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.
> x <- array(list(1
+ ,593408
+ ,280190
+ ,313218
+ ,44148
+ ,125326
+ ,223560
+ ,2
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+ ,280408
+ ,309664
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+ ,3
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+ ,221353
+ ,7
+ ,619567
+ ,289802
+ ,329765
+ ,51349
+ ,143565
+ ,224844
+ ,8
+ ,625809
+ ,290726
+ ,335083
+ ,58672
+ ,149946
+ ,230418
+ ,9
+ ,619916
+ ,292300
+ ,327616
+ ,61271
+ ,149147
+ ,232189
+ ,10
+ ,587625
+ ,278506
+ ,309119
+ ,53145
+ ,134339
+ ,231219
+ ,11
+ ,565742
+ ,269826
+ ,295916
+ ,46211
+ ,122683
+ ,228209
+ ,12
+ ,557274
+ ,265861
+ ,291413
+ ,40744
+ ,115614
+ ,227941
+ ,1
+ ,560576
+ ,269034
+ ,291542
+ ,41248
+ ,116566
+ ,228128
+ ,2
+ ,548854
+ ,264176
+ ,284678
+ ,39032
+ ,111272
+ ,226309
+ ,3
+ ,531673
+ ,255198
+ ,276475
+ ,35907
+ ,104609
+ ,221990
+ ,4
+ ,525919
+ ,253353
+ ,272566
+ ,33335
+ ,101802
+ ,220386
+ ,5
+ ,511038
+ ,246057
+ ,264981
+ ,23988
+ ,94542
+ ,217415
+ ,6
+ ,498662
+ ,235372
+ ,263290
+ ,23099
+ ,93051
+ ,210394
+ ,7
+ ,555362
+ ,258556
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+ ,46390
+ ,124129
+ ,213985
+ ,8
+ ,564591
+ ,260993
+ ,303598
+ ,51588
+ ,130374
+ ,214552
+ ,9
+ ,541657
+ ,254663
+ ,286994
+ ,51579
+ ,123946
+ ,211797
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+ ,250643
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+ ,114971
+ ,208512
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+ ,39215
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+ ,205708
+ ,12
+ ,514258
+ ,247105
+ ,267153
+ ,38433
+ ,104919
+ ,206890
+ ,1
+ ,516922
+ ,248541
+ ,268381
+ ,37676
+ ,104782
+ ,207069
+ ,2
+ ,507561
+ ,245039
+ ,262522
+ ,36055
+ ,101281
+ ,205305
+ ,3
+ ,492622
+ ,237080
+ ,255542
+ ,32986
+ ,94545
+ ,201504
+ ,4
+ ,490243
+ ,237085
+ ,253158
+ ,30953
+ ,93248
+ ,200517
+ ,5
+ ,469357
+ ,225554
+ ,243803
+ ,23558
+ ,84031
+ ,195771
+ ,6
+ ,477580
+ ,226839
+ ,250741
+ ,22487
+ ,87486
+ ,195259
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+ ,528379
+ ,247934
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+ ,43528
+ ,115867
+ ,197579
+ ,8
+ ,533590
+ ,248333
+ ,285257
+ ,47913
+ ,120327
+ ,196985
+ ,9
+ ,517945
+ ,246969
+ ,270976
+ ,48621
+ ,117008
+ ,194382
+ ,10
+ ,506174
+ ,245098
+ ,261076
+ ,42169
+ ,108811
+ ,191580
+ ,11
+ ,501866
+ ,246263
+ ,255603
+ ,38444
+ ,104519
+ ,190765
+ ,12
+ ,516141
+ ,255765
+ ,260376
+ ,38692
+ ,106758
+ ,191480
+ ,1
+ ,528222
+ ,264319
+ ,263903
+ ,38124
+ ,109337
+ ,192277
+ ,2
+ ,532638
+ ,268347
+ ,264291
+ ,37886
+ ,109078
+ ,191632
+ ,3
+ ,536322
+ ,273046
+ ,263276
+ ,37310
+ ,108293
+ ,190757
+ ,4
+ ,536535
+ ,273963
+ ,262572
+ ,34689
+ ,106534
+ ,190995
+ ,5
+ ,523597
+ ,267430
+ ,256167
+ ,26450
+ ,99197
+ ,189081
+ ,6
+ ,536214
+ ,271993
+ ,264221
+ ,25565
+ ,103493
+ ,190028
+ ,7
+ ,586570
+ ,292710
+ ,293860
+ ,46562
+ ,130676
+ ,196146
+ ,8
+ ,596594
+ ,295881
+ ,300713
+ ,52653
+ ,137448
+ ,197070
+ ,9
+ ,580523
+ ,293299
+ ,287224
+ ,54807
+ ,134704
+ ,194893
+ ,10
+ ,564478
+ ,288576
+ ,275902
+ ,47534
