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)
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> x <- array(list(9769,9321,9939,9336,10195,9464,10010,10213,9563,9890,9305,9391,9928,8686,9843,9627,10074,9503,10119,10000,9313,9866,9172,9241,9659,8904,9755,9080,9435,8971,10063,9793,9454,9759,8820,9403,9676,8642,9402,9610,9294,9448,10319,9548,9801,9596,8923,9746,9829,9125,9782,9441,9162,9915,10444,10209,9985,9842,9429,10132,9849,9172,10313,9819,9955,10048,10082,10541,10208,10233,9439,9963,10158,9225,10474,9757,10490,10281,10444,10640,10695,10786,9832,9747,10411,9511,10402,9701,10540,10112,10915,11183,10384,10834,9886,10216,10943,9867,10203,10837,10573,10647,11502,10656,10866,10835,9945,10331,10718,9462,10579,10633,10346,10757,11207,11013,11015,10765,10042,10661),dim=c(1,120),dimnames=list(c(''),1:120))
> y <- array(NA,dim=c(1,120),dimnames=list(c(''),1:120))
> 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 = 'Include Monthly Dummies'
> par1 = '1'
> 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
M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9769 1 0 0 0 0 0 0 0 0 0 0 1
2 9321 0 1 0 0 0 0 0 0 0 0 0 2
3 9939 0 0 1 0 0 0 0 0 0 0 0 3
4 9336 0 0 0 1 0 0 0 0 0 0 0 4
5 10195 0 0 0 0 1 0 0 0 0 0 0 5
6 9464 0 0 0 0 0 1 0 0 0 0 0 6
7 10010 0 0 0 0 0 0 1 0 0 0 0 7
8 10213 0 0 0 0 0 0 0 1 0 0 0 8
9 9563 0 0 0 0 0 0 0 0 1 0 0 9
10 9890 0 0 0 0 0 0 0 0 0 1 0 10
11 9305 0 0 0 0 0 0 0 0 0 0 1 11
12 9391 0 0 0 0 0 0 0 0 0 0 0 12
13 9928 1 0 0 0 0 0 0 0 0 0 0 13
14 8686 0 1 0 0 0 0 0 0 0 0 0 14
15 9843 0 0 1 0 0 0 0 0 0 0 0 15
16 9627 0 0 0 1 0 0 0 0 0 0 0 16
17 10074 0 0 0 0 1 0 0 0 0 0 0 17
18 9503 0 0 0 0 0 1 0 0 0 0 0 18
19 10119 0 0 0 0 0 0 1 0 0 0 0 19
20 10000 0 0 0 0 0 0 0 1 0 0 0 20
21 9313 0 0 0 0 0 0 0 0 1 0 0 21
22 9866 0 0 0 0 0 0 0 0 0 1 0 22
23 9172 0 0 0 0 0 0 0 0 0 0 1 23
24 9241 0 0 0 0 0 0 0 0 0 0 0 24
25 9659 1 0 0 0 0 0 0 0 0 0 0 25
26 8904 0 1 0 0 0 0 0 0 0 0 0 26
27 9755 0 0 1 0 0 0 0 0 0 0 0 27
28 9080 0 0 0 1 0 0 0 0 0 0 0 28
29 9435 0 0 0 0 1 0 0 0 0 0 0 29
30 8971 0 0 0 0 0 1 0 0 0 0 0 30
31 10063 0 0 0 0 0 0 1 0 0 0 0 31
32 9793 0 0 0 0 0 0 0 1 0 0 0 32
33 9454 0 0 0 0 0 0 0 0 1 0 0 33
34 9759 0 0 0 0 0 0 0 0 0 1 0 34
35 8820 0 0 0 0 0 0 0 0 0 0 1 35
36 9403 0 0 0 0 0 0 0 0 0 0 0 36
37 9676 1 0 0 0 0 0 0 0 0 0 0 37
38 8642 0 1 0 0 0 0 0 0 0 0 0 38
39 9402 0 0 1 0 0 0 0 0 0 0 0 39
40 9610 0 0 0 1 0 0 0 0 0 0 0 40
41 9294 0 0 0 0 1 0 0 0 0 0 0 41
42 9448 0 0 0 0 0 1 0 0 0 0 0 42
43 10319 0 0 0 0 0 0 1 0 0 0 0 43
44 9548 0 0 0 0 0 0 0 1 0 0 0 44
45 9801 0 0 0 0 0 0 0 0 1 0 0 45
46 9596 0 0 0 0 0 0 0 0 0 1 0 46
47 8923 0 0 0 0 0 0 0 0 0 0 1 47
48 9746 0 0 0 0 0 0 0 0 0 0 0 48
49 9829 1 0 0 0 0 0 0 0 0 0 0 49
50 9125 0 1 0 0 0 0 0 0 0 0 0 50
51 9782 0 0 1 0 0 0 0 0 0 0 0 51
52 9441 0 0 0 1 0 0 0 0 0 0 0 52
53 9162 0 0 0 0 1 0 0 0 0 0 0 53
54 9915 0 0 0 0 0 1 0 0 0 0 0 54
55 10444 0 0 0 0 0 0 1 0 0 0 0 55
56 10209 0 0 0 0 0 0 0 1 0 0 0 56
57 9985 0 0 0 0 0 0 0 0 1 0 0 57
