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(2000
+ ,1
+ ,501
+ ,134
+ ,368
+ ,6.70
+ ,8.50
+ ,8.70
+ ,2000
+ ,2
+ ,485
+ ,124
+ ,361
+ ,6.80
+ ,8.40
+ ,8.60
+ ,2000
+ ,3
+ ,464
+ ,113
+ ,351
+ ,6.70
+ ,8.40
+ ,8.60
+ ,2000
+ ,4
+ ,460
+ ,109
+ ,351
+ ,6.60
+ ,8.30
+ ,8.50
+ ,2001
+ ,5
+ ,467
+ ,109
+ ,358
+ ,6.40
+ ,8.20
+ ,8.50
+ ,2001
+ ,6
+ ,460
+ ,106
+ ,354
+ ,6.30
+ ,8.20
+ ,8.50
+ ,2001
+ ,7
+ ,448
+ ,101
+ ,347
+ ,6.30
+ ,8.10
+ ,8.50
+ ,2001
+ ,8
+ ,443
+ ,98
+ ,345
+ ,6.50
+ ,8.10
+ ,8.50
+ ,2001
+ ,9
+ ,436
+ ,93
+ ,343
+ ,6.50
+ ,8.10
+ ,8.50
+ ,2001
+ ,10
+ ,431
+ ,91
+ ,340
+ ,6.40
+ ,8.10
+ ,8.50
+ ,2001
+ ,11
+ ,484
+ ,122
+ ,362
+ ,6.20
+ ,8.10
+ ,8.50
+ ,2001
+ ,12
+ ,510
+ ,139
+ ,370
+ ,6.20
+ ,8.10
+ ,8.60
+ ,2001
+ ,13
+ ,513
+ ,140
+ ,373
+ ,6.50
+ ,8.10
+ ,8.60
+ ,2001
+ ,14
+ ,503
+ ,132
+ ,371
+ ,7.00
+ ,8.20
+ ,8.60
+ ,2001
+ ,15
+ ,471
+ ,117
+ ,354
+ ,7.20
+ ,8.20
+ ,8.70
+ ,2001
+ ,16
+ ,471
+ ,114
+ ,357
+ ,7.30
+ ,8.30
+ ,8.70
+ ,2002
+ ,17
+ ,476
+ ,113
+ ,363
+ ,7.40
+ ,8.20
+ ,8.70
+ ,2002
+ ,18
+ ,475
+ ,110
+ ,364
+ ,7.40
+ ,8.30
+ ,8.80
+ ,2002
+ ,19
+ ,470
+ ,107
+ ,363
+ ,7.40
+ ,8.30
+ ,8.80
+ ,2002
+ ,20
+ ,461
+ ,103
+ ,358
+ ,7.30
+ ,8.40
+ ,8.90
+ ,2002
+ ,21
+ ,455
+ ,98
+ ,357
+ ,7.40
+ ,8.50
+ ,8.90
+ ,2002
+ ,22
+ ,456
+ ,98
+ ,357
+ ,7.40
+ ,8.50
+ ,8.90
+ ,2002
+ ,23
+ ,517
+ ,137
+ ,380
+ ,7.60
+ ,8.60
+ ,9.00
+ ,2002
+ ,24
+ ,525
+ ,148
+ ,378
+ ,7.60
+ ,8.60
+ ,9.00
+ ,2002
+ ,25
+ ,523
+ ,147
+ ,376
+ ,7.70
+ ,8.70
+ ,9.00
+ ,2002
+ ,26
+ ,519
+ ,139
+ ,380
+ ,7.70
+ ,8.70
+ ,9.00
+ ,2002
+ ,27
+ ,509
+ ,130
+ ,379
+ ,7.80
+ ,8.80
+ ,9.00
+ ,2002
+ ,28
+ ,512
+ ,128
+ ,384
+ ,7.80
+ ,8.80
+ ,9.00
+ ,2003
+ ,29
+ ,519
+ ,127
+ ,392
+ ,8.00
+ ,8.90
+ ,9.10
+ ,2003
+ ,30
+ ,517
+ ,123
+ ,394
+ ,8.10
+ ,9.00
+ ,9.10
+ ,2003
+ ,31
+ ,510
+ ,118
+ ,392
+ ,8.10
+ ,9.00
+ ,9.10
+ ,2003
+ ,32
+ ,509
+ ,114
+ ,396
+ ,8.20
+ ,9.00
+ ,9.10
+ ,2003
+ ,33
+ ,501
+ ,108
+ ,392
+ ,8.10
+ ,9.00
+ ,9.10
+ ,2003
+ ,34
+ ,507
+ ,111
+ ,396
+ ,8.10
+ ,9.10
+ ,9.10
+ ,2003
+ ,35
+ ,569
+ ,151
+ ,419
+ ,8.10
+ ,9.10
+ ,9.10
+ ,2003
+ ,36
+ ,580
+ ,159
+ ,421
+ ,8.10
+ ,9.00
+ ,9.10
+ ,2003
+ ,37
+ ,578
+ ,158
+ ,420
+ ,8.20
+ ,9.10
+ ,9.10
+ ,2003
+ ,38
+ ,565
+ ,148
+ ,418
+ ,8.20
+ ,9.00
+ ,9.10
+ ,2003
+ ,39
+ ,547
+ ,138
+ ,410
+ ,8.30
+ ,9.10
+ ,9.10
+ ,2003
+ ,40
+ ,555
+ ,137
+ ,418
+ ,8.40
+ ,9.10
+ ,9.20
+ ,2004
+ ,41
+ ,562
+ ,136
+ ,426
+ ,8.60
+ ,9.20
+ ,9.30
+ ,2004
+ ,42
+ ,561
+ ,133
+ ,428
+ ,8.60
+ ,9.20
+ ,9.30
+ ,2004
+ ,43
+ ,555
+ ,126
+ ,430
+ ,8.40
+ ,9.20
+ ,9.30
+ ,2004
+ ,44
+ ,544
+ ,120
+ ,424
+ ,8.00
+ ,9.20
+ ,9.20
+ ,2004
+ ,45
+ ,537
+ ,114
+ ,423
+ ,7.90
+ ,9.20
+ ,9.20
+ ,2004
+ ,46
+ ,543
+ ,116
+ ,427
+ ,8.10
+ ,9.30
+ ,9.20
+ ,2004
+ ,47
+ ,594
+ ,153
+ ,441
+ ,8.50
+ ,9.30
+ ,9.20
+ ,2004
+ ,48
+ ,611
+ ,162
+ ,449
+ ,8.80
+ ,9.30
+ ,9.20
+ ,2004
+ ,49
+ ,613
+ ,161
+ ,452
+ ,8.80
+ ,9.30
+ ,9.20
+ ,2004
+ ,50
+ ,611
+ ,149
+ ,462
+ ,8.50
+ ,9.30
+ ,9.20
+ ,2004
+ ,51
+ ,594
+ ,139
+ ,455
+ ,8.30
+ ,9.40
+ ,9.20
+ ,2004
+ ,52
+ ,595
+ ,135
+ ,461
+ ,8.30
+ ,9.40
+ ,9.20
+ ,2005
+ ,53
+ ,591
+ ,130
+ ,461
+ ,8.30
+ ,9.30
+ ,9.20
+ ,2005
+ ,54
+ ,589
+ ,127
+ ,463
+ ,8.40
+ ,9.30
+ ,9.20
+ ,2005
+ ,55
+ ,584
+ ,122
+ ,462
+ ,8.50
+ ,9.30
+ ,9.20
+ ,2005
+ ,56
+ ,573
+ ,117
+ ,456
+ ,8.50
+ ,9.30
+ ,9.20
+ ,2005
+ ,57
+ ,567
+ ,112
+ ,455
+ ,8.60
+ ,9.20
+ ,9.10
+ ,2005
+ ,58
+ ,569
+ ,113
+ ,456
+ ,8.50
+ ,9.20
+ ,9.10
+ ,2005
+ ,59
+ ,621
+ ,149
+ ,472
+ ,8.60
+ ,9.20
+ ,9.00
+ ,2005
+ ,60
+ ,629
+ ,157
+ ,472
+ ,8.60
+ ,9.10
+ ,8.90
+ ,2005
+ ,61
+ ,628
+ ,157
+ ,471
+ ,8.60
+ ,9.10
+ ,8.90
+ ,2005
+ ,62
+ ,612
+ ,147
+ ,465
+ ,8.50
+ ,9.10
+ ,9.00
+ ,2005
+ ,63
+ ,595
+ ,137
+ ,459
+ ,8.40
+ ,9.10
+ ,8.90
+ ,2005
+ ,64
+ ,597
+ ,132
+ ,465
+ ,8.40
+ ,9.00
+ ,8.80
+ ,2006
+ ,65
+ ,593
+ ,125
+ ,468
+ ,8.50
+ ,8.90
+ ,8.70
+ ,2006
+ ,66
+ ,590
+ ,123
+ ,467
+ ,8.50
+ ,8.80
+ ,8.60
+ ,2006
+ ,67
+ ,580
+ ,117
+ ,463
+ ,8.50
+ ,8.70
+ ,8.50
+ ,2006
+ ,68
+ ,574
+ ,114
+ ,460
+ ,8.60
+ ,8.60
+ ,8.50
+ ,2006
+ ,69
+ ,573
+ ,111
+ ,462
+ ,8.60
+ ,8.60
+ ,8.40
+ ,2006
+ ,70
+ ,573
+ ,112
+ ,461
+ ,8.40
+ ,8.50
+ ,8.30
+ ,2006
+ ,71
+ ,620
+ ,144
+ ,476
+ ,8.20
+ ,8.40
+ ,8.20
+ ,2006
+ ,72
+ ,626
+ ,150
+ ,476
+ ,8.00
+ ,8.40
+ ,8.20
+ ,2006
+ ,73
+ ,620
+ ,149
+ ,471
+ ,8.00
+ ,8.30
+ ,8.10
+ ,2006
+ ,74
+ ,588
+ ,134
+ ,453
+ ,8.00
+ ,8.20
+ ,8.00
+ ,2006
+ ,75
+ ,566
+ ,123
+ ,443
+ ,8.00
+ ,8.20
+ ,7.90
+ ,2006
+ ,76
+ ,557
+ ,116
+ ,442
+ ,7.90
+ ,8.00
+ ,7.80
+ ,2007
+ ,77
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+ ,117
+ ,444
+ ,7.90
+ ,7.90
+ ,7.60
+ ,2007
+ ,78
+ ,549
+ ,111
+ ,438
+ ,7.90
+ ,7.80
+ ,7.50
+ ,2007
+ ,79
+ ,532
+ ,105
+ ,427
+ ,7.90
+ ,7.70
+ ,7.40
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+ ,80
+ ,526
+ ,102
+ ,424
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+ ,7.60
