R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
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(9.2,0,9.1,0,9.1,0,9.1,0,9.1,0,9.2,0,9.3,0,9.3,0,9.3,0,9.3,0,9.3,0,9.4,0,9.4,0,9.4,0,9.5,0,9.5,0,9.4,0,9.4,0,9.3,0,9.4,0,9.4,0,9.2,0,9.1,0,9.1,0,9.1,0,9.1,0,9,0,8.9,0,8.8,0,8.7,0,8.5,0,8.3,0,8.1,0,7.8,0,7.6,0,7.5,0,7.4,0,7.3,0,7.1,0,6.9,0,6.8,0,6.8,0,6.8,0,6.9,0,6.7,0,6.6,0,6.5,0,6.4,0,6.3,0,6.3,0,6.3,0,6.5,0,6.6,0,6.5,0,6.4,0,6.5,0,6.7,0,7.1,0,7.1,0,7.2,1,7.2,1,7.3,1,7.3,1,7.3,1,7.4,1,7.4,1,7.6,1,7.6,1,7.6,1,7.7,1,7.8,1,7.9,1,8.1,1,8.1,1,8.1,1,8.2,1,8.2,1,8.2,1,8.2,1,8.2,1,8.2,1,8.3,1,8.3,1,8.4,1,8.4,1,8.4,1,8.3,1,8,1,8,1,8.2,1,8.6,1,8.7,1,8.7,1,8.5,1,8.4,1,8.4,1,8.4,1,8.5,1,8.5,1,8.5,1,8.5,1,8.5,1,8.4,1,8.4,1,8.4,1,8.5,1,8.5,1,8.6,1,8.6,1,8.6,1,8.5,1,8.4,1,8.4,1,8.3,1,8.2,1,8.1,1,8.2,1,8.1,1,8,1,7.9,1,7.8,1,7.7,1,7.7,1,7.9,1,7.8,1,7.6,1,7.4,1,7.3,1,7.1,1,7.1,1,7,1,7,1),dim=c(2,132),dimnames=list(c('Werkloosheid','SabenaFailliet'),1:132))
> y <- array(NA,dim=c(2,132),dimnames=list(c('Werkloosheid','SabenaFailliet'),1:132))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Werkloosheid SabenaFailliet t
1 9.2 0 1
2 9.1 0 2
3 9.1 0 3
4 9.1 0 4
5 9.1 0 5
6 9.2 0 6
7 9.3 0 7
8 9.3 0 8
9 9.3 0 9
10 9.3 0 10
11 9.3 0 11
12 9.4 0 12
13 9.4 0 13
14 9.4 0 14
15 9.5 0 15
16 9.5 0 16
17 9.4 0 17
18 9.4 0 18
19 9.3 0 19
20 9.4 0 20
21 9.4 0 21
22 9.2 0 22
23 9.1 0 23
24 9.1 0 24
25 9.1 0 25
26 9.1 0 26
27 9.0 0 27
28 8.9 0 28
29 8.8 0 29
30 8.7 0 30
31 8.5 0 31
32 8.3 0 32
33 8.1 0 33
34 7.8 0 34
35 7.6 0 35
36 7.5 0 36
37 7.4 0 37
38 7.3 0 38
39 7.1 0 39
40 6.9 0 40
41 6.8 0 41
42 6.8 0 42
43 6.8 0 43
44 6.9 0 44
45 6.7 0 45
46 6.6 0 46
47 6.5 0 47
48 6.4 0 48
49 6.3 0 49
50 6.3 0 50
51 6.3 0 51
52 6.5 0 52
53 6.6 0 53
54 6.5 0 54
55 6.4 0 55
56 6.5 0 56
57 6.7 0 57
58 7.1 0 58
59 7.1 0 59
60 7.2 1 60
61 7.2 1 61
62 7.3 1 62
63 7.3 1 63
64 7.3 1 64
65 7.4 1 65
66 7.4 1 66
67 7.6 1 67
68 7.6 1 68
69 7.6 1 69
70 7.7 1 70
71 7.8 1 71
72 7.9 1 72
73 8.1 1 73
74 8.1 1 74
75 8.1 1 75
76 8.2 1 76
77 8.2 1 77
78 8.2 1 78
79 8.2 1 79
80 8.2 1 80
81 8.2 1 81
82 8.3 1 82
83 8.3 1 83
84 8.4 1 84
85 8.4 1 85
86 8.4 1 86
87 8.3 1 87
88 8.0 1 88
89 8.0 1 89
90 8.2 1 90
91 8.6 1 91
92 8.7 1 92
93 8.7 1 93
94 8.5 1 94
95 8.4 1 95
96 8.4 1 96
97 8.4 1 97
98 8.5 1 98
99 8.5 1 99
100 8.5 1 100
101 8.5 1 101
102 8.5 1 102
103 8.4 1 103
104 8.4 1 104
105 8.4 1 105
106 8.5 1 106
107 8.5 1 107
108 8.6 1 108
109 8.6 1 109
110 8.6 1 110
111 8.5 1 111
112 8.4 1 112
113 8.4 1 113
114 8.3 1 114
115 8.2 1 115
116 8.1 1 116
117 8.2 1 117
118 8.1 1 118
119 8.0 1 119
120 7.9 1 120
121 7.8 1 121
122 7.7 1 122
123 7.7 1 123
124 7.9 1 124
125 7.8 1 125
126 7.6 1 126
127 7.4 1 127
128 7.3 1 128
129 7.1 1 129
130 7.1 1 130
131 7.0 1 131
132 7.0 1 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) SabenaFailliet t
8.76672 1.36176 -0.02177
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.6222 -0.5807 0.2289 0.6020 1.0905