58 9842 0 0 0 0 0 0 0 0 0 1 0 58
59 9429 0 0 0 0 0 0 0 0 0 0 1 59
60 10132 0 0 0 0 0 0 0 0 0 0 0 60
61 9849 1 0 0 0 0 0 0 0 0 0 0 61
62 9172 0 1 0 0 0 0 0 0 0 0 0 62
63 10313 0 0 1 0 0 0 0 0 0 0 0 63
64 9819 0 0 0 1 0 0 0 0 0 0 0 64
65 9955 0 0 0 0 1 0 0 0 0 0 0 65
66 10048 0 0 0 0 0 1 0 0 0 0 0 66
67 10082 0 0 0 0 0 0 1 0 0 0 0 67
68 10541 0 0 0 0 0 0 0 1 0 0 0 68
69 10208 0 0 0 0 0 0 0 0 1 0 0 69
70 10233 0 0 0 0 0 0 0 0 0 1 0 70
71 9439 0 0 0 0 0 0 0 0 0 0 1 71
72 9963 0 0 0 0 0 0 0 0 0 0 0 72
73 10158 1 0 0 0 0 0 0 0 0 0 0 73
74 9225 0 1 0 0 0 0 0 0 0 0 0 74
75 10474 0 0 1 0 0 0 0 0 0 0 0 75
76 9757 0 0 0 1 0 0 0 0 0 0 0 76
77 10490 0 0 0 0 1 0 0 0 0 0 0 77
78 10281 0 0 0 0 0 1 0 0 0 0 0 78
79 10444 0 0 0 0 0 0 1 0 0 0 0 79
80 10640 0 0 0 0 0 0 0 1 0 0 0 80
81 10695 0 0 0 0 0 0 0 0 1 0 0 81
82 10786 0 0 0 0 0 0 0 0 0 1 0 82
83 9832 0 0 0 0 0 0 0 0 0 0 1 83
84 9747 0 0 0 0 0 0 0 0 0 0 0 84
85 10411 1 0 0 0 0 0 0 0 0 0 0 85
86 9511 0 1 0 0 0 0 0 0 0 0 0 86
87 10402 0 0 1 0 0 0 0 0 0 0 0 87
88 9701 0 0 0 1 0 0 0 0 0 0 0 88
89 10540 0 0 0 0 1 0 0 0 0 0 0 89
90 10112 0 0 0 0 0 1 0 0 0 0 0 90
91 10915 0 0 0 0 0 0 1 0 0 0 0 91
92 11183 0 0 0 0 0 0 0 1 0 0 0 92
93 10384 0 0 0 0 0 0 0 0 1 0 0 93
94 10834 0 0 0 0 0 0 0 0 0 1 0 94
95 9886 0 0 0 0 0 0 0 0 0 0 1 95
96 10216 0 0 0 0 0 0 0 0 0 0 0 96
97 10943 1 0 0 0 0 0 0 0 0 0 0 97
98 9867 0 1 0 0 0 0 0 0 0 0 0 98
99 10203 0 0 1 0 0 0 0 0 0 0 0 99
100 10837 0 0 0 1 0 0 0 0 0 0 0 100
101 10573 0 0 0 0 1 0 0 0 0 0 0 101
102 10647 0 0 0 0 0 1 0 0 0 0 0 102
103 11502 0 0 0 0 0 0 1 0 0 0 0 103
104 10656 0 0 0 0 0 0 0 1 0 0 0 104
105 10866 0 0 0 0 0 0 0 0 1 0 0 105
106 10835 0 0 0 0 0 0 0 0 0 1 0 106
107 9945 0 0 0 0 0 0 0 0 0 0 1 107
108 10331 0 0 0 0 0 0 0 0 0 0 0 108
109 10718 1 0 0 0 0 0 0 0 0 0 0 109
110 9462 0 1 0 0 0 0 0 0 0 0 0 110
111 10579 0 0 1 0 0 0 0 0 0 0 0 111
112 10633 0 0 0 1 0 0 0 0 0 0 0 112
113 10346 0 0 0 0 1 0 0 0 0 0 0 113
114 10757 0 0 0 0 0 1 0 0 0 0 0 114
115 11207 0 0 0 0 0 0 1 0 0 0 0 115
116 11013 0 0 0 0 0 0 0 1 0 0 0 116
117 11015 0 0 0 0 0 0 0 0 1 0 0 117
118 10765 0 0 0 0 0 0 0 0 0 1 0 118
119 10042 0 0 0 0 0 0 0 0 0 0 1 119
120 10661 0 0 0 0 0 0 0 0 0 0 0 120
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
9152.70 332.63 -580.93 285.70 -10.47 200.77
M6 M7 M8 M9 M10 M11
97.90 682.73 540.77 278.50 379.63 -392.73
t
11.07
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-778.00 -180.08 10.45 169.75 786.20
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9152.7028 106.5971 85.863 < 2e-16 ***
M1 332.6329 132.2155 2.516 0.01336 *
M2 -580.9338 132.1670 -4.395 2.61e-05 ***
M3 285.6996 132.1231 2.162 0.03282 *
M4 -10.4670 132.0838 -0.079 0.93699
M5 200.7664 132.0491 1.520 0.13136
M6 97.8997 132.0191 0.742 0.45998
M7 682.7331 131.9936 5.172 1.09e-06 ***
M8 540.7665 131.9728 4.098 8.15e-05 ***
M9 278.4999 131.9566 2.111 0.03714 *
M10 379.6332 131.9450 2.877 0.00484 **
M11 -392.7334 131.9381 -2.977 0.00360 **
t 11.0666 0.7814 14.163 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 295 on 107 degrees of freedom