+ ,123725
+ ,193246
+ ,11
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+ ,43565
+ ,118277
+ ,192484
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+ ,42622
+ ,120528
+ ,197394
+ ,2
+ ,574761
+ ,298522
+ ,276239
+ ,41761
+ ,118240
+ ,196598
+ ,3
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+ ,271037
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+ ,112514
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+ ,4
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+ ,266148
+ ,35438
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+ ,5
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+ ,259497
+ ,27356
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+ ,191942
+ ,6
+ ,544686
+ ,277891
+ ,266795
+ ,26149
+ ,102082
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+ ,7
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+ ,8
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+ ,265641
+ ,38504
+ ,106365
+ ,197787
+ ,3
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+ ,277799
+ ,262525
+ ,36393
+ ,102304
+ ,197622
+ ,4
+ ,530577
+ ,271980
+ ,258597
+ ,33740
+ ,97968
+ ,196683
+ ,5
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+ ,266730
+ ,253849
+ ,26131
+ ,92462
+ ,194590
+ ,6
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+ ,256221
+ ,23885
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+ ,194316
+ ,7
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+ ,285378
+ ,286895
+ ,43899
+ ,120092
+ ,199598
+ ,8
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+ ,294610
+ ,49871
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+ ,199055
+ ,9
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+ ,280363
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+ ,197482
+ ,10
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+ ,277686
+ ,269926
+ ,45493
+ ,114045
+ ,196440
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+ ,274371
+ ,264341
+ ,41124
+ ,108120
+ ,195338
+ ,12
+ ,540735
+ ,277466
+ ,263269
+ ,39385
+ ,105698
+ ,195589
+ ,1
+ ,561649
+ ,290604
+ ,271045
+ ,41472
+ ,111203
+ ,198936
+ ,2
+ ,558685
+ ,290770
+ ,267915
+ ,41688
+ ,110030
+ ,198262
+ ,3
+ ,545732
+ ,283654
+ ,262078
+ ,38711
+ ,104009
+ ,197275
+ ,4
+ ,536352
+ ,278601
+ ,257751
+ ,36840
+ ,99772
+ ,196007
+ ,5
+ ,527676
+ ,274405
+ ,253271
+ ,35141
+ ,96301
+ ,194447
+ ,6
+ ,530455
+ ,272817
+ ,257638
+ ,37443
+ ,97680
+ ,193951
+ ,7
+ ,581744
+ ,294292
+ ,287452
+ ,51905
+ ,121563
+ ,198396
+ ,8
+ ,598714
+ ,300562
+ ,298152
+ ,60016
+ ,134210
+ ,199486
+ ,9
+ ,583775
+ ,298982
+ ,284793
+ ,58611
+ ,133111
+ ,198688
+ ,10
+ ,571477
+ ,296917
+ ,274560
+ ,52097
+ ,124527
+ ,196729)
+ ,dim=c(7
+ ,82)
+ ,dimnames=list(c('Maand'
+ ,'Werkzoekenden'
+ ,'Mannen'
+ ,'Vrouwen'
+ ,'Beroepsinschakelingstijd'
+ ,'<25jaar'
+ ,'inactiviteitsduur>=2jaar')
+ ,1:82))
> y <- array(NA,dim=c(7,82),dimnames=list(c('Maand','Werkzoekenden','Mannen','Vrouwen','Beroepsinschakelingstijd','<25jaar','inactiviteitsduur>=2jaar'),1:82))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> 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
