R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> x <- c(126.64
+ ,126.81
+ ,125.84
+ ,126.77
+ ,124.34
+ ,124.4
+ ,120.48
+ ,118.54
+ ,117.66
+ ,116.97
+ ,120.11
+ ,119.16
+ ,116.9
+ ,116.11
+ ,114.98
+ ,113.65
+ ,115.82
+ ,117.59
+ ,118.57
+ ,118.07
+ ,114.98
+ ,114.04
+ ,115.02
+ ,114.28
+ ,115.04
+ ,116.7
+ ,119.21
+ ,118.39
+ ,116.5
+ ,115.46
+ ,117.59
+ ,117.33
+ ,116.2
+ ,116.83
+ ,118.99
+ ,118.62
+ ,121.09
+ ,122.4
+ ,123.76
+ ,125.33
+ ,123.23
+ ,122.52
+ ,123.64
+ ,124.67
+ ,124.71
+ ,122.53
+ ,124.4
+ ,125.45
+ ,125.35
+ ,124.3
+ ,127.03
+ ,128.51
+ ,128.1
+ ,128.94
+ ,129.67
+ ,129.87
+ ,131.12
+ ,132.68
+ ,132.24
+ ,133.63
+ ,129.91
+ ,127.93
+ ,131.17
+ ,130.86
+ ,133.48
+ ,134.08
+ ,136.02
+ ,132.8
+ ,132.37
+ ,133.05
+ ,132.57
+ ,130.7
+ ,130.5
+ ,129.67
+ ,127.8
+ ,126.82
+ ,126.85
+ ,128.28
+ ,128.3
+ ,126.82
+ ,125.08
+ ,128.53
+ ,130.34
+ ,131.52
+ ,132.59
+ ,131.17
+ ,132.72
+ ,133.36
+ ,132.82
+ ,132.9
+ ,130.9
+ ,129.41
+ ,128.67
+ ,129.28
+ ,130.91
+ ,131.06
+ ,130.84
+ ,131.41
+ ,133.22
+ ,132.06
+ ,132.48
+ ,134.38
+ ,135.22
+ ,134.89
+ ,136.09
+ ,136.33
+ ,136.32
+ ,137.48
+ ,136.53
+ ,136.8
+ ,138.03
+ ,137.39
+ ,137.55
+ ,136.08
+ ,134.78
+ ,133.28
+ ,133.57
+ ,134.84
+ ,133.02
+ ,133.49
+ ,133.77
+ ,134.34
+ ,134.5
+ ,134.03
+ ,135.51
+ ,136.53
+ ,135.95
+ ,134.32
+ ,132.44
+ ,133.61
+ ,131.02
+ ,130.05
+ ,128.21
+ ,129.03
+ ,130.34
+ ,131.57
+ ,132.63
+ ,132.06
+ ,134.44
+ ,134.1
+ ,132.49
+ ,134.23
+ ,134.92
+ ,135.61
+ ,134.53
+ ,133.86
+ ,133.89
+ ,135.33
+ ,135.86
+ ,136.22
+ ,137.38
+ ,137.31
+ ,136.89
+ ,138.01
+ ,136.72
+ ,135.77
+ ,137.52
+ ,135.61
+ ,132.94
+ ,134.12
+ ,132.55
+ ,134.11
+ ,134.19
+ ,135.57
+ ,135.05
+ ,134.32
+ ,133.61
+ ,134.75
+ ,133.1
+ ,133.26
+ ,131.63
+ ,132.47
+ ,132.45
+ ,133.33
+ ,133.57
+ ,134.13
+ ,133.92
+ ,132.62
+ ,132.3
+ ,133.26
+ ,132.6
+ ,134.38
+ ,134.17
+ ,135.46
+ ,135.09
+ ,134.96
+ ,133.85
+ ,132.59
+ ,131.15
+ ,130.91
+ ,131.07
+ ,130.78
+ ,129.95
+ ,131.41
+ ,131.21
+ ,130.68
+ ,130.46
+ ,131.12
+ ,132.99
+ ,133.02
+ ,133.39
+ ,134.07
+ ,135.6
+ ,135.66
+ ,135.53
+ ,135.82
+ ,136.9
+ ,137.97
+ ,138.09
+ ,136.91
+ ,134.76
+ ,135.13
+ ,134.66
+ ,132.95
+ ,132.25
+ ,134.3
+ ,134.3
+ ,134.76
+ ,134.81
+ ,134.51
+ ,135.11
+ ,134.32
+ ,133.51
+ ,134.02
+ ,132.76
+ ,133.39
+ ,132.05
+ ,131.87
+ ,133.03
+ ,132.57
+ ,132.1
+ ,130.7
+ ,129.2
+ ,129.77
+ ,131.02
+ ,131.55
+ ,133.17
+ ,133.08
+ ,133.24
+ ,130.74
+ ,129.91
+ ,130.03
+ ,131.13
+ ,129.55
+ ,130.22
+ ,130.61
+ ,129.27
+ ,129.68
+ ,130.1
+ ,130.83
+ ,130.95
+ ,131.73
+ ,131.86
+ ,132.44
+ ,132.35
+ ,133.16
+ ,133.62
+ ,132.54
+ ,132.69
+ ,133.5
+ ,133.36
+ ,134.23
+ ,132.41
+ ,133.02
+ ,132.88
+ ,130.76
+ ,130.33
+ ,129.79
+ ,128.65
+ ,129.14
+ ,127.35
+ ,127.74
+ ,126.31
+ ,125.95
+ ,126.36
+ ,126.15
+ ,125.6
+ ,126.2
+ ,126.73
+ ,125.68
+ ,122.49
+ ,122.07
+ ,123.4
+ ,123.01
+ ,123.03
+ ,122.33
+ ,122.42
+ ,122.68
+ ,124.69
+ ,123.3
+ ,124.17
+ ,124.38
+ ,123.19