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+ ,81
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+ ,95
+ ,416
+ ,7.90
+ ,7.60
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+ ,7.40
+ ,7.60
+ ,7.20
+ ,2007
+ ,83
+ ,555
+ ,124
+ ,431
+ ,7.20
+ ,7.60
+ ,7.20
+ ,2007
+ ,84
+ ,565
+ ,130
+ ,434
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+ ,7.20
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+ ,85
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+ ,124
+ ,418
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+ ,7.50
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+ ,86
+ ,527
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+ ,412
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+ ,7.50
+ ,7.00
+ ,2007
+ ,87
+ ,510
+ ,106
+ ,404
+ ,7.20
+ ,7.40
+ ,7.00
+ ,2007
+ ,88
+ ,514
+ ,105
+ ,409
+ ,7.20
+ ,7.40
+ ,6.90
+ ,2008
+ ,89
+ ,517
+ ,105
+ ,412
+ ,7.10
+ ,7.40
+ ,6.90
+ ,2008
+ ,90
+ ,508
+ ,101
+ ,406
+ ,6.90
+ ,7.30
+ ,6.80
+ ,2008
+ ,91
+ ,493
+ ,95
+ ,398
+ ,6.80
+ ,7.30
+ ,6.80
+ ,2008
+ ,92
+ ,490
+ ,93
+ ,397
+ ,6.80
+ ,7.40
+ ,6.80
+ ,2008
+ ,93
+ ,469
+ ,84
+ ,385
+ ,6.80
+ ,7.50
+ ,6.90
+ ,2008
+ ,94
+ ,478
+ ,87
+ ,390
+ ,6.90
+ ,7.60
+ ,7.00
+ ,2008
+ ,95
+ ,528
+ ,116
+ ,413
+ ,7.10
+ ,7.60
+ ,7.00
+ ,2008
+ ,96
+ ,534
+ ,120
+ ,413
+ ,7.20
+ ,7.70
+ ,7.10
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+ ,109
+ ,397
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+ ,105
+ ,397
+ ,7.10
+ ,8.10
+ ,7.50
+ ,2008
+ ,100
+ ,516
+ ,107
+ ,409
+ ,7.20
+ ,8.40
+ ,7.70
+ ,2009
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+ ,528
+ ,109
+ ,419
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+ ,8.70
+ ,8.10
+ ,2009
+ ,102
+ ,533
+ ,109
+ ,424
+ ,7.70
+ ,9.00
+ ,8.40
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+ ,103
+ ,536
+ ,108
+ ,428
+ ,7.80
+ ,9.30
+ ,8.60
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+ ,104
+ ,537
+ ,107
+ ,430
+ ,7.70
+ ,9.40
+ ,8.80
+ ,2009
+ ,105
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+ ,99
+ ,424
+ ,7.70
+ ,9.50
+ ,8.90
+ ,2009
+ ,106
+ ,536
+ ,103
+ ,433
+ ,7.80
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+ ,9.10
+ ,2009
+ ,107
+ ,587
+ ,131
+ ,456
+ ,8.00
+ ,9.80
+ ,9.20
+ ,2009
+ ,108
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+ ,459
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+ ,9.30
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+ ,9.40
+ ,2009
+ ,111
+ ,558
+ ,118
+ ,439
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+ ,10.00
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+ ,575
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+ ,9.50
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+ ,113
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+ ,121
+ ,460
+ ,8.40
+ ,10.10
+ ,9.70
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+ ,114
+ ,575
+ ,118
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+ ,115
+ ,563
+ ,113
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+ ,107
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+ ,9.70
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+ ,117
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+ ,10.20
+ ,9.70
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+ ,118
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+ ,10.10
+ ,9.60
+ ,2010
+ ,119
+ ,601
+ ,130
+ ,471
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+ ,124
+ ,551
+ ,109
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+ ,428
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+ ,9.90
+ ,9.50
+ ,2011
+ ,130
+ ,519
+ ,92
+ ,426
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+ ,10.00
+ ,9.50
+ ,2011
+ ,131
+ ,572
+ ,120
+ ,452
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+ ,10.10
+ ,9.60
+ ,2011
+ ,132
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+ ,127
+ ,455
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+ ,10.20
+ ,9.70
+ ,2011
+ ,133
+ ,563
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+ ,2011
+ ,134
+ ,548
+ ,114
+ ,434
+ ,7.20
+ ,10.50
+ ,9.90
+ ,2011
+ ,135
+ ,539
+ ,108
+ ,431
+ ,7.20
+ ,10.60
+ ,10.00
+ ,2011
+ ,136
+ ,541
+ ,106
+ ,435
+ ,7.10
+ ,10.70
+ ,10.00
+ ,2012
+ ,137
+ ,562
+ ,111
+ ,450
+ ,7.10
+ ,10.80
+ ,10.10
+ ,2012
+ ,138
+ ,559
+ ,110
+ ,449
+ ,7.10
+ ,10.90
+ ,10.20
+ ,2012
+ ,139
+ ,546
+ ,104
+ ,442
+ ,7.20
+ ,11.00
+ ,10.30
+ ,2012
+ ,140
+ ,536
+ ,100
+ ,437
+ ,7.30
+ ,11.20
+ ,10.30
+ ,2012
+ ,141
+ ,528
+ ,96
+ ,431
+ ,7.40
+ ,11.30
+ ,10.40
+ ,2012
+ ,142
+ ,530
+ ,98
+ ,433
+ ,7.40
+ ,11.40
+ ,10.50
+ ,2012
+ ,143
+ ,582
+ ,122
+ ,460
+ ,7.50
+ ,11.50
+ ,10.50
+ ,2012
+ ,144
+ ,599
+ ,134
+ ,465
+ ,7.40
+ ,11.50
+ ,10.60
+ ,2012
+ ,145
+ ,584
+ ,133
+ ,451
+ ,7.40
+ ,11.60
+ ,10.60)
+ ,dim=c(8
+ ,145)
+ ,dimnames=list(c('Jaartal'
+ ,'t'
+ ,'Totale_werkloosheid'
