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.
Natural language support but running in an English locale
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(1.3954
+ ,1.0685
+ ,1.4790
+ ,1.1010
+ ,1.4619
+ ,1.0996
+ ,1.4670
+ ,1.0978
+ ,1.4799
+ ,1.0893
+ ,1.4508
+ ,1.1018
+ ,1.4678
+ ,1.0931
+ ,1.4824
+ ,1.0842
+ ,1.5189
+ ,1.0409
+ ,1.5348
+ ,1.0245
+ ,1.5666
+ ,0.9994
+ ,1.5446
+ ,1.0090
+ ,1.5803
+ ,0.9947
+ ,1.5718
+ ,1.0080
+ ,1.5832
+ ,0.9986
+ ,1.5801
+ ,1.0184
+ ,1.5605
+ ,1.0357
+ ,1.5416
+ ,1.0556
+ ,1.5479
+ ,1.0409
+ ,1.5580
+ ,1.0474
+ ,1.5790
+ ,1.0219
+ ,1.5554
+ ,1.0427
+ ,1.5761
+ ,1.0205
+ ,1.5360
+ ,1.0490
+ ,1.5621
+ ,1.0344
+ ,1.5773
+ ,1.0193
+ ,1.5710
+ ,1.0238
+ ,1.5925
+ ,1.0165
+ ,1.5844
+ ,1.0218
+ ,1.5696
+ ,1.0370
+ ,1.5540
+ ,1.0508
+ ,1.5012
+ ,1.0813
+ ,1.4676
+ ,1.0970
+ ,1.4770
+ ,1.0989
+ ,1.4660
+ ,1.1018
+ ,1.4241
+ ,1.1166
+ ,1.4214
+ ,1.1319
+ ,1.4469
+ ,1.1020
+ ,1.4618
+ ,1.0884
+ ,1.3834
+ ,1.1263
+ ,1.3412
+ ,1.1345
+ ,1.3437
+ ,1.1337
+ ,1.2630
+ ,1.1660
+ ,1.2759
+ ,1.1550
+ ,1.2743
+ ,1.1782
+ ,1.2797
+ ,1.1856
+ ,1.2573
+ ,1.2219
+ ,1.2705
+ ,1.2130
+ ,1.2680
+ ,1.2230
+ ,1.3371
+ ,1.1767
+ ,1.3885
+ ,1.1077
+ ,1.4060
+ ,1.0672
+ ,1.3855
+ ,1.0840
+ ,1.3431
+ ,1.1154
+ ,1.3257
+ ,1.1184
+ ,1.2978
+ ,1.1570
+ ,1.2793
+ ,1.1625
+ ,1.2945
+ ,1.1627
+ ,1.2890
+ ,1.1578
+ ,1.2848
+ ,1.1533
+ ,1.2694
+ ,1.1684
+ ,1.2636
+ ,1.1597
+ ,1.2900
+ ,1.1888
+ ,1.3559
+ ,1.1296
+ ,1.3305
+ ,1.1424
+ ,1.3482
+ ,1.1317
+ ,1.3146
+ ,1.1581
+ ,1.3027
+ ,1.1672
+ ,1.3247
+ ,1.1391
+ ,1.3267
+ ,1.1357
+ ,1.3621
+ ,1.1065
+ ,1.3479
+ ,1.1232
+ ,1.4011
+ ,1.0845
+ ,1.4135
+ ,1.0676
+ ,1.3964
+ ,1.0863
+ ,1.4010
+ ,1.0792
+ ,1.3955
+ ,1.0799
+ ,1.4077
+ ,1.0817
+ ,1.3975
+ ,1.0869
+ ,1.3949
+ ,1.0843
+ ,1.4138
+ ,1.0747
+ ,1.4210
+ ,1.0711
+ ,1.4253
+ ,1.0688
+ ,1.4169
+ ,1.0828
+ ,1.4174
+ ,1.0746
+ ,1.4346
+ ,1.0568
+ ,1.4296
+ ,1.0600
+ ,1.4311
+ ,1.0593
+ ,1.4594
+ ,1.0370
+ ,1.4722
+ ,1.0288
+ ,1.4669
+ ,1.0295
+ ,1.4571
+ ,1.0352
+ ,1.4709
+ ,1.0324
+ ,1.4893
+ ,1.0186
+ ,1.4997
+ ,1.0094
+ ,1.4713
+ ,1.0258
+ ,1.4846
+ ,1.0170
+ ,1.4914
+ ,1.0117
+ ,1.4859
+ ,1.0175
+ ,1.4957
+ ,1.0064
+ ,1.4843
+ ,1.0168
+ ,1.4619
+ ,1.0340
+ ,1.4340
+ ,1.0423
+ ,1.4426
+ ,1.0356
+ ,1.4318
+ ,1.0348)
+ ,dim=c(2
+ ,105)
+ ,dimnames=list(c('eu/us'
+ ,'us/ch')
+ ,1:105))
> y <- array(NA,dim=c(2,105),dimnames=list(c('eu/us','us/ch'),1:105))
> 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
eu/us us/ch t
1 1.3954 1.0685 1
2 1.4790 1.1010 2
3 1.4619 1.0996 3
4 1.4670 1.0978 4
5 1.4799 1.0893 5
6 1.4508 1.1018 6
7 1.4678 1.0931 7
8 1.4824 1.0842 8
9 1.5189 1.0409 9
10 1.5348 1.0245 10
11 1.5666 0.9994 11
12 1.5446 1.0090 12
13 1.5803 0.9947 13
14 1.5718 1.0080 14
15 1.5832 0.9986 15
16 1.5801 1.0184 16
17 1.5605 1.0357 17
18 1.5416 1.0556 18
19 1.5479 1.0409 19
20 1.5580 1.0474 20
21 1.5790 1.0219 21
