R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(43880
+ ,25222
+ ,43110
+ ,21333
+ ,44496
+ ,19778
+ ,44164
+ ,25943
+ ,40399
+ ,21698
+ ,36763
+ ,20077
+ ,37903
+ ,25673
+ ,35532
+ ,19094
+ ,35533
+ ,19306
+ ,32110
+ ,15443
+ ,33374
+ ,15179
+ ,35462
+ ,18288
+ ,33508
+ ,18264
+ ,36080
+ ,16406
+ ,34560
+ ,15678
+ ,38737
+ ,19657
+ ,38144
+ ,18821
+ ,37594
+ ,19493
+ ,36424
+ ,21078
+ ,36843
+ ,19296
+ ,37246
+ ,19985
+ ,38661
+ ,16972
+ ,40454
+ ,16951
+ ,44928
+ ,23126
+ ,48441
+ ,24890
+ ,48140
+ ,21042
+ ,45998
+ ,20842
+ ,47369
+ ,23904
+ ,49554
+ ,22578
+ ,47510
+ ,25452
+ ,44873
+ ,21928
+ ,45344
+ ,25227
+ ,42413
+ ,26210
+ ,36912
+ ,17436
+ ,43452
+ ,21258
+ ,42142
+ ,25638
+ ,44382
+ ,23516
+ ,43636
+ ,23891
+ ,44167
+ ,24617
+ ,44423
+ ,26174
+ ,42868
+ ,23339
+ ,43908
+ ,23660
+ ,42013
+ ,26500
+ ,38846
+ ,22469
+ ,35087
+ ,23163
+ ,33026
+ ,16170
+ ,34646
+ ,18267
+ ,37135
+ ,20561
+ ,37985
+ ,20372
+ ,43121
+ ,19017
+ ,43722
+ ,18242
+ ,43630
+ ,20937
+ ,42234
+ ,22065
+ ,39351
+ ,16731
+ ,39327
+ ,21943
+ ,35704
+ ,19254
+ ,30466
+ ,16397
+ ,28155
+ ,13644
+ ,29257
+ ,14375
+ ,29998
+ ,14814
+ ,32529
+ ,16061
+ ,34787
+ ,14784
+ ,33855
+ ,12824
+ ,34556
+ ,18282
+ ,31348
+ ,14936
+ ,30805
+ ,15701
+ ,28353
+ ,16394
+ ,24514
+ ,13085
+ ,21106
+ ,11431
+ ,21346
+ ,9334
+ ,23335
+ ,10921
+ ,24379
+ ,11725
+ ,26290
+ ,13077
+ ,30084
+ ,11794
+ ,29429
+ ,11047
+ ,30632
+ ,16797
+ ,27349
+ ,11482
+ ,27264
+ ,12657
+ ,27474
+ ,15277
+ ,24482
+ ,12385
+ ,21453
+ ,11996
+ ,18788
+ ,8395
+ ,19282
+ ,8928
+ ,19713
+ ,9937
+ ,21917
+ ,11468
+ ,23812
+ ,9554
+ ,23785
+ ,9226
+ ,24696
+ ,11021
+ ,24562
+ ,10065
+ ,23580
+ ,9939
+ ,24939
+ ,11179
+ ,23899
+ ,11943
+ ,21454
+ ,10792
+ ,19761
+ ,8080
+ ,19815
+ ,8603
+ ,20780
+ ,11561
+ ,23462
+ ,10449
+ ,25005
+ ,8197
+ ,24725
+ ,7602
+ ,26198
+ ,9521
+ ,27543
+ ,10412
+ ,26471
+ ,10860
+ ,26558
+ ,11538
+ ,25317
+ ,11420
+ ,22896
+ ,10408
+ ,22248
+ ,5998
+ ,23406
+ ,8356
+ ,25073
+ ,10569
+ ,27691
+ ,9660
+ ,30599
+ ,9304
+ ,31948
+ ,9114
+ ,32946
+ ,10492
+ ,34012
+ ,12388
+ ,32936
+ ,10003
+ ,32974
+ ,14029
+ ,30951
+ ,12452
+ ,29812
+ ,12332
+ ,29010
+ ,8064
+ ,31068
+ ,10931
+ ,32447
+ ,12631
+ ,34844
+ ,13656
+ ,35676
+ ,11005
+ ,35387
+ ,8879
+ ,36488
+ ,11536
+ ,35652
+ ,13698
+ ,33488
+ ,10853
+ ,32914
+ ,15107
+ ,29781
+ ,13604
+ ,27951
+ ,12231)
+ ,dim=c(2
+ ,129)
+ ,dimnames=list(c('OPENVAC'
+ ,'OntvangenJobs')
+ ,1:129))
> y <- array(NA,dim=c(2,129),dimnames=list(c('OPENVAC','OntvangenJobs'),1:129))
> 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 = '1'
> #'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.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
OPENVAC OntvangenJobs
1 43880 25222
2 43110 21333
3 44496 19778
4 44164 25943
5 40399 21698
6 36763 20077
7 37903 25673
8 35532 19094
9 35533 19306
10 32110 15443
11 33374 15179
12 35462 18288
13 33508 18264
14 36080 16406
15 34560 15678
16 38737 19657
17 38144 18821