+ ,'Jonger_dan_25'
+ ,'Vanaf_25'
+ ,'België'
+ ,'Euroraad'
+ ,'EU-27')
+ ,1:145))
> y <- array(NA,dim=c(8,145),dimnames=list(c('Jaartal','t','Totale_werkloosheid','Jonger_dan_25','Vanaf_25','België','Euroraad','EU-27'),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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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
Totale_werkloosheid Jaartal t Jonger_dan_25 Vanaf_25 Belgi\303\253
1 501 2000 1 134 368 6.7
2 485 2000 2 124 361 6.8
3 464 2000 3 113 351 6.7
4 460 2000 4 109 351 6.6
5 467 2001 5 109 358 6.4
6 460 2001 6 106 354 6.3
7 448 2001 7 101 347 6.3
8 443 2001 8 98 345 6.5
9 436 2001 9 93 343 6.5
10 431 2001 10 91 340 6.4
11 484 2001 11 122 362 6.2
12 510 2001 12 139 370 6.2
13 513 2001 13 140 373 6.5
14 503 2001 14 132 371 7.0
15 471 2001 15 117 354 7.2
16 471 2001 16 114 357 7.3
17 476 2002 17 113 363 7.4
18 475 2002 18 110 364 7.4
19 470 2002 19 107 363 7.4
20 461 2002 20 103 358 7.3
21 455 2002 21 98 357 7.4
22 456 2002 22 98 357 7.4
23 517 2002 23 137 380 7.6
24 525 2002 24 148 378 7.6
25 523 2002 25 147 376 7.7
26 519 2002 26 139 380 7.7
27 509 2002 27 130 379 7.8
28 512 2002 28 128 384 7.8
29 519 2003 29 127 392 8.0
30 517 2003 30 123 394 8.1
31 510 2003 31 118 392 8.1
32 509 2003 32 114 396 8.2
33 501 2003 33 108 392 8.1
34 507 2003 34 111 396 8.1
35 569 2003 35 151 419 8.1
36 580 2003 36 159 421 8.1
37 578 2003 37 158 420 8.2
38 565 2003 38 148 418 8.2
39 547 2003 39 138 410 8.3
40 555 2003 40 137 418 8.4
41 562 2004 41 136 426 8.6
42 561 2004 42 133 428 8.6
43 555 2004 43 126 430 8.4
44 544 2004 44 120 424 8.0
45 537 2004 45 114 423 7.9
46 543 2004 46 116 427 8.1
47 594 2004 47 153 441 8.5
48 611 2004 48 162 449 8.8
49 613 2004 49 161 452 8.8
50 611 2004 50 149 462 8.5
51 594 2004 51 139 455 8.3
52 595 2004 52 135 461 8.3
53 591 2005 53 130 461 8.3
54 589 2005 54 127 463 8.4
55 584 2005 55 122 462 8.5
56 573 2005 56 117 456 8.5
57 567 2005 57 112 455 8.6
58 569 2005 58 113 456 8.5
59 621 2005 59 149 472 8.6
60 629 2005 60 157 472 8.6
61 628 2005 61 157 471 8.6
62 612 2005 62 147 465 8.5
63 595 2005 63 137 459 8.4
64 597 2005 64 132 465 8.4
65 593 2006 65 125 468 8.5
66 590 2006 66 123 467 8.5
67 580 2006 67 117 463 8.5
68 574 2006 68 114 460 8.6
69 573 2006 69 111 462 8.6
70 573 2006 70 112 461 8.4
71 620 2006 71 144 476 8.2
72 626 2006 72 150 476 8.0
73 620 2006 73 149 471 8.0
74 588 2006 74 134 453 8.0
75 566 2006 75 123 443 8.0
76 557 2006 76 116 442 7.9
77 561 2007 77 117 444 7.9
78 549 2007 78 111 438 7.9
79 532 2007 79 105 427 7.9
80 526 2007 80 102 424 8.0
81 511 2007 81 95 416 7.9
82 499 2007 82 93 406 7.4
83 555 2007 83 124 431 7.2
84 565 2007 84 130 434 7.0
85 542 2007 85 124 418 6.9
86 527 2007 86 115 412 7.1
87 510 2007 87 106 404 7.2
88 514 2007 88 105 409 7.2
89 517 2008 89 105 412 7.1
90 508 2008 90 101 406 6.9
91 493 2008 91 95 398 6.8
92 490 2008 92 93 397 6.8
93 469 2008 93 84 385 6.8
94 478 2008 94 87 390 6.9
95 528 2008 95 116 413 7.1
96 534 2008 96 120 413 7.2
97 518 2008 97 117 401 7.2
98 506 2008 98 109 397 7.1
99 502 2008 99 105 397 7.1
100 516 2008 100 107 409 7.2
101 528 2009 101 109 419 7.5
102 533 2009 102 109 424 7.7
103 536 2009 103 108 428 7.8
104 537 2009 104 107 430 7.7
105 524 2009 105 99 424 7.7
106 536 2009 106 103 433 7.8
107 587 2009 107 131 456 8.0
108 597 2009 108 137 459 8.1
109 581 2009 109 135 446 8.1
110 564 2009 110 124 441 8.0
111 558 2009 111 118 439 8.1
112 575 2010 112 121 454 8.2
113 580 2010 113 121 460 8.4
114 575 2010 114 118 457 8.5
115 563 2010 115 113 451 8.5
116 552 2010 116 107 444 8.5
117 537 2010 117 100 437 8.5
118 545 2010 118 102 443 8.5
119 601 2010 119 130 471 8.4
120 604 2010 120 136 469 8.3
121 586 2010 121 133 454 8.2
122 564 2010 122 120 444 8.1
123 549 2010 123 112 436 7.9
124 551 2010 124 109 442 7.6
125 556 2011 125 110 446 7.3
126 548 2011 126 106 442 7.1
127 540 2011 127 102 438 7.0
128 531 2011 128 98 433 7.1
129 521 2011 129 92 428 7.1
130 519 2011 130 92 426 7.1
131 572 2011 131 120 452 7.3
132 581 2011 132 127 455 7.3
133 563 2011 133 124 439 7.3
134 548 2011 134 114 434 7.2
135 539 2011 135 108 431 7.2
136 541 2011 136 106 435 7.1
137 562 2012 137 111 450 7.1
138 559 2012 138 110 449 7.1
139 546 2012 139 104 442 7.2
140 536 2012 140 100 437 7.3
141 528 2012 141 96 431 7.4
142 530 2012 142 98 433 7.4
143 582 2012 143 122 460 7.5
144 599 2012 144 134 465 7.4
145 584 2012 145 133 451 7.4
Euroraad EU-27
1 8.5 8.7
2 8.4 8.6
3 8.4 8.6
4 8.3 8.5
5 8.2 8.5
6 8.2 8.5
7 8.1 8.5
8 8.1 8.5
9 8.1 8.5
10 8.1 8.5
11 8.1 8.5
12 8.1 8.6
13 8.1 8.6
14 8.2 8.6
15 8.2 8.7
16 8.3 8.7
17 8.2 8.7
18 8.3 8.8
19 8.3 8.8
20 8.4 8.9
21 8.5 8.9
22 8.5 8.9
23 8.6 9.0
24 8.6 9.0
25 8.7 9.0
26 8.7 9.0
27 8.8 9.0
28 8.8 9.0
29 8.9 9.1
30 9.0 9.1
31 9.0 9.1
32 9.0 9.1
33 9.0 9.1
34 9.1 9.1
35 9.1 9.1
36 9.0 9.1
37 9.1 9.1
38 9.0 9.1
39 9.1 9.1
40 9.1 9.2
41 9.2 9.3
