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.
R is a collaborative project with many contributors.
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.
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+ ,397
+ ,7.10
+ ,7.10
+ ,8.10
+ ,8.10
+ ,7.50
+ ,7.5
+ ,100
+ ,1
+ ,100
+ ,516
+ ,107
+ ,107
+ ,409
+ ,409
+ ,7.20
+ ,7.20
+ ,8.40
+ ,8.40
+ ,7.70
+ ,7.7
+ ,101
+ ,1
+ ,101
+ ,528
+ ,109
+ ,109
+ ,419
+ ,419
+ ,7.50
+ ,7.50
+ ,8.70
+ ,8.70
+ ,8.10
+ ,8.1
+ ,102
+ ,1
+ ,102
+ ,533
+ ,109
+ ,109
+ ,424
+ ,424
+ ,7.70
+ ,7.70
+ ,9.00
+ ,9.00
+ ,8.40
+ ,8.4
+ ,103
+ ,1
+ ,103
+ ,536
+ ,108
+ ,108
+ ,428
+ ,428
+ ,7.80
+ ,7.80
+ ,9.30
+ ,9.30
+ ,8.60
+ ,8.6
+ ,104
+ ,1
+ ,104
+ ,537
+ ,107
+ ,107
+ ,430
+ ,430
+ ,7.70
+ ,7.70
+ ,9.40
+ ,9.40
+ ,8.80
+ ,8.8
+ ,0
+ ,0
+ ,105
+ ,524
+ ,99
+ ,0
+ ,424
+ ,0
+ ,7.70
+ ,0.00
+ ,9.50
+ ,0.00
+ ,8.90
+ ,0
+ ,0
+ ,0
+ ,106
+ ,536
+ ,103
+ ,0
+ ,433
+ ,0
+ ,7.80
+ ,0.00
+ ,9.60
+ ,0.00
+ ,9.10
+ ,0
+ ,0
+ ,0
+ ,107
+ ,587
+ ,131
+ ,0
+ ,456
+ ,0
+ ,8.00
+ ,0.00
+ ,9.80
+ ,0.00
+ ,9.20
+ ,0
+ ,0
+ ,0
+ ,108
+ ,597
+ ,137
+ ,0
+ ,459
+ ,0
+ ,8.10
+ ,0.00
+ ,9.80
+ ,0.00
+ ,9.30
+ ,0
+ ,0
+ ,0
+ ,109
+ ,581
+ ,135
+ ,0
+ ,446
+ ,0
+ ,8.10
+ ,0.00
+ ,9.90
+ ,0.00
+ ,9.40
+ ,0
+ ,110
+ ,1
+ ,110
+ ,564
+ ,124
+ ,124
+ ,441
+ ,441
+ ,8.00
+ ,8.00
+ ,10.00
+ ,10.00
+ ,9.40
+ ,9.4
+ ,111
+ ,1
+ ,111
+ ,558
+ ,118
+ ,118
+ ,439
+ ,439
+ ,8.10
+ ,8.10
+ ,10.00
+ ,10.00
+ ,9.50
+ ,9.5
+ ,112
+ ,1
+ ,112
+ ,575
+ ,121
+ ,121
+ ,454
+ ,454
+ ,8.20
+ ,8.20
+ ,10.10
+ ,10.10
+ ,9.50
+ ,9.5
+ ,113
+ ,1
+ ,113
+ ,580
+ ,121
+ ,121
+ ,460
+ ,460
+ ,8.40
+ ,8.40
+ ,10.10
+ ,10.10
+ ,9.70
+ ,9.7
+ ,114
+ ,1
+ ,114
+ ,575
+ ,118
+ ,118
+ ,457
+ ,457
+ ,8.50
+ ,8.50
+ ,10.10
+ ,10.10
+ ,9.70
+ ,9.7
+ ,115
+ ,1
+ ,115
+ ,563
+ ,113
+ ,113
+ ,451
+ ,451
+ ,8.50
+ ,8.50
+ ,10.10
+ ,10.10
+ ,9.70
+ ,9.7
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+ ,1
+ ,116
+ ,552
+ ,107
+ ,107
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+ ,444
+ ,8.50
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+ ,10.20
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+ ,0
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+ ,0
+ ,0
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+ ,601
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+ ,0
+ ,0
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+ ,604
+ ,136
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+ ,469
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+ ,8.30
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+ ,0
+ ,0
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+ ,586
+ ,133
+ ,0
+ ,454
+ ,0
+ ,8.20
+ ,0.00
+ ,10.10
+ ,0.00
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+ ,0
+ ,122
+ ,1
+ ,122
+ ,564
+ ,120
+ ,120
+ ,444
+ ,444
+ ,8.10
+ ,8.10
+ ,10.10
+ ,10.10
+ ,9.60
+ ,9.6
+ ,123
+ ,1
+ ,123
+ ,549
+ ,112
+ ,112
+ ,436
+ ,436
+ ,7.90
+ ,7.90
+ ,10.10
+ ,10.10
+ ,9.60
+ ,9.6
+ ,124
+ ,1
+ ,124
+ ,551
+ ,109
+ ,109
+ ,442
+ ,442
+ ,7.60
+ ,7.60
+ ,10.10
+ ,10.10
+ ,9.60
+ ,9.6
+ ,125
+ ,1
+ ,125
+ ,556
+ ,110
+ ,110
+ ,446
+ ,446
+ ,7.30
+ ,7.30
+ ,10.00
+ ,10.00
+ ,9.50
+ ,9.5
+ ,126
+ ,1
+ ,126
+ ,548
+ ,106
+ ,106
+ ,442
+ ,442
+ ,7.10
+ ,7.10
+ ,9.90
+ ,9.90
+ ,9.50
+ ,9.5
+ ,127
+ ,1
+ ,127
+ ,540
+ ,102
+ ,102
+ ,438
+ ,438
+ ,7.00
+ ,7.00
+ ,9.90
+ ,9.90
+ ,9.40
+ ,9.4
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+ ,1
+ ,128
+ ,531
+ ,98
+ ,98
+ ,433
+ ,433
+ ,7.10
+ ,7.10
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+ ,9.90
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+ ,0
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+ ,521
+ ,92
+ ,0
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+ ,7.10
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+ ,0
+ ,0
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+ ,10.00
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+ ,0
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+ ,572
+ ,120
+ ,0
+ ,452
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+ ,0.00
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+ ,0
+ ,0
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+ ,581
+ ,127
+ ,0
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+ ,0
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+ ,0.00
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+ ,0
+ ,0
+ ,0
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+ ,563
+ ,124
+ ,0
+ ,439
+ ,0
+ ,7.30
+ ,0.00
+ ,10.30
+ ,0.00
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+ ,0
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+ ,1
+ ,134
+ ,548
+ ,114
+ ,114
+ ,434
+ ,434
+ ,7.20
+ ,7.20
+ ,10.50
+ ,10.50
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+ ,9.9
