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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2000
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+ ,7.50
+ ,0.00
+ ,6.90
+ ,0
+ ,2008
+ ,0
+ ,0
+ ,94
+ ,478
+ ,87
+ ,0
+ ,390
+ ,0
+ ,6.90
+ ,0.00
+ ,7.60
+ ,0.00
+ ,7.00
+ ,0
+ ,2008
+ ,0
+ ,0
+ ,95
+ ,528
+ ,116
+ ,0
+ ,413
+ ,0
+ ,7.10
+ ,0.00
+ ,7.60
+ ,0.00
+ ,7.00
+ ,0
+ ,2008
+ ,0
+ ,0
+ ,96
+ ,534
+ ,120
+ ,0
+ ,413
+ ,0
+ ,7.20
+ ,0.00
+ ,7.70
+ ,0.00
+ ,7.10
+ ,0
+ ,2008
+ ,0
+ ,0
+ ,97
+ ,518
+ ,117
+ ,0
+ ,401
+ ,0
+ ,7.20
+ ,0.00
+ ,7.70
+ ,0.00
+ ,7.20
+ ,0
+ ,2008
+ ,98
+ ,1
+ ,98
+ ,506
+ ,109
+ ,109
+ ,397
+ ,397
+ ,7.10
+ ,7.10
+ ,7.90
+ ,7.90
+ ,7.30
+ ,7.3
+ ,2008
+ ,99
+ ,1
+ ,99
+ ,502
+ ,105
+ ,105
+ ,397
+ ,397
+ ,7.10
+ ,7.10
+ ,8.10
+ ,8.10
+ ,7.50
+ ,7.5
+ ,2008
+ ,100
+ ,1
+ ,100
+ ,516
+ ,107
+ ,107
+ ,409
+ ,409
+ ,7.20
+ ,7.20
+ ,8.40
+ ,8.40
+ ,7.70
+ ,7.7
+ ,2009
+ ,101
+ ,1
+ ,101
+ ,528
+ ,109
+ ,109
+ ,419
+ ,419
+ ,7.50
+ ,7.50
+ ,8.70
+ ,8.70
+ ,8.10
+ ,8.1
+ ,2009
+ ,102
+ ,1
+ ,102
+ ,533
+ ,109
+ ,109
+ ,424
+ ,424
+ ,7.70
+ ,7.70
+ ,9.00
+ ,9.00
+ ,8.40
+ ,8.4
+ ,2009
+ ,103
+ ,1
+ ,103
+ ,536
+ ,108
+ ,108
+ ,428
+ ,428
+ ,7.80
+ ,7.80
+ ,9.30
+ ,9.30
+ ,8.60
+ ,8.6
+ ,2009
+ ,104
+ ,1
+ ,104
+ ,537
+ ,107
+ ,107
+ ,430
+ ,430
+ ,7.70
+ ,7.70
+ ,9.40
+ ,9.40
+ ,8.80
+ ,8.8
+ ,2009
+ ,0
+ ,0
+ ,105
+ ,524
+ ,99
+ ,0
+ ,424
+ ,0
+ ,7.70
+ ,0.00
+ ,9.50
+ ,0.00
+ ,8.90
+ ,0
+ ,2009
+ ,0
+ ,0
+ ,106
+ ,536
+ ,103
+ ,0
+ ,433
+ ,0
+ ,7.80
+ ,0.00
+ ,9.60
+ ,0.00
+ ,9.10
+ ,0
+ ,2009
+ ,0
+ ,0
+ ,107
+ ,587
+ ,131
+ ,0
+ ,456
+ ,0
+ ,8.00
+ ,0.00
+ ,9.80
+ ,0.00
+ ,9.20
+ ,0
+ ,2009
+ ,0
+ ,0
+ ,108
+ ,597
+ ,137
+ ,0
+ ,459
+ ,0
+ ,8.10
+ ,0.00
+ ,9.80
+ ,0.00
+ ,9.30
+ ,0
+ ,2009
+ ,0
+ ,0
+ ,109
+ ,581
+ ,135
+ ,0
+ ,446
+ ,0
+ ,8.10
+ ,0.00
+ ,9.90
+ ,0.00
+ ,9.40
+ ,0
+ ,2009
+ ,110
+ ,1
+ ,110
+ ,564
+ ,124
+ ,124
+ ,441
+ ,441
+ ,8.00
+ ,8.00
+ ,10.00
+ ,10.00
+ ,9.40
+ ,9.4
+ ,2009
+ ,111
+ ,1
+ ,111
+ ,558
+ ,118
+ ,118
+ ,439
+ ,439
+ ,8.10
+ ,8.10
+ ,10.00
+ ,10.00
+ ,9.50
+ ,9.5
+ ,2010
+ ,112
+ ,1
+ ,112
+ ,575
+ ,121
+ ,121
+ ,454
+ ,454
+ ,8.20
+ ,8.20
+ ,10.10
+ ,10.10
+ ,9.50
+ ,9.5
+ ,2010
+ ,113
+ ,1
+ ,113
+ ,580
+ ,121
+ ,121
+ ,460
+ ,460
+ ,8.40
+ ,8.40
+ ,10.10
+ ,10.10
+ ,9.70
+ ,9.7
+ ,2010
+ ,114
+ ,1
+ ,114
+ ,575
+ ,118
+ ,118
+ ,457
+ ,457
+ ,8.50
+ ,8.50
+ ,10.10
+ ,10.10
+ ,9.70
+ ,9.7
+ ,2010
+ ,115
+ ,1
+ ,115
+ ,563
+ ,113
+ ,113
+ ,451
+ ,451
+ ,8.50
+ ,8.50
+ ,10.10
+ ,10.10
+ ,9.70
+ ,9.7
+ ,2010
+ ,116
+ ,1
+ ,116
+ ,552
+ ,107
+ ,107
+ ,444
+ ,444
+ ,8.50
+ ,8.50
+ ,10.20
+ ,10.20
+ ,9.70
+ ,9.7
+ ,2010
+ ,0
+ ,0
+ ,117
+ ,537
+ ,100
+ ,0
+ ,437
+ ,0
+ ,8.50
+ ,0.00
+ ,10.20
+ ,0.00
+ ,9.70
+ ,0
+ ,2010
+ ,0
+ ,0
+ ,118
+ ,545
+ ,102
+ ,0
+ ,443
+ ,0
+ ,8.50
+ ,0.00
+ ,10.10
+ ,0.00
+ ,9.60
+ ,0
+ ,2010
+ ,0
+ ,0
+ ,119
+ ,601
+ ,130
+ ,0
+ ,471
+ ,0
+ ,8.40
+ ,0.00
+ ,10.10
+ ,0.00
+ ,9.60
+ ,0
+ ,2010
+ ,0
+ ,0
+ ,120
+ ,604
+ ,136
+ ,0
+ ,469
+ ,0
+ ,8.30
+ ,0.00
+ ,10.10
+ ,0.00
+ ,9.60
+ ,0
+ ,2010
+ ,0
+ ,0
+ ,121
+ ,586
+ ,133
+ ,0
+ ,454
+ ,0
+ ,8.20
+ ,0.00
+ ,10.10
+ ,0.00
+ ,9.60
+ ,0
+ ,2010
+ ,122
+ ,1
+ ,122
+ ,564
+ ,120
+ ,120
+ ,444
+ ,444
+ ,8.10
+ ,8.10
+ ,10.10
+ ,10.10
+ ,9.60
+ ,9.6
+ ,2010
+ ,123
+ ,1
+ ,123
+ ,549
+ ,112
+ ,112
+ ,436
+ ,436
+ ,7.90
+ ,7.90
+ ,10.10
+ ,10.10
+ ,9.60
+ ,9.6
+ ,2010
+ ,124
+ ,1
+ ,124
+ ,551
+ ,109
+ ,109
+ ,442
+ ,442
+ ,7.60
+ ,7.60
+ ,10.10
+ ,10.10
+ ,9.60
+ ,9.6
+ ,2011
+ ,125
+ ,1
+ ,125
+ ,556
+ ,110
+ ,110
+ ,446
+ ,446
+ ,7.30
+ ,7.30
+ ,10.00
+ ,10.00
+ ,9.50
+ ,9.5
+ ,2011
+ ,126
+ ,1
+ ,126
+ ,548
+ ,106
+ ,106
+ ,442
+ ,442
+ ,7.10
+ ,7.10
+ ,9.90
+ ,9.90
+ ,9.50
+ ,9.5
+ ,2011
+ ,127
+ ,1
+ ,127
+ ,540
+ ,102
+ ,102
+ ,438
+ ,438
+ ,7.00
+ ,7.00
+ ,9.90
+ ,9.90
+ ,9.40
+ ,9.4
+ ,2011
+ ,128
+ ,1
+ ,128
+ ,531
+ ,98
+ ,98
+ ,433
+ ,433
+ ,7.10
+ ,7.10
+ ,9.90
+ ,9.90
+ ,9.40
+ ,9.4
+ ,2011
+ ,0
+ ,0
+ ,129
+ ,521
+ ,92
+ ,0
+ ,428
+ ,0
+ ,7.10
+ ,0.00
+ ,9.90
+ ,0.00
+ ,9.50
+ ,0
+ ,2011
+ ,0
+ ,0
+ ,130
+ ,519
+ ,92
+ ,0
+ ,426
+ ,0
+ ,7.10
+ ,0.00
+ ,10.00
+ ,0.00
+ ,9.50
+ ,0
+ ,2011
+ ,0
+ ,0
+ ,131
+ ,572
+ ,120
+ ,0
+ ,452
+ ,0
+ ,7.30
+ ,0.00
+ ,10.10
+ ,0.00
+ ,9.60
+ ,0
+ ,2011
+ ,0
+ ,0
+ ,132
+ ,581
+ ,127
+ ,0
+ ,455
+ ,0
+ ,7.30
+ ,0.00
+ ,10.20
+ ,0.00
+ ,9.70
+ ,0
+ ,2011
+ ,0
+ ,0
+ ,133
+ ,563
+ ,124
+ ,0
+ ,439
+ ,0
+ ,7.30
+ ,0.00
+ ,10.30
+ ,0.00
+ ,9.80
+ ,0
+ ,2011
+ ,134
+ ,1
+ ,134
+ ,548
+ ,114
+ ,114
+ ,434
+ ,434
+ ,7.20
+ ,7.20
+ ,10.50
+ ,10.50
+ ,9.90
+ ,9.9
+ ,2011
+ ,135
+ ,1
+ ,135
+ ,539
+ ,108
+ ,108
+ ,431
+ ,431
+ ,7.20
+ ,7.20
+ ,10.60
+ ,10.60
+ ,10.00
+ ,10
+ ,2011
+ ,136
+ ,1
+ ,136
+ ,541
+ ,106
+ ,106
+ ,435
+ ,435
+ ,7.10
+ ,7.10
+ ,10.70
+ ,10.70
+ ,10.00
+ ,10
+ ,2012
+ ,137
+ ,1
+ ,137
+ ,562
+ ,111
+ ,111
+ ,450
+ ,450
+ ,7.10
+ ,7.10
+ ,10.80
