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(337
+ ,74
+ ,232
+ ,31
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+ ,9
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+ ,44
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+ ,118
+ ,44
+ ,35
+ ,610
+ ,79
+ ,508
+ ,23
+ ,313
+ ,86
+ ,198
+ ,29)
+ ,dim=c(4
+ ,121)
+ ,dimnames=list(c('Totaal'
+ ,'InbrengInContanten'
+ ,'InbrengInNatura'
+ ,'TeStortenBedrag')
+ ,1:121))
> y <- array(NA,dim=c(4,121),dimnames=list(c('Totaal','InbrengInContanten','InbrengInNatura','TeStortenBedrag'),1:121))
> 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'
> 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, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Totaal InbrengInContanten InbrengInNatura TeStortenBedrag
1 337 74 232 31
2 430 35 386 9
3 169 44 102 23
4 133 53 52 28
5 76 42 17 17
6 328 128 165 35
7 175 50 106 19
8 169 97 31 42
9 165 76 69 20
10 141 36 85 21
11 92 48 27 17
12 233 22 206 5
13 110 42 51 17
14 170 113 45 12
15 94 49 22 23
16 125 78 22 25
17 100 65 21 14
18 8434 91 8313 29
19 126 37 79 10
20 381 111 241 30
21 799 155 587 57
22 150 81 25 44
23 190 87 83 19
24 165 65 78 22
25 162 102 42 18
26 137 70 51 17
27 131 74 40 17
28 162 80 57 26
29 141 80 28 34
30 247 101 83 63
31 175 65 93 17
32 357 160 175 21
33 107 62 29 16
34 310 68 223 20
35 116 58 20 37
36 376 70 280 25
37 230 115 90 25
38 54 33 7 14
39 194 44 135 15
40 171 73 78 21
41 311 46 248 17
42 290 81 186 22
43 4435 2053 687 1695
44 440 101 307 32
45 1430 341 1048 41
46 820 314 477 29
47 223 141 43 39
48 426 270 122 34
49 1693 320 566 807
50 2068 44 2010 13
51 832 589 222 20
52 416 149 236 30
53 372 79 262 31
54 5266 751 3929 586
55 633 155 456 22
56 191 107 35 48
57 337 172 138 26
58 280 106 122 52
59 619 149 270 200
60 2423 2125 243 55
61 538 297 189 52
62 294 93 180 20
63 430 293 116 21
64 737 325 321 92
65 541 169 346 26
66 1214 209 878 126
67 929 130 760 39
68 1288 67 1201 20
69 321 152 148 21
70 1912 388 1498 25
71 146 62 59 25
72 357 97 225 35
73 473 158 280 35
74 153 55 87 11
75 681 521 142 19
76 337 109 208 20
77 433 70 332 31
78 751 116 610 26
79 655 126 475 55
80 233 150 36 46
81 118 73 20 25
82 146 83 42 21
83 365 197 153 16
84 653 112 519 22
85 434 168 168 97
86 231 62 156 12
87 123 50 57 16
88 259 113 104 42
89 98 46 28 23
90 2107 222 1839 46
91 715 61 622 31
92 136 73 31 32
93 180 111 45 25
94 172 63 79 31
95 170 58 79 33
96 380 131 205 45
97 813 110 674 29
98 708 399 295 14
99 193 79 93 22
100 248 76 149 23
101 725 184 524 17
102 13007 326 12645 36
103 976 129 824 22
104 185 63 98 24
105 234 92 68 75
106 185 72 89 24
107 217 64 130 23
108 802 358 404 40
109 705 76 571 57
110 304 117 156 30
111 395 230 129 37
112 439 161 254 24
113 321 73 228 20
114 1015 231 736 48
115 340 57 256 27
116 372 133 49 190
117 1772 80 1666 26
118 163 101 38 24
119 197 118 44 35
120 610 79 508 23
121 313 86 198 29
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InbrengInContanten InbrengInNatura TeStortenBedrag