18 37594 19493
19 36424 21078
20 36843 19296
21 37246 19985
22 38661 16972
23 40454 16951
24 44928 23126
25 48441 24890
26 48140 21042
27 45998 20842
28 47369 23904
29 49554 22578
30 47510 25452
31 44873 21928
32 45344 25227
33 42413 26210
34 36912 17436
35 43452 21258
36 42142 25638
37 44382 23516
38 43636 23891
39 44167 24617
40 44423 26174
41 42868 23339
42 43908 23660
43 42013 26500
44 38846 22469
45 35087 23163
46 33026 16170
47 34646 18267
48 37135 20561
49 37985 20372
50 43121 19017
51 43722 18242
52 43630 20937
53 42234 22065
54 39351 16731
55 39327 21943
56 35704 19254
57 30466 16397
58 28155 13644
59 29257 14375
60 29998 14814
61 32529 16061
62 34787 14784
63 33855 12824
64 34556 18282
65 31348 14936
66 30805 15701
67 28353 16394
68 24514 13085
69 21106 11431
70 21346 9334
71 23335 10921
72 24379 11725
73 26290 13077
74 30084 11794
75 29429 11047
76 30632 16797
77 27349 11482
78 27264 12657
79 27474 15277
80 24482 12385
81 21453 11996
82 18788 8395
83 19282 8928
84 19713 9937
85 21917 11468
86 23812 9554
87 23785 9226
88 24696 11021
89 24562 10065
90 23580 9939
91 24939 11179
92 23899 11943
93 21454 10792
94 19761 8080
95 19815 8603
96 20780 11561
97 23462 10449
98 25005 8197
99 24725 7602
100 26198 9521
101 27543 10412
102 26471 10860
103 26558 11538
104 25317 11420
105 22896 10408
106 22248 5998
107 23406 8356
108 25073 10569
109 27691 9660
110 30599 9304
111 31948 9114
112 32946 10492
113 34012 12388
114 32936 10003
115 32974 14029
116 30951 12452
117 29812 12332
118 29010 8064
119 31068 10931
120 32447 12631
121 34844 13656
122 35676 11005
123 35387 8879
124 36488 11536
125 35652 13698
126 33488 10853
127 32914 15107
128 29781 13604
129 27951 12231
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) OntvangenJobs
13329.223 1.261
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7804.9 -2489.1 -696.8 2178.6 10859.6
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.333e+04 1.069e+03 12.46 <2e-16 ***
OntvangenJobs 1.261e+00 6.426e-02 19.63 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3966 on 127 degrees of freedom
Multiple R-squared: 0.7521, Adjusted R-squared: 0.7501
F-statistic: 385.2 on 1 and 127 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,] 0.115330292 0.23066058 0.88466971
[2,] 0.303117147 0.60623429 0.69688285
[3,] 0.423787567 0.84757513 0.57621243
[4,] 0.465607926 0.93121585 0.53439207
[5,] 0.432881073 0.86576215 0.56711893
[6,] 0.384772345 0.76954469 0.61522766
[7,] 0.288832377 0.57766475 0.71116762
[8,] 0.213477519 0.42695504 0.78652248
[9,] 0.185499224 0.37099845 0.81450078
[10,] 0.138156021 0.27631204 0.86184398
[11,] 0.094193389 0.18838678 0.90580661
[12,] 0.063973007 0.12794601 0.93602699
[13,] 0.042778217 0.08555643 0.95722178
[14,] 0.026459202 0.05291840 0.97354080
[15,] 0.022004427 0.04400885 0.97799557
[16,] 0.013377920 0.02675584 0.98662208
[17,] 0.008038171 0.01607634 0.99196183
[18,] 0.008229190 0.01645838 0.99177081
[19,] 0.014609402 0.02921880 0.98539060