+ ,10.80
+ ,10.10
+ ,10.1
+ ,2012
+ ,138
+ ,1
+ ,138
+ ,559
+ ,110
+ ,110
+ ,449
+ ,449
+ ,7.10
+ ,7.10
+ ,10.90
+ ,10.90
+ ,10.20
+ ,10.2
+ ,2012
+ ,139
+ ,1
+ ,139
+ ,546
+ ,104
+ ,104
+ ,442
+ ,442
+ ,7.20
+ ,7.20
+ ,11.00
+ ,11.00
+ ,10.30
+ ,10.3
+ ,2012
+ ,140
+ ,1
+ ,140
+ ,536
+ ,100
+ ,100
+ ,437
+ ,437
+ ,7.30
+ ,7.30
+ ,11.20
+ ,11.20
+ ,10.30
+ ,10.3
+ ,2012
+ ,0
+ ,0
+ ,141
+ ,528
+ ,96
+ ,0
+ ,431
+ ,0
+ ,7.40
+ ,0.00
+ ,11.30
+ ,0.00
+ ,10.40
+ ,0
+ ,2012
+ ,0
+ ,0
+ ,142
+ ,530
+ ,98
+ ,0
+ ,433
+ ,0
+ ,7.40
+ ,0.00
+ ,11.40
+ ,0.00
+ ,10.50
+ ,0
+ ,2012
+ ,0
+ ,0
+ ,143
+ ,582
+ ,122
+ ,0
+ ,460
+ ,0
+ ,7.50
+ ,0.00
+ ,11.50
+ ,0.00
+ ,10.50
+ ,0
+ ,2012
+ ,0
+ ,0
+ ,144
+ ,599
+ ,134
+ ,0
+ ,465
+ ,0
+ ,7.40
+ ,0.00
+ ,11.50
+ ,0.00
+ ,10.60
+ ,0
+ ,2012
+ ,0
+ ,0
+ ,145
+ ,584
+ ,133
+ ,0
+ ,451
+ ,0
+ ,7.40
+ ,0.00
+ ,11.60
+ ,0.00
+ ,10.60
+ ,0)
+ ,dim=c(15
+ ,145)
+ ,dimnames=list(c('jaartal'
+ ,'S_t'
+ ,'s'
+ ,'t'
+ ,'Totaal'
+ ,'jongerdan25jaar'
+ ,'<25jaar_s'
+ ,'vanaf25jaar'
+ ,'vanaf25_s'
+ ,'België'
+ ,'België_s'
+ ,'Eurogebied'
+ ,'Eurogebied_s'
+ ,'EU-27'
+ ,'EU-27_s
')
+ ,1:145))
> y <- array(NA,dim=c(15,145),dimnames=list(c('jaartal','S_t','s','t','Totaal','jongerdan25jaar','<25jaar_s','vanaf25jaar','vanaf25_s','België','België_s','Eurogebied','Eurogebied_s','EU-27','EU-27_s
'),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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'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 jaartal S_t s t jongerdan25jaar <25jaar_s vanaf25jaar vanaf25_s
1 501 2000 0 0 1 134 0 368 0
2 485 2000 2 1 2 124 124 361 361
3 464 2000 3 1 3 113 113 351 351
4 460 2000 4 1 4 109 109 351 351
5 467 2001 5 1 5 109 109 358 358
6 460 2001 6 1 6 106 106 354 354
7 448 2001 7 1 7 101 101 347 347
8 443 2001 8 1 8 98 98 345 345
9 436 2001 0 0 9 93 0 343 0
10 431 2001 0 0 10 91 0 340 0
11 484 2001 0 0 11 122 0 362 0
12 510 2001 0 0 12 139 0 370 0
13 513 2001 0 0 13 140 0 373 0
14 503 2001 14 1 14 132 132 371 371
15 471 2001 15 1 15 117 117 354 354
16 471 2001 16 1 16 114 114 357 357
17 476 2002 17 1 17 113 113 363 363
18 475 2002 18 1 18 110 110 364 364
19 470 2002 19 1 19 107 107 363 363
20 461 2002 20 1 20 103 103 358 358
21 455 2002 0 0 21 98 0 357 0
22 456 2002 0 0 22 98 0 357 0
23 517 2002 0 0 23 137 0 380 0
24 525 2002 0 0 24 148 0 378 0
25 523 2002 0 0 25 147 0 376 0
26 519 2002 26 1 26 139 139 380 380
27 509 2002 27 1 27 130 130 379 379
28 512 2002 28 1 28 128 128 384 384
29 519 2003 29 1 29 127 127 392 392
30 517 2003 30 1 30 123 123 394 394
31 510 2003 31 1 31 118 118 392 392
32 509 2003 32 1 32 114 114 396 396
33 501 2003 0 0 33 108 0 392 0
34 507 2003 0 0 34 111 0 396 0
35 569 2003 0 0 35 151 0 419 0
36 580 2003 0 0 36 159 0 421 0
37 578 2003 0 0 37 158 0 420 0
38 565 2003 38 1 38 148 148 418 418
39 547 2003 39 1 39 138 138 410 410
40 555 2003 40 1 40 137 137 418 418
41 562 2004 41 1 41 136 136 426 426
42 561 2004 42 1 42 133 133 428 428
43 555 2004 43 1 43 126 126 430 430
44 544 2004 44 1 44 120 120 424 424
45 537 2004 0 0 45 114 0 423 0
46 543 2004 0 0 46 116 0 427 0
47 594 2004 0 0 47 153 0 441 0
48 611 2004 0 0 48 162 0 449 0
49 613 2004 0 0 49 161 0 452 0
50 611 2004 50 1 50 149 149 462 462
51 594 2004 51 1 51 139 139 455 455
52 595 2004 52 1 52 135 135 461 461
53 591 2005 53 1 53 130 130 461 461
54 589 2005 54 1 54 127 127 463 463
55 584 2005 55 1 55 122 122 462 462
56 573 2005 56 1 56 117 117 456 456
57 567 2005 0 0 57 112 0 455 0
58 569 2005 0 0 58 113 0 456 0
59 621 2005 0 0 59 149 0 472 0
60 629 2005 0 0 60 157 0 472 0
61 628 2005 0 0 61 157 0 471 0
62 612 2005 62 1 62 147 147 465 465
63 595 2005 63 1 63 137 137 459 459
64 597 2005 64 1 64 132 132 465 465
65 593 2006 65 1 65 125 125 468 468
66 590 2006 66 1 66 123 123 467 467
67 580 2006 67 1 67 117 117 463 463
68 574 2006 68 1 68 114 114 460 460
69 573 2006 0 0 69 111 0 462 0
70 573 2006 0 0 70 112 0 461 0
71 620 2006 0 0 71 144 0 476 0
72 626 2006 0 0 72 150 0 476 0
73 620 2006 0 0 73 149 0 471 0
74 588 2006 74 1 74 134 134 453 453
75 566 2006 75 1 75 123 123 443 443
76 557 2006 76 1 76 116 116 442 442
77 561 2007 77 1 77 117 117 444 444
78 549 2007 78 1 78 111 111 438 438
79 532 2007 79 1 79 105 105 427 427
80 526 2007 80 1 80 102 102 424 424
81 511 2007 0 0 81 95 0 416 0
82 499 2007 0 0 82 93 0 406 0
83 555 2007 0 0 83 124 0 431 0
84 565 2007 0 0 84 130 0 434 0
85 542 2007 0 0 85 124 0 418 0
86 527 2007 86 1 86 115 115 412 412
87 510 2007 87 1 87 106 106 404 404
88 514 2007 88 1 88 105 105 409 409
89 517 2008 89 1 89 105 105 412 412
90 508 2008 90 1 90 101 101 406 406
91 493 2008 91 1 91 95 95 398 398
92 490 2008 92 1 92 93 93 397 397
93 469 2008 0 0 93 84 0 385 0
94 478 2008 0 0 94 87 0 390 0
95 528 2008 0 0 95 116 0 413 0
96 534 2008 0 0 96 120 0 413 0
97 518 2008 0 0 97 117 0 401 0
98 506 2008 98 1 98 109 109 397 397
