R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(30/09/2000
+ ,501
+ ,134
+ ,368
+ ,6.7
+ ,8.5
+ ,8.7
+ ,31/10/2000
+ ,485
+ ,124
+ ,361
+ ,6.8
+ ,8.4
+ ,8.6
+ ,30/11/2000
+ ,464
+ ,113
+ ,351
+ ,6.7
+ ,8.4
+ ,8.6
+ ,31/12/2000
+ ,460
+ ,109
+ ,351
+ ,6.6
+ ,8.3
+ ,8.5
+ ,31/01/2001
+ ,467
+ ,109
+ ,358
+ ,6.4
+ ,8.2
+ ,8.5
+ ,28/02/2001
+ ,460
+ ,106
+ ,354
+ ,6.3
+ ,8.2
+ ,8.5
+ ,31/03/2001
+ ,448
+ ,101
+ ,347
+ ,6.3
+ ,8.1
+ ,8.5
+ ,30/04/2001
+ ,443
+ ,98
+ ,345
+ ,6.5
+ ,8.1
+ ,8.5
+ ,31/05/2001
+ ,436
+ ,93
+ ,343
+ ,6.5
+ ,8.1
+ ,8.5
+ ,30/06/2001
+ ,431
+ ,91
+ ,340
+ ,6.4
+ ,8.1
+ ,8.5
+ ,31/07/2001
+ ,484
+ ,122
+ ,362
+ ,6.2
+ ,8.1
+ ,8.5
+ ,31/08/2001
+ ,510
+ ,139
+ ,370
+ ,6.2
+ ,8.1
+ ,8.6
+ ,30/09/2001
+ ,513
+ ,140
+ ,373
+ ,6.5
+ ,8.1
+ ,8.6
+ ,31/10/2001
+ ,503
+ ,132
+ ,371
+ ,7
+ ,8.2
+ ,8.6
+ ,30/11/2001
+ ,471
+ ,117
+ ,354
+ ,7.2
+ ,8.2
+ ,8.7
+ ,31/12/2001
+ ,471
+ ,114
+ ,357
+ ,7.3
+ ,8.3
+ ,8.7
+ ,31/01/2002
+ ,476
+ ,113
+ ,363
+ ,7.4
+ ,8.2
+ ,8.7
+ ,28/02/2002
+ ,475
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+ ,8.3
+ ,8.8
+ ,31/03/2002
+ ,470
+ ,107
+ ,363
+ ,7.4
+ ,8.3
+ ,8.8
+ ,30/04/2002
+ ,461
+ ,103
+ ,358
+ ,7.3
+ ,8.4
+ ,8.9
+ ,31/05/2002
+ ,455
+ ,98
+ ,357
+ ,7.4
+ ,8.5
+ ,8.9
+ ,30/06/2002
+ ,456
+ ,98
+ ,357
+ ,7.4
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+ ,517
+ ,137
+ ,380
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+ ,8.6
+ ,9
+ ,31/08/2002
+ ,525
+ ,148
+ ,378
+ ,7.6
+ ,8.6
+ ,9
+ ,30/09/2002
+ ,523
+ ,147
+ ,376
+ ,7.7
+ ,8.7
+ ,9
+ ,31/10/2002
+ ,519
+ ,139
+ ,380
+ ,7.7
+ ,8.7
+ ,9
+ ,30/11/2002
+ ,509
+ ,130
+ ,379
+ ,7.8
+ ,8.8
+ ,9
+ ,31/12/2002
+ ,512
+ ,128
+ ,384
+ ,7.8
+ ,8.8
+ ,9
+ ,31/01/2003
+ ,519
+ ,127
+ ,392
+ ,8
+ ,8.9
+ ,9.1
+ ,28/02/2003
+ ,517
+ ,123
+ ,394
+ ,8.1
+ ,9
+ ,9.1
+ ,31/03/2003
+ ,510
+ ,118
+ ,392
+ ,8.1
+ ,9
+ ,9.1
+ ,30/04/2003
+ ,509
+ ,114
+ ,396
+ ,8.2
+ ,9
+ ,9.1
+ ,31/05/2003
+ ,501
+ ,108
+ ,392
+ ,8.1
+ ,9
+ ,9.1
+ ,30/06/2003
+ ,507
+ ,111
+ ,396
+ ,8.1
+ ,9.1
+ ,9.1
+ ,31/07/2003
+ ,569
+ ,151
+ ,419
+ ,8.1
+ ,9.1
+ ,9.1
+ ,31/08/2003
+ ,580
+ ,159
+ ,421
+ ,8.1
+ ,9
+ ,9.1
+ ,30/09/2003
+ ,578
+ ,158
+ ,420
+ ,8.2
+ ,9.1
+ ,9.1
+ ,31/10/2003
+ ,565
+ ,148
+ ,418
+ ,8.2
+ ,9
+ ,9.1
+ ,30/11/2003
+ ,547
+ ,138
+ ,410
+ ,8.3
+ ,9.1
+ ,9.1
+ ,31/12/2003
+ ,555
+ ,137
+ ,418
+ ,8.4
+ ,9.1
+ ,9.2
+ ,31/01/2004
+ ,562
+ ,136
+ ,426
+ ,8.6
+ ,9.2
+ ,9.3
+ ,29/02/2004
+ ,561
+ ,133
+ ,428
+ ,8.6
+ ,9.2
+ ,9.3
+ ,31/03/2004
+ ,555
+ ,126
+ ,430
+ ,8.4
+ ,9.2
+ ,9.3
+ ,30/04/2004
+ ,544
+ ,120
+ ,424
+ ,8
+ ,9.2
+ ,9.2
+ ,31/05/2004
+ ,537
+ ,114
+ ,423
+ ,7.9
+ ,9.2
+ ,9.2
+ ,30/06/2004
+ ,543
+ ,116
+ ,427
+ ,8.1
+ ,9.3
+ ,9.2
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+ ,9.3
+ ,9.2
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+ ,9.4
+ ,9.2
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+ ,9.2
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+ ,465
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+ ,9.1
+ ,9
+ ,30/11/2005
+ ,595
+ ,137
+ ,459
+ ,8.4
+ ,9.1
+ ,8.9
+ ,31/12/2005
+ ,597
+ ,132
+ ,465
+ ,8.4
+ ,9
+ ,8.8
+ ,31/01/2006
+ ,593
+ ,125
+ ,468
+ ,8.5
+ ,8.9
+ ,8.7
+ ,28/02/2006
+ ,590
+ ,123
+ ,467
+ ,8.5
+ ,8.8
+ ,8.6
+ ,31/03/2006
+ ,580
+ ,117
+ ,463
+ ,8.5
+ ,8.7
+ ,8.5
+ ,30/04/2006
+ ,574
+ ,114
+ ,460
+ ,8.6
+ ,8.6
+ ,8.5
+ ,31/05/2006
+ ,573
+ ,111
+ ,462
+ ,8.6
+ ,8.6
+ ,8.4
+ ,30/06/2006
+ ,573
+ ,112
+ ,461
+ ,8.4
+ ,8.5
+ ,8.3
+ ,31/07/2006
+ ,620
+ ,144
+ ,476
+ ,8.2
+ ,8.4
+ ,8.2
+ ,31/08/2006
+ ,626
+ ,150
+ ,476
+ ,8
+ ,8.4
+ ,8.2
+ ,30/09/2006
+ ,620
+ ,149
+ ,471
+ ,8
+ ,8.3
+ ,8.1
+ ,31/10/2006
+ ,588
+ ,134
+ ,453
+ ,8
+ ,8.2
+ ,8
+ ,30/11/2006
+ ,566
+ ,123
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+ ,8
+ ,8.2
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+ ,557
+ ,116
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+ ,8
+ ,7.8
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+ ,7
+ ,30/11/2007
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+ ,508
+ ,101
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+ ,7.3
+ ,6.8
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+ ,493
+ ,95
+ ,398
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+ ,7.3
+ ,6.8
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+ ,490
+ ,93
+ ,397
+ ,6.8
+ ,7.4
+ ,6.8
+ ,31/05/2008
+ ,469
+ ,84
+ ,385
+ ,6.8
+ ,7.5
+ ,6.9
+ ,30/06/2008
+ ,478
+ ,87
+ ,390
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+ ,7.6
+ ,7
+ ,31/07/2008
