+ ,4
+ ,961
+ ,412
+ ,1.7
+ ,2886
+ ,1345
+ ,16229
+ ,3.6
+ ,1231
+ ,470
+ ,1.3
+ ,3318
+ ,1900
+ ,15180
+ ,4.8
+ ,794
+ ,491
+ ,1.2
+ ,4166
+ ,1480
+ ,16215
+ ,5.9
+ ,420
+ ,504
+ ,1.1
+ ,6401
+ ,1472
+ ,15801
+ ,10.4
+ ,331
+ ,484
+ ,1.4
+ ,9209
+ ,3823
+ ,15751
+ ,12.3
+ ,312
+ ,474
+ ,1.5
+ ,9820
+ ,4454
+ ,16477
+ ,15.5
+ ,692
+ ,508
+ ,1.4
+ ,7470
+ ,3357
+ ,17324
+ ,16.7
+ ,1221
+ ,492
+ ,1.1
+ ,8207
+ ,5393
+ ,16919
+ ,18.8
+ ,1272
+ ,452
+ ,1.1
+ ,9564
+ ,8329
+ ,16438
+ ,15.2
+ ,622
+ ,457
+ ,1
+ ,5309
+ ,4152
+ ,16239
+ ,11.3
+ ,479
+ ,457
+ ,1.4
+ ,3385
+ ,4042
+ ,15613
+ ,6.3
+ ,757
+ ,471
+ ,1.3
+ ,3706
+ ,7747
+ ,15821
+ ,3.2
+ ,463
+ ,451
+ ,1.2
+ ,2733
+ ,1451
+ ,15678
+ ,5.3
+ ,534
+ ,493
+ ,1.5
+ ,3045
+ ,911
+ ,14671
+ ,2.4
+ ,731
+ ,514
+ ,1.6
+ ,3449
+ ,406
+ ,15876
+ ,6.5
+ ,498
+ ,522
+ ,1.8
+ ,5542
+ ,1387
+ ,15563
+ ,10.4
+ ,629
+ ,490
+ ,1.5
+ ,10072
+ ,2150
+ ,15711
+ ,12.6
+ ,542
+ ,484
+ ,1.3
+ ,9418
+ ,1577
+ ,15583
+ ,16.8
+ ,519
+ ,506
+ ,1.6
+ ,7516
+ ,2642
+ ,16405
+ ,17.7
+ ,1585
+ ,501
+ ,1.6
+ ,7840
+ ,4273
+ ,16701
+ ,16.2
+ ,956
+ ,462
+ ,1.8
+ ,10081
+ ,8064
+ ,16194
+ ,15.7
+ ,633
+ ,465
+ ,1.8
+ ,4956
+ ,3243
+ ,16024
+ ,13.3
+ ,561
+ ,454
+ ,1.6
+ ,3641
+ ,1112
+ ,14728
+ ,6.9
+ ,976
+ ,464
+ ,1.8
+ ,3970
+ ,2280
+ ,14776
+ ,4
+ ,565
+ ,427
+ ,2
+ ,2931
+ ,505
+ ,15399
+ ,1.5
+ ,151
+ ,460
+ ,1.3
+ ,3170
+ ,744
+ ,14286
+ ,2.9
+ ,588
+ ,473
+ ,1.1
+ ,3889
+ ,1369
+ ,15646
+ ,3.9
+ ,1043
+ ,465
+ ,1
+ ,4850
+ ,531
+ ,14543
+ ,9
+ ,398
+ ,422
+ ,1.2
+ ,8037
+ ,1041
+ ,15673
+ ,14.5
+ ,902
+ ,415
+ ,1.2
+ ,12370
+ ,2076
+ ,15171
+ ,16.7
+ ,180
+ ,413
+ ,1.3
+ ,6712
+ ,577
+ ,15999
+ ,22.3
+ ,150
+ ,420
+ ,1.3
+ ,7297
+ ,5080
+ ,16260
+ ,16.4
+ ,1805
+ ,363
+ ,1.4
+ ,10613
+ ,6584
+ ,16123
+ ,17.9
+ ,86
+ ,376
+ ,1.1
+ ,5184
+ ,3761
+ ,16144
+ ,13.6
+ ,1093
+ ,380
+ ,0.9
+ ,3506
+ ,294
+ ,15005
+ ,9.2
+ ,925
+ ,384
+ ,1
+ ,3810
+ ,5020
+ ,14806
+ ,6.5
+ ,750
+ ,346
+ ,1.1
+ ,2692
+ ,1141
+ ,15019
+ ,7.1
+ ,1038
+ ,389
+ ,1.4
+ ,3073
+ ,3805
+ ,13909
+ ,6
+ ,679
+ ,407
+ ,1.5
+ ,3713
+ ,2127
+ ,15211
+ ,8
+ ,848
+ ,393
+ ,1.8
+ ,4555
+ ,2531
+ ,14385
+ ,13.1
+ ,300
+ ,346
+ ,1.8
+ ,7807
+ ,3682
+ ,15144
+ ,14.1
+ ,1379
+ ,348
+ ,1.8
+ ,10869
+ ,3263
+ ,14659
+ ,17.5
+ ,901
+ ,353
+ ,1.7
+ ,9682
+ ,2798
+ ,15989
+ ,17
+ ,1606
+ ,364
+ ,1.5
+ ,7704
+ ,5936
+ ,16262
+ ,17.1
+ ,422
+ ,305
+ ,1.1
+ ,9826
+ ,10568
+ ,16021
+ ,13.8
+ ,968
+ ,307
+ ,1.3
+ ,5456
+ ,5296
+ ,15662
+ ,10.1
+ ,319
+ ,312
+ ,1.6
+ ,3677
+ ,1870
+ ,14531
+ ,6.9
+ ,583
+ ,312
+ ,1.9
+ ,3431
+ ,4390
+ ,14544
+ ,2.4
+ ,765
+ ,286
+ ,1.9
+ ,2765
+ ,3707
+ ,15071
+ ,6.5
+ ,963
+ ,324
+ ,2
+ ,3483
+ ,5201
+ ,14236
+ ,5.1
+ ,392
+ ,336
+ ,2.2
+ ,3445
+ ,3748
+ ,14771
+ ,5.9
+ ,919
+ ,327
+ ,2.2
+ ,6081
+ ,5282
+ ,14804
+ ,8.9
+ ,339
+ ,302
+ ,2
+ ,8767
+ ,5349