99 502 2008 99 1 99 105 105 397 397
100 516 2008 100 1 100 107 107 409 409
101 528 2009 101 1 101 109 109 419 419
102 533 2009 102 1 102 109 109 424 424
103 536 2009 103 1 103 108 108 428 428
104 537 2009 104 1 104 107 107 430 430
105 524 2009 0 0 105 99 0 424 0
106 536 2009 0 0 106 103 0 433 0
107 587 2009 0 0 107 131 0 456 0
108 597 2009 0 0 108 137 0 459 0
109 581 2009 0 0 109 135 0 446 0
110 564 2009 110 1 110 124 124 441 441
111 558 2009 111 1 111 118 118 439 439
112 575 2010 112 1 112 121 121 454 454
113 580 2010 113 1 113 121 121 460 460
114 575 2010 114 1 114 118 118 457 457
115 563 2010 115 1 115 113 113 451 451
116 552 2010 116 1 116 107 107 444 444
117 537 2010 0 0 117 100 0 437 0
118 545 2010 0 0 118 102 0 443 0
119 601 2010 0 0 119 130 0 471 0
120 604 2010 0 0 120 136 0 469 0
121 586 2010 0 0 121 133 0 454 0
122 564 2010 122 1 122 120 120 444 444
123 549 2010 123 1 123 112 112 436 436
124 551 2010 124 1 124 109 109 442 442
125 556 2011 125 1 125 110 110 446 446
126 548 2011 126 1 126 106 106 442 442
127 540 2011 127 1 127 102 102 438 438
128 531 2011 128 1 128 98 98 433 433
129 521 2011 0 0 129 92 0 428 0
130 519 2011 0 0 130 92 0 426 0
131 572 2011 0 0 131 120 0 452 0
132 581 2011 0 0 132 127 0 455 0
133 563 2011 0 0 133 124 0 439 0
134 548 2011 134 1 134 114 114 434 434
135 539 2011 135 1 135 108 108 431 431
136 541 2011 136 1 136 106 106 435 435
137 562 2012 137 1 137 111 111 450 450
138 559 2012 138 1 138 110 110 449 449
139 546 2012 139 1 139 104 104 442 442
140 536 2012 140 1 140 100 100 437 437
141 528 2012 0 0 141 96 0 431 0
142 530 2012 0 0 142 98 0 433 0
143 582 2012 0 0 143 122 0 460 0
144 599 2012 0 0 144 134 0 465 0
145 584 2012 0 0 145 133 0 451 0
Belgi\303\253 Belgi\303\253_s Eurogebied Eurogebied_s EU-27 EU-27_s\r
1 6.7 0.0 8.5 0.0 8.7 0.0
2 6.8 6.8 8.4 8.4 8.6 8.6
3 6.7 6.7 8.4 8.4 8.6 8.6
4 6.6 6.6 8.3 8.3 8.5 8.5
5 6.4 6.4 8.2 8.2 8.5 8.5
6 6.3 6.3 8.2 8.2 8.5 8.5
7 6.3 6.3 8.1 8.1 8.5 8.5
8 6.5 6.5 8.1 8.1 8.5 8.5
9 6.5 0.0 8.1 0.0 8.5 0.0
10 6.4 0.0 8.1 0.0 8.5 0.0
11 6.2 0.0 8.1 0.0 8.5 0.0
12 6.2 0.0 8.1 0.0 8.6 0.0
13 6.5 0.0 8.1 0.0 8.6 0.0
14 7.0 7.0 8.2 8.2 8.6 8.6
15 7.2 7.2 8.2 8.2 8.7 8.7
16 7.3 7.3 8.3 8.3 8.7 8.7
17 7.4 7.4 8.2 8.2 8.7 8.7
18 7.4 7.4 8.3 8.3 8.8 8.8
19 7.4 7.4 8.3 8.3 8.8 8.8
20 7.3 7.3 8.4 8.4 8.9 8.9
21 7.4 0.0 8.5 0.0 8.9 0.0
22 7.4 0.0 8.5 0.0 8.9 0.0
23 7.6 0.0 8.6 0.0 9.0 0.0
24 7.6 0.0 8.6 0.0 9.0 0.0
25 7.7 0.0 8.7 0.0 9.0 0.0
26 7.7 7.7 8.7 8.7 9.0 9.0
27 7.8 7.8 8.8 8.8 9.0 9.0
28 7.8 7.8 8.8 8.8 9.0 9.0
29 8.0 8.0 8.9 8.9 9.1 9.1
30 8.1 8.1 9.0 9.0 9.1 9.1
31 8.1 8.1 9.0 9.0 9.1 9.1
32 8.2 8.2 9.0 9.0 9.1 9.1
33 8.1 0.0 9.0 0.0 9.1 0.0
34 8.1 0.0 9.1 0.0 9.1 0.0
35 8.1 0.0 9.1 0.0 9.1 0.0
36 8.1 0.0 9.0 0.0 9.1 0.0
37 8.2 0.0 9.1 0.0 9.1 0.0
38 8.2 8.2 9.0 9.0 9.1 9.1
39 8.3 8.3 9.1 9.1 9.1 9.1
40 8.4 8.4 9.1 9.1 9.2 9.2
41 8.6 8.6 9.2 9.2 9.3 9.3
42 8.6 8.6 9.2 9.2 9.3 9.3
43 8.4 8.4 9.2 9.2 9.3 9.3
44 8.0 8.0 9.2 9.2 9.2 9.2
45 7.9 0.0 9.2 0.0 9.2 0.0
46 8.1 0.0 9.3 0.0 9.2 0.0
47 8.5 0.0 9.3 0.0 9.2 0.0
48 8.8 0.0 9.3 0.0 9.2 0.0
49 8.8 0.0 9.3 0.0 9.2 0.0
50 8.5 8.5 9.3 9.3 9.2 9.2
51 8.3 8.3 9.4 9.4 9.2 9.2
52 8.3 8.3 9.4 9.4 9.2 9.2
53 8.3 8.3 9.3 9.3 9.2 9.2
54 8.4 8.4 9.3 9.3 9.2 9.2
55 8.5 8.5 9.3 9.3 9.2 9.2
56 8.5 8.5 9.3 9.3 9.2 9.2
57 8.6 0.0 9.2 0.0 9.1 0.0
58 8.5 0.0 9.2 0.0 9.1 0.0
59 8.6 0.0 9.2 0.0 9.0 0.0
60 8.6 0.0 9.1 0.0 8.9 0.0
61 8.6 0.0 9.1 0.0 8.9 0.0
62 8.5 8.5 9.1 9.1 9.0 9.0
63 8.4 8.4 9.1 9.1 8.9 8.9
64 8.4 8.4 9.0 9.0 8.8 8.8
65 8.5 8.5 8.9 8.9 8.7 8.7
66 8.5 8.5 8.8 8.8 8.6 8.6
67 8.5 8.5 8.7 8.7 8.5 8.5
68 8.6 8.6 8.6 8.6 8.5 8.5
69 8.6 0.0 8.6 0.0 8.4 0.0
70 8.4 0.0 8.5 0.0 8.3 0.0
71 8.2 0.0 8.4 0.0 8.2 0.0
72 8.0 0.0 8.4 0.0 8.2 0.0
73 8.0 0.0 8.3 0.0 8.1 0.0
74 8.0 8.0 8.2 8.2 8.0 8.0
75 8.0 8.0 8.2 8.2 7.9 7.9
76 7.9 7.9 8.0 8.0 7.8 7.8
77 7.9 7.9 7.9 7.9 7.6 7.6
78 7.9 7.9 7.8 7.8 7.5 7.5
79 7.9 7.9 7.7 7.7 7.4 7.4
80 8.0 8.0 7.6 7.6 7.3 7.3
81 7.9 0.0 7.6 0.0 7.3 0.0
82 7.4 0.0 7.6 0.0 7.2 0.0
83 7.2 0.0 7.6 0.0 7.2 0.0
84 7.0 0.0 7.6 0.0 7.2 0.0
85 6.9 0.0 7.5 0.0 7.1 0.0
86 7.1 7.1 7.5 7.5 7.0 7.0
87 7.2 7.2 7.4 7.4 7.0 7.0
88 7.2 7.2 7.4 7.4 6.9 6.9
89 7.1 7.1 7.4 7.4 6.9 6.9
90 6.9 6.9 7.3 7.3 6.8 6.8
91 6.8 6.8 7.3 7.3 6.8 6.8
92 6.8 6.8 7.4 7.4 6.8 6.8
93 6.8 0.0 7.5 0.0 6.9 0.0
94 6.9 0.0 7.6 0.0 7.0 0.0
95 7.1 0.0 7.6 0.0 7.0 0.0
96 7.2 0.0 7.7 0.0 7.1 0.0
97 7.2 0.0 7.7 0.0 7.2 0.0
98 7.1 7.1 7.9 7.9 7.3 7.3
99 7.1 7.1 8.1 8.1 7.5 7.5
100 7.2 7.2 8.4 8.4 7.7 7.7
101 7.5 7.5 8.7 8.7 8.1 8.1
102 7.7 7.7 9.0 9.0 8.4 8.4
103 7.8 7.8 9.3 9.3 8.6 8.6
104 7.7 7.7 9.4 9.4 8.8 8.8
105 7.7 0.0 9.5 0.0 8.9 0.0
106 7.8 0.0 9.6 0.0 9.1 0.0
107 8.0 0.0 9.8 0.0 9.2 0.0
108 8.1 0.0 9.8 0.0 9.3 0.0
109 8.1 0.0 9.9 0.0 9.4 0.0
110 8.0 8.0 10.0 10.0 9.4 9.4
111 8.1 8.1 10.0 10.0 9.5 9.5
112 8.2 8.2 10.1 10.1 9.5 9.5
113 8.4 8.4 10.1 10.1 9.7 9.7
