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Type 'q()' to quit R. > x <- c(2350.44,2440.25,2408.64,2472.81,2407.6,2454.62,2448.05,2497.84,2645.64,2756.76,2849.27,2921.44,2981.85,3080.58,3106.22,3119.31,3061.26,3097.31,3161.69,3257.16,3277.01,3295.32,3363.99,3494.17,3667.03,3813.06,3917.96,3895.51,3801.06,3570.12,3701.61,3862.27,3970.1,4138.52,4199.75,4290.89,4443.91,4502.64,4356.98,4591.27,4696.96,4621.4,4562.84,4202.52,4296.49,4435.23,4105.18,4116.68,3844.49,3720.98,3674.4,3857.62,3801.06,3504.37,3032.6,3047.03,2962.34,2197.82,2014.45,1862.83,1905.41,1810.99,1670.07,1864.44,2052.02,2029.6,2070.83,2293.41,2443.27,2513.17) > par9 = '0' > par8 = '0' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '1' > par1 = 'FALSE' > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par6 <- 11 > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$coef + try2 <- sqrt(diag(arima.out$var.coef)) + try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names))) + dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv')) + try.data.frame[,1] <- try1 + for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i] + try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2] + try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5) + vector <- rep(NA,length(names)) + vector[is.na(try.data.frame[,4])] <- 0 + maxi <- which.max(try.data.frame[,4]) + continue <- max(try.data.frame[,4],na.rm=TRUE) > .05 + vector[maxi] <- 0 + list(summary=try.data.frame,next.vector=vector,continue=continue) + } > arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){ + nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3] + coeff <- matrix(NA, nrow=nrc*2, ncol=nrc) + pval <- matrix(NA, nrow=nrc*2, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep='')) + if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep='')) + arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML') + mylist[[1]] <- arima.out + last.arma <- armaGR(arima.out, names, length(series)) + mystop <- FALSE + i <- 1 + coeff[i,] <- last.arma[[1]][,1] + pval [i,] <- last.arma[[1]][,4] + i <- 2 + aic <- arima.out$aic + while(!mystop){ + mylist[[i]] <- arima.out + arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector) + aic <- c(aic, arima.out$aic) + last.arma <- armaGR(arima.out, names, length(series)) + mystop <- !last.arma$continue + coeff[i,] <- last.arma[[1]][,1] + pval [i,] <- last.arma[[1]][,4] + i <- i+1 + } + list(coeff, pval, mylist, aic=aic) + } > arimaSelectplot <- function(arimaSelect.out,noms,choix){ + noms <- names(arimaSelect.out[[3]][[1]]$coef) + coeff <- arimaSelect.out[[1]] + k <- min(which(is.na(coeff[,1])))-1 + coeff <- coeff[1:k,] + pval <- arimaSelect.out[[2]][1:k,] + aic <- arimaSelect.out$aic[1:k] + coeff[coeff==0] <- NA + n <- ncol(coeff) + if(missing(choix)) choix <- k + layout(matrix(c(1,1,1,2, + 3,3,3,2, + 3,3,3,4, + 5,6,7,7),nr=4), + widths=c(10,35,45,15), + heights=c(30,30,15,15)) + couleurs <- rainbow(75)[1:50]#(50) + ticks <- pretty(coeff) + par(mar=c(1,1,3,1)) + plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA) + points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA) + title('aic',line=2) + par(mar=c(3,0,0,0)) + plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1)) + rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)), + xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)), + ytop = rep(1,50), + ybottom= rep(0,50),col=couleurs,border=NA) + axis(1,ticks) + rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0) + text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2) + par(mar=c(1,1,3,1)) + image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks)) + for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) { + if(pval[j,i]<.01) symb = 'green' + else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange' + else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red' + else symb = 'black' + polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5), + c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5), + col=symb) + if(j==choix) { + rect(xleft=i-.5, + xright=i+.5, + ybottom=k-j+1.5, + ytop=k-j+.5, + lwd=4) + text(i, + k-j+1, + round(coeff[j,i],2), + cex=1.2, + font=2) + } + else{ + rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5) + text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1) + } + } + axis(3,1:n,noms) + par(mar=c(0.5,0,0,0.5)) + plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8)) + cols <- c('green','orange','red','black') + niv <- c('0','0.01','0.05','0.1') + for(i in 0:3){ + polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i), + c(.4 ,.7 , .4 , .4), + col=cols[i+1]) + text(2*i,0.5,niv[i+1],cex=1.5) + } + text(8,.5,1,cex=1.5) + text(4,0,'p-value',cex=2) + box() + residus <- arimaSelect.out[[3]][[choix]]$res + par(mar=c(1,2,4,1)) + acf(residus,main='') + title('acf',line=.5) + par(mar=c(1,2,4,1)) + pacf(residus,main='') + title('pacf',line=.5) + par(mar=c(2,2,4,1)) + qqnorm(residus,main='') + title('qq-norm',line=.5) + qqline(residus) + residus + } > if (par2 == 0) x <- log(x) > if (par2 != 0) x <- x^par2 > (selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5))) [[1]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.3112516 -0.01404531 0.1528977 0.07302858 0.2836749 -0.2341154 [2,] 0.3111731 -0.01395184 0.1530634 0.07270953 0.2837041 -0.2343458 [3,] 0.3075595 0.00000000 0.1490761 0.07456498 0.2809840 -0.2345150 [4,] 0.3036403 0.00000000 0.1591967 0.07914200 0.2907032 -0.2388803 [5,] 0.3099162 0.00000000 0.1546916 0.06536032 0.2945806 -0.2495987 [6,] 0.3219351 0.00000000 0.1722114 0.00000000 0.3114744 -0.2563570 [7,] 0.3347811 0.00000000 0.0000000 0.00000000 0.3224007 -0.2296240 [8,] 0.2772807 0.00000000 0.0000000 0.00000000 0.2628630 0.0000000 [9,] NA NA NA NA NA NA [10,] NA NA NA NA NA NA [11,] NA NA NA NA NA NA [12,] NA NA NA NA NA NA [13,] NA NA NA NA NA NA [14,] NA NA NA NA NA NA [15,] NA NA NA NA NA NA [16,] NA NA NA NA NA NA [17,] NA NA NA NA NA NA [18,] NA NA NA NA NA NA [19,] NA NA NA NA NA NA [20,] NA NA NA NA NA NA [21,] NA NA NA NA NA NA [22,] NA NA NA NA NA NA [,7] [,8] [,9] [,10] [,11] [1,] -0.05397746 0.04281570 -0.001164903 -0.2355032 0.2897575 [2,] -0.05389406 0.04251355 0.000000000 -0.2357857 0.2897728 [3,] -0.05734166 0.04530153 0.000000000 -0.2368503 0.2898494 [4,] -0.04858676 0.00000000 0.000000000 -0.2373438 0.2974084 [5,] 0.00000000 0.00000000 0.000000000 -0.2463997 0.2925207 [6,] 0.00000000 0.00000000 0.000000000 -0.2501680 0.2928648 [7,] 0.00000000 0.00000000 0.000000000 -0.2721093 0.3206334 [8,] 0.00000000 0.00000000 0.000000000 -0.2793667 0.2447819 [9,] NA NA NA NA NA [10,] NA NA NA NA NA [11,] NA NA NA NA NA [12,] NA NA NA NA NA [13,] NA NA NA NA NA [14,] NA NA NA NA NA [15,] NA NA NA NA NA [16,] NA NA NA NA NA [17,] NA NA NA NA NA [18,] NA NA NA NA NA [19,] NA NA NA NA NA [20,] NA NA NA NA NA [21,] NA NA NA NA NA [22,] NA NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.00809 0.90281 0.19591 0.53387 0.01918 0.05428 0.64401 0.71436 0.9919 [2,] 0.00797 0.90336 0.18986 0.52208 0.01914 0.04967 0.64345 0.70775 NA [3,] 0.00662 NA 0.18294 0.50767 0.01815 0.04949 0.61163 0.68337 NA [4,] 0.00712 NA 0.14512 0.48190 0.01283 