22 1.5554 1.0427 22
23 1.5761 1.0205 23
24 1.5360 1.0490 24
25 1.5621 1.0344 25
26 1.5773 1.0193 26
27 1.5710 1.0238 27
28 1.5925 1.0165 28
29 1.5844 1.0218 29
30 1.5696 1.0370 30
31 1.5540 1.0508 31
32 1.5012 1.0813 32
33 1.4676 1.0970 33
34 1.4770 1.0989 34
35 1.4660 1.1018 35
36 1.4241 1.1166 36
37 1.4214 1.1319 37
38 1.4469 1.1020 38
39 1.4618 1.0884 39
40 1.3834 1.1263 40
41 1.3412 1.1345 41
42 1.3437 1.1337 42
43 1.2630 1.1660 43
44 1.2759 1.1550 44
45 1.2743 1.1782 45
46 1.2797 1.1856 46
47 1.2573 1.2219 47
48 1.2705 1.2130 48
49 1.2680 1.2230 49
50 1.3371 1.1767 50
51 1.3885 1.1077 51
52 1.4060 1.0672 52
53 1.3855 1.0840 53
54 1.3431 1.1154 54
55 1.3257 1.1184 55
56 1.2978 1.1570 56
57 1.2793 1.1625 57
58 1.2945 1.1627 58
59 1.2890 1.1578 59
60 1.2848 1.1533 60
61 1.2694 1.1684 61
62 1.2636 1.1597 62
63 1.2900 1.1888 63
64 1.3559 1.1296 64
65 1.3305 1.1424 65
66 1.3482 1.1317 66
67 1.3146 1.1581 67
68 1.3027 1.1672 68
69 1.3247 1.1391 69
70 1.3267 1.1357 70
71 1.3621 1.1065 71
72 1.3479 1.1232 72
73 1.4011 1.0845 73
74 1.4135 1.0676 74
75 1.3964 1.0863 75
76 1.4010 1.0792 76
77 1.3955 1.0799 77
78 1.4077 1.0817 78
79 1.3975 1.0869 79
80 1.3949 1.0843 80
81 1.4138 1.0747 81
82 1.4210 1.0711 82
83 1.4253 1.0688 83
84 1.4169 1.0828 84
85 1.4174 1.0746 85
86 1.4346 1.0568 86
87 1.4296 1.0600 87
88 1.4311 1.0593 88
89 1.4594 1.0370 89
90 1.4722 1.0288 90
91 1.4669 1.0295 91
92 1.4571 1.0352 92
93 1.4709 1.0324 93
94 1.4893 1.0186 94
95 1.4997 1.0094 95
96 1.4713 1.0258 96
97 1.4846 1.0170 97
98 1.4914 1.0117 98
99 1.4859 1.0175 99
100 1.4957 1.0064 100
101 1.4843 1.0168 101
102 1.4619 1.0340 102
103 1.4340 1.0423 103
104 1.4426 1.0356 104
105 1.4318 1.0348 105
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `us/ch` t
3.146894 -1.527587 -0.001141
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.118127 -0.013494 0.001233 0.016407 0.047671
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.147e+00 5.106e-02 61.63 <2e-16 ***
`us/ch` -1.528e+00 4.677e-02 -32.66 <2e-16 ***
t -1.141e-03 8.722e-05 -13.08 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.02708 on 102 degrees of freedom
Multiple R-squared: 0.9226, Adjusted R-squared: 0.9211
F-statistic: 608.1 on 2 and 102 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,] 0.56474926 8.705015e-01 4.352507e-01
[2,] 0.40163990 8.032798e-01 5.983601e-01
[3,] 0.37255780 7.451156e-01 6.274422e-01
[4,] 0.67635030 6.472994e-01 3.236497e-01
[5,] 0.64270883 7.145823e-01 3.572912e-01
[6,] 0.64252459 7.149508e-01 3.574754e-01
[7,] 0.61479276 7.704145e-01 3.852072e-01
[8,] 0.58257914 8.348417e-01 4.174209e-01
[9,] 0.51964768 9.607046e-01 4.803523e-01
[10,] 0.47082054 9.416411e-01 5.291795e-01
[11,] 0.39591886 7.918377e-01 6.040811e-01
[12,] 0.38064762 7.612952e-01 6.193524e-01
[13,] 0.39028234 7.805647e-01 6.097177e-01
[14,] 0.38787498 7.757500e-01 6.121250e-01