Multiple R-squared: 0.7758, Adjusted R-squared: 0.7507
F-statistic: 30.86 on 12 and 107 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.71427936 0.57144129 0.28572064
[2,] 0.62743573 0.74512854 0.37256427
[3,] 0.50422804 0.99154393 0.49577196
[4,] 0.40772904 0.81545808 0.59227096
[5,] 0.33245657 0.66491314 0.66754343
[6,] 0.26519542 0.53039084 0.73480458
[7,] 0.19422346 0.38844692 0.80577654
[8,] 0.14633280 0.29266560 0.85366720
[9,] 0.09737182 0.19474364 0.90262818
[10,] 0.06376494 0.12752989 0.93623506
[11,] 0.04352503 0.08705006 0.95647497
[12,] 0.02876026 0.05752053 0.97123974
[13,] 0.03072564 0.06145127 0.96927436
[14,] 0.10761081 0.21522162 0.89238919
[15,] 0.12520529 0.25041058 0.87479471
[16,] 0.11801013 0.23602026 0.88198987
[17,] 0.08602372 0.17204743 0.91397628
[18,] 0.08368378 0.16736757 0.91631622
[19,] 0.06168733 0.12337466 0.93831267
[20,] 0.05209251 0.10418501 0.94790749
[21,] 0.05648991 0.11297981 0.94351009
[22,] 0.04448909 0.08897817 0.95551091
[23,] 0.03126390 0.06252779 0.96873610
[24,] 0.02666552 0.05333104 0.97333448
[25,] 0.07536645 0.15073291 0.92463355
[26,] 0.09542522 0.19085044 0.90457478
[27,] 0.12219936 0.24439872 0.87780064
[28,] 0.18319800 0.36639600 0.81680200
[29,] 0.21668630 0.43337261 0.78331370
[30,] 0.32512957 0.65025915 0.67487043
[31,] 0.29998667 0.59997334 0.70001333
[32,] 0.26106548 0.52213096 0.73893452
[33,] 0.37558312 0.75116625 0.62441688
[34,] 0.35325013 0.70650026 0.64674987
[35,] 0.38699758 0.77399517 0.61300242
[36,] 0.34889174 0.69778347 0.65110826
[37,] 0.31509835 0.63019669 0.68490165
[38,] 0.52000849 0.95998301 0.47999151
[39,] 0.66959224 0.66081551 0.33040776
[40,] 0.68218209 0.63563582 0.31781791
[41,] 0.68665358 0.62669284 0.31334642
[42,] 0.72370854 0.55258292 0.27629146
[43,] 0.72240033 0.55519935 0.27759967
[44,] 0.72076586 0.55846827 0.27923414
[45,] 0.84147554 0.31704893 0.15852446
[46,] 0.82836951 0.34326097 0.17163049
[47,] 0.79923722 0.40152557 0.20076278
[48,] 0.83971939 0.32056123 0.16028061
[49,] 0.82230954 0.35538091 0.17769046
[50,] 0.79701596 0.40596807 0.20298404
[51,] 0.80030648 0.39938704 0.19969352
[52,] 0.84969621 0.30060758 0.15030379
[53,] 0.84856711 0.30286578 0.15143289
[54,] 0.84819676 0.30360648 0.15180324
[55,] 0.83528016 0.32943967 0.16471984
[56,] 0.80402368 0.39195263 0.19597632
[57,] 0.76936636 0.46126728 0.23063364
[58,] 0.74846510 0.50306980 0.25153490
[59,] 0.70234161 0.59531677 0.29765839
[60,] 0.73008293 0.53983414 0.26991707
[61,] 0.72663567 0.54672867 0.27336433
[62,] 0.75830737 0.48338525 0.24169263
[63,] 0.74241838 0.51516324 0.25758162
[64,] 0.78430352 0.43139297 0.21569648
[65,] 0.74984331 0.50031338 0.25015669
[66,] 0.77640810 0.44718380 0.22359190
[67,] 0.79359388 0.41281224 0.20640612
[68,] 0.76523491 0.46953019 0.23476509
[69,] 0.77340665 0.45318671 0.22659335
[70,] 0.74281910 0.51436179 0.25718090
[71,] 0.68382339 0.63235321 0.31617661
[72,] 0.63544685 0.72910630 0.36455315
