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(153.3 + ,154.5 + ,155.2 + ,156.9 + ,157 + ,157.4 + ,157.2 + ,157.5 + ,158 + ,158.5 + ,159 + ,159.3 + ,160 + ,160.8 + ,161.9 + ,162.5 + ,162.7 + ,162.8 + ,162.9 + ,163 + ,164 + ,164.7 + ,164.8 + ,164.9 + ,165 + ,165.8 + ,166.1 + ,167.2 + ,167.7 + ,168.3 + ,168.6 + ,168.9 + ,169.1 + ,169.5 + ,169.6 + ,169.7 + ,169.8 + ,170.4 + ,170.9 + ,171.9 + ,171.9 + ,172 + ,172 + ,172.4 + ,173 + ,173.7 + ,173.8 + ,173.8 + ,173.9 + ,174.6 + ,175 + ,175.9 + ,176 + ,175.1 + ,175.6 + ,175.9 + ,176.7 + ,176.1 + ,176.1 + ,176.2 + ,176.3 + ,177.8 + ,178.5 + ,179.4 + ,179.5 + ,179.6 + ,179.7 + ,179.7 + ,179.8 + ,179.9 + ,180.2 + ,180.4 + ,180.4 + ,181.3 + ,181.9 + ,182.5 + ,182.7 + ,183.1 + ,183.6 + ,183.7 + ,183.8 + ,183.9 + ,184.1 + ,184.4 + ,184.5 + ,185.9 + ,186.6 + ,187.6 + ,187.8 + ,187.9 + ,188 + ,188.3 + ,188.4 + ,188.5 + ,188.5 + ,188.6 + ,188.6 + ,189.4 + ,190 + ,191.9 + ,192.5 + ,193 + ,193.5 + ,193.9 + ,194.2 + ,194.9 + ,194.9 + ,194.9 + ,194.9 + ,195.5 + ,196 + ,196.2 + ,196.2 + ,196.2 + ,196.2 + ,197 + ,197.7 + ,198 + ,198.2 + ,198.5 + ,198.6 + ,199.5 + ,200 + ,201.3 + ,202.2 + ,202.9 + ,203.5 + ,203.5 + ,204 + ,204.1 + ,204.3 + ,204.5 + ,204.8 + ,205.1 + ,205.7 + ,206.5 + ,206.9 + ,207.1 + ,207.8 + ,208 + ,208.5 + ,208.6 + ,209 + ,209.1 + ,209.7 + ,209.8 + ,209.9 + ,210 + ,210.8 + ,211.4 + ,211.7 + ,212 + ,212.2 + ,212.4 + ,212.9 + ,213.4 + ,213.7 + ,214 + ,214.3 + ,214.8 + ,215 + ,215.9 + ,216.4 + ,216.9 + ,217.2 + ,217.5 + ,217.9 + ,218.1 + ,218.6 + ,218.9 + ,219.3 + ,220.4 + ,220.9 + ,221 + ,221.8 + ,222 + ,222.2 + ,222.5 + ,222.9 + ,223.1 + ,223.4 + ,224 + ,225.1 + ,225.5 + ,225.9 + ,226.3 + ,226.5 + ,227 + ,227.3 + ,227.8 + ,228.1 + ,228.4 + ,228.5 + ,228.8 + ,229 + ,229.1 + ,229.3 + ,229.6 + ,229.9 + ,230 + ,230.2 + ,230.8 + ,231 + ,231.7 + ,231.9 + ,233 + ,235.1 + ,236 + ,236.9 + ,237.1 + ,237.5 + ,238.2 + ,238.9 + ,239.1 + ,240 + ,240.2 + ,240.5 + ,240.7 + ,241.1 + ,241.4 + ,242.2 + ,242.9 + ,243.2 + ,243.9) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '1' > 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,] 0.04174316 0.1745754 -0.1224354 0.1065258 -0.1203752 0.09138246 [2,] 0.00000000 0.1797354 -0.1156506 0.1481954 -0.1204037 0.09254202 [3,] 0.00000000 0.1775642 -0.1173974 0.1530856 -0.1896268 0.00000000 [4,] 0.00000000 0.1799986 0.0000000 0.1533272 -0.1811964 0.00000000 [5,] 0.00000000 0.1867174 0.0000000 0.1570760 0.0000000 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,] -0.7715889 [2,] -0.7713256 [3,] -0.7056481 [4,] -0.7042846 [5,] -1.2542883 [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,] 0.93219 0.05679 0.23658 0.82923 0.30476 0.40356 0 [2,] NA 0.00907 0.09357 0.03346 0.30565 0.39584 0 [3,] NA 0.00993 0.08845 0.02745 0.05361 NA 0 [4,] NA 0.01140 NA 0.02612 0.06529 NA 0 [5,] NA 0.00838 NA 0.02223 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 0.0417 0.1746 -0.1224 0.1065 -0.1204 0.0914 -0.7716 s.e. 0.4900 0.0912 0.1032 0.4933 0.1170 0.1092 0.0994 sigma^2 estimated as 0.1041: log likelihood = -66.81, aic = 149.62 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0417 0.1746 -0.1224 0.1065 -0.1204 0.0914 -0.7716 s.e. 0.4900 0.0912 0.1032 0.4933 0.1170 0.1092 0.0994 sigma^2 estimated as 0.1041: log likelihood = -66.81, aic = 149.62 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1797 -0.1157 0.1482 -0.1204 0.0925 -0.7713 s.e. 0 0.0683 0.0687 0.0692 0.1173 0.1088 0.0996 sigma^2 estimated as 0.1041: log likelihood = -66.81, aic = 147.63 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1776 -0.1174 0.1531 -0.1896 0 -0.7056 s.e. 0 0.0683 0.0686 0.0690 0.0977 0 0.0873 sigma^2 estimated as 0.1046: log likelihood = -67.17, aic = 146.34 [[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 0.1800 0 0.1533 -0.1812 0 -0.7043 s.e. 0 0.0705 0 0.0685 0.0978 0 0.0859 sigma^2 estimated as 0.1062: log likelihood = -68.62, aic = 147.25 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 149.6215 147.6264 146.3364 147.2461 148.7817 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 > postscript(file="/var/www/html/rcomp/tmp/1dn9r1259846851.