0.004915 0.999983 1.000051 0.999970
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.03361 -0.02907 -0.00815 -0.00430 0.99712
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0049151 0.0638073 0.077 0.939
InbrengInContanten 0.9999832 0.0002533 3948.401 <2e-16 ***
InbrengInNatura 1.0000515 0.0000380 26319.552 <2e-16 ***
TeStortenBedrag 0.9999702 0.0003966 2521.322 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5868 on 117 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.685e+08 on 3 and 117 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,] 1.500410e-40 3.000820e-40 1.000000e+00
[2,] 1.495184e-01 2.990369e-01 8.504816e-01
[3,] 7.288422e-02 1.457684e-01 9.271158e-01
[4,] 2.067923e-01 4.135846e-01 7.932077e-01
[5,] 1.266176e-01 2.532352e-01 8.733824e-01
[6,] 7.746255e-02 1.549251e-01 9.225374e-01
[7,] 4.453696e-02 8.907393e-02 9.554630e-01
[8,] 3.322556e-02 6.645112e-02 9.667744e-01
[9,] 2.100158e-02 4.200316e-02 9.789984e-01
[10,] 1.195607e-02 2.391213e-02 9.880439e-01
[11,] 5.999443e-03 1.199889e-02 9.940006e-01
[12,] 3.099587e-03 6.199175e-03 9.969004e-01
[13,] 1.456128e-03 2.912255e-03 9.985439e-01
[14,] 7.145802e-03 1.429160e-02 9.928542e-01
[15,] 8.512293e-03 1.702459e-02 9.914877e-01
[16,] 6.508993e-03 1.301799e-02 9.934910e-01
[17,] 3.677131e-02 7.354263e-02 9.632287e-01
[18,] 2.390844e-02 4.781688e-02 9.760916e-01
[19,] 1.508633e-02 3.017266e-02 9.849137e-01
[20,] 4.516285e-02 9.032571e-02 9.548371e-01
[21,] 3.058548e-02 6.117095e-02 9.694145e-01
[22,] 5.842983e-02 1.168597e-01 9.415702e-01
[23,] 8.221458e-02 1.644292e-01 9.177854e-01
[24,] 7.847846e-02 1.569569e-01 9.215215e-01
[25,] 5.773756e-02 1.154751e-01 9.422624e-01
[26,] 9.150886e-02 1.830177e-01 9.084911e-01
[27,] 6.824126e-02 1.364825e-01 9.317587e-01
[28,] 1.183504e-01 2.367009e-01 8.816496e-01
[29,] 2.855679e-01 5.711358e-01 7.144321e-01
[30,] 4.130184e-01 8.260368e-01 5.869816e-01
[31,] 3.561668e-01 7.123336e-01 6.438332e-01
[32,] 3.047397e-01 6.094795e-01 6.952603e-01
[33,] 2.561371e-01 5.122743e-01 7.438629e-01
[34,] 3.456694e-01 6.913389e-01 6.543306e-01
[35,] 2.953986e-01 5.907973e-01 7.046014e-01
[36,] 4.004323e-01 8.008647e-01 5.995677e-01
[37,] 3.561371e-01 7.122743e-01 6.438629e-01
[38,] 3.050845e-01 6.101691e-01 6.949155e-01
[39,] 2.715010e-01 5.430020e-01 7.284990e-01
[40,] 2.281133e-01 4.562266e-01 7.718867e-01
[41,] 1.885102e-01 3.770205e-01 8.114898e-01
[42,] 1.533108e-01 3.066216e-01 8.466892e-01
[43,] 1.245435e-01 2.490871e-01 8.754565e-01
[44,] 1.575056e-01 3.150112e-01 8.424944e-01
[45,] 2.048758e-01 4.097516e-01 7.951242e-01
[46,] 2.796586e-01 5.593172e-01 7.203414e-01
[47,] 2.358529e-01 4.717057e-01 7.641471e-01
[48,] 2.157198e-01 4.314396e-01 7.842802e-01
[49,] 1.785558e-01 3.571116e-01 8.214442e-01
[50,] 2.544749e-01 5.089497e-01 7.455251e-01
[51,] 3.316126e-01 6.632252e-01 6.683874e-01
[52,] 2.843836e-01 5.687673e-01 7.156164e-01
[53,] 2.420958e-01 4.841916e-01 7.579042e-01
[54,] 2.551730e-01 5.103461e-01 7.448270e-01
[55,] 2.153272e-01 4.306545e-01 7.846728e-01