Werkzoekenden Maand Mannen Vrouwen Beroepsinschakelingstijd <25jaar
1 593408 1 280190 313218 44148 125326
2 590072 2 280408 309664 42065 122716
3 579799 3 276836 302963 38546 116615
4 574205 4 275216 298989 35324 113719
5 572775 5 274352 298423 26599 110737
6 572942 6 271311 301631 24935 112093
7 619567 7 289802 329765 51349 143565
8 625809 8 290726 335083 58672 149946
9 619916 9 292300 327616 61271 149147
10 587625 10 278506 309119 53145 134339
11 565742 11 269826 295916 46211 122683
12 557274 12 265861 291413 40744 115614
13 560576 1 269034 291542 41248 116566
14 548854 2 264176 284678 39032 111272
15 531673 3 255198 276475 35907 104609
16 525919 4 253353 272566 33335 101802
17 511038 5 246057 264981 23988 94542
18 498662 6 235372 263290 23099 93051
19 555362 7 258556 296806 46390 124129
20 564591 8 260993 303598 51588 130374
21 541657 9 254663 286994 51579 123946
22 527070 10 250643 276427 45390 114971
23 509846 11 243422 266424 39215 105531
24 514258 12 247105 267153 38433 104919
25 516922 1 248541 268381 37676 104782
26 507561 2 245039 262522 36055 101281
27 492622 3 237080 255542 32986 94545
28 490243 4 237085 253158 30953 93248
29 469357 5 225554 243803 23558 84031
30 477580 6 226839 250741 22487 87486
31 528379 7 247934 280445 43528 115867
32 533590 8 248333 285257 47913 120327
33 517945 9 246969 270976 48621 117008
34 506174 10 245098 261076 42169 108811
35 501866 11 246263 255603 38444 104519
36 516141 12 255765 260376 38692 106758
37 528222 1 264319 263903 38124 109337
38 532638 2 268347 264291 37886 109078
39 536322 3 273046 263276 37310 108293
40 536535 4 273963 262572 34689 106534
41 523597 5 267430 256167 26450 99197
42 536214 6 271993 264221 25565 103493
43 586570 7 292710 293860 46562 130676
44 596594 8 295881 300713 52653 137448
45 580523 9 293299 287224 54807 134704
46 564478 10 288576 275902 47534 123725
47 557560 11 286445 271115 43565 118277
48 575093 12 297584 277509 44051 121225
49 580112 1 300431 279681 42622 120528
50 574761 2 298522 276239 41761 118240
51 563250 3 292213 271037 39086 112514
52 551531 4 285383 266148 35438 107304
53 537034 5 277537 259497 27356 100001
54 544686 6 277891 266795 26149 102082
55 600991 7 302686 298305 47034 130455
56 604378 8 300653 303725 53091 135574
57 586111 9 296369 289742 55718 132540
58 563668 10 287224 276444 47637 119920
59 548604 11 279998 268606 43237 112454
60 551174 12 283495 267679 40597 109415
61 555654 1 285775 269879 39884 109843
62 547970 2 282329 265641 38504 106365
63 540324 3 277799 262525 36393 102304
64 530577 4 271980 258597 33740 97968
65 520579 5 266730 253849 26131 92462
66 518654 6 262433 256221 23885 92286
67 572273 7 285378 286895 43899 120092
68 581302 8 286692 294610 49871 126656
69 563280 9 282917 280363 52292 124144
70 547612 10 277686 269926 45493 114045
71 538712 11 274371 264341 41124 108120
72 540735 12 277466 263269 39385 105698
73 561649 1 290604 271045 41472 111203
74 558685 2 290770 267915 41688 110030
75 545732 3 283654 262078 38711 104009
76 536352 4 278601 257751 36840 99772
77 527676 5 274405 253271 35141 96301
78 530455 6 272817 257638 37443 97680
79 581744 7 294292 287452 51905 121563
80 598714 8 300562 298152 60016 134210