[15,] 0.32649629 6.529926e-01 6.735037e-01
[16,] 0.26815867 5.363173e-01 7.318413e-01
[17,] 0.24309893 4.861979e-01 7.569011e-01
[18,] 0.20992599 4.198520e-01 7.900740e-01
[19,] 0.24955072 4.991014e-01 7.504493e-01
[20,] 0.21669448 4.333890e-01 7.833055e-01
[21,] 0.18367934 3.673587e-01 8.163207e-01
[22,] 0.16064994 3.212999e-01 8.393501e-01
[23,] 0.12298618 2.459724e-01 8.770138e-01
[24,] 0.09583845 1.916769e-01 9.041615e-01
[25,] 0.08022029 1.604406e-01 9.197797e-01
[26,] 0.07806511 1.561302e-01 9.219349e-01
[27,] 0.12890988 2.578198e-01 8.710901e-01
[28,] 0.23210638 4.642128e-01 7.678936e-01
[29,] 0.30350175 6.070035e-01 6.964983e-01
[30,] 0.41095476 8.219095e-01 5.890452e-01
[31,] 0.58136949 8.372610e-01 4.186305e-01
[32,] 0.73290450 5.341910e-01 2.670955e-01
[33,] 0.86508268 2.698346e-01 1.349173e-01
[34,] 0.96717601 6.564798e-02 3.282399e-02
[35,] 0.99412048 1.175904e-02 5.879522e-03
[36,] 0.99931304 1.373923e-03 6.869617e-04
[37,] 0.99982732 3.453524e-04 1.726762e-04
[38,] 0.99999163 1.673656e-05 8.368281e-06
[39,] 0.99999939 1.212722e-06 6.063608e-07
[40,] 0.99999920 1.602165e-06 8.010825e-07
[41,] 0.99999843 3.142362e-06 1.571181e-06
[42,] 0.99999871 2.580852e-06 1.290426e-06
[43,] 0.99999917 1.668109e-06 8.340547e-07
[44,] 0.99999989 2.170901e-07 1.085450e-07
[45,] 1.00000000 6.393079e-10 3.196540e-10
[46,] 1.00000000 1.439107e-10 7.195535e-11
[47,] 1.00000000 3.930224e-11 1.965112e-11
[48,] 1.00000000 2.709448e-11 1.354724e-11
[49,] 1.00000000 2.662643e-11 1.331321e-11
[50,] 1.00000000 3.669902e-12 1.834951e-12
[51,] 1.00000000 8.450036e-12 4.225018e-12
[52,] 1.00000000 7.810466e-12 3.905233e-12
[53,] 1.00000000 2.203419e-11 1.101710e-11
[54,] 1.00000000 2.856002e-11 1.428001e-11
[55,] 1.00000000 6.637453e-12 3.318726e-12
[56,] 1.00000000 2.576669e-12 1.288334e-12
[57,] 1.00000000 4.025768e-16 2.012884e-16
[58,] 1.00000000 5.556639e-17 2.778320e-17
[59,] 1.00000000 1.380031e-16 6.900155e-17
[60,] 1.00000000 6.032099e-16 3.016050e-16
[61,] 1.00000000 2.018248e-15 1.009124e-15
[62,] 1.00000000 3.409018e-15 1.704509e-15
[63,] 1.00000000 1.587722e-15 7.938608e-16
[64,] 1.00000000 7.377410e-15 3.688705e-15
[65,] 1.00000000 3.231178e-14 1.615589e-14
[66,] 1.00000000 5.928054e-14 2.964027e-14
[67,] 1.00000000 2.663953e-13 1.331976e-13
[68,] 1.00000000 1.099258e-12 5.496289e-13
[69,] 1.00000000 5.068464e-13 2.534232e-13
[70,] 1.00000000 1.758271e-12 8.791354e-13
[71,] 1.00000000 2.144778e-12 1.072389e-12
[72,] 1.00000000 4.190425e-13 2.095212e-13
[73,] 1.00000000 2.024130e-12 1.012065e-12
[74,] 1.00000000 8.832045e-12 4.416023e-12
[75,] 1.00000000 1.376355e-11 6.881774e-12
[76,] 1.00000000 4.789849e-11 2.394924e-11
[77,] 1.00000000 2.130004e-10 1.065002e-10
[78,] 1.00000000 1.016671e-09 5.083355e-10
[79,] 1.00000000 2.398079e-10 1.199040e-10
[80,] 1.00000000 4.014503e-10 2.007252e-10
[81,] 1.00000000 2.288963e-09 1.144481e-09