114 8.5 8.5 10.1 10.1 9.7 9.7
115 8.5 8.5 10.1 10.1 9.7 9.7
116 8.5 8.5 10.2 10.2 9.7 9.7
117 8.5 0.0 10.2 0.0 9.7 0.0
118 8.5 0.0 10.1 0.0 9.6 0.0
119 8.4 0.0 10.1 0.0 9.6 0.0
120 8.3 0.0 10.1 0.0 9.6 0.0
121 8.2 0.0 10.1 0.0 9.6 0.0
122 8.1 8.1 10.1 10.1 9.6 9.6
123 7.9 7.9 10.1 10.1 9.6 9.6
124 7.6 7.6 10.1 10.1 9.6 9.6
125 7.3 7.3 10.0 10.0 9.5 9.5
126 7.1 7.1 9.9 9.9 9.5 9.5
127 7.0 7.0 9.9 9.9 9.4 9.4
128 7.1 7.1 9.9 9.9 9.4 9.4
129 7.1 0.0 9.9 0.0 9.5 0.0
130 7.1 0.0 10.0 0.0 9.5 0.0
131 7.3 0.0 10.1 0.0 9.6 0.0
132 7.3 0.0 10.2 0.0 9.7 0.0
133 7.3 0.0 10.3 0.0 9.8 0.0
134 7.2 7.2 10.5 10.5 9.9 9.9
135 7.2 7.2 10.6 10.6 10.0 10.0
136 7.1 7.1 10.7 10.7 10.0 10.0
137 7.1 7.1 10.8 10.8 10.1 10.1
138 7.1 7.1 10.9 10.9 10.2 10.2
139 7.2 7.2 11.0 11.0 10.3 10.3
140 7.3 7.3 11.2 11.2 10.3 10.3
141 7.4 0.0 11.3 0.0 10.4 0.0
142 7.4 0.0 11.4 0.0 10.5 0.0
143 7.5 0.0 11.5 0.0 10.5 0.0
144 7.4 0.0 11.5 0.0 10.6 0.0
145 7.4 0.0 11.6 0.0 10.6 0.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) jaartal S_t
-2.352e+02 1.181e-01 8.982e-03
s t jongerdan25jaar
6.302e-01 -9.697e-03 9.923e-01
`<25jaar_s` vanaf25jaar vanaf25_s
1.003e-02 1.003e+00 -4.020e-03
`Belgi\\303\\253` `Belgi\\303\\253_s` Eurogebied
-9.729e-02 -2.486e-02 -1.743e-01
Eurogebied_s `EU-27` `EU-27_s\\r`
-6.170e-01 1.232e-01 5.481e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.16523 -0.15789 -0.01104 0.19541 1.14185
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.352e+02 3.722e+02 -0.632 0.529
jaartal 1.181e-01 1.861e-01 0.635 0.527
S_t 8.982e-03 7.895e-03 1.138 0.257
s 6.301e-01 1.370e+00 0.460 0.646
t -9.697e-03 1.624e-02 -0.597 0.551
jongerdan25jaar 9.923e-01 4.534e-03 218.882 <2e-16 ***
`<25jaar_s` 1.003e-02 9.299e-03 1.079 0.283
vanaf25jaar 1.003e+00 4.638e-03 216.190 <2e-16 ***
vanaf25_s -4.020e-03 6.187e-03 -0.650 0.517
`Belgi\\303\\253` -9.729e-02 1.704e-01 -0.571 0.569
`Belgi\\303\\253_s` -2.486e-02 2.412e-01 -0.103 0.918
Eurogebied -1.743e-01 5.481e-01 -0.318 0.751
Eurogebied_s -6.170e-01 7.710e-01 -0.800 0.425
`EU-27` 1.232e-01 5.222e-01 0.236 0.814
`EU-27_s\\r` 5.481e-01 7.245e-01 0.757 0.451
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5078 on 130 degrees of freedom
Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
F-statistic: 8.189e+04 on 14 and 130 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,] 0.0261451063 0.0522902125 0.9738549
[2,] 0.2832710630 0.5665421259 0.7167289
[3,] 0.2168047825 0.4336095650 0.7831952
[4,] 0.1266512225 0.2533024450 0.8733488
[5,] 0.2720923233 0.5441846466 0.7279077
[6,] 0.1835663042 0.3671326083 0.8164337
[7,] 0.1488926462 0.2977852924 0.8511074
[8,] 0.3257520179 0.6515040359 0.6742480
[9,] 0.2780229545 0.5560459090 0.7219770
[10,] 0.2061614230 0.4123228460 0.7938386
[11,] 0.1526586166 0.3053172332 0.8473414
[12,] 0.1115744427 0.2231488855 0.8884256
[13,] 0.0779103929 0.1558207858 0.9220896
[14,] 0.0541937352 0.1083874705 0.9458063
[15,] 0.1108964852 0.2217929704 0.8891035
[16,] 0.0864061337 0.1728122674 0.9135939
[17,] 0.1226637633 0.2453275265 0.8773362
[18,] 0.1531662537 0.3063325073 0.8468337
[19,] 0.1556237518 0.3112475036 0.8443762
[20,] 0.1706362278 0.3412724557 0.8293638
[21,] 0.1543967119 0.3087934237 0.8456033
[22,] 0.1306022728 0.2612045456 0.8693977
[23,] 0.1546177160 0.3092354320 0.8453823
[24,] 0.1243769223 0.2487538447 0.8756231
[25,] 0.0976686211 0.1953372422 0.9023314
[26,] 0.1324829280 0.2649658561 0.8675171
[27,] 0.1643626535 0.3287253071 0.8356373
[28,] 0.2685795015 0.5371590030 0.7314205
[29,] 0.2358345767 0.4716691534 0.7641654
[30,] 0.1939334546 0.3878669093 0.8060665
[31,] 0.1551338467 0.3102676933 0.8448662
[32,] 0.1224017367 0.2448034733 0.8775983
[33,] 0.1371719995 0.2743439989 0.8628280
[34,] 0.1357808553 0.2715617105 0.8642191
[35,] 0.1410630548 0.2821261095 0.8589369
[36,] 0.1251423725 0.2502847449 0.8748576
[37,] 0.1421758902 0.2843517804 0.8578241
[38,] 0.1350818765 0.2701637530 0.8649181
[39,] 0.1229172530 0.2458345061 0.8770827
[40,] 0.1343786553 0.2687573105 0.8656213
[41,] 0.1235552197 0.2471104393 0.8764448
[42,] 0.0985053996 0.1970107992 0.9014946
[43,] 0.0770214773 0.1540429545 0.9229785
[44,] 0.0601148477 0.1202296953 0.9398852
[45,] 0.0501328072 0.1002656144 0.9498672
[46,] 0.0611792490 0.1223584981 0.9388208
[47,] 0.0558224351 0.1116448702 0.9441776
[48,] 0.0431874449 0.0863748897 0.9568126
[49,] 0.0322575861 0.0645151721 0.9677424
[50,] 0.0238460191 0.0476920381 0.9761540
[51,] 0.0174852652 0.0349705303 0.9825147
[52,] 0.0138958637 0.0277917273 0.9861041
[53,] 0.0105509514 0.0211019028 0.9894490
[54,] 0.0074015785 0.0148031569 0.9925984
[55,] 0.0052379100 0.0104758200 0.9947621
[56,] 0.0037253108 0.0074506216 0.9962747
[57,] 0.0077844173 0.0155688346 0.9922156
[58,] 0.0068759602 0.0137519204 0.9931240
[59,] 0.0191143413 0.0382286826 0.9808857