+ ,122.16
+ ,120.66
+ ,120.92
+ ,120.67
+ ,120.68
+ ,121.1
+ ,120.86
+ ,121.48
+ ,123.48
+ ,121.72
+ ,123.16
+ ,123.84
+ ,124.57
+ ,124.3
+ ,124.22
+ ,124.43
+ ,123.33
+ ,122.86
+ ,121.25
+ ,122.16
+ ,122.62
+ ,123.44
+ ,124
+ ,124.75
+ ,124.8
+ ,125.93
+ ,126.28
+ ,126.04
+ ,125.04
+ ,123.76
+ ,125.34
+ ,126.99
+ ,126.34
+ ,127.42
+ ,126.18
+ ,125.3
+ ,123.5
+ ,125.32
+ ,124.65
+ ,124.03
+ ,125.11
+ ,125.46
+ ,124.7
+ ,124.48
+ ,124.76
+ ,125.81
+ ,124.95
+ ,123.66
+ ,122.66
+ ,119.34
+ ,117.84
+ ,120.97
+ ,117.38
+ ,118.06
+ ,116.99
+ ,115.55
+ ,114.17
+ ,115.32
+ ,112.49
+ ,111.93
+ ,112.08
+ ,111.63
+ ,109.53
+ ,111.35
+ ,110.79
+ ,113.06
+ ,112.62
+ ,110.65
+ ,112.36
+ ,113.74
+ ,111.73
+ ,109.86
+ ,109.32
+ ,109.99
+ ,109.84
+ ,111.13
+ ,112.43
+ ,111.77
+ ,112.15
+ ,112.89
+ ,112.12
+ ,113.1
+ ,111.09
+ ,110.76
+ ,109.59
+ ,109.99
+ ,110.25
+ ,108.31
+ ,108.79
+ ,108.14)
> par10 = 'FALSE'
> par9 = '0'
> par8 = '0'
> par7 = '0'
> par6 = '0'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '15'
> par1 <- 45 #cut off periods
> par2 <- as.numeric(par2) #lambda
> par3 <- as.numeric(par3) #degree of non-seasonal differencing
> par4 <- as.numeric(par4) #degree of seasonal differencing
> par5 <- 5 #seasonal period
> par6 <- as.numeric(par6) #p
> par7 <- as.numeric(par7) #q
> par8 <- as.numeric(par8) #P
> par9 <- as.numeric(par9) #Q
> if (par10 == 'TRUE') par10 <- TRUE
> if (par10 == 'FALSE') par10 <- FALSE
> if (par2 == 0) x <- log(x)
> if (par2 != 0) x <- x^par2
> lx <- length(x)
> first <- lx - 2*par1
> nx <- lx - par1
> nx1 <- nx + 1
> fx <- lx - nx
> if (fx < 1) {
+ fx <- par5
+ nx1 <- lx + fx - 1
+ first <- lx - 2*fx
+ }
> first <- 1
> if (fx < 3) fx <- round(lx/10,0)
> (arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))

Call:
arima(x = x[1:nx], order = c(par6, par3, par7), seasonal = list(order = c(par8, 
    par4, par9), period = par5), include.mean = par10, method = "ML")


sigma^2 estimated as 1.505:  log likelihood = -547.03,  aic = 1096.06
> (forecast <- predict(arima.out,par1))
$pred
Time Series:
Start = 339 
End = 383 
Frequency = 1 
 [1] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[11] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[21] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[31] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[41] 124.76 124.76 124.76 124.76 124.76

$se
Time Series:
Start = 339 
End = 383 
Frequency = 1 
 [1] 1.226664 1.734765 2.124645 2.453329 2.742905 3.004702 3.245449 3.469531
 [9] 3.679993 3.879054 4.068386 4.249290 4.422802 4.589758 4.750851 4.906658
[17] 5.057667 5.204296 5.346906 5.485810 5.621283 5.753566 5.882876 6.009404
[25] 6.133322 6.254786 6.373935 6.490898 6.605790 6.718718 6.829779 6.939062
[33] 7.046651 7.152621 7.257045 7.359987 7.461508 7.561667 7.660517 7.758107
[41] 7.854485 7.949694 8.043777 8.136771 8.228715

> (lb <- forecast$pred - 1.96 * forecast$se)
Time Series:
Start = 339 
End = 383 
Frequency = 1 