0.04507 0.66164 NA NA [5,] 0.00577 NA 0.15539 0.54593 0.01166 0.03281 NA NA NA [6,] 0.00365 NA 0.10221 NA 0.00616 0.02811 NA NA NA [7,] 0.00300 NA NA NA 0.00533 0.05001 NA NA NA [8,] 0.01314 NA NA NA 0.02148 NA NA NA NA [9,] NA NA NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA NA [17,] NA NA NA NA NA NA NA NA NA [18,] NA NA NA NA NA NA NA NA NA [19,] NA NA NA NA NA NA NA NA NA [20,] NA NA NA NA NA NA NA NA NA [21,] NA NA NA NA NA NA NA NA NA [22,] NA NA NA NA NA NA NA NA NA [,10] [,11] [1,] 0.04237 0.01248 [2,] 0.03620 0.01245 [3,] 0.03479 0.01239 [4,] 0.03471 0.00942 [5,] 0.02594 0.01029 [6,] 0.02386 0.01045 [7,] 0.01503 0.00544 [8,] 0.01460 0.02700 [9,] NA NA [10,] NA NA [11,] NA NA [12,] NA NA [13,] NA NA [14,] NA NA [15,] NA NA [16,] NA NA [17,] NA NA [18,] NA NA [19,] NA NA [20,] NA NA [21,] NA NA [22,] NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 0.3113 -0.0140 0.1529 0.0730 0.2837 -0.2341 -0.0540 0.0428 s.e. 0.1135 0.1145 0.1169 0.1167 0.1177 0.1192 0.1162 0.1164 ar9 ar10 ar11 -0.0012 -0.2355 0.2898 s.e. 0.1142 0.1135 0.1124 sigma^2 estimated as 19768: log likelihood = -440.19, aic = 904.38 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 0.3113 -0.0140 0.1529 0.0730 0.2837 -0.2341 -0.0540 0.0428 s.e. 0.1135 0.1145 0.1169 0.1167 0.1177 0.1192 0.1162 0.1164 ar9 ar10 ar11 -0.0012 -0.2355 0.2898 s.e. 0.1142 0.1135 0.1124 sigma^2 estimated as 19768: log likelihood = -440.19, aic = 904.38 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3112 -0.0140 0.1531 0.0727 0.2837 -0.2343 -0.0539 0.0425 0 s.e. 0.1133 0.1144 0.1154 0.1129 0.1178 0.1169 0.1158 0.1129 0 ar10 ar11 -0.2358 0.2898 s.e. 0.1100 0.1124 sigma^2 estimated as 19768: log likelihood = -440.19, aic = 902.38 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.3076 0 0.1491 0.0746 0.2810 -0.2345 -0.0573 0.0453 0 s.e. 0.1093 0 0.1106 0.1119 0.1157 0.1170 0.1123 0.1105 0 ar10 ar11 -0.2369 0.2898 s.e. 0.1097 0.1124 sigma^2 estimated as 19772: log likelihood = -440.2, aic = 900.4 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.3036 0 0.1592 0.0791 0.2907 -0.2389 -0.0486 0 0 -0.2373 s.e. 0.1090 0 0.1079 0.1118 0.1134 0.1168 0.1105 0 0 0.1099 ar11 0.2974 s.e. 0.1109 sigma^2 estimated as 19824: log likelihood = -440.28, aic = 898.57 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.3099 0 0.1547 0.0654 0.2946 -0.2496 0 0 0 -0.2464 s.e. 0.1084 0 0.1076 0.1076 0.1133 0.1143 0 0 0 0.1080 ar11 0.2925 s.e. 0.1105 sigma^2 estimated as 19882: log likelihood = -440.38, aic = 896.76 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.3219 0 0.1722 0 0.3115 -0.2564 0 0 0 -0.2502 0.2929 s.e. 0.1066 0 0.1038 0 0.1099 0.1141 0 0 0 0.1080 0.1109 sigma^2 estimated as 19999: log likelihood = -440.56, aic = 895.13 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.3348 0 0 0 0.3224 -0.2296 0 0 0 -0.2721 0.3206 s.e. 0.1085 0 0 0 0.1117 0.1149 0 0 0 0.1089 0.1114 sigma^2 estimated as 20798: log likelihood = -441.92, aic = 895.84 [[3]][[9]] NULL [[3]][[10]] NULL [[3]][[11]] NULL $aic [1] 904.3834 902.3835 900.3983 898.5660 896.7590 895.1268 895.8423 897.6832 Warning messages: 1: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 2: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 3: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 4: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 5: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 6: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 7: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE > postscript(file="/var/www/html/rcomp/tmp/129ok1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > resid <- arimaSelectplot(selection) > dev.off() null device 1 > resid Time Series: Start = 1 End = 70 Frequency = 1 [1] 2.350438 77.682539 -50.617920 65.025862 -76.209907 53.017259 [7] -33.190893 66.546572 106.991995 87.511201 42.044702 67.798503 [13] -18.709571 69.884501 -47.618974 33.899820 -81.321369 68.234662 [19] 58.605524 71.168387 -20.900167 23.362070 30.885954 102.208258 [25] 88.602541 99.023155 34.673757 -47.081072 -107.176446 -219.821762 [31] 176.207476 114.969535 98.184466 171.021538 62.910164 -40.469037 [37] 82.626584 -30.114581 -213.362574 269.429788 121.757132 -137.791843 [43] -39.232883 -269.013762 68.276223 132.172350 -315.452335 90.441566 [49] -231.785580 -34.964086 -74.745253 282.632166 -189.101258 -266.629993 [55] -254.024703 166.648781 -293.581384 -566.906400 77.489672 47.405633 [61] 7.284146 -13.266420 43.588018 62.516767 96.040548 21.430092 [67] 61.282579 51.650018 175.554724 21.423116 > postscript(file="/var/www/html/rcomp/tmp/2kv0a1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(resid,length(resid)/2, main='Residual Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/31ode1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4xgnu1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > cpgram(resid, main='Residual Cumulative Periodogram') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/54i8w1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(resid, main='Residual Histogram', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/6lbna1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7b4hr1261592811.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(resid, main='Residual Normal Q-Q Plot') > qqline(resid) > dev.off() null device 1 > ncols <- length(selection[[1]][1,]) > nrows <- length(selection[[2]][,1])-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,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Iteration', header=TRUE) > for (i in 1:ncols) { + a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE) + } > a<-table.row.end(a) > for (j in 1:nrows) { + a<-table.row.start(a) + mydum <- 'Estimates (' + mydum <- paste(mydum,j) + mydum <- paste(mydum,')') + a<-table.element(a,mydum, header=TRUE) + for (i in 1:ncols) { + a<-table.element(a,round(selection[[1]][j,i],4)) + } + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'(p-val)', header=TRUE) + for (i in 1:ncols) { + mydum <- '(' + mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='') + mydum <- paste(mydum,')') + a<-table.element(a,mydum) + } + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/8thzm1261592811.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Value', 1,TRUE) > a<-table.row.end(a) > for (i in (par4*par5+par3):length(resid)) { + a<-table.row.start(a) + a<-table.element(a,resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/9syw51261592811.tab") > > try(system("convert tmp/129ok1261592811.ps tmp/129ok1261592811.png",intern=TRUE)) character(0) > try(system("convert tmp/2kv0a1261592811.ps tmp/2kv0a1261592811.png",intern=TRUE)) character(0) > try(system("convert tmp/31ode1261592811.ps tmp/31ode1261592811.png",intern=TRUE)) character(0) > try(system("convert tmp/4xgnu1261592811.ps tmp/4xgnu1261592811.png",intern=TRUE)) character(0) > try(system("convert tmp/54i8w1261592811.ps tmp/54i8w1261592811.png",intern=TRUE)) character(0) > try(system("convert tmp/6lbna1261592811.ps tmp/6lbna1261592811.png",intern=TRUE)) character(0) > try(system("convert tmp/7b4hr1261592811.ps tmp/7b4hr1261592811.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.324 1.043 4.084