[20,] 0.017703350 0.03540670 0.98229665
[21,] 0.032266205 0.06453241 0.96773380
[22,] 0.132270090 0.26454018 0.86772991
[23,] 0.199858509 0.39971702 0.80014149
[24,] 0.205526403 0.41105281 0.79447360
[25,] 0.347900489 0.69580098 0.65209951
[26,] 0.305433961 0.61086792 0.69456604
[27,] 0.292263349 0.58452670 0.70773665
[28,] 0.244175720 0.48835144 0.75582428
[29,] 0.250245669 0.50049134 0.74975433
[30,] 0.207961648 0.41592330 0.79203835
[31,] 0.190278199 0.38055640 0.80972180
[32,] 0.181983632 0.36396726 0.81801637
[33,] 0.151110626 0.30222125 0.84888937
[34,] 0.120679765 0.24135953 0.87932023
[35,] 0.094945259 0.18989052 0.90505474
[36,] 0.076246026 0.15249205 0.92375397
[37,] 0.058509134 0.11701827 0.94149087
[38,] 0.045081425 0.09016285 0.95491857
[39,] 0.046890498 0.09378100 0.95310950
[40,] 0.041651405 0.08330281 0.95834860
[41,] 0.087388674 0.17477735 0.91261133
[42,] 0.074007934 0.14801587 0.92599207
[43,] 0.064240339 0.12848068 0.93575966
[44,] 0.054857235 0.10971447 0.94514277
[45,] 0.043072965 0.08614593 0.95692704
[46,] 0.055475551 0.11095110 0.94452445
[47,] 0.094652556 0.18930511 0.90534744
[48,] 0.097496497 0.19499299 0.90250350
[49,] 0.081942435 0.16388487 0.91805757
[50,] 0.089193692 0.17838738 0.91080631
[51,] 0.074448748 0.14889750 0.92555125
[52,] 0.065078200 0.13015640 0.93492180
[53,] 0.072365748 0.14473150 0.92763425
[54,] 0.072375963 0.14475193 0.92762404
[55,] 0.066959231 0.13391846 0.93304077
[56,] 0.058973440 0.11794688 0.94102656
[57,] 0.047642563 0.09528513 0.95235744
[58,] 0.041940401 0.08388080 0.95805960
[59,] 0.041835494 0.08367099 0.95816451
[60,] 0.034512016 0.06902403 0.96548798
[61,] 0.027413316 0.05482663 0.97258668
[62,] 0.023379470 0.04675894 0.97662053
[63,] 0.031229427 0.06245885 0.96877057
[64,] 0.041033553 0.08206711 0.95896645
[65,] 0.067203770 0.13440754 0.93279623
[66,] 0.065927106 0.13185421 0.93407289
[67,] 0.062560674 0.12512135 0.93743933
[68,] 0.058098386 0.11619677 0.94190161
[69,] 0.051975654 0.10395131 0.94802435
[70,] 0.043646274 0.08729255 0.95635373
[71,] 0.036938925 0.07387785 0.96306107
[72,] 0.033772161 0.06754432 0.96622784
[73,] 0.025279204 0.05055841 0.97472080
[74,] 0.019685143 0.03937029 0.98031486
[75,] 0.022839524 0.04567905 0.97716048
[76,] 0.023868117 0.04773623 0.97613188
[77,] 0.041605908 0.08321182 0.95839409
[78,] 0.045709506 0.09141901 0.95429049
[79,] 0.053151936 0.10630387 0.94684806
[80,] 0.073512950 0.14702590 0.92648705
[81,] 0.098543976 0.19708795 0.90145602
[82,] 0.083439340 0.16687868 0.91656066
[83,] 0.069442378 0.13888476 0.93055762
[84,] 0.062085787 0.12417157 0.93791421
[85,] 0.051846574 0.10369315 0.94815343
[86,] 0.045905429 0.09181086 0.95409457
[87,] 0.041486729 0.08297346 0.95851327
[88,] 0.050948169 0.10189634 0.94905183
[89,] 0.078618894 0.15723779 0.92138111
[90,] 0.092770393 0.18554079 0.90722961
[91,] 0.129996943 0.25999389 0.87000306
[92,] 0.303142958 0.60628592 0.69685704
[93,] 0.354647188 0.70929438 0.64535281