 [1] 122.3557 121.3599 120.5957 119.9515 119.3839 118.8708 118.3989 117.9597
 [9] 117.5472 117.1571 116.7860 116.4314 116.0913 115.7641 115.4483 115.1430
[17] 114.8470 114.5596 114.2801 114.0078 113.7423 113.4830 113.2296 112.9816
[25] 112.7387 112.5006 112.2671 112.0378 111.8127 111.5913 111.3736 111.1594
[33] 110.9486 110.7409 110.5362 110.3344 110.1354 109.9391 109.7454 109.5541
[41] 109.3652 109.1786 108.9942 108.8119 108.6317
> (ub <- forecast$pred + 1.96 * forecast$se)
Time Series:
Start = 339 
End = 383 
Frequency = 1 
 [1] 127.1643 128.1601 128.9243 129.5685 130.1361 130.6492 131.1211 131.5603
 [9] 131.9728 132.3629 132.7340 133.0886 133.4287 133.7559 134.0717 134.3770
[17] 134.6730 134.9604 135.2399 135.5122 135.7777 136.0370 136.2904 136.5384
[25] 136.7813 137.0194 137.2529 137.4822 137.7073 137.9287 138.1464 138.3606
[33] 138.5714 138.7791 138.9838 139.1856 139.3846 139.5809 139.7746 139.9659
[41] 140.1548 140.3414 140.5258 140.7081 140.8883
> if (par2 == 0) {
+ x <- exp(x)
+ forecast$pred <- exp(forecast$pred)
+ lb <- exp(lb)
+ ub <- exp(ub)
+ }
> if (par2 != 0) {
+ x <- x^(1/par2)
+ forecast$pred <- forecast$pred^(1/par2)
+ lb <- lb^(1/par2)
+ ub <- ub^(1/par2)
+ }
> if (par2 < 0) {
+ olb <- lb
+ lb <- ub
+ ub <- olb
+ }
> (actandfor <- c(x[1:nx], forecast$pred))
  [1] 126.64 126.81 125.84 126.77 124.34 124.40 120.48 118.54 117.66 116.97
 [11] 120.11 119.16 116.90 116.11 114.98 113.65 115.82 117.59 118.57 118.07
 [21] 114.98 114.04 115.02 114.28 115.04 116.70 119.21 118.39 116.50 115.46
 [31] 117.59 117.33 116.20 116.83 118.99 118.62 121.09 122.40 123.76 125.33
 [41] 123.23 122.52 123.64 124.67 124.71 122.53 124.40 125.45 125.35 124.30
 [51] 127.03 128.51 128.10 128.94 129.67 129.87 131.12 132.68 132.24 133.63
 [61] 129.91 127.93 131.17 130.86 133.48 134.08 136.02 132.80 132.37 133.05
 [71] 132.57 130.70 130.50 129.67 127.80 126.82 126.85 128.28 128.30 126.82
 [81] 125.08 128.53 130.34 131.52 132.59 131.17 132.72 133.36 132.82 132.90
 [91] 130.90 129.41 128.67 129.28 130.91 131.06 130.84 131.41 133.22 132.06
[101] 132.48 134.38 135.22 134.89 136.09 136.33 136.32 137.48 136.53 136.80
[111] 138.03 137.39 137.55 136.08 134.78 133.28 133.57 134.84 133.02 133.49
[121] 133.77 134.34 134.50 134.03 135.51 136.53 135.95 134.32 132.44 133.61
[131] 131.02 130.05 128.21 129.03 130.34 131.57 132.63 132.06 134.44 134.10
[141] 132.49 134.23 134.92 135.61 134.53 133.86 133.89 135.33 135.86 136.22
[151] 137.38 137.31 136.89 138.01 136.72 135.77 137.52 135.61 132.94 134.12
[161] 132.55 134.11 134.19 135.57 135.05 134.32 133.61 134.75 133.10 133.26
[171] 131.63 132.47 132.45 133.33 133.57 134.13 133.92 132.62 132.30 133.26
[181] 132.60 134.38 134.17 135.46 135.09 134.96 133.85 132.59 131.15 130.91
[191] 131.07 130.78 129.95 131.41 131.21 130.68 130.46 131.12 132.99 133.02
[201] 133.39 134.07 135.60 135.66 135.53 135.82 136.90 137.97 138.09 136.91
[211] 134.76 135.13 134.66 132.95 132.25 134.30 134.30 134.76 134.81 134.51