42 9.2 9.3
43 9.2 9.3
44 9.2 9.2
45 9.2 9.2
46 9.3 9.2
47 9.3 9.2
48 9.3 9.2
49 9.3 9.2
50 9.3 9.2
51 9.4 9.2
52 9.4 9.2
53 9.3 9.2
54 9.3 9.2
55 9.3 9.2
56 9.3 9.2
57 9.2 9.1
58 9.2 9.1
59 9.2 9.0
60 9.1 8.9
61 9.1 8.9
62 9.1 9.0
63 9.1 8.9
64 9.0 8.8
65 8.9 8.7
66 8.8 8.6
67 8.7 8.5
68 8.6 8.5
69 8.6 8.4
70 8.5 8.3
71 8.4 8.2
72 8.4 8.2
73 8.3 8.1
74 8.2 8.0
75 8.2 7.9
76 8.0 7.8
77 7.9 7.6
78 7.8 7.5
79 7.7 7.4
80 7.6 7.3
81 7.6 7.3
82 7.6 7.2
83 7.6 7.2
84 7.6 7.2
85 7.5 7.1
86 7.5 7.0
87 7.4 7.0
88 7.4 6.9
89 7.4 6.9
90 7.3 6.8
91 7.3 6.8
92 7.4 6.8
93 7.5 6.9
94 7.6 7.0
95 7.6 7.0
96 7.7 7.1
97 7.7 7.2
98 7.9 7.3
99 8.1 7.5
100 8.4 7.7
101 8.7 8.1
102 9.0 8.4
103 9.3 8.6
104 9.4 8.8
105 9.5 8.9
106 9.6 9.1
107 9.8 9.2
108 9.8 9.3
109 9.9 9.4
110 10.0 9.4
111 10.0 9.5
112 10.1 9.5
113 10.1 9.7
114 10.1 9.7
115 10.1 9.7
116 10.2 9.7
117 10.2 9.7
118 10.1 9.6
119 10.1 9.6
120 10.1 9.6
121 10.1 9.6
122 10.1 9.6
123 10.1 9.6
124 10.1 9.6
125 10.0 9.5
126 9.9 9.5
127 9.9 9.4
128 9.9 9.4
129 9.9 9.5
130 10.0 9.5
131 10.1 9.6
132 10.2 9.7
133 10.3 9.8
134 10.5 9.9
135 10.6 10.0
136 10.7 10.0
137 10.8 10.1
138 10.9 10.2
139 11.0 10.3
140 11.2 10.3
141 11.3 10.4
142 11.4 10.5
143 11.5 10.5
144 11.5 10.6
145 11.6 10.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Jaartal t Jonger_dan_25
-84.859846 0.043127 0.001109 0.995879
Vanaf_25 `Belgi\\303\\253` Euroraad `EU-27`
1.000234 -0.076660 -0.461901 0.385382
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.1065 -0.1462 -0.0059 0.1496 1.1182
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -84.859846 336.990516 -0.252 0.802
Jaartal 0.043127 0.168533 0.256 0.798
t 0.001109 0.014254 0.078 0.938
Jonger_dan_25 0.995879 0.003930 253.403 <2e-16 ***
Vanaf_25 1.000234 0.003058 327.134 <2e-16 ***
`Belgi\\303\\253` -0.076660 0.113462 -0.676 0.500
Euroraad -0.461901 0.372345 -1.241 0.217
`EU-27` 0.385382 0.350866 1.098 0.274
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5042 on 137 degrees of freedom
Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
F-statistic: 1.661e+05 on 7 and 137 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,] 0.0441230367 0.088246073 0.9558770
[2,] 0.3573185288 0.714637058 0.6426815
[3,] 0.2968624315 0.593724863 0.7031376
[4,] 0.1934032779 0.386806556 0.8065967
[5,] 0.1157020034 0.231404007 0.8842980
[6,] 0.1047585833 0.209517167 0.8952414
[7,] 0.0663808915 0.132761783 0.9336191
[8,] 0.2389261690 0.477852338 0.7610738
[9,] 0.2425732441 0.485146488 0.7574268
[10,] 0.2040925088 0.408185018 0.7959075
[11,] 0.1472493216 0.294498643 0.8527507
[12,] 0.2223422871 0.444684574 0.7776577
[13,] 0.1841944158 0.368388832 0.8158056
[14,] 0.1821302240 0.364260448 0.8178698
[15,] 0.2417708528 0.483541706 0.7582291
[16,] 0.1867755290 0.373551058 0.8132245
[17,] 0.1452078133 0.290415627 0.8547922
[18,] 0.1207206855 0.241441371 0.8792793
[19,] 0.0883050196 0.176610039 0.9116950
[20,] 0.0632875752 0.126575150 0.9367124
[21,] 0.0452865733 0.090573147 0.9547134
[22,] 0.1479019044 0.295803809 0.8520981
[23,] 0.2637105374 0.527421075 0.7362895
[24,] 0.2188830133 0.437766027 0.7811170
[25,] 0.2636168466 0.527233693 0.7363832
[26,] 0.2346543120 0.469308624 0.7653457
[27,] 0.2133129031 0.426625806 0.7866871
[28,] 0.3093186181 0.618637236 0.6906814
[29,] 0.3645665795 0.729133159 0.6354334
[30,] 0.3134188357 0.626837671 0.6865812
[31,] 0.2658351750 0.531670350 0.7341648
[32,] 0.2208755724 0.441751145 0.7791244
[33,] 0.3618166032 0.723633206 0.6381834
[34,] 0.3121495474 0.624299095 0.6878505
[35,] 0.2649296144 0.529859229 0.7350704
[36,] 0.2236140440 0.447228088 0.7763860
[37,] 0.2086492044 0.417298409 0.7913508
[38,] 0.1959938038 0.391987608 0.8040062
[39,] 0.1734772637 0.346954527 0.8265227
[40,] 0.1456633976 0.291326795 0.8543366
[41,] 0.1194010095 0.238802019 0.8805990
[42,] 0.1682530021 0.336506004 0.8317470
[43,] 0.1371067244 0.274213449 0.8628933
[44,] 0.2123592540 0.424718508 0.7876407
[45,] 0.1800091922 0.360018384 0.8199908
[46,] 0.1489512916 0.297902583 0.8510487
[47,] 0.1210496124 0.242099225 0.8789504
[48,] 0.0973733494 0.194746699 0.9026267
[49,] 0.0772537227 0.154507445 0.9227463
[50,] 0.0604042694 0.120808539 0.9395957
[51,] 0.0468166912 0.093633382 0.9531833
[52,] 0.0357297953 0.071459591 0.9642702
[53,] 0.0853567861 0.170713572 0.9146432
[54,] 0.0680704161 0.136140832 0.9319296
[55,] 0.0537999153 0.107599831 0.9462001
[56,] 0.0419611809 0.083922362 0.9580388
[57,] 0.0324296373 0.064859275 0.9675704
[58,] 0.0251521359 0.050304272 0.9748479
[59,] 0.0187285749 0.037457150 0.9812714
[60,] 0.0138277202 0.027655440 0.9861723
[61,] 0.0099413130 0.019882626 0.9900587
[62,] 0.0070506769 0.014101354 0.9929493
[63,] 0.0049350625 0.009870125 0.9950649
[64,] 0.0094021867 0.018804373 0.9905978
[65,] 0.0073446888 0.014689378 0.9926553
[66,] 0.0309156180 0.061831236 0.9690844
[67,] 0.0236197171 0.047239434 0.9763803
