R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(17140
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+ ,17)
+ ,dim=c(5
+ ,133)
+ ,dimnames=list(c('Total_size'
+ ,'Time_RFC'
+ ,'PR_views'
+ ,'Blogged'
+ ,'Reviewed')
+ ,1:133))
> y <- array(NA,dim=c(5,133),dimnames=list(c('Total_size','Time_RFC','PR_views','Blogged','Reviewed'),1:133))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Time_RFC Total_size PR_views Blogged Reviewed
1 101645 17140 88 20 11
2 101011 27570 41 30 13
3 7176 1423 1 0 0
4 96560 22996 129 42 17
5 175824 39992 107 57 20
6 341570 117105 190 94 21
7 103597 23789 66 27 16
8 112611 26706 36 46 20
9 85574 24266 71 37 21
10 220801 44418 105 51 18
11 92661 35232 133 40 17
12 133328 40909 79 56 20
13 61361 13294 51 27 12
14 125930 32387 207 37 17
15 82316 21233 34 27 10
16 102010 44332 66 28 13
17 101523 61056 76 59 22
18 41566 13497 42 0 9
19 99923 32334 115 44 25
20 22648 44339 44 12 13
21 46698 10288 35 14 13
22 131698 65622 74 60 19
23 91735 16563 103 7 18
24 79863 29011 134 29 22
25 108043 34553 29 45 14
26 98866 23517 140 25 13
27 120445 51009 72 36 16
28 116048 33416 45 50 20
29 250047 83305 58 41 18
30 136084 27142 69 27 13
31 92499 21399 57 25 18
32 135781 24874 98 45 14
33 74408 34988 61 29 7
34 81240 45549 89 58 17
35 133368 32755 54 37 16
36 79619 20760 123 42 11
37 59194 37636 247 7 24
38 139942 65461 46 54 22
39 118612 30080 72 54 12
40 72880 24094 41 14 19
41 65475 69008 24 16 13
42 99643 54968 45 33 17
43 71965 46090 33 32 15
44 77272 27507 27 21 16
45 49289 10672 36 15 24
46 135131 34029 87 38 15
47 108446 46300 90 22 17
48 89746 24760 114 28 18
49 44296 18779 31 10 20
50 77648 21280 45 31 16
51 181528 40662 69 32 16
52 134019 28987 51 32 18
53 124064 22827 34 43 22
54 92630 18513 60 27 8
55 121848 30594 45 37 17
56 52915 24006 54 20 18
57 81872 27913 25 32 16
58 58981 42744 38 0 23
59 53515 12934 52 5 22
60 60812 22574 67 26 13
61 56375 41385 74 10 13
62 65490 18653 38 27 16
63 80949 18472 30 11 16
64 76302 30976 26 29 20
65 104011 63339 67 25 22
66 98104 25568 132 55 17
67 67989 33747 42 23 18
68 30989 4154 35 5 17
69 135458 19474 118 43 12
70 73504 35130 68 23 7
71 63123 39067 43 34 17
72 61254 13310 76 36 14
73 74914 65892 64 35 23
74 31774 4143 48 0 17
75 81437 28579 64 37 14
76 87186 51776 56 28 15
77 50090 21152 71 16 17
78 65745 38084 75 26 21
79 56653 27717 39 38 18
80 158399 32928 42 23 18
81 46455 11342 39 22 17
82 73624 19499 93 30 17
83 38395 16380 38 16 16
84 91899 36874 60 18 15
85 139526 48259 71 28 21
86 52164 16734 52 32 16
87 51567 28207 27 21 14
88 70551 30143 59 23 15
89 84856 41369 40 29 17
90 102538 45833 79 50 15
91 86678 29156 44 12 15
92 85709 35944 65 21 10
93 34662 36278 10 18 6
94 150580 45588 124 27 22
95 99611 45097 81 41 21
96 19349 3895 15 13 1
97 99373 28394 92 12 18
98 86230 18632 42 21 17
99 30837 2325 10 8 4
100 31706 25139 24 26 10
101 89806 27975 64 27 16
102 62088 14483 45 13 16
103 40151 13127 22 16 9
104 27634 5839 56 2 16
105 76990 24069 94 42 17
106 37460 3738 19 5 7
