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(9.9,9.8,9.3,8.3,8,8.5,10.4,11.1,10.9,10,9.2,9.2,9.5,9.6,9.5,9.1,8.9,9,10.1,10.3,10.2,9.6,9.2,9.3,9.4,9.4,9.2,9,9,9,9.8,10,9.8,9.3,9,9,9.1,9.1,9.1,9.2,8.8,8.3,8.4,8.1,7.7,7.9,7.9,8,7.9,7.6,7.1,6.8,6.5,6.9,8.2,8.7,8.3,7.9,7.5,7.8) > 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,] 0.3972418 -0.2384387 -0.3375464 -0.1810917 -0.06376095 0.03799798 [2,] 0.4017781 -0.2390855 -0.3376647 -0.1806533 -0.05824398 0.04443675 [3,] 0.4018523 -0.2464833 -0.3443788 -0.1872210 -0.04483457 0.00000000 [4,] 0.4092200 -0.2346282 -0.3336220 -0.1994528 0.00000000 0.00000000 [5,] 0.4069086 -0.2294865 -0.3178840 -0.1851291 0.00000000 0.00000000 [6,] 0.4120106 -0.2286755 -0.3295240 -0.1904080 0.00000000 0.00000000 [7,] 0.4112297 -0.2398203 -0.3400009 -0.1903533 0.00000000 0.00000000 [8,] 0.4795662 -0.2131821 -0.4209330 0.0000000 0.00000000 0.00000000 [9,] 0.4683133 -0.2573895 -0.4112028 0.0000000 0.00000000 0.00000000 [10,] 0.3189707 0.0000000 -0.5657600 0.0000000 0.00000000 0.00000000 [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,] -0.09542806 -0.1173324 -0.03306469 -0.07000491 0.1377180 [2,] -0.09181167 -0.1288630 0.00000000 -0.08248008 0.1432810 [3,] -0.07807969 -0.1367953 0.00000000 -0.09157466 0.1460329 [4,] -0.07579919 -0.1199034 0.00000000 -0.08219505 0.1434296 [5,] 0.00000000 -0.1366957 0.00000000 -0.05907872 0.1512122 [6,] 0.00000000 -0.1430007 0.00000000 0.00000000 0.1282592 [7,] 0.00000000 -0.2055991 0.00000000 0.00000000 0.0000000 [8,] 0.00000000 -0.1320153 0.00000000 0.00000000 0.0000000 [9,] 0.00000000 0.0000000 0.00000000 0.00000000 0.0000000 [10,] 0.00000000 0.0000000 0.00000000 0.00000000 0.0000000 [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,] 0.00376 0.09831 0.02361 0.23674 0.68194 0.83578 0.59931 0.51264 0.84937 [2,] 0.00289 0.09712 0.02348 0.23808 0.70375 0.80529 0.61129 0.44486 NA [3,] 0.00284 0.08039 0.01909 0.21541 0.75430 NA 0.64970 0.40967 NA [4,] 0.00209 0.08413 0.01950 0.17235 NA NA 0.65945 0.44485 NA [5,] 0.00222 0.08965 0.02146 0.19440 NA NA NA 0.36942 NA [6,] 0.00191 0.09233 0.01554 0.18148 NA NA NA 0.34783 NA [7,] 0.00210 0.07927 0.01299 0.18411 NA NA NA 0.13736 NA [8,] 0.00016 0.11891 0.00092 NA NA NA NA 0.30718 NA [9,] 0.00024 0.05116 0.00129 NA NA NA NA NA NA [10,] 0.00135 NA 0.00000 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,] 0.68068 0.38522 [2,] 0.59937 0.35870 [3,] 0.54937 0.34911 [4,] 0.58376 0.35757 [5,] 0.67429 0.32930 [6,] NA 0.37647 [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 0.3972 -0.2384 -0.3375 -0.1811 -0.0638 0.0380 -0.0954 -0.1173 s.e. 0.1304 0.1414 0.1444 0.1511 0.1546 0.1823 0.1804 0.1779 ar9 ar10 ar11 -0.0331 -0.0700 0.1377 s.e. 0.1732 0.1691 0.1572 sigma^2 estimated as 0.1051: log likelihood = -18.52, aic = 61.03 [[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 0.3972 -0.2384 -0.3375 -0.1811 -0.0638 0.0380 -0.0954 -0.1173 s.e. 0.1304 0.1414 0.1444 0.1511 0.1546 0.1823 0.1804 0.1779 ar9 ar10 ar11 -0.0331 -0.0700 0.1377 s.e. 0.1732 0.1691 0.1572 sigma^2 estimated as 0.1051: log likelihood = -18.52, aic = 61.03 [[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 0.4018 -0.2391 -0.3377 -0.1807 -0.0582 0.0444 -0.0918 -0.1289 0 s.e. 0.1281 0.1414 0.1444 0.1513 0.1523 0.1793 0.1795 0.1673 0 ar10 ar11 -0.0825 0.1433 s.e. 0.1560 0.1546 sigma^2 estimated as 0.1052: log likelihood = -18.54, aic = 59.07 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.4019 -0.2465 -0.3444 -0.1872 -0.0448 0 -0.0781 -0.1368 0 s.e. 0.1280 0.1381 0.1422 0.1492 0.1425 0 0.1709 0.1645 0 ar10 ar11 -0.0916 0.1460 s.e. 0.1519 0.1545 sigma^2 estimated as 0.1053: log