81 583775 9 298982 284793 58611 133111
82 571477 10 296917 274560 52097 124527
inactiviteitsduur>=2jaar
1 223560
2 223789
3 223893
4 221010
5 221742
6 221353
7 224844
8 230418
9 232189
10 231219
11 228209
12 227941
13 228128
14 226309
15 221990
16 220386
17 217415
18 210394
19 213985
20 214552
21 211797
22 208512
23 205708
24 206890
25 207069
26 205305
27 201504
28 200517
29 195771
30 195259
31 197579
32 196985
33 194382
34 191580
35 190765
36 191480
37 192277
38 191632
39 190757
40 190995
41 189081
42 190028
43 196146
44 197070
45 194893
46 193246
47 192484
48 194924
49 197394
50 196598
51 194409
52 193431
53 191942
54 193323
55 199654
56 198422
57 198219
58 197157
59 195115
60 197296
61 198178
62 197787
63 197622
64 196683
65 194590
66 194316
67 199598
68 199055
69 197482
70 196440
71 195338
72 195589
73 198936
74 198262
75 197275
76 196007
77 194447
78 193951
79 198396
80 199486
81 198688
82 196729
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Maand
9.414e-13 -1.476e-13
Mannen Vrouwen
1.000e+00 1.000e+00
Beroepsinschakelingstijd `<25jaar`
-2.529e-17 1.323e-17
`inactiviteitsduur>=2jaar`
-2.190e-17
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.902e-12 -2.216e-12 3.290e-13 2.116e-12 5.552e-12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.414e-13 9.126e-12 1.030e-01 0.918
Maand -1.476e-13 1.132e-13 -1.304e+00 0.196
Mannen 1.000e+00 2.592e-17 3.858e+16 <2e-16 ***
Vrouwen 1.000e+00 8.912e-17 1.122e+16 <2e-16 ***
Beroepsinschakelingstijd -2.529e-17 1.129e-16 -2.240e-01 0.823
`<25jaar` 1.323e-17 1.552e-16 8.500e-02 0.932
`inactiviteitsduur>=2jaar` -2.190e-17 6.165e-17 -3.550e-01 0.723
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.947e-12 on 75 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.681e+33 on 6 and 75 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,] 2.350026e-01 4.700052e-01 7.649974e-01
[2,] 2.122074e-01 4.244148e-01 7.877926e-01
[3,] 1.498592e-01 2.997185e-01 8.501408e-01
[4,] 2.221367e-06 4.442735e-06 9.999978e-01
[5,] 1.000000e+00 3.690504e-50 1.845252e-50
[6,] 3.402170e-08 6.804339e-08 1.000000e+00
[7,] 3.870585e-02 7.741171e-02 9.612941e-01
[8,] 1.105758e-13 2.211516e-13 1.000000e+00
[9,] 1.088475e-11 2.176950e-11 1.000000e+00
[10,] 2.342924e-13 4.685848e-13 1.000000e+00
[11,] 4.003136e-13 8.006273e-13 1.000000e+00
[12,] 1.000000e+00 5.135897e-43 2.567948e-43
[13,] 9.995350e-01 9.300854e-04 4.650427e-04
[14,] 2.409615e-11 4.819230e-11 1.000000e+00
[15,] 9.725710e-01 5.485795e-02 2.742897e-02
[16,] 4.353589e-14 8.707178e-14 1.000000e+00
[17,] 1.287558e-10 2.575115e-10 1.000000e+00
[18,] 9.980981e-01 3.803784e-03 1.901892e-03
[19,] 3.725147e-14 7.450294e-14 1.000000e+00
[20,] 5.034710e-18 1.006942e-17 1.000000e+00
[21,] 9.754886e-01 4.902288e-02 2.451144e-02
[22,] 1.000000e+00 4.781881e-36 2.390941e-36
[23,] 5.497514e-15 1.099503e-14 1.000000e+00
[24,] 1.000000e+00 5.198678e-30 2.599339e-30
[25,] 8.586536e-01 2.826929e-01 1.413464e-01
[26,] 8.903491e-01 2.193019e-01 1.096509e-01
[27,] 8.947882e-01 2.104236e-01 1.052118e-01
[28,] 9.999896e-01 2.086230e-05 1.043115e-05
[29,] 1.000000e+00 2.951038e-36 1.475519e-36