[221] 135.11 134.32 133.51 134.02 132.76 133.39 132.05 131.87 133.03 132.57
[231] 132.10 130.70 129.20 129.77 131.02 131.55 133.17 133.08 133.24 130.74
[241] 129.91 130.03 131.13 129.55 130.22 130.61 129.27 129.68 130.10 130.83
[251] 130.95 131.73 131.86 132.44 132.35 133.16 133.62 132.54 132.69 133.50
[261] 133.36 134.23 132.41 133.02 132.88 130.76 130.33 129.79 128.65 129.14
[271] 127.35 127.74 126.31 125.95 126.36 126.15 125.60 126.20 126.73 125.68
[281] 122.49 122.07 123.40 123.01 123.03 122.33 122.42 122.68 124.69 123.30
[291] 124.17 124.38 123.19 122.16 120.66 120.92 120.67 120.68 121.10 120.86
[301] 121.48 123.48 121.72 123.16 123.84 124.57 124.30 124.22 124.43 123.33
[311] 122.86 121.25 122.16 122.62 123.44 124.00 124.75 124.80 125.93 126.28
[321] 126.04 125.04 123.76 125.34 126.99 126.34 127.42 126.18 125.30 123.50
[331] 125.32 124.65 124.03 125.11 125.46 124.70 124.48 124.76 124.76 124.76
[341] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[351] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[361] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[371] 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76 124.76
[381] 124.76 124.76 124.76
> (perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
Time Series:
Start = 339 
End = 383 
Frequency = 1 
 [1] 0.009832193 0.013904821 0.017029858 0.019664387 0.021985453 0.024083857
 [7] 0.026013538 0.027809642 0.029496580 0.031092125 0.032609696 0.034059717
[13] 0.035450477 0.036788699 0.038079921 0.039328773 0.040539171 0.041714463
[19] 0.042857537 0.043970905 0.045056770 0.046117074 0.047153543 0.048167713
[25] 0.049160966 0.050134545 0.051089575 0.052027077 0.052947981 0.053853141
[31] 0.054743335 0.055619284 0.056481650 0.057331046 0.058168040 0.058993160
[37] 0.059806897 0.060609710 0.061402027 0.062184250 0.062956755 0.063719895
[43] 0.064474003 0.065219392 0.065956358
> postscript(file="/var/www/html/rcomp/tmp/1hdij1292756619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> opar <- par(mar=c(4,4,2,2),las=1)
> ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
> plot(x,ylim=ylim,type='n',xlim=c(first,lx))
> usr <- par('usr')
> rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
> rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
> abline(h= (-3:3)*2 , col ='gray', lty =3)
> polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
> lines(nx1:lx, lb , lty=2)
> lines(nx1:lx, ub , lty=2)
> lines(x, lwd=2)
> lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
> box()
> par(opar)
> dev.off()
null device 
          1 
> prob.dec <- array(NA, dim=fx)
> prob.sdec <- array(NA, dim=fx)
> prob.ldec <- array(NA, dim=fx)
> prob.pval <- array(NA, dim=fx)
> perf.pe <- array(0, dim=fx)
> perf.mape <- array(0, dim=fx)
> perf.mape1 <- array(0, dim=fx)
> perf.se <- array(0, dim=fx)
> perf.mse <- array(0, dim=fx)
> perf.mse1 <- array(0, dim=fx)
> perf.rmse <- array(0, dim=fx)
> for (i in 1:fx) {