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+ ,1
+ ,135
+ ,539
+ ,108
+ ,108
+ ,431
+ ,431
+ ,7.20
+ ,7.20
+ ,10.60
+ ,10.60
+ ,10.00
+ ,10
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+ ,1
+ ,136
+ ,541
+ ,106
+ ,106
+ ,435
+ ,435
+ ,7.10
+ ,7.10
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+ ,10.70
+ ,10.00
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+ ,1
+ ,137
+ ,562
+ ,111
+ ,111
+ ,450
+ ,450
+ ,7.10
+ ,7.10
+ ,10.80
+ ,10.80
+ ,10.10
+ ,10.1
+ ,138
+ ,1
+ ,138
+ ,559
+ ,110
+ ,110
+ ,449
+ ,449
+ ,7.10
+ ,7.10
+ ,10.90
+ ,10.90
+ ,10.20
+ ,10.2
+ ,139
+ ,1
+ ,139
+ ,546
+ ,104
+ ,104
+ ,442
+ ,442
+ ,7.20
+ ,7.20
+ ,11.00
+ ,11.00
+ ,10.30
+ ,10.3
+ ,140
+ ,1
+ ,140
+ ,536
+ ,100
+ ,100
+ ,437
+ ,437
+ ,7.30
+ ,7.30
+ ,11.20
+ ,11.20
+ ,10.30
+ ,10.3
+ ,0
+ ,0
+ ,141
+ ,528
+ ,96
+ ,0
+ ,431
+ ,0
+ ,7.40
+ ,0.00
+ ,11.30
+ ,0.00
+ ,10.40
+ ,0
+ ,0
+ ,0
+ ,142
+ ,530
+ ,98
+ ,0
+ ,433
+ ,0
+ ,7.40
+ ,0.00
+ ,11.40
+ ,0.00
+ ,10.50
+ ,0
+ ,0
+ ,0
+ ,143
+ ,582
+ ,122
+ ,0
+ ,460
+ ,0
+ ,7.50
+ ,0.00
+ ,11.50
+ ,0.00
+ ,10.50
+ ,0
+ ,0
+ ,0
+ ,144
+ ,599
+ ,134
+ ,0
+ ,465
+ ,0
+ ,7.40
+ ,0.00
+ ,11.50
+ ,0.00
+ ,10.60
+ ,0
+ ,0
+ ,0
+ ,145
+ ,584
+ ,133
+ ,0
+ ,451
+ ,0
+ ,7.40
+ ,0.00
+ ,11.60
+ ,0.00
+ ,10.60
+ ,0)
+ ,dim=c(14
+ ,145)
+ ,dimnames=list(c('S_t'
+ ,'s'
+ ,'t'
+ ,'Totale_werkloosheid'
+ ,'Jonger_dan_25'
+ ,'Jonger_dan_25_s'
+ ,'Vanaf_25'
+ ,'Vanaf_25_s'
+ ,'Belgie'
+ ,'Belgie_s'
+ ,'Euroraad'
+ ,'Euroraad_s'
+ ,'EU-27'
+ ,'EU-27_s')
+ ,1:145))
> y <- array(NA,dim=c(14,145),dimnames=list(c('S_t','s','t','Totale_werkloosheid','Jonger_dan_25','Jonger_dan_25_s','Vanaf_25','Vanaf_25_s','Belgie','Belgie_s','Euroraad','Euroraad_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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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 S_t s t Jonger_dan_25 Jonger_dan_25_s Vanaf_25
1 501 0 0 1 134 0 368
2 485 2 1 2 124 124 361
3 464 3 1 3 113 113 351
4 460 4 1 4 109 109 351
5 467 5 1 5 109 109 358
6 460 6 1 6 106 106 354
7 448 7 1 7 101 101 347
8 443 8 1 8 98 98 345
9 436 0 0 9 93 0 343
10 431 0 0 10 91 0 340
11 484 0 0 11 122 0 362
12 510 0 0 12 139 0 370
13 513 0 0 13 140 0 373
14 503 14 1 14 132 132 371
15 471 15 1 15 117 117 354
16 471 16 1 16 114 114 357
17 476 17 1 17 113 113 363
18 475 18 1 18 110 110 364
19 470 19 1 19 107 107 363
20 461 20 1 20 103 103 358
21 455 0 0 21 98 0 357
22 456 0 0 22 98 0 357
23 517 0 0 23 137 0 380
24 525 0 0 24 148 0 378
25 523 0 0 25 147 0 376
26 519 26 1 26 139 139 380
27 509 27 1 27 130 130 379
28 512 28 1 28 128 128 384
29 519 29 1 29 127 127 392
30 517 30 1 30 123 123 394
31 510 31 1 31 118 118 392
32 509 32 1 32 114 114 396
33 501 0 0 33 108 0 392
34 507 0 0 34 111 0 396
35 569 0 0 35 151 0 419
36 580 0 0 36 159 0 421
37 578 0 0 37 158 0 420
38 565 38 1 38 148 148 418
39 547 39 1 39 138 138 410
40 555 40 1 40 137 137 418
41 562 41 1 41 136 136 426
42 561 42 1 42 133 133 428
43 555 43 1 43 126 126 430
44 544 44 1 44 120 120 424
45 537 0 0 45 114 0 423
46 543 0 0 46 116 0 427
47 594 0 0 47 153 0 441
48 611 0 0 48 162 0 449
49 613 0 0 49 161 0 452
50 611 50 1 50 149 149 462
51 594 51 1 51 139 139 455
52 595 52 1 52 135 135 461
53 591 53 1 53 130 130 461
54 589 54 1 54 127 127 463
55 584 55 1 55 122 122 462
56 573 56 1 56 117 117 456
57 567 0 0 57 112 0 455
58 569 0 0 58 113 0 456
59 621 0 0 59 149 0 472
60 629 0 0 60 157 0 472
61 628 0 0 61 157 0 471
62 612 62 1 62 147 147 465
63 595 63 1 63 137 137 459
64 597 64 1 64 132 132 465
65 593 65 1 65 125 125 468
66 590 66 1 66 123 123 467
67 580 67 1 67 117 117 463
68 574 68 1 68 114 114 460
69 573 0 0 69 111 0 462
70 573 0 0 70 112 0 461
71 620 0 0 71 144 0 476
72 626 0 0 72 150 0 476
73 620 0 0 73 149 0 471
74 588 74 1 74 134 134 453
75 566 75 1 75 123 123 443
76 557 76 1 76 116 116 442
77 561 77 1 77 117 117 444
78 549 78 1 78 111 111 438
79 532 79 1 79 105 105 427
80 526 80 1 80 102 102 424
81 511 0 0 81 95 0 416
82 499 0 0 82 93 0 406
83 555 0 0 83 124 0 431
84 565 0 0 84 130 0 434
85 542 0 0 85 124 0 418
86 527 86 1 86 115 115 412
87 510 87 1 87 106 106 404
88 514 88 1 88 105 105 409
89 517 89 1 89 105 105 412
90 508 90 1 90 101 101 406
91 493 91 1 91 95 95 398
92 490 92 1 92 93 93 397
93 469 0 0 93 84 0 385
94 478 0 0 94 87 0 390
95 528 0 0 95 116 0 413
96 534 0 0 96 120 0 413
97 518 0 0 97 117 0 401
98 506 98 1 98 109 109 397
99 502 99 1 99 105 105 397
100 516 100 1 100 107 107 409
101 528 101 1 101 109 109 419
102 533 102 1 102 109 109 424
103 536 103 1 103 108 108 428
104 537 104 1 104 107 107 430
105 524 0 0 105 99 0 424
106 536 0 0 106 103 0 433
107 587 0 0 107 131 0 456
108 597 0 0 108 137 0 459
109 581 0 0 109 135 0 446
110 564 110 1 110 124 124 441
111 558 111 1 111 118 118 439
112 575 112 1 112 121 121 454