+ ,15597
+ ,15.7
+ ,327
+ ,299
+ ,2.3
+ ,9407
+ ,6249
+ ,15418
+ ,16.5
+ ,397
+ ,311
+ ,2.6
+ ,6551
+ ,5517
+ ,16903
+ ,18.1
+ ,1268
+ ,315
+ ,3.2
+ ,12480
+ ,8640
+ ,16350
+ ,17.4
+ ,1137
+ ,264
+ ,3.2
+ ,9530
+ ,15767
+ ,16393
+ ,13.6
+ ,1000
+ ,278
+ ,3.1
+ ,5960
+ ,8850
+ ,15685
+ ,10.1
+ ,915
+ ,278
+ ,2.8
+ ,3252
+ ,5582
+ ,14556
+ ,6.9
+ ,905
+ ,287
+ ,2.3
+ ,3717
+ ,6496
+ ,14850
+ ,2.4
+ ,243
+ ,279
+ ,1.9
+ ,2642
+ ,3255
+ ,15391
+ ,0.8
+ ,537
+ ,324
+ ,1.9
+ ,2989
+ ,6189
+ ,13704
+ ,3.3
+ ,551
+ ,354
+ ,2
+ ,3607
+ ,6452
+ ,15409
+ ,6.3
+ ,482
+ ,354
+ ,2
+ ,5366
+ ,5099
+ ,15098
+ ,12.2
+ ,199
+ ,360
+ ,1.8
+ ,8898
+ ,6833
+ ,15254
+ ,13.9
+ ,650
+ ,363
+ ,1.6
+ ,9435
+ ,7046
+ ,15522
+ ,15.6
+ ,533
+ ,385
+ ,1.4
+ ,7328
+ ,7739
+ ,16669
+ ,18.1
+ ,1071
+ ,412
+ ,0.2
+ ,8594
+ ,10142
+ ,16238
+ ,18.5
+ ,469
+ ,370
+ ,0.3
+ ,11349
+ ,16054
+ ,16246
+ ,15
+ ,335
+ ,389
+ ,0.4
+ ,5797
+ ,7721
+ ,15424
+ ,10.7
+ ,598
+ ,395
+ ,0.7
+ ,3621
+ ,6182
+ ,14952
+ ,9.5
+ ,1200
+ ,417
+ ,1
+ ,3851
+ ,6490
+ ,15008
+ ,2.2
+ ,844
+ ,404
+ ,1.1)
+ ,dim=c(7
+ ,84)
+ ,dimnames=list(c('Huwelijken'
+ ,'Bevolkingsgroei'
+ ,'Geboren'
+ ,'Temperatuur'
+ ,'Neerslag'
+ ,'Werkloosheid'
+ ,'Inflatie')
+ ,1:84))
> y <- array(NA,dim=c(7,84),dimnames=list(c('Huwelijken','Bevolkingsgroei','Geboren','Temperatuur','Neerslag','Werkloosheid','Inflatie'),1:84))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Huwelijken Bevolkingsgroei Geboren Temperatuur Neerslag Werkloosheid
1 3111 5140 17153 2.5 766 332
2 3995 4749 15579 1.8 294 369
3 5245 3635 16755 7.3 235 384
4 5588 4305 16585 9.9 462 373
5 10681 5805 16572 13.2 919 378
6 10516 4260 16325 17.8 346 426
7 7496 3869 17913 18.8 298 423
8 9935 7325 17572 19.3 92 397
9 10249 9280 17338 13.9 516 422
10 6271 6222 17087 7.5 843 409
11 3616 3272 15864 8.0 395 430
12 3724 7598 15554 4.0 961 412
13 2886 1345 16229 3.6 1231 470
14 3318 1900 15180 4.8 794 491
15 4166 1480 16215 5.9 420 504
16 6401 1472 15801 10.4 331 484
17 9209 3823 15751 12.3 312 474
18 9820 4454 16477 15.5 692 508
19 7470 3357 17324 16.7 1221 492
20 8207 5393 16919 18.8 1272 452
21 9564 8329 16438 15.2 622 457
22 5309 4152 16239 11.3 479 457
23 3385 4042 15613 6.3 757 471
24 3706 7747 15821 3.2 463 451
25 2733 1451 15678 5.3 534 493
26 3045 911 14671 2.4 731 514
27 3449 406 15876 6.5 498 522
28 5542 1387 15563 10.4 629 490
29 10072 2150 15711 12.6 542 484
30 9418 1577 15583 16.8 519 506
31 7516 2642 16405 17.7 1585 501
32 7840 4273 16701 16.2 956 462
33 10081 8064 16194 15.7 633 465
34 4956 3243 16024 13.3 561 454
35 3641 1112 14728 6.9 976 464
36 3970 2280 14776 4.0 565 427
37 2931 505 15399 1.5 151 460
38 3170 744 14286 2.9 588 473
39 3889 1369 15646 3.9 1043 465
40 4850 531 14543 9.0 398 422
41 8037 1041 15673 14.5 902 415
42 12370 2076 15171 16.7 180 413