[60,] 0.0142067381 0.0284134763 0.9857933
[61,] 0.0101845110 0.0203690220 0.9898155
[62,] 0.0070932046 0.0141864092 0.9929068
[63,] 0.0048647448 0.0097294897 0.9951353
[64,] 0.0034863550 0.0069727099 0.9965136
[65,] 0.0031090170 0.0062180340 0.9968910
[66,] 0.0024424069 0.0048848139 0.9975576
[67,] 0.0033507169 0.0067014338 0.9966493
[68,] 0.0030248190 0.0060496380 0.9969752
[69,] 0.0020482144 0.0040964288 0.9979518
[70,] 0.0013801235 0.0027602470 0.9986199
[71,] 0.0009201144 0.0018402287 0.9990799
[72,] 0.0005938972 0.0011877944 0.9994061
[73,] 0.0012711125 0.0025422251 0.9987289
[74,] 0.0008463125 0.0016926250 0.9991537
[75,] 0.0005299884 0.0010599768 0.9994700
[76,] 0.0006210571 0.0012421142 0.9993789
[77,] 0.0005498986 0.0010997973 0.9994501
[78,] 0.0052709222 0.0105418443 0.9947291
[79,] 0.0073774558 0.0147549117 0.9926225
[80,] 0.0056224332 0.0112448663 0.9943776
[81,] 0.0038437595 0.0076875191 0.9961562
[82,] 0.0027395164 0.0054790327 0.9972605
[83,] 0.0018506042 0.0037012085 0.9981494
[84,] 0.0011861155 0.0023722311 0.9988139
[85,] 0.0007502134 0.0015004269 0.9992498
[86,] 0.0004649505 0.0009299009 0.9995350
[87,] 0.0002745486 0.0005490972 0.9997255
[88,] 0.0001693124 0.0003386248 0.9998307
[89,] 0.0004578464 0.0009156928 0.9995422
[90,] 0.0006677925 0.0013355850 0.9993322
[91,] 0.0006267036 0.0012534072 0.9993733
[92,] 0.0004487179 0.0008974359 0.9995513
[93,] 0.0017577428 0.0035154855 0.9982423
[94,] 0.0046666360 0.0093332720 0.9953334
[95,] 0.0035783052 0.0071566103 0.9964217
[96,] 0.0059598749 0.0119197498 0.9940401
[97,] 0.0036722545 0.0073445091 0.9963277
[98,] 0.0378104964 0.0756209928 0.9621895
[99,] 0.0523905178 0.1047810356 0.9476095
[100,] 0.0380896701 0.0761793402 0.9619103
[101,] 0.0322259653 0.0644519306 0.9677740
[102,] 0.0573476700 0.1146953399 0.9426523
[103,] 0.0508883493 0.1017766986 0.9491117
[104,] 0.0391517410 0.0783034819 0.9608483
[105,] 0.0742539593 0.1485079186 0.9257460
[106,] 0.0906382136 0.1812764272 0.9093618
[107,] 0.0570665275 0.1141330550 0.9429335
[108,] 0.0375064821 0.0750129641 0.9624935
[109,] 0.0837190927 0.1674381853 0.9162809
[110,] 0.0500814792 0.1001629585 0.9499185
> postscript(file="/var/fisher/rcomp/tmp/1cmty1352155974.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2nq3m1352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3o0zf1352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4qels1352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5lt651352155974.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
-0.963476744 0.190052043 0.191588088 0.177590113 -0.034204702 -0.043813547
7 8 9 10 11 12
-0.119505713 -0.089854364 -0.314763442 -0.321977340 -0.153964865 0.951925137
13 14 15 16 17 18
-0.009668193 -0.059206778 -0.087813437 0.015284273 -0.158799476 0.862338580
19 20 21 22 23 24
-0.131132323 -0.127681336 -0.208192618 0.801504620 0.072102022 -0.828523781
25 26 27 28 29 30
0.206093282 0.039018947 0.151129155 0.163130365 0.094993540 0.199157446
31 32 33 34 35 36
0.209126597 -0.763217183 0.902326945 -0.058420168 -0.804728135 0.243393462
37 38 39 40 41 42
0.275298407 -0.810868664 -0.705538230 0.253097691 0.184960866 0.195412949
43 44 45 46 47 48
-0.809069453 0.216312455 -0.134461010 -0.083409829 0.210628526 0.296749308
49 50 51 52 53 54
0.290650852 0.341650642 0.411643866 -0.570299547 0.245051208 -0.732281753
55 56 57 58 59 60
0.291210017 0.295948520 -0.157890588 -0.152974947 0.111112417 0.176977287
61 62 63 64 65 66
0.189386644 0.216805597 -0.711672194 0.296764615 0.200130400 0.192292004
67 68 69 70 71 72
0.190026054 0.127031777 -0.204602016 -0.209098028 -0.019548458 0.016647176
73 74 75 76 77 78
0.027138597 1.020557203 0.101437579 -0.985869494 -0.047887815 -0.052769088
79 80 81 82 83 84
-0.064188670 -0.060053574 -0.310991233 -0.325816532 -0.165940414 0.862118864
85 86 87 88 89 90
-0.145581917 -0.089984045 -0.145273486 -0.068516760 -0.194204257 0.771736840
91 92 93 94 95 96
-0.235981079 -0.152702830 -0.388079074 0.645871475 -1.165231578 0.889938904
97 98 99 100 101 102
-0.103115481 -0.089780386 -0.055582500 0.071402148 -0.032168073 0.035496242
103 104 105 106 107 108
0.159141168 0.097494840 0.808995343 -0.172562280 0.031208511 1.076135204
109 110 111 112 113 114
0.110878385 -0.815578660 1.141849932 0.128285725 -0.972981951 0.043146780
115 116 117 118 119 120
-0.952114716 1.132812992 -0.119085698 -0.115449570 0.023040994 -0.925610318
121 122 123 124 125 126
-0.907938128 0.045390500 1.030205339 0.009243198 -0.153934044 -0.252506996
127 128 129 130 131 132
-0.192612610 -0.176725345 0.678461125 0.711008709 -0.110782965 -1.050499642
133 134 135 136 137 138
-0.015279443 -0.057802145 -0.034773163 0.042828437 0.945172185 -0.041052798
139 140 141 142 143 144
-0.011039506 -0.836906224 0.861498929 -1.113802320 0.033646752 0.099647603
145
0.157081275
> postscript(file="/var/fisher/rcomp/tmp/6evq91352155974.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 -0.963476744 NA