113 580 113 1 113 121 121 460
114 575 114 1 114 118 118 457
115 563 115 1 115 113 113 451
116 552 116 1 116 107 107 444
117 537 0 0 117 100 0 437
118 545 0 0 118 102 0 443
119 601 0 0 119 130 0 471
120 604 0 0 120 136 0 469
121 586 0 0 121 133 0 454
122 564 122 1 122 120 120 444
123 549 123 1 123 112 112 436
124 551 124 1 124 109 109 442
125 556 125 1 125 110 110 446
126 548 126 1 126 106 106 442
127 540 127 1 127 102 102 438
128 531 128 1 128 98 98 433
129 521 0 0 129 92 0 428
130 519 0 0 130 92 0 426
131 572 0 0 131 120 0 452
132 581 0 0 132 127 0 455
133 563 0 0 133 124 0 439
134 548 134 1 134 114 114 434
135 539 135 1 135 108 108 431
136 541 136 1 136 106 106 435
137 562 137 1 137 111 111 450
138 559 138 1 138 110 110 449
139 546 139 1 139 104 104 442
140 536 140 1 140 100 100 437
141 528 0 0 141 96 0 431
142 530 0 0 142 98 0 433
143 582 0 0 143 122 0 460
144 599 0 0 144 134 0 465
145 584 0 0 145 133 0 451
Vanaf_25_s Belgie Belgie_s Euroraad Euroraad_s EU-27 EU-27_s
1 0 6.7 0.0 8.5 0.0 8.7 0.0
2 361 6.8 6.8 8.4 8.4 8.6 8.6
3 351 6.7 6.7 8.4 8.4 8.6 8.6
4 351 6.6 6.6 8.3 8.3 8.5 8.5
5 358 6.4 6.4 8.2 8.2 8.5 8.5
6 354 6.3 6.3 8.2 8.2 8.5 8.5
7 347 6.3 6.3 8.1 8.1 8.5 8.5
8 345 6.5 6.5 8.1 8.1 8.5 8.5
9 0 6.5 0.0 8.1 0.0 8.5 0.0
10 0 6.4 0.0 8.1 0.0 8.5 0.0
11 0 6.2 0.0 8.1 0.0 8.5 0.0
12 0 6.2 0.0 8.1 0.0 8.6 0.0
13 0 6.5 0.0 8.1 0.0 8.6 0.0
14 371 7.0 7.0 8.2 8.2 8.6 8.6
15 354 7.2 7.2 8.2 8.2 8.7 8.7
16 357 7.3 7.3 8.3 8.3 8.7 8.7
17 363 7.4 7.4 8.2 8.2 8.7 8.7
18 364 7.4 7.4 8.3 8.3 8.8 8.8
19 363 7.4 7.4 8.3 8.3 8.8 8.8
20 358 7.3 7.3 8.4 8.4 8.9 8.9
21 0 7.4 0.0 8.5 0.0 8.9 0.0
22 0 7.4 0.0 8.5 0.0 8.9 0.0
23 0 7.6 0.0 8.6 0.0 9.0 0.0
24 0 7.6 0.0 8.6 0.0 9.0 0.0
25 0 7.7 0.0 8.7 0.0 9.0 0.0
26 380 7.7 7.7 8.7 8.7 9.0 9.0
27 379 7.8 7.8 8.8 8.8 9.0 9.0
28 384 7.8 7.8 8.8 8.8 9.0 9.0
29 392 8.0 8.0 8.9 8.9 9.1 9.1
30 394 8.1 8.1 9.0 9.0 9.1 9.1
31 392 8.1 8.1 9.0 9.0 9.1 9.1
32 396 8.2 8.2 9.0 9.0 9.1 9.1
33 0 8.1 0.0 9.0 0.0 9.1 0.0
34 0 8.1 0.0 9.1 0.0 9.1 0.0
35 0 8.1 0.0 9.1 0.0 9.1 0.0
36 0 8.1 0.0 9.0 0.0 9.1 0.0
37 0 8.2 0.0 9.1 0.0 9.1 0.0
38 418 8.2 8.2 9.0 9.0 9.1 9.1
39 410 8.3 8.3 9.1 9.1 9.1 9.1
40 418 8.4 8.4 9.1 9.1 9.2 9.2
41 426 8.6 8.6 9.2 9.2 9.3 9.3
42 428 8.6 8.6 9.2 9.2 9.3 9.3
43 430 8.4 8.4 9.2 9.2 9.3 9.3
44 424 8.0 8.0 9.2 9.2 9.2 9.2
45 0 7.9 0.0 9.2 0.0 9.2 0.0
46 0 8.1 0.0 9.3 0.0 9.2 0.0
47 0 8.5 0.0 9.3 0.0 9.2 0.0
48 0 8.8 0.0 9.3 0.0 9.2 0.0
49 0 8.8 0.0 9.3 0.0 9.2 0.0
50 462 8.5 8.5 9.3 9.3 9.2 9.2
51 455 8.3 8.3 9.4 9.4 9.2 9.2
52 461 8.3 8.3 9.4 9.4 9.2 9.2
53 461 8.3 8.3 9.3 9.3 9.2 9.2
54 463 8.4 8.4 9.3 9.3 9.2 9.2
55 462 8.5 8.5 9.3 9.3 9.2 9.2
56 456 8.5 8.5 9.3 9.3 9.2 9.2
57 0 8.6 0.0 9.2 0.0 9.1 0.0
58 0 8.5 0.0 9.2 0.0 9.1 0.0
59 0 8.6 0.0 9.2 0.0 9.0 0.0
60 0 8.6 0.0 9.1 0.0 8.9 0.0
61 0 8.6 0.0 9.1 0.0 8.9 0.0
62 465 8.5 8.5 9.1 9.1 9.0 9.0
63 459 8.4 8.4 9.1 9.1 8.9 8.9
64 465 8.4 8.4 9.0 9.0 8.8 8.8
65 468 8.5 8.5 8.9 8.9 8.7 8.7
66 467 8.5 8.5 8.8 8.8 8.6 8.6
67 463 8.5 8.5 8.7 8.7 8.5 8.5
68 460 8.6 8.6 8.6 8.6 8.5 8.5
69 0 8.6 0.0 8.6 0.0 8.4 0.0
70 0 8.4 0.0 8.5 0.0 8.3 0.0
71 0 8.2 0.0 8.4 0.0 8.2 0.0
72 0 8.0 0.0 8.4 0.0 8.2 0.0
73 0 8.0 0.0 8.3 0.0 8.1 0.0
74 453 8.0 8.0 8.2 8.2 8.0 8.0
75 443 8.0 8.0 8.2 8.2 7.9 7.9
76 442 7.9 7.9 8.0 8.0 7.8 7.8
77 444 7.9 7.9 7.9 7.9 7.6 7.6
78 438 7.9 7.9 7.8 7.8 7.5 7.5
79 427 7.9 7.9 7.7 7.7 7.4 7.4
80 424 8.0 8.0 7.6 7.6 7.3 7.3
81 0 7.9 0.0 7.6 0.0 7.3 0.0
82 0 7.4 0.0 7.6 0.0 7.2 0.0
83 0 7.2 0.0 7.6 0.0 7.2 0.0
84 0 7.0 0.0 7.6 0.0 7.2 0.0
85 0 6.9 0.0 7.5 0.0 7.1 0.0
86 412 7.1 7.1 7.5 7.5 7.0 7.0
87 404 7.2 7.2 7.4 7.4 7.0 7.0
88 409 7.2 7.2 7.4 7.4 6.9 6.9
89 412 7.1 7.1 7.4 7.4 6.9 6.9
90 406 6.9 6.9 7.3 7.3 6.8 6.8
91 398 6.8 6.8 7.3 7.3 6.8 6.8
92 397 6.8 6.8 7.4 7.4 6.8 6.8
93 0 6.8 0.0 7.5 0.0 6.9 0.0
94 0 6.9 0.0 7.6 0.0 7.0 0.0
95 0 7.1 0.0 7.6 0.0 7.0 0.0
96 0 7.2 0.0 7.7 0.0 7.1 0.0
97 0 7.2 0.0 7.7 0.0 7.2 0.0
98 397 7.1 7.1 7.9 7.9 7.3 7.3
99 397 7.1 7.1 8.1 8.1 7.5 7.5
100 409 7.2 7.2 8.4 8.4 7.7 7.7
101 419 7.5 7.5 8.7 8.7 8.1 8.1
102 424 7.7 7.7 9.0 9.0 8.4 8.4
103 428 7.8 7.8 9.3 9.3 8.6 8.6
104 430 7.7 7.7 9.4 9.4 8.8 8.8
105 0 7.7 0.0 9.5 0.0 8.9 0.0
106 0 7.8 0.0 9.6 0.0 9.1 0.0
107 0 8.0 0.0 9.8 0.0 9.2 0.0
108 0 8.1 0.0 9.8 0.0 9.3 0.0
109 0 8.1 0.0 9.9 0.0 9.4 0.0
110 441 8.0 8.0 10.0 10.0 9.4 9.4
111 439 8.1 8.1 10.0 10.0 9.5 9.5
112 454 8.2 8.2 10.1 10.1 9.5 9.5
113 460 8.4 8.4 10.1 10.1 9.7 9.7
114 457 8.5 8.5 10.1 10.1 9.7 9.7
115 451 8.5 8.5 10.1 10.1 9.7 9.7
116 444 8.5 8.5 10.2 10.2 9.7 9.7
117 0 8.5 0.0 10.2 0.0 9.7 0.0
118 0 8.5 0.0 10.1 0.0 9.6 0.0
119 0 8.4 0.0 10.1 0.0 9.6 0.0
120 0 8.3 0.0 10.1 0.0 9.6 0.0
121 0 8.2 0.0 10.1 0.0 9.6 0.0
122 444 8.1 8.1 10.1 10.1 9.6 9.6
123 436 7.9 7.9 10.1 10.1 9.6 9.6
124 442 7.6 7.6 10.1 10.1 9.6 9.6