107 54157 18625 35 37 15
108 49862 36341 32 17 14
109 84337 24548 35 38 14
110 64175 21792 48 37 18
111 59382 26263 49 29 12
112 119308 23686 48 32 16
113 76702 49303 62 35 21
114 103425 25659 96 17 19
115 70344 28904 45 20 16
116 43410 2781 63 7 1
117 104838 29236 71 46 16
118 62215 19546 26 24 10
119 69304 22818 48 40 19
120 53117 32689 29 3 12
121 19764 5752 19 10 2
122 86680 22197 45 37 14
123 84105 20055 45 17 17
124 77945 25272 67 28 19
125 89113 82206 30 19 14
126 91005 32073 36 29 11
127 40248 5444 34 8 4
128 64187 20154 36 10 16
129 50857 36944 34 15 20
130 56613 8019 37 15 12
131 62792 30884 46 28 15
132 72535 19540 44 17 16
133 98146 27114 37 15 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Total_size PR_views Blogged Reviewed
8640.072 0.888 232.320 1190.859 223.203
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-61388 -17423 -1806 12537 101115
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8640.0720 8522.6496 1.014 0.31260
Total_size 0.8880 0.1732 5.127 1.06e-06 ***
PR_views 232.3205 73.7294 3.151 0.00203 **
Blogged 1190.8593 198.0295 6.014 1.77e-08 ***
Reviewed 223.2034 581.7239 0.384 0.70184
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 28110 on 128 degrees of freedom
Multiple R-squared: 0.6051, Adjusted R-squared: 0.5928
F-statistic: 49.03 on 4 and 128 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,] 0.3046620 6.093239e-01 6.953380e-01
[2,] 0.3347316 6.694632e-01 6.652684e-01
[3,] 0.7969376 4.061247e-01 2.030624e-01
[4,] 0.8682211 2.635578e-01 1.317789e-01
[5,] 0.8529416 2.941167e-01 1.470584e-01
[6,] 0.7927150 4.145699e-01 2.072850e-01
[7,] 0.7146080 5.707841e-01 2.853920e-01
[8,] 0.6306790 7.386421e-01 3.693210e-01
[9,] 0.5999679 8.000641e-01 4.000321e-01
[10,] 0.9087789 1.824421e-01 9.122106e-02
[11,] 0.8879649 2.240702e-01 1.120351e-01
[12,] 0.8494132 3.011735e-01 1.505868e-01
[13,] 0.8734420 2.531160e-01 1.265580e-01
[14,] 0.8405427 3.189145e-01 1.594573e-01
[15,] 0.8701652 2.596696e-01 1.298348e-01
[16,] 0.9366611 1.266778e-01 6.333891e-02
[17,] 0.9210833 1.578333e-01 7.891666e-02
[18,] 0.8940110 2.119780e-01 1.059890e-01
[19,] 0.8642146 2.715708e-01 1.357854e-01
[20,] 0.8268983 3.462034e-01 1.731017e-01
[21,] 0.7832847 4.334307e-01 2.167153e-01
[22,] 0.9906099 1.878027e-02 9.390133e-03
[23,] 0.9956870 8.626064e-03 4.313032e-03
[24,] 0.9945928 1.081442e-02 5.407208e-03
[25,] 0.9940596 1.188072e-02 5.940358e-03
[26,] 0.9941973 1.160547e-02 5.802734e-03
[27,] 0.9987964 2.407250e-03 1.203625e-03
[28,] 0.9990413 1.917302e-03 9.586511e-04
[29,] 0.9988779 2.244290e-03 1.122145e-03
[30,] 0.9996826 6.348768e-04 3.174384e-04
[31,] 0.9996055 7.890097e-04 3.945049e-04
[32,] 0.9994065 1.186986e-03 5.934928e-04
[33,] 0.9991144 1.771141e-03 8.855706e-04
[34,] 0.9994293 1.141403e-03 5.707017e-04
[35,] 0.9991838 1.632346e-03 8.161728e-04
[36,] 0.9991048 1.790329e-03 8.951645e-04
[37,] 0.9986990 2.602067e-03 1.301034e-03
[38,] 0.9981104 3.779219e-03 1.889610e-03
[39,] 0.9981398 3.720370e-03 1.860185e-03
[40,] 0.9972883 5.423480e-03 2.711740e-03
