R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(33,62,39,64,45,62,46,64,45,64,45,69,49,69,50,65,54,56,59,58,58,53,56,62,48,55,50,60,52,59,53,58,55,53,43,57,42,57,38,53,41,54,41,53,39,57,34,57,27,55,15,49,14,50,31,49,41,54,43,58,46,58,42,52,45,56,45,52,40,59,35,53,36,52,38,53,39,51,32,50,24,56,21,52,12,46,29,48,36,46,31,48,28,48,30,49,38,53,27,48,40,51,40,48,44,50,47,55,45,52,42,53,38,52,46,55,37,53,41,53,40,56,33,54,34,52,36,55,36,54,38,59,42,56,35,56,25,51,24,53,22,52,27,51,17,46,30,49,30,46,34,55,37,57,36,53,33,52,33,53,33,50,37,54,40,53,35,50,37,51,43,52,42,47,33,51,39,49,40,53,37,52,44,45,42,53,43,51,40,48,30,48,30,48,31,48,18,40,24,43,22,40,26,39,28,39,23,36,17,41,12,39,9,40,19,39,21,46,18,40,18,37,15,37,24,44,18,41,19,40,30,36,33,38,35,43,36,42,47,45,46,46),dim=c(2,121),dimnames=list(c('Alg_E','Spaar'),1:121))
> y <- array(NA,dim=c(2,121),dimnames=list(c('Alg_E','Spaar'),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 = 'Include Monthly Dummies'
> par1 = '2'
> #'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
Spaar Alg_E M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 62 33 1 0 0 0 0 0 0 0 0 0 0
2 64 39 0 1 0 0 0 0 0 0 0 0 0
3 62 45 0 0 1 0 0 0 0 0 0 0 0
4 64 46 0 0 0 1 0 0 0 0 0 0 0
5 64 45 0 0 0 0 1 0 0 0 0 0 0
6 69 45 0 0 0 0 0 1 0 0 0 0 0
7 69 49 0 0 0 0 0 0 1 0 0 0 0
8 65 50 0 0 0 0 0 0 0 1 0 0 0
9 56 54 0 0 0 0 0 0 0 0 1 0 0
10 58 59 0 0 0 0 0 0 0 0 0 1 0
11 53 58 0 0 0 0 0 0 0 0 0 0 1
12 62 56 0 0 0 0 0 0 0 0 0 0 0
13 55 48 1 0 0 0 0 0 0 0 0 0 0
14 60 50 0 1 0 0 0 0 0 0 0 0 0
15 59 52 0 0 1 0 0 0 0 0 0 0 0
16 58 53 0 0 0 1 0 0 0 0 0 0 0
17 53 55 0 0 0 0 1 0 0 0 0 0 0
18 57 43 0 0 0 0 0 1 0 0 0 0 0
19 57 42 0 0 0 0 0 0 1 0 0 0 0
20 53 38 0 0 0 0 0 0 0 1 0 0 0
21 54 41 0 0 0 0 0 0 0 0 1 0 0
22 53 41 0 0 0 0 0 0 0 0 0 1 0
23 57 39 0 0 0 0 0 0 0 0 0 0 1
24 57 34 0 0 0 0 0 0 0 0 0 0 0
25 55 27 1 0 0 0 0 0 0 0 0 0 0
26 49 15 0 1 0 0 0 0 0 0 0 0 0
27 50 14 0 0 1 0 0 0 0 0 0 0 0
28 49 31 0 0 0 1 0 0 0 0 0 0 0
29 54 41 0 0 0 0 1 0 0 0 0 0 0
30 58 43 0 0 0 0 0 1 0 0 0 0 0
31 58 46 0 0 0 0 0 0 1 0 0 0 0
32 52 42 0 0 0 0 0 0 0 1 0 0 0
33 56 45 0 0 0 0 0 0 0 0 1 0 0
34 52 45 0 0 0 0 0 0 0 0 0 1 0
35 59 40 0 0 0 0 0 0 0 0 0 0 1
36 53 35 0 0 0 0 0 0 0 0 0 0 0
37 52 36 1 0 0 0 0 0 0 0 0 0 0
38 53 38 0 1 0 0 0 0 0 0 0 0 0
39 51 39 0 0 1 0 0 0 0 0 0 0 0
40 50 32 0 0 0 1 0 0 0 0 0 0 0
41 56 24 0 0 0 0 1 0 0 0 0 0 0
42 52 21 0 0 0 0 0 1 0 0 0 0 0
43 46 12 0 0 0 0 0 0 1 0 0 0 0
44 48 29 0 0 0 0 0 0 0 1 0 0 0
45 46 36 0 0 0 0 0 0 0 0 1 0 0
46 48 31 0 0 0 0 0 0 0 0 0 1 0
47 48 28 0 0 0 0 0 0 0 0 0 0 1
48 49 30 0 0 0 0 0 0 0 0 0 0 0
49 53 38 1 0 0 0 0 0 0 0 0 0 0
50 48 27 0 1 0 0 0 0 0 0 0 0 0
51 51 40 0 0 1 0 0 0 0 0 0 0 0
52 48 40 0 0 0 1 0 0 0 0 0 0 0
53 50 44 0 0 0 0 1 0 0 0 0 0 0
54 55 47 0 0 0 0 0 1 0 0 0 0 0
55 52 45 0 0 0 0 0 0 1 0 0 0 0
56 53 42 0 0 0 0 0 0 0 1 0 0 0
57 52 38 0 0 0 0 0 0 0 0 1 0 0