1 0.190052043 -0.963476744
2 0.191588088 0.190052043
3 0.177590113 0.191588088
4 -0.034204702 0.177590113
5 -0.043813547 -0.034204702
6 -0.119505713 -0.043813547
7 -0.089854364 -0.119505713
8 -0.314763442 -0.089854364
9 -0.321977340 -0.314763442
10 -0.153964865 -0.321977340
11 0.951925137 -0.153964865
12 -0.009668193 0.951925137
13 -0.059206778 -0.009668193
14 -0.087813437 -0.059206778
15 0.015284273 -0.087813437
16 -0.158799476 0.015284273
17 0.862338580 -0.158799476
18 -0.131132323 0.862338580
19 -0.127681336 -0.131132323
20 -0.208192618 -0.127681336
21 0.801504620 -0.208192618
22 0.072102022 0.801504620
23 -0.828523781 0.072102022
24 0.206093282 -0.828523781
25 0.039018947 0.206093282
26 0.151129155 0.039018947
27 0.163130365 0.151129155
28 0.094993540 0.163130365
29 0.199157446 0.094993540
30 0.209126597 0.199157446
31 -0.763217183 0.209126597
32 0.902326945 -0.763217183
33 -0.058420168 0.902326945
34 -0.804728135 -0.058420168
35 0.243393462 -0.804728135
36 0.275298407 0.243393462
37 -0.810868664 0.275298407
38 -0.705538230 -0.810868664
39 0.253097691 -0.705538230
40 0.184960866 0.253097691
41 0.195412949 0.184960866
42 -0.809069453 0.195412949
43 0.216312455 -0.809069453
44 -0.134461010 0.216312455
45 -0.083409829 -0.134461010
46 0.210628526 -0.083409829
47 0.296749308 0.210628526
48 0.290650852 0.296749308
49 0.341650642 0.290650852
50 0.411643866 0.341650642
51 -0.570299547 0.411643866
52 0.245051208 -0.570299547
53 -0.732281753 0.245051208
54 0.291210017 -0.732281753
55 0.295948520 0.291210017
56 -0.157890588 0.295948520
57 -0.152974947 -0.157890588
58 0.111112417 -0.152974947
59 0.176977287 0.111112417
60 0.189386644 0.176977287
61 0.216805597 0.189386644
62 -0.711672194 0.216805597
63 0.296764615 -0.711672194
64 0.200130400 0.296764615
65 0.192292004 0.200130400
66 0.190026054 0.192292004
67 0.127031777 0.190026054
68 -0.204602016 0.127031777
69 -0.209098028 -0.204602016
70 -0.019548458 -0.209098028
71 0.016647176 -0.019548458
72 0.027138597 0.016647176
73 1.020557203 0.027138597
74 0.101437579 1.020557203
75 -0.985869494 0.101437579
76 -0.047887815 -0.985869494
77 -0.052769088 -0.047887815
78 -0.064188670 -0.052769088
79 -0.060053574 -0.064188670
80 -0.310991233 -0.060053574
81 -0.325816532 -0.310991233
82 -0.165940414 -0.325816532
83 0.862118864 -0.165940414
84 -0.145581917 0.862118864
85 -0.089984045 -0.145581917
86 -0.145273486 -0.089984045
87 -0.068516760 -0.145273486
88 -0.194204257 -0.068516760
89 0.771736840 -0.194204257
90 -0.235981079 0.771736840
91 -0.152702830 -0.235981079
92 -0.388079074 -0.152702830
93 0.645871475 -0.388079074
94 -1.165231578 0.645871475
95 0.889938904 -1.165231578
96 -0.103115481 0.889938904
97 -0.089780386 -0.103115481
98 -0.055582500 -0.089780386
99 0.071402148 -0.055582500
100 -0.032168073 0.071402148
101 0.035496242 -0.032168073
102 0.159141168 0.035496242
103 0.097494840 0.159141168
104 0.808995343 0.097494840
105 -0.172562280 0.808995343
106 0.031208511 -0.172562280
107 1.076135204 0.031208511
108 0.110878385 1.076135204
109 -0.815578660 0.110878385
110 1.141849932 -0.815578660
111 0.128285725 1.141849932
112 -0.972981951 0.128285725
113 0.043146780 -0.972981951
114 -0.952114716 0.043146780
115 1.132812992 -0.952114716
116 -0.119085698 1.132812992
117 -0.115449570 -0.119085698
118 0.023040994 -0.115449570
119 -0.925610318 0.023040994
120 -0.907938128 -0.925610318
121 0.045390500 -0.907938128
122 1.030205339 0.045390500
123 0.009243198 1.030205339
124 -0.153934044 0.009243198
125 -0.252506996 -0.153934044
126 -0.192612610 -0.252506996
127 -0.176725345 -0.192612610
128 0.678461125 -0.176725345
129 0.711008709 0.678461125
130 -0.110782965 0.711008709
131 -1.050499642 -0.110782965
132 -0.015279443 -1.050499642
133 -0.057802145 -0.015279443
134 -0.034773163 -0.057802145
135 0.042828437 -0.034773163
136 0.945172185 0.042828437
137 -0.041052798 0.945172185
138 -0.011039506 -0.041052798
139 -0.836906224 -0.011039506
140 0.861498929 -0.836906224
141 -1.113802320 0.861498929
142 0.033646752 -1.113802320
143 0.099647603 0.033646752
144 0.157081275 0.099647603
145 NA 0.157081275
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.190052043 -0.963476744
[2,] 0.191588088 0.190052043
[3,] 0.177590113 0.191588088
[4,] -0.034204702 0.177590113
[5,] -0.043813547 -0.034204702
[6,] -0.119505713 -0.043813547
[7,] -0.089854364 -0.119505713
[8,] -0.314763442 -0.089854364
[9,] -0.321977340 -0.314763442
[10,] -0.153964865 -0.321977340
[11,] 0.951925137 -0.153964865
[12,] -0.009668193 0.951925137
[13,] -0.059206778 -0.009668193
[14,] -0.087813437 -0.059206778
[15,] 0.015284273 -0.087813437
[16,] -0.158799476 0.015284273
[17,] 0.862338580 -0.158799476
[18,] -0.131132323 0.862338580
[19,] -0.127681336 -0.131132323
[20,] -0.208192618 -0.127681336
[21,] 0.801504620 -0.208192618