[94,] 0.334222013 0.66844403 0.66577799
[95,] 0.315647237 0.63129447 0.68435276
[96,] 0.292870674 0.58574135 0.70712933
[97,] 0.264470799 0.52894160 0.73552920
[98,] 0.256647234 0.51329447 0.74335277
[99,] 0.264844248 0.52968850 0.73515575
[100,] 0.322668348 0.64533670 0.67733165
[101,] 0.503190423 0.99361915 0.49680958
[102,] 0.584621534 0.83075693 0.41537847
[103,] 0.765076520 0.46984696 0.23492348
[104,] 0.909825986 0.18034803 0.09017401
[105,] 0.941596842 0.11680632 0.05840316
[106,] 0.937396483 0.12520703 0.06260352
[107,] 0.928714629 0.14257074 0.07128537
[108,] 0.911944490 0.17611102 0.08805551
[109,] 0.890470485 0.21905903 0.10952951
[110,] 0.865271656 0.26945669 0.13472834
[111,] 0.811843642 0.37631272 0.18815636
[112,] 0.762329977 0.47534005 0.23767002
[113,] 0.744177033 0.51164593 0.25582297
[114,] 0.808625522 0.38274896 0.19137448
[115,] 0.791208810 0.41758238 0.20879119
[116,] 0.708914634 0.58217073 0.29108537
[117,] 0.642179945 0.71564011 0.35782006
[118,] 0.571260228 0.85747954 0.42873977
[119,] 0.474821738 0.94964348 0.52517826
[120,] 0.509439905 0.98112019 0.49056009
> postscript(file="/var/www/html/rcomp/tmp/1mtks1290755448.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xl2d1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3xl2d1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xl2d1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58cjy1290755448.ps",horizontal=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 = 129
Frequency = 1
1 2 3 4 5 6 7
-1259.1160 2875.6764 6222.8367 -1884.4386 -295.6602 -1887.2610 -7804.9156
8 9 10 11 12 13 14
-1878.5051 -2144.8787 -695.8773 901.0785 -931.9808 -2855.7121 2059.5906
15 16 17 18 19 20 21
1457.7415 616.4414 1077.8015 -319.7224 -3488.7185 -822.2667 -1288.2310
22 23 24 25 26 27 28
3926.7535 5746.2386 2432.3514 3720.6011 8272.6845 6382.9238 3892.1407
29 30 31 32 33 34 35
7749.4870 2080.8088 3888.2646 198.5780 -3972.1780 1592.5584 3312.2661
36 37 38 39 40 41 42
-3521.7737 1394.4849 175.5363 -209.0923 -1916.7749 103.7166 738.8726
43 44 45 46 47 48 49
-4737.9249 -2821.0426 -7455.3128 -696.7671 -1721.4957 -2125.6800 -1037.3139
50 51 52 53 54 55 56
5807.6071 7386.0342 3895.1101 1076.4807 4920.7018 -1676.6533 -1908.2965
57 58 59 60 61 62 63
-3543.0586 -2381.9852 -2201.9197 -2014.5849 -1056.2967 2812.2510 4352.1958
64 65 66 67 68 69 70
-1830.4137 -818.4508 -2326.2660 -5652.2750 -5317.9764 -6639.9578 -3755.2291
71 72 73 74 75 76 77
-3767.7476 -3737.7495 -3531.8869 1880.2280 2167.3416 -3881.5371 -461.2788
78 79 80 81 82 83 84
-2028.1844 -5122.5188 -4467.1390 -7005.5337 -5128.9658 -5307.1834 -6148.7305
85 86 87 88 89 90 91
-5875.6220 -1566.6923 -1180.0199 -2532.8673 -1461.1636 -2284.2529 -2489.1363
92 93 94 95 96 97 98
-4492.6903 -5486.0533 -3758.6889 -4364.2946 -7129.9133 -3045.4630 1337.7511
99 100 101 102 103 104 105
1808.1629 860.9272 1082.2013 -554.8147 -1322.9058 -2415.0846 -3559.7539
106 107 108 109 110 111 112