+ ,528
+ ,116
+ ,413
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+ ,8.7
+ ,8.1
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+ ,533
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+ ,7.7
+ ,9
+ ,8.4
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+ ,536
+ ,108
+ ,428
+ ,7.8
+ ,9.3
+ ,8.6
+ ,30/04/2009
+ ,537
+ ,107
+ ,430
+ ,7.7
+ ,9.4
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+ ,99
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+ ,7.7
+ ,9.5
+ ,8.9
+ ,30/06/2009
+ ,536
+ ,103
+ ,433
+ ,7.8
+ ,9.6
+ ,9.1
+ ,31/07/2009
+ ,587
+ ,131
+ ,456
+ ,8
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+ ,9.2
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+ ,597
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+ ,9.4
+ ,31/10/2009
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+ ,10
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+ ,558
+ ,118
+ ,439
+ ,8.1
+ ,10
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+ ,575
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+ ,113
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+ ,540
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+ ,30/06/2011
+ ,519
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+ ,548
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+ ,30/11/2011
+ ,539
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+ ,31/12/2011
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+ ,7.1
+ ,10.7
+ ,10
+ ,31/01/2012
+ ,562
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+ ,7.1
+ ,10.8
+ ,10.1
+ ,29/02/2012
+ ,559
+ ,110
+ ,449
+ ,7.1
+ ,10.9
+ ,10.2
+ ,31/03/2012
+ ,546
+ ,104
+ ,442
+ ,7.2
+ ,11
+ ,10.3
+ ,30/04/2012
+ ,536
+ ,100
+ ,437
+ ,7.3
+ ,11.2
+ ,10.3
+ ,31/05/2012
+ ,528
+ ,96
+ ,431
+ ,7.4
+ ,11.3
+ ,10.4
+ ,30/06/2012
+ ,530
+ ,98
+ ,433
+ ,7.4
+ ,11.4
+ ,10.5
+ ,31/07/2012
+ ,582
+ ,122
+ ,460
+ ,7.5
+ ,11.5
+ ,10.5
+ ,31/08/2012
+ ,599
+ ,134
+ ,465
+ ,7.4
+ ,11.5
+ ,10.6
+ ,30/09/2012
+ ,584
+ ,133
+ ,451
+ ,7.4
+ ,11.6
+ ,10.6)
+ ,dim=c(7
+ ,145)
+ ,dimnames=list(c('maand'
+ ,'Totaal'
+ ,'jongerdan25jaar'
+ ,'vanaf25jaar'
+ ,'België'
+ ,'Eurogebied'
+ ,'eu27
')
+ ,1:145))
> y <- array(NA,dim=c(7,145),dimnames=list(c('maand','Totaal','jongerdan25jaar','vanaf25jaar','België','Eurogebied','eu27
'),1:145))
> 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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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, 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
Totaal maand jongerdan25jaar vanaf25jaar Belgi\353 Eurogebied eu27\r
1 501 0.001666667 134 368 6.7 8.5 8.7
2 485 0.001550000 124 361 6.8 8.4 8.6
3 464 0.001363636 113 351 6.7 8.4 8.6
4 460 0.001291667 109 351 6.6 8.3 8.5
5 467 0.015492254 109 358 6.4 8.2 8.5
6 460 0.006996502 106 354 6.3 8.2 8.5
7 448 0.005164085 101 347 6.3 8.1 8.5
8 443 0.003748126 98 345 6.5 8.1 8.5
9 436 0.003098451 93 343 6.5 8.1 8.5
10 431 0.002498751 91 340 6.4 8.1 8.5
11 484 0.002213179 122 362 6.2 8.1 8.5
12 510 0.001936532 139 370 6.2 8.1 8.6
13 513 0.001665834 140 373 6.5 8.1 8.6
14 503 0.001549225 132 371 7.0 8.2 8.6
15 471 0.001362955 117 354 7.2 8.2 8.7
16 471 0.001291021 114 357 7.3 8.3 8.7
17 476 0.015484515 113 363 7.4 8.2 8.7
18 475 0.006993007 110 364 7.4 8.3 8.8
19 470 0.005161505 107 363 7.4 8.3 8.8
20 461 0.003746254 103 358 7.3 8.4 8.9
21 455 0.003096903 98 357 7.4 8.5 8.9
22 456 0.002497502 98 357 7.4 8.5 8.9
23 517 0.002212074 137 380 7.6 8.6 9.0
24 525 0.001935564 148 378 7.6 8.6 9.0
25 523 0.001665002 147 376 7.7 8.7 9.0
26 519 0.001548452 139 380 7.7 8.7 9.0
27 509 0.001362274 130 379 7.8 8.8 9.0
28 512 0.001290376 128 384 7.8 8.8 9.0
29 519 0.015476785 127 392 8.0 8.9 9.1
30 517 0.006989516 123 394 8.1 9.0 9.1
31 510 0.005158928 118 392 8.1 9.0 9.1
32 509 0.003744383 114 396 8.2 9.0 9.1
33 501 0.003095357 108 392 8.1 9.0 9.1
34 507 0.002496256 111 396 8.1 9.1 9.1
35 569 0.002210969 151 419 8.1 9.1 9.1
36 580 0.001934598 159 421 8.1 9.0 9.1
37 578 0.001664170 158 420 8.2 9.1 9.1
38 565 0.001547678 148 418 8.2 9.0 9.1
39 547 0.001361594 138 410 8.3 9.1 9.1
40 555 0.001289732 137 418 8.4 9.1 9.2
41 562 0.015469062 136 426 8.6 9.2 9.3
42 561 0.007235529 133 428 8.6 9.2 9.3
43 555 0.005156354 126 430 8.4 9.2 9.3
44 544 0.003742515 120 424 8.0 9.2 9.2
45 537 0.003093812 114 423 7.9 9.2 9.2
46 543 0.002495010 116 427 8.1 9.3 9.2
47 594 0.002209866 153 441 8.5 9.3 9.2
48 611 0.001933633 162 449 8.8 9.3 9.2
49 613 0.001663340 161 452 8.8 9.3 9.2
50 611 0.001546906 149 462 8.5 9.3 9.2