[82,] 0.99999999 1.196600e-08 5.982999e-09
[83,] 0.99999998 4.156806e-08 2.078403e-08
[84,] 0.99999989 2.101150e-07 1.050575e-07
[85,] 0.99999950 9.932232e-07 4.966116e-07
[86,] 0.99999855 2.899269e-06 1.449634e-06
[87,] 0.99999708 5.844512e-06 2.922256e-06
[88,] 0.99998520 2.959205e-05 1.479603e-05
[89,] 0.99992833 1.433388e-04 7.166942e-05
[90,] 0.99970091 5.981723e-04 2.990861e-04
[91,] 0.99910422 1.791568e-03 8.957839e-04
[92,] 0.99789769 4.204628e-03 2.102314e-03
[93,] 0.99571088 8.578233e-03 4.289116e-03
[94,] 0.98731472 2.537057e-02 1.268528e-02
> postscript(file="/var/www/html/freestat/rcomp/tmp/1hxht1290507380.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2hxht1290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3s6ge1290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4s6ge1290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5s6ge1290507380.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 = 105
Frequency = 1
1 2 3 4 5
-0.1181268340 0.0162609431 -0.0018364652 0.0016550919 0.0027118189
6 7 8 9 10
-0.0061521358 -0.0013009261 0.0008447662 -0.0276585200 -0.0356697271
11 12 13 14 15
-0.0410709375 -0.0472648933 -0.0322681685 -0.0193100539 -0.0211281548
16 17 18 19 20
0.0071592726 0.0151277335 0.0277679196 0.0127536097 0.0339241355
21 22 23 24 25
0.0171118905 0.0264269045 0.0143556952 0.0189331259 0.0238715747
26 27 28 29 30
0.0171462302 0.0188615829 0.0303514137 0.0314888356 0.0410493647
31 32 33 34 35
0.0476712726 0.0426038765 0.0341281989 0.0475718264 0.0421430405
36 37 38 39 40
0.0239925350 0.0458058227 0.0267721967 0.0220382321 0.0026749768
41 42 43 44 45
-0.0258576002 -0.0234384565 -0.0536561968 -0.0564184363 -0.0214372145
46 47 48 49 50
-0.0035918607 0.0306007454 0.0313464377 0.0452635166 0.0447774705
51 52 53 54 55
-0.0080847910 -0.0513108349 -0.0450061672 -0.0382987354 -0.0499747627
56 57 58 59 60
-0.0177687074 -0.0267257682 -0.0100790379 -0.0219229992 -0.0318559259
61 62 63 64 65
-0.0230481554 -0.0409969458 0.0309970369 0.0076051239 0.0028994452
66 67 68 69 70
0.0053954817 0.0132649806 0.0164072315 -0.0033767386 -0.0054293201
71 72 73 74 75
-0.0134936355 -0.0010417265 -0.0058181144 -0.0180931148 -0.0054860326
76 77 78 79 80
-0.0105906844 -0.0138801608 0.0022107080 0.0010953712 -0.0043351409
81 82 83 84 85
0.0010412408 0.0038831421 0.0058109059 0.0199383311 0.0090533340
86 87 88 89 90
0.0002035058 0.0012329958 0.0028048982 -0.0018190698 -0.0004040668
91 92 93 94 95
-0.0034935432 -0.0034450867 0.0072188838 0.0056794019 0.0031668183
96 97 98 99 100
0.0009604513 0.0019589023 0.0018039064 0.0063051215 0.0002901234
101 102 103 104 105
0.0059182369 0.0109339392 -0.0031458792 -0.0036394964 -0.0145203526
> postscript(file="/var/www/html/freestat/rcomp/tmp/62xfz1290507380.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 = 105
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.1181268340 NA