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.766724 0.145792 60.132 < 2e-16 ***
SabenaFailliet 1.361758 0.267915 5.083 1.28e-06 ***
t -0.021772 0.003496 -6.228 6.15e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7779 on 129 degrees of freedom
Multiple R-squared: 0.2326, Adjusted R-squared: 0.2207
F-statistic: 19.55 on 2 and 129 DF, p-value: 3.843e-08
> 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,] 8.327603e-04 1.665521e-03 9.991672e-01
[2,] 2.568006e-04 5.136013e-04 9.997432e-01
[3,] 3.031783e-05 6.063567e-05 9.999697e-01
[4,] 2.776969e-06 5.553937e-06 9.999972e-01
[5,] 2.391610e-07 4.783220e-07 9.999998e-01
[6,] 2.131175e-08 4.262351e-08 1.000000e+00
[7,] 2.261609e-09 4.523218e-09 1.000000e+00
[8,] 1.858924e-10 3.717848e-10 1.000000e+00
[9,] 1.520698e-11 3.041396e-11 1.000000e+00
[10,] 2.056265e-12 4.112530e-12 1.000000e+00
[11,] 1.883280e-13 3.766559e-13 1.000000e+00
[12,] 7.577357e-14 1.515471e-13 1.000000e+00
[13,] 3.011152e-14 6.022303e-14 1.000000e+00
[14,] 1.528131e-13 3.056261e-13 1.000000e+00
[15,] 4.086433e-14 8.172866e-14 1.000000e+00
[16,] 1.248939e-14 2.497877e-14 1.000000e+00
[17,] 2.468355e-13 4.936710e-13 1.000000e+00
[18,] 5.276152e-12 1.055230e-11 1.000000e+00
[19,] 2.001819e-11 4.003638e-11 1.000000e+00
[20,] 3.941354e-11 7.882707e-11 1.000000e+00
[21,] 5.940515e-11 1.188103e-10 1.000000e+00
[22,] 1.868807e-10 3.737613e-10 1.000000e+00
[23,] 1.045272e-09 2.090545e-09 1.000000e+00
[24,] 8.498647e-09 1.699729e-08 1.000000e+00
[25,] 8.477190e-08 1.695438e-07 9.999999e-01
[26,] 1.856204e-06 3.712408e-06 9.999981e-01
[27,] 4.696308e-05 9.392616e-05 9.999530e-01
[28,] 8.562499e-04 1.712500e-03 9.991438e-01
[29,] 1.256209e-02 2.512418e-02 9.874379e-01
[30,] 7.399233e-02 1.479847e-01 9.260077e-01
[31,] 1.977302e-01 3.954605e-01 8.022698e-01
[32,] 3.500027e-01 7.000054e-01 6.499973e-01
[33,] 4.923071e-01 9.846142e-01 5.076929e-01
[34,] 6.291608e-01 7.416785e-01 3.708392e-01
[35,] 7.446632e-01 5.106736e-01 2.553368e-01
[36,] 8.159845e-01 3.680309e-01 1.840155e-01
[37,] 8.472848e-01 3.054305e-01 1.527152e-01
[38,] 8.577521e-01 2.844958e-01 1.422479e-01
[39,] 8.505995e-01 2.988010e-01 1.494005e-01
[40,] 8.463353e-01 3.073295e-01 1.536647e-01
[41,] 8.394724e-01 3.210552e-01 1.605276e-01
[42,] 8.309841e-01 3.380318e-01 1.690159e-01
[43,] 8.222464e-01 3.555073e-01 1.777536e-01
[44,] 8.152778e-01 3.694444e-01 1.847222e-01
[45,] 8.005149e-01 3.989703e-01 1.994851e-01
[46,] 7.802677e-01 4.394645e-01 2.197323e-01
[47,] 7.427211e-01 5.145579e-01 2.572789e-01
[48,] 7.022002e-01 5.955996e-01 2.977998e-01
[49,] 6.618857e-01 6.762286e-01 3.381143e-01
[50,] 6.286060e-01 7.427880e-01 3.713940e-01
[51,] 6.000800e-01 7.998400e-01 3.999200e-01
[52,] 5.860361e-01 8.279277e-01 4.139639e-01
[53,] 6.265697e-01 7.468606e-01 3.734303e-01
[54,] 6.621869e-01 6.756261e-01 3.378131e-01
[55,] 6.790644e-01 6.418712e-01 3.209356e-01
[56,] 7.063055e-01 5.873891e-01 2.936945e-01