51 594 0.001360915 139 455 8.3 9.4 9.2
52 595 0.001289088 135 461 8.3 9.4 9.2
53 591 0.015461347 130 461 8.3 9.3 9.2
54 589 0.006982544 127 463 8.4 9.3 9.2
55 584 0.005153782 122 462 8.5 9.3 9.2
56 573 0.003740648 117 456 8.5 9.3 9.2
57 567 0.003092269 112 455 8.6 9.2 9.1
58 569 0.002493766 113 456 8.5 9.2 9.1
59 621 0.002208764 149 472 8.6 9.2 9.0
60 629 0.001932668 157 472 8.6 9.1 8.9
61 628 0.001662510 157 471 8.6 9.1 8.9
62 612 0.001546135 147 465 8.5 9.1 9.0
63 595 0.001360236 137 459 8.4 9.1 8.9
64 597 0.001288446 132 465 8.4 9.0 8.8
65 593 0.015453639 125 468 8.5 8.9 8.7
66 590 0.006979063 123 467 8.5 8.8 8.6
67 580 0.005151213 117 463 8.5 8.7 8.5
68 574 0.003738784 114 460 8.6 8.6 8.5
69 573 0.003090728 111 462 8.6 8.6 8.4
70 573 0.002492522 112 461 8.4 8.5 8.3
71 620 0.002207663 144 476 8.2 8.4 8.2
72 626 0.001931705 150 476 8.0 8.4 8.2
73 620 0.001661682 149 471 8.0 8.3 8.1
74 588 0.001545364 134 453 8.0 8.2 8.0
75 566 0.001359558 123 443 8.0 8.2 7.9
76 557 0.001287803 116 442 7.9 8.0 7.8
77 561 0.015445939 117 444 7.9 7.9 7.6
78 549 0.006975585 111 438 7.9 7.8 7.5
79 532 0.005148646 105 427 7.9 7.7 7.4
80 526 0.003736921 102 424 8.0 7.6 7.3
81 511 0.003089188 95 416 7.9 7.6 7.3
82 499 0.002491281 93 406 7.4 7.6 7.2
83 555 0.002206563 124 431 7.2 7.6 7.2
84 565 0.001930742 130 434 7.0 7.6 7.2
85 542 0.001660854 124 418 6.9 7.5 7.1
86 527 0.001544594 115 412 7.1 7.5 7.0
87 510 0.001358880 106 404 7.2 7.4 7.0
88 514 0.001287162 105 409 7.2 7.4 6.9
89 517 0.015438247 105 412 7.1 7.4 6.9
90 508 0.007221116 101 406 6.9 7.3 6.8
91 493 0.005146082 95 398 6.8 7.3 6.8
92 490 0.003735060 93 397 6.8 7.4 6.8
93 469 0.003087649 84 385 6.8 7.5 6.9
94 478 0.002490040 87 390 6.9 7.6 7.0
95 528 0.002205464 116 413 7.1 7.6 7.0
96 534 0.001929781 120 413 7.2 7.7 7.1
97 518 0.001660027 117 401 7.2 7.7 7.2
98 506 0.001543825 109 397 7.1 7.9 7.3
99 502 0.001358204 105 397 7.1 8.1 7.5
100 516 0.001286521 107 409 7.2 8.4 7.7
101 528 0.015430562 109 419 7.5 8.7 8.1
102 533 0.006968641 109 424 7.7 9.0 8.4
103 536 0.005143521 108 428 7.8 9.3 8.6
104 537 0.003733201 107 430 7.7 9.4 8.8
105 524 0.003086112 99 424 7.7 9.5 8.9
106 536 0.002488800 103 433 7.8 9.6 9.1
107 587 0.002204366 131 456 8.0 9.8 9.2
108 597 0.001928820 137 459 8.1 9.8 9.3
109 581 0.001659200 135 446 8.1 9.9 9.4
110 564 0.001543056 124 441 8.0 10.0 9.4
111 558 0.001357527 118 439 8.1 10.0 9.5
112 575 0.001285880 121 454 8.2 10.1 9.5
113 580 0.015422886 121 460 8.4 10.1 9.7
114 575 0.006965174 118 457 8.5 10.1 9.7
115 563 0.005140962 113 451 8.5 10.1 9.7
116 552 0.003731343 107 444 8.5 10.2 9.7
117 537 0.003084577 100 437 8.5 10.2 9.7
118 545 0.002487562 102 443 8.5 10.1 9.6
119 601 0.002203269 130 471 8.4 10.1 9.6
120 604 0.001927861 136 469 8.3 10.1 9.6
121 586 0.001658375 133 454 8.2 10.1 9.6
122 564 0.001542289 120 444 8.1 10.1 9.6
123 549 0.001356852 112 436 7.9 10.1 9.6
124 551 0.001285240 109 442 7.6 10.1 9.6
125 556 0.015415216 110 446 7.3 10.0 9.5
126 548 0.006961711 106 442 7.1 9.9 9.5
127 540 0.005138405 102 438 7.0 9.9 9.4
128 531 0.003729488 98 433 7.1 9.9 9.4
129 521 0.003083043 92 428 7.1 9.9 9.5
130 519 0.002486325 92 426 7.1 10.0 9.5
131 572 0.002202174 120 452 7.3 10.1 9.6
132 581 0.001926902 127 455 7.3 10.2 9.7
133 563 0.001657550 124 439 7.3 10.3 9.8
134 548 0.001541522 114 434 7.2 10.5 9.9
135 539 0.001356177 108 431 7.2 10.6 10.0
136 541 0.001284601 106 435 7.1 10.7 10.0
137 562 0.015407555 111 450 7.1 10.8 10.1
138 559 0.007206759 110 449 7.1 10.9 10.2
139 546 0.005135852 104 442 7.2 11.0 10.3
140 536 0.003727634 100 437 7.3 11.2 10.3
141 528 0.003081511 96 431 7.4 11.3 10.4
142 530 0.002485089 98 433 7.4 11.4 10.5
143 582 0.002201079 122 460 7.5 11.5 10.5
144 599 0.001925944 134 465 7.4 11.5 10.6
145 584 0.001656726 133 451 7.4 11.6 10.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) maand jongerdan25jaar vanaf25jaar
1.24933 -1.50811 0.99370 1.00184
`Belgi\\353` Eurogebied `eu27\\r`
-0.12009 -0.09617 0.05790
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.09765 -0.13044 -0.00502 0.14183 1.14799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.249326 0.654923 1.908 0.0585 .
maand -1.508111 11.405643 -0.132 0.8950
jongerdan25jaar 0.993701 0.003313 299.922 <2e-16 ***
vanaf25jaar 1.001841 0.002804 357.313 <2e-16 ***
`Belgi\\353` -0.120094 0.108289 -1.109 0.2693
Eurogebied -0.096165 0.208858 -0.460 0.6459
`eu27\\r` 0.057902 0.213170 0.272 0.7863
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5052 on 138 degrees of freedom
Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
F-statistic: 1.93e+05 on 6 and 138 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,] 0.099868151 0.199736302 0.9001318
[2,] 0.041295088 0.082590176 0.9587049
[3,] 0.143442506 0.286885012 0.8565575
[4,] 0.113892754 0.227785508 0.8861072
[5,] 0.096421379 0.192842759 0.9035786
[6,] 0.054118560 0.108237119 0.9458814
[7,] 0.080980396 0.161960792 0.9190196
[8,] 0.048622833 0.097245666 0.9513772
[9,] 0.211385077 0.422770153 0.7886149
[10,] 0.206278769 0.412557537 0.7937212
[11,] 0.160133283 0.320266565 0.8398667
[12,] 0.112314429 0.224628857 0.8876856
[13,] 0.229611800 0.459223600 0.7703882
[14,] 0.177579575 0.355159150 0.8224204
[15,] 0.222001378 0.444002756 0.7779986
[16,] 0.250061513 0.500123027 0.7499385
[17,] 0.198096277 0.396192555 0.8019037
[18,] 0.151509260 0.303018520 0.8484907
[19,] 0.116777966 0.233555931 0.8832220
[20,] 0.086058346 0.172116693 0.9139417
[21,] 0.064228398 0.128456796 0.9357716
[22,] 0.047240160 0.094480320 0.9527598
[23,] 0.142582531 0.285165062 0.8574175
[24,] 0.283941342 0.567882684 0.7160587
[25,] 0.233986223 0.467972447 0.7660138
[26,] 0.273627509 0.547255018 0.7263725
[27,] 0.249003173 0.498006346 0.7509968
[28,] 0.233597031 0.467194062 0.7664030
[29,] 0.306495516 0.612991032 0.6935045
[30,] 0.342594463 0.685188927 0.6574055
[31,] 0.295712764 0.591425528 0.7042872
[32,] 0.249300849 0.498601697 0.7506992
[33,] 0.206976878 0.413953755 0.7930231
[34,] 0.345180911 0.690361823 0.6548191
[35,] 0.300603005 0.601206010 0.6993970
[36,] 0.255807612 0.511615224 0.7441924
[37,] 0.219145726 0.438291453 0.7808543
[38,] 0.214460162 0.428920324 0.7855398
[39,] 0.206130341 0.412260683 0.7938697
[40,] 0.184543244 0.369086489 0.8154568
[41,] 0.153271432 0.306542864 0.8467286
[42,] 0.126532394 0.253064789 0.8734676
[43,] 0.187392915 0.374785829 0.8126071
[44,] 0.154947009 0.309894018 0.8450530
[45,] 0.230394123 0.460788245 0.7696059
[46,] 0.195796004 0.391592007 0.8042040
[47,] 0.163462616 0.326925232 0.8365374
[48,] 0.133853298 0.267706596 0.8661467
[49,] 0.108224348 0.216448696 0.8917757
[50,] 0.090863533 0.181727065 0.9091365
[51,] 0.074639671 0.149279343 0.9253603
[52,] 0.060318915 0.120637831 0.9396811
[53,] 0.047188226 0.094376453 0.9528118
[54,] 0.078328440 0.156656881 0.9216716
[55,] 0.061894807 0.123789613 0.9381052
[56,] 0.048109580 0.096219160 0.9518904
[57,] 0.036869598 0.073739197 0.9631304
[58,] 0.027948661 0.055897321 0.9720513
[59,] 0.021240377 0.042480755 0.9787596
[60,] 0.015690781 0.031381563 0.9843092
[61,] 0.011407018 0.022814036 0.9885930
[62,] 0.008222447 0.016444893 0.9917776
[63,] 0.005877072 0.011754144 0.9941229
[64,] 0.004141206 0.008282412 0.9958588
[65,] 0.011642082 0.023284164 0.9883579
[66,] 0.008532994 0.017065988 0.9914670
[67,] 0.027638269 0.055276539 0.9723617
[68,] 0.020688753 0.041377506 0.9793112
[69,] 0.015233200 0.030466400 0.9847668
[70,] 0.011113435 0.022226871 0.9888866
[71,] 0.008036125 0.016072250 0.9919639
[72,] 0.005858305 0.011716610 0.9941417
[73,] 0.004386726 0.008773453 0.9956133
[74,] 0.003072387 0.006144775 0.9969276
[75,] 0.009441040 0.018882081 0.9905590
[76,] 0.006706452 0.013412903 0.9932935
[77,] 0.004697185 0.009394370 0.9953028
[78,] 0.003340225 0.006680449 0.9966598
[79,] 0.002315418 0.004630836 0.9976846
[80,] 0.001563191 0.003126382 0.9984368
[81,] 0.003250387 0.006500774 0.9967496
[82,] 0.002321904 0.004643808 0.9976781
[83,] 0.001636976 0.003273952 0.9983630
[84,] 0.001306550 0.002613100 0.9986934
[85,] 0.002185945 0.004371890 0.9978141
[86,] 0.007194434 0.014388867 0.9928056
[87,] 0.016191322 0.032382643 0.9838087
[88,] 0.011680577 0.023361154 0.9883194
[89,] 0.008489702 0.016979404 0.9915103
[90,] 0.006455675 0.012911350 0.9935443
[91,] 0.004925142 0.009850284 0.9950749
[92,] 0.003479917 0.006959834 0.9965201
[93,] 0.002590254 0.005180508 0.9974097
[94,] 0.002061848 0.004123696 0.9979382
[95,] 0.001805072 0.003610144 0.9981949
[96,] 0.002593259 0.005186518 0.9974067
[97,] 0.001943188 0.003886376 0.9980568
[98,] 0.001256985 0.002513969 0.9987430
[99,] 0.006369226 0.012738452 0.9936308
[100,] 0.004541962 0.009083924 0.9954580
[101,] 0.012735546 0.025471093 0.9872645
[102,] 0.026046062 0.052092123 0.9739539
[103,] 0.018623091 0.037246181 0.9813769
[104,] 0.032136003 0.064272006 0.9678640
[105,] 0.023172405 0.046344811 0.9768276
[106,] 0.053219205 0.106438410 0.9467808
[107,] 0.092009727 0.184019454 0.9079903
[108,] 0.069901597 0.139803194 0.9300984
[109,] 0.051019166 0.102038331 0.9489808