likelihood = -18.57, aic = 57.13 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.4092 -0.2346 -0.3336 -0.1995 0 0 -0.0758 -0.1199 0 s.e. 0.1262 0.1332 0.1383 0.1441 0 0 0.1710 0.1557 0 ar10 ar11 -0.0822 0.1434 s.e. 0.1491 0.1545 sigma^2 estimated as 0.1056: log likelihood = -18.62, aic = 55.23 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.4069 -0.2295 -0.3179 -0.1851 0 0 0 -0.1367 0 -0.0591 s.e. 0.1264 0.1327 0.1340 0.1408 0 0 0 0.1510 0 0.1398 ar11 0.1512 s.e. 0.1536 sigma^2 estimated as 0.1059: log likelihood = -18.71, aic = 53.43 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.4120 -0.2287 -0.3295 -0.1904 0 0 0 -0.143 0 0 s.e. 0.1261 0.1334 0.1318 0.1406 0 0 0 0.151 0 0 ar11 0.1283 s.e. 0.1438 sigma^2 estimated as 0.1064: log likelihood = -18.8, aic = 51.6 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.4112 -0.2398 -0.3400 -0.1904 0 0 0 -0.2056 0 0 s.e. 0.1272 0.1341 0.1323 0.1415 0 0 0 0.1363 0 0 ar11 0 s.e. 0 sigma^2 estimated as 0.1084: log likelihood = -19.19, aic = 50.38 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4796 -0.2132 -0.4209 0 0 0 0 -0.1320 0 0 0 s.e. 0.1186 0.1346 0.1201 0 0 0 0 0.1281 0 0 0 sigma^2 estimated as 0.1122: log likelihood = -20.08, aic = 50.15 [[3]][[10]] 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 0.4683 -0.2574 -0.4112 0 0 0 0 0 0 0 0 s.e. 0.1193 0.1292 0.1213 0 0 0 0 0 0 0 0 sigma^2 estimated as 0.1147: log likelihood = -20.59, aic = 49.17 [[3]][[11]] NULL $aic [1] 61.03404 59.07049 57.13191 55.23087 53.42701 51.60407 50.37743 50.15143 [9] 49.17230 51.03587 There were 11 warnings (use warnings() to see them) > postscript(file="/var/www/html/rcomp/tmp/1ynn61260475950.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 = 60 Frequency = 1 [1] 0.009899989 -0.068508432 -0.348802672 -0.645968002 -0.001501770 [6] 0.177503090 1.177423725 -0.184461278 0.166822146 0.155120615 [11] -0.142154008 0.060759505 -0.275994110 -0.369456218 -0.069614480 [16] -0.204068885 0.002706633 0.049586576 0.837209657 -0.371646190 [21] 0.130586063 -0.049367695 -0.062510436 0.091771330 -0.296508801 [26] -0.185573498 -0.133140771 -0.065217069 0.042184754 -0.133718458 [31] 0.717759439 -0.174650602 -0.087751063 -0.025897210 -0.035080710 [36] -0.070441327 -0.182818247 -0.170192166 0.025738948 0.141120280 [41] -0.446831325 -0.286935751 0.272321113 -0.640007189 -0.439368477 [46] 0.351228736 -0.319979285 -0.013003224 -0.064590765 -0.227429726 [51] -0.344124692 -0.184180500 -0.411561608 0.257675729 0.912097013 [56] -0.129212275 -0.135069175 0.450583687 -0.110029091 0.219888386 > postscript(file="/var/www/html/rcomp/tmp/2d1xw1260475950.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/3mw7b1260475950.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/42ph51260475950.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/5jqma1260475950.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/6c6181260475950.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/7kde61260475950.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/8ssz51260475950.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/9yx771260475950.tab") > > system("convert tmp/1ynn61260475950.ps tmp/1ynn61260475950.png") > system("convert tmp/2d1xw1260475950.ps tmp/2d1xw1260475950.png") > system("convert tmp/3mw7b1260475950.ps tmp/3mw7b1260475950.png") > system("convert tmp/42ph51260475950.ps tmp/42ph51260475950.png") > system("convert tmp/5jqma1260475950.ps tmp/5jqma1260475950.png") > system("convert tmp/6c6181260475950.ps tmp/6c6181260475950.png") > system("convert tmp/7kde61260475950.ps tmp/7kde61260475950.png") > > > proc.time() user system elapsed 3.141 1.051 4.326