[57,] 7.298424e-01 5.403152e-01 2.701576e-01
[58,] 7.630510e-01 4.738980e-01 2.369490e-01
[59,] 8.059642e-01 3.880716e-01 1.940358e-01
[60,] 8.443125e-01 3.113750e-01 1.556875e-01
[61,] 8.870581e-01 2.258837e-01 1.129419e-01
[62,] 9.155030e-01 1.689939e-01 8.449696e-02
[63,] 9.424167e-01 1.151666e-01 5.758330e-02
[64,] 9.657698e-01 6.846042e-02 3.423021e-02
[65,] 9.809608e-01 3.807849e-02 1.903925e-02
[66,] 9.899918e-01 2.001634e-02 1.000817e-02
[67,] 9.949405e-01 1.011904e-02 5.059521e-03
[68,] 9.973180e-01 5.363976e-03 2.681988e-03
[69,] 9.986003e-01 2.799393e-03 1.399697e-03
[70,] 9.992923e-01 1.415462e-03 7.077312e-04
[71,] 9.996283e-01 7.434433e-04 3.717217e-04
[72,] 9.998046e-01 3.908083e-04 1.954042e-04
[73,] 9.998984e-01 2.032784e-04 1.016392e-04
[74,] 9.999483e-01 1.033660e-04 5.168299e-05
[75,] 9.999747e-01 5.069459e-05 2.534729e-05
[76,] 9.999882e-01 2.360304e-05 1.180152e-05
[77,] 9.999938e-01 1.239342e-05 6.196709e-06
[78,] 9.999968e-01 6.487177e-06 3.243588e-06
[79,] 9.999980e-01 3.943445e-06 1.971722e-06
[80,] 9.999987e-01 2.507782e-06 1.253891e-06
[81,] 9.999992e-01 1.660470e-06 8.302349e-07
[82,] 9.999995e-01 9.202862e-07 4.601431e-07
[83,] 9.999999e-01 1.192385e-07 5.961924e-08
[84,] 1.000000e+00 6.094416e-09 3.047208e-09
[85,] 1.000000e+00 5.555612e-10 2.777806e-10
[86,] 1.000000e+00 3.665141e-10 1.832570e-10
[87,] 1.000000e+00 3.453726e-10 1.726863e-10
[88,] 1.000000e+00 3.792389e-10 1.896195e-10
[89,] 1.000000e+00 2.859731e-10 1.429865e-10
[90,] 1.000000e+00 1.318496e-10 6.592480e-11
[91,] 1.000000e+00 5.454277e-11 2.727139e-11
[92,] 1.000000e+00 1.937940e-11 9.689702e-12
[93,] 1.000000e+00 1.299936e-11 6.499681e-12
[94,] 1.000000e+00 9.036249e-12 4.518125e-12
[95,] 1.000000e+00 6.552735e-12 3.276367e-12
[96,] 1.000000e+00 5.009777e-12 2.504888e-12
[97,] 1.000000e+00 4.101821e-12 2.050911e-12
[98,] 1.000000e+00 1.186436e-12 5.932181e-13
[99,] 1.000000e+00 2.303822e-13 1.151911e-13
[100,] 1.000000e+00 2.282320e-14 1.141160e-14
[101,] 1.000000e+00 9.056465e-15 4.528232e-15
[102,] 1.000000e+00 3.836486e-15 1.918243e-15
[103,] 1.000000e+00 1.133939e-14 5.669697e-15
[104,] 1.000000e+00 5.388631e-14 2.694316e-14
[105,] 1.000000e+00 3.268729e-13 1.634365e-13
[106,] 1.000000e+00 1.889052e-12 9.445261e-13
[107,] 1.000000e+00 9.166419e-12 4.583209e-12
[108,] 1.000000e+00 5.734954e-11 2.867477e-11
[109,] 1.000000e+00 3.237586e-10 1.618793e-10
[110,] 1.000000e+00 1.451499e-09 7.257494e-10
[111,] 1.000000e+00 4.095942e-09 2.047971e-09
[112,] 1.000000e+00 2.708970e-08 1.354485e-08
[113,] 9.999999e-01 1.708275e-07 8.541376e-08
[114,] 9.999995e-01 9.751863e-07 4.875931e-07
[115,] 9.999977e-01 4.637260e-06 2.318630e-06
[116,] 9.999922e-01 1.554817e-05 7.774083e-06
[117,] 9.999892e-01 2.163536e-05 1.081768e-05
[118,] 9.999875e-01 2.491925e-05 1.245963e-05
[119,] 9.999154e-01 1.692425e-04 8.462123e-05
[120,] 9.997486e-01 5.027794e-04 2.513897e-04