[56,] 2.908402e-01 5.816804e-01 7.091598e-01
[57,] 2.495741e-01 4.991481e-01 7.504259e-01
[58,] 3.357436e-01 6.714872e-01 6.642564e-01
[59,] 2.886986e-01 5.773971e-01 7.113014e-01
[60,] 3.498616e-01 6.997232e-01 6.501384e-01
[61,] 3.022043e-01 6.044085e-01 6.977957e-01
[62,] 2.585990e-01 5.171979e-01 7.414010e-01
[63,] 2.171922e-01 4.343844e-01 7.828078e-01
[64,] 3.252178e-01 6.504355e-01 6.747822e-01
[65,] 2.780995e-01 5.561990e-01 7.219005e-01
[66,] 2.344830e-01 4.689661e-01 7.655170e-01
[67,] 1.954519e-01 3.909038e-01 8.045481e-01
[68,] 1.600407e-01 3.200813e-01 8.399593e-01
[69,] 1.835179e-01 3.670359e-01 8.164821e-01
[70,] 1.493167e-01 2.986335e-01 8.506833e-01
[71,] 1.195871e-01 2.391742e-01 8.804129e-01
[72,] 1.818974e-01 3.637948e-01 8.181026e-01
[73,] 2.630525e-01 5.261050e-01 7.369475e-01
[74,] 3.578495e-01 7.156989e-01 6.421505e-01
[75,] 3.062698e-01 6.125397e-01 6.937302e-01
[76,] 2.579745e-01 5.159490e-01 7.420255e-01
[77,] 3.314602e-01 6.629203e-01 6.685398e-01
[78,] 2.805990e-01 5.611979e-01 7.194010e-01
[79,] 3.943317e-01 7.886635e-01 6.056683e-01
[80,] 4.854483e-01 9.708967e-01 5.145517e-01
[81,] 4.257871e-01 8.515741e-01 5.742129e-01
[82,] 3.673757e-01 7.347514e-01 6.326243e-01
[83,] 4.740721e-01 9.481442e-01 5.259279e-01
[84,] 4.157158e-01 8.314316e-01 5.842842e-01
[85,] 5.398065e-01 9.203870e-01 4.601935e-01
[86,] 4.770990e-01 9.541981e-01 5.229010e-01
[87,] 5.758107e-01 8.483786e-01 4.241893e-01
[88,] 6.894582e-01 6.210836e-01 3.105418e-01
[89,] 6.265630e-01 7.468740e-01 3.734370e-01
[90,] 7.415202e-01 5.169596e-01 2.584798e-01
[91,] 6.805641e-01 6.388718e-01 3.194359e-01
[92,] 6.180933e-01 7.638133e-01 3.819067e-01
[93,] 7.764718e-01 4.470565e-01 2.235282e-01
[94,] 7.172477e-01 5.655046e-01 2.827523e-01
[95,] 6.473173e-01 7.053653e-01 3.526827e-01
[96,] 6.608742e-01 6.782517e-01 3.391258e-01
[97,] 7.461878e-01 5.076243e-01 2.538122e-01
[98,] 6.722892e-01 6.554216e-01 3.277108e-01
[99,] 8.455238e-01 3.089524e-01 1.544762e-01
[100,] 7.845599e-01 4.308803e-01 2.154401e-01
[101,] 7.115781e-01 5.768438e-01 2.884219e-01
[102,] 7.054787e-01 5.890426e-01 2.945213e-01
[103,] 8.032062e-01 3.935877e-01 1.967938e-01
[104,] 9.814181e-01 3.716378e-02 1.858189e-02
[105,] 1.000000e+00 1.492617e-81 7.463087e-82
[106,] 1.000000e+00 4.896853e-65 2.448427e-65
[107,] 1.000000e+00 4.283534e-53 2.141767e-53
[108,] 1.000000e+00 1.761769e-40 8.808847e-41
> postscript(file="/var/wessaorg/rcomp/tmp/1pqwi1353054559.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fyxk1353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/376oz1353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4q5fc1353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5h34s1353054559.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 = 121
Frequency = 1
1 2 3 4 5
-0.0146971842 -0.0239376525 -0.0087441294 -0.0058692906 -0.0045794595
6 7 8 9 10
-0.0102225303 -0.0089688930 -1.0036327284 -0.0065979234 -1.0080624410
11 12 13 14 15
-0.0049938627 -0.0150055119 -0.0063303391 -0.0049805583 -0.0045406116