[41,] 0.9960969 7.806169e-03 3.903084e-03
[42,] 0.9945208 1.095842e-02 5.479212e-03
[43,] 0.9921203 1.575943e-02 7.879715e-03
[44,] 0.9996980 6.039430e-04 3.019715e-04
[45,] 0.9998974 2.051061e-04 1.025530e-04
[46,] 0.9999392 1.216858e-04 6.084292e-05
[47,] 0.9999280 1.440442e-04 7.202210e-05
[48,] 0.9999562 8.757716e-05 4.378858e-05
[49,] 0.9999438 1.124946e-04 5.624732e-05
[50,] 0.9999193 1.613399e-04 8.066993e-05
[51,] 0.9998777 2.445582e-04 1.222791e-04
[52,] 0.9998213 3.574251e-04 1.787126e-04
[53,] 0.9997649 4.701571e-04 2.350785e-04
[54,] 0.9997734 4.532596e-04 2.266298e-04
[55,] 0.9996436 7.128538e-04 3.564269e-04
[56,] 0.9996820 6.360649e-04 3.180325e-04
[57,] 0.9995304 9.391497e-04 4.695748e-04
[58,] 0.9992976 1.404743e-03 7.023716e-04
[59,] 0.9993908 1.218307e-03 6.091534e-04
[60,] 0.9991057 1.788630e-03 8.943149e-04
[61,] 0.9987358 2.528447e-03 1.264223e-03
[62,] 0.9989468 2.106371e-03 1.053185e-03
[63,] 0.9984804 3.039293e-03 1.519647e-03
[64,] 0.9985425 2.915013e-03 1.457506e-03
[65,] 0.9982576 3.484838e-03 1.742419e-03
[66,] 0.9993412 1.317613e-03 6.588067e-04
[67,] 0.9991780 1.644069e-03 8.220346e-04
[68,] 0.9988017 2.396564e-03 1.198282e-03
[69,] 0.9983057 3.388634e-03 1.694317e-03
[70,] 0.9983898 3.220479e-03 1.610240e-03
[71,] 0.9988946 2.210835e-03 1.105418e-03
[72,] 0.9989079 2.184112e-03 1.092056e-03
[73,] 0.9999966 6.849314e-06 3.424657e-06
[74,] 0.9999946 1.087564e-05 5.437819e-06
[75,] 0.9999940 1.194388e-05 5.971941e-06
[76,] 0.9999937 1.269203e-05 6.346013e-06
[77,] 0.9999888 2.230036e-05 1.115018e-05
[78,] 0.9999958 8.424346e-06 4.212173e-06
[79,] 0.9999957 8.575637e-06 4.287819e-06
[80,] 0.9999928 1.443775e-05 7.218873e-06
[81,] 0.9999875 2.498265e-05 1.249133e-05
[82,] 0.9999771 4.575960e-05 2.287980e-05
[83,] 0.9999636 7.273610e-05 3.636805e-05
[84,] 0.9999575 8.502007e-05 4.251003e-05
[85,] 0.9999239 1.521869e-04 7.609344e-05
[86,] 0.9998952 2.095039e-04 1.047519e-04
[87,] 0.9999243 1.514681e-04 7.573404e-05
[88,] 0.9998747 2.506832e-04 1.253416e-04
[89,] 0.9997906 4.187811e-04 2.093905e-04
[90,] 0.9997032 5.936445e-04 2.968223e-04
[91,] 0.9996590 6.819694e-04 3.409847e-04
[92,] 0.9993869 1.226236e-03 6.131181e-04
[93,] 0.9995828 8.343410e-04 4.171705e-04
[94,] 0.9993147 1.370540e-03 6.852699e-04
[95,] 0.9987834 2.433139e-03 1.216570e-03
[96,] 0.9979952 4.009569e-03 2.004784e-03
[97,] 0.9984952 3.009623e-03 1.504811e-03
[98,] 0.9985452 2.909686e-03 1.454843e-03
[99,] 0.9974074 5.185188e-03 2.592594e-03
[100,] 0.9969277 6.144623e-03 3.072311e-03
[101,] 0.9961072 7.785624e-03 3.892812e-03
[102,] 0.9936999 1.260025e-02 6.300125e-03
[103,] 0.9927687 1.446270e-02 7.231349e-03
[104,] 0.9905963 1.880746e-02 9.403730e-03
[105,] 0.9978325 4.335075e-03 2.167537e-03
[106,] 0.9985783 2.843427e-03 1.421714e-03
[107,] 0.9973487 5.302631e-03 2.651316e-03
[108,] 0.9945919 1.081630e-02 5.408148e-03
[109,] 0.9895238 2.095234e-02 1.047617e-02
[110,] 0.9808615 3.827700e-02 1.913850e-02
[111,] 0.9647560 7.048791e-02 3.524395e-02
