R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(115.65 + ,116.00 + ,115.92 + ,116.10 + ,116.44 + ,116.65 + ,117.45 + ,117.58 + ,117.43 + ,117.24 + ,117.25 + ,117.29 + ,117.83 + ,118.22 + ,118.11 + ,118.23 + ,118.15 + ,118.23 + ,119.03 + ,119.38 + ,118.97 + ,118.78 + ,118.97 + ,118.94 + ,119.86 + ,120.09 + ,120.13 + ,120.15 + ,119.90 + ,120.00 + ,120.84 + ,121.17 + ,120.81 + ,121.00 + ,121.12 + ,121.29 + ,122.09 + ,121.88 + ,121.31 + ,121.33 + ,121.45 + ,121.67 + ,122.78 + ,122.84 + ,122.34 + ,122.37 + ,122.72 + ,122.68 + ,122.78 + ,123.08 + ,122.92 + ,123.51 + ,124.18 + ,124.05 + ,124.36 + ,123.87 + ,123.84 + ,123.85 + ,123.83 + ,123.84 + ,124.27 + ,124.56 + ,124.57 + ,124.87 + ,125.08 + ,124.86 + ,124.89 + ,124.58 + ,124.83 + ,124.97 + ,125.19 + ,125.42 + ,125.74 + ,126.07 + ,126.35 + ,126.69 + ,126.85 + ,127.12 + ,127.43 + ,127.49 + ,128.05 + ,127.85 + ,128.35 + ,128.29 + ,128.38 + ,128.80 + ,129.18 + ,130.14 + ,130.77 + ,131.19 + ,131.32 + ,131.41 + ,131.61 + ,131.69 + ,131.94 + ,131.70 + ,132.54 + ,132.74 + ,133.02 + ,132.76 + ,133.05 + ,132.74 + ,133.16 + ,133.10 + ,133.37 + ,133.15 + ,133.18 + ,133.29 + ,133.76 + ,134.51 + ,134.82 + ,134.71 + ,134.52 + ,134.86 + ,135.11 + ,135.28 + ,135.61 + ,135.22 + ,135.47 + ,135.42 + ,135.85 + ,136.27 + ,136.30 + ,136.85 + ,137.05 + ,137.03 + ,137.45 + ,137.49 + ,137.55 + ,138.04 + ,138.03 + ,137.75 + ,138.27 + ,138.99 + ,139.74 + ,139.70 + ,139.97 + ,140.21 + ,140.78 + ,140.80 + ,140.64 + ,140.42 + ,140.85 + ,140.96 + ,141.04 + ,141.71 + ,141.60 + ,142.11 + ,142.59 + ,142.56 + ,143.00 + ,143.18 + ,143.15 + ,143.10 + ,143.45 + ,143.59 + ,143.92 + ,144.66 + ,144.34 + ,144.82 + ,144.49 + ,144.41 + ,144.99 + ,144.95 + ,145.00 + ,145.66 + ,146.68 + ,147.38 + ,147.94 + ,149.12 + ,149.95 + ,150.19 + ,151.16 + ,151.74 + ,152.56 + ,152.09 + ,152.46 + ,152.66 + ,152.38 + ,152.59 + ,152.88 + ,153.29 + ,152.35 + ,152.49 + ,152.20 + ,151.57 + ,151.55 + ,151.79 + ,151.52 + ,151.76 + ,151.92 + ,152.20 + ,152.75 + ,153.49 + ,153.78 + ,154.10 + ,154.62 + ,154.65 + ,154.81 + ,154.92 + ,155.40 + ,155.63 + ,155.76) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '0.0' > 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,] 0.8738196 -0.09862202 0.04272620 -0.7067984 0.9217500 0.03768859 [2,] 0.8634392 -0.09162225 0.04014423 -0.6969727 0.9686646 0.00000000 [3,] 0.9044439 -0.06838034 0.00000000 -0.7403458 0.9687779 0.00000000 [4,] 0.7748633 0.00000000 0.00000000 -0.6457425 0.9663868 0.00000000 [5,] NA NA NA NA NA NA [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,] -0.7744216 [2,] -0.8105774 [3,] -0.8100308 [4,] -0.8015660 [5,] NA [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,] 4e-05 0.33059 0.57005 0.00042 0 0.77398 1e-05 [2,] 6e-05 0.35161 0.59258 0.00054 0 NA 0e+00 [3,] 2e-05 0.44118 NA 0.00013 0 NA 0e+00 [4,] 1e-05 NA NA 0.00209 0 NA 0e+00 [5,] NA NA NA NA NA NA NA [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 0.8738 -0.0986 0.0427 -0.7068 0.9217 0.0377 -0.7744 s.e. 0.2084 0.1011 0.0751 0.1968 0.1719 0.1311 0.1701 sigma^2 estimated as 5.425e-06: log likelihood = 933.42, aic = -1850.83 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8738 -0.0986 0.0427 -0.7068 0.9217 0.0377 -0.7744 s.e. 0.2084 0.1011 0.0751 0.1968 0.1719 0.1311 0.1701 sigma^2 estimated as 5.425e-06: log likelihood = 933.42, aic = -1850.83 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8634 -0.0916 0.0401 -0.6970 0.9687 0 -0.8106 s.e. 0.2094 0.0981 0.0749 0.1981 0.0289 0 0.0897 sigma^2 estimated as 5.417e-06: log likelihood = 933.37, aic = -1852.74 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.9044 -0.0684 0 -0.7403 0.9688 0 -0.8100 s.e. 0.2042 0.0886 0 0.1897 0.0290 0 0.0903 sigma^2 estimated as 5.423e-06: log likelihood = 933.23, aic = -1854.46 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -1850.832 -1852.739 -1854.463 -1855.830 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 > postscript(file="/var/www/html/rcomp/tmp/1m0nz1291465019.ps",horizontal=F,onefile=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 = 203 Frequency = 1 [1] 4.750565e-03 2.475048e-03 -1.061944e-03 1.215053e-03 2.119360e-03 [6] 9.275203e-04 5.208163e-03 -3.468793e-04 -1.805393e-03 -1.693009e-03 [11] -6.005234e-05 3.133807e-05 3.522647e-03 1.318950e-03 -1.154282e-03 [16] 1.882827e-04 -1.920971e-03 7.949035e-05 3.673758e-03 1.643352e-03 [21] -3.308238e-03 -7.578033e-04 1.462512e-03 -6.568995e-04 5.284292e-03 [26] -7.176661e-04 4.304999e-04 -8.680630e-04 -2.671665e-03 4.266114e-04 [31] 3.043253e-03 1.011603e-03 -1.976120e-03 2.385845e-03 5.427233e-06 [36] 1.025656e-03 2.533182e-03 -3.997237e-03 -4.028932e-03 2.912758e-04 [41] 1.279706e-03 1.079076e-03 4.411387e-03 -1.725872e-03 -2.433925e-03 [46] 7.399869e-04 2.108899e-03 -1.062215e-03 -3.259713e-03 1.913388e-03 [51] -2.434548e-04 4.286997e-03 4.473956e-03 -2.974562e-03 -2.816490e-03 [56] -4.868042e-03 2.645952e-03 1.782704e-04 -1.148983e-03 1.050062e-04 [61] 2.530140e-04 1.159800e-03 1.090696e-03 9.090020e-04 2.619808e-04 [66] -2.449297e-03 -4.021560e-03 -1.943539e-03 4.338477e-03 9.333824e-04 [71] 7.975009e-04 1.436881e-03 -1.038161e-03 1.205505e-03 2.821043e-03 [76] 6.269181e-04 -4.002840e-04 1.759371e-03 -1.728337e-03 6.812203e-04 [81] 5.081314e-03 -2.670937e-03 2.833135e-03 -1.611416e-03 -2.450204e-03 [86] 1.914362e-03 2.979098e-03 5.145886e-03 2.441323e-03 1.673922e-03 [91] -3.333299e-03 6.389145e-04 1.123016e-03 2.666791e-04 7.515174e-05 [96] -2.430168e-03 3.949783e-03 -9.435498e-04 1.691441e-03 -4.924939e-03 [101] 9.452192e-04 -3.200709e-03 8.825301e-04 -3.152638e-04 1.994617e-03 [106] -1.784070e-03 -9.694040e-04 1.166858e-03 