+ locSD <- (ub[i] - forecast$pred[i]) / 1.96
+ perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
+ perf.se[i] = (x[nx+i] - forecast$pred[i])^2
+ prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
+ prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
+ prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
+ prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
+ }
> perf.mape[1] = abs(perf.pe[1])
> perf.mse[1] = abs(perf.se[1])
> for (i in 2:fx) {
+ perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
+ perf.mape1[i] = perf.mape[i] / i
+ perf.mse[i] = perf.mse[i-1] + perf.se[i]
+ perf.mse1[i] = perf.mse[i] / i
+ }
> perf.rmse = sqrt(perf.mse1)
> postscript(file="/var/www/html/rcomp/tmp/2oexc1292756619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
> dum <- forecast$pred
> dum[1:par1] <- x[(nx+1):lx]
> lines(dum, lty=1)
> lines(ub,lty=3)
> lines(lb,lty=3)
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'Y[t]',1,header=TRUE)
> a<-table.element(a,'F[t]',1,header=TRUE)
> a<-table.element(a,'95% LB',1,header=TRUE)
> a<-table.element(a,'95% UB',1,header=TRUE)
> a<-table.element(a,'p-value<br />(H0: Y[t] = F[t])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
> a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
> mylab <- paste('P(F[t]>Y[',nx,sep='')
> mylab <- paste(mylab,'])',sep='')
> a<-table.element(a,mylab,1,header=TRUE)
> a<-table.row.end(a)
> for (i in (nx-par5):nx) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.element(a,'-')
+ a<-table.row.end(a)
+ }
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(x[nx+i],4))
+ a<-table.element(a,round(forecast$pred[i],4))
+ a<-table.element(a,round(lb[i],4))
+ a<-table.element(a,round(ub[i],4))
+ a<-table.element(a,round((1-prob.pval[i]),4))
+ a<-table.element(a,round((1-prob.dec[i]),4))
+ a<-table.element(a,round((1-prob.sdec[i]),4))
+ a<-table.element(a,round((1-prob.ldec[i]),4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/3dfd61292756619.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'time',1,header=TRUE)
> a<-table.element(a,'% S.E.',1,header=TRUE)
> a<-table.element(a,'PE',1,header=TRUE)
> a<-table.element(a,'MAPE',1,header=TRUE)
> a<-table.element(a,'Sq.E',1,header=TRUE)
> a<-table.element(a,'MSE',1,header=TRUE)
> a<-table.element(a,'RMSE',1,header=TRUE)
> a<-table.row.end(a)
> for (i in 1:fx) {
+ a<-table.row.start(a)
+ a<-table.element(a,nx+i,header=TRUE)
+ a<-table.element(a,round(perc.se[i],4))
+ a<-table.element(a,round(perf.pe[i],4))
+ a<-table.element(a,round(perf.mape1[i],4))
+ a<-table.element(a,round(perf.se[i],4))
+ a<-table.element(a,round(perf.mse1[i],4))
+ a<-table.element(a,round(perf.rmse[i],4))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4gfbu1292756619.tab") 
> 
> try(system("convert tmp/1hdij1292756619.ps tmp/1hdij1292756619.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oexc1292756619.ps tmp/2oexc1292756619.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.791   0.362   1.727