125 446 7.3 7.3 10.0 10.0 9.5 9.5
126 442 7.1 7.1 9.9 9.9 9.5 9.5
127 438 7.0 7.0 9.9 9.9 9.4 9.4
128 433 7.1 7.1 9.9 9.9 9.4 9.4
129 0 7.1 0.0 9.9 0.0 9.5 0.0
130 0 7.1 0.0 10.0 0.0 9.5 0.0
131 0 7.3 0.0 10.1 0.0 9.6 0.0
132 0 7.3 0.0 10.2 0.0 9.7 0.0
133 0 7.3 0.0 10.3 0.0 9.8 0.0
134 434 7.2 7.2 10.5 10.5 9.9 9.9
135 431 7.2 7.2 10.6 10.6 10.0 10.0
136 435 7.1 7.1 10.7 10.7 10.0 10.0
137 450 7.1 7.1 10.8 10.8 10.1 10.1
138 449 7.1 7.1 10.9 10.9 10.2 10.2
139 442 7.2 7.2 11.0 11.0 10.3 10.3
140 437 7.3 7.3 11.2 11.2 10.3 10.3
141 0 7.4 0.0 11.3 0.0 10.4 0.0
142 0 7.4 0.0 11.4 0.0 10.5 0.0
143 0 7.5 0.0 11.5 0.0 10.5 0.0
144 0 7.4 0.0 11.5 0.0 10.6 0.0
145 0 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) S_t s t
1.063e+00 8.734e-03 5.594e-01 -9.267e-05
Jonger_dan_25 Jonger_dan_25_s Vanaf_25 Vanaf_25_s
9.916e-01 7.722e-03 1.003e+00 -3.388e-03
Belgie Belgie_s Euroraad Euroraad_s
-9.366e-02 -1.721e-02 -1.696e-01 -6.513e-01
`EU-27` `EU-27_s`
1.211e-01 5.877e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.15867 -0.15887 -0.00415 0.21622 1.11814
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.063e+00 1.055e+00 1.008 0.316
S_t 8.734e-03 7.867e-03 1.110 0.269
s 5.594e-01 1.362e+00 0.411 0.682
t -9.267e-05 5.869e-03 -0.016 0.987
Jonger_dan_25 9.916e-01 4.367e-03 227.045 <2e-16 ***
Jonger_dan_25_s 7.722e-03 8.536e-03 0.905 0.367
Vanaf_25 1.003e+00 4.621e-03 217.039 <2e-16 ***
Vanaf_25_s -3.388e-03 6.093e-03 -0.556 0.579
Belgie -9.366e-02 1.700e-01 -0.551 0.583
Belgie_s -1.721e-02 2.404e-01 -0.072 0.943
Euroraad -1.696e-01 5.468e-01 -0.310 0.757
Euroraad_s -6.513e-01 7.674e-01 -0.849 0.398
`EU-27` 1.211e-01 5.210e-01 0.232 0.817
`EU-27_s` 5.877e-01 7.202e-01 0.816 0.416
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5066 on 131 degrees of freedom
Multiple R-squared: 0.9999, Adjusted R-squared: 0.9999
F-statistic: 8.859e+04 on 13 and 131 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,] 3.516125e-40 7.032249e-40 1.0000000
[2,] 6.812808e-02 1.362562e-01 0.9318719
[3,] 1.725093e-01 3.450185e-01 0.8274907
[4,] 1.379085e-01 2.758170e-01 0.8620915
[5,] 7.727830e-02 1.545566e-01 0.9227217
[6,] 2.031052e-01 4.062105e-01 0.7968948
[7,] 1.330114e-01 2.660228e-01 0.8669886
[8,] 1.073574e-01 2.147148e-01 0.8926426
[9,] 2.907086e-01 5.814172e-01 0.7092914
[10,] 2.189906e-01 4.379813e-01 0.7810094
[11,] 1.586038e-01 3.172076e-01 0.8413962
[12,] 1.125829e-01 2.251657e-01 0.8874171
[13,] 8.309283e-02 1.661857e-01 0.9169072
[14,] 5.753368e-02 1.150674e-01 0.9424663
[15,] 3.955200e-02 7.910400e-02 0.9604480
[16,] 9.294046e-02 1.858809e-01 0.9070595
[17,] 7.233142e-02 1.446628e-01 0.9276686
[18,] 1.076679e-01 2.153357e-01 0.8923321
[19,] 1.325514e-01 2.651027e-01 0.8674486
[20,] 1.345101e-01 2.690202e-01 0.8654899
[21,] 1.457296e-01 2.914591e-01 0.8542704
[22,] 1.454834e-01 2.909669e-01 0.8545166
[23,] 1.235557e-01 2.471113e-01 0.8764443
[24,] 1.290567e-01 2.581133e-01 0.8709433
[25,] 1.037141e-01 2.074282e-01 0.8962859
[26,] 8.192013e-02 1.638403e-01 0.9180799
[27,] 1.137645e-01 2.275291e-01 0.8862355
[28,] 1.408761e-01 2.817522e-01 0.8591239
[29,] 2.389078e-01 4.778156e-01 0.7610922
[30,] 2.094606e-01 4.189211e-01 0.7905394
[31,] 1.708093e-01 3.416186e-01 0.8291907
[32,] 1.355661e-01 2.711322e-01 0.8644339
[33,] 1.062575e-01 2.125151e-01 0.8937425
[34,] 1.206306e-01 2.412611e-01 0.8793694
[35,] 1.216682e-01 2.433365e-01 0.8783318
[36,] 1.266372e-01 2.532744e-01 0.8733628
[37,] 1.137395e-01 2.274790e-01 0.8862605
[38,] 1.275602e-01 2.551204e-01 0.8724398
[39,] 1.218464e-01 2.436927e-01 0.8781536
[40,] 1.118255e-01 2.236511e-01 0.8881745
[41,] 1.232381e-01 2.464763e-01 0.8767619
[42,] 1.138625e-01 2.277251e-01 0.8861375
[43,] 9.044819e-02 1.808964e-01 0.9095518
[44,] 7.046416e-02 1.409283e-01 0.9295358
[45,] 5.483813e-02 1.096763e-01 0.9451619
[46,] 4.703648e-02 9.407296e-02 0.9529635
[47,] 5.477162e-02 1.095432e-01 0.9452284
[48,] 5.004530e-02 1.000906e-01 0.9499547
[49,] 3.923732e-02 7.847464e-02 0.9607627
[50,] 2.943296e-02 5.886592e-02 0.9705670
[51,] 2.176420e-02 4.352839e-02 0.9782358
[52,] 1.607052e-02 3.214104e-02 0.9839295
[53,] 1.274198e-02 2.548395e-02 0.9872580
[54,] 9.759814e-03 1.951963e-02 0.9902402
[55,] 6.840307e-03 1.368061e-02 0.9931597
[56,] 4.838563e-03 9.677127e-03 0.9951614
[57,] 3.457989e-03 6.915979e-03 0.9965420
[58,] 8.062719e-03 1.612544e-02 0.9919373
[59,] 6.939739e-03 1.387948e-02 0.9930603
[60,] 2.157113e-02 4.314227e-02 0.9784289
[61,] 1.576534e-02 3.153068e-02 0.9842347
[62,] 1.131273e-02 2.262547e-02 0.9886873
[63,] 7.950666e-03 1.590133e-02 0.9920493
[64,] 5.501918e-03 1.100384e-02 0.9944981
[65,] 3.972869e-03 7.945739e-03 0.9960271
[66,] 3.574490e-03 7.148980e-03 0.9964255
[67,] 2.833977e-03 5.667953e-03 0.9971660