[30,] 1.000000e+00 1.740043e-34 8.700215e-35
[31,] 1.000000e+00 3.204426e-25 1.602213e-25
[32,] 1.000000e+00 4.398160e-34 2.199080e-34
[33,] 3.504414e-01 7.008828e-01 6.495586e-01
[34,] 6.191097e-25 1.238219e-24 1.000000e+00
[35,] 4.845993e-01 9.691986e-01 5.154007e-01
[36,] 9.999278e-01 1.443799e-04 7.218993e-05
[37,] 9.999995e-01 9.089825e-07 4.544912e-07
[38,] 1.000000e+00 1.310911e-10 6.554554e-11
[39,] 1.000000e+00 2.260945e-26 1.130472e-26
[40,] 9.999660e-01 6.809760e-05 3.404880e-05
[41,] 1.000000e+00 4.655319e-27 2.327660e-27
[42,] 1.000000e+00 1.158425e-21 5.792125e-22
[43,] 6.920513e-28 1.384103e-27 1.000000e+00
[44,] 8.885257e-01 2.229487e-01 1.114743e-01
[45,] 1.709921e-01 3.419842e-01 8.290079e-01
[46,] 1.732641e-01 3.465282e-01 8.267359e-01
[47,] 1.393940e-26 2.787881e-26 1.000000e+00
[48,] 9.206473e-40 1.841295e-39 1.000000e+00
[49,] 1.596652e-37 3.193305e-37 1.000000e+00
[50,] 1.000000e+00 5.962639e-23 2.981320e-23
[51,] 1.756311e-04 3.512621e-04 9.998244e-01
[52,] 1.000000e+00 6.857256e-15 3.428628e-15
[53,] 1.000000e+00 3.374109e-18 1.687054e-18
[54,] 8.968009e-01 2.063982e-01 1.031991e-01
[55,] 3.348844e-01 6.697688e-01 6.651156e-01
[56,] 9.352283e-01 1.295435e-01 6.477174e-02
[57,] 9.301978e-01 1.396045e-01 6.980224e-02
[58,] 9.691810e-01 6.163795e-02 3.081898e-02
[59,] 1.157607e-15 2.315213e-15 1.000000e+00
[60,] 9.708744e-24 1.941749e-23 1.000000e+00
[61,] 9.058340e-13 1.811668e-12 1.000000e+00
[62,] 9.998194e-01 3.611613e-04 1.805806e-04
[63,] 9.996456e-01 7.087755e-04 3.543877e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1lh3k1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29xzl1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/39uq41356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xyqg1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5bv481356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 82
Frequency = 1
1 2 3 4 5
2.442012e-12 -3.783616e-12 -6.902202e-12 9.870854e-13 6.680176e-13
6 7 8 9 10
2.135044e-12 -2.139489e-12 4.819259e-12 7.304701e-13 -7.572114e-13
11 12 13 14 15
2.060345e-12 3.064410e-12 -2.931621e-12 4.619779e-13 5.551570e-12
16 17 18 19 20
2.324786e-12 -4.856985e-12 -8.522308e-13 -1.251050e-12 1.678996e-12
21 22 23 24 25
1.114263e-12 -5.885638e-13 -1.173087e-12 -3.248534e-12 -1.880104e-12
26 27 28 29 30
-6.459688e-12 3.850548e-12 -1.547485e-12 5.662357e-13 4.783489e-12
31 32 33 34 35
3.981421e-13 5.274861e-12 -2.636543e-12 -4.507796e-12 2.299936e-12
36 37 38 39 40
-5.897317e-12 1.386313e-12 -1.060007e-12 4.255802e-12 3.575454e-12
41 42 43 44 45
-1.352364e-13 2.848482e-12 5.509565e-13 -3.928331e-12 2.731566e-12
46 47 48 49 50
1.719005e-13 2.530821e-13 8.422845e-13 1.322415e-12 2.129707e-13
51 52 53 54 55
3.657816e-12 -4.550455e-12 3.322481e-12 -3.398555e-12 -3.186382e-12
56 57 58 59 60
-2.504931e-12 1.580268e-12 1.425172e-13 -5.660066e-13 1.125062e-12
61 62 63 64 65
-4.010247e-12 -3.020594e-12 -1.792518e-12 2.641835e-12 -1.633314e-12
66 67 68 69 70
-2.240847e-12 1.467892e-12 1.813880e-12 1.343137e-13 -1.167479e-12
71 72 73 74 75
-3.252305e-12 1.183560e-12 4.995257e-13 -2.473517e-12 3.498031e-12
76 77 78 79 80