16 17 18 19 20
-0.0039948956 -0.0044894645 0.5693851859 -0.0080648844 -1.0145703589
21 22 23 24 25
-0.0308451450 -0.0035322437 0.9928356538 -0.0071860845 -0.0048314228
26 27 28 29 30
-1.0058610528 -0.0052275509 -1.0057339164 -1.0040018410 -0.0056169732
31 32 33 34 35
-0.0081077052 0.9893811455 -0.0048920473 -1.0146624599 0.9961309101
36 37 38 39 40
0.9825849362 -0.0068765265 -0.0043048412 -0.0106821907 -1.0070818375
41 42 43 44 45
-0.0164081006 0.9875204615 0.0446854646 -0.0180770578 -0.0519449673
46 47 48 49 50
-0.0233510325 -0.0036027404 -0.0056580656 -0.0046222060 0.8927022793
51 52 53 54 55
0.9941211130 0.9863240119 -0.0161582769 -0.1771746247 -0.0251433258
56 57 58 59 60
0.9965078966 0.9916368110 -0.0078697165 -0.0103549570 0.0198276077
61 62 63 64 65
-0.0081187765 0.9879708934 -0.0053514537 -1.0132536321 -0.0191247331
66 67 68 69 70
0.9571330366 -0.0407100638 -0.0650427569 -0.0093625319 0.9251920004
71 72 73 74 75
-0.0061684281 -0.0138318848 -0.0156418164 -0.0081453373 -1.0029287002
76 77 78 79 80
-0.0132028438 -0.0199138710 -1.0336080901 -1.0256232577 0.9971174202
81 82 83 84 85
-0.0039757038 -0.0050603627 -1.0090149780 -0.0291082923 0.9921431449
86 87 88 89 90
0.9884485634 -0.0065350708 -0.0071238063 0.9951001290 -0.0945239590
91 92 93 94 95
0.9650013139 -0.0043333214 -1.0046262268 -1.0070025885 -0.0070267201
96 97 98 99 100
-1.0119337603 -0.0369149205 -0.0130015795 -1.0077238882 -0.0106281358
101 102 103 104 105
-0.0283082124 -0.6495499781 0.9554702115 -0.0081898646 -1.0046373511
106 107 108 109 110
-0.0075755544 -0.0098508258 -0.0185261587 0.9686547407 0.9899074015
111 112 113 114 115
-1.0065994455 -0.0145808103 -0.0148361411 -0.0375128533 -0.0163373621
116 117 118 119 120
0.0004592492 -0.0885917188 -0.0044631885 -0.0041590610 -0.0290650839
121
-0.0128048515
> postscript(file="/var/wessaorg/rcomp/tmp/64xeq1353054559.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0146971842 NA
1 -0.0239376525 -0.0146971842
2 -0.0087441294 -0.0239376525
3 -0.0058692906 -0.0087441294
4 -0.0045794595 -0.0058692906
5 -0.0102225303 -0.0045794595
6 -0.0089688930 -0.0102225303
7 -1.0036327284 -0.0089688930
8 -0.0065979234 -1.0036327284
9 -1.0080624410 -0.0065979234
10 -0.0049938627 -1.0080624410
11 -0.0150055119 -0.0049938627
12 -0.0063303391 -0.0150055119
13 -0.0049805583 -0.0063303391
14 -0.0045406116 -0.0049805583
15 -0.0039948956 -0.0045406116
16 -0.0044894645 -0.0039948956
17 0.5693851859 -0.0044894645
18 -0.0080648844 0.5693851859
19 -1.0145703589 -0.0080648844
20 -0.0308451450 -1.0145703589
21 -0.0035322437 -0.0308451450
22 0.9928356538 -0.0035322437
23 -0.0071860845 0.9928356538
24 -0.0048314228 -0.0071860845
25 -1.0058610528 -0.0048314228
26 -0.0052275509 -1.0058610528
27 -1.0057339164 -0.0052275509
28 -1.0040018410 -1.0057339164
29 -0.0056169732 -1.0040018410
30 -0.0081077052 -0.0056169732
31 0.9893811455 -0.0081077052
32 -0.0048920473 0.9893811455
33 -1.0146624599 -0.0048920473
34 0.9961309101 -1.0146624599
35 0.9825849362 0.9961309101