1 0.0162609431 -0.1181268340
2 -0.0018364652 0.0162609431
3 0.0016550919 -0.0018364652
4 0.0027118189 0.0016550919
5 -0.0061521358 0.0027118189
6 -0.0013009261 -0.0061521358
7 0.0008447662 -0.0013009261
8 -0.0276585200 0.0008447662
9 -0.0356697271 -0.0276585200
10 -0.0410709375 -0.0356697271
11 -0.0472648933 -0.0410709375
12 -0.0322681685 -0.0472648933
13 -0.0193100539 -0.0322681685
14 -0.0211281548 -0.0193100539
15 0.0071592726 -0.0211281548
16 0.0151277335 0.0071592726
17 0.0277679196 0.0151277335
18 0.0127536097 0.0277679196
19 0.0339241355 0.0127536097
20 0.0171118905 0.0339241355
21 0.0264269045 0.0171118905
22 0.0143556952 0.0264269045
23 0.0189331259 0.0143556952
24 0.0238715747 0.0189331259
25 0.0171462302 0.0238715747
26 0.0188615829 0.0171462302
27 0.0303514137 0.0188615829
28 0.0314888356 0.0303514137
29 0.0410493647 0.0314888356
30 0.0476712726 0.0410493647
31 0.0426038765 0.0476712726
32 0.0341281989 0.0426038765
33 0.0475718264 0.0341281989
34 0.0421430405 0.0475718264
35 0.0239925350 0.0421430405
36 0.0458058227 0.0239925350
37 0.0267721967 0.0458058227
38 0.0220382321 0.0267721967
39 0.0026749768 0.0220382321
40 -0.0258576002 0.0026749768
41 -0.0234384565 -0.0258576002
42 -0.0536561968 -0.0234384565
43 -0.0564184363 -0.0536561968
44 -0.0214372145 -0.0564184363
45 -0.0035918607 -0.0214372145
46 0.0306007454 -0.0035918607
47 0.0313464377 0.0306007454
48 0.0452635166 0.0313464377
49 0.0447774705 0.0452635166
50 -0.0080847910 0.0447774705
51 -0.0513108349 -0.0080847910
52 -0.0450061672 -0.0513108349
53 -0.0382987354 -0.0450061672
54 -0.0499747627 -0.0382987354
55 -0.0177687074 -0.0499747627
56 -0.0267257682 -0.0177687074
57 -0.0100790379 -0.0267257682
58 -0.0219229992 -0.0100790379
59 -0.0318559259 -0.0219229992
60 -0.0230481554 -0.0318559259
61 -0.0409969458 -0.0230481554
62 0.0309970369 -0.0409969458
63 0.0076051239 0.0309970369
64 0.0028994452 0.0076051239
65 0.0053954817 0.0028994452
66 0.0132649806 0.0053954817
67 0.0164072315 0.0132649806
68 -0.0033767386 0.0164072315
69 -0.0054293201 -0.0033767386
70 -0.0134936355 -0.0054293201
71 -0.0010417265 -0.0134936355
72 -0.0058181144 -0.0010417265
73 -0.0180931148 -0.0058181144
74 -0.0054860326 -0.0180931148
75 -0.0105906844 -0.0054860326
76 -0.0138801608 -0.0105906844
77 0.0022107080 -0.0138801608
78 0.0010953712 0.0022107080
79 -0.0043351409 0.0010953712
80 0.0010412408 -0.0043351409
81 0.0038831421 0.0010412408
82 0.0058109059 0.0038831421
83 0.0199383311 0.0058109059
84 0.0090533340 0.0199383311
85 0.0002035058 0.0090533340
86 0.0012329958 0.0002035058
87 0.0028048982 0.0012329958
88 -0.0018190698 0.0028048982
89 -0.0004040668 -0.0018190698
90 -0.0034935432 -0.0004040668
91 -0.0034450867 -0.0034935432
92 0.0072188838 -0.0034450867
93 0.0056794019 0.0072188838
94 0.0031668183 0.0056794019
95 0.0009604513 0.0031668183