[110,] 0.050604469 0.101208937 0.9493955
[111,] 0.047113125 0.094226249 0.9528869
[112,] 0.076272678 0.152545357 0.9237273
[113,] 0.061200952 0.122401903 0.9387990
[114,] 0.089735303 0.179470607 0.9102647
[115,] 0.067530238 0.135060477 0.9324698
[116,] 0.058486538 0.116973076 0.9415135
[117,] 0.044340411 0.088680822 0.9556596
[118,] 0.032489321 0.064978641 0.9675107
[119,] 0.026519694 0.053039388 0.9734803
[120,] 0.035409524 0.070819049 0.9645905
[121,] 0.075692833 0.151385665 0.9243072
[122,] 0.067133567 0.134267134 0.9328664
[123,] 0.083581481 0.167162962 0.9164185
[124,] 0.062754897 0.125509793 0.9372451
[125,] 0.040658816 0.081317633 0.9593412
[126,] 0.100405878 0.200811756 0.8995941
> postscript(file="/var/wessaorg/rcomp/tmp/1qmhu1352123336.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2atmi1352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3m36v1352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4r1301352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58ikr1352123336.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 = 145
Frequency = 1
1 2 3 4 5 6
-0.962139570 -0.004229500 -0.067391519 -0.108530684 -0.133640020 -0.169992420
7 8 9 10 11 12
-0.200975954 -0.194305752 -0.223096224 -0.243083218 -0.112784232 0.973355022
13 14 15 16 17 18
0.009749545 0.032530610 -0.012697919 -0.015600870 -0.009149839 0.961132803
19 20 21 22 23 24
-0.058684006 -0.084989163 -0.093994625 0.905101412 0.135812994 -0.791635348
25 26 27 28 29 30
0.226966712 0.169035547 0.135533762 0.113620767 0.141830542 0.121779048
31 32 33 34 35 36
0.091207629 -0.931476770 1.025108437 0.045351839 -0.745482891 0.291190627
37 38 39 40 41 42
0.307951462 -0.761144961 -0.788055310 0.197025457 0.225224558 0.190228486
43 44 45 46 47 48
-0.884699864 0.044176596 -0.004761997 0.033202042 0.288082054 0.365650761
49 50 51 52 53 54
0.353420132 0.223217384 0.158437401 -0.877914335 0.102348958 -0.921007560
55 56 57 58 59 60
0.058591793 0.036015657 0.013568818 0.005114040 0.219774515 0.265921470
61 62 63 64 65 66
0.267355471 0.197441833 -0.860996331 0.092526937 0.072457478 0.045094543
67 68 69 70 71 72
0.008085061 -0.005023934 -0.022790100 -0.043397358 0.102265076 0.115622262
73 74 75 76 77 78
0.114297134 1.048960455 0.003598886 -1.064211155 -0.038279438 -0.081623697
79 80 81 82 83 84
-0.105741800 -0.113059573 -0.155405408 -0.204747325 -0.079971340 0.927861767
85 86 87 88 89 90
-0.096710434 -0.112716520 -0.152560603 -0.162384458 -0.158576782 0.787039370
91 92 93 94 95 96
-0.251160283 -0.254427751 -0.286169013 0.738454496 -1.097646073 0.942968793
97 98 99 100 101 102
-0.060027165 -0.101792977 -0.119615073 -0.099944281 -0.042713161 -0.029183910
103 104 105 106 107 108
-0.016322192 -0.042403949 0.921105408 -0.061127803 0.109915974 1.147987677
109 110 111 112 113 114
0.162748562 -0.899898203 1.071931886 0.084724474 -0.892565351 0.093317086
115 116 117 118 119 120
-0.929879002 1.052709385 0.020533027 0.017355165 0.129720823 -0.841228855
121 122 123 124 125 126
-0.844919408 0.079427139 1.019470341 -0.046610706 -0.066222822 -0.130436169
127 128 129 130 131 132
-0.157234325 -0.163337373 0.801312443 0.813711886 -0.030384700 -0.988406820
133 134 135 136 137 138
0.025580047 -0.026941494 -0.055662644 -0.078126672 0.950870763 -0.062127889
139 140 141 142 143 144
-0.074317514 -1.061186504 0.939528596 -1.048629973 0.074018099 0.122180925
145
0.150872716
> postscript(file="/var/wessaorg/rcomp/tmp/6uvjv1352123336.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 = 145
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.962139570 NA
1 -0.004229500 -0.962139570
2 -0.067391519 -0.004229500
3 -0.108530684 -0.067391519
4 -0.133640020 -0.108530684
5 -0.169992420 -0.133640020
6 -0.200975954 -0.169992420
7 -0.194305752 -0.200975954
8 -0.223096224 -0.194305752
9 -0.243083218 -0.223096224
10 -0.112784232 -0.243083218
11 0.973355022 -0.112784232
12 0.009749545 0.973355022
13 0.032530610 0.009749545
14 -0.012697919 0.032530610
15 -0.015600870 -0.012697919
16 -0.009149839 -0.015600870
17 0.961132803 -0.009149839
18 -0.058684006 0.961132803
19 -0.084989163 -0.058684006
20 -0.093994625 -0.084989163
21 0.905101412 -0.093994625
22 0.135812994 0.905101412
23 -0.791635348 0.135812994
24 0.226966712 -0.791635348
25 0.169035547 0.226966712
26 0.135533762 0.169035547
27 0.113620767 0.135533762
28 0.141830542 0.113620767
29 0.121779048 0.141830542
30 0.091207629 0.121779048
31 -0.931476770 0.091207629
32 1.025108437 -0.931476770
33 0.045351839 1.025108437
34 -0.745482891 0.045351839