58 55 46 0 0 0 0 0 0 0 0 0 1 0
59 53 37 0 0 0 0 0 0 0 0 0 0 1
60 53 41 0 0 0 0 0 0 0 0 0 0 0
61 56 40 1 0 0 0 0 0 0 0 0 0 0
62 54 33 0 1 0 0 0 0 0 0 0 0 0
63 52 34 0 0 1 0 0 0 0 0 0 0 0
64 55 36 0 0 0 1 0 0 0 0 0 0 0
65 54 36 0 0 0 0 1 0 0 0 0 0 0
66 59 38 0 0 0 0 0 1 0 0 0 0 0
67 56 42 0 0 0 0 0 0 1 0 0 0 0
68 56 35 0 0 0 0 0 0 0 1 0 0 0
69 51 25 0 0 0 0 0 0 0 0 1 0 0
70 53 24 0 0 0 0 0 0 0 0 0 1 0
71 52 22 0 0 0 0 0 0 0 0 0 0 1
72 51 27 0 0 0 0 0 0 0 0 0 0 0
73 46 17 1 0 0 0 0 0 0 0 0 0 0
74 49 30 0 1 0 0 0 0 0 0 0 0 0
75 46 30 0 0 1 0 0 0 0 0 0 0 0
76 55 34 0 0 0 1 0 0 0 0 0 0 0
77 57 37 0 0 0 0 1 0 0 0 0 0 0
78 53 36 0 0 0 0 0 1 0 0 0 0 0
79 52 33 0 0 0 0 0 0 1 0 0 0 0
80 53 33 0 0 0 0 0 0 0 1 0 0 0
81 50 33 0 0 0 0 0 0 0 0 1 0 0
82 54 37 0 0 0 0 0 0 0 0 0 1 0
83 53 40 0 0 0 0 0 0 0 0 0 0 1
84 50 35 0 0 0 0 0 0 0 0 0 0 0
85 51 37 1 0 0 0 0 0 0 0 0 0 0
86 52 43 0 1 0 0 0 0 0 0 0 0 0
87 47 42 0 0 1 0 0 0 0 0 0 0 0
88 51 33 0 0 0 1 0 0 0 0 0 0 0
89 49 39 0 0 0 0 1 0 0 0 0 0 0
90 53 40 0 0 0 0 0 1 0 0 0 0 0
91 52 37 0 0 0 0 0 0 1 0 0 0 0
92 45 44 0 0 0 0 0 0 0 1 0 0 0
93 53 42 0 0 0 0 0 0 0 0 1 0 0
94 51 43 0 0 0 0 0 0 0 0 0 1 0
95 48 40 0 0 0 0 0 0 0 0 0 0 1
96 48 30 0 0 0 0 0 0 0 0 0 0 0
97 48 30 1 0 0 0 0 0 0 0 0 0 0
98 48 31 0 1 0 0 0 0 0 0 0 0 0
99 40 18 0 0 1 0 0 0 0 0 0 0 0
100 43 24 0 0 0 1 0 0 0 0 0 0 0
101 40 22 0 0 0 0 1 0 0 0 0 0 0
102 39 26 0 0 0 0 0 1 0 0 0 0 0
103 39 28 0 0 0 0 0 0 1 0 0 0 0
104 36 23 0 0 0 0 0 0 0 1 0 0 0
105 41 17 0 0 0 0 0 0 0 0 1 0 0
106 39 12 0 0 0 0 0 0 0 0 0 1 0
107 40 9 0 0 0 0 0 0 0 0 0 0 1
108 39 19 0 0 0 0 0 0 0 0 0 0 0
109 46 21 1 0 0 0 0 0 0 0 0 0 0
110 40 18 0 1 0 0 0 0 0 0 0 0 0
111 37 18 0 0 1 0 0 0 0 0 0 0 0
112 37 15 0 0 0 1 0 0 0 0 0 0 0
113 44 24 0 0 0 0 1 0 0 0 0 0 0
114 41 18 0 0 0 0 0 1 0 0 0 0 0
115 40 19 0 0 0 0 0 0 1 0 0 0 0
116 36 30 0 0 0 0 0 0 0 1 0 0 0
117 38 33 0 0 0 0 0 0 0 0 1 0 0
118 43 35 0 0 0 0 0 0 0 0 0 1 0
119 42 36 0 0 0 0 0 0 0 0 0 0 1
120 45 47 0 0 0 0 0 0 0 0 0 0 0
121 46 46 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Alg_E M1 M2 M3 M4
35.29086 0.43529 1.76715 2.30586 -0.24237 0.73529
M5 M6 M7 M8 M9 M10
0.83413 2.76941 1.44353 -1.52234 -1.43529 -0.92704
M11
0.01764
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.08119 -2.95176 0.04469 2.47411 11.35184
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 35.29086 2.32391 15.186 < 2e-16 ***
Alg_E 0.43529 0.04562 9.541 5.16e-16 ***
M1 1.76715 2.30985 0.765 0.446
M2 2.30586 2.36714 0.974 0.332
M3 -0.24237 2.36531 -0.102 0.919
M4 0.73529 2.36362 0.311 0.756
M5 0.83413 2.36392 0.353 0.725
M6 2.76941 2.36322 1.172 0.244
M7 1.44353 2.36318 0.611 0.543
M8 -1.52234 2.36381 -0.644 0.521
M9 -1.43529 2.36362 -0.607 0.545
M10 -0.92704 2.36477 -0.392 0.696
M11 0.01764 2.36329 0.007 0.994
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.284 on 108 degrees of freedom
Multiple R-squared: 0.473, Adjusted R-squared: 0.4144