1.992159e-12 2.774342e-12 3.236428e-12 2.599101e-13 -2.245541e-12
81 82
2.566834e-12 -2.679739e-12
> postscript(file="/var/wessaorg/rcomp/tmp/6ytjc1356052691.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 = 82
Frequency = 1
lag(myerror, k = 1) myerror
0 2.442012e-12 NA
1 -3.783616e-12 2.442012e-12
2 -6.902202e-12 -3.783616e-12
3 9.870854e-13 -6.902202e-12
4 6.680176e-13 9.870854e-13
5 2.135044e-12 6.680176e-13
6 -2.139489e-12 2.135044e-12
7 4.819259e-12 -2.139489e-12
8 7.304701e-13 4.819259e-12
9 -7.572114e-13 7.304701e-13
10 2.060345e-12 -7.572114e-13
11 3.064410e-12 2.060345e-12
12 -2.931621e-12 3.064410e-12
13 4.619779e-13 -2.931621e-12
14 5.551570e-12 4.619779e-13
15 2.324786e-12 5.551570e-12
16 -4.856985e-12 2.324786e-12
17 -8.522308e-13 -4.856985e-12
18 -1.251050e-12 -8.522308e-13
19 1.678996e-12 -1.251050e-12
20 1.114263e-12 1.678996e-12
21 -5.885638e-13 1.114263e-12
22 -1.173087e-12 -5.885638e-13
23 -3.248534e-12 -1.173087e-12
24 -1.880104e-12 -3.248534e-12
25 -6.459688e-12 -1.880104e-12
26 3.850548e-12 -6.459688e-12
27 -1.547485e-12 3.850548e-12
28 5.662357e-13 -1.547485e-12
29 4.783489e-12 5.662357e-13
30 3.981421e-13 4.783489e-12
31 5.274861e-12 3.981421e-13
32 -2.636543e-12 5.274861e-12
33 -4.507796e-12 -2.636543e-12
34 2.299936e-12 -4.507796e-12
35 -5.897317e-12 2.299936e-12
36 1.386313e-12 -5.897317e-12
37 -1.060007e-12 1.386313e-12
38 4.255802e-12 -1.060007e-12
39 3.575454e-12 4.255802e-12
40 -1.352364e-13 3.575454e-12
41 2.848482e-12 -1.352364e-13
42 5.509565e-13 2.848482e-12
43 -3.928331e-12 5.509565e-13
44 2.731566e-12 -3.928331e-12
45 1.719005e-13 2.731566e-12
46 2.530821e-13 1.719005e-13
47 8.422845e-13 2.530821e-13
48 1.322415e-12 8.422845e-13
49 2.129707e-13 1.322415e-12
50 3.657816e-12 2.129707e-13
51 -4.550455e-12 3.657816e-12
52 3.322481e-12 -4.550455e-12
53 -3.398555e-12 3.322481e-12
54 -3.186382e-12 -3.398555e-12
55 -2.504931e-12 -3.186382e-12
56 1.580268e-12 -2.504931e-12
57 1.425172e-13 1.580268e-12
58 -5.660066e-13 1.425172e-13
59 1.125062e-12 -5.660066e-13
60 -4.010247e-12 1.125062e-12
61 -3.020594e-12 -4.010247e-12
62 -1.792518e-12 -3.020594e-12
63 2.641835e-12 -1.792518e-12
64 -1.633314e-12 2.641835e-12
65 -2.240847e-12 -1.633314e-12
66 1.467892e-12 -2.240847e-12
67 1.813880e-12 1.467892e-12
68 1.343137e-13 1.813880e-12
69 -1.167479e-12 1.343137e-13
70 -3.252305e-12 -1.167479e-12
71 1.183560e-12 -3.252305e-12
72 4.995257e-13 1.183560e-12
73 -2.473517e-12 4.995257e-13
74 3.498031e-12 -2.473517e-12
75 1.992159e-12 3.498031e-12
76 2.774342e-12 1.992159e-12
77 3.236428e-12 2.774342e-12
78 2.599101e-13 3.236428e-12
79 -2.245541e-12 2.599101e-13
80 2.566834e-12 -2.245541e-12
81 -2.679739e-12 2.566834e-12
82 NA -2.679739e-12
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.783616e-12 2.442012e-12
[2,] -6.902202e-12 -3.783616e-12
[3,] 9.870854e-13 -6.902202e-12
[4,] 6.680176e-13 9.870854e-13
[5,] 2.135044e-12 6.680176e-13
[6,] -2.139489e-12 2.135044e-12
[7,] 4.819259e-12 -2.139489e-12
[8,] 7.304701e-13 4.819259e-12
[9,] -7.572114e-13 7.304701e-13
[10,] 2.060345e-12 -7.572114e-13
[11,] 3.064410e-12 2.060345e-12
[12,] -2.931621e-12 3.064410e-12