35 0.291190627 -0.745482891
36 0.307951462 0.291190627
37 -0.761144961 0.307951462
38 -0.788055310 -0.761144961
39 0.197025457 -0.788055310
40 0.225224558 0.197025457
41 0.190228486 0.225224558
42 -0.884699864 0.190228486
43 0.044176596 -0.884699864
44 -0.004761997 0.044176596
45 0.033202042 -0.004761997
46 0.288082054 0.033202042
47 0.365650761 0.288082054
48 0.353420132 0.365650761
49 0.223217384 0.353420132
50 0.158437401 0.223217384
51 -0.877914335 0.158437401
52 0.102348958 -0.877914335
53 -0.921007560 0.102348958
54 0.058591793 -0.921007560
55 0.036015657 0.058591793
56 0.013568818 0.036015657
57 0.005114040 0.013568818
58 0.219774515 0.005114040
59 0.265921470 0.219774515
60 0.267355471 0.265921470
61 0.197441833 0.267355471
62 -0.860996331 0.197441833
63 0.092526937 -0.860996331
64 0.072457478 0.092526937
65 0.045094543 0.072457478
66 0.008085061 0.045094543
67 -0.005023934 0.008085061
68 -0.022790100 -0.005023934
69 -0.043397358 -0.022790100
70 0.102265076 -0.043397358
71 0.115622262 0.102265076
72 0.114297134 0.115622262
73 1.048960455 0.114297134
74 0.003598886 1.048960455
75 -1.064211155 0.003598886
76 -0.038279438 -1.064211155
77 -0.081623697 -0.038279438
78 -0.105741800 -0.081623697
79 -0.113059573 -0.105741800
80 -0.155405408 -0.113059573
81 -0.204747325 -0.155405408
82 -0.079971340 -0.204747325
83 0.927861767 -0.079971340
84 -0.096710434 0.927861767
85 -0.112716520 -0.096710434
86 -0.152560603 -0.112716520
87 -0.162384458 -0.152560603
88 -0.158576782 -0.162384458
89 0.787039370 -0.158576782
90 -0.251160283 0.787039370
91 -0.254427751 -0.251160283
92 -0.286169013 -0.254427751
93 0.738454496 -0.286169013
94 -1.097646073 0.738454496
95 0.942968793 -1.097646073
96 -0.060027165 0.942968793
97 -0.101792977 -0.060027165
98 -0.119615073 -0.101792977
99 -0.099944281 -0.119615073
100 -0.042713161 -0.099944281
101 -0.029183910 -0.042713161
102 -0.016322192 -0.029183910
103 -0.042403949 -0.016322192
104 0.921105408 -0.042403949
105 -0.061127803 0.921105408
106 0.109915974 -0.061127803
107 1.147987677 0.109915974
108 0.162748562 1.147987677
109 -0.899898203 0.162748562
110 1.071931886 -0.899898203
111 0.084724474 1.071931886
112 -0.892565351 0.084724474
113 0.093317086 -0.892565351
114 -0.929879002 0.093317086
115 1.052709385 -0.929879002
116 0.020533027 1.052709385
117 0.017355165 0.020533027
118 0.129720823 0.017355165
119 -0.841228855 0.129720823
120 -0.844919408 -0.841228855
121 0.079427139 -0.844919408
122 1.019470341 0.079427139
123 -0.046610706 1.019470341
124 -0.066222822 -0.046610706
125 -0.130436169 -0.066222822
126 -0.157234325 -0.130436169
127 -0.163337373 -0.157234325
128 0.801312443 -0.163337373
129 0.813711886 0.801312443
130 -0.030384700 0.813711886
131 -0.988406820 -0.030384700
132 0.025580047 -0.988406820
133 -0.026941494 0.025580047
134 -0.055662644 -0.026941494
135 -0.078126672 -0.055662644
136 0.950870763 -0.078126672
137 -0.062127889 0.950870763
138 -0.074317514 -0.062127889
139 -1.061186504 -0.074317514
140 0.939528596 -1.061186504
141 -1.048629973 0.939528596
142 0.074018099 -1.048629973
143 0.122180925 0.074018099
144 0.150872716 0.122180925
145 NA 0.150872716
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.004229500 -0.962139570
[2,] -0.067391519 -0.004229500
[3,] -0.108530684 -0.067391519
[4,] -0.133640020 -0.108530684
[5,] -0.169992420 -0.133640020
[6,] -0.200975954 -0.169992420
[7,] -0.194305752 -0.200975954
[8,] -0.223096224 -0.194305752
[9,] -0.243083218 -0.223096224
[10,] -0.112784232 -0.243083218
[11,] 0.973355022 -0.112784232
[12,] 0.009749545 0.973355022
[13,] 0.032530610 0.009749545
[14,] -0.012697919 0.032530610
[15,] -0.015600870 -0.012697919
[16,] -0.009149839 -0.015600870
[17,] 0.961132803 -0.009149839
[18,] -0.058684006 0.961132803
[19,] -0.084989163 -0.058684006
[20,] -0.093994625 -0.084989163
[21,] 0.905101412 -0.093994625
[22,] 0.135812994 0.905101412
[23,] -0.791635348 0.135812994
[24,] 0.226966712 -0.791635348
[25,] 0.169035547 0.226966712
[26,] 0.135533762 0.169035547
[27,] 0.113620767 0.135533762
[28,] 0.141830542 0.113620767
[29,] 0.121779048 0.141830542
[30,] 0.091207629 0.121779048
[31,] -0.931476770 0.091207629
[32,] 1.025108437 -0.931476770
[33,] 0.045351839 1.025108437
[34,] -0.745482891 0.045351839
[35,] 0.291190627 -0.745482891
[36,] 0.307951462 0.291190627
[37,] -0.761144961 0.307951462
[38,] -0.788055310 -0.761144961
[39,] 0.197025457 -0.788055310
[40,] 0.225224558 0.197025457
[41,] 0.190228486 0.225224558
[42,] -0.884699864 0.190228486
[43,] 0.044176596 -0.884699864
