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Type 'q()' to quit R. > x <- c(46402 + ,45329 + ,42185 + ,49341 + ,50472 + ,33020 + ,29567 + ,22870 + ,25730 + ,32609 + ,23536 + ,15135 + ,36776 + ,29982 + ,38062 + ,34226 + ,24906 + ,30233 + ,27405 + ,20784 + ,22886 + ,25425 + ,20838 + ,15655 + ,37158 + ,36364 + ,43213 + ,31635 + ,30113 + ,29985 + ,20919 + ,19429 + ,21427 + ,26064 + ,20109 + ,15369 + ,35466 + ,25954 + ,33504 + ,28115 + ,28501 + ,28618 + ,21434 + ,20177 + ,21484 + ,25642 + ,23515 + ,12941 + ,36190 + ,37785 + ,38407 + ,33326 + ,30304 + ,27576 + ,27048 + ,17291 + ,21018 + ,26792 + ,19426 + ,13927 + ,35647 + ,31746 + ,31277 + ,31583 + ,25607 + ,28151 + ,24947 + ,18077 + ,23429 + ,26313 + ,18862 + ,14753 + ,36409 + ,33163 + ,34122 + ,35225 + ,28249 + ,30374 + ,26311 + ,22069 + ,23651 + ,28628 + ,23187 + ,14727 + ,43080 + ,32519 + ,39657 + ,33614 + ,28671 + ,34243 + ,27336 + ,22916 + ,24537 + ,26128 + ,22602 + ,15744 + ,41086 + ,39690 + ,43129 + ,37863 + ,35953 + ,29133 + ,24693 + ,22205 + ,21725 + ,27192 + ,21790 + ,13253 + ,37702 + ,30364 + ,32609 + ,30212 + ,29965 + ,28352 + ,25814 + ,22414 + ,20506 + ,28806 + ,22228 + ,13971 + ,36845 + ,35338 + ,35022 + ,34777 + ,26887 + ,23970 + ,22780 + ,17351 + ,21382 + ,24561 + ,17409 + ,11514 + ,31514 + ,27071 + ,29462 + ,26105 + ,22397 + ,23843 + ,21705 + ,18089 + ,20764 + ,25316 + ,17704 + ,15548 + ,28029 + ,29383 + ,36438 + ,32034 + ,22679 + ,24319 + ,18004 + ,17537 + ,20366 + ,22782 + ,19169 + ,13807 + ,29743 + ,25591 + ,29096 + ,26482 + ,22405 + ,27044 + ,17970 + ,18730 + ,19684 + ,19785 + ,18479 + ,10698 + ,31956 + ,29506 + ,34506 + ,27165 + ,26736 + ,23691 + ,18157 + ,17328 + ,18205 + ,20995 + ,17382 + ,9367 + ,31124 + ,26551 + ,30651 + ,25859 + ,25100 + ,25778 + ,20418 + ,18688 + ,20424 + ,24776 + ,19814 + ,12738) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '-0.6' > par1 = 'FALSE' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$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,] 0.08113261 0.1839356 0.2384050 -0.9040672 0.01816943 -0.02504986 [2,] 0.07542631 0.1822962 0.2394308 -0.9010737 0.00000000 -0.02890907 [3,] 0.06629676 0.1794593 0.2419035 -0.8991719 0.00000000 0.00000000 [4,] 0.00000000 0.1443215 0.2196775 -1.1671985 0.00000000 0.00000000 [5,] 0.00000000 0.0000000 0.1777671 -1.2617931 0.00000000 0.00000000 [6,] NA NA NA NA NA NA [7,] NA NA NA NA NA NA [8,] NA NA NA NA NA NA [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 [,7] [1,] -0.8845687 [2,] -1.1440343 [3,] -1.1347488 [4,] -1.1327273 [5,] -1.1279828 [6,] NA [7,] NA [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.49002 0.07114 0.00954 0 0.86631 0.80481 0 [2,] 0.50701 0.07486 0.00931 0 NA 0.76826 0 [3,] 0.54776 0.08135 0.00878 0 NA NA 0 [4,] NA 0.11988 0.01190 0 NA NA 0 [5,] NA NA 0.03113 0 NA NA 0 [6,] NA NA NA NA NA NA NA [7,] NA NA NA NA NA NA NA [8,] NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0811 0.1839 0.2384 -0.9041 0.0182 -0.0250 -0.8846 s.e. 0.1173 0.1013 0.0910 0.0802 0.1078 0.1012 0.1059 sigma^2 estimated as 1.902e-08: log likelihood = 1327.3, aic = -2638.59 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0811 0.1839 0.2384 -0.9041 0.0182 -0.0250 -0.8846 s.e. 0.1173 0.1013 0.0910 0.0802 0.1078 0.1012 0.1059 sigma^2 estimated as 1.902e-08: log likelihood = 1327.3, aic = -2638.59 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0754 0.1823 0.2394 -0.9011 0 -0.0289 -1.1440 s.e. 0.1135 0.1018 0.0911 0.0793 0 0.0980 0.1067 sigma^2 estimated as 1.457e-08: log likelihood = 1327.28, aic = -2640.56 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0663 0.1795 0.2419 -0.8992 0 0 -1.1347 s.e. 0.1101 0.1024 0.0913 0.0801 0 0 0.1028 sigma^2 estimated as 1.481e-08: log likelihood = 1327.24, aic = -2642.47 [[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 ma1 sar1 sar2 sma1 0 0.1443 0.2197 -1.1672 0 0 -1.1327 s.e. 0 0.0924 0.0865 0.0881 0 0 0.1027 sigma^2 estimated as 1.094e-08: log likelihood = 1327.08, aic = -2644.15 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -2638.590 -2640.561 -2642.475 -2644.151 -2643.672 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 log(s2) : NaNs produced 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 > postscript(file="/var/www/html/freestat/rcomp/tmp/1s5ba1230109597.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 = 192 Frequency = 1 [1] 9.152020e-07 4.266487e-07 3.391873e-07 1.199924e-07 7.705594e-08 [6] 4.660696e-07 5.231303e-07 7.825737e-07 5.388615e-07 2.002084e-07 [11] 5.864146e-07 4.460019e-07 -6.616669e-06 9.101175e-05 -1.151196e-04 [16] 4.828129e-05 2.758735e-04 -1.359727e-04 -1.872565e-04 -1.254673e-04 [21] -8.649485e-06 7.126544e-05 -1.195454e-05 -1.746212e-04 -1.364978e-04 [26] -1.390395e-04 -1.343224e-04 1.215522e-04 4.021117e-05 -4.314788e-05 [31] 1.638749e-04 2.155577e-06 -2.068868e-05 -8.389388e-05 -1.536440e-05 [36] -1.109611e-04 -5.715704e-05 1.588354e-04 2.048062e-05 5.812190e-05 [41] -8.822421e-05 -1.026562e-04 1.299312e-05 -8.731413e-05 -4.023757e-05 [46] -4.717009e-05 -1.780034e-04 1.618594e-04 -6.857887e-05 -2.208388e-04 [51] -1.076809e-04 -4.625725e-06 2.081160e-05 4.401346e-05 -1.578202e-04 [56] 1.707757e-04 3.455174e-05 -4.598296e-05 3.454451e-05 1.116856e-06 [61] -5.705256e-05 -5.775839e-05 9.024418e-05 -2.147851e-05 8.064247e-05 [66] -7.495956e-05 -1.107772e-04 1.604139e-05 -1.121006e-04 -2.464280e-05 [71] 9.300305e-05 -6.295038e-05 -6.932434e-05 -7.432880e-05 2.937943e-05 [76] -8.452569e-05 1.660066e-05 -6.806613e-05 -7.535901e-05 -1.545454e-04 [81] -2.247298e-05 -9.406531e-06 -5.909943e-05 2.921336e-05 -8.164976e-05 [86] 6.360704e-05 -3.915926e-05 4.323925e-05 4.576133e-05 -1.133352e-04 [91] -7.148139e-05 -1.152193e-04 6.081477e-06 1.255033e-04 4.649095e-06 [96] -6.717768e-05 -3.918841e-05 -8.964889e-05 -4.738380e-05 -9.692708e-06 [101] -7.268409e-05 1.350075e-04 1.174634e-04 -4.009506e-05 8.156777e-05 [106] 2.075384e-05 1.097363e-05 1.575166e-04 -1.857622e-06 6.215920e-05 [111] 5.818562e-05 6.052442e-05 -7.282137e-05 -2.217589e-05 -7.656462e-05 [116] -1.241424e-04 1.087276e-04 -5.971391e-05 -4.225475e-05 3.550674e-05 [121] 3.154346e-05 -6.887260e-05 1.553252e-05 -3.977979e-05 1.093961e-04 [126] 1.870877e-04 5.386472e-05 1.095971e-04 -8.253230e-05 -1.271696e-05 [131] 1.424340e-04 2.402920e-04 -2.409054e-05 -2.551566e-05 -2.405638e-05 [136] 6.582200e-05 9.544613e-05 -1.941352e-05 -7.473114e-05 -7.248755e-05 [141] -9.468192e-05 -9.099558e-05 7.590612e-05 -2.733609e-04 1.416279e-04 [146] -1.146002e-05 -1.065656e-04 -1.194212e-04 1.695162e-04 7.475245e-05 [151] 2.330836e-04 -3.020603e-05 -1.047838e-04 -3.595151e-05 -6.498074e-05 [156] -9.116173e-05 3.432802e-05 1.131476e-04 4.964983e-05 2.088357e-05 [161] 3.706218e-05 -1.630639e-04 1.472768e-04 -1.056667e-04 -2.543747e-05 [166] 1.342816e-04 -3.028127e-05 2.619551e-04 -1.616648e-04 -1.659125e-04 [171] -2.087482e-04 4.543966e-05 -8.668933e-05 6.224034e-05 1.500342e-04 [176] 2.569671e-05 2.723550e-05 1.118636e-05 5.539547e-06 4.428155e-04 [181] -1.589900e-04 -1.372200e-04 -1.777190e-04 2.457982e-05 -9.202936e-05 [186] -1.042854e-04 -4.872751e-05 -6.613946e-05 -5.477141e-05 -9.466293e-05 [191] -6.946534e-05 -2.254337e-05 > postscript(file="/var/www/html/freestat/rcomp/tmp/23ags1230109597.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/freestat/rcomp/tmp/3x25y1230109597.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/freestat/rcomp/tmp/49sm81230109597.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/freestat/rcomp/tmp/5oq0w1230109597.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/freestat/rcomp/tmp/6nlaa1230109597.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/freestat/rcomp/tmp/7n6731230109597.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/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,'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/freestat/rcomp/tmp/8r3ji1230109597.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/freestat/rcomp/tmp/9u5mt1230109597.tab") > > system("convert tmp/1s5ba1230109597.ps tmp/1s5ba1230109597.png") > system("convert tmp/23ags1230109597.ps tmp/23ags1230109597.png") > system("convert tmp/3x25y1230109597.ps tmp/3x25y1230109597.png") > system("convert tmp/49sm81230109597.ps tmp/49sm81230109597.png") > system("convert tmp/5oq0w1230109597.ps tmp/5oq0w1230109597.png") > system("convert tmp/6nlaa1230109597.ps tmp/6nlaa1230109597.png") > system("convert tmp/7n6731230109597.ps tmp/7n6731230109597.png") > > > proc.time() user system elapsed 17.292 2.063 18.663