1354.1218 -461.7791 -1585.8065 2178.6209 5535.6068 7124.2341 6384.3056
113 114 115 116 117 118 119
5059.0774 6991.0306 1951.4542 1917.3608 929.7044 5510.4902 3952.6404
120 121 122 123 124 125 126
3187.6067 4291.8805 8467.3119 10859.6152 8609.6166 5046.9102 6471.0137
127 128 129
531.8846 -705.5373 -803.9148
> postscript(file="/var/www/html/rcomp/tmp/68cjy1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 129
Frequency = 1
lag(myerror, k = 1) myerror
0 -1259.1160 NA
1 2875.6764 -1259.1160
2 6222.8367 2875.6764
3 -1884.4386 6222.8367
4 -295.6602 -1884.4386
5 -1887.2610 -295.6602
6 -7804.9156 -1887.2610
7 -1878.5051 -7804.9156
8 -2144.8787 -1878.5051
9 -695.8773 -2144.8787
10 901.0785 -695.8773
11 -931.9808 901.0785
12 -2855.7121 -931.9808
13 2059.5906 -2855.7121
14 1457.7415 2059.5906
15 616.4414 1457.7415
16 1077.8015 616.4414
17 -319.7224 1077.8015
18 -3488.7185 -319.7224
19 -822.2667 -3488.7185
20 -1288.2310 -822.2667
21 3926.7535 -1288.2310
22 5746.2386 3926.7535
23 2432.3514 5746.2386
24 3720.6011 2432.3514
25 8272.6845 3720.6011
26 6382.9238 8272.6845
27 3892.1407 6382.9238
28 7749.4870 3892.1407
29 2080.8088 7749.4870
30 3888.2646 2080.8088
31 198.5780 3888.2646
32 -3972.1780 198.5780
33 1592.5584 -3972.1780
34 3312.2661 1592.5584
35 -3521.7737 3312.2661
36 1394.4849 -3521.7737
37 175.5363 1394.4849
38 -209.0923 175.5363
39 -1916.7749 -209.0923
40 103.7166 -1916.7749
41 738.8726 103.7166
42 -4737.9249 738.8726
43 -2821.0426 -4737.9249
44 -7455.3128 -2821.0426
45 -696.7671 -7455.3128
46 -1721.4957 -696.7671
47 -2125.6800 -1721.4957
48 -1037.3139 -2125.6800
49 5807.6071 -1037.3139
50 7386.0342 5807.6071
51 3895.1101 7386.0342
52 1076.4807 3895.1101
53 4920.7018 1076.4807
54 -1676.6533 4920.7018
55 -1908.2965 -1676.6533
56 -3543.0586 -1908.2965
57 -2381.9852 -3543.0586
58 -2201.9197 -2381.9852
59 -2014.5849 -2201.9197
60 -1056.2967 -2014.5849
61 2812.2510 -1056.2967
62 4352.1958 2812.2510
63 -1830.4137 4352.1958
64 -818.4508 -1830.4137
65 -2326.2660 -818.4508
66 -5652.2750 -2326.2660
67 -5317.9764 -5652.2750
68 -6639.9578 -5317.9764
69 -3755.2291 -6639.9578
70 -3767.7476 -3755.2291
71 -3737.7495 -3767.7476
72 -3531.8869 -3737.7495
73 1880.2280 -3531.8869
74 2167.3416 1880.2280
75 -3881.5371 2167.3416
76 -461.2788 -3881.5371
77 -2028.1844 -461.2788
78 -5122.5188 -2028.1844
79 -4467.1390 -5122.5188
80 -7005.5337 -4467.1390
81 -5128.9658 -7005.5337
82 -5307.1834 -5128.9658
83 -6148.7305 -5307.1834
84 -5875.6220 -6148.7305
85 -1566.6923 -5875.6220
86 -1180.0199 -1566.6923
87 -2532.8673 -1180.0199
88 -1461.1636 -2532.8673
89 -2284.2529 -1461.1636
90 -2489.1363 -2284.2529
91 -4492.6903 -2489.1363
92 -5486.0533 -4492.6903
93 -3758.6889 -5486.0533
94 -4364.2946 -3758.6889
95 -7129.9133 -4364.2946
96 -3045.4630 -7129.9133
97 1337.7511 -3045.4630
98 1808.1629 1337.7511
99 860.9272 1808.1629
100 1082.2013 860.9272
101 -554.8147 1082.2013
102 -1322.9058 -554.8147
103 -2415.0846 -1322.9058
104 -3559.7539 -2415.0846
