R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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.
> 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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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
1 2.4
2 2.4
3 2.4
4 2.1
5 2.0
6 2.0
7 2.1
8 2.1
9 2.0
10 2.0
11 2.0
12 1.7
13 1.3
14 1.2
15 1.1
16 1.4
17 1.5
18 1.4
19 1.1
20 1.1
21 1.0
22 1.4
23 1.3
24 1.2
25 1.5
26 1.6
27 1.8
28 1.5
29 1.3
30 1.6
31 1.6
32 1.8
33 1.8
34 1.6
35 1.8
36 2.0
37 1.3
38 1.1
39 1.0
40 1.2
41 1.2
42 1.3
43 1.3
44 1.4
45 1.1
46 0.9
47 1.0
48 1.1
49 1.4
50 1.5
51 1.8
52 1.8
53 1.8
54 1.7
55 1.5
56 1.1
57 1.3
58 1.6
59 1.9
60 1.9
61 2.0
62 2.2
63 2.2
64 2.0
65 2.3
66 2.6
67 3.2
68 3.2
69 3.1
70 2.8
71 2.3
72 1.9
73 1.9
74 2.0
75 2.0
76 1.8
77 1.6
78 1.4
79 0.2
80 0.3
81 0.4
82 0.7
83 1.0
84 1.1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Bevolkingsgroei Geboren Temperatuur
168.8345 0.2799 -0.1749 404.3768
Neerslag Werkloosheid Inflatie
-0.6047 6.7320 547.2348
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3286.4 -950.4 -175.9 838.1 4137.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 168.83447 3247.53402 0.052 0.959
Bevolkingsgroei 0.27994 0.06594 4.245 6.04e-05 ***
Geboren -0.17487 0.24892 -0.703 0.484
Temperatuur 404.37675 34.23012 11.813 < 2e-16 ***
Neerslag -0.60471 0.43523 -1.389 0.169
Werkloosheid 6.73205 3.35067 2.009 0.048 *
Inflatie 547.23479 329.27994 1.662 0.101
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1439 on 77 degrees of freedom
Multiple R-squared: 0.7614, Adjusted R-squared: 0.7428
F-statistic: 40.96 on 6 and 77 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,] 0.10361755 0.2072351 0.8963825
[2,] 0.08991660 0.1798332 0.9100834
[3,] 0.07357965 0.1471593 0.9264204
[4,] 0.48334016 0.9666803 0.5166598
[5,] 0.44635110 0.8927022 0.5536489
[6,] 0.57401033 0.8519793 0.4259897
[7,] 0.50910801 0.9817840 0.4908920
[8,] 0.54787294 0.9042541 0.4521271
[9,] 0.48218122 0.9643624 0.5178188
[10,] 0.44857725 0.8971545 0.5514227
[11,] 0.44749627 0.8949925 0.5525037
[12,] 0.36944051 0.7388810 0.6305595
[13,] 0.35252089 0.7050418 0.6474791
[14,] 0.35165115 0.7033023 0.6483488
[15,] 0.28288434 0.5657687 0.7171157
[16,] 0.25123853 0.5024771 0.7487615
[17,] 0.19528068 0.3905614 0.8047193
[18,] 0.15890841 0.3178168 0.8410916
[19,] 0.11808411 0.2361682 0.8819159
[20,] 0.33353374 0.6670675 0.6664663
[21,] 0.27996211 0.5599242 0.7200379
[22,] 0.26517271 0.5303454 0.7348273
[23,] 0.21900944 0.4380189 0.7809906
[24,] 0.17476092 0.3495218 0.8252391
[25,] 0.27212828 0.5442566 0.7278717
[26,] 0.23475757 0.4695151 0.7652424
[27,] 0.18676056 0.3735211 0.8132394
[28,] 0.16289886 0.3257977 0.8371011
[29,] 0.13116278 0.2623256 0.8688372
[30,] 0.14608978 0.2921796 0.8539102
[31,] 0.12167843 0.2433569 0.8783216
[32,] 0.12288404 0.2457681 0.8771160
[33,] 0.52085957 0.9582809 0.4791404
[34,] 0.77627743 0.4474451 0.2237226
[35,] 0.73635332 0.5272934 0.2636467
[36,] 0.74484423 0.5103115 0.2551558
[37,] 0.73103986 0.5379203 0.2689601
[38,] 0.68021310 0.6395738 0.3197869
[39,] 0.63904596 0.7219081 0.3609540
[40,] 0.58480501 0.8303900 0.4151950
[41,] 0.56972624 0.8605475 0.4302738
[42,] 0.50976419 0.9804716 0.4902358
[43,] 0.59077929 0.8184414 0.4092207
[44,] 0.54699575 0.9060085 0.4530043
[45,] 0.64777444 0.7044511 0.3522256
[46,] 0.81749130 0.3650174 0.1825087
[47,] 0.79057334 0.4188533 0.2094267
[48,] 0.77933386 0.4413323 0.2206661
[49,] 0.73008428 0.5398314 0.2699157