96 0.0019589023 0.0009604513
97 0.0018039064 0.0019589023
98 0.0063051215 0.0018039064
99 0.0002901234 0.0063051215
100 0.0059182369 0.0002901234
101 0.0109339392 0.0059182369
102 -0.0031458792 0.0109339392
103 -0.0036394964 -0.0031458792
104 -0.0145203526 -0.0036394964
105 NA -0.0145203526
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0162609431 -0.1181268340
[2,] -0.0018364652 0.0162609431
[3,] 0.0016550919 -0.0018364652
[4,] 0.0027118189 0.0016550919
[5,] -0.0061521358 0.0027118189
[6,] -0.0013009261 -0.0061521358
[7,] 0.0008447662 -0.0013009261
[8,] -0.0276585200 0.0008447662
[9,] -0.0356697271 -0.0276585200
[10,] -0.0410709375 -0.0356697271
[11,] -0.0472648933 -0.0410709375
[12,] -0.0322681685 -0.0472648933
[13,] -0.0193100539 -0.0322681685
[14,] -0.0211281548 -0.0193100539
[15,] 0.0071592726 -0.0211281548
[16,] 0.0151277335 0.0071592726
[17,] 0.0277679196 0.0151277335
[18,] 0.0127536097 0.0277679196
[19,] 0.0339241355 0.0127536097
[20,] 0.0171118905 0.0339241355
[21,] 0.0264269045 0.0171118905
[22,] 0.0143556952 0.0264269045
[23,] 0.0189331259 0.0143556952
[24,] 0.0238715747 0.0189331259
[25,] 0.0171462302 0.0238715747
[26,] 0.0188615829 0.0171462302
[27,] 0.0303514137 0.0188615829
[28,] 0.0314888356 0.0303514137
[29,] 0.0410493647 0.0314888356
[30,] 0.0476712726 0.0410493647
[31,] 0.0426038765 0.0476712726
[32,] 0.0341281989 0.0426038765
[33,] 0.0475718264 0.0341281989
[34,] 0.0421430405 0.0475718264
[35,] 0.0239925350 0.0421430405
[36,] 0.0458058227 0.0239925350
[37,] 0.0267721967 0.0458058227
[38,] 0.0220382321 0.0267721967
[39,] 0.0026749768 0.0220382321
[40,] -0.0258576002 0.0026749768
[41,] -0.0234384565 -0.0258576002
[42,] -0.0536561968 -0.0234384565
[43,] -0.0564184363 -0.0536561968
[44,] -0.0214372145 -0.0564184363
[45,] -0.0035918607 -0.0214372145
[46,] 0.0306007454 -0.0035918607
[47,] 0.0313464377 0.0306007454
[48,] 0.0452635166 0.0313464377
[49,] 0.0447774705 0.0452635166
[50,] -0.0080847910 0.0447774705
[51,] -0.0513108349 -0.0080847910
[52,] -0.0450061672 -0.0513108349
[53,] -0.0382987354 -0.0450061672
[54,] -0.0499747627 -0.0382987354
[55,] -0.0177687074 -0.0499747627
[56,] -0.0267257682 -0.0177687074
[57,] -0.0100790379 -0.0267257682
[58,] -0.0219229992 -0.0100790379
[59,] -0.0318559259 -0.0219229992
[60,] -0.0230481554 -0.0318559259
[61,] -0.0409969458 -0.0230481554
[62,] 0.0309970369 -0.0409969458
[63,] 0.0076051239 0.0309970369
[64,] 0.0028994452 0.0076051239
[65,] 0.0053954817 0.0028994452
[66,] 0.0132649806 0.0053954817
[67,] 0.0164072315 0.0132649806
[68,] -0.0033767386 0.0164072315
[69,] -0.0054293201 -0.0033767386
[70,] -0.0134936355 -0.0054293201
[71,] -0.0010417265 -0.0134936355
[72,] -0.0058181144 -0.0010417265
[73,] -0.0180931148 -0.0058181144
[74,] -0.0054860326 -0.0180931148
[75,] -0.0105906844 -0.0054860326
[76,] -0.0138801608 -0.0105906844
[77,] 0.0022107080 -0.0138801608