[68,] 0.0177937217 0.035587443 0.9822063
[69,] 0.0132957379 0.026591476 0.9867043
[70,] 0.0097026420 0.019405284 0.9902974
[71,] 0.0071932105 0.014386421 0.9928068
[72,] 0.0057800557 0.011560111 0.9942199
[73,] 0.0041756246 0.008351249 0.9958244
[74,] 0.0080388545 0.016077709 0.9919611
[75,] 0.0060116980 0.012023396 0.9939883
[76,] 0.0042729007 0.008545801 0.9957271
[77,] 0.0031472113 0.006294423 0.9968528
[78,] 0.0022416102 0.004483220 0.9977584
[79,] 0.0015517449 0.003103490 0.9984483
[80,] 0.0027181261 0.005436252 0.9972819
[81,] 0.0020589649 0.004117930 0.9979410
[82,] 0.0014764021 0.002952804 0.9985236
[83,] 0.0011870150 0.002374030 0.9988130
[84,] 0.0016869014 0.003373803 0.9983131
[85,] 0.0058806148 0.011761230 0.9941194
[86,] 0.0151041295 0.030208259 0.9848959
[87,] 0.0113876456 0.022775291 0.9886124
[88,] 0.0081214915 0.016242983 0.9918785
[89,] 0.0057841877 0.011568375 0.9942158
[90,] 0.0041947247 0.008389449 0.9958053
[91,] 0.0028804626 0.005760925 0.9971195
[92,] 0.0019347063 0.003869413 0.9980653
[93,] 0.0013286283 0.002657257 0.9986714
[94,] 0.0010604722 0.002120944 0.9989395
[95,] 0.0012145448 0.002429090 0.9987855
[96,] 0.0010136277 0.002027255 0.9989864
[97,] 0.0006436949 0.001287390 0.9993563
[98,] 0.0023534575 0.004706915 0.9976465
[99,] 0.0017502910 0.003500582 0.9982497
[100,] 0.0076184378 0.015236876 0.9923816
[101,] 0.0119480600 0.023896120 0.9880519
[102,] 0.0093772916 0.018754583 0.9906227
[103,] 0.0164155658 0.032831132 0.9835844
[104,] 0.0119509657 0.023901931 0.9880490
[105,] 0.0299097473 0.059819495 0.9700903
[106,] 0.0574369466 0.114873893 0.9425631
[107,] 0.0410948859 0.082189772 0.9589051
[108,] 0.0288160627 0.057632125 0.9711839
[109,] 0.0249079026 0.049815805 0.9750921
[110,] 0.0260044945 0.052008989 0.9739955
[111,] 0.0427253266 0.085450653 0.9572747
[112,] 0.0327664504 0.065532901 0.9672335
[113,] 0.0555698401 0.111139680 0.9444302
[114,] 0.0528936703 0.105787341 0.9471063
[115,] 0.0489782356 0.097956471 0.9510218
[116,] 0.0494801822 0.098960364 0.9505198
[117,] 0.0359346922 0.071869384 0.9640653
[118,] 0.0223799436 0.044759887 0.9776201
[119,] 0.0189573541 0.037914708 0.9810426
[120,] 0.0168841898 0.033768380 0.9831158
[121,] 0.0123757382 0.024751476 0.9876243
[122,] 0.0225683386 0.045136677 0.9774317
[123,] 0.1930831920 0.386166384 0.8069168
[124,] 0.1301768929 0.260353786 0.8698231
> postscript(file="/var/wessaorg/rcomp/tmp/1kueu1352142897.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fxry1352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3inzd1352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ws9y1352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5vblz1352142897.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
-0.842685591 0.116645547 0.064877508 0.031965796 -0.075430892 -0.095632975
7 8 9 10 11 12
-0.161899462 -0.159571713 -0.180818658 -0.197133525 -0.090963373 0.937579539
13 14 15 16 17 18
-0.037111940 0.013797322 -0.068357438 -0.028676167 -0.116962016 0.876983213
19 20 21 22 23 24
-0.136255188 -0.152692635 -0.120317573 0.878573900 0.055796494 -0.899509199
25 26 27 28 29 30
0.149585288 0.114569687 0.130459435 0.119937692 0.092691591 0.128485645
31 32 33 34 35 36
0.107238700 -0.903625569 1.063808363 0.120317473 -0.721322495 0.263881280
37 38 39 40 41 42
0.312741655 -0.775302032 -0.761894829 0.200130193 0.172884092 0.158943356
43 44 45 46 47 48
-0.886814730 0.096627617 0.063359213 0.131079032 0.309846160 0.366954748
49 50 51 52 53 54
0.361022556 0.285118916 0.275293952 -0.743704560 0.145262931 -0.861011787
55 56 57 58 59 60
0.125173175 0.104862677 0.083395785 0.078508456 0.268226223 0.292436469
61 62 63 64 65 66
0.291562053 0.204440648 -0.805604262 0.163624045 0.089850808 0.073081883
67 68 69 70 71 72
0.040529980 -0.010764278 0.013833233 -0.005903744 0.098374666 0.106662071
73 74 75 76 77 78
0.094950922 1.028584630 0.023020856 -1.068210829 -0.077907099 -0.109990778
79 80 81 82 83 84
-0.140903898 -0.153659910 -0.189410859 -0.196212770 -0.090744953 0.916840117
85 86 87 88 89 90
-0.120568463 -0.103493990 -0.178345659 -0.146207827 -0.198811949 0.762014991
91 92 93 94 95 96
-0.269614630 -0.232541602 -0.260280889 0.765121881 -1.106520661 0.924173997
97 98 99 100 101 102
-0.125027421 -0.111994192 -0.114284327 -0.040799714 -0.071718576 -0.035710066
103 104 105 106 107 108
0.027283455 -0.016967036 0.958010334 -0.051940262 0.126137821 1.118182700
109 110 111 112 113 114
0.119526822 -0.887221675 1.056537823 0.075010481 -0.989247175 0.005648667
115 116 117 118 119 120
-1.014661831 1.007330555 -0.020988488 -0.022910882 0.077156633 -0.906421720
121 122 123 124 125 126
-0.924048573 0.015940731 0.968402435 -0.069472803 -0.141173599 -0.219353129
127 128 129 130 131 132
-0.205138294 -0.213895557 0.722900258 0.768450054 -0.100364298 -1.065674010
133 134 135 136 137 138
-0.067748880 -0.062724196 -0.080206505 -0.051970054 0.928540995 -0.068802896