36 -0.0068765265 0.9825849362
37 -0.0043048412 -0.0068765265
38 -0.0106821907 -0.0043048412
39 -1.0070818375 -0.0106821907
40 -0.0164081006 -1.0070818375
41 0.9875204615 -0.0164081006
42 0.0446854646 0.9875204615
43 -0.0180770578 0.0446854646
44 -0.0519449673 -0.0180770578
45 -0.0233510325 -0.0519449673
46 -0.0036027404 -0.0233510325
47 -0.0056580656 -0.0036027404
48 -0.0046222060 -0.0056580656
49 0.8927022793 -0.0046222060
50 0.9941211130 0.8927022793
51 0.9863240119 0.9941211130
52 -0.0161582769 0.9863240119
53 -0.1771746247 -0.0161582769
54 -0.0251433258 -0.1771746247
55 0.9965078966 -0.0251433258
56 0.9916368110 0.9965078966
57 -0.0078697165 0.9916368110
58 -0.0103549570 -0.0078697165
59 0.0198276077 -0.0103549570
60 -0.0081187765 0.0198276077
61 0.9879708934 -0.0081187765
62 -0.0053514537 0.9879708934
63 -1.0132536321 -0.0053514537
64 -0.0191247331 -1.0132536321
65 0.9571330366 -0.0191247331
66 -0.0407100638 0.9571330366
67 -0.0650427569 -0.0407100638
68 -0.0093625319 -0.0650427569
69 0.9251920004 -0.0093625319
70 -0.0061684281 0.9251920004
71 -0.0138318848 -0.0061684281
72 -0.0156418164 -0.0138318848
73 -0.0081453373 -0.0156418164
74 -1.0029287002 -0.0081453373
75 -0.0132028438 -1.0029287002
76 -0.0199138710 -0.0132028438
77 -1.0336080901 -0.0199138710
78 -1.0256232577 -1.0336080901
79 0.9971174202 -1.0256232577
80 -0.0039757038 0.9971174202
81 -0.0050603627 -0.0039757038
82 -1.0090149780 -0.0050603627
83 -0.0291082923 -1.0090149780
84 0.9921431449 -0.0291082923
85 0.9884485634 0.9921431449
86 -0.0065350708 0.9884485634
87 -0.0071238063 -0.0065350708
88 0.9951001290 -0.0071238063
89 -0.0945239590 0.9951001290
90 0.9650013139 -0.0945239590
91 -0.0043333214 0.9650013139
92 -1.0046262268 -0.0043333214
93 -1.0070025885 -1.0046262268
94 -0.0070267201 -1.0070025885
95 -1.0119337603 -0.0070267201
96 -0.0369149205 -1.0119337603
97 -0.0130015795 -0.0369149205
98 -1.0077238882 -0.0130015795
99 -0.0106281358 -1.0077238882
100 -0.0283082124 -0.0106281358
101 -0.6495499781 -0.0283082124
102 0.9554702115 -0.6495499781
103 -0.0081898646 0.9554702115
104 -1.0046373511 -0.0081898646
105 -0.0075755544 -1.0046373511
106 -0.0098508258 -0.0075755544
107 -0.0185261587 -0.0098508258
108 0.9686547407 -0.0185261587
109 0.9899074015 0.9686547407
110 -1.0065994455 0.9899074015
111 -0.0145808103 -1.0065994455
112 -0.0148361411 -0.0145808103
113 -0.0375128533 -0.0148361411
114 -0.0163373621 -0.0375128533
115 0.0004592492 -0.0163373621
116 -0.0885917188 0.0004592492
117 -0.0044631885 -0.0885917188
118 -0.0041590610 -0.0044631885
119 -0.0290650839 -0.0041590610
120 -0.0128048515 -0.0290650839
121 NA -0.0128048515
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0239376525 -0.0146971842
[2,] -0.0087441294 -0.0239376525
[3,] -0.0058692906 -0.0087441294
[4,] -0.0045794595 -0.0058692906
[5,] -0.0102225303 -0.0045794595
[6,] -0.0089688930 -0.0102225303
[7,] -1.0036327284 -0.0089688930
[8,] -0.0065979234 -1.0036327284
[9,] -1.0080624410 -0.0065979234
[10,] -0.0049938627 -1.0080624410