[13,] 4.619779e-13 -2.931621e-12
[14,] 5.551570e-12 4.619779e-13
[15,] 2.324786e-12 5.551570e-12
[16,] -4.856985e-12 2.324786e-12
[17,] -8.522308e-13 -4.856985e-12
[18,] -1.251050e-12 -8.522308e-13
[19,] 1.678996e-12 -1.251050e-12
[20,] 1.114263e-12 1.678996e-12
[21,] -5.885638e-13 1.114263e-12
[22,] -1.173087e-12 -5.885638e-13
[23,] -3.248534e-12 -1.173087e-12
[24,] -1.880104e-12 -3.248534e-12
[25,] -6.459688e-12 -1.880104e-12
[26,] 3.850548e-12 -6.459688e-12
[27,] -1.547485e-12 3.850548e-12
[28,] 5.662357e-13 -1.547485e-12
[29,] 4.783489e-12 5.662357e-13
[30,] 3.981421e-13 4.783489e-12
[31,] 5.274861e-12 3.981421e-13
[32,] -2.636543e-12 5.274861e-12
[33,] -4.507796e-12 -2.636543e-12
[34,] 2.299936e-12 -4.507796e-12
[35,] -5.897317e-12 2.299936e-12
[36,] 1.386313e-12 -5.897317e-12
[37,] -1.060007e-12 1.386313e-12
[38,] 4.255802e-12 -1.060007e-12
[39,] 3.575454e-12 4.255802e-12
[40,] -1.352364e-13 3.575454e-12
[41,] 2.848482e-12 -1.352364e-13
[42,] 5.509565e-13 2.848482e-12
[43,] -3.928331e-12 5.509565e-13
[44,] 2.731566e-12 -3.928331e-12
[45,] 1.719005e-13 2.731566e-12
[46,] 2.530821e-13 1.719005e-13
[47,] 8.422845e-13 2.530821e-13
[48,] 1.322415e-12 8.422845e-13
[49,] 2.129707e-13 1.322415e-12
[50,] 3.657816e-12 2.129707e-13
[51,] -4.550455e-12 3.657816e-12
[52,] 3.322481e-12 -4.550455e-12
[53,] -3.398555e-12 3.322481e-12
[54,] -3.186382e-12 -3.398555e-12
[55,] -2.504931e-12 -3.186382e-12
[56,] 1.580268e-12 -2.504931e-12
[57,] 1.425172e-13 1.580268e-12
[58,] -5.660066e-13 1.425172e-13
[59,] 1.125062e-12 -5.660066e-13
[60,] -4.010247e-12 1.125062e-12
[61,] -3.020594e-12 -4.010247e-12
[62,] -1.792518e-12 -3.020594e-12
[63,] 2.641835e-12 -1.792518e-12
[64,] -1.633314e-12 2.641835e-12
[65,] -2.240847e-12 -1.633314e-12
[66,] 1.467892e-12 -2.240847e-12
[67,] 1.813880e-12 1.467892e-12
[68,] 1.343137e-13 1.813880e-12
[69,] -1.167479e-12 1.343137e-13
[70,] -3.252305e-12 -1.167479e-12
[71,] 1.183560e-12 -3.252305e-12
[72,] 4.995257e-13 1.183560e-12
[73,] -2.473517e-12 4.995257e-13
[74,] 3.498031e-12 -2.473517e-12
[75,] 1.992159e-12 3.498031e-12
[76,] 2.774342e-12 1.992159e-12
[77,] 3.236428e-12 2.774342e-12
[78,] 2.599101e-13 3.236428e-12
[79,] -2.245541e-12 2.599101e-13
[80,] 2.566834e-12 -2.245541e-12
[81,] -2.679739e-12 2.566834e-12
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.783616e-12 2.442012e-12
2 -6.902202e-12 -3.783616e-12
3 9.870854e-13 -6.902202e-12
4 6.680176e-13 9.870854e-13
5 2.135044e-12 6.680176e-13
6 -2.139489e-12 2.135044e-12
7 4.819259e-12 -2.139489e-12
8 7.304701e-13 4.819259e-12
9 -7.572114e-13 7.304701e-13
10 2.060345e-12 -7.572114e-13
11 3.064410e-12 2.060345e-12
12 -2.931621e-12 3.064410e-12
13 4.619779e-13 -2.931621e-12
14 5.551570e-12 4.619779e-13
15 2.324786e-12 5.551570e-12
16 -4.856985e-12 2.324786e-12
17 -8.522308e-13 -4.856985e-12
18 -1.251050e-12 -8.522308e-13
19 1.678996e-12 -1.251050e-12
20 1.114263e-12 1.678996e-12
21 -5.885638e-13 1.114263e-12
22 -1.173087e-12 -5.885638e-13
23 -3.248534e-12 -1.173087e-12
24 -1.880104e-12 -3.248534e-12
25 -6.459688e-12 -1.880104e-12
26 3.850548e-12 -6.459688e-12
27 -1.547485e-12 3.850548e-12
28 5.662357e-13 -1.547485e-12