43 6712 577 15999 22.3 150 420
44 7297 5080 16260 16.4 1805 363
45 10613 6584 16123 17.9 86 376
46 5184 3761 16144 13.6 1093 380
47 3506 294 15005 9.2 925 384
48 3810 5020 14806 6.5 750 346
49 2692 1141 15019 7.1 1038 389
50 3073 3805 13909 6.0 679 407
51 3713 2127 15211 8.0 848 393
52 4555 2531 14385 13.1 300 346
53 7807 3682 15144 14.1 1379 348
54 10869 3263 14659 17.5 901 353
55 9682 2798 15989 17.0 1606 364
56 7704 5936 16262 17.1 422 305
57 9826 10568 16021 13.8 968 307
58 5456 5296 15662 10.1 319 312
59 3677 1870 14531 6.9 583 312
60 3431 4390 14544 2.4 765 286
61 2765 3707 15071 6.5 963 324
62 3483 5201 14236 5.1 392 336
63 3445 3748 14771 5.9 919 327
64 6081 5282 14804 8.9 339 302
65 8767 5349 15597 15.7 327 299
66 9407 6249 15418 16.5 397 311
67 6551 5517 16903 18.1 1268 315
68 12480 8640 16350 17.4 1137 264
69 9530 15767 16393 13.6 1000 278
70 5960 8850 15685 10.1 915 278
71 3252 5582 14556 6.9 905 287
72 3717 6496 14850 2.4 243 279
73 2642 3255 15391 0.8 537 324
74 2989 6189 13704 3.3 551 354
75 3607 6452 15409 6.3 482 354
76 5366 5099 15098 12.2 199 360
77 8898 6833 15254 13.9 650 363
78 9435 7046 15522 15.6 533 385
79 7328 7739 16669 18.1 1071 412
80 8594 10142 16238 18.5 469 370
81 11349 16054 16246 15.0 335 389
82 5797 7721 15424 10.7 598 395
83 3621 6182 14952 9.5 1200 417
84 3851 6490 15008 2.2 844 404
Inflatie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2.4 1 0 0 0 0 0 0 0 0 0 0 1
2 2.4 0 1 0 0 0 0 0 0 0 0 0 2
3 2.4 0 0 1 0 0 0 0 0 0 0 0 3
4 2.1 0 0 0 1 0 0 0 0 0 0 0 4
5 2.0 0 0 0 0 1 0 0 0 0 0 0 5
6 2.0 0 0 0 0 0 1 0 0 0 0 0 6
7 2.1 0 0 0 0 0 0 1 0 0 0 0 7
8 2.1 0 0 0 0 0 0 0 1 0 0 0 8
9 2.0 0 0 0 0 0 0 0 0 1 0 0 9
10 2.0 0 0 0 0 0 0 0 0 0 1 0 10
11 2.0 0 0 0 0 0 0 0 0 0 0 1 11
12 1.7 0 0 0 0 0 0 0 0 0 0 0 12
13 1.3 1 0 0 0 0 0 0 0 0 0 0 13
14 1.2 0 1 0 0 0 0 0 0 0 0 0 14
15 1.1 0 0 1 0 0 0 0 0 0 0 0 15
16 1.4 0 0 0 1 0 0 0 0 0 0 0 16
17 1.5 0 0 0 0 1 0 0 0 0 0 0 17
18 1.4 0 0 0 0 0 1 0 0 0 0 0 18
19 1.1 0 0 0 0 0 0 1 0 0 0 0 19
20 1.1 0 0 0 0 0 0 0 1 0 0 0 20
21 1.0 0 0 0 0 0 0 0 0 1 0 0 21
22 1.4 0 0 0 0 0 0 0 0 0 1 0 22
23 1.3 0 0 0 0 0 0 0 0 0 0 1 23
24 1.2 0 0 0 0 0 0 0 0 0 0 0 24
25 1.5 1 0 0 0 0 0 0 0 0 0 0 25
26 1.6 0 1 0 0 0 0 0 0 0 0 0 26
27 1.8 0 0 1 0 0 0 0 0 0 0 0 27
28 1.5 0 0 0 1 0 0 0 0 0 0 0 28
29 1.3 0 0 0 0 1 0 0 0 0 0 0 29
30 1.6 0 0 0 0 0 1 0 0 0 0 0 30
31 1.6 0 0 0 0 0 0 1 0 0 0 0 31
32 1.8 0 0 0 0 0 0 0 1 0 0 0 32
33 1.8 0 0 0 0 0 0 0 0 1 0 0 33
34 1.6 0 0 0 0 0 0 0 0 0 1 0 34
35 1.8 0 0 0 0 0 0 0 0 0 0 1 35
36 2.0 0 0 0 0 0 0 0 0 0 0 0 36
37 1.3 1 0 0 0 0 0 0 0 0 0 0 37
38 1.1 0 1 0 0 0 0 0 0 0 0 0 38
39 1.0 0 0 1 0 0 0 0 0 0 0 0 39
40 1.2 0 0 0 1 0 0 0 0 0 0 0 40
41 1.2 0 0 0 0 1 0 0 0 0 0 0 41
42 1.3 0 0 0 0 0 1 0 0 0 0 0 42
43 1.3 0 0 0 0 0 0 1 0 0 0 0 43
44 1.4 0 0 0 0 0 0 0 1 0 0 0 44
45 1.1 0 0 0 0 0 0 0 0 1 0 0 45