[68,] 3.863151e-03 7.726302e-03 0.9961368
[69,] 3.557354e-03 7.114708e-03 0.9964426
[70,] 2.501896e-03 5.003793e-03 0.9974981
[71,] 1.697859e-03 3.395718e-03 0.9983021
[72,] 1.121604e-03 2.243208e-03 0.9988784
[73,] 7.279747e-04 1.455949e-03 0.9992720
[74,] 1.878766e-03 3.757533e-03 0.9981212
[75,] 1.245817e-03 2.491635e-03 0.9987542
[76,] 7.923338e-04 1.584668e-03 0.9992077
[77,] 9.213603e-04 1.842721e-03 0.9990786
[78,] 8.131389e-04 1.626278e-03 0.9991869
[79,] 7.179017e-03 1.435803e-02 0.9928210
[80,] 9.826813e-03 1.965363e-02 0.9901732
[81,] 7.716252e-03 1.543250e-02 0.9922837
[82,] 5.353073e-03 1.070615e-02 0.9946469
[83,] 3.831911e-03 7.663822e-03 0.9961681
[84,] 2.589553e-03 5.179105e-03 0.9974104
[85,] 1.713623e-03 3.427246e-03 0.9982864
[86,] 1.112033e-03 2.224067e-03 0.9988880
[87,] 7.076897e-04 1.415379e-03 0.9992923
[88,] 4.265106e-04 8.530212e-04 0.9995735
[89,] 2.683851e-04 5.367702e-04 0.9997316
[90,] 7.234420e-04 1.446884e-03 0.9992766
[91,] 1.051512e-03 2.103025e-03 0.9989485
[92,] 9.889008e-04 1.977802e-03 0.9990111
[93,] 7.343289e-04 1.468658e-03 0.9992657
[94,] 2.950233e-03 5.900467e-03 0.9970498
[95,] 6.506550e-03 1.301310e-02 0.9934935
[96,] 5.193112e-03 1.038622e-02 0.9948069
[97,] 8.288425e-03 1.657685e-02 0.9917116
[98,] 5.246212e-03 1.049242e-02 0.9947538
[99,] 5.006337e-02 1.001267e-01 0.9499366
[100,] 6.836698e-02 1.367340e-01 0.9316330
[101,] 5.104016e-02 1.020803e-01 0.9489598
[102,] 4.449915e-02 8.899830e-02 0.9555009
[103,] 7.840974e-02 1.568195e-01 0.9215903
[104,] 7.139820e-02 1.427964e-01 0.9286018
[105,] 5.717709e-02 1.143542e-01 0.9428229
[106,] 1.052133e-01 2.104265e-01 0.8947867
[107,] 1.153333e-01 2.306666e-01 0.8846667
[108,] 8.873674e-02 1.774735e-01 0.9112633
[109,] 5.881099e-02 1.176220e-01 0.9411890
[110,] 3.320330e-02 6.640661e-02 0.9667967
[111,] 1.851897e-02 3.703793e-02 0.9814810
[112,] 1.485063e-02 2.970126e-02 0.9851494
> postscript(file="/var/fisher/rcomp/tmp/1da6o1352155635.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2star1352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fgeh1352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4uh5u1352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5h7781352155635.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
-0.977461935 0.185609810 0.153168996 0.119484119 0.010198533 -0.013653186
7 8 9 10 11 12
-0.111427088 -0.100987285 -0.312023755 -0.329497799 -0.150659633 0.957290446
13 14 15 16 17 18
-0.014727112 -0.049427368 -0.125843774 -0.041821809 -0.119051316 0.881977245
19 20 21 22 23 24
-0.129239132 -0.143081355 -0.205376412 0.794716255 0.080264206 -0.821407669
25 26 27 28 29 30
0.202347525 0.060872882 0.138711204 0.131279343 0.159467683 0.242286921
31 32 33 34 35 36
0.229181450 -0.769053237 0.905451389 -0.063757092 -0.793384397 0.251269788
37 38 39 40 41 42
0.272153096 -0.786202316 -0.712664867 0.222350564 0.250538904 0.240871601
43 44 45 46 47 48
-0.793713713 0.216958361 -0.128932873 -0.087818291 0.220639168 0.301528861
49 50 51 52 53 54
0.284597535 0.342157150 0.382952151 -0.625336089 0.280504514 -0.718076520
55 56 57 58 59 60
0.280420635 0.265249732 -0.158868797 -0.162605380 0.115712073 0.178219227
61 62 63 64 65 66
0.181183781 0.216218083 -0.742596511 0.237216659 0.225194286 0.203452057
67 68 69 70 71 72
0.177415279 0.094165814 -0.203272290 -0.215482993 -0.012985483 0.018825796
73 74 75 76 77 78
0.020017365 1.011941119 0.061460993 -1.056885989 -0.004145621 -0.031215114
79 80 81 82 83 84
-0.060866397 -0.073241442 -0.306559521 -0.329283963 -0.159061455 0.864134167
85 86 87 88 89 90
-0.154490894 -0.091600227 -0.181549692 -0.117420765 -0.135598874 0.816531833
91 92 93 94 95 96
-0.211444423 -0.139887790 -0.380458030 0.644718105 -1.158669488 0.889274412
97 98 99 100 101 102
-0.113504492 -0.091431459 -0.080393402 0.034132069 0.028048524 0.077798602
103 104 105 106 107 108
0.186134105 0.107091074 0.816110607 -0.173901664 0.036115992 1.075299513
109 110 111 112 113 114
0.100762221 -0.826874069 1.099545941 0.193882411 -0.931236656 0.067600520
115 116 117 118 119 120
-0.947570383 1.118142629 -0.114921821 -0.120095993 0.025651552 -0.927427698
121 122 123 124 125 126
-0.918847446 0.019662643 0.979227382 -0.061633295 -0.111993436 -0.229704433
127 128 129 130 131 132
-0.183368101 -0.186250009 0.686984912 0.709782742 -0.105776651 -1.050589103
133 134 135 136 137 138
-0.024919073 -0.077363216 -0.080459192 -0.017407038 0.996341481 -0.002289937
139 140 141 142 143 144
0.003634924 -0.835073702 0.869688389 -1.114293852 0.036385027 0.101543389
145
0.150395515
> postscript(file="/var/fisher/rcomp/tmp/6jbr41352155635.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 -0.977461935 NA
1 0.185609810 -0.977461935
2 0.153168996 0.185609810
3 0.119484119 0.153168996