139 140 141 142 143 144
-0.077682739 -0.994059802 1.005068900 -0.980613341 0.164725110 0.165697698
145
0.209935507
> postscript(file="/var/wessaorg/rcomp/tmp/6b7up1352142897.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 -0.842685591 NA
1 0.116645547 -0.842685591
2 0.064877508 0.116645547
3 0.031965796 0.064877508
4 -0.075430892 0.031965796
5 -0.095632975 -0.075430892
6 -0.161899462 -0.095632975
7 -0.159571713 -0.161899462
8 -0.180818658 -0.159571713
9 -0.197133525 -0.180818658
10 -0.090963373 -0.197133525
11 0.937579539 -0.090963373
12 -0.037111940 0.937579539
13 0.013797322 -0.037111940
14 -0.068357438 0.013797322
15 -0.028676167 -0.068357438
16 -0.116962016 -0.028676167
17 0.876983213 -0.116962016
18 -0.136255188 0.876983213
19 -0.152692635 -0.136255188
20 -0.120317573 -0.152692635
21 0.878573900 -0.120317573
22 0.055796494 0.878573900
23 -0.899509199 0.055796494
24 0.149585288 -0.899509199
25 0.114569687 0.149585288
26 0.130459435 0.114569687
27 0.119937692 0.130459435
28 0.092691591 0.119937692
29 0.128485645 0.092691591
30 0.107238700 0.128485645
31 -0.903625569 0.107238700
32 1.063808363 -0.903625569
33 0.120317473 1.063808363
34 -0.721322495 0.120317473
35 0.263881280 -0.721322495
36 0.312741655 0.263881280
37 -0.775302032 0.312741655
38 -0.761894829 -0.775302032
39 0.200130193 -0.761894829
40 0.172884092 0.200130193
41 0.158943356 0.172884092
42 -0.886814730 0.158943356
43 0.096627617 -0.886814730
44 0.063359213 0.096627617
45 0.131079032 0.063359213
46 0.309846160 0.131079032
47 0.366954748 0.309846160
48 0.361022556 0.366954748
49 0.285118916 0.361022556
50 0.275293952 0.285118916
51 -0.743704560 0.275293952
52 0.145262931 -0.743704560
53 -0.861011787 0.145262931
54 0.125173175 -0.861011787
55 0.104862677 0.125173175
56 0.083395785 0.104862677
57 0.078508456 0.083395785
58 0.268226223 0.078508456
59 0.292436469 0.268226223
60 0.291562053 0.292436469
61 0.204440648 0.291562053
62 -0.805604262 0.204440648
63 0.163624045 -0.805604262
64 0.089850808 0.163624045
65 0.073081883 0.089850808
66 0.040529980 0.073081883
67 -0.010764278 0.040529980
68 0.013833233 -0.010764278
69 -0.005903744 0.013833233
70 0.098374666 -0.005903744
71 0.106662071 0.098374666
72 0.094950922 0.106662071
73 1.028584630 0.094950922
74 0.023020856 1.028584630
75 -1.068210829 0.023020856
76 -0.077907099 -1.068210829
77 -0.109990778 -0.077907099
78 -0.140903898 -0.109990778
79 -0.153659910 -0.140903898
80 -0.189410859 -0.153659910
81 -0.196212770 -0.189410859
82 -0.090744953 -0.196212770
83 0.916840117 -0.090744953
84 -0.120568463 0.916840117
85 -0.103493990 -0.120568463
86 -0.178345659 -0.103493990
87 -0.146207827 -0.178345659
88 -0.198811949 -0.146207827
89 0.762014991 -0.198811949
90 -0.269614630 0.762014991
91 -0.232541602 -0.269614630
92 -0.260280889 -0.232541602
93 0.765121881 -0.260280889
94 -1.106520661 0.765121881
95 0.924173997 -1.106520661
96 -0.125027421 0.924173997
97 -0.111994192 -0.125027421
98 -0.114284327 -0.111994192
99 -0.040799714 -0.114284327
100 -0.071718576 -0.040799714
101 -0.035710066 -0.071718576
102 0.027283455 -0.035710066
103 -0.016967036 0.027283455
104 0.958010334 -0.016967036
105 -0.051940262 0.958010334
106 0.126137821 -0.051940262
107 1.118182700 0.126137821
108 0.119526822 1.118182700
109 -0.887221675 0.119526822
110 1.056537823 -0.887221675
111 0.075010481 1.056537823
112 -0.989247175 0.075010481
113 0.005648667 -0.989247175
114 -1.014661831 0.005648667
115 1.007330555 -1.014661831
116 -0.020988488 1.007330555
117 -0.022910882 -0.020988488
118 0.077156633 -0.022910882
119 -0.906421720 0.077156633
120 -0.924048573 -0.906421720
121 0.015940731 -0.924048573
122 0.968402435 0.015940731
123 -0.069472803 0.968402435
124 -0.141173599 -0.069472803
125 -0.219353129 -0.141173599
126 -0.205138294 -0.219353129
127 -0.213895557 -0.205138294
128 0.722900258 -0.213895557
129 0.768450054 0.722900258
130 -0.100364298 0.768450054
131 -1.065674010 -0.100364298
132 -0.067748880 -1.065674010
133 -0.062724196 -0.067748880
134 -0.080206505 -0.062724196
135 -0.051970054 -0.080206505
136 0.928540995 -0.051970054
137 -0.068802896 0.928540995
138 -0.077682739 -0.068802896
139 -0.994059802 -0.077682739
140 1.005068900 -0.994059802
141 -0.980613341 1.005068900
142 0.164725110 -0.980613341
143 0.165697698 0.164725110
144 0.209935507 0.165697698
145 NA 0.209935507
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.116645547 -0.842685591
[2,] 0.064877508 0.116645547
[3,] 0.031965796 0.064877508
[4,] -0.075430892 0.031965796
[5,] -0.095632975 -0.075430892
[6,] -0.161899462 -0.095632975
[7,] -0.159571713 -0.161899462
[8,] -0.180818658 -0.159571713
[9,] -0.197133525 -0.180818658
[10,] -0.090963373 -0.197133525
[11,] 0.937579539 -0.090963373
[12,] -0.037111940 0.937579539
[13,] 0.013797322 -0.037111940
[14,] -0.068357438 0.013797322
[15,] -0.028676167 -0.068357438