[22,] 0.072102022 0.801504620
[23,] -0.828523781 0.072102022
[24,] 0.206093282 -0.828523781
[25,] 0.039018947 0.206093282
[26,] 0.151129155 0.039018947
[27,] 0.163130365 0.151129155
[28,] 0.094993540 0.163130365
[29,] 0.199157446 0.094993540
[30,] 0.209126597 0.199157446
[31,] -0.763217183 0.209126597
[32,] 0.902326945 -0.763217183
[33,] -0.058420168 0.902326945
[34,] -0.804728135 -0.058420168
[35,] 0.243393462 -0.804728135
[36,] 0.275298407 0.243393462
[37,] -0.810868664 0.275298407
[38,] -0.705538230 -0.810868664
[39,] 0.253097691 -0.705538230
[40,] 0.184960866 0.253097691
[41,] 0.195412949 0.184960866
[42,] -0.809069453 0.195412949
[43,] 0.216312455 -0.809069453
[44,] -0.134461010 0.216312455
[45,] -0.083409829 -0.134461010
[46,] 0.210628526 -0.083409829
[47,] 0.296749308 0.210628526
[48,] 0.290650852 0.296749308
[49,] 0.341650642 0.290650852
[50,] 0.411643866 0.341650642
[51,] -0.570299547 0.411643866
[52,] 0.245051208 -0.570299547
[53,] -0.732281753 0.245051208
[54,] 0.291210017 -0.732281753
[55,] 0.295948520 0.291210017
[56,] -0.157890588 0.295948520
[57,] -0.152974947 -0.157890588
[58,] 0.111112417 -0.152974947
[59,] 0.176977287 0.111112417
[60,] 0.189386644 0.176977287
[61,] 0.216805597 0.189386644
[62,] -0.711672194 0.216805597
[63,] 0.296764615 -0.711672194
[64,] 0.200130400 0.296764615
[65,] 0.192292004 0.200130400
[66,] 0.190026054 0.192292004
[67,] 0.127031777 0.190026054
[68,] -0.204602016 0.127031777
[69,] -0.209098028 -0.204602016
[70,] -0.019548458 -0.209098028
[71,] 0.016647176 -0.019548458
[72,] 0.027138597 0.016647176
[73,] 1.020557203 0.027138597
[74,] 0.101437579 1.020557203
[75,] -0.985869494 0.101437579
[76,] -0.047887815 -0.985869494
[77,] -0.052769088 -0.047887815
[78,] -0.064188670 -0.052769088
[79,] -0.060053574 -0.064188670
[80,] -0.310991233 -0.060053574
[81,] -0.325816532 -0.310991233
[82,] -0.165940414 -0.325816532
[83,] 0.862118864 -0.165940414
[84,] -0.145581917 0.862118864
[85,] -0.089984045 -0.145581917
[86,] -0.145273486 -0.089984045
[87,] -0.068516760 -0.145273486
[88,] -0.194204257 -0.068516760
[89,] 0.771736840 -0.194204257
[90,] -0.235981079 0.771736840
[91,] -0.152702830 -0.235981079
[92,] -0.388079074 -0.152702830
[93,] 0.645871475 -0.388079074
[94,] -1.165231578 0.645871475
[95,] 0.889938904 -1.165231578
[96,] -0.103115481 0.889938904
[97,] -0.089780386 -0.103115481
[98,] -0.055582500 -0.089780386
[99,] 0.071402148 -0.055582500
[100,] -0.032168073 0.071402148
[101,] 0.035496242 -0.032168073
[102,] 0.159141168 0.035496242
[103,] 0.097494840 0.159141168
[104,] 0.808995343 0.097494840
[105,] -0.172562280 0.808995343
[106,] 0.031208511 -0.172562280
[107,] 1.076135204 0.031208511
[108,] 0.110878385 1.076135204
[109,] -0.815578660 0.110878385
[110,] 1.141849932 -0.815578660
[111,] 0.128285725 1.141849932
[112,] -0.972981951 0.128285725
[113,] 0.043146780 -0.972981951
[114,] -0.952114716 0.043146780
[115,] 1.132812992 -0.952114716
[116,] -0.119085698 1.132812992
[117,] -0.115449570 -0.119085698
[118,] 0.023040994 -0.115449570
[119,] -0.925610318 0.023040994
[120,] -0.907938128 -0.925610318
[121,] 0.045390500 -0.907938128
[122,] 1.030205339 0.045390500
[123,] 0.009243198 1.030205339
[124,] -0.153934044 0.009243198
[125,] -0.252506996 -0.153934044
[126,] -0.192612610 -0.252506996
[127,] -0.176725345 -0.192612610
[128,] 0.678461125 -0.176725345
[129,] 0.711008709 0.678461125
[130,] -0.110782965 0.711008709
[131,] -1.050499642 -0.110782965
[132,] -0.015279443 -1.050499642
[133,] -0.057802145 -0.015279443
[134,] -0.034773163 -0.057802145
[135,] 0.042828437 -0.034773163
[136,] 0.945172185 0.042828437
[137,] -0.041052798 0.945172185
[138,] -0.011039506 -0.041052798
[139,] -0.836906224 -0.011039506
[140,] 0.861498929 -0.836906224
[141,] -1.113802320 0.861498929
[142,] 0.033646752 -1.113802320
[143,] 0.099647603 0.033646752
[144,] 0.157081275 0.099647603
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.190052043 -0.963476744
2 0.191588088 0.190052043
3 0.177590113 0.191588088
4 -0.034204702 0.177590113
5 -0.043813547 -0.034204702
6 -0.119505713 -0.043813547
7 -0.089854364 -0.119505713
8 -0.314763442 -0.089854364
9 -0.321977340 -0.314763442
10 -0.153964865 -0.321977340
11 0.951925137 -0.153964865
12 -0.009668193 0.951925137
13 -0.059206778 -0.009668193
14 -0.087813437 -0.059206778
15 0.015284273 -0.087813437
16 -0.158799476 0.015284273
17 0.862338580 -0.158799476
18 -0.131132323 0.862338580
19 -0.127681336 -0.131132323
20 -0.208192618 -0.127681336
21 0.801504620 -0.208192618
22 0.072102022 0.801504620
23 -0.828523781 0.072102022
24 0.206093282 -0.828523781
25 0.039018947 0.206093282
26 0.151129155 0.039018947
27 0.163130365 0.151129155
28 0.094993540 0.163130365
29 0.199157446 0.094993540
30 0.209126597 0.199157446
31 -0.763217183 0.209126597
32 0.902326945 -0.763217183
33 -0.058420168 0.902326945