[44,] -0.004761997 0.044176596
[45,] 0.033202042 -0.004761997
[46,] 0.288082054 0.033202042
[47,] 0.365650761 0.288082054
[48,] 0.353420132 0.365650761
[49,] 0.223217384 0.353420132
[50,] 0.158437401 0.223217384
[51,] -0.877914335 0.158437401
[52,] 0.102348958 -0.877914335
[53,] -0.921007560 0.102348958
[54,] 0.058591793 -0.921007560
[55,] 0.036015657 0.058591793
[56,] 0.013568818 0.036015657
[57,] 0.005114040 0.013568818
[58,] 0.219774515 0.005114040
[59,] 0.265921470 0.219774515
[60,] 0.267355471 0.265921470
[61,] 0.197441833 0.267355471
[62,] -0.860996331 0.197441833
[63,] 0.092526937 -0.860996331
[64,] 0.072457478 0.092526937
[65,] 0.045094543 0.072457478
[66,] 0.008085061 0.045094543
[67,] -0.005023934 0.008085061
[68,] -0.022790100 -0.005023934
[69,] -0.043397358 -0.022790100
[70,] 0.102265076 -0.043397358
[71,] 0.115622262 0.102265076
[72,] 0.114297134 0.115622262
[73,] 1.048960455 0.114297134
[74,] 0.003598886 1.048960455
[75,] -1.064211155 0.003598886
[76,] -0.038279438 -1.064211155
[77,] -0.081623697 -0.038279438
[78,] -0.105741800 -0.081623697
[79,] -0.113059573 -0.105741800
[80,] -0.155405408 -0.113059573
[81,] -0.204747325 -0.155405408
[82,] -0.079971340 -0.204747325
[83,] 0.927861767 -0.079971340
[84,] -0.096710434 0.927861767
[85,] -0.112716520 -0.096710434
[86,] -0.152560603 -0.112716520
[87,] -0.162384458 -0.152560603
[88,] -0.158576782 -0.162384458
[89,] 0.787039370 -0.158576782
[90,] -0.251160283 0.787039370
[91,] -0.254427751 -0.251160283
[92,] -0.286169013 -0.254427751
[93,] 0.738454496 -0.286169013
[94,] -1.097646073 0.738454496
[95,] 0.942968793 -1.097646073
[96,] -0.060027165 0.942968793
[97,] -0.101792977 -0.060027165
[98,] -0.119615073 -0.101792977
[99,] -0.099944281 -0.119615073
[100,] -0.042713161 -0.099944281
[101,] -0.029183910 -0.042713161
[102,] -0.016322192 -0.029183910
[103,] -0.042403949 -0.016322192
[104,] 0.921105408 -0.042403949
[105,] -0.061127803 0.921105408
[106,] 0.109915974 -0.061127803
[107,] 1.147987677 0.109915974
[108,] 0.162748562 1.147987677
[109,] -0.899898203 0.162748562
[110,] 1.071931886 -0.899898203
[111,] 0.084724474 1.071931886
[112,] -0.892565351 0.084724474
[113,] 0.093317086 -0.892565351
[114,] -0.929879002 0.093317086
[115,] 1.052709385 -0.929879002
[116,] 0.020533027 1.052709385
[117,] 0.017355165 0.020533027
[118,] 0.129720823 0.017355165
[119,] -0.841228855 0.129720823
[120,] -0.844919408 -0.841228855
[121,] 0.079427139 -0.844919408
[122,] 1.019470341 0.079427139
[123,] -0.046610706 1.019470341
[124,] -0.066222822 -0.046610706
[125,] -0.130436169 -0.066222822
[126,] -0.157234325 -0.130436169
[127,] -0.163337373 -0.157234325
[128,] 0.801312443 -0.163337373
[129,] 0.813711886 0.801312443
[130,] -0.030384700 0.813711886
[131,] -0.988406820 -0.030384700
[132,] 0.025580047 -0.988406820
[133,] -0.026941494 0.025580047
[134,] -0.055662644 -0.026941494
[135,] -0.078126672 -0.055662644
[136,] 0.950870763 -0.078126672
[137,] -0.062127889 0.950870763
[138,] -0.074317514 -0.062127889
[139,] -1.061186504 -0.074317514
[140,] 0.939528596 -1.061186504
[141,] -1.048629973 0.939528596
[142,] 0.074018099 -1.048629973
[143,] 0.122180925 0.074018099
[144,] 0.150872716 0.122180925
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.004229500 -0.962139570
2 -0.067391519 -0.004229500
3 -0.108530684 -0.067391519
4 -0.133640020 -0.108530684
5 -0.169992420 -0.133640020
6 -0.200975954 -0.169992420
7 -0.194305752 -0.200975954
8 -0.223096224 -0.194305752
9 -0.243083218 -0.223096224
10 -0.112784232 -0.243083218
11 0.973355022 -0.112784232
12 0.009749545 0.973355022
13 0.032530610 0.009749545
14 -0.012697919 0.032530610
15 -0.015600870 -0.012697919
16 -0.009149839 -0.015600870
17 0.961132803 -0.009149839
18 -0.058684006 0.961132803
19 -0.084989163 -0.058684006
20 -0.093994625 -0.084989163
21 0.905101412 -0.093994625
22 0.135812994 0.905101412
23 -0.791635348 0.135812994
24 0.226966712 -0.791635348
25 0.169035547 0.226966712
26 0.135533762 0.169035547
27 0.113620767 0.135533762
28 0.141830542 0.113620767
29 0.121779048 0.141830542
30 0.091207629 0.121779048
31 -0.931476770 0.091207629
32 1.025108437 -0.931476770
33 0.045351839 1.025108437
34 -0.745482891 0.045351839
35 0.291190627 -0.745482891
36 0.307951462 0.291190627
37 -0.761144961 0.307951462
38 -0.788055310 -0.761144961
39 0.197025457 -0.788055310
40 0.225224558 0.197025457
41 0.190228486 0.225224558
42 -0.884699864 0.190228486
43 0.044176596 -0.884699864
44 -0.004761997 0.044176596
45 0.033202042 -0.004761997
46 0.288082054 0.033202042