[50,] 0.66160486 0.6767903 0.3383951
[51,] 0.61587487 0.7682503 0.3841251
[52,] 0.56152916 0.8769417 0.4384708
[53,] 0.50656080 0.9868784 0.4934392
[54,] 0.42603960 0.8520792 0.5739604
[55,] 0.34727486 0.6945497 0.6527251
[56,] 0.27401015 0.5480203 0.7259898
[57,] 0.21235190 0.4247038 0.7876481
[58,] 0.31078394 0.6215679 0.6892161
[59,] 0.69937551 0.6012490 0.3006245
[60,] 0.61372231 0.7725554 0.3862777
[61,] 0.52060017 0.9587997 0.4793998
[62,] 0.44181943 0.8836389 0.5581806
[63,] 0.31876996 0.6375399 0.6812300
[64,] 0.31391719 0.6278344 0.6860828
[65,] 0.20272606 0.4054521 0.7972739
> postscript(file="/var/www/html/rcomp/tmp/1k3b71292948361.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cuaa1292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3cuaa1292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cuaa1292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cuaa1292948361.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
406.70145 873.46464 280.22952 -269.94228 3363.85406 1058.39052
7 8 9 10 11 12
-2042.39142 -782.20736 1270.05306 977.47447 -1680.05265 -592.14408
13 14 15 16 17 18
591.79210 -151.17223 291.62097 553.42000 1927.33828 1250.27331
19 20 21 22 23 24
-537.99761 -990.83876 543.91235 -1304.89207 -1157.09881 -571.74254
25 26 27 28 29 30
-1060.44404 322.35210 -883.70816 -238.31571 3311.57411 771.02790
31 32 33 34 35 36
-971.01569 -672.52655 405.24643 -2289.44499 -572.33270 501.88997
37 38 39 40 41 42
990.21794 687.74556 1448.95608 179.33081 1548.99394 4136.98424
43 44 45 46 47 48
-3286.37480 -200.65981 1100.94586 -1103.83722 -414.46118 -281.15167
49 50 51 52 53 54
-798.14930 -1305.18266 -744.24800 -2237.08268 1060.08168 2511.69243
55 56 57 58 59 60
2351.34522 -597.68816 1727.20963 -323.84086 -52.07804 1103.54265
61 62 63 64 65 66
-1127.85736 -943.49357 -425.42013 500.81283 405.73614 236.36541
67 68 69 70 71 72
-2630.60632 2874.63087 -648.69380 -878.09496 -1367.70430 584.97141
73 74 75 76 77 78
1033.69532 -994.80361 -1407.12915 -1811.66209 936.74431 664.13301
79 80 81 82 83 84
-1646.97677 -1426.79767 826.27353 -843.46481 -2134.16776 788.87020
> postscript(file="/var/www/html/rcomp/tmp/65l9d1292948361.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
0 406.70145 NA
1 873.46464 406.70145
2 280.22952 873.46464
3 -269.94228 280.22952
4 3363.85406 -269.94228
5 1058.39052 3363.85406
6 -2042.39142 1058.39052
7 -782.20736 -2042.39142
8 1270.05306 -782.20736
9 977.47447 1270.05306
10 -1680.05265 977.47447
11 -592.14408 -1680.05265
12 591.79210 -592.14408
13 -151.17223 591.79210
14 291.62097 -151.17223
15 553.42000 291.62097
16 1927.33828 553.42000
17 1250.27331 1927.33828
18 -537.99761 1250.27331
19 -990.83876 -537.99761
20 543.91235 -990.83876
21 -1304.89207 543.91235
22 -1157.09881 -1304.89207
23 -571.74254 -1157.09881
24 -1060.44404 -571.74254
25 322.35210 -1060.44404
26 -883.70816 322.35210
27 -238.31571 -883.70816
28 3311.57411 -238.31571
29 771.02790 3311.57411
30 -971.01569 771.02790
31 -672.52655 -971.01569
32 405.24643 -672.52655
33 -2289.44499 405.24643
34 -572.33270 -2289.44499
35 501.88997 -572.33270
36 990.21794 501.88997
37 687.74556 990.21794
38 1448.95608 687.74556
39 179.33081 1448.95608
40 1548.99394 179.33081
41 4136.98424 1548.99394
42 -3286.37480 4136.98424
43 -200.65981 -3286.37480
44 1100.94586 -200.65981
45 -1103.83722 1100.94586
46 -414.46118 -1103.83722
47 -281.15167 -414.46118
48 -798.14930 -281.15167
49 -1305.18266 -798.14930
50 -744.24800 -1305.18266
51 -2237.08268 -744.24800