[121,] 9.990435e-01 1.913003e-03 9.565014e-04
> postscript(file="/var/www/html/rcomp/tmp/1xesm1229951896.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jasb1229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bj6p1229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43i181229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5mw6l1229951896.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 = 132
Frequency = 1
1 2 3 4 5 6
0.45504857 0.37682071 0.39859285 0.42036499 0.44213714 0.56390928
7 8 9 10 11 12
0.68568142 0.70745356 0.72922570 0.75099784 0.77276999 0.89454213
13 14 15 16 17 18
0.91631427 0.93808641 1.05985855 1.08163069 1.00340284 1.02517498
19 20 21 22 23 24
0.94694712 1.06871926 1.09049140 0.91226354 0.83403569 0.85580783
25 26 27 28 29 30
0.87757997 0.89935211 0.82112425 0.74289639 0.66466854 0.58644068
31 32 33 34 35 36
0.40821282 0.22998496 0.05175710 -0.22647076 -0.40469861 -0.48292647
37 38 39 40 41 42
-0.56115433 -0.63938219 -0.81761005 -0.99583791 -1.07406576 -1.05229362
43 44 45 46 47 48
-1.03052148 -0.90874934 -1.08697720 -1.16520506 -1.24343291 -1.32166077
49 50 51 52 53 54
-1.39988863 -1.37811649 -1.35634435 -1.13457221 -1.01280006 -1.09102792
55 56 57 58 59 60
-1.16925578 -1.04748364 -0.82571150 -0.40393936 -0.38216721 -1.62215326
61 62 63 64 65 66
-1.60038112 -1.47860898 -1.45683684 -1.43506470 -1.31329256 -1.29152041
67 68 69 70 71 72
-1.06974827 -1.04797613 -1.02620399 -0.90443185 -0.78265971 -0.66088756
73 74 75 76 77 78
-0.43911542 -0.41734328 -0.39557114 -0.27379900 -0.25202686 -0.23025471
79 80 81 82 83 84
-0.20848257 -0.18671043 -0.16493829 -0.04316615 -0.02139401 0.10037814
85 86 87 88 89 90
0.12215028 0.14392242 0.06569456 -0.21253330 -0.19076116 0.03101099
91 92 93 94 95 96
0.45278313 0.57455527 0.59632741 0.41809955 0.33987169 0.36164384
97 98 99 100 101 102
0.38341598 0.50518812 0.52696026 0.54873240 0.57050454 0.59227669
103 104 105 106 107 108
0.51404883 0.53582097 0.55759311 0.67936525 0.70113739 0.82290954
109 110 111 112 113 114
0.84468168 0.86645382 0.78822596 0.70999810 0.73177024 0.65354239
115 116 117 118 119 120
0.57531453 0.49708667 0.61885881 0.54063095 0.46240309 0.38417524
121 122 123 124 125 126
0.30594738 0.22771952 0.24949166 0.47126380 0.39303594 0.21480809
127 128 129 130 131 132
0.03658023 -0.04164763 -0.21987549 -0.19810335 -0.27633121 -0.25455906
> postscript(file="/var/www/html/rcomp/tmp/6u6mt1229951896.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 0.45504857 NA
1 0.37682071 0.45504857
2 0.39859285 0.37682071
3 0.42036499 0.39859285
4 0.44213714 0.42036499
5 0.56390928 0.44213714
6 0.68568142 0.56390928
7 0.70745356 0.68568142
8 0.72922570 0.70745356
9 0.75099784 0.72922570
10 0.77276999 0.75099784
11 0.89454213 0.77276999
12 0.91631427 0.89454213
13 0.93808641 0.91631427
14 1.05985855 0.93808641
15 1.08163069 1.05985855
16 1.00340284 1.08163069
17 1.02517498 1.00340284
18 0.94694712 1.02517498