F-statistic: 8.077 on 12 and 108 DF, p-value: 1.254e-10
> 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,] 0.02152816 0.04305632 0.97847184
[2,] 0.07536491 0.15072982 0.92463509
[3,] 0.53708096 0.92583808 0.46291904
[4,] 0.84653421 0.30693157 0.15346579
[5,] 0.94163408 0.11673185 0.05836592
[6,] 0.91151848 0.17696305 0.08848152
[7,] 0.88333230 0.23333541 0.11666770
[8,] 0.85036786 0.29926427 0.14963214
[9,] 0.83068606 0.33862788 0.16931394
[10,] 0.79872542 0.40254915 0.20127458
[11,] 0.84618703 0.30762594 0.15381297
[12,] 0.84230379 0.31539243 0.15769621
[13,] 0.84825833 0.30348334 0.15174167
[14,] 0.80344016 0.39311968 0.19655984
[15,] 0.77817588 0.44364825 0.22182412
[16,] 0.75800149 0.48399702 0.24199851
[17,] 0.75035839 0.49928321 0.24964161
[18,] 0.70067602 0.59864796 0.29932398
[19,] 0.64471041 0.71057918 0.35528959
[20,] 0.66091999 0.67816001 0.33908001
[21,] 0.62731058 0.74537884 0.37268942
[22,] 0.60543942 0.78912115 0.39456058
[23,] 0.58679704 0.82640591 0.41320296
[24,] 0.58552078 0.82895843 0.41447922
[25,] 0.54459140 0.91081721 0.45540860
[26,] 0.58909432 0.82181135 0.41090568
[27,] 0.58015281 0.83969438 0.41984719
[28,] 0.59537784 0.80924433 0.40462216
[29,] 0.56966139 0.86067721 0.43033861
[30,] 0.56415019 0.87169962 0.43584981
[31,] 0.50390927 0.99218147 0.49609073
[32,] 0.45286128 0.90572255 0.54713872
[33,] 0.42368892 0.84737785 0.57631108
[34,] 0.38413665 0.76827329 0.61586335
[35,] 0.36766261 0.73532522 0.63233739
[36,] 0.34780902 0.69561805 0.65219098
[37,] 0.36923806 0.73847611 0.63076194
[38,] 0.39221818 0.78443637 0.60778182
[39,] 0.38860125 0.77720250 0.61139875
[40,] 0.40056843 0.80113686 0.59943157
[41,] 0.36107921 0.72215843 0.63892079
[42,] 0.31102942 0.62205884 0.68897058
[43,] 0.26341028 0.52682056 0.73658972
[44,] 0.22240058 0.44480117 0.77759942
[45,] 0.19062844 0.38125688 0.80937156
[46,] 0.16377770 0.32755541 0.83622230
[47,] 0.14510454 0.29020909 0.85489546
[48,] 0.13424490 0.26848980 0.86575510
[49,] 0.11747565 0.23495131 0.88252435
[50,] 0.09797789 0.19595579 0.90202211
[51,] 0.10831665 0.21663331 0.89168335
[52,] 0.09558659 0.19117318 0.90441341
[53,] 0.15270494 0.30540988 0.84729506
[54,] 0.16563501 0.33127001 0.83436499
[55,] 0.21502432 0.43004864 0.78497568
[56,] 0.27162450 0.54324900 0.72837550
[57,] 0.29037605 0.58075210 0.70962395
[58,] 0.28374377 0.56748754 0.71625623
[59,] 0.25917825 0.51835651 0.74082175
[60,] 0.24888828 0.49777656 0.75111172
[61,] 0.26106136 0.52212271 0.73893864
[62,] 0.31917997 0.63835993 0.68082003
[63,] 0.32082022 0.64164044 0.67917978
[64,] 0.32770133 0.65540267 0.67229867
[65,] 0.59799032 0.80401935 0.40200968
[66,] 0.58753403 0.82493193 0.41246597
[67,] 0.63157783 0.73684434 0.36842217
[68,] 0.63440594 0.73118812 0.36559406
[69,] 0.63529781 0.72940438 0.36470219
[70,] 0.59910949 0.80178102 0.40089051
[71,] 0.55368048 0.89263903 0.44631952