[73,] 0.89163823 0.21672353 0.10836177
[74,] 0.87624040 0.24751921 0.12375960
[75,] 0.91001570 0.17996860 0.08998430
[76,] 0.92048478 0.15903043 0.07951522
[77,] 0.95588465 0.08823070 0.04411535
[78,] 0.97927869 0.04144262 0.02072131
[79,] 0.96865702 0.06268597 0.03134298
[80,] 0.94834066 0.10331869 0.05165934
[81,] 0.93978929 0.12042141 0.06021071
[82,] 0.92830763 0.14338474 0.07169237
[83,] 0.95301617 0.09396765 0.04698383
[84,] 0.95546620 0.08906760 0.04453380
[85,] 0.95059261 0.09881478 0.04940739
[86,] 0.94732620 0.10534760 0.05267380
[87,] 0.89460795 0.21078410 0.10539205
[88,] 0.95744886 0.08510228 0.04255114
[89,] 0.93443045 0.13113910 0.06556955
> postscript(file="/var/wessaorg/rcomp/tmp/1b6cw1354653195.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/2fu4z1354653195.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/346e21354653195.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/401vb1354653195.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5v1nd1354653195.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 120
Frequency = 1
1 2 3 4 5 6
272.5977273 727.0977273 467.3977273 149.4977273 786.1977273 146.9977273
7 8 9 10 11 12
97.0977273 430.9977273 32.1977273 246.9977273 423.2977273 105.4977273
13 14 15 16 17 18
298.7982323 -40.7017677 238.5982323 307.6982323 532.3982323 53.1982323
19 20 21 22 23 24
73.2982323 85.1982323 -350.6017677 90.1982323 157.4982323 -177.3017677
25 26 27 28 29 30
-103.0012626 44.4987374 17.7987374 -372.1012626 -239.4012626 -611.6012626
31 32 33 34 35 36
-115.5012626 -254.6012626 -342.4012626 -149.6012626 -327.3012626 -148.1012626
37 38 39 40 41 42
-218.8007576 -350.3007576 -468.0007576 25.0992424 -513.2007576 -267.4007576
43 44 45 46 47 48
7.6992424 -632.4007576 -128.2007576 -445.4007576 -357.1007576 62.0992424
49 50 51 52 53 54
-198.6002525 -0.1002525 -220.8002525 -276.7002525 -778.0002525 66.7997475
55 56 57 58 59 60
-0.1002525 -104.2002525 -77.0002525 -332.2002525 16.0997475 315.2997475
61 62 63 64 65 66
-311.3997475 -85.8997475 177.4002525 -31.4997475 -117.7997475 67.0002525
67 68 69 70 71 72
-494.8997475 95.0002525 13.2002525 -73.9997475 -106.6997475 13.5002525
73 74 75 76 77 78
-135.1992424 -165.6992424 205.6007576 -226.2992424 284.4007576 167.2007576
79 80 81 82 83 84
-265.6992424 61.2007576 367.4007576 346.2007576 153.5007576 -335.2992424
85 86 87 88 89 90
-14.9987374 -12.4987374 0.8012626 -415.0987374 201.6012626 -134.5987374
91 92 93 94 95 96
72.5012626 471.4012626 -76.3987374 261.4012626 74.7012626 0.9012626
97 98 99 100 101 102
384.2017677 210.7017677 -330.9982323 588.1017677 101.8017677 267.6017677
103 104 105 106 107 108
526.7017677 -188.3982323 272.8017677 129.6017677 0.9017677 -16.8982323
109 110 111 112 113 114
26.4022727 -327.0977273 -87.7977273 251.3022727 -257.9977273 244.8022727
115 116 117 118 119 120
98.9022727 35.8022727 289.0022727 -73.1977273 -34.8977273 180.3022727
> postscript(file="/var/wessaorg/rcomp/tmp/6dagh1354653195.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 272.5977273 NA