46 0.9 0 0 0 0 0 0 0 0 0 1 0 46
47 1.0 0 0 0 0 0 0 0 0 0 0 1 47
48 1.1 0 0 0 0 0 0 0 0 0 0 0 48
49 1.4 1 0 0 0 0 0 0 0 0 0 0 49
50 1.5 0 1 0 0 0 0 0 0 0 0 0 50
51 1.8 0 0 1 0 0 0 0 0 0 0 0 51
52 1.8 0 0 0 1 0 0 0 0 0 0 0 52
53 1.8 0 0 0 0 1 0 0 0 0 0 0 53
54 1.7 0 0 0 0 0 1 0 0 0 0 0 54
55 1.5 0 0 0 0 0 0 1 0 0 0 0 55
56 1.1 0 0 0 0 0 0 0 1 0 0 0 56
57 1.3 0 0 0 0 0 0 0 0 1 0 0 57
58 1.6 0 0 0 0 0 0 0 0 0 1 0 58
59 1.9 0 0 0 0 0 0 0 0 0 0 1 59
60 1.9 0 0 0 0 0 0 0 0 0 0 0 60
61 2.0 1 0 0 0 0 0 0 0 0 0 0 61
62 2.2 0 1 0 0 0 0 0 0 0 0 0 62
63 2.2 0 0 1 0 0 0 0 0 0 0 0 63
64 2.0 0 0 0 1 0 0 0 0 0 0 0 64
65 2.3 0 0 0 0 1 0 0 0 0 0 0 65
66 2.6 0 0 0 0 0 1 0 0 0 0 0 66
67 3.2 0 0 0 0 0 0 1 0 0 0 0 67
68 3.2 0 0 0 0 0 0 0 1 0 0 0 68
69 3.1 0 0 0 0 0 0 0 0 1 0 0 69
70 2.8 0 0 0 0 0 0 0 0 0 1 0 70
71 2.3 0 0 0 0 0 0 0 0 0 0 1 71
72 1.9 0 0 0 0 0 0 0 0 0 0 0 72
73 1.9 1 0 0 0 0 0 0 0 0 0 0 73
74 2.0 0 1 0 0 0 0 0 0 0 0 0 74
75 2.0 0 0 1 0 0 0 0 0 0 0 0 75
76 1.8 0 0 0 1 0 0 0 0 0 0 0 76
77 1.6 0 0 0 0 1 0 0 0 0 0 0 77
78 1.4 0 0 0 0 0 1 0 0 0 0 0 78
79 0.2 0 0 0 0 0 0 1 0 0 0 0 79
80 0.3 0 0 0 0 0 0 0 1 0 0 0 80
81 0.4 0 0 0 0 0 0 0 0 1 0 0 81
82 0.7 0 0 0 0 0 0 0 0 0 1 0 82
83 1.0 0 0 0 0 0 0 0 0 0 0 1 83
84 1.1 0 0 0 0 0 0 0 0 0 0 0 84
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bevolkingsgroei Geboren Temperatuur
7270.78638 0.08699 -0.17589 -38.56507
Neerslag Werkloosheid Inflatie M1
-0.25852 -1.41320 30.23237 -578.71164
M2 M3 M4 M5
-409.63044 610.84433 2147.89575 5925.56545
M6 M7 M8 M9
7199.88387 4930.08035 5866.94385 6643.60367
M10 M11 t
2285.87677 221.40777 -12.33681
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1310.1 -391.0 -89.8 197.1 3546.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7270.78638 5587.84783 1.301 0.197791
Bevolkingsgroei 0.08699 0.07236 1.202 0.233658
Geboren -0.17589 0.35502 -0.495 0.621965
Temperatuur -38.56507 65.39072 -0.590 0.557394
Neerslag -0.25852 0.28927 -0.894 0.374773
Werkloosheid -1.41320 2.38505 -0.593 0.555555
Inflatie 30.23237 216.27922 0.140 0.889263
M1 -578.71164 566.87661 -1.021 0.311097
M2 -409.63044 495.31257 -0.827 0.411255
M3 610.84433 593.26757 1.030 0.307001
M4 2147.89575 661.34976 3.248 0.001842 **
M5 5925.56545 864.43802 6.855 3.13e-09 ***
M6 7199.88387 1011.01701 7.121 1.06e-09 ***
M7 4930.08035 1336.90096 3.688 0.000464 ***
M8 5866.94385 1207.37346 4.859 7.78e-06 ***
M9 6643.60367 1001.65762 6.633 7.69e-09 ***
M10 2285.87677 770.86126 2.965 0.004224 **
M11 221.40777 550.56299 0.402 0.688895
t -12.33681 9.47939 -1.301 0.197704
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 830.8 on 65 degrees of freedom
Multiple R-squared: 0.9329, Adjusted R-squared: 0.9143
F-statistic: 50.21 on 18 and 65 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.3265175609 0.6530351219 0.6734824