[72,] 0.52873467 0.94253066 0.47126533
[73,] 0.55068572 0.89862855 0.44931428
[74,] 0.51200906 0.97598189 0.48799094
[75,] 0.58674730 0.82650541 0.41325270
[76,] 0.71289359 0.57421281 0.28710641
[77,] 0.75764201 0.48471598 0.24235799
[78,] 0.90556076 0.18887848 0.09443924
[79,] 0.94355771 0.11288459 0.05644229
[80,] 0.95230978 0.09538044 0.04769022
[81,] 0.97632393 0.04735214 0.02367607
[82,] 0.96663187 0.06673625 0.03336813
[83,] 0.98404069 0.03191862 0.01595931
[84,] 0.97860089 0.04279823 0.02139911
[85,] 0.98640207 0.02719585 0.01359793
[86,] 0.98690495 0.02619009 0.01309505
[87,] 0.98262754 0.03474491 0.01737246
[88,] 0.96781143 0.06437713 0.03218857
[89,] 0.92744666 0.14510668 0.07255334
[90,] 0.93768918 0.12462165 0.06231082
> postscript(file="/var/www/html/rcomp/tmp/1t1ze1258731936.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22y1u1258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jdfk1258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4h1lt1258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57ufa1258731936.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 = 121
Frequency = 1
1 2 3 4 5 6
10.57753302 9.42711028 7.36362141 7.95067868 8.28712354 11.35183722
7 8 9 10 11 12
10.93657740 9.46716330 -1.36103925 -2.04571316 -7.55511401 2.33310179
13 14 15 16 17 18
-2.95176180 0.63896075 1.31661717 -1.09632557 -7.06573967 0.22240986
19 20 21 22 23 24
1.98358165 2.69059915 2.29768292 0.78944061 4.71532608 6.90940085
25 26 27 28 29 30
6.18925094 4.87398198 8.85749736 -0.52002651 0.02826882 1.22240986
31 32 33 34 35 36
1.24243637 -0.05054613 2.55653764 -1.95170467 6.28003976 2.47411453
37 38 39 40 41 42
-0.72832594 -1.13760340 -1.02466066 0.04468717 9.42813628 4.79870892
43 44 45 46 47 48
4.04217128 1.60817604 -3.52588547 0.14230382 0.50347561 0.65054613
49 50 51 52 53 54
-0.59889859 -1.34945387 -1.45994698 -5.43760340 -5.27759014 -3.51873543
55 56 57 58 59 60
-4.32227731 0.94945387 1.60354189 0.61300901 1.58589873 -0.13760340
61 62 63 64 65 66
1.53052877 2.03882821 2.15177094 3.30354189 2.20470042 4.39884146
67 68 69 70 71 72
0.98358165 6.99645811 6.26226406 8.18930807 7.11519354 3.95640510
73 74 75 76 77 78
1.54211415 -1.65531283 -2.10708377 4.17411453 4.76941410 -0.73058590
79 80 81 82 83 84
0.90115854 4.86703076 1.77997349 3.53058590 0.28003976 -0.52588547
85 86 87 88 89 90
-2.16361226 -4.31403500 -6.33051962 0.60940085 -4.10115854 -2.47173118
91 92 93 94 95 96
-0.83998675 -7.92111878 0.86239660 -2.08113203 -4.71996024 -0.34945387
97 98 99 100 101 102
-2.11660802 -3.09059915 -2.88364792 -3.47302226 -5.70129108 -10.37772269
103 104 105 106 107 108
-9.92240986 -7.78010604 -0.25544537 -0.58725608 0.77391571 -4.56130434
109 110 111 112 113 114
-0.19903113 -5.43187698 -5.88364792 -5.55544537 -2.57186372 -4.89543212
115 116 117 118 119 120
-5.00483297 -10.82711028 -10.22002651 -6.59884146 -8.97881495 -10.74932132