19 1.06871926 0.94694712
20 1.09049140 1.06871926
21 0.91226354 1.09049140
22 0.83403569 0.91226354
23 0.85580783 0.83403569
24 0.87757997 0.85580783
25 0.89935211 0.87757997
26 0.82112425 0.89935211
27 0.74289639 0.82112425
28 0.66466854 0.74289639
29 0.58644068 0.66466854
30 0.40821282 0.58644068
31 0.22998496 0.40821282
32 0.05175710 0.22998496
33 -0.22647076 0.05175710
34 -0.40469861 -0.22647076
35 -0.48292647 -0.40469861
36 -0.56115433 -0.48292647
37 -0.63938219 -0.56115433
38 -0.81761005 -0.63938219
39 -0.99583791 -0.81761005
40 -1.07406576 -0.99583791
41 -1.05229362 -1.07406576
42 -1.03052148 -1.05229362
43 -0.90874934 -1.03052148
44 -1.08697720 -0.90874934
45 -1.16520506 -1.08697720
46 -1.24343291 -1.16520506
47 -1.32166077 -1.24343291
48 -1.39988863 -1.32166077
49 -1.37811649 -1.39988863
50 -1.35634435 -1.37811649
51 -1.13457221 -1.35634435
52 -1.01280006 -1.13457221
53 -1.09102792 -1.01280006
54 -1.16925578 -1.09102792
55 -1.04748364 -1.16925578
56 -0.82571150 -1.04748364
57 -0.40393936 -0.82571150
58 -0.38216721 -0.40393936
59 -1.62215326 -0.38216721
60 -1.60038112 -1.62215326
61 -1.47860898 -1.60038112
62 -1.45683684 -1.47860898
63 -1.43506470 -1.45683684
64 -1.31329256 -1.43506470
65 -1.29152041 -1.31329256
66 -1.06974827 -1.29152041
67 -1.04797613 -1.06974827
68 -1.02620399 -1.04797613
69 -0.90443185 -1.02620399
70 -0.78265971 -0.90443185
71 -0.66088756 -0.78265971
72 -0.43911542 -0.66088756
73 -0.41734328 -0.43911542
74 -0.39557114 -0.41734328
75 -0.27379900 -0.39557114
76 -0.25202686 -0.27379900
77 -0.23025471 -0.25202686
78 -0.20848257 -0.23025471
79 -0.18671043 -0.20848257
80 -0.16493829 -0.18671043
81 -0.04316615 -0.16493829
82 -0.02139401 -0.04316615
83 0.10037814 -0.02139401
84 0.12215028 0.10037814
85 0.14392242 0.12215028
86 0.06569456 0.14392242
87 -0.21253330 0.06569456
88 -0.19076116 -0.21253330
89 0.03101099 -0.19076116
90 0.45278313 0.03101099
91 0.57455527 0.45278313
92 0.59632741 0.57455527
93 0.41809955 0.59632741
94 0.33987169 0.41809955
95 0.36164384 0.33987169
96 0.38341598 0.36164384
97 0.50518812 0.38341598
98 0.52696026 0.50518812
99 0.54873240 0.52696026
100 0.57050454 0.54873240
101 0.59227669 0.57050454
102 0.51404883 0.59227669
103 0.53582097 0.51404883
104 0.55759311 0.53582097
105 0.67936525 0.55759311
106 0.70113739 0.67936525
107 0.82290954 0.70113739
108 0.84468168 0.82290954
109 0.86645382 0.84468168
110 0.78822596 0.86645382
111 0.70999810 0.78822596
112 0.73177024 0.70999810
113 0.65354239 0.73177024
114 0.57531453 0.65354239
115 0.49708667 0.57531453
116 0.61885881 0.49708667
117 0.54063095 0.61885881
118 0.46240309 0.54063095
119 0.38417524 0.46240309
120 0.30594738 0.38417524
121 0.22771952 0.30594738
122 0.24949166 0.22771952
123 0.47126380 0.24949166
124 0.39303594 0.47126380
125 0.21480809 0.39303594
126 0.03658023 0.21480809
127 -0.04164763 0.03658023
128 -0.21987549 -0.04164763
129 -0.19810335 -0.21987549
130 -0.27633121 -0.19810335