[11,] -0.0150055119 -0.0049938627
[12,] -0.0063303391 -0.0150055119
[13,] -0.0049805583 -0.0063303391
[14,] -0.0045406116 -0.0049805583
[15,] -0.0039948956 -0.0045406116
[16,] -0.0044894645 -0.0039948956
[17,] 0.5693851859 -0.0044894645
[18,] -0.0080648844 0.5693851859
[19,] -1.0145703589 -0.0080648844
[20,] -0.0308451450 -1.0145703589
[21,] -0.0035322437 -0.0308451450
[22,] 0.9928356538 -0.0035322437
[23,] -0.0071860845 0.9928356538
[24,] -0.0048314228 -0.0071860845
[25,] -1.0058610528 -0.0048314228
[26,] -0.0052275509 -1.0058610528
[27,] -1.0057339164 -0.0052275509
[28,] -1.0040018410 -1.0057339164
[29,] -0.0056169732 -1.0040018410
[30,] -0.0081077052 -0.0056169732
[31,] 0.9893811455 -0.0081077052
[32,] -0.0048920473 0.9893811455
[33,] -1.0146624599 -0.0048920473
[34,] 0.9961309101 -1.0146624599
[35,] 0.9825849362 0.9961309101
[36,] -0.0068765265 0.9825849362
[37,] -0.0043048412 -0.0068765265
[38,] -0.0106821907 -0.0043048412
[39,] -1.0070818375 -0.0106821907
[40,] -0.0164081006 -1.0070818375
[41,] 0.9875204615 -0.0164081006
[42,] 0.0446854646 0.9875204615
[43,] -0.0180770578 0.0446854646
[44,] -0.0519449673 -0.0180770578
[45,] -0.0233510325 -0.0519449673
[46,] -0.0036027404 -0.0233510325
[47,] -0.0056580656 -0.0036027404
[48,] -0.0046222060 -0.0056580656
[49,] 0.8927022793 -0.0046222060
[50,] 0.9941211130 0.8927022793
[51,] 0.9863240119 0.9941211130
[52,] -0.0161582769 0.9863240119
[53,] -0.1771746247 -0.0161582769
[54,] -0.0251433258 -0.1771746247
[55,] 0.9965078966 -0.0251433258
[56,] 0.9916368110 0.9965078966
[57,] -0.0078697165 0.9916368110
[58,] -0.0103549570 -0.0078697165
[59,] 0.0198276077 -0.0103549570
[60,] -0.0081187765 0.0198276077
[61,] 0.9879708934 -0.0081187765
[62,] -0.0053514537 0.9879708934
[63,] -1.0132536321 -0.0053514537
[64,] -0.0191247331 -1.0132536321
[65,] 0.9571330366 -0.0191247331
[66,] -0.0407100638 0.9571330366
[67,] -0.0650427569 -0.0407100638
[68,] -0.0093625319 -0.0650427569
[69,] 0.9251920004 -0.0093625319
[70,] -0.0061684281 0.9251920004
[71,] -0.0138318848 -0.0061684281
[72,] -0.0156418164 -0.0138318848
[73,] -0.0081453373 -0.0156418164
[74,] -1.0029287002 -0.0081453373
[75,] -0.0132028438 -1.0029287002
[76,] -0.0199138710 -0.0132028438
[77,] -1.0336080901 -0.0199138710
[78,] -1.0256232577 -1.0336080901
[79,] 0.9971174202 -1.0256232577
[80,] -0.0039757038 0.9971174202
[81,] -0.0050603627 -0.0039757038
[82,] -1.0090149780 -0.0050603627
[83,] -0.0291082923 -1.0090149780
[84,] 0.9921431449 -0.0291082923
[85,] 0.9884485634 0.9921431449
[86,] -0.0065350708 0.9884485634
[87,] -0.0071238063 -0.0065350708
[88,] 0.9951001290 -0.0071238063
[89,] -0.0945239590 0.9951001290
[90,] 0.9650013139 -0.0945239590
[91,] -0.0043333214 0.9650013139
[92,] -1.0046262268 -0.0043333214
[93,] -1.0070025885 -1.0046262268
[94,] -0.0070267201 -1.0070025885
[95,] -1.0119337603 -0.0070267201
[96,] -0.0369149205 -1.0119337603
[97,] -0.0130015795 -0.0369149205
[98,] -1.0077238882 -0.0130015795
[99,] -0.0106281358 -1.0077238882
[100,] -0.0283082124 -0.0106281358