1 727.0977273 272.5977273
2 467.3977273 727.0977273
3 149.4977273 467.3977273
4 786.1977273 149.4977273
5 146.9977273 786.1977273
6 97.0977273 146.9977273
7 430.9977273 97.0977273
8 32.1977273 430.9977273
9 246.9977273 32.1977273
10 423.2977273 246.9977273
11 105.4977273 423.2977273
12 298.7982323 105.4977273
13 -40.7017677 298.7982323
14 238.5982323 -40.7017677
15 307.6982323 238.5982323
16 532.3982323 307.6982323
17 53.1982323 532.3982323
18 73.2982323 53.1982323
19 85.1982323 73.2982323
20 -350.6017677 85.1982323
21 90.1982323 -350.6017677
22 157.4982323 90.1982323
23 -177.3017677 157.4982323
24 -103.0012626 -177.3017677
25 44.4987374 -103.0012626
26 17.7987374 44.4987374
27 -372.1012626 17.7987374
28 -239.4012626 -372.1012626
29 -611.6012626 -239.4012626
30 -115.5012626 -611.6012626
31 -254.6012626 -115.5012626
32 -342.4012626 -254.6012626
33 -149.6012626 -342.4012626
34 -327.3012626 -149.6012626
35 -148.1012626 -327.3012626
36 -218.8007576 -148.1012626
37 -350.3007576 -218.8007576
38 -468.0007576 -350.3007576
39 25.0992424 -468.0007576
40 -513.2007576 25.0992424
41 -267.4007576 -513.2007576
42 7.6992424 -267.4007576
43 -632.4007576 7.6992424
44 -128.2007576 -632.4007576
45 -445.4007576 -128.2007576
46 -357.1007576 -445.4007576
47 62.0992424 -357.1007576
48 -198.6002525 62.0992424
49 -0.1002525 -198.6002525
50 -220.8002525 -0.1002525
51 -276.7002525 -220.8002525
52 -778.0002525 -276.7002525
53 66.7997475 -778.0002525
54 -0.1002525 66.7997475
55 -104.2002525 -0.1002525
56 -77.0002525 -104.2002525
57 -332.2002525 -77.0002525
58 16.0997475 -332.2002525
59 315.2997475 16.0997475
60 -311.3997475 315.2997475
61 -85.8997475 -311.3997475
62 177.4002525 -85.8997475
63 -31.4997475 177.4002525
64 -117.7997475 -31.4997475
65 67.0002525 -117.7997475
66 -494.8997475 67.0002525
67 95.0002525 -494.8997475
68 13.2002525 95.0002525
69 -73.9997475 13.2002525
70 -106.6997475 -73.9997475
71 13.5002525 -106.6997475
72 -135.1992424 13.5002525
73 -165.6992424 -135.1992424
74 205.6007576 -165.6992424
75 -226.2992424 205.6007576
76 284.4007576 -226.2992424
77 167.2007576 284.4007576
78 -265.6992424 167.2007576
79 61.2007576 -265.6992424
80 367.4007576 61.2007576
81 346.2007576 367.4007576
82 153.5007576 346.2007576
83 -335.2992424 153.5007576
84 -14.9987374 -335.2992424
85 -12.4987374 -14.9987374
86 0.8012626 -12.4987374
87 -415.0987374 0.8012626
88 201.6012626 -415.0987374
89 -134.5987374 201.6012626
90 72.5012626 -134.5987374
91 471.4012626 72.5012626
92 -76.3987374 471.4012626
93 261.4012626 -76.3987374
94 74.7012626 261.4012626
95 0.9012626 74.7012626
96 384.2017677 0.9012626
97 210.7017677 384.2017677
98 -330.9982323 210.7017677
99 588.1017677 -330.9982323
100 101.8017677 588.1017677
101 267.6017677 101.8017677
102 526.7017677 267.6017677
103 -188.3982323 526.7017677
104 272.8017677 -188.3982323
105 129.6017677 272.8017677
106 0.9017677 129.6017677
107 -16.8982323 0.9017677