105 1354.1218 -3559.7539
106 -461.7791 1354.1218
107 -1585.8065 -461.7791
108 2178.6209 -1585.8065
109 5535.6068 2178.6209
110 7124.2341 5535.6068
111 6384.3056 7124.2341
112 5059.0774 6384.3056
113 6991.0306 5059.0774
114 1951.4542 6991.0306
115 1917.3608 1951.4542
116 929.7044 1917.3608
117 5510.4902 929.7044
118 3952.6404 5510.4902
119 3187.6067 3952.6404
120 4291.8805 3187.6067
121 8467.3119 4291.8805
122 10859.6152 8467.3119
123 8609.6166 10859.6152
124 5046.9102 8609.6166
125 6471.0137 5046.9102
126 531.8846 6471.0137
127 -705.5373 531.8846
128 -803.9148 -705.5373
129 NA -803.9148
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2875.6764 -1259.1160
[2,] 6222.8367 2875.6764
[3,] -1884.4386 6222.8367
[4,] -295.6602 -1884.4386
[5,] -1887.2610 -295.6602
[6,] -7804.9156 -1887.2610
[7,] -1878.5051 -7804.9156
[8,] -2144.8787 -1878.5051
[9,] -695.8773 -2144.8787
[10,] 901.0785 -695.8773
[11,] -931.9808 901.0785
[12,] -2855.7121 -931.9808
[13,] 2059.5906 -2855.7121
[14,] 1457.7415 2059.5906
[15,] 616.4414 1457.7415
[16,] 1077.8015 616.4414
[17,] -319.7224 1077.8015
[18,] -3488.7185 -319.7224
[19,] -822.2667 -3488.7185
[20,] -1288.2310 -822.2667
[21,] 3926.7535 -1288.2310
[22,] 5746.2386 3926.7535
[23,] 2432.3514 5746.2386
[24,] 3720.6011 2432.3514
[25,] 8272.6845 3720.6011
[26,] 6382.9238 8272.6845
[27,] 3892.1407 6382.9238
[28,] 7749.4870 3892.1407
[29,] 2080.8088 7749.4870
[30,] 3888.2646 2080.8088
[31,] 198.5780 3888.2646
[32,] -3972.1780 198.5780
[33,] 1592.5584 -3972.1780
[34,] 3312.2661 1592.5584
[35,] -3521.7737 3312.2661
[36,] 1394.4849 -3521.7737
[37,] 175.5363 1394.4849
[38,] -209.0923 175.5363
[39,] -1916.7749 -209.0923
[40,] 103.7166 -1916.7749
[41,] 738.8726 103.7166
[42,] -4737.9249 738.8726
[43,] -2821.0426 -4737.9249
[44,] -7455.3128 -2821.0426
[45,] -696.7671 -7455.3128
[46,] -1721.4957 -696.7671
[47,] -2125.6800 -1721.4957
[48,] -1037.3139 -2125.6800
[49,] 5807.6071 -1037.3139
[50,] 7386.0342 5807.6071
[51,] 3895.1101 7386.0342
[52,] 1076.4807 3895.1101
[53,] 4920.7018 1076.4807
[54,] -1676.6533 4920.7018
[55,] -1908.2965 -1676.6533
[56,] -3543.0586 -1908.2965
[57,] -2381.9852 -3543.0586
[58,] -2201.9197 -2381.9852
[59,] -2014.5849 -2201.9197
[60,] -1056.2967 -2014.5849
[61,] 2812.2510 -1056.2967
[62,] 4352.1958 2812.2510
[63,] -1830.4137 4352.1958
[64,] -818.4508 -1830.4137
[65,] -2326.2660 -818.4508
[66,] -5652.2750 -2326.2660
[67,] -5317.9764 -5652.2750
[68,] -6639.9578 -5317.9764
[69,] -3755.2291 -6639.9578
[70,] -3767.7476 -3755.2291
[71,] -3737.7495 -3767.7476
[72,] -3531.8869 -3737.7495
[73,] 1880.2280 -3531.8869
[74,] 2167.3416 1880.2280
[75,] -3881.5371 2167.3416
[76,] -461.2788 -3881.5371
[77,] -2028.1844 -461.2788
[78,] -5122.5188 -2028.1844
[79,] -4467.1390 -5122.5188
[80,] -7005.5337 -4467.1390
[81,] -5128.9658 -7005.5337
[82,] -5307.1834 -5128.9658
[83,] -6148.7305 -5307.1834
[84,] -5875.6220 -6148.7305
[85,] -1566.6923 -5875.6220
[86,] -1180.0199 -1566.6923
[87,] -2532.8673 -1180.0199