131 -0.25455906 -0.27633121
132 NA -0.25455906
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.37682071 0.45504857
[2,] 0.39859285 0.37682071
[3,] 0.42036499 0.39859285
[4,] 0.44213714 0.42036499
[5,] 0.56390928 0.44213714
[6,] 0.68568142 0.56390928
[7,] 0.70745356 0.68568142
[8,] 0.72922570 0.70745356
[9,] 0.75099784 0.72922570
[10,] 0.77276999 0.75099784
[11,] 0.89454213 0.77276999
[12,] 0.91631427 0.89454213
[13,] 0.93808641 0.91631427
[14,] 1.05985855 0.93808641
[15,] 1.08163069 1.05985855
[16,] 1.00340284 1.08163069
[17,] 1.02517498 1.00340284
[18,] 0.94694712 1.02517498
[19,] 1.06871926 0.94694712
[20,] 1.09049140 1.06871926
[21,] 0.91226354 1.09049140
[22,] 0.83403569 0.91226354
[23,] 0.85580783 0.83403569
[24,] 0.87757997 0.85580783
[25,] 0.89935211 0.87757997
[26,] 0.82112425 0.89935211
[27,] 0.74289639 0.82112425
[28,] 0.66466854 0.74289639
[29,] 0.58644068 0.66466854
[30,] 0.40821282 0.58644068
[31,] 0.22998496 0.40821282
[32,] 0.05175710 0.22998496
[33,] -0.22647076 0.05175710
[34,] -0.40469861 -0.22647076
[35,] -0.48292647 -0.40469861
[36,] -0.56115433 -0.48292647
[37,] -0.63938219 -0.56115433
[38,] -0.81761005 -0.63938219
[39,] -0.99583791 -0.81761005
[40,] -1.07406576 -0.99583791
[41,] -1.05229362 -1.07406576
[42,] -1.03052148 -1.05229362
[43,] -0.90874934 -1.03052148
[44,] -1.08697720 -0.90874934
[45,] -1.16520506 -1.08697720
[46,] -1.24343291 -1.16520506
[47,] -1.32166077 -1.24343291
[48,] -1.39988863 -1.32166077
[49,] -1.37811649 -1.39988863
[50,] -1.35634435 -1.37811649
[51,] -1.13457221 -1.35634435
[52,] -1.01280006 -1.13457221
[53,] -1.09102792 -1.01280006
[54,] -1.16925578 -1.09102792
[55,] -1.04748364 -1.16925578
[56,] -0.82571150 -1.04748364
[57,] -0.40393936 -0.82571150
[58,] -0.38216721 -0.40393936
[59,] -1.62215326 -0.38216721
[60,] -1.60038112 -1.62215326
[61,] -1.47860898 -1.60038112
[62,] -1.45683684 -1.47860898
[63,] -1.43506470 -1.45683684
[64,] -1.31329256 -1.43506470
[65,] -1.29152041 -1.31329256
[66,] -1.06974827 -1.29152041
[67,] -1.04797613 -1.06974827
[68,] -1.02620399 -1.04797613
[69,] -0.90443185 -1.02620399
[70,] -0.78265971 -0.90443185
[71,] -0.66088756 -0.78265971
[72,] -0.43911542 -0.66088756
[73,] -0.41734328 -0.43911542
[74,] -0.39557114 -0.41734328
[75,] -0.27379900 -0.39557114
[76,] -0.25202686 -0.27379900
[77,] -0.23025471 -0.25202686
[78,] -0.20848257 -0.23025471
[79,] -0.18671043 -0.20848257
[80,] -0.16493829 -0.18671043
[81,] -0.04316615 -0.16493829
[82,] -0.02139401 -0.04316615
[83,] 0.10037814 -0.02139401
[84,] 0.12215028 0.10037814
[85,] 0.14392242 0.12215028
[86,] 0.06569456 0.14392242
[87,] -0.21253330 0.06569456
[88,] -0.19076116 -0.21253330
[89,] 0.03101099 -0.19076116
[90,] 0.45278313 0.03101099
[91,] 0.57455527 0.45278313
[92,] 0.59632741 0.57455527
[93,] 0.41809955 0.59632741
[94,] 0.33987169 0.41809955
[95,] 0.36164384 0.33987169
[96,] 0.38341598 0.36164384
[97,] 0.50518812 0.38341598
[98,] 0.52696026 0.50518812