52 1060.08168 -2237.08268
53 2511.69243 1060.08168
54 2351.34522 2511.69243
55 -597.68816 2351.34522
56 1727.20963 -597.68816
57 -323.84086 1727.20963
58 -52.07804 -323.84086
59 1103.54265 -52.07804
60 -1127.85736 1103.54265
61 -943.49357 -1127.85736
62 -425.42013 -943.49357
63 500.81283 -425.42013
64 405.73614 500.81283
65 236.36541 405.73614
66 -2630.60632 236.36541
67 2874.63087 -2630.60632
68 -648.69380 2874.63087
69 -878.09496 -648.69380
70 -1367.70430 -878.09496
71 584.97141 -1367.70430
72 1033.69532 584.97141
73 -994.80361 1033.69532
74 -1407.12915 -994.80361
75 -1811.66209 -1407.12915
76 936.74431 -1811.66209
77 664.13301 936.74431
78 -1646.97677 664.13301
79 -1426.79767 -1646.97677
80 826.27353 -1426.79767
81 -843.46481 826.27353
82 -2134.16776 -843.46481
83 788.87020 -2134.16776
84 NA 788.87020
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 873.46464 406.70145
[2,] 280.22952 873.46464
[3,] -269.94228 280.22952
[4,] 3363.85406 -269.94228
[5,] 1058.39052 3363.85406
[6,] -2042.39142 1058.39052
[7,] -782.20736 -2042.39142
[8,] 1270.05306 -782.20736
[9,] 977.47447 1270.05306
[10,] -1680.05265 977.47447
[11,] -592.14408 -1680.05265
[12,] 591.79210 -592.14408
[13,] -151.17223 591.79210
[14,] 291.62097 -151.17223
[15,] 553.42000 291.62097
[16,] 1927.33828 553.42000
[17,] 1250.27331 1927.33828
[18,] -537.99761 1250.27331
[19,] -990.83876 -537.99761
[20,] 543.91235 -990.83876
[21,] -1304.89207 543.91235
[22,] -1157.09881 -1304.89207
[23,] -571.74254 -1157.09881
[24,] -1060.44404 -571.74254
[25,] 322.35210 -1060.44404
[26,] -883.70816 322.35210
[27,] -238.31571 -883.70816
[28,] 3311.57411 -238.31571
[29,] 771.02790 3311.57411
[30,] -971.01569 771.02790
[31,] -672.52655 -971.01569
[32,] 405.24643 -672.52655
[33,] -2289.44499 405.24643
[34,] -572.33270 -2289.44499
[35,] 501.88997 -572.33270
[36,] 990.21794 501.88997
[37,] 687.74556 990.21794
[38,] 1448.95608 687.74556
[39,] 179.33081 1448.95608
[40,] 1548.99394 179.33081
[41,] 4136.98424 1548.99394
[42,] -3286.37480 4136.98424
[43,] -200.65981 -3286.37480
[44,] 1100.94586 -200.65981
[45,] -1103.83722 1100.94586
[46,] -414.46118 -1103.83722
[47,] -281.15167 -414.46118
[48,] -798.14930 -281.15167
[49,] -1305.18266 -798.14930
[50,] -744.24800 -1305.18266
[51,] -2237.08268 -744.24800
[52,] 1060.08168 -2237.08268
[53,] 2511.69243 1060.08168
[54,] 2351.34522 2511.69243
[55,] -597.68816 2351.34522
[56,] 1727.20963 -597.68816
[57,] -323.84086 1727.20963
[58,] -52.07804 -323.84086
[59,] 1103.54265 -52.07804
[60,] -1127.85736 1103.54265
[61,] -943.49357 -1127.85736
[62,] -425.42013 -943.49357
[63,] 500.81283 -425.42013
[64,] 405.73614 500.81283
[65,] 236.36541 405.73614
[66,] -2630.60632 236.36541
[67,] 2874.63087 -2630.60632
[68,] -648.69380 2874.63087
[69,] -878.09496 -648.69380
[70,] -1367.70430 -878.09496
[71,] 584.97141 -1367.70430
[72,] 1033.69532 584.97141
[73,] -994.80361 1033.69532
[74,] -1407.12915 -994.80361
[75,] -1811.66209 -1407.12915
[76,] 936.74431 -1811.66209
[77,] 664.13301 936.74431
[78,] -1646.97677 664.13301
[79,] -1426.79767 -1646.97677
[80,] 826.27353 -1426.79767
[81,] -843.46481 826.27353
[82,] -2134.16776 -843.46481
[83,] 788.87020 -2134.16776
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 873.46464 406.70145
2 280.22952 873.46464
3 -269.94228 280.22952
4 3363.85406 -269.94228
5 1058.39052 3363.85406
6 -2042.39142 1058.39052
7 -782.20736 -2042.39142
8 1270.05306 -782.20736
9 977.47447 1270.05306
10 -1680.05265 977.47447
11 -592.14408 -1680.05265