[88,] -1461.1636 -2532.8673
[89,] -2284.2529 -1461.1636
[90,] -2489.1363 -2284.2529
[91,] -4492.6903 -2489.1363
[92,] -5486.0533 -4492.6903
[93,] -3758.6889 -5486.0533
[94,] -4364.2946 -3758.6889
[95,] -7129.9133 -4364.2946
[96,] -3045.4630 -7129.9133
[97,] 1337.7511 -3045.4630
[98,] 1808.1629 1337.7511
[99,] 860.9272 1808.1629
[100,] 1082.2013 860.9272
[101,] -554.8147 1082.2013
[102,] -1322.9058 -554.8147
[103,] -2415.0846 -1322.9058
[104,] -3559.7539 -2415.0846
[105,] 1354.1218 -3559.7539
[106,] -461.7791 1354.1218
[107,] -1585.8065 -461.7791
[108,] 2178.6209 -1585.8065
[109,] 5535.6068 2178.6209
[110,] 7124.2341 5535.6068
[111,] 6384.3056 7124.2341
[112,] 5059.0774 6384.3056
[113,] 6991.0306 5059.0774
[114,] 1951.4542 6991.0306
[115,] 1917.3608 1951.4542
[116,] 929.7044 1917.3608
[117,] 5510.4902 929.7044
[118,] 3952.6404 5510.4902
[119,] 3187.6067 3952.6404
[120,] 4291.8805 3187.6067
[121,] 8467.3119 4291.8805
[122,] 10859.6152 8467.3119
[123,] 8609.6166 10859.6152
[124,] 5046.9102 8609.6166
[125,] 6471.0137 5046.9102
[126,] 531.8846 6471.0137
[127,] -705.5373 531.8846
[128,] -803.9148 -705.5373
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2875.6764 -1259.1160
2 6222.8367 2875.6764
3 -1884.4386 6222.8367
4 -295.6602 -1884.4386
5 -1887.2610 -295.6602
6 -7804.9156 -1887.2610
7 -1878.5051 -7804.9156
8 -2144.8787 -1878.5051
9 -695.8773 -2144.8787
10 901.0785 -695.8773
11 -931.9808 901.0785
12 -2855.7121 -931.9808
13 2059.5906 -2855.7121
14 1457.7415 2059.5906
15 616.4414 1457.7415
16 1077.8015 616.4414
17 -319.7224 1077.8015
18 -3488.7185 -319.7224
19 -822.2667 -3488.7185
20 -1288.2310 -822.2667
21 3926.7535 -1288.2310
22 5746.2386 3926.7535
23 2432.3514 5746.2386
24 3720.6011 2432.3514
25 8272.6845 3720.6011
26 6382.9238 8272.6845
27 3892.1407 6382.9238
28 7749.4870 3892.1407
29 2080.8088 7749.4870
30 3888.2646 2080.8088
31 198.5780 3888.2646
32 -3972.1780 198.5780
33 1592.5584 -3972.1780
34 3312.2661 1592.5584
35 -3521.7737 3312.2661
36 1394.4849 -3521.7737
37 175.5363 1394.4849
38 -209.0923 175.5363
39 -1916.7749 -209.0923
40 103.7166 -1916.7749
41 738.8726 103.7166
42 -4737.9249 738.8726
43 -2821.0426 -4737.9249
44 -7455.3128 -2821.0426
45 -696.7671 -7455.3128
46 -1721.4957 -696.7671
47 -2125.6800 -1721.4957
48 -1037.3139 -2125.6800
49 5807.6071 -1037.3139
50 7386.0342 5807.6071
51 3895.1101 7386.0342
52 1076.4807 3895.1101
53 4920.7018 1076.4807
54 -1676.6533 4920.7018
55 -1908.2965 -1676.6533
56 -3543.0586 -1908.2965
57 -2381.9852 -3543.0586
58 -2201.9197 -2381.9852
59 -2014.5849 -2201.9197
60 -1056.2967 -2014.5849
61 2812.2510 -1056.2967
62 4352.1958 2812.2510
63 -1830.4137 4352.1958
64 -818.4508 -1830.4137
65 -2326.2660 -818.4508
66 -5652.2750 -2326.2660
67 -5317.9764 -5652.2750
68 -6639.9578 -5317.9764
69 -3755.2291 -6639.9578
70 -3767.7476 -3755.2291
71 -3737.7495 -3767.7476
72 -3531.8869 -3737.7495
73 1880.2280 -3531.8869
74 2167.3416 1880.2280
75 -3881.5371 2167.3416
76 -461.2788 -3881.5371