[16,] -0.116962016 -0.028676167
[17,] 0.876983213 -0.116962016
[18,] -0.136255188 0.876983213
[19,] -0.152692635 -0.136255188
[20,] -0.120317573 -0.152692635
[21,] 0.878573900 -0.120317573
[22,] 0.055796494 0.878573900
[23,] -0.899509199 0.055796494
[24,] 0.149585288 -0.899509199
[25,] 0.114569687 0.149585288
[26,] 0.130459435 0.114569687
[27,] 0.119937692 0.130459435
[28,] 0.092691591 0.119937692
[29,] 0.128485645 0.092691591
[30,] 0.107238700 0.128485645
[31,] -0.903625569 0.107238700
[32,] 1.063808363 -0.903625569
[33,] 0.120317473 1.063808363
[34,] -0.721322495 0.120317473
[35,] 0.263881280 -0.721322495
[36,] 0.312741655 0.263881280
[37,] -0.775302032 0.312741655
[38,] -0.761894829 -0.775302032
[39,] 0.200130193 -0.761894829
[40,] 0.172884092 0.200130193
[41,] 0.158943356 0.172884092
[42,] -0.886814730 0.158943356
[43,] 0.096627617 -0.886814730
[44,] 0.063359213 0.096627617
[45,] 0.131079032 0.063359213
[46,] 0.309846160 0.131079032
[47,] 0.366954748 0.309846160
[48,] 0.361022556 0.366954748
[49,] 0.285118916 0.361022556
[50,] 0.275293952 0.285118916
[51,] -0.743704560 0.275293952
[52,] 0.145262931 -0.743704560
[53,] -0.861011787 0.145262931
[54,] 0.125173175 -0.861011787
[55,] 0.104862677 0.125173175
[56,] 0.083395785 0.104862677
[57,] 0.078508456 0.083395785
[58,] 0.268226223 0.078508456
[59,] 0.292436469 0.268226223
[60,] 0.291562053 0.292436469
[61,] 0.204440648 0.291562053
[62,] -0.805604262 0.204440648
[63,] 0.163624045 -0.805604262
[64,] 0.089850808 0.163624045
[65,] 0.073081883 0.089850808
[66,] 0.040529980 0.073081883
[67,] -0.010764278 0.040529980
[68,] 0.013833233 -0.010764278
[69,] -0.005903744 0.013833233
[70,] 0.098374666 -0.005903744
[71,] 0.106662071 0.098374666
[72,] 0.094950922 0.106662071
[73,] 1.028584630 0.094950922
[74,] 0.023020856 1.028584630
[75,] -1.068210829 0.023020856
[76,] -0.077907099 -1.068210829
[77,] -0.109990778 -0.077907099
[78,] -0.140903898 -0.109990778
[79,] -0.153659910 -0.140903898
[80,] -0.189410859 -0.153659910
[81,] -0.196212770 -0.189410859
[82,] -0.090744953 -0.196212770
[83,] 0.916840117 -0.090744953
[84,] -0.120568463 0.916840117
[85,] -0.103493990 -0.120568463
[86,] -0.178345659 -0.103493990
[87,] -0.146207827 -0.178345659
[88,] -0.198811949 -0.146207827
[89,] 0.762014991 -0.198811949
[90,] -0.269614630 0.762014991
[91,] -0.232541602 -0.269614630
[92,] -0.260280889 -0.232541602
[93,] 0.765121881 -0.260280889
[94,] -1.106520661 0.765121881
[95,] 0.924173997 -1.106520661
[96,] -0.125027421 0.924173997
[97,] -0.111994192 -0.125027421
[98,] -0.114284327 -0.111994192
[99,] -0.040799714 -0.114284327
[100,] -0.071718576 -0.040799714
[101,] -0.035710066 -0.071718576
[102,] 0.027283455 -0.035710066
[103,] -0.016967036 0.027283455
[104,] 0.958010334 -0.016967036
[105,] -0.051940262 0.958010334
[106,] 0.126137821 -0.051940262
[107,] 1.118182700 0.126137821
[108,] 0.119526822 1.118182700
[109,] -0.887221675 0.119526822
[110,] 1.056537823 -0.887221675
[111,] 0.075010481 1.056537823
[112,] -0.989247175 0.075010481
[113,] 0.005648667 -0.989247175
[114,] -1.014661831 0.005648667
[115,] 1.007330555 -1.014661831
[116,] -0.020988488 1.007330555
[117,] -0.022910882 -0.020988488
[118,] 0.077156633 -0.022910882
[119,] -0.906421720 0.077156633
[120,] -0.924048573 -0.906421720
[121,] 0.015940731 -0.924048573
[122,] 0.968402435 0.015940731
[123,] -0.069472803 0.968402435
[124,] -0.141173599 -0.069472803
[125,] -0.219353129 -0.141173599
[126,] -0.205138294 -0.219353129
[127,] -0.213895557 -0.205138294
[128,] 0.722900258 -0.213895557
[129,] 0.768450054 0.722900258
[130,] -0.100364298 0.768450054
[131,] -1.065674010 -0.100364298
[132,] -0.067748880 -1.065674010
[133,] -0.062724196 -0.067748880
[134,] -0.080206505 -0.062724196
[135,] -0.051970054 -0.080206505
[136,] 0.928540995 -0.051970054
[137,] -0.068802896 0.928540995
[138,] -0.077682739 -0.068802896
[139,] -0.994059802 -0.077682739
[140,] 1.005068900 -0.994059802
[141,] -0.980613341 1.005068900
[142,] 0.164725110 -0.980613341
[143,] 0.165697698 0.164725110
[144,] 0.209935507 0.165697698
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.116645547 -0.842685591
2 0.064877508 0.116645547
3 0.031965796 0.064877508
4 -0.075430892 0.031965796
5 -0.095632975 -0.075430892
6 -0.161899462 -0.095632975
7 -0.159571713 -0.161899462
8 -0.180818658 -0.159571713
9 -0.197133525 -0.180818658
10 -0.090963373 -0.197133525
11 0.937579539 -0.090963373
12 -0.037111940 0.937579539
13 0.013797322 -0.037111940
14 -0.068357438 0.013797322
15 -0.028676167 -0.068357438
16 -0.116962016 -0.028676167
17 0.876983213 -0.116962016
18 -0.136255188 0.876983213
19 -0.152692635 -0.136255188
20 -0.120317573 -0.152692635
21 0.878573900 -0.120317573
22 0.055796494 0.878573900
23 -0.899509199 0.055796494
24 0.149585288 -0.899509199
25 0.114569687 0.149585288
26 0.130459435 0.114569687
27 0.119937692 0.130459435
28 0.092691591 0.119937692