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 = 224 Frequency = 1 [1] 0.088507768 0.040511339 0.027112427 0.021707584 0.017407022 [6] 0.014851368 0.012531664 0.011240324 0.010460142 0.009886852 [11] 0.009463764 -0.078692807 -0.523744804 -0.288361715 0.345483732 [16] -0.816905506 0.145034189 -0.099401540 0.223502798 -0.145276377 [21] 0.352369552 0.114222564 -0.381596316 -0.149274079 -0.364506463 [26] -0.131497049 -0.428928898 -0.012802945 0.417547581 0.253567809 [31] 0.237899815 -0.013676724 -0.525393296 -0.106285789 -0.109709084 [36] -0.077913991 -0.276618298 -0.255097320 -0.167453242 0.003327197 [41] -0.183243797 -0.168630990 0.018910032 0.211950611 -0.064813519 [46] 0.107025348 -0.138322204 -0.178522728 -0.154390446 -0.093082839 [51] -0.185887464 -0.113283903 -0.082978168 -1.165402959 0.609201819 [56] 0.160757523 0.142372144 -1.164210212 -0.048489966 0.174606377 [61] -0.130754518 0.735513692 0.028739738 -0.265108226 -0.060254640 [66] 0.018001704 -0.018092384 -0.284436409 -0.444364607 -0.195822495 [71] 0.272672298 0.108489888 -0.235168566 0.058753413 0.061307560 [76] -0.407444998 0.100129136 0.523400943 0.217166038 -0.273460585 [81] -0.481738709 0.008694683 0.144438298 0.177084520 -0.089020819 [86] 0.398869073 0.054882204 -0.012209758 0.031580367 0.064785717 [91] -0.131952244 0.114010843 -0.324872329 -0.034888399 -0.110274403 [96] -0.024989177 -0.081718654 -0.193232000 0.043959428 1.053540701 [101] 0.273574898 0.177320467 0.142402013 0.117920576 -0.092937629 [106] 0.529608128 -0.234523880 -0.237465236 -0.035307892 -0.423297790 [111] -0.039811468 -0.728950589 -0.088288759 0.016905909 -0.215737013 [116] 0.626124547 0.364266173 -0.059863601 0.022781207 0.138327515 [121] -0.007716440 -0.087510845 -0.096373415 0.241809203 0.616466522 [126] 0.374566597 0.159450456 -0.444107325 0.172825571 -0.170808387 [131] 0.094935546 0.088639182 0.207429700 -0.631581830 0.080294502 [136] -0.054924224 0.113633307 -0.006186746 0.422956175 -0.238091449 [141] 0.036437373 -0.153323002 0.278396093 -0.081639273 0.468294608 [146] -0.781560717 -0.434163961 -0.698414660 0.577281588 0.371075880 [151] -0.197286798 -0.011194434 -0.229578327 0.006643725 0.368679928 [156] 0.279885890 0.007854526 -0.406002916 -0.174266375 -0.268313066 [161] -0.174937936 0.673768945 0.020295007 0.118771705 -0.158404821 [166] 0.083032851 0.157521497 -0.036022841 0.201644886 -0.238804045 [171] -0.019928073 0.461223303 -0.056133916 -0.402453852 0.450557249 [176] -0.112790608 -0.223838008 0.141286495 0.094855688 -0.080524093 [181] -0.010032828 0.164912185 0.682482831 -0.427247123 -0.099120422 [186] -0.012827841 -0.261600157 0.227060102 -0.024542082 0.234879271 [191] -0.068690190 0.028064542 -0.238300019 -0.112041738 -0.214994102 [196] -0.576858533 -0.106447262 0.027772378 -0.159338834 -0.181329243 [201] -0.045273538 0.368164584 -0.182844024 0.433977630 -0.146317420 [206] 0.588171838 1.471744928 -0.043719222 0.220558651 -0.278086296 [211] -0.067938349 0.414285632 0.345630681 -0.275424852 0.548354542 [216] -0.156962708 -0.036735541 -0.274017463 -0.234934345 -0.154977479 [221] 0.445242923 0.323484905 -0.214187104 0.339258439 > postscript(file="/var/www/html/rcomp/tmp/2z22o1259846851.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/3k6qf1259846851.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/4bnzw1259846851.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/55b1v1259846852.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/6khxk1259846852.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/775qc1259846852.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/85ei81259846852.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/9gr1a1259846852.tab") > system("convert tmp/1dn9r1259846851.ps tmp/1dn9r1259846851.png") > system("convert tmp/2z22o1259846851.ps tmp/2z22o1259846851.png") > system("convert tmp/3k6qf1259846851.ps tmp/3k6qf1259846851.png") > system("convert tmp/4bnzw1259846851.ps tmp/4bnzw1259846851.png") > system("convert tmp/55b1v1259846852.ps tmp/55b1v1259846852.png") > system("convert tmp/6khxk1259846852.ps tmp/6khxk1259846852.png") > system("convert tmp/775qc1259846852.ps tmp/775qc1259846852.png") > > > proc.time() user system elapsed 11.246 1.470 11.970