29 4.783489e-12 5.662357e-13
30 3.981421e-13 4.783489e-12
31 5.274861e-12 3.981421e-13
32 -2.636543e-12 5.274861e-12
33 -4.507796e-12 -2.636543e-12
34 2.299936e-12 -4.507796e-12
35 -5.897317e-12 2.299936e-12
36 1.386313e-12 -5.897317e-12
37 -1.060007e-12 1.386313e-12
38 4.255802e-12 -1.060007e-12
39 3.575454e-12 4.255802e-12
40 -1.352364e-13 3.575454e-12
41 2.848482e-12 -1.352364e-13
42 5.509565e-13 2.848482e-12
43 -3.928331e-12 5.509565e-13
44 2.731566e-12 -3.928331e-12
45 1.719005e-13 2.731566e-12
46 2.530821e-13 1.719005e-13
47 8.422845e-13 2.530821e-13
48 1.322415e-12 8.422845e-13
49 2.129707e-13 1.322415e-12
50 3.657816e-12 2.129707e-13
51 -4.550455e-12 3.657816e-12
52 3.322481e-12 -4.550455e-12
53 -3.398555e-12 3.322481e-12
54 -3.186382e-12 -3.398555e-12
55 -2.504931e-12 -3.186382e-12
56 1.580268e-12 -2.504931e-12
57 1.425172e-13 1.580268e-12
58 -5.660066e-13 1.425172e-13
59 1.125062e-12 -5.660066e-13
60 -4.010247e-12 1.125062e-12
61 -3.020594e-12 -4.010247e-12
62 -1.792518e-12 -3.020594e-12
63 2.641835e-12 -1.792518e-12
64 -1.633314e-12 2.641835e-12
65 -2.240847e-12 -1.633314e-12
66 1.467892e-12 -2.240847e-12
67 1.813880e-12 1.467892e-12
68 1.343137e-13 1.813880e-12
69 -1.167479e-12 1.343137e-13
70 -3.252305e-12 -1.167479e-12
71 1.183560e-12 -3.252305e-12
72 4.995257e-13 1.183560e-12
73 -2.473517e-12 4.995257e-13
74 3.498031e-12 -2.473517e-12
75 1.992159e-12 3.498031e-12
76 2.774342e-12 1.992159e-12
77 3.236428e-12 2.774342e-12
78 2.599101e-13 3.236428e-12
79 -2.245541e-12 2.599101e-13
80 2.566834e-12 -2.245541e-12
81 -2.679739e-12 2.566834e-12
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7u1em1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ng5k1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9drsh1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10qkmf1356052691.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1118nn1356052691.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1233ts1356052691.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13er0x1356052691.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14qq8p1356052691.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15i5911356052691.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16fj5m1356052692.tab")
+ }
>
> try(system("convert tmp/1lh3k1356052691.ps tmp/1lh3k1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/29xzl1356052691.ps tmp/29xzl1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/39uq41356052691.ps tmp/39uq41356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xyqg1356052691.ps tmp/4xyqg1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bv481356052691.ps tmp/5bv481356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ytjc1356052691.ps tmp/6ytjc1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u1em1356052691.ps tmp/7u1em1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ng5k1356052691.ps tmp/8ng5k1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/9drsh1356052691.ps tmp/9drsh1356052691.png",intern=TRUE))
character(0)
> try(system("convert tmp/10qkmf1356052691.ps tmp/10qkmf1356052691.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.940 1.237 8.197