[2,] 0.1820276955 0.3640553910 0.8179723
[3,] 0.2270637998 0.4541275996 0.7729362
[4,] 0.1307835327 0.2615670654 0.8692165
[5,] 0.0717721706 0.1435443412 0.9282278
[6,] 0.0471501632 0.0943003264 0.9528498
[7,] 0.0274469848 0.0548939695 0.9725530
[8,] 0.0364090860 0.0728181720 0.9635909
[9,] 0.0199769434 0.0399538869 0.9800231
[10,] 0.0148790388 0.0297580776 0.9851210
[11,] 0.0102765245 0.0205530491 0.9897235
[12,] 0.0057933978 0.0115867956 0.9942066
[13,] 0.0034986557 0.0069973115 0.9965013
[14,] 0.0028607903 0.0057215805 0.9971392
[15,] 0.0019360788 0.0038721576 0.9980639
[16,] 0.0010594066 0.0021188132 0.9989406
[17,] 0.0004773502 0.0009547004 0.9995226
[18,] 0.0002231395 0.0004462791 0.9997769
[19,] 0.0001754740 0.0003509480 0.9998245
[20,] 0.0002334575 0.0004669149 0.9997665
[21,] 0.0277012759 0.0554025518 0.9722987
[22,] 0.0283938716 0.0567877432 0.9716061
[23,] 0.0432817680 0.0865635360 0.9567182
[24,] 0.0367453718 0.0734907436 0.9632546
[25,] 0.0234584862 0.0469169724 0.9765415
[26,] 0.0165205043 0.0330410086 0.9834795
[27,] 0.0109368317 0.0218736635 0.9890632
[28,] 0.0061664885 0.0123329770 0.9938335
[29,] 0.0033642056 0.0067284113 0.9966358
[30,] 0.0020583899 0.0041167798 0.9979416
[31,] 0.0031537568 0.0063075136 0.9968462
[32,] 0.0039444592 0.0078889185 0.9960555
[33,] 0.0025274366 0.0050548732 0.9974726
[34,] 0.0339225307 0.0678450614 0.9660775
[35,] 0.0843707541 0.1687415082 0.9156292
[36,] 0.0581534968 0.1163069935 0.9418465
[37,] 0.0350855157 0.0701710313 0.9649145
[38,] 0.0194618761 0.0389237522 0.9805381
[39,] 0.0110451005 0.0220902010 0.9889549
[40,] 0.0054973323 0.0109946645 0.9945027
[41,] 0.0021503787 0.0043007574 0.9978496
> postscript(file="/var/www/rcomp/tmp/1kj9w1292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2kj9w1292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3dsqh1292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4dsqh1292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5dsqh1292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 84
Frequency = 1
1 2 3 4 5 6
-307.77908 79.91165 843.57932 -273.84193 1176.55135 -62.37435
7 8 9 10 11 12
-468.01598 615.12534 -106.70676 326.57278 -276.97018 -390.37603
13 14 15 16 17 18
173.77996 182.25358 207.82628 959.19031 -160.24433 -465.70314
19 20 21 22 23 24
-119.66272 -517.89321 -562.00525 -318.05087 -363.93121 -315.69775
25 26 27 28 29 30
-25.48517 -34.63121 -279.75233 296.25975 1080.50408 -630.09789
31 32 33 34 35 36
105.20421 -866.75869 92.41793 -393.70848 194.07633 387.23347
37 38 39 40 41 42
67.62633 124.70960 168.32137 -553.37911 -644.81256 2140.21643
43 44 45 46 47 48
-741.50777 -1310.10206 727.53699 24.06723 313.64207 199.17768
49 50 51 52 53 54
196.41615 -119.12277 -20.31829 -894.86910 -1054.49268 714.30608
55 56 57 58 59 60
2268.39533 -1232.60659 -309.58967 -226.53250 106.14258 -286.27674