[112,] 0.9595868 8.082636e-02 4.041318e-02
[113,] 0.9260958 1.478084e-01 7.390418e-02
[114,] 0.9190351 1.619297e-01 8.096487e-02
[115,] 0.8553614 2.892772e-01 1.446386e-01
[116,] 0.8024788 3.950424e-01 1.975212e-01
[117,] 0.6784655 6.430689e-01 3.215345e-01
[118,] 0.5249161 9.501679e-01 4.750839e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1m7bo1324044221.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/27au71324044221.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/31pjw1324044221.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43lal1324044221.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5g5v71324044221.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 133
Frequency = 1
1 2 3 4 5 6
31068.0117 19736.2541 -2960.0138 -16280.3720 34469.7621 68171.9609
7 8 9 10 11 12
22774.7304 12648.9134 -9858.0577 83572.6862 -29592.4805 -1144.6990
13 14 15 16 17 18
-5764.1080 -7416.2644 12536.9300 2424.3355 -54162.2209 9174.3255
19 20 21 22 23 24
-22124.3577 -52779.0800 1217.3105 -28098.4285 32104.3268 -25115.1288
25 26 27 28 29 30
5269.1153 4144.8782 3339.7639 3273.1241 101114.7593 52256.9214
31 32 33 34 35 36
17825.2423 25571.9351 -15570.2462 -61388.3238 35463.1916 -28502.6646
37 38 39 40 41 42
-53942.8300 -6730.9411 -450.9776 12406.4647 -31975.1336 -11355.7927
43 44 45 46 47 48
-26725.0331 9353.8088 -411.0982 27460.6496 7789.4057 -4727.1658
49 50 51 52 53 54
-4594.3863 -830.9892 79071.2796 45665.0200 31137.2704 19672.3589
55 56 57 58 59 60
27729.8366 -17422.5106 958.4697 -1577.5219 10444.1224 -17303.2027
61 62 63 64 65 66
-21016.8297 -4266.5372 32265.5040 -4884.0247 -11121.4183 -33198.4362
67 68 69 70 71 72
-11783.2326 780.2116 28225.8407 -11081.4294 -34481.9526 -22857.4693
73 74 75 76 77 78
-53920.3130 4509.1098 -14636.3263 -17133.1245 -16675.9715 -29787.2452
79 80 81 82 83 84
-34930.5326 79354.0379 -11310.6100 -13457.1924 -16243.6646 11792.1353
85 86 87 88 89 90
33505.9370 -25095.2525 -16526.3831 -9300.7255 -8141.8661 -28046.0287
91 92 93 94 95 96
24286.9890 2809.8101 -31290.7623 35586.4497 -21405.5894 -11939.0071
97 98 99 100 101 102
25837.6495 22484.7814 7389.4389 -38027.5264 5731.2112 11080.2024
103 104 105 106 107 108
-6319.4548 -5154.0143 -28671.9766 13569.7809 -26563.1019 -21852.5251
109 110 111 112 113 114
-2610.3708 -23047.1687 -21176.6336 36804.6664 -36490.2640 25211.5423
115 116 117 118 119 120
-1805.6342 9104.9951 -4609.1256 -634.8751 -22625.0362 2460.8434
121 122 123 124 125 126
-10752.9270 687.9672 23162.6360 -6286.9609 -25246.6375 8530.4671
127 128 129 130 131 132
8455.0808 13806.8275 -20815.1307 11714.8813 -20651.8624 12505.4789
133
35175.5398
> postscript(file="/var/wessaorg/rcomp/tmp/6tser1324044221.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 = 133
Frequency = 1
lag(myerror, k = 1) myerror
0 31068.0117 NA
1 19736.2541 31068.0117
2 -2960.0138 19736.2541
3 -16280.3720 -2960.0138
4 34469.7621 -16280.3720
5 68171.9609 34469.7621
6 22774.7304 68171.9609
7 12648.9134 22774.7304
8 -9858.0577 12648.9134
9 83572.6862 -9858.0577