[101,] -0.6495499781 -0.0283082124
[102,] 0.9554702115 -0.6495499781
[103,] -0.0081898646 0.9554702115
[104,] -1.0046373511 -0.0081898646
[105,] -0.0075755544 -1.0046373511
[106,] -0.0098508258 -0.0075755544
[107,] -0.0185261587 -0.0098508258
[108,] 0.9686547407 -0.0185261587
[109,] 0.9899074015 0.9686547407
[110,] -1.0065994455 0.9899074015
[111,] -0.0145808103 -1.0065994455
[112,] -0.0148361411 -0.0145808103
[113,] -0.0375128533 -0.0148361411
[114,] -0.0163373621 -0.0375128533
[115,] 0.0004592492 -0.0163373621
[116,] -0.0885917188 0.0004592492
[117,] -0.0044631885 -0.0885917188
[118,] -0.0041590610 -0.0044631885
[119,] -0.0290650839 -0.0041590610
[120,] -0.0128048515 -0.0290650839
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0239376525 -0.0146971842
2 -0.0087441294 -0.0239376525
3 -0.0058692906 -0.0087441294
4 -0.0045794595 -0.0058692906
5 -0.0102225303 -0.0045794595
6 -0.0089688930 -0.0102225303
7 -1.0036327284 -0.0089688930
8 -0.0065979234 -1.0036327284
9 -1.0080624410 -0.0065979234
10 -0.0049938627 -1.0080624410
11 -0.0150055119 -0.0049938627
12 -0.0063303391 -0.0150055119
13 -0.0049805583 -0.0063303391
14 -0.0045406116 -0.0049805583
15 -0.0039948956 -0.0045406116
16 -0.0044894645 -0.0039948956
17 0.5693851859 -0.0044894645
18 -0.0080648844 0.5693851859
19 -1.0145703589 -0.0080648844
20 -0.0308451450 -1.0145703589
21 -0.0035322437 -0.0308451450
22 0.9928356538 -0.0035322437
23 -0.0071860845 0.9928356538
24 -0.0048314228 -0.0071860845
25 -1.0058610528 -0.0048314228
26 -0.0052275509 -1.0058610528
27 -1.0057339164 -0.0052275509
28 -1.0040018410 -1.0057339164
29 -0.0056169732 -1.0040018410
30 -0.0081077052 -0.0056169732
31 0.9893811455 -0.0081077052
32 -0.0048920473 0.9893811455
33 -1.0146624599 -0.0048920473
34 0.9961309101 -1.0146624599
35 0.9825849362 0.9961309101
36 -0.0068765265 0.9825849362
37 -0.0043048412 -0.0068765265
38 -0.0106821907 -0.0043048412
39 -1.0070818375 -0.0106821907
40 -0.0164081006 -1.0070818375
41 0.9875204615 -0.0164081006
42 0.0446854646 0.9875204615
43 -0.0180770578 0.0446854646
44 -0.0519449673 -0.0180770578
45 -0.0233510325 -0.0519449673
46 -0.0036027404 -0.0233510325
47 -0.0056580656 -0.0036027404
48 -0.0046222060 -0.0056580656
49 0.8927022793 -0.0046222060
50 0.9941211130 0.8927022793
51 0.9863240119 0.9941211130
52 -0.0161582769 0.9863240119
53 -0.1771746247 -0.0161582769
54 -0.0251433258 -0.1771746247
55 0.9965078966 -0.0251433258
56 0.9916368110 0.9965078966
57 -0.0078697165 0.9916368110
58 -0.0103549570 -0.0078697165
59 0.0198276077 -0.0103549570
60 -0.0081187765 0.0198276077
61 0.9879708934 -0.0081187765
62 -0.0053514537 0.9879708934
63 -1.0132536321 -0.0053514537
64 -0.0191247331 -1.0132536321
65 0.9571330366 -0.0191247331
66 -0.0407100638 0.9571330366
67 -0.0650427569 -0.0407100638
68 -0.0093625319 -0.0650427569
69 0.9251920004 -0.0093625319
70 -0.0061684281 0.9251920004
71 -0.0138318848 -0.0061684281
72 -0.0156418164 -0.0138318848
73 -0.0081453373 -0.0156418164
74 -1.0029287002 -0.0081453373