47 0.365650761 0.288082054
48 0.353420132 0.365650761
49 0.223217384 0.353420132
50 0.158437401 0.223217384
51 -0.877914335 0.158437401
52 0.102348958 -0.877914335
53 -0.921007560 0.102348958
54 0.058591793 -0.921007560
55 0.036015657 0.058591793
56 0.013568818 0.036015657
57 0.005114040 0.013568818
58 0.219774515 0.005114040
59 0.265921470 0.219774515
60 0.267355471 0.265921470
61 0.197441833 0.267355471
62 -0.860996331 0.197441833
63 0.092526937 -0.860996331
64 0.072457478 0.092526937
65 0.045094543 0.072457478
66 0.008085061 0.045094543
67 -0.005023934 0.008085061
68 -0.022790100 -0.005023934
69 -0.043397358 -0.022790100
70 0.102265076 -0.043397358
71 0.115622262 0.102265076
72 0.114297134 0.115622262
73 1.048960455 0.114297134
74 0.003598886 1.048960455
75 -1.064211155 0.003598886
76 -0.038279438 -1.064211155
77 -0.081623697 -0.038279438
78 -0.105741800 -0.081623697
79 -0.113059573 -0.105741800
80 -0.155405408 -0.113059573
81 -0.204747325 -0.155405408
82 -0.079971340 -0.204747325
83 0.927861767 -0.079971340
84 -0.096710434 0.927861767
85 -0.112716520 -0.096710434
86 -0.152560603 -0.112716520
87 -0.162384458 -0.152560603
88 -0.158576782 -0.162384458
89 0.787039370 -0.158576782
90 -0.251160283 0.787039370
91 -0.254427751 -0.251160283
92 -0.286169013 -0.254427751
93 0.738454496 -0.286169013
94 -1.097646073 0.738454496
95 0.942968793 -1.097646073
96 -0.060027165 0.942968793
97 -0.101792977 -0.060027165
98 -0.119615073 -0.101792977
99 -0.099944281 -0.119615073
100 -0.042713161 -0.099944281
101 -0.029183910 -0.042713161
102 -0.016322192 -0.029183910
103 -0.042403949 -0.016322192
104 0.921105408 -0.042403949
105 -0.061127803 0.921105408
106 0.109915974 -0.061127803
107 1.147987677 0.109915974
108 0.162748562 1.147987677
109 -0.899898203 0.162748562
110 1.071931886 -0.899898203
111 0.084724474 1.071931886
112 -0.892565351 0.084724474
113 0.093317086 -0.892565351
114 -0.929879002 0.093317086
115 1.052709385 -0.929879002
116 0.020533027 1.052709385
117 0.017355165 0.020533027
118 0.129720823 0.017355165
119 -0.841228855 0.129720823
120 -0.844919408 -0.841228855
121 0.079427139 -0.844919408
122 1.019470341 0.079427139
123 -0.046610706 1.019470341
124 -0.066222822 -0.046610706
125 -0.130436169 -0.066222822
126 -0.157234325 -0.130436169
127 -0.163337373 -0.157234325
128 0.801312443 -0.163337373
129 0.813711886 0.801312443
130 -0.030384700 0.813711886
131 -0.988406820 -0.030384700
132 0.025580047 -0.988406820
133 -0.026941494 0.025580047
134 -0.055662644 -0.026941494
135 -0.078126672 -0.055662644
136 0.950870763 -0.078126672
137 -0.062127889 0.950870763
138 -0.074317514 -0.062127889
139 -1.061186504 -0.074317514
140 0.939528596 -1.061186504
141 -1.048629973 0.939528596
142 0.074018099 -1.048629973
143 0.122180925 0.074018099
144 0.150872716 0.122180925
> 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/wessaorg/rcomp/tmp/7sfwd1352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89gpd1352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9v98d1352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1014t61352123336.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11k10p1352123336.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/wessaorg/rcomp/tmp/12x93n1352123337.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/wessaorg/rcomp/tmp/13rldy1352123337.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/wessaorg/rcomp/tmp/14m6uz1352123337.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/wessaorg/rcomp/tmp/15gmzx1352123337.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/wessaorg/rcomp/tmp/16t2br1352123337.tab")
+ }
>
> try(system("convert tmp/1qmhu1352123336.ps tmp/1qmhu1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/2atmi1352123336.ps tmp/2atmi1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m36v1352123336.ps tmp/3m36v1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/4r1301352123336.ps tmp/4r1301352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/58ikr1352123336.ps tmp/58ikr1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uvjv1352123336.ps tmp/6uvjv1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sfwd1352123336.ps tmp/7sfwd1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/89gpd1352123336.ps tmp/89gpd1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v98d1352123336.ps tmp/9v98d1352123336.png",intern=TRUE))
character(0)
> try(system("convert tmp/1014t61352123336.ps tmp/1014t61352123336.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.370 1.411 10.774