61 62 63 64 65 66
50.86178 144.59242 -526.67468 393.44188 -306.40847 -981.32810
67 68 69 70 71 72
-955.92693 3546.67456 -939.22042 190.14233 -453.50519 -126.45439
73 74 75 76 77 78
-155.41997 -377.71327 -392.98167 73.19820 -91.09739 -715.01902
79 80 81 82 83 84
-88.48614 -234.43936 1097.56718 397.50952 480.54560 532.39376
> postscript(file="/var/www/rcomp/tmp/66kp21292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 84
Frequency = 1
lag(myerror, k = 1) myerror
0 -307.77908 NA
1 79.91165 -307.77908
2 843.57932 79.91165
3 -273.84193 843.57932
4 1176.55135 -273.84193
5 -62.37435 1176.55135
6 -468.01598 -62.37435
7 615.12534 -468.01598
8 -106.70676 615.12534
9 326.57278 -106.70676
10 -276.97018 326.57278
11 -390.37603 -276.97018
12 173.77996 -390.37603
13 182.25358 173.77996
14 207.82628 182.25358
15 959.19031 207.82628
16 -160.24433 959.19031
17 -465.70314 -160.24433
18 -119.66272 -465.70314
19 -517.89321 -119.66272
20 -562.00525 -517.89321
21 -318.05087 -562.00525
22 -363.93121 -318.05087
23 -315.69775 -363.93121
24 -25.48517 -315.69775
25 -34.63121 -25.48517
26 -279.75233 -34.63121
27 296.25975 -279.75233
28 1080.50408 296.25975
29 -630.09789 1080.50408
30 105.20421 -630.09789
31 -866.75869 105.20421
32 92.41793 -866.75869
33 -393.70848 92.41793
34 194.07633 -393.70848
35 387.23347 194.07633
36 67.62633 387.23347
37 124.70960 67.62633
38 168.32137 124.70960
39 -553.37911 168.32137
40 -644.81256 -553.37911
41 2140.21643 -644.81256
42 -741.50777 2140.21643
43 -1310.10206 -741.50777
44 727.53699 -1310.10206
45 24.06723 727.53699
46 313.64207 24.06723
47 199.17768 313.64207
48 196.41615 199.17768
49 -119.12277 196.41615
50 -20.31829 -119.12277
51 -894.86910 -20.31829
52 -1054.49268 -894.86910
53 714.30608 -1054.49268
54 2268.39533 714.30608
55 -1232.60659 2268.39533
56 -309.58967 -1232.60659
57 -226.53250 -309.58967
58 106.14258 -226.53250
59 -286.27674 106.14258
60 50.86178 -286.27674
61 144.59242 50.86178
62 -526.67468 144.59242
63 393.44188 -526.67468
64 -306.40847 393.44188
65 -981.32810 -306.40847
66 -955.92693 -981.32810
67 3546.67456 -955.92693
68 -939.22042 3546.67456
69 190.14233 -939.22042
70 -453.50519 190.14233
71 -126.45439 -453.50519
72 -155.41997 -126.45439
73 -377.71327 -155.41997
74 -392.98167 -377.71327
75 73.19820 -392.98167
76 -91.09739 73.19820
77 -715.01902 -91.09739
78 -88.48614 -715.01902
79 -234.43936 -88.48614
80 1097.56718 -234.43936
81 397.50952 1097.56718
82 480.54560 397.50952
83 532.39376 480.54560
84 NA 532.39376
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 79.91165 -307.77908
[2,] 843.57932 79.91165
[3,] -273.84193 843.57932
[4,] 1176.55135 -273.84193
[5,] -62.37435 1176.55135
[6,] -468.01598 -62.37435
[7,] 615.12534 -468.01598
[8,] -106.70676 615.12534
[9,] 326.57278 -106.70676
[10,] -276.97018 326.57278