[78,] 0.0010953712 0.0022107080
[79,] -0.0043351409 0.0010953712
[80,] 0.0010412408 -0.0043351409
[81,] 0.0038831421 0.0010412408
[82,] 0.0058109059 0.0038831421
[83,] 0.0199383311 0.0058109059
[84,] 0.0090533340 0.0199383311
[85,] 0.0002035058 0.0090533340
[86,] 0.0012329958 0.0002035058
[87,] 0.0028048982 0.0012329958
[88,] -0.0018190698 0.0028048982
[89,] -0.0004040668 -0.0018190698
[90,] -0.0034935432 -0.0004040668
[91,] -0.0034450867 -0.0034935432
[92,] 0.0072188838 -0.0034450867
[93,] 0.0056794019 0.0072188838
[94,] 0.0031668183 0.0056794019
[95,] 0.0009604513 0.0031668183
[96,] 0.0019589023 0.0009604513
[97,] 0.0018039064 0.0019589023
[98,] 0.0063051215 0.0018039064
[99,] 0.0002901234 0.0063051215
[100,] 0.0059182369 0.0002901234
[101,] 0.0109339392 0.0059182369
[102,] -0.0031458792 0.0109339392
[103,] -0.0036394964 -0.0031458792
[104,] -0.0145203526 -0.0036394964
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0162609431 -0.1181268340
2 -0.0018364652 0.0162609431
3 0.0016550919 -0.0018364652
4 0.0027118189 0.0016550919
5 -0.0061521358 0.0027118189
6 -0.0013009261 -0.0061521358
7 0.0008447662 -0.0013009261
8 -0.0276585200 0.0008447662
9 -0.0356697271 -0.0276585200
10 -0.0410709375 -0.0356697271
11 -0.0472648933 -0.0410709375
12 -0.0322681685 -0.0472648933
13 -0.0193100539 -0.0322681685
14 -0.0211281548 -0.0193100539
15 0.0071592726 -0.0211281548
16 0.0151277335 0.0071592726
17 0.0277679196 0.0151277335
18 0.0127536097 0.0277679196
19 0.0339241355 0.0127536097
20 0.0171118905 0.0339241355
21 0.0264269045 0.0171118905
22 0.0143556952 0.0264269045
23 0.0189331259 0.0143556952
24 0.0238715747 0.0189331259
25 0.0171462302 0.0238715747
26 0.0188615829 0.0171462302
27 0.0303514137 0.0188615829
28 0.0314888356 0.0303514137
29 0.0410493647 0.0314888356
30 0.0476712726 0.0410493647
31 0.0426038765 0.0476712726
32 0.0341281989 0.0426038765
33 0.0475718264 0.0341281989
34 0.0421430405 0.0475718264
35 0.0239925350 0.0421430405
36 0.0458058227 0.0239925350
37 0.0267721967 0.0458058227
38 0.0220382321 0.0267721967
39 0.0026749768 0.0220382321
40 -0.0258576002 0.0026749768
41 -0.0234384565 -0.0258576002
42 -0.0536561968 -0.0234384565
43 -0.0564184363 -0.0536561968
44 -0.0214372145 -0.0564184363
45 -0.0035918607 -0.0214372145
46 0.0306007454 -0.0035918607
47 0.0313464377 0.0306007454
48 0.0452635166 0.0313464377
49 0.0447774705 0.0452635166
50 -0.0080847910 0.0447774705
51 -0.0513108349 -0.0080847910
52 -0.0450061672 -0.0513108349
53 -0.0382987354 -0.0450061672
54 -0.0499747627 -0.0382987354
55 -0.0177687074 -0.0499747627
56 -0.0267257682 -0.0177687074
57 -0.0100790379 -0.0267257682
58 -0.0219229992 -0.0100790379
59 -0.0318559259 -0.0219229992
60 -0.0230481554 -0.0318559259
61 -0.0409969458 -0.0230481554
62 0.0309970369 -0.0409969458
63 0.0076051239 0.0309970369
64 0.0028994452 0.0076051239
65 0.0053954817 0.0028994452
66 0.0132649806 0.0053954817