4.069208e-04 3.870707e-03 [111] 1.143133e-03 -3.006940e-03 -3.001022e-03 2.665018e-03 -1.170855e-03 [116] 1.460322e-03 1.737110e-03 -2.930388e-03 9.273221e-04 -5.189036e-04 [121] 1.752459e-04 8.204693e-04 -7.021373e-04 2.837365e-03 -1.895958e-04 [126] -1.008634e-03 5.135660e-04 1.228572e-05 -4.588858e-04 4.268641e-03 [131] -2.062370e-03 -2.055501e-03 1.009348e-03 2.763239e-03 4.207739e-03 [136] -2.875133e-03 6.732516e-04 8.972489e-04 1.133603e-03 -4.580680e-04 [141] -2.074565e-03 -1.417631e-03 2.275691e-03 8.345033e-04 -2.685725e-03 [146] 2.323389e-03 -2.464031e-03 2.595478e-03 1.778453e-03 -1.322239e-03 [151] 3.454459e-04 9.655196e-04 -8.680406e-04 -1.100694e-04 1.094929e-03 [156] 8.910970e-04 -5.008242e-04 2.091372e-03 -3.665727e-03 2.058448e-03 [161] -4.090233e-03 -5.087892e-04 1.654669e-03 -6.432859e-04 2.116841e-04 [166] 4.868128e-03 4.791566e-03 3.591228e-03 1.529603e-04 3.924324e-03 [171] 3.794695e-03 -1.699302e-03 4.831063e-03 2.051249e-03 1.312177e-03 [176] -4.421912e-03 2.029571e-03 1.410730e-04 -4.592156e-03 9.630389e-04 [181] -7.527229e-04 -1.104884e-03 -7.207656e-03 4.643638e-04 -3.082597e-03 [186] -4.055879e-03 -1.972814e-03 3.046807e-03 -2.083927e-03 1.718535e-03 [191] -2.641183e-04 1.304253e-03 1.281207e-03 1.179201e-03 1.641114e-03 [196] 1.580651e-04 2.015960e-03 -3.648742e-04 -1.727327e-03 7.859407e-04 [201] 2.708882e-03 1.785693e-04 -9.192792e-04 > postscript(file="/var/www/html/rcomp/tmp/2m0nz1291465019.ps",horizontal=F,onefile=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/3f9n21291465019.ps",horizontal=F,onefile=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/4f9n21291465019.ps",horizontal=F,onefile=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/5f9n21291465019.ps",horizontal=F,onefile=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/6f9n21291465019.ps",horizontal=F,onefile=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/7pi4n1291465019.ps",horizontal=F,onefile=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/83akd1291465019.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/9ejjy1291465019.tab") > > try(system("convert tmp/1m0nz1291465019.ps tmp/1m0nz1291465019.png",intern=TRUE)) character(0) > try(system("convert tmp/2m0nz1291465019.ps tmp/2m0nz1291465019.png",intern=TRUE)) character(0) > try(system("convert tmp/3f9n21291465019.ps tmp/3f9n21291465019.png",intern=TRUE)) character(0) > try(system("convert tmp/4f9n21291465019.ps tmp/4f9n21291465019.png",intern=TRUE)) character(0) > try(system("convert tmp/5f9n21291465019.ps tmp/5f9n21291465019.png",intern=TRUE)) character(0) > try(system("convert tmp/6f9n21291465019.ps tmp/6f9n21291465019.png",intern=TRUE)) character(0) > try(system("convert tmp/7pi4n1291465019.ps tmp/7pi4n1291465019.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.485 2.160 26.277