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Type 'q()' to quit R. > x <- c(2.83,2.72,2.73,2.72,2.77,2.61,2.47,2.30,2.38,2.43,2.39,2.60,2.84,2.87,2.92,2.08,3.33,3.48,3.57,3.66,3.77,3.75,3.75,3.81,3.82,3.89,4.05,4.10,4.07,4.26,4.40,4.61,4.63,4.48,4.46,4.45,4.32,4.52,4.21,3.97,4.12,4.50,4.73,5.26,4.52,4.94,4.95,3.52,3.85,2.41,2.95,2.68,2.53,2.44,2.16,2.20,2.10,2.29,2.03,2.05,2.07) > par9 = '0' > par8 = '0' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '2' > 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,] -1.196735 -0.8649558 -0.6785985 -0.6987135 -0.45323442 -0.4546610 [2,] -1.184217 -0.8441901 -0.6364407 -0.6452463 -0.40680174 -0.4110502 [3,] -1.176577 -0.8081142 -0.5902430 -0.6094270 -0.37041671 -0.3510651 [4,] -1.155004 -0.7767930 -0.5642532 -0.5824444 -0.32377629 -0.3071580 [5,] -1.129089 -0.6965078 -0.4359351 -0.3999394 -0.06578604 0.0000000 [6,] -1.110571 -0.6673997 -0.3918858 -0.3303384 0.00000000 0.0000000 [7,] -1.093889 -0.6797327 -0.3763737 -0.2866476 0.00000000 0.0000000 [8,] -1.093888 -0.6759762 -0.3897136 -0.2867356 0.00000000 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,] -0.5426240 -0.46794985 -0.25873643 -0.19806665 -0.09313655 [2,] -0.4769790 -0.40562126 -0.18206423 -0.09200481 0.00000000 [3,] -0.4191220 -0.32806120 -0.07139555 0.00000000 0.00000000 [4,] -0.3599493 -0.24282752 0.00000000 0.00000000 0.00000000 [5,] -0.1457532 -0.16183875 0.00000000 0.00000000 0.00000000 [6,] -0.1648087 -0.16850270 0.00000000 0.00000000 0.00000000 [7,] 0.0000000 -0.05014109 0.00000000 0.00000000 0.00000000 [8,] 0.0000000 0.00000000 0.00000000 0.00000000 0.00000000 [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 0.00009 0.00486 0.00541 0.07041 0.06551 0.02884 0.04961 0.25215 [2,] 0 0.00011 0.00620 0.00666 0.09070 0.08390 0.03654 0.06505 0.35247 [3,] 0 0.00014 0.00830 0.00877 0.11468 0.11551 0.05012 0.08824 0.56530 [4,] 0 0.00015 0.00996 0.01038 0.14105 0.14286 0.05470 0.04634 NA [5,] 0 0.00045 0.02989 0.03418 0.62062 NA 0.21630 0.14090 NA [6,] 0 0.00041 0.02866 0.00809 NA NA 0.14029 0.12355 NA [7,] 0 0.00042 0.04088 0.02003 NA NA NA 0.50625 NA [8,] 0 0.00046 0.03423 0.02063 NA 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,] 0.32418 0.49796 [2,] 0.46524 NA [3,] NA NA [4,] NA NA [5,] NA NA [6,] NA NA [7,] NA NA [8,] NA NA [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 -1.1967 -0.8650 -0.6786 -0.6987 -0.4532 -0.4547 -0.5426 -0.4679 s.e. 0.1297 0.2029 0.2300 0.2400 0.2450 0.2413 0.2409 0.2324 ar9 ar10 ar11 -0.2587 -0.1981 -0.0931 s.e. 0.2233 0.1989 0.1364 sigma^2 estimated as 0.1231: log likelihood = -23.14, aic = 70.27 [[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 -1.1967 -0.8650 -0.6786 -0.6987 -0.4532 -0.4547 -0.5426 -0.4679 s.e. 0.1297 0.2029 0.2300 0.2400 0.2450 0.2413 0.2409 0.2324 ar9 ar10 ar11 -0.2587 -0.1981 -0.0931 s.e. 0.2233 0.1989 0.1364 sigma^2 estimated as 0.1231: log likelihood = -23.14, aic = 70.27 [[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 -1.1842 -0.8442 -0.6364 -0.6452 -0.4068 -0.4111 -0.477 -0.4056 s.e. 0.1291 0.2016 0.2227 0.2279 0.2358 0.2331 0.222 0.2150 ar9 ar10 ar11 -0.1821 -0.092 0 s.e. 0.1940 0.125 0 sigma^2 estimated as 0.1243: log likelihood = -23.37, aic = 68.74 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 -1.1766 -0.8081 -0.5902 -0.6094 -0.3704 -0.3511 -0.4191 -0.3281 s.e. 0.1294 