10 -29592.4805 83572.6862
11 -1144.6990 -29592.4805
12 -5764.1080 -1144.6990
13 -7416.2644 -5764.1080
14 12536.9300 -7416.2644
15 2424.3355 12536.9300
16 -54162.2209 2424.3355
17 9174.3255 -54162.2209
18 -22124.3577 9174.3255
19 -52779.0800 -22124.3577
20 1217.3105 -52779.0800
21 -28098.4285 1217.3105
22 32104.3268 -28098.4285
23 -25115.1288 32104.3268
24 5269.1153 -25115.1288
25 4144.8782 5269.1153
26 3339.7639 4144.8782
27 3273.1241 3339.7639
28 101114.7593 3273.1241
29 52256.9214 101114.7593
30 17825.2423 52256.9214
31 25571.9351 17825.2423
32 -15570.2462 25571.9351
33 -61388.3238 -15570.2462
34 35463.1916 -61388.3238
35 -28502.6646 35463.1916
36 -53942.8300 -28502.6646
37 -6730.9411 -53942.8300
38 -450.9776 -6730.9411
39 12406.4647 -450.9776
40 -31975.1336 12406.4647
41 -11355.7927 -31975.1336
42 -26725.0331 -11355.7927
43 9353.8088 -26725.0331
44 -411.0982 9353.8088
45 27460.6496 -411.0982
46 7789.4057 27460.6496
47 -4727.1658 7789.4057
48 -4594.3863 -4727.1658
49 -830.9892 -4594.3863
50 79071.2796 -830.9892
51 45665.0200 79071.2796
52 31137.2704 45665.0200
53 19672.3589 31137.2704
54 27729.8366 19672.3589
55 -17422.5106 27729.8366
56 958.4697 -17422.5106
57 -1577.5219 958.4697
58 10444.1224 -1577.5219
59 -17303.2027 10444.1224
60 -21016.8297 -17303.2027
61 -4266.5372 -21016.8297
62 32265.5040 -4266.5372
63 -4884.0247 32265.5040
64 -11121.4183 -4884.0247
65 -33198.4362 -11121.4183
66 -11783.2326 -33198.4362
67 780.2116 -11783.2326
68 28225.8407 780.2116
69 -11081.4294 28225.8407
70 -34481.9526 -11081.4294
71 -22857.4693 -34481.9526
72 -53920.3130 -22857.4693
73 4509.1098 -53920.3130
74 -14636.3263 4509.1098
75 -17133.1245 -14636.3263
76 -16675.9715 -17133.1245
77 -29787.2452 -16675.9715
78 -34930.5326 -29787.2452
79 79354.0379 -34930.5326
80 -11310.6100 79354.0379
81 -13457.1924 -11310.6100
82 -16243.6646 -13457.1924
83 11792.1353 -16243.6646
84 33505.9370 11792.1353
85 -25095.2525 33505.9370
86 -16526.3831 -25095.2525
87 -9300.7255 -16526.3831
88 -8141.8661 -9300.7255
89 -28046.0287 -8141.8661
90 24286.9890 -28046.0287
91 2809.8101 24286.9890
92 -31290.7623 2809.8101
93 35586.4497 -31290.7623
94 -21405.5894 35586.4497
95 -11939.0071 -21405.5894
96 25837.6495 -11939.0071
97 22484.7814 25837.6495
98 7389.4389 22484.7814
99 -38027.5264 7389.4389
100 5731.2112 -38027.5264
101 11080.2024 5731.2112
102 -6319.4548 11080.2024
103 -5154.0143 -6319.4548
104 -28671.9766 -5154.0143
105 13569.7809 -28671.9766
106 -26563.1019 13569.7809
107 -21852.5251 -26563.1019
108 -2610.3708 -21852.5251
109 -23047.1687 -2610.3708
110 -21176.6336 -23047.1687
111 36804.6664 -21176.6336
112 -36490.2640 36804.6664
113 25211.5423 -36490.2640
114 -1805.6342 25211.5423
115 9104.9951 -1805.6342
116 -4609.1256 9104.9951
117 -634.8751 -4609.1256
118 -22625.0362 -634.8751
119 2460.8434 -22625.0362
120 -10752.9270 2460.8434
121 687.9672 -10752.9270
122 23162.6360 687.9672
123 -6286.9609 23162.6360
124 -25246.6375 -6286.9609
125 8530.4671 -25246.6375
126 8455.0808 8530.4671