121
-11.08118915
> postscript(file="/var/www/html/rcomp/tmp/6d1bc1258731936.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 = 121
Frequency = 1
lag(myerror, k = 1) myerror
0 10.57753302 NA
1 9.42711028 10.57753302
2 7.36362141 9.42711028
3 7.95067868 7.36362141
4 8.28712354 7.95067868
5 11.35183722 8.28712354
6 10.93657740 11.35183722
7 9.46716330 10.93657740
8 -1.36103925 9.46716330
9 -2.04571316 -1.36103925
10 -7.55511401 -2.04571316
11 2.33310179 -7.55511401
12 -2.95176180 2.33310179
13 0.63896075 -2.95176180
14 1.31661717 0.63896075
15 -1.09632557 1.31661717
16 -7.06573967 -1.09632557
17 0.22240986 -7.06573967
18 1.98358165 0.22240986
19 2.69059915 1.98358165
20 2.29768292 2.69059915
21 0.78944061 2.29768292
22 4.71532608 0.78944061
23 6.90940085 4.71532608
24 6.18925094 6.90940085
25 4.87398198 6.18925094
26 8.85749736 4.87398198
27 -0.52002651 8.85749736
28 0.02826882 -0.52002651
29 1.22240986 0.02826882
30 1.24243637 1.22240986
31 -0.05054613 1.24243637
32 2.55653764 -0.05054613
33 -1.95170467 2.55653764
34 6.28003976 -1.95170467
35 2.47411453 6.28003976
36 -0.72832594 2.47411453
37 -1.13760340 -0.72832594
38 -1.02466066 -1.13760340
39 0.04468717 -1.02466066
40 9.42813628 0.04468717
41 4.79870892 9.42813628
42 4.04217128 4.79870892
43 1.60817604 4.04217128
44 -3.52588547 1.60817604
45 0.14230382 -3.52588547
46 0.50347561 0.14230382
47 0.65054613 0.50347561
48 -0.59889859 0.65054613
49 -1.34945387 -0.59889859
50 -1.45994698 -1.34945387
51 -5.43760340 -1.45994698
52 -5.27759014 -5.43760340
53 -3.51873543 -5.27759014
54 -4.32227731 -3.51873543
55 0.94945387 -4.32227731
56 1.60354189 0.94945387
57 0.61300901 1.60354189
58 1.58589873 0.61300901
59 -0.13760340 1.58589873
60 1.53052877 -0.13760340
61 2.03882821 1.53052877
62 2.15177094 2.03882821
63 3.30354189 2.15177094
64 2.20470042 3.30354189
65 4.39884146 2.20470042
66 0.98358165 4.39884146
67 6.99645811 0.98358165
68 6.26226406 6.99645811
69 8.18930807 6.26226406
70 7.11519354 8.18930807
71 3.95640510 7.11519354
72 1.54211415 3.95640510
73 -1.65531283 1.54211415
74 -2.10708377 -1.65531283
75 4.17411453 -2.10708377
76 4.76941410 4.17411453
77 -0.73058590 4.76941410
78 0.90115854 -0.73058590
79 4.86703076 0.90115854
80 1.77997349 4.86703076
81 3.53058590 1.77997349
82 0.28003976 3.53058590
83 -0.52588547 0.28003976
84 -2.16361226 -0.52588547
85 -4.31403500 -2.16361226
86 -6.33051962 -4.31403500
87 0.60940085 -6.33051962
88 -4.10115854 0.60940085
89 -2.47173118 -4.10115854
90 -0.83998675 -2.47173118
91 -7.92111878 -0.83998675
92 0.86239660 -7.92111878
93 -2.08113203 0.86239660
94 -4.71996024 -2.08113203
95 -0.34945387 -4.71996024
96 -2.11660802 -0.34945387
97 -3.09059915 -2.11660802
98 -2.88364792 -3.09059915
99 -3.47302226 -2.88364792
100 -5.70129108 -3.47302226
101 -10.37772269 -5.70129108
102 -9.92240986 -10.37772269
103 -7.78010604 -9.92240986
104 -0.25544537 -7.78010604