108 26.4022727 -16.8982323
109 -327.0977273 26.4022727
110 -87.7977273 -327.0977273
111 251.3022727 -87.7977273
112 -257.9977273 251.3022727
113 244.8022727 -257.9977273
114 98.9022727 244.8022727
115 35.8022727 98.9022727
116 289.0022727 35.8022727
117 -73.1977273 289.0022727
118 -34.8977273 -73.1977273
119 180.3022727 -34.8977273
120 NA 180.3022727
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 727.0977273 272.5977273
[2,] 467.3977273 727.0977273
[3,] 149.4977273 467.3977273
[4,] 786.1977273 149.4977273
[5,] 146.9977273 786.1977273
[6,] 97.0977273 146.9977273
[7,] 430.9977273 97.0977273
[8,] 32.1977273 430.9977273
[9,] 246.9977273 32.1977273
[10,] 423.2977273 246.9977273
[11,] 105.4977273 423.2977273
[12,] 298.7982323 105.4977273
[13,] -40.7017677 298.7982323
[14,] 238.5982323 -40.7017677
[15,] 307.6982323 238.5982323
[16,] 532.3982323 307.6982323
[17,] 53.1982323 532.3982323
[18,] 73.2982323 53.1982323
[19,] 85.1982323 73.2982323
[20,] -350.6017677 85.1982323
[21,] 90.1982323 -350.6017677
[22,] 157.4982323 90.1982323
[23,] -177.3017677 157.4982323
[24,] -103.0012626 -177.3017677
[25,] 44.4987374 -103.0012626
[26,] 17.7987374 44.4987374
[27,] -372.1012626 17.7987374
[28,] -239.4012626 -372.1012626
[29,] -611.6012626 -239.4012626
[30,] -115.5012626 -611.6012626
[31,] -254.6012626 -115.5012626
[32,] -342.4012626 -254.6012626
[33,] -149.6012626 -342.4012626
[34,] -327.3012626 -149.6012626
[35,] -148.1012626 -327.3012626
[36,] -218.8007576 -148.1012626
[37,] -350.3007576 -218.8007576
[38,] -468.0007576 -350.3007576
[39,] 25.0992424 -468.0007576
[40,] -513.2007576 25.0992424
[41,] -267.4007576 -513.2007576
[42,] 7.6992424 -267.4007576
[43,] -632.4007576 7.6992424
[44,] -128.2007576 -632.4007576
[45,] -445.4007576 -128.2007576
[46,] -357.1007576 -445.4007576
[47,] 62.0992424 -357.1007576
[48,] -198.6002525 62.0992424
[49,] -0.1002525 -198.6002525
[50,] -220.8002525 -0.1002525
[51,] -276.7002525 -220.8002525
[52,] -778.0002525 -276.7002525
[53,] 66.7997475 -778.0002525
[54,] -0.1002525 66.7997475
[55,] -104.2002525 -0.1002525
[56,] -77.0002525 -104.2002525
[57,] -332.2002525 -77.0002525
[58,] 16.0997475 -332.2002525
[59,] 315.2997475 16.0997475
[60,] -311.3997475 315.2997475
[61,] -85.8997475 -311.3997475
[62,] 177.4002525 -85.8997475
[63,] -31.4997475 177.4002525
[64,] -117.7997475 -31.4997475
[65,] 67.0002525 -117.7997475
[66,] -494.8997475 67.0002525
[67,] 95.0002525 -494.8997475
[68,] 13.2002525 95.0002525
[69,] -73.9997475 13.2002525
[70,] -106.6997475 -73.9997475
[71,] 13.5002525 -106.6997475
[72,] -135.1992424 13.5002525
[73,] -165.6992424 -135.1992424
[74,] 205.6007576 -165.6992424
[75,] -226.2992424 205.6007576
[76,] 284.4007576 -226.2992424
[77,] 167.2007576 284.4007576
[78,] -265.6992424 167.2007576
[79,] 61.2007576 -265.6992424
[80,] 367.4007576 61.2007576
[81,] 346.2007576 367.4007576
[82,] 153.5007576 346.2007576
[83,] -335.2992424 153.5007576
[84,] -14.9987374 -335.2992424
[85,] -12.4987374 -14.9987374