29 0.128485645 0.092691591
30 0.107238700 0.128485645
31 -0.903625569 0.107238700
32 1.063808363 -0.903625569
33 0.120317473 1.063808363
34 -0.721322495 0.120317473
35 0.263881280 -0.721322495
36 0.312741655 0.263881280
37 -0.775302032 0.312741655
38 -0.761894829 -0.775302032
39 0.200130193 -0.761894829
40 0.172884092 0.200130193
41 0.158943356 0.172884092
42 -0.886814730 0.158943356
43 0.096627617 -0.886814730
44 0.063359213 0.096627617
45 0.131079032 0.063359213
46 0.309846160 0.131079032
47 0.366954748 0.309846160
48 0.361022556 0.366954748
49 0.285118916 0.361022556
50 0.275293952 0.285118916
51 -0.743704560 0.275293952
52 0.145262931 -0.743704560
53 -0.861011787 0.145262931
54 0.125173175 -0.861011787
55 0.104862677 0.125173175
56 0.083395785 0.104862677
57 0.078508456 0.083395785
58 0.268226223 0.078508456
59 0.292436469 0.268226223
60 0.291562053 0.292436469
61 0.204440648 0.291562053
62 -0.805604262 0.204440648
63 0.163624045 -0.805604262
64 0.089850808 0.163624045
65 0.073081883 0.089850808
66 0.040529980 0.073081883
67 -0.010764278 0.040529980
68 0.013833233 -0.010764278
69 -0.005903744 0.013833233
70 0.098374666 -0.005903744
71 0.106662071 0.098374666
72 0.094950922 0.106662071
73 1.028584630 0.094950922
74 0.023020856 1.028584630
75 -1.068210829 0.023020856
76 -0.077907099 -1.068210829
77 -0.109990778 -0.077907099
78 -0.140903898 -0.109990778
79 -0.153659910 -0.140903898
80 -0.189410859 -0.153659910
81 -0.196212770 -0.189410859
82 -0.090744953 -0.196212770
83 0.916840117 -0.090744953
84 -0.120568463 0.916840117
85 -0.103493990 -0.120568463
86 -0.178345659 -0.103493990
87 -0.146207827 -0.178345659
88 -0.198811949 -0.146207827
89 0.762014991 -0.198811949
90 -0.269614630 0.762014991
91 -0.232541602 -0.269614630
92 -0.260280889 -0.232541602
93 0.765121881 -0.260280889
94 -1.106520661 0.765121881
95 0.924173997 -1.106520661
96 -0.125027421 0.924173997
97 -0.111994192 -0.125027421
98 -0.114284327 -0.111994192
99 -0.040799714 -0.114284327
100 -0.071718576 -0.040799714
101 -0.035710066 -0.071718576
102 0.027283455 -0.035710066
103 -0.016967036 0.027283455
104 0.958010334 -0.016967036
105 -0.051940262 0.958010334
106 0.126137821 -0.051940262
107 1.118182700 0.126137821
108 0.119526822 1.118182700
109 -0.887221675 0.119526822
110 1.056537823 -0.887221675
111 0.075010481 1.056537823
112 -0.989247175 0.075010481
113 0.005648667 -0.989247175
114 -1.014661831 0.005648667
115 1.007330555 -1.014661831
116 -0.020988488 1.007330555
117 -0.022910882 -0.020988488
118 0.077156633 -0.022910882
119 -0.906421720 0.077156633
120 -0.924048573 -0.906421720
121 0.015940731 -0.924048573
122 0.968402435 0.015940731
123 -0.069472803 0.968402435
124 -0.141173599 -0.069472803
125 -0.219353129 -0.141173599
126 -0.205138294 -0.219353129
127 -0.213895557 -0.205138294
128 0.722900258 -0.213895557
129 0.768450054 0.722900258
130 -0.100364298 0.768450054
131 -1.065674010 -0.100364298
132 -0.067748880 -1.065674010
133 -0.062724196 -0.067748880
134 -0.080206505 -0.062724196
135 -0.051970054 -0.080206505
136 0.928540995 -0.051970054
137 -0.068802896 0.928540995
138 -0.077682739 -0.068802896
139 -0.994059802 -0.077682739
140 1.005068900 -0.994059802
141 -0.980613341 1.005068900
142 0.164725110 -0.980613341
143 0.165697698 0.164725110
144 0.209935507 0.165697698
> 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/79j1v1352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8h7671352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9tabh1352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/106ig01352142897.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1171f81352142897.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/12j9a91352142898.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/13vegr1352142898.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/14vav31352142898.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/157us61352142898.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/16zv2b1352142898.tab")
+ }
>
> try(system("convert tmp/1kueu1352142897.ps tmp/1kueu1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fxry1352142897.ps tmp/2fxry1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/3inzd1352142897.ps tmp/3inzd1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ws9y1352142897.ps tmp/4ws9y1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/5vblz1352142897.ps tmp/5vblz1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b7up1352142897.ps tmp/6b7up1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/79j1v1352142897.ps tmp/79j1v1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h7671352142897.ps tmp/8h7671352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tabh1352142897.ps tmp/9tabh1352142897.png",intern=TRUE))
character(0)
> try(system("convert tmp/106ig01352142897.ps tmp/106ig01352142897.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.248 1.096 9.360