67 0.0164072315 0.0132649806
68 -0.0033767386 0.0164072315
69 -0.0054293201 -0.0033767386
70 -0.0134936355 -0.0054293201
71 -0.0010417265 -0.0134936355
72 -0.0058181144 -0.0010417265
73 -0.0180931148 -0.0058181144
74 -0.0054860326 -0.0180931148
75 -0.0105906844 -0.0054860326
76 -0.0138801608 -0.0105906844
77 0.0022107080 -0.0138801608
78 0.0010953712 0.0022107080
79 -0.0043351409 0.0010953712
80 0.0010412408 -0.0043351409
81 0.0038831421 0.0010412408
82 0.0058109059 0.0038831421
83 0.0199383311 0.0058109059
84 0.0090533340 0.0199383311
85 0.0002035058 0.0090533340
86 0.0012329958 0.0002035058
87 0.0028048982 0.0012329958
88 -0.0018190698 0.0028048982
89 -0.0004040668 -0.0018190698
90 -0.0034935432 -0.0004040668
91 -0.0034450867 -0.0034935432
92 0.0072188838 -0.0034450867
93 0.0056794019 0.0072188838
94 0.0031668183 0.0056794019
95 0.0009604513 0.0031668183
96 0.0019589023 0.0009604513
97 0.0018039064 0.0019589023
98 0.0063051215 0.0018039064
99 0.0002901234 0.0063051215
100 0.0059182369 0.0002901234
101 0.0109339392 0.0059182369
102 -0.0031458792 0.0109339392
103 -0.0036394964 -0.0031458792
104 -0.0145203526 -0.0036394964
> 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/freestat/rcomp/tmp/7d6w21290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8d6w21290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9d6w21290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10ogw51290507380.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/119gcb1290507380.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/freestat/rcomp/tmp/12czby1290507380.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/freestat/rcomp/tmp/138qq71290507380.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/freestat/rcomp/tmp/14ur7v1290507380.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/freestat/rcomp/tmp/15fr6j1290507380.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/freestat/rcomp/tmp/161amp1290507380.tab")
+ }
>
> try(system("convert tmp/1hxht1290507380.ps tmp/1hxht1290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hxht1290507380.ps tmp/2hxht1290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/3s6ge1290507380.ps tmp/3s6ge1290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/4s6ge1290507380.ps tmp/4s6ge1290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s6ge1290507380.ps tmp/5s6ge1290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/62xfz1290507380.ps tmp/62xfz1290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/7d6w21290507380.ps tmp/7d6w21290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d6w21290507380.ps tmp/8d6w21290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d6w21290507380.ps tmp/9d6w21290507380.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ogw51290507380.ps tmp/10ogw51290507380.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.542 2.574 6.013