[86,] 0.8012626 -12.4987374
[87,] -415.0987374 0.8012626
[88,] 201.6012626 -415.0987374
[89,] -134.5987374 201.6012626
[90,] 72.5012626 -134.5987374
[91,] 471.4012626 72.5012626
[92,] -76.3987374 471.4012626
[93,] 261.4012626 -76.3987374
[94,] 74.7012626 261.4012626
[95,] 0.9012626 74.7012626
[96,] 384.2017677 0.9012626
[97,] 210.7017677 384.2017677
[98,] -330.9982323 210.7017677
[99,] 588.1017677 -330.9982323
[100,] 101.8017677 588.1017677
[101,] 267.6017677 101.8017677
[102,] 526.7017677 267.6017677
[103,] -188.3982323 526.7017677
[104,] 272.8017677 -188.3982323
[105,] 129.6017677 272.8017677
[106,] 0.9017677 129.6017677
[107,] -16.8982323 0.9017677
[108,] 26.4022727 -16.8982323
[109,] -327.0977273 26.4022727
[110,] -87.7977273 -327.0977273
[111,] 251.3022727 -87.7977273
[112,] -257.9977273 251.3022727
[113,] 244.8022727 -257.9977273
[114,] 98.9022727 244.8022727
[115,] 35.8022727 98.9022727
[116,] 289.0022727 35.8022727
[117,] -73.1977273 289.0022727
[118,] -34.8977273 -73.1977273
[119,] 180.3022727 -34.8977273
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 727.0977273 272.5977273
2 467.3977273 727.0977273
3 149.4977273 467.3977273
4 786.1977273 149.4977273
5 146.9977273 786.1977273
6 97.0977273 146.9977273
7 430.9977273 97.0977273
8 32.1977273 430.9977273
9 246.9977273 32.1977273
10 423.2977273 246.9977273
11 105.4977273 423.2977273
12 298.7982323 105.4977273
13 -40.7017677 298.7982323
14 238.5982323 -40.7017677
15 307.6982323 238.5982323
16 532.3982323 307.6982323
17 53.1982323 532.3982323
18 73.2982323 53.1982323
19 85.1982323 73.2982323
20 -350.6017677 85.1982323
21 90.1982323 -350.6017677
22 157.4982323 90.1982323
23 -177.3017677 157.4982323
24 -103.0012626 -177.3017677
25 44.4987374 -103.0012626
26 17.7987374 44.4987374
27 -372.1012626 17.7987374
28 -239.4012626 -372.1012626
29 -611.6012626 -239.4012626
30 -115.5012626 -611.6012626
31 -254.6012626 -115.5012626
32 -342.4012626 -254.6012626
33 -149.6012626 -342.4012626
34 -327.3012626 -149.6012626
35 -148.1012626 -327.3012626
36 -218.8007576 -148.1012626
37 -350.3007576 -218.8007576
38 -468.0007576 -350.3007576
39 25.0992424 -468.0007576
40 -513.2007576 25.0992424
41 -267.4007576 -513.2007576
42 7.6992424 -267.4007576
43 -632.4007576 7.6992424
44 -128.2007576 -632.4007576
45 -445.4007576 -128.2007576
46 -357.1007576 -445.4007576
47 62.0992424 -357.1007576
48 -198.6002525 62.0992424
49 -0.1002525 -198.6002525
50 -220.8002525 -0.1002525
51 -276.7002525 -220.8002525
52 -778.0002525 -276.7002525
53 66.7997475 -778.0002525
54 -0.1002525 66.7997475
55 -104.2002525 -0.1002525
56 -77.0002525 -104.2002525
57 -332.2002525 -77.0002525
58 16.0997475 -332.2002525
59 315.2997475 16.0997475
60 -311.3997475 315.2997475
61 -85.8997475 -311.3997475
62 177.4002525 -85.8997475
63 -31.4997475 177.4002525
64 -117.7997475 -31.4997475
65 67.0002525 -117.7997475
66 -494.8997475 67.0002525
67 95.0002525 -494.8997475
68 13.2002525 95.0002525
69 -73.9997475 13.2002525
70 -106.6997475 -73.9997475