77 -2028.1844 -461.2788
78 -5122.5188 -2028.1844
79 -4467.1390 -5122.5188
80 -7005.5337 -4467.1390
81 -5128.9658 -7005.5337
82 -5307.1834 -5128.9658
83 -6148.7305 -5307.1834
84 -5875.6220 -6148.7305
85 -1566.6923 -5875.6220
86 -1180.0199 -1566.6923
87 -2532.8673 -1180.0199
88 -1461.1636 -2532.8673
89 -2284.2529 -1461.1636
90 -2489.1363 -2284.2529
91 -4492.6903 -2489.1363
92 -5486.0533 -4492.6903
93 -3758.6889 -5486.0533
94 -4364.2946 -3758.6889
95 -7129.9133 -4364.2946
96 -3045.4630 -7129.9133
97 1337.7511 -3045.4630
98 1808.1629 1337.7511
99 860.9272 1808.1629
100 1082.2013 860.9272
101 -554.8147 1082.2013
102 -1322.9058 -554.8147
103 -2415.0846 -1322.9058
104 -3559.7539 -2415.0846
105 1354.1218 -3559.7539
106 -461.7791 1354.1218
107 -1585.8065 -461.7791
108 2178.6209 -1585.8065
109 5535.6068 2178.6209
110 7124.2341 5535.6068
111 6384.3056 7124.2341
112 5059.0774 6384.3056
113 6991.0306 5059.0774
114 1951.4542 6991.0306
115 1917.3608 1951.4542
116 929.7044 1917.3608
117 5510.4902 929.7044
118 3952.6404 5510.4902
119 3187.6067 3952.6404
120 4291.8805 3187.6067
121 8467.3119 4291.8805
122 10859.6152 8467.3119
123 8609.6166 10859.6152
124 5046.9102 8609.6166
125 6471.0137 5046.9102
126 531.8846 6471.0137
127 -705.5373 531.8846
128 -803.9148 -705.5373
> 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/www/html/rcomp/tmp/7il0j1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8il0j1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tuzm1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tuzm1290755448.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/11fvgs1290755448.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/www/html/rcomp/tmp/12ivwy1290755448.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/www/html/rcomp/tmp/13pftr1290755448.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/www/html/rcomp/tmp/14z6bc1290755448.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/www/html/rcomp/tmp/15lori1290755448.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/www/html/rcomp/tmp/1667qo1290755448.tab")
+ }
>
> try(system("convert tmp/1mtks1290755448.ps tmp/1mtks1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xl2d1290755448.ps tmp/2xl2d1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xl2d1290755448.ps tmp/3xl2d1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xl2d1290755448.ps tmp/4xl2d1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/58cjy1290755448.ps tmp/58cjy1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/68cjy1290755448.ps tmp/68cjy1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/7il0j1290755448.ps tmp/7il0j1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/8il0j1290755448.ps tmp/8il0j1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tuzm1290755448.ps tmp/9tuzm1290755448.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tuzm1290755448.ps tmp/10tuzm1290755448.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.319 1.649 7.415