105 -0.58725608 -0.25544537
106 0.77391571 -0.58725608
107 -4.56130434 0.77391571
108 -0.19903113 -4.56130434
109 -5.43187698 -0.19903113
110 -5.88364792 -5.43187698
111 -5.55544537 -5.88364792
112 -2.57186372 -5.55544537
113 -4.89543212 -2.57186372
114 -5.00483297 -4.89543212
115 -10.82711028 -5.00483297
116 -10.22002651 -10.82711028
117 -6.59884146 -10.22002651
118 -8.97881495 -6.59884146
119 -10.74932132 -8.97881495
120 -11.08118915 -10.74932132
121 NA -11.08118915
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.42711028 10.57753302
[2,] 7.36362141 9.42711028
[3,] 7.95067868 7.36362141
[4,] 8.28712354 7.95067868
[5,] 11.35183722 8.28712354
[6,] 10.93657740 11.35183722
[7,] 9.46716330 10.93657740
[8,] -1.36103925 9.46716330
[9,] -2.04571316 -1.36103925
[10,] -7.55511401 -2.04571316
[11,] 2.33310179 -7.55511401
[12,] -2.95176180 2.33310179
[13,] 0.63896075 -2.95176180
[14,] 1.31661717 0.63896075
[15,] -1.09632557 1.31661717
[16,] -7.06573967 -1.09632557
[17,] 0.22240986 -7.06573967
[18,] 1.98358165 0.22240986
[19,] 2.69059915 1.98358165
[20,] 2.29768292 2.69059915
[21,] 0.78944061 2.29768292
[22,] 4.71532608 0.78944061
[23,] 6.90940085 4.71532608
[24,] 6.18925094 6.90940085
[25,] 4.87398198 6.18925094
[26,] 8.85749736 4.87398198
[27,] -0.52002651 8.85749736
[28,] 0.02826882 -0.52002651
[29,] 1.22240986 0.02826882
[30,] 1.24243637 1.22240986
[31,] -0.05054613 1.24243637
[32,] 2.55653764 -0.05054613
[33,] -1.95170467 2.55653764
[34,] 6.28003976 -1.95170467
[35,] 2.47411453 6.28003976
[36,] -0.72832594 2.47411453
[37,] -1.13760340 -0.72832594
[38,] -1.02466066 -1.13760340
[39,] 0.04468717 -1.02466066
[40,] 9.42813628 0.04468717
[41,] 4.79870892 9.42813628
[42,] 4.04217128 4.79870892
[43,] 1.60817604 4.04217128
[44,] -3.52588547 1.60817604
[45,] 0.14230382 -3.52588547
[46,] 0.50347561 0.14230382
[47,] 0.65054613 0.50347561
[48,] -0.59889859 0.65054613
[49,] -1.34945387 -0.59889859
[50,] -1.45994698 -1.34945387
[51,] -5.43760340 -1.45994698
[52,] -5.27759014 -5.43760340
[53,] -3.51873543 -5.27759014
[54,] -4.32227731 -3.51873543
[55,] 0.94945387 -4.32227731
[56,] 1.60354189 0.94945387
[57,] 0.61300901 1.60354189
[58,] 1.58589873 0.61300901
[59,] -0.13760340 1.58589873
[60,] 1.53052877 -0.13760340
[61,] 2.03882821 1.53052877
[62,] 2.15177094 2.03882821
[63,] 3.30354189 2.15177094
[64,] 2.20470042 3.30354189
[65,] 4.39884146 2.20470042
[66,] 0.98358165 4.39884146
[67,] 6.99645811 0.98358165
[68,] 6.26226406 6.99645811
[69,] 8.18930807 6.26226406
[70,] 7.11519354 8.18930807
[71,] 3.95640510 7.11519354
[72,] 1.54211415 3.95640510
[73,] -1.65531283 1.54211415
[74,] -2.10708377 -1.65531283
[75,] 4.17411453 -2.10708377
[76,] 4.76941410 4.17411453
[77,] -0.73058590 4.76941410
[78,] 0.90115854 -0.73058590
[79,] 4.86703076 0.90115854
[80,] 1.77997349 4.86703076
[81,] 3.53058590 1.77997349
[82,] 0.28003976 3.53058590