127 13806.8275 8455.0808
128 -20815.1307 13806.8275
129 11714.8813 -20815.1307
130 -20651.8624 11714.8813
131 12505.4789 -20651.8624
132 35175.5398 12505.4789
133 NA 35175.5398
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 19736.2541 31068.0117
[2,] -2960.0138 19736.2541
[3,] -16280.3720 -2960.0138
[4,] 34469.7621 -16280.3720
[5,] 68171.9609 34469.7621
[6,] 22774.7304 68171.9609
[7,] 12648.9134 22774.7304
[8,] -9858.0577 12648.9134
[9,] 83572.6862 -9858.0577
[10,] -29592.4805 83572.6862
[11,] -1144.6990 -29592.4805
[12,] -5764.1080 -1144.6990
[13,] -7416.2644 -5764.1080
[14,] 12536.9300 -7416.2644
[15,] 2424.3355 12536.9300
[16,] -54162.2209 2424.3355
[17,] 9174.3255 -54162.2209
[18,] -22124.3577 9174.3255
[19,] -52779.0800 -22124.3577
[20,] 1217.3105 -52779.0800
[21,] -28098.4285 1217.3105
[22,] 32104.3268 -28098.4285
[23,] -25115.1288 32104.3268
[24,] 5269.1153 -25115.1288
[25,] 4144.8782 5269.1153
[26,] 3339.7639 4144.8782
[27,] 3273.1241 3339.7639
[28,] 101114.7593 3273.1241
[29,] 52256.9214 101114.7593
[30,] 17825.2423 52256.9214
[31,] 25571.9351 17825.2423
[32,] -15570.2462 25571.9351
[33,] -61388.3238 -15570.2462
[34,] 35463.1916 -61388.3238
[35,] -28502.6646 35463.1916
[36,] -53942.8300 -28502.6646
[37,] -6730.9411 -53942.8300
[38,] -450.9776 -6730.9411
[39,] 12406.4647 -450.9776
[40,] -31975.1336 12406.4647
[41,] -11355.7927 -31975.1336
[42,] -26725.0331 -11355.7927
[43,] 9353.8088 -26725.0331
[44,] -411.0982 9353.8088
[45,] 27460.6496 -411.0982
[46,] 7789.4057 27460.6496
[47,] -4727.1658 7789.4057
[48,] -4594.3863 -4727.1658
[49,] -830.9892 -4594.3863
[50,] 79071.2796 -830.9892
[51,] 45665.0200 79071.2796
[52,] 31137.2704 45665.0200
[53,] 19672.3589 31137.2704
[54,] 27729.8366 19672.3589
[55,] -17422.5106 27729.8366
[56,] 958.4697 -17422.5106
[57,] -1577.5219 958.4697
[58,] 10444.1224 -1577.5219
[59,] -17303.2027 10444.1224
[60,] -21016.8297 -17303.2027
[61,] -4266.5372 -21016.8297
[62,] 32265.5040 -4266.5372
[63,] -4884.0247 32265.5040
[64,] -11121.4183 -4884.0247
[65,] -33198.4362 -11121.4183
[66,] -11783.2326 -33198.4362
[67,] 780.2116 -11783.2326
[68,] 28225.8407 780.2116
[69,] -11081.4294 28225.8407
[70,] -34481.9526 -11081.4294
[71,] -22857.4693 -34481.9526
[72,] -53920.3130 -22857.4693
[73,] 4509.1098 -53920.3130
[74,] -14636.3263 4509.1098
[75,] -17133.1245 -14636.3263
[76,] -16675.9715 -17133.1245
[77,] -29787.2452 -16675.9715
[78,] -34930.5326 -29787.2452
[79,] 79354.0379 -34930.5326
[80,] -11310.6100 79354.0379
[81,] -13457.1924 -11310.6100
[82,] -16243.6646 -13457.1924
[83,] 11792.1353 -16243.6646
[84,] 33505.9370 11792.1353
[85,] -25095.2525 33505.9370
[86,] -16526.3831 -25095.2525
[87,] -9300.7255 -16526.3831
[88,] -8141.8661 -9300.7255
[89,] -28046.0287 -8141.8661
[90,] 24286.9890 -28046.0287
[91,] 2809.8101 24286.9890
[92,] -31290.7623 2809.8101
[93,] 35586.4497 -31290.7623
[94,] -21405.5894 35586.4497
[95,] -11939.0071 -21405.5894
[96,] 25837.6495 -11939.0071
[97,] 22484.7814 25837.6495
[98,] 7389.4389 22484.7814
[99,] -38027.5264 7389.4389