12 591.79210 -592.14408
13 -151.17223 591.79210
14 291.62097 -151.17223
15 553.42000 291.62097
16 1927.33828 553.42000
17 1250.27331 1927.33828
18 -537.99761 1250.27331
19 -990.83876 -537.99761
20 543.91235 -990.83876
21 -1304.89207 543.91235
22 -1157.09881 -1304.89207
23 -571.74254 -1157.09881
24 -1060.44404 -571.74254
25 322.35210 -1060.44404
26 -883.70816 322.35210
27 -238.31571 -883.70816
28 3311.57411 -238.31571
29 771.02790 3311.57411
30 -971.01569 771.02790
31 -672.52655 -971.01569
32 405.24643 -672.52655
33 -2289.44499 405.24643
34 -572.33270 -2289.44499
35 501.88997 -572.33270
36 990.21794 501.88997
37 687.74556 990.21794
38 1448.95608 687.74556
39 179.33081 1448.95608
40 1548.99394 179.33081
41 4136.98424 1548.99394
42 -3286.37480 4136.98424
43 -200.65981 -3286.37480
44 1100.94586 -200.65981
45 -1103.83722 1100.94586
46 -414.46118 -1103.83722
47 -281.15167 -414.46118
48 -798.14930 -281.15167
49 -1305.18266 -798.14930
50 -744.24800 -1305.18266
51 -2237.08268 -744.24800
52 1060.08168 -2237.08268
53 2511.69243 1060.08168
54 2351.34522 2511.69243
55 -597.68816 2351.34522
56 1727.20963 -597.68816
57 -323.84086 1727.20963
58 -52.07804 -323.84086
59 1103.54265 -52.07804
60 -1127.85736 1103.54265
61 -943.49357 -1127.85736
62 -425.42013 -943.49357
63 500.81283 -425.42013
64 405.73614 500.81283
65 236.36541 405.73614
66 -2630.60632 236.36541
67 2874.63087 -2630.60632
68 -648.69380 2874.63087
69 -878.09496 -648.69380
70 -1367.70430 -878.09496
71 584.97141 -1367.70430
72 1033.69532 584.97141
73 -994.80361 1033.69532
74 -1407.12915 -994.80361
75 -1811.66209 -1407.12915
76 936.74431 -1811.66209
77 664.13301 936.74431
78 -1646.97677 664.13301
79 -1426.79767 -1646.97677
80 826.27353 -1426.79767
81 -843.46481 826.27353
82 -2134.16776 -843.46481
83 788.87020 -2134.16776
> 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/html/rcomp/tmp/7yd9g1292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8yd9g1292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98mq01292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108mq01292948361.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11umoo1292948361.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/html/rcomp/tmp/12x55c1292948361.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/html/rcomp/tmp/13bxll1292948361.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/html/rcomp/tmp/14fx191292948361.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/html/rcomp/tmp/15iyzx1292948361.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/html/rcomp/tmp/163yyl1292948361.tab")
+ }
>
> try(system("convert tmp/1k3b71292948361.ps tmp/1k3b71292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/2cuaa1292948361.ps tmp/2cuaa1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cuaa1292948361.ps tmp/3cuaa1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cuaa1292948361.ps tmp/4cuaa1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/5cuaa1292948361.ps tmp/5cuaa1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/65l9d1292948361.ps tmp/65l9d1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yd9g1292948361.ps tmp/7yd9g1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yd9g1292948361.ps tmp/8yd9g1292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/98mq01292948361.ps tmp/98mq01292948361.png",intern=TRUE))
character(0)
> try(system("convert tmp/108mq01292948361.ps tmp/108mq01292948361.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.800 1.648 6.230