[99,] 0.54873240 0.52696026
[100,] 0.57050454 0.54873240
[101,] 0.59227669 0.57050454
[102,] 0.51404883 0.59227669
[103,] 0.53582097 0.51404883
[104,] 0.55759311 0.53582097
[105,] 0.67936525 0.55759311
[106,] 0.70113739 0.67936525
[107,] 0.82290954 0.70113739
[108,] 0.84468168 0.82290954
[109,] 0.86645382 0.84468168
[110,] 0.78822596 0.86645382
[111,] 0.70999810 0.78822596
[112,] 0.73177024 0.70999810
[113,] 0.65354239 0.73177024
[114,] 0.57531453 0.65354239
[115,] 0.49708667 0.57531453
[116,] 0.61885881 0.49708667
[117,] 0.54063095 0.61885881
[118,] 0.46240309 0.54063095
[119,] 0.38417524 0.46240309
[120,] 0.30594738 0.38417524
[121,] 0.22771952 0.30594738
[122,] 0.24949166 0.22771952
[123,] 0.47126380 0.24949166
[124,] 0.39303594 0.47126380
[125,] 0.21480809 0.39303594
[126,] 0.03658023 0.21480809
[127,] -0.04164763 0.03658023
[128,] -0.21987549 -0.04164763
[129,] -0.19810335 -0.21987549
[130,] -0.27633121 -0.19810335
[131,] -0.25455906 -0.27633121
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.37682071 0.45504857
2 0.39859285 0.37682071
3 0.42036499 0.39859285
4 0.44213714 0.42036499
5 0.56390928 0.44213714
6 0.68568142 0.56390928
7 0.70745356 0.68568142
8 0.72922570 0.70745356
9 0.75099784 0.72922570
10 0.77276999 0.75099784
11 0.89454213 0.77276999
12 0.91631427 0.89454213
13 0.93808641 0.91631427
14 1.05985855 0.93808641
15 1.08163069 1.05985855
16 1.00340284 1.08163069
17 1.02517498 1.00340284
18 0.94694712 1.02517498
19 1.06871926 0.94694712
20 1.09049140 1.06871926
21 0.91226354 1.09049140
22 0.83403569 0.91226354
23 0.85580783 0.83403569
24 0.87757997 0.85580783
25 0.89935211 0.87757997
26 0.82112425 0.89935211
27 0.74289639 0.82112425
28 0.66466854 0.74289639
29 0.58644068 0.66466854
30 0.40821282 0.58644068
31 0.22998496 0.40821282
32 0.05175710 0.22998496
33 -0.22647076 0.05175710
34 -0.40469861 -0.22647076
35 -0.48292647 -0.40469861
36 -0.56115433 -0.48292647
37 -0.63938219 -0.56115433
38 -0.81761005 -0.63938219
39 -0.99583791 -0.81761005
40 -1.07406576 -0.99583791
41 -1.05229362 -1.07406576
42 -1.03052148 -1.05229362
43 -0.90874934 -1.03052148
44 -1.08697720 -0.90874934
45 -1.16520506 -1.08697720
46 -1.24343291 -1.16520506
47 -1.32166077 -1.24343291
48 -1.39988863 -1.32166077
49 -1.37811649 -1.39988863
50 -1.35634435 -1.37811649
51 -1.13457221 -1.35634435
52 -1.01280006 -1.13457221
53 -1.09102792 -1.01280006
54 -1.16925578 -1.09102792
55 -1.04748364 -1.16925578
56 -0.82571150 -1.04748364
57 -0.40393936 -0.82571150
58 -0.38216721 -0.40393936
59 -1.62215326 -0.38216721
60 -1.60038112 -1.62215326
61 -1.47860898 -1.60038112
62 -1.45683684 -1.47860898
63 -1.43506470 -1.45683684
64 -1.31329256 -1.43506470
65 -1.29152041 -1.31329256
66 -1.06974827 -1.29152041
67 -1.04797613 -1.06974827
68 -1.02620399 -1.04797613
69 -0.90443185 -1.02620399
70 -0.78265971 -0.90443185
71 -0.66088756 -0.78265971
72 -0.43911542 -0.66088756
73 -0.41734328 -0.43911542