[100,] 5731.2112 -38027.5264
[101,] 11080.2024 5731.2112
[102,] -6319.4548 11080.2024
[103,] -5154.0143 -6319.4548
[104,] -28671.9766 -5154.0143
[105,] 13569.7809 -28671.9766
[106,] -26563.1019 13569.7809
[107,] -21852.5251 -26563.1019
[108,] -2610.3708 -21852.5251
[109,] -23047.1687 -2610.3708
[110,] -21176.6336 -23047.1687
[111,] 36804.6664 -21176.6336
[112,] -36490.2640 36804.6664
[113,] 25211.5423 -36490.2640
[114,] -1805.6342 25211.5423
[115,] 9104.9951 -1805.6342
[116,] -4609.1256 9104.9951
[117,] -634.8751 -4609.1256
[118,] -22625.0362 -634.8751
[119,] 2460.8434 -22625.0362
[120,] -10752.9270 2460.8434
[121,] 687.9672 -10752.9270
[122,] 23162.6360 687.9672
[123,] -6286.9609 23162.6360
[124,] -25246.6375 -6286.9609
[125,] 8530.4671 -25246.6375
[126,] 8455.0808 8530.4671
[127,] 13806.8275 8455.0808
[128,] -20815.1307 13806.8275
[129,] 11714.8813 -20815.1307
[130,] -20651.8624 11714.8813
[131,] 12505.4789 -20651.8624
[132,] 35175.5398 12505.4789
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 19736.2541 31068.0117
2 -2960.0138 19736.2541
3 -16280.3720 -2960.0138
4 34469.7621 -16280.3720
5 68171.9609 34469.7621
6 22774.7304 68171.9609
7 12648.9134 22774.7304
8 -9858.0577 12648.9134
9 83572.6862 -9858.0577
10 -29592.4805 83572.6862
11 -1144.6990 -29592.4805
12 -5764.1080 -1144.6990
13 -7416.2644 -5764.1080
14 12536.9300 -7416.2644
15 2424.3355 12536.9300
16 -54162.2209 2424.3355
17 9174.3255 -54162.2209
18 -22124.3577 9174.3255
19 -52779.0800 -22124.3577
20 1217.3105 -52779.0800
21 -28098.4285 1217.3105
22 32104.3268 -28098.4285
23 -25115.1288 32104.3268
24 5269.1153 -25115.1288
25 4144.8782 5269.1153
26 3339.7639 4144.8782
27 3273.1241 3339.7639
28 101114.7593 3273.1241
29 52256.9214 101114.7593
30 17825.2423 52256.9214
31 25571.9351 17825.2423
32 -15570.2462 25571.9351
33 -61388.3238 -15570.2462
34 35463.1916 -61388.3238
35 -28502.6646 35463.1916
36 -53942.8300 -28502.6646
37 -6730.9411 -53942.8300
38 -450.9776 -6730.9411
39 12406.4647 -450.9776
40 -31975.1336 12406.4647
41 -11355.7927 -31975.1336
42 -26725.0331 -11355.7927
43 9353.8088 -26725.0331
44 -411.0982 9353.8088
45 27460.6496 -411.0982
46 7789.4057 27460.6496
47 -4727.1658 7789.4057
48 -4594.3863 -4727.1658
49 -830.9892 -4594.3863
50 79071.2796 -830.9892
51 45665.0200 79071.2796
52 31137.2704 45665.0200
53 19672.3589 31137.2704
54 27729.8366 19672.3589
55 -17422.5106 27729.8366
56 958.4697 -17422.5106
57 -1577.5219 958.4697
58 10444.1224 -1577.5219
59 -17303.2027 10444.1224
60 -21016.8297 -17303.2027
61 -4266.5372 -21016.8297
62 32265.5040 -4266.5372
63 -4884.0247 32265.5040
64 -11121.4183 -4884.0247
65 -33198.4362 -11121.4183
66 -11783.2326 -33198.4362
67 780.2116 -11783.2326
68 28225.8407 780.2116
69 -11081.4294 28225.8407
70 -34481.9526 -11081.4294
71 -22857.4693 -34481.9526
72 -53920.3130 -22857.4693
73 4509.1098 -53920.3130
74 -14636.3263 4509.1098
75 -17133.1245 -14636.3263
76 -16675.9715 -17133.1245
77 -29787.2452 -16675.9715
78 -34930.5326 -29787.2452
79 79354.0379 -34930.5326