34 -0.804728135 -0.058420168
35 0.243393462 -0.804728135
36 0.275298407 0.243393462
37 -0.810868664 0.275298407
38 -0.705538230 -0.810868664
39 0.253097691 -0.705538230
40 0.184960866 0.253097691
41 0.195412949 0.184960866
42 -0.809069453 0.195412949
43 0.216312455 -0.809069453
44 -0.134461010 0.216312455
45 -0.083409829 -0.134461010
46 0.210628526 -0.083409829
47 0.296749308 0.210628526
48 0.290650852 0.296749308
49 0.341650642 0.290650852
50 0.411643866 0.341650642
51 -0.570299547 0.411643866
52 0.245051208 -0.570299547
53 -0.732281753 0.245051208
54 0.291210017 -0.732281753
55 0.295948520 0.291210017
56 -0.157890588 0.295948520
57 -0.152974947 -0.157890588
58 0.111112417 -0.152974947
59 0.176977287 0.111112417
60 0.189386644 0.176977287
61 0.216805597 0.189386644
62 -0.711672194 0.216805597
63 0.296764615 -0.711672194
64 0.200130400 0.296764615
65 0.192292004 0.200130400
66 0.190026054 0.192292004
67 0.127031777 0.190026054
68 -0.204602016 0.127031777
69 -0.209098028 -0.204602016
70 -0.019548458 -0.209098028
71 0.016647176 -0.019548458
72 0.027138597 0.016647176
73 1.020557203 0.027138597
74 0.101437579 1.020557203
75 -0.985869494 0.101437579
76 -0.047887815 -0.985869494
77 -0.052769088 -0.047887815
78 -0.064188670 -0.052769088
79 -0.060053574 -0.064188670
80 -0.310991233 -0.060053574
81 -0.325816532 -0.310991233
82 -0.165940414 -0.325816532
83 0.862118864 -0.165940414
84 -0.145581917 0.862118864
85 -0.089984045 -0.145581917
86 -0.145273486 -0.089984045
87 -0.068516760 -0.145273486
88 -0.194204257 -0.068516760
89 0.771736840 -0.194204257
90 -0.235981079 0.771736840
91 -0.152702830 -0.235981079
92 -0.388079074 -0.152702830
93 0.645871475 -0.388079074
94 -1.165231578 0.645871475
95 0.889938904 -1.165231578
96 -0.103115481 0.889938904
97 -0.089780386 -0.103115481
98 -0.055582500 -0.089780386
99 0.071402148 -0.055582500
100 -0.032168073 0.071402148
101 0.035496242 -0.032168073
102 0.159141168 0.035496242
103 0.097494840 0.159141168
104 0.808995343 0.097494840
105 -0.172562280 0.808995343
106 0.031208511 -0.172562280
107 1.076135204 0.031208511
108 0.110878385 1.076135204
109 -0.815578660 0.110878385
110 1.141849932 -0.815578660
111 0.128285725 1.141849932
112 -0.972981951 0.128285725
113 0.043146780 -0.972981951
114 -0.952114716 0.043146780
115 1.132812992 -0.952114716
116 -0.119085698 1.132812992
117 -0.115449570 -0.119085698
118 0.023040994 -0.115449570
119 -0.925610318 0.023040994
120 -0.907938128 -0.925610318
121 0.045390500 -0.907938128
122 1.030205339 0.045390500
123 0.009243198 1.030205339
124 -0.153934044 0.009243198
125 -0.252506996 -0.153934044
126 -0.192612610 -0.252506996
127 -0.176725345 -0.192612610
128 0.678461125 -0.176725345
129 0.711008709 0.678461125
130 -0.110782965 0.711008709
131 -1.050499642 -0.110782965
132 -0.015279443 -1.050499642
133 -0.057802145 -0.015279443
134 -0.034773163 -0.057802145
135 0.042828437 -0.034773163
136 0.945172185 0.042828437
137 -0.041052798 0.945172185
138 -0.011039506 -0.041052798
139 -0.836906224 -0.011039506
140 0.861498929 -0.836906224
141 -1.113802320 0.861498929
142 0.033646752 -1.113802320
143 0.099647603 0.033646752
144 0.157081275 0.099647603
> 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/fisher/rcomp/tmp/7vu2r1352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8skqq1352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9d9sh1352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10xtt61352155974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11hspx1352155974.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/fisher/rcomp/tmp/12f1an1352155974.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/fisher/rcomp/tmp/13s0j11352155974.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/fisher/rcomp/tmp/140gfx1352155974.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/fisher/rcomp/tmp/159biy1352155974.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/fisher/rcomp/tmp/16l18p1352155974.tab")
+ }
>
> try(system("convert tmp/1cmty1352155974.ps tmp/1cmty1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nq3m1352155974.ps tmp/2nq3m1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/3o0zf1352155974.ps tmp/3o0zf1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qels1352155974.ps tmp/4qels1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lt651352155974.ps tmp/5lt651352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/6evq91352155974.ps tmp/6evq91352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vu2r1352155974.ps tmp/7vu2r1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/8skqq1352155974.ps tmp/8skqq1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d9sh1352155974.ps tmp/9d9sh1352155974.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xtt61352155974.ps tmp/10xtt61352155974.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.694 1.120 9.812