4 0.010198533 0.119484119
5 -0.013653186 0.010198533
6 -0.111427088 -0.013653186
7 -0.100987285 -0.111427088
8 -0.312023755 -0.100987285
9 -0.329497799 -0.312023755
10 -0.150659633 -0.329497799
11 0.957290446 -0.150659633
12 -0.014727112 0.957290446
13 -0.049427368 -0.014727112
14 -0.125843774 -0.049427368
15 -0.041821809 -0.125843774
16 -0.119051316 -0.041821809
17 0.881977245 -0.119051316
18 -0.129239132 0.881977245
19 -0.143081355 -0.129239132
20 -0.205376412 -0.143081355
21 0.794716255 -0.205376412
22 0.080264206 0.794716255
23 -0.821407669 0.080264206
24 0.202347525 -0.821407669
25 0.060872882 0.202347525
26 0.138711204 0.060872882
27 0.131279343 0.138711204
28 0.159467683 0.131279343
29 0.242286921 0.159467683
30 0.229181450 0.242286921
31 -0.769053237 0.229181450
32 0.905451389 -0.769053237
33 -0.063757092 0.905451389
34 -0.793384397 -0.063757092
35 0.251269788 -0.793384397
36 0.272153096 0.251269788
37 -0.786202316 0.272153096
38 -0.712664867 -0.786202316
39 0.222350564 -0.712664867
40 0.250538904 0.222350564
41 0.240871601 0.250538904
42 -0.793713713 0.240871601
43 0.216958361 -0.793713713
44 -0.128932873 0.216958361
45 -0.087818291 -0.128932873
46 0.220639168 -0.087818291
47 0.301528861 0.220639168
48 0.284597535 0.301528861
49 0.342157150 0.284597535
50 0.382952151 0.342157150
51 -0.625336089 0.382952151
52 0.280504514 -0.625336089
53 -0.718076520 0.280504514
54 0.280420635 -0.718076520
55 0.265249732 0.280420635
56 -0.158868797 0.265249732
57 -0.162605380 -0.158868797
58 0.115712073 -0.162605380
59 0.178219227 0.115712073
60 0.181183781 0.178219227
61 0.216218083 0.181183781
62 -0.742596511 0.216218083
63 0.237216659 -0.742596511
64 0.225194286 0.237216659
65 0.203452057 0.225194286
66 0.177415279 0.203452057
67 0.094165814 0.177415279
68 -0.203272290 0.094165814
69 -0.215482993 -0.203272290
70 -0.012985483 -0.215482993
71 0.018825796 -0.012985483
72 0.020017365 0.018825796
73 1.011941119 0.020017365
74 0.061460993 1.011941119
75 -1.056885989 0.061460993
76 -0.004145621 -1.056885989
77 -0.031215114 -0.004145621
78 -0.060866397 -0.031215114
79 -0.073241442 -0.060866397
80 -0.306559521 -0.073241442
81 -0.329283963 -0.306559521
82 -0.159061455 -0.329283963
83 0.864134167 -0.159061455
84 -0.154490894 0.864134167
85 -0.091600227 -0.154490894
86 -0.181549692 -0.091600227
87 -0.117420765 -0.181549692
88 -0.135598874 -0.117420765
89 0.816531833 -0.135598874
90 -0.211444423 0.816531833
91 -0.139887790 -0.211444423
92 -0.380458030 -0.139887790
93 0.644718105 -0.380458030
94 -1.158669488 0.644718105
95 0.889274412 -1.158669488
96 -0.113504492 0.889274412
97 -0.091431459 -0.113504492
98 -0.080393402 -0.091431459
99 0.034132069 -0.080393402
100 0.028048524 0.034132069
101 0.077798602 0.028048524
102 0.186134105 0.077798602
103 0.107091074 0.186134105
104 0.816110607 0.107091074
105 -0.173901664 0.816110607
106 0.036115992 -0.173901664
107 1.075299513 0.036115992
108 0.100762221 1.075299513
109 -0.826874069 0.100762221
110 1.099545941 -0.826874069
111 0.193882411 1.099545941
112 -0.931236656 0.193882411
113 0.067600520 -0.931236656
114 -0.947570383 0.067600520
115 1.118142629 -0.947570383
116 -0.114921821 1.118142629
117 -0.120095993 -0.114921821
118 0.025651552 -0.120095993
119 -0.927427698 0.025651552
120 -0.918847446 -0.927427698
121 0.019662643 -0.918847446
122 0.979227382 0.019662643
123 -0.061633295 0.979227382
124 -0.111993436 -0.061633295
125 -0.229704433 -0.111993436
126 -0.183368101 -0.229704433
127 -0.186250009 -0.183368101
128 0.686984912 -0.186250009
129 0.709782742 0.686984912
130 -0.105776651 0.709782742
131 -1.050589103 -0.105776651
132 -0.024919073 -1.050589103
133 -0.077363216 -0.024919073
134 -0.080459192 -0.077363216
135 -0.017407038 -0.080459192
136 0.996341481 -0.017407038
137 -0.002289937 0.996341481
138 0.003634924 -0.002289937
139 -0.835073702 0.003634924
140 0.869688389 -0.835073702
141 -1.114293852 0.869688389
142 0.036385027 -1.114293852
143 0.101543389 0.036385027
144 0.150395515 0.101543389
145 NA 0.150395515
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.185609810 -0.977461935
[2,] 0.153168996 0.185609810
[3,] 0.119484119 0.153168996
[4,] 0.010198533 0.119484119
[5,] -0.013653186 0.010198533
[6,] -0.111427088 -0.013653186
[7,] -0.100987285 -0.111427088
[8,] -0.312023755 -0.100987285
[9,] -0.329497799 -0.312023755
[10,] -0.150659633 -0.329497799
[11,] 0.957290446 -0.150659633
[12,] -0.014727112 0.957290446
[13,] -0.049427368 -0.014727112
[14,] -0.125843774 -0.049427368
[15,] -0.041821809 -0.125843774
[16,] -0.119051316 -0.041821809
[17,] 0.881977245 -0.119051316
[18,] -0.129239132 0.881977245
[19,] -0.143081355 -0.129239132
[20,] -0.205376412 -0.143081355
[21,] 0.794716255 -0.205376412
[22,] 0.080264206 0.794716255
[23,] -0.821407669 0.080264206