[83,] -0.52588547 0.28003976
[84,] -2.16361226 -0.52588547
[85,] -4.31403500 -2.16361226
[86,] -6.33051962 -4.31403500
[87,] 0.60940085 -6.33051962
[88,] -4.10115854 0.60940085
[89,] -2.47173118 -4.10115854
[90,] -0.83998675 -2.47173118
[91,] -7.92111878 -0.83998675
[92,] 0.86239660 -7.92111878
[93,] -2.08113203 0.86239660
[94,] -4.71996024 -2.08113203
[95,] -0.34945387 -4.71996024
[96,] -2.11660802 -0.34945387
[97,] -3.09059915 -2.11660802
[98,] -2.88364792 -3.09059915
[99,] -3.47302226 -2.88364792
[100,] -5.70129108 -3.47302226
[101,] -10.37772269 -5.70129108
[102,] -9.92240986 -10.37772269
[103,] -7.78010604 -9.92240986
[104,] -0.25544537 -7.78010604
[105,] -0.58725608 -0.25544537
[106,] 0.77391571 -0.58725608
[107,] -4.56130434 0.77391571
[108,] -0.19903113 -4.56130434
[109,] -5.43187698 -0.19903113
[110,] -5.88364792 -5.43187698
[111,] -5.55544537 -5.88364792
[112,] -2.57186372 -5.55544537
[113,] -4.89543212 -2.57186372
[114,] -5.00483297 -4.89543212
[115,] -10.82711028 -5.00483297
[116,] -10.22002651 -10.82711028
[117,] -6.59884146 -10.22002651
[118,] -8.97881495 -6.59884146
[119,] -10.74932132 -8.97881495
[120,] -11.08118915 -10.74932132
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.42711028 10.57753302
2 7.36362141 9.42711028
3 7.95067868 7.36362141
4 8.28712354 7.95067868
5 11.35183722 8.28712354
6 10.93657740 11.35183722
7 9.46716330 10.93657740
8 -1.36103925 9.46716330
9 -2.04571316 -1.36103925
10 -7.55511401 -2.04571316
11 2.33310179 -7.55511401
12 -2.95176180 2.33310179
13 0.63896075 -2.95176180
14 1.31661717 0.63896075
15 -1.09632557 1.31661717
16 -7.06573967 -1.09632557
17 0.22240986 -7.06573967
18 1.98358165 0.22240986
19 2.69059915 1.98358165
20 2.29768292 2.69059915
21 0.78944061 2.29768292
22 4.71532608 0.78944061
23 6.90940085 4.71532608
24 6.18925094 6.90940085
25 4.87398198 6.18925094
26 8.85749736 4.87398198
27 -0.52002651 8.85749736
28 0.02826882 -0.52002651
29 1.22240986 0.02826882
30 1.24243637 1.22240986
31 -0.05054613 1.24243637
32 2.55653764 -0.05054613
33 -1.95170467 2.55653764
34 6.28003976 -1.95170467
35 2.47411453 6.28003976
36 -0.72832594 2.47411453
37 -1.13760340 -0.72832594
38 -1.02466066 -1.13760340
39 0.04468717 -1.02466066
40 9.42813628 0.04468717
41 4.79870892 9.42813628
42 4.04217128 4.79870892
43 1.60817604 4.04217128
44 -3.52588547 1.60817604
45 0.14230382 -3.52588547
46 0.50347561 0.14230382
47 0.65054613 0.50347561
48 -0.59889859 0.65054613
49 -1.34945387 -0.59889859
50 -1.45994698 -1.34945387
51 -5.43760340 -1.45994698
52 -5.27759014 -5.43760340
53 -3.51873543 -5.27759014
54 -4.32227731 -3.51873543
55 0.94945387 -4.32227731
56 1.60354189 0.94945387
57 0.61300901 1.60354189
58 1.58589873 0.61300901
59 -0.13760340 1.58589873
60 1.53052877 -0.13760340
61 2.03882821 1.53052877
62 2.15177094 2.03882821
63 3.30354189 2.15177094
64 2.20470042 3.30354189
65 4.39884146 2.20470042
66 0.98358165 4.39884146