71 13.5002525 -106.6997475
72 -135.1992424 13.5002525
73 -165.6992424 -135.1992424
74 205.6007576 -165.6992424
75 -226.2992424 205.6007576
76 284.4007576 -226.2992424
77 167.2007576 284.4007576
78 -265.6992424 167.2007576
79 61.2007576 -265.6992424
80 367.4007576 61.2007576
81 346.2007576 367.4007576
82 153.5007576 346.2007576
83 -335.2992424 153.5007576
84 -14.9987374 -335.2992424
85 -12.4987374 -14.9987374
86 0.8012626 -12.4987374
87 -415.0987374 0.8012626
88 201.6012626 -415.0987374
89 -134.5987374 201.6012626
90 72.5012626 -134.5987374
91 471.4012626 72.5012626
92 -76.3987374 471.4012626
93 261.4012626 -76.3987374
94 74.7012626 261.4012626
95 0.9012626 74.7012626
96 384.2017677 0.9012626
97 210.7017677 384.2017677
98 -330.9982323 210.7017677
99 588.1017677 -330.9982323
100 101.8017677 588.1017677
101 267.6017677 101.8017677
102 526.7017677 267.6017677
103 -188.3982323 526.7017677
104 272.8017677 -188.3982323
105 129.6017677 272.8017677
106 0.9017677 129.6017677
107 -16.8982323 0.9017677
108 26.4022727 -16.8982323
109 -327.0977273 26.4022727
110 -87.7977273 -327.0977273
111 251.3022727 -87.7977273
112 -257.9977273 251.3022727
113 244.8022727 -257.9977273
114 98.9022727 244.8022727
115 35.8022727 98.9022727
116 289.0022727 35.8022727
117 -73.1977273 289.0022727
118 -34.8977273 -73.1977273
119 180.3022727 -34.8977273
> 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/7sdw21354653195.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/8c96f1354653195.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/9jjf41354653195.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/10p6ko1354653195.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11yom01354653195.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12sq6q1354653195.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13kds91354653195.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/146nti1354653195.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/158ip81354653195.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/16g8151354653195.tab")
+ }
>
> try(system("convert tmp/1b6cw1354653195.ps tmp/1b6cw1354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fu4z1354653195.ps tmp/2fu4z1354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/346e21354653195.ps tmp/346e21354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/401vb1354653195.ps tmp/401vb1354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v1nd1354653195.ps tmp/5v1nd1354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dagh1354653195.ps tmp/6dagh1354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sdw21354653195.ps tmp/7sdw21354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/8c96f1354653195.ps tmp/8c96f1354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jjf41354653195.ps tmp/9jjf41354653195.png",intern=TRUE))
character(0)
> try(system("convert tmp/10p6ko1354653195.ps tmp/10p6ko1354653195.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.852 0.897 7.764