[24,] 0.202347525 -0.821407669
[25,] 0.060872882 0.202347525
[26,] 0.138711204 0.060872882
[27,] 0.131279343 0.138711204
[28,] 0.159467683 0.131279343
[29,] 0.242286921 0.159467683
[30,] 0.229181450 0.242286921
[31,] -0.769053237 0.229181450
[32,] 0.905451389 -0.769053237
[33,] -0.063757092 0.905451389
[34,] -0.793384397 -0.063757092
[35,] 0.251269788 -0.793384397
[36,] 0.272153096 0.251269788
[37,] -0.786202316 0.272153096
[38,] -0.712664867 -0.786202316
[39,] 0.222350564 -0.712664867
[40,] 0.250538904 0.222350564
[41,] 0.240871601 0.250538904
[42,] -0.793713713 0.240871601
[43,] 0.216958361 -0.793713713
[44,] -0.128932873 0.216958361
[45,] -0.087818291 -0.128932873
[46,] 0.220639168 -0.087818291
[47,] 0.301528861 0.220639168
[48,] 0.284597535 0.301528861
[49,] 0.342157150 0.284597535
[50,] 0.382952151 0.342157150
[51,] -0.625336089 0.382952151
[52,] 0.280504514 -0.625336089
[53,] -0.718076520 0.280504514
[54,] 0.280420635 -0.718076520
[55,] 0.265249732 0.280420635
[56,] -0.158868797 0.265249732
[57,] -0.162605380 -0.158868797
[58,] 0.115712073 -0.162605380
[59,] 0.178219227 0.115712073
[60,] 0.181183781 0.178219227
[61,] 0.216218083 0.181183781
[62,] -0.742596511 0.216218083
[63,] 0.237216659 -0.742596511
[64,] 0.225194286 0.237216659
[65,] 0.203452057 0.225194286
[66,] 0.177415279 0.203452057
[67,] 0.094165814 0.177415279
[68,] -0.203272290 0.094165814
[69,] -0.215482993 -0.203272290
[70,] -0.012985483 -0.215482993
[71,] 0.018825796 -0.012985483
[72,] 0.020017365 0.018825796
[73,] 1.011941119 0.020017365
[74,] 0.061460993 1.011941119
[75,] -1.056885989 0.061460993
[76,] -0.004145621 -1.056885989
[77,] -0.031215114 -0.004145621
[78,] -0.060866397 -0.031215114
[79,] -0.073241442 -0.060866397
[80,] -0.306559521 -0.073241442
[81,] -0.329283963 -0.306559521
[82,] -0.159061455 -0.329283963
[83,] 0.864134167 -0.159061455
[84,] -0.154490894 0.864134167
[85,] -0.091600227 -0.154490894
[86,] -0.181549692 -0.091600227
[87,] -0.117420765 -0.181549692
[88,] -0.135598874 -0.117420765
[89,] 0.816531833 -0.135598874
[90,] -0.211444423 0.816531833
[91,] -0.139887790 -0.211444423
[92,] -0.380458030 -0.139887790
[93,] 0.644718105 -0.380458030
[94,] -1.158669488 0.644718105
[95,] 0.889274412 -1.158669488
[96,] -0.113504492 0.889274412
[97,] -0.091431459 -0.113504492
[98,] -0.080393402 -0.091431459
[99,] 0.034132069 -0.080393402
[100,] 0.028048524 0.034132069
[101,] 0.077798602 0.028048524
[102,] 0.186134105 0.077798602
[103,] 0.107091074 0.186134105
[104,] 0.816110607 0.107091074
[105,] -0.173901664 0.816110607
[106,] 0.036115992 -0.173901664
[107,] 1.075299513 0.036115992
[108,] 0.100762221 1.075299513
[109,] -0.826874069 0.100762221
[110,] 1.099545941 -0.826874069
[111,] 0.193882411 1.099545941
[112,] -0.931236656 0.193882411
[113,] 0.067600520 -0.931236656
[114,] -0.947570383 0.067600520
[115,] 1.118142629 -0.947570383
[116,] -0.114921821 1.118142629
[117,] -0.120095993 -0.114921821
[118,] 0.025651552 -0.120095993
[119,] -0.927427698 0.025651552
[120,] -0.918847446 -0.927427698
[121,] 0.019662643 -0.918847446
[122,] 0.979227382 0.019662643
[123,] -0.061633295 0.979227382
[124,] -0.111993436 -0.061633295
[125,] -0.229704433 -0.111993436
[126,] -0.183368101 -0.229704433
[127,] -0.186250009 -0.183368101
[128,] 0.686984912 -0.186250009
[129,] 0.709782742 0.686984912
[130,] -0.105776651 0.709782742
[131,] -1.050589103 -0.105776651
[132,] -0.024919073 -1.050589103
[133,] -0.077363216 -0.024919073
[134,] -0.080459192 -0.077363216
[135,] -0.017407038 -0.080459192
[136,] 0.996341481 -0.017407038
[137,] -0.002289937 0.996341481
[138,] 0.003634924 -0.002289937
[139,] -0.835073702 0.003634924
[140,] 0.869688389 -0.835073702
[141,] -1.114293852 0.869688389
[142,] 0.036385027 -1.114293852
[143,] 0.101543389 0.036385027
[144,] 0.150395515 0.101543389
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.185609810 -0.977461935
2 0.153168996 0.185609810
3 0.119484119 0.153168996
4 0.010198533 0.119484119
5 -0.013653186 0.010198533
6 -0.111427088 -0.013653186
7 -0.100987285 -0.111427088
8 -0.312023755 -0.100987285
9 -0.329497799 -0.312023755
10 -0.150659633 -0.329497799
11 0.957290446 -0.150659633
12 -0.014727112 0.957290446
13 -0.049427368 -0.014727112
14 -0.125843774 -0.049427368
15 -0.041821809 -0.125843774
16 -0.119051316 -0.041821809
17 0.881977245 -0.119051316
18 -0.129239132 0.881977245
19 -0.143081355 -0.129239132
20 -0.205376412 -0.143081355
21 0.794716255 -0.205376412
22 0.080264206 0.794716255
23 -0.821407669 0.080264206
24 0.202347525 -0.821407669
25 0.060872882 0.202347525
26 0.138711204 0.060872882
27 0.131279343 0.138711204
28 0.159467683 0.131279343
29 0.242286921 0.159467683
30 0.229181450 0.242286921
31 -0.769053237 0.229181450
32 0.905451389 -0.769053237
33 -0.063757092 0.905451389