[11,] -390.37603 -276.97018
[12,] 173.77996 -390.37603
[13,] 182.25358 173.77996
[14,] 207.82628 182.25358
[15,] 959.19031 207.82628
[16,] -160.24433 959.19031
[17,] -465.70314 -160.24433
[18,] -119.66272 -465.70314
[19,] -517.89321 -119.66272
[20,] -562.00525 -517.89321
[21,] -318.05087 -562.00525
[22,] -363.93121 -318.05087
[23,] -315.69775 -363.93121
[24,] -25.48517 -315.69775
[25,] -34.63121 -25.48517
[26,] -279.75233 -34.63121
[27,] 296.25975 -279.75233
[28,] 1080.50408 296.25975
[29,] -630.09789 1080.50408
[30,] 105.20421 -630.09789
[31,] -866.75869 105.20421
[32,] 92.41793 -866.75869
[33,] -393.70848 92.41793
[34,] 194.07633 -393.70848
[35,] 387.23347 194.07633
[36,] 67.62633 387.23347
[37,] 124.70960 67.62633
[38,] 168.32137 124.70960
[39,] -553.37911 168.32137
[40,] -644.81256 -553.37911
[41,] 2140.21643 -644.81256
[42,] -741.50777 2140.21643
[43,] -1310.10206 -741.50777
[44,] 727.53699 -1310.10206
[45,] 24.06723 727.53699
[46,] 313.64207 24.06723
[47,] 199.17768 313.64207
[48,] 196.41615 199.17768
[49,] -119.12277 196.41615
[50,] -20.31829 -119.12277
[51,] -894.86910 -20.31829
[52,] -1054.49268 -894.86910
[53,] 714.30608 -1054.49268
[54,] 2268.39533 714.30608
[55,] -1232.60659 2268.39533
[56,] -309.58967 -1232.60659
[57,] -226.53250 -309.58967
[58,] 106.14258 -226.53250
[59,] -286.27674 106.14258
[60,] 50.86178 -286.27674
[61,] 144.59242 50.86178
[62,] -526.67468 144.59242
[63,] 393.44188 -526.67468
[64,] -306.40847 393.44188
[65,] -981.32810 -306.40847
[66,] -955.92693 -981.32810
[67,] 3546.67456 -955.92693
[68,] -939.22042 3546.67456
[69,] 190.14233 -939.22042
[70,] -453.50519 190.14233
[71,] -126.45439 -453.50519
[72,] -155.41997 -126.45439
[73,] -377.71327 -155.41997
[74,] -392.98167 -377.71327
[75,] 73.19820 -392.98167
[76,] -91.09739 73.19820
[77,] -715.01902 -91.09739
[78,] -88.48614 -715.01902
[79,] -234.43936 -88.48614
[80,] 1097.56718 -234.43936
[81,] 397.50952 1097.56718
[82,] 480.54560 397.50952
[83,] 532.39376 480.54560
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 79.91165 -307.77908
2 843.57932 79.91165
3 -273.84193 843.57932
4 1176.55135 -273.84193
5 -62.37435 1176.55135
6 -468.01598 -62.37435
7 615.12534 -468.01598
8 -106.70676 615.12534
9 326.57278 -106.70676
10 -276.97018 326.57278
11 -390.37603 -276.97018
12 173.77996 -390.37603
13 182.25358 173.77996
14 207.82628 182.25358
15 959.19031 207.82628
16 -160.24433 959.19031
17 -465.70314 -160.24433
18 -119.66272 -465.70314
19 -517.89321 -119.66272
20 -562.00525 -517.89321
21 -318.05087 -562.00525
22 -363.93121 -318.05087
23 -315.69775 -363.93121
24 -25.48517 -315.69775
25 -34.63121 -25.48517
26 -279.75233 -34.63121
27 296.25975 -279.75233
28 1080.50408 296.25975
29 -630.09789 1080.50408
30 105.20421 -630.09789
31 -866.75869 105.20421
32 92.41793 -866.75869
33 -393.70848 92.41793