75 -0.0132028438 -1.0029287002
76 -0.0199138710 -0.0132028438
77 -1.0336080901 -0.0199138710
78 -1.0256232577 -1.0336080901
79 0.9971174202 -1.0256232577
80 -0.0039757038 0.9971174202
81 -0.0050603627 -0.0039757038
82 -1.0090149780 -0.0050603627
83 -0.0291082923 -1.0090149780
84 0.9921431449 -0.0291082923
85 0.9884485634 0.9921431449
86 -0.0065350708 0.9884485634
87 -0.0071238063 -0.0065350708
88 0.9951001290 -0.0071238063
89 -0.0945239590 0.9951001290
90 0.9650013139 -0.0945239590
91 -0.0043333214 0.9650013139
92 -1.0046262268 -0.0043333214
93 -1.0070025885 -1.0046262268
94 -0.0070267201 -1.0070025885
95 -1.0119337603 -0.0070267201
96 -0.0369149205 -1.0119337603
97 -0.0130015795 -0.0369149205
98 -1.0077238882 -0.0130015795
99 -0.0106281358 -1.0077238882
100 -0.0283082124 -0.0106281358
101 -0.6495499781 -0.0283082124
102 0.9554702115 -0.6495499781
103 -0.0081898646 0.9554702115
104 -1.0046373511 -0.0081898646
105 -0.0075755544 -1.0046373511
106 -0.0098508258 -0.0075755544
107 -0.0185261587 -0.0098508258
108 0.9686547407 -0.0185261587
109 0.9899074015 0.9686547407
110 -1.0065994455 0.9899074015
111 -0.0145808103 -1.0065994455
112 -0.0148361411 -0.0145808103
113 -0.0375128533 -0.0148361411
114 -0.0163373621 -0.0375128533
115 0.0004592492 -0.0163373621
116 -0.0885917188 0.0004592492
117 -0.0044631885 -0.0885917188
118 -0.0041590610 -0.0044631885
119 -0.0290650839 -0.0041590610
120 -0.0128048515 -0.0290650839
> 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/7dcgi1353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/81sj51353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9br3t1353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10otdn1353054559.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i8e41353054559.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/12qvuo1353054559.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/138nw51353054559.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/14fj141353054560.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/1594ek1353054560.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/16y9aw1353054560.tab")
+ }
>
> try(system("convert tmp/1pqwi1353054559.ps tmp/1pqwi1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fyxk1353054559.ps tmp/2fyxk1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/376oz1353054559.ps tmp/376oz1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/4q5fc1353054559.ps tmp/4q5fc1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h34s1353054559.ps tmp/5h34s1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/64xeq1353054559.ps tmp/64xeq1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dcgi1353054559.ps tmp/7dcgi1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/81sj51353054559.ps tmp/81sj51353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/9br3t1353054559.ps tmp/9br3t1353054559.png",intern=TRUE))
character(0)
> try(system("convert tmp/10otdn1353054559.ps tmp/10otdn1353054559.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.667 0.890 7.591