34 -0.793384397 -0.063757092
35 0.251269788 -0.793384397
36 0.272153096 0.251269788
37 -0.786202316 0.272153096
38 -0.712664867 -0.786202316
39 0.222350564 -0.712664867
40 0.250538904 0.222350564
41 0.240871601 0.250538904
42 -0.793713713 0.240871601
43 0.216958361 -0.793713713
44 -0.128932873 0.216958361
45 -0.087818291 -0.128932873
46 0.220639168 -0.087818291
47 0.301528861 0.220639168
48 0.284597535 0.301528861
49 0.342157150 0.284597535
50 0.382952151 0.342157150
51 -0.625336089 0.382952151
52 0.280504514 -0.625336089
53 -0.718076520 0.280504514
54 0.280420635 -0.718076520
55 0.265249732 0.280420635
56 -0.158868797 0.265249732
57 -0.162605380 -0.158868797
58 0.115712073 -0.162605380
59 0.178219227 0.115712073
60 0.181183781 0.178219227
61 0.216218083 0.181183781
62 -0.742596511 0.216218083
63 0.237216659 -0.742596511
64 0.225194286 0.237216659
65 0.203452057 0.225194286
66 0.177415279 0.203452057
67 0.094165814 0.177415279
68 -0.203272290 0.094165814
69 -0.215482993 -0.203272290
70 -0.012985483 -0.215482993
71 0.018825796 -0.012985483
72 0.020017365 0.018825796
73 1.011941119 0.020017365
74 0.061460993 1.011941119
75 -1.056885989 0.061460993
76 -0.004145621 -1.056885989
77 -0.031215114 -0.004145621
78 -0.060866397 -0.031215114
79 -0.073241442 -0.060866397
80 -0.306559521 -0.073241442
81 -0.329283963 -0.306559521
82 -0.159061455 -0.329283963
83 0.864134167 -0.159061455
84 -0.154490894 0.864134167
85 -0.091600227 -0.154490894
86 -0.181549692 -0.091600227
87 -0.117420765 -0.181549692
88 -0.135598874 -0.117420765
89 0.816531833 -0.135598874
90 -0.211444423 0.816531833
91 -0.139887790 -0.211444423
92 -0.380458030 -0.139887790
93 0.644718105 -0.380458030
94 -1.158669488 0.644718105
95 0.889274412 -1.158669488
96 -0.113504492 0.889274412
97 -0.091431459 -0.113504492
98 -0.080393402 -0.091431459
99 0.034132069 -0.080393402
100 0.028048524 0.034132069
101 0.077798602 0.028048524
102 0.186134105 0.077798602
103 0.107091074 0.186134105
104 0.816110607 0.107091074
105 -0.173901664 0.816110607
106 0.036115992 -0.173901664
107 1.075299513 0.036115992
108 0.100762221 1.075299513
109 -0.826874069 0.100762221
110 1.099545941 -0.826874069
111 0.193882411 1.099545941
112 -0.931236656 0.193882411
113 0.067600520 -0.931236656
114 -0.947570383 0.067600520
115 1.118142629 -0.947570383
116 -0.114921821 1.118142629
117 -0.120095993 -0.114921821
118 0.025651552 -0.120095993
119 -0.927427698 0.025651552
120 -0.918847446 -0.927427698
121 0.019662643 -0.918847446
122 0.979227382 0.019662643
123 -0.061633295 0.979227382
124 -0.111993436 -0.061633295
125 -0.229704433 -0.111993436
126 -0.183368101 -0.229704433
127 -0.186250009 -0.183368101
128 0.686984912 -0.186250009
129 0.709782742 0.686984912
130 -0.105776651 0.709782742
131 -1.050589103 -0.105776651
132 -0.024919073 -1.050589103
133 -0.077363216 -0.024919073
134 -0.080459192 -0.077363216
135 -0.017407038 -0.080459192
136 0.996341481 -0.017407038
137 -0.002289937 0.996341481
138 0.003634924 -0.002289937
139 -0.835073702 0.003634924
140 0.869688389 -0.835073702
141 -1.114293852 0.869688389
142 0.036385027 -1.114293852
143 0.101543389 0.036385027
144 0.150395515 0.101543389
> 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/7u55z1352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/82r731352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9opb81352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10acfs1352155635.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111kh71352155635.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/12721y1352155635.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/132od61352155635.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/14owmu1352155635.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/15eaqg1352155635.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/16e1gi1352155635.tab")
+ }
>
> try(system("convert tmp/1da6o1352155635.ps tmp/1da6o1352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/2star1352155635.ps tmp/2star1352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fgeh1352155635.ps tmp/3fgeh1352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uh5u1352155635.ps tmp/4uh5u1352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h7781352155635.ps tmp/5h7781352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jbr41352155635.ps tmp/6jbr41352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u55z1352155635.ps tmp/7u55z1352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/82r731352155635.ps tmp/82r731352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/9opb81352155635.ps tmp/9opb81352155635.png",intern=TRUE))
character(0)
> try(system("convert tmp/10acfs1352155635.ps tmp/10acfs1352155635.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.606 1.158 9.766