34 194.07633 -393.70848
35 387.23347 194.07633
36 67.62633 387.23347
37 124.70960 67.62633
38 168.32137 124.70960
39 -553.37911 168.32137
40 -644.81256 -553.37911
41 2140.21643 -644.81256
42 -741.50777 2140.21643
43 -1310.10206 -741.50777
44 727.53699 -1310.10206
45 24.06723 727.53699
46 313.64207 24.06723
47 199.17768 313.64207
48 196.41615 199.17768
49 -119.12277 196.41615
50 -20.31829 -119.12277
51 -894.86910 -20.31829
52 -1054.49268 -894.86910
53 714.30608 -1054.49268
54 2268.39533 714.30608
55 -1232.60659 2268.39533
56 -309.58967 -1232.60659
57 -226.53250 -309.58967
58 106.14258 -226.53250
59 -286.27674 106.14258
60 50.86178 -286.27674
61 144.59242 50.86178
62 -526.67468 144.59242
63 393.44188 -526.67468
64 -306.40847 393.44188
65 -981.32810 -306.40847
66 -955.92693 -981.32810
67 3546.67456 -955.92693
68 -939.22042 3546.67456
69 190.14233 -939.22042
70 -453.50519 190.14233
71 -126.45439 -453.50519
72 -155.41997 -126.45439
73 -377.71327 -155.41997
74 -392.98167 -377.71327
75 73.19820 -392.98167
76 -91.09739 73.19820
77 -715.01902 -91.09739
78 -88.48614 -715.01902
79 -234.43936 -88.48614
80 1097.56718 -234.43936
81 397.50952 1097.56718
82 480.54560 397.50952
83 532.39376 480.54560
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7yt651292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8yt651292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9yt651292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1092o81292950569.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11dlmw1292950569.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12nc4z1292950569.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13cd1a1292950569.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/145mid1292950569.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/158ng11292950569.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/164wwa1292950569.tab")
+ }
>
> try(system("convert tmp/1kj9w1292950569.ps tmp/1kj9w1292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kj9w1292950569.ps tmp/2kj9w1292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dsqh1292950569.ps tmp/3dsqh1292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dsqh1292950569.ps tmp/4dsqh1292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dsqh1292950569.ps tmp/5dsqh1292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/66kp21292950569.ps tmp/66kp21292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yt651292950569.ps tmp/7yt651292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yt651292950569.ps tmp/8yt651292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yt651292950569.ps tmp/9yt651292950569.png",intern=TRUE))
character(0)
> try(system("convert tmp/1092o81292950569.ps tmp/1092o81292950569.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.440 1.750 5.171