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Type 'q()' to quit R. > x <- c(137.7,148.3,152.2,169.4,168.6,161.1,174.1,179,190.6,190,181.6,174.8,180.5,196.8,193.8,197,216.3,221.4,217.9,229.7,227.4,204.2,196.6,198.8,207.5,190.7,201.6,210.5,223.5,223.8,231.2,244,234.7,250.2,265.7,287.6,283.3,295.4,312.3,333.8,347.7,383.2,407.1,413.6,362.7,321.9,239.4,191,159.7,163.4,157.6,166.2,176.7,198.3,226.2,216.2,235.9,226.9,242.3,253.1) > 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] [,7] [1,] 0.5412134 0.2865185 -0.3482466 0.1949029 -0.2810255 -0.14422295 0.3822198 [2,] 0.5398583 0.2883652 -0.3379784 0.1817152 -0.2810894 -0.12644244 0.3702486 [3,] 0.5381186 0.2749533 -0.3191276 0.1799899 -0.3067427 -0.10861440 0.3519226 [4,] 0.5572923 0.2527514 -0.3174541 0.2101516 -0.3272481 -0.08454919 0.3413084 [5,] 0.5736040 0.2381674 -0.3086673 0.1975353 -0.3620915 0.00000000 0.3155665 [6,] 0.5228979 0.2941556 -0.2286949 0.0000000 -0.2672712 0.00000000 0.2695513 [7,] 0.4755927 0.2081274 0.0000000 0.0000000 -0.3395102 0.00000000 0.2196065 [8,] 0.4692092 0.2310738 0.0000000 0.0000000 -0.3164932 0.00000000 0.1134523 [9,] 0.4484099 0.2021003 0.0000000 0.0000000 -0.2638338 0.00000000 0.0000000 [10,] 0.5618569 0.0000000 0.0000000 0.0000000 -0.2385116 0.00000000 0.0000000 [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 [15,] NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA [17,] NA NA NA NA NA NA NA [18,] NA NA NA NA NA NA NA [19,] NA NA NA NA NA NA NA [20,] NA NA NA NA NA NA NA [21,] NA NA NA NA NA NA NA [22,] NA NA NA NA NA NA NA [,8] [,9] [,10] [,11] [1,] -0.2348227 -0.09467556 0.07483036 -0.03422885 [2,] -0.2219320 -0.10027049 0.05110597 0.00000000 [3,] -0.2132428 -0.06609842 0.00000000 0.00000000 [4,] -0.2574538 0.00000000 0.00000000 0.00000000 [5,] -0.2725133 0.00000000 0.00000000 0.00000000 [6,] -0.2300411 0.00000000 0.00000000 0.00000000 [7,] -0.1606934 0.00000000 0.00000000 0.00000000 [8,] 0.0000000 0.00000000 0.00000000 0.00000000 [9,] 0.0000000 0.00000000 0.00000000 0.00000000 [10,] 0.0000000 0.00000000 0.00000000 0.00000000 [11,] NA NA NA NA [12,] NA NA NA NA [13,] NA NA NA NA [14,] NA NA NA NA [15,] NA NA NA NA [16,] NA NA NA NA [17,] NA NA NA NA [18,] NA NA NA NA [19,] NA NA NA NA [20,] NA NA NA NA [21,] NA NA NA NA [22,] NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.00015 0.05963 0.03584 0.25512 0.10059