80 -11310.6100 79354.0379
81 -13457.1924 -11310.6100
82 -16243.6646 -13457.1924
83 11792.1353 -16243.6646
84 33505.9370 11792.1353
85 -25095.2525 33505.9370
86 -16526.3831 -25095.2525
87 -9300.7255 -16526.3831
88 -8141.8661 -9300.7255
89 -28046.0287 -8141.8661
90 24286.9890 -28046.0287
91 2809.8101 24286.9890
92 -31290.7623 2809.8101
93 35586.4497 -31290.7623
94 -21405.5894 35586.4497
95 -11939.0071 -21405.5894
96 25837.6495 -11939.0071
97 22484.7814 25837.6495
98 7389.4389 22484.7814
99 -38027.5264 7389.4389
100 5731.2112 -38027.5264
101 11080.2024 5731.2112
102 -6319.4548 11080.2024
103 -5154.0143 -6319.4548
104 -28671.9766 -5154.0143
105 13569.7809 -28671.9766
106 -26563.1019 13569.7809
107 -21852.5251 -26563.1019
108 -2610.3708 -21852.5251
109 -23047.1687 -2610.3708
110 -21176.6336 -23047.1687
111 36804.6664 -21176.6336
112 -36490.2640 36804.6664
113 25211.5423 -36490.2640
114 -1805.6342 25211.5423
115 9104.9951 -1805.6342
116 -4609.1256 9104.9951
117 -634.8751 -4609.1256
118 -22625.0362 -634.8751
119 2460.8434 -22625.0362
120 -10752.9270 2460.8434
121 687.9672 -10752.9270
122 23162.6360 687.9672
123 -6286.9609 23162.6360
124 -25246.6375 -6286.9609
125 8530.4671 -25246.6375
126 8455.0808 8530.4671
127 13806.8275 8455.0808
128 -20815.1307 13806.8275
129 11714.8813 -20815.1307
130 -20651.8624 11714.8813
131 12505.4789 -20651.8624
132 35175.5398 12505.4789
> 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/704k81324044221.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/8v0bh1324044221.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/98n6f1324044221.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/10x0nz1324044221.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11q6vt1324044221.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12s6f91324044221.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13cag51324044221.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/142vx31324044221.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/150hcv1324044221.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/16indr1324044221.tab")
+ }
>
> try(system("convert tmp/1m7bo1324044221.ps tmp/1m7bo1324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/27au71324044221.ps tmp/27au71324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/31pjw1324044221.ps tmp/31pjw1324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/43lal1324044221.ps tmp/43lal1324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g5v71324044221.ps tmp/5g5v71324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tser1324044221.ps tmp/6tser1324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/704k81324044221.ps tmp/704k81324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v0bh1324044221.ps tmp/8v0bh1324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/98n6f1324044221.ps tmp/98n6f1324044221.png",intern=TRUE))
character(0)
> try(system("convert tmp/10x0nz1324044221.ps tmp/10x0nz1324044221.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.285 0.593 4.901