0.1966 0.2149 0.2236 0.2308 0.2192 0.2089 0.1888 ar9 ar10 ar11 -0.0714 0 0 s.e. 0.1234 0 0 sigma^2 estimated as 0.1257: log likelihood = -23.64, aic = 67.27 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 -1.1550 -0.7768 -0.5643 -0.5824 -0.3238 -0.3072 -0.3599 -0.2428 s.e. 0.1244 0.1899 0.2109 0.2190 0.2166 0.2065 0.1831 0.1190 ar9 ar10 ar11 0 0 0 s.e. 0 0 0 sigma^2 estimated as 0.1265: log likelihood = -23.8, aic = 65.61 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 -1.1291 -0.6965 -0.4359 -0.3999 -0.0658 0 -0.1458 -0.1618 0 s.e. 0.1260 0.1862 0.1953 0.1840 0.1321 0 0.1165 0.1083 0 ar10 ar11 0 0 s.e. 0 0 sigma^2 estimated as 0.1318: log likelihood = -24.88, aic = 65.76 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 -1.1106 -0.6674 -0.3919 -0.3303 0 0 -0.1648 -0.1685 0 0 s.e. 0.1208 0.1771 0.1743 0.1201 0 0 0.1101 0.1077 0 0 ar11 0 s.e. 0 sigma^2 estimated as 0.1324: log likelihood = -25, aic = 64.01 [[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 -1.0939 -0.6797 -0.3764 -0.2866 0 0 0 -0.0501 0 0 s.e. 0.1224 0.1809 0.1797 0.1197 0 0 0 0.0749 0 0 ar11 0 s.e. 0 sigma^2 estimated as 0.1380: log likelihood = -26.09, aic = 64.18 [[3]][[9]] NULL [[3]][[10]] NULL [[3]][[11]] NULL $aic [1] 70.27330 68.73571 67.27305 65.60636 65.75809 64.00532 64.18106 62.62453 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/1n7l31260446770.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 = 61 Frequency = 1 [1] 0.001265614 -0.004042793 0.070755145 0.056827042 0.087957139 [6] -0.139637000 -0.143298564 -0.133163292 0.168050081 0.166682341 [11] 0.047575168 0.215649186 0.305675448 -0.060252853 -0.120026741 [16] -0.929417211 1.072129696 0.527093127 -0.176388930 -0.269299319 [21] 0.165802626 -0.456546592 -0.124806945 -0.043585563 0.090827242 [26] -0.038802398 0.146953612 0.027614136 -0.129898982 0.102272434 [31] 0.121676957 0.106214076 -0.090051054 -0.283005372 -0.168583853 [36] -0.020310178 -0.143153716 0.216760184 -0.192063694 -0.302360086 [41] 0.200187372 0.598317114 0.253364443 0.459607646 -0.851451641 [46] 0.000700255 0.039993738 -1.488495027 -0.001784410 -0.633840036 [51] 0.573121236 0.417460870 0.354459708 -0.063299402 0.199342901 [56] -0.106277799 0.226123712 0.211308120 -0.062678870 -0.016706943 [61] 0.075443854 > postscript(file="/var/www/html/rcomp/tmp/2zq5c1260446770.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/3jz9p1260446770.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/4z8p41260446770.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/5zfrr1260446770.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/6eeun1260446770.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/7rzrv1260446770.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/8tywt1260446770.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/98eyc1260446770.tab") > > system("convert tmp/1n7l31260446770.ps tmp/1n7l31260446770.png") > system("convert tmp/2zq5c1260446770.ps tmp/2zq5c1260446770.png") > system("convert tmp/3jz9p1260446770.ps tmp/3jz9p1260446770.png") > system("convert tmp/4z8p41260446770.ps tmp/4z8p41260446770.png") > system("convert tmp/5zfrr1260446770.ps tmp/5zfrr1260446770.png") > system("convert tmp/6eeun1260446770.ps tmp/6eeun1260446770.png") > system("convert tmp/7rzrv1260446770.ps tmp/7rzrv1260446770.png") > > > proc.time() user system elapsed 4.012 1.082 13.075