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Type 'q()' to quit R. > x <- c(627000,696000,825000,677000,656000,785000,412000,352000,839000,729000,696000,641000,695000,638000,762000,635000,721000,854000,418000,367000,824000,687000,601000,676000,740000,691000,683000,594000,729000,731000,386000,331000,707000,715000,657000,653000,642000,643000,718000,654000,632000,731000,392000,344000,792000,852000,649000,629000,685000,617000,715000,715000,629000,916000,531000,357000,917000,828000,708000,858000,775000,785000,1006000,789000,734000,906000,532000,387000,991000,841000) > par9 = '0' > par8 = '0' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '-0.5' > 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,] -0.5765419 -0.6145476 -0.4719197 -0.5395842 -0.2456981 -0.2020255 [2,] -0.5809945 -0.6217042 -0.4817977 -0.5545101 -0.2619337 -0.2225454 [3,] -0.5476658 -0.6015808 -0.4949631 -0.5504067 -0.2512704 -0.2435686 [4,] -0.5404655 -0.6228517 -0.5102411 -0.5527338 -0.2969936 -0.2555328 [5,] -0.5235636 -0.5959836 -0.4774645 -0.5725074 -0.2689931 -0.2236348 [6,] -0.5007099 -0.5033462 -0.3784458 -0.4483063 -0.1213115 0.0000000 [7,] -0.4522184 -0.4783770 -0.3102523 -0.3938668 0.0000000 0.0000000 [8,] -0.4670954 -0.5171785 -0.2816308 -0.4119720 0.0000000 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,] -0.1843827 0.02595372 0.1972954 0.1395014 0.2442126 [2,] -0.2059426 0.00000000 0.1797456 0.1262063 0.2369574 [3,] -0.2309995 0.00000000 0.1284935 0.0000000 0.1878372 [4,] -0.2827676 0.00000000 0.0000000 0.0000000 0.1516986 [5,] -0.2979954 0.00000000 0.0000000 0.0000000 0.0000000 [6,] -0.2007628 0.00000000 0.0000000 0.0000000 0.0000000 [7,] -0.1673274 0.00000000 0.0000000 0.0000000 0.0000000 [8,] 0.0000000 0.00000000 0.0000000 0.0000000 0.0000000 [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,] 0.00006 0.00019 0.00998 0.00621 0.24583 0.35289 0.40755 0.90078 0.30423 [2,] 0.00003 0.00006 0.00358 0.00041 0.11725 0.11913 0.14396 NA 0.16908 [3,] 0.00005 0.00009 0.00294 0.00049 0.13935 0.09037 0.10009 NA 0.28792 [4,] 0.00007 0.00005 0.00236 0.00054 0.07549 0.07871 0.03483 NA NA [5,] 0.00015 0.00010 0.00417 0.00038 0.10821 0.12460 0.02827 NA NA [6,] 0.00031 0.00037 0.01387 0.00129 0.38239 NA 0.09923 NA NA [7,] 0.00038 0.00057 0.01893 0.00152 NA NA 0.15193 NA NA [8,] 0.00030 0.00023 0.03532 0.00110 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,] 0.39854 0.09467 [2,] 0.31867 0.07750 [3,] NA 0.13395 [4,] NA 0.21589 [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 -0.5765 -0.6145 -0.4719 -0.5396 -0.2457 -0.2020 -0.1844 0.0260 s.e. 0.1334 0.1542 0.1771 0.1900 0.2096 0.2157 0.2210 0.2073 ar9 ar10 ar11 0.1973 0.1395 0.2442 s.e. 0.1903 0.1640 0.1437 sigma^2 estimated as 3.018e-09: log likelihood = 476.97, aic = -929.94 [[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 -0.5765 -0.6145 -0.4719 -0.5396 -0.2457 -0.2020 -0.1844 0.0260 s.e. 0.1334 0.1542 0.1771 0.1900 0.2096 0.2157 0.2210 0.2073 ar9 ar10 ar11 0.1973 0.1395 0.2442 s.e. 0.1903 0.1640 0.1437 sigma^2 estimated as 3.018e-09: log likelihood = 476.97, aic = -929.94 [[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 -0.5810 -0.6217 -0.4818 -0.5545 -0.2619 -0.2225 -0.2059 0 s.e. 0.1284 0.1433 0.1588 0.1480 0.1648 0.1407 0.1391 0 ar9 ar10 ar11 0.1797 0.1262 0.2370 s.e. 0.1291 0.1255 0.1319 sigma^2 estimated as 3.019e-09: log likelihood = 476.96, aic = -931.92 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 -0.5477 -0.6016 -0.4950 -0.5504 -0.2513 -0.2436 -0.2310 0 s.e. 0.1252 0.1427 0.1597 0.1493 0.1677 0.1415 0.1383 0 ar9 ar10 ar11 0.1285 0 0.1878 s.e. 0.1198 0 0.1236 sigma^2 estimated as 