74 -0.39557114 -0.41734328
75 -0.27379900 -0.39557114
76 -0.25202686 -0.27379900
77 -0.23025471 -0.25202686
78 -0.20848257 -0.23025471
79 -0.18671043 -0.20848257
80 -0.16493829 -0.18671043
81 -0.04316615 -0.16493829
82 -0.02139401 -0.04316615
83 0.10037814 -0.02139401
84 0.12215028 0.10037814
85 0.14392242 0.12215028
86 0.06569456 0.14392242
87 -0.21253330 0.06569456
88 -0.19076116 -0.21253330
89 0.03101099 -0.19076116
90 0.45278313 0.03101099
91 0.57455527 0.45278313
92 0.59632741 0.57455527
93 0.41809955 0.59632741
94 0.33987169 0.41809955
95 0.36164384 0.33987169
96 0.38341598 0.36164384
97 0.50518812 0.38341598
98 0.52696026 0.50518812
99 0.54873240 0.52696026
100 0.57050454 0.54873240
101 0.59227669 0.57050454
102 0.51404883 0.59227669
103 0.53582097 0.51404883
104 0.55759311 0.53582097
105 0.67936525 0.55759311
106 0.70113739 0.67936525
107 0.82290954 0.70113739
108 0.84468168 0.82290954
109 0.86645382 0.84468168
110 0.78822596 0.86645382
111 0.70999810 0.78822596
112 0.73177024 0.70999810
113 0.65354239 0.73177024
114 0.57531453 0.65354239
115 0.49708667 0.57531453
116 0.61885881 0.49708667
117 0.54063095 0.61885881
118 0.46240309 0.54063095
119 0.38417524 0.46240309
120 0.30594738 0.38417524
121 0.22771952 0.30594738
122 0.24949166 0.22771952
123 0.47126380 0.24949166
124 0.39303594 0.47126380
125 0.21480809 0.39303594
126 0.03658023 0.21480809
127 -0.04164763 0.03658023
128 -0.21987549 -0.04164763
129 -0.19810335 -0.21987549
130 -0.27633121 -0.19810335
131 -0.25455906 -0.27633121
> 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/7dmro1229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v91x1229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r4js1229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101vui1229951896.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/116hwz1229951896.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/12x2571229951896.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/13dkwz1229951896.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/14qg831229951896.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/15w1np1229951897.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/16qory1229951897.tab")
+ }
>
> system("convert tmp/1xesm1229951896.ps tmp/1xesm1229951896.png")
> system("convert tmp/2jasb1229951896.ps tmp/2jasb1229951896.png")
> system("convert tmp/3bj6p1229951896.ps tmp/3bj6p1229951896.png")
> system("convert tmp/43i181229951896.ps tmp/43i181229951896.png")
> system("convert tmp/5mw6l1229951896.ps tmp/5mw6l1229951896.png")
> system("convert tmp/6u6mt1229951896.ps tmp/6u6mt1229951896.png")
> system("convert tmp/7dmro1229951896.ps tmp/7dmro1229951896.png")
> system("convert tmp/8v91x1229951896.ps tmp/8v91x1229951896.png")
> system("convert tmp/9r4js1229951896.ps tmp/9r4js1229951896.png")
> system("convert tmp/101vui1229951896.ps tmp/101vui1229951896.png")
>
>
> proc.time()
user system elapsed
3.380 1.672 3.786