67 6.99645811 0.98358165
68 6.26226406 6.99645811
69 8.18930807 6.26226406
70 7.11519354 8.18930807
71 3.95640510 7.11519354
72 1.54211415 3.95640510
73 -1.65531283 1.54211415
74 -2.10708377 -1.65531283
75 4.17411453 -2.10708377
76 4.76941410 4.17411453
77 -0.73058590 4.76941410
78 0.90115854 -0.73058590
79 4.86703076 0.90115854
80 1.77997349 4.86703076
81 3.53058590 1.77997349
82 0.28003976 3.53058590
83 -0.52588547 0.28003976
84 -2.16361226 -0.52588547
85 -4.31403500 -2.16361226
86 -6.33051962 -4.31403500
87 0.60940085 -6.33051962
88 -4.10115854 0.60940085
89 -2.47173118 -4.10115854
90 -0.83998675 -2.47173118
91 -7.92111878 -0.83998675
92 0.86239660 -7.92111878
93 -2.08113203 0.86239660
94 -4.71996024 -2.08113203
95 -0.34945387 -4.71996024
96 -2.11660802 -0.34945387
97 -3.09059915 -2.11660802
98 -2.88364792 -3.09059915
99 -3.47302226 -2.88364792
100 -5.70129108 -3.47302226
101 -10.37772269 -5.70129108
102 -9.92240986 -10.37772269
103 -7.78010604 -9.92240986
104 -0.25544537 -7.78010604
105 -0.58725608 -0.25544537
106 0.77391571 -0.58725608
107 -4.56130434 0.77391571
108 -0.19903113 -4.56130434
109 -5.43187698 -0.19903113
110 -5.88364792 -5.43187698
111 -5.55544537 -5.88364792
112 -2.57186372 -5.55544537
113 -4.89543212 -2.57186372
114 -5.00483297 -4.89543212
115 -10.82711028 -5.00483297
116 -10.22002651 -10.82711028
117 -6.59884146 -10.22002651
118 -8.97881495 -6.59884146
119 -10.74932132 -8.97881495
120 -11.08118915 -10.74932132
> 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/7jh8k1258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ykra1258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98txj1258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109u871258731936.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ti2e1258731936.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/12gn7f1258731936.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/13f1501258731937.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/14nsds1258731937.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/15f9nv1258731937.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/16lyaz1258731937.tab")
+ }
>
> system("convert tmp/1t1ze1258731936.ps tmp/1t1ze1258731936.png")
> system("convert tmp/22y1u1258731936.ps tmp/22y1u1258731936.png")
> system("convert tmp/3jdfk1258731936.ps tmp/3jdfk1258731936.png")
> system("convert tmp/4h1lt1258731936.ps tmp/4h1lt1258731936.png")
> system("convert tmp/57ufa1258731936.ps tmp/57ufa1258731936.png")
> system("convert tmp/6d1bc1258731936.ps tmp/6d1bc1258731936.png")
> system("convert tmp/7jh8k1258731936.ps tmp/7jh8k1258731936.png")
> system("convert tmp/8ykra1258731936.ps tmp/8ykra1258731936.png")
> system("convert tmp/98txj1258731936.ps tmp/98txj1258731936.png")
> system("convert tmp/109u871258731936.ps tmp/109u871258731936.png")
>
>
> proc.time()
user system elapsed
3.305 1.629 3.729