3.083e-09: log likelihood = 476.46, aic = -932.93 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 -0.5405 -0.6229 -0.5102 -0.5527 -0.2970 -0.2555 -0.2828 0 0 s.e. 0.1271 0.1423 0.1608 0.1512 0.1642 0.1429 0.1310 0 0 ar10 ar11 0 0.1517 s.e. 0 0.1213 sigma^2 estimated as 3.156e-09: log likelihood = 475.9, aic = -933.81 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 -0.5236 -0.5960 -0.4775 -0.5725 -0.269 -0.2236 -0.2980 0 0 s.e. 0.1294 0.1436 0.1605 0.1524 0.165 0.1436 0.1327 0 0 ar10 ar11 0 0 s.e. 0 0 sigma^2 estimated as 3.263e-09: log likelihood = 475.15, aic = -934.3 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 -0.5007 -0.5033 -0.3784 -0.4483 -0.1213 0 -0.2008 0 0 0 s.e. 0.1313 0.1337 0.1495 0.1330 0.1379 0 0.1200 0 0 0 ar11 0 s.e. 0 sigma^2 estimated as 3.417e-09: log likelihood = 473.98, aic = -933.96 [[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 -0.4522 -0.4784 -0.3103 -0.3939 0 0 -0.1673 0 0 0 s.e. 0.1205 0.1319 0.1288 0.1188 0 0 0.1154 0 0 0 ar11 0 s.e. 0 sigma^2 estimated as 3.469e-09: log likelihood = 473.6, aic = -935.2 [[3]][[9]] NULL [[3]][[10]] NULL [[3]][[11]] NULL $aic [1] -929.9383 -931.9227 -932.9281 -933.8088 -934.3011 -933.9601 -935.1965 [8] -935.1580 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/1lmbs1261244555.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] 7.291309e-07 2.763219e-07 9.832833e-08 1.741793e-07 1.564046e-07 [6] 3.263366e-08 4.275807e-07 4.937276e-07 -1.229523e-07 -3.502913e-08 [11] -5.613771e-09 -6.678293e-07 -4.651768e-06 9.326607e-05 2.145675e-05 [16] 2.692326e-05 -7.878593e-05 6.966377e-06 -2.016535e-05 -3.591654e-05 [21] 3.567255e-05 4.791865e-05 9.460364e-05 -9.813623e-05 -6.361382e-06 [26] -3.991371e-05 8.559726e-05 -1.897079e-05 -5.835482e-06 1.011958e-04 [31] 1.149840e-05 3.387130e-05 7.383687e-06 -5.131242e-05 -8.840393e-05 [36] 1.100834e-05 6.509691e-05 -3.342985e-05 -4.652854e-05 -3.388061e-05 [41] 9.199618e-05 -7.948117e-05 -6.758322e-06 -2.352663e-05 -2.347778e-05 [46] -1.085955e-04 5.956787e-05 5.447233e-05 -4.266467e-05 6.262961e-05 [51] 1.949944e-05 -5.436911e-05 1.006224e-05 -9.250415e-05 -1.547822e-04 [56] 7.202940e-05 -1.463295e-05 8.051618e-05 -3.729370e-05 -4.311844e-05 [61] 7.312588e-06 -8.484104e-05 -5.608451e-05 5.084477e-05 3.814093e-05 [66] 9.173569e-05 2.253168e-05 3.701576e-05 -2.009291e-06 4.221051e-05 > postscript(file="/var/www/html/rcomp/tmp/2kvfk1261244555.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/3h2xi1261244555.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/4h9kf1261244555.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/5xt541261244555.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/6gk6p1261244555.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/7o3uy1261244555.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/892d31261244555.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/9vye01261244555.tab") > > try(system("convert tmp/1lmbs1261244555.ps tmp/1lmbs1261244555.png",intern=TRUE)) character(0) > try(system("convert tmp/2kvfk1261244555.ps tmp/2kvfk1261244555.png",intern=TRUE)) character(0) > try(system("convert tmp/3h2xi1261244555.ps tmp/3h2xi1261244555.png",intern=TRUE)) character(0) > try(system("convert tmp/4h9kf1261244555.ps tmp/4h9kf1261244555.png",intern=TRUE)) character(0) > try(system("convert tmp/5xt541261244555.ps tmp/5xt541261244555.png",intern=TRUE)) character(0) > try(system("convert tmp/6gk6p1261244555.ps tmp/6gk6p1261244555.png",intern=TRUE)) character(0) > try(system("convert tmp/7o3uy1261244555.ps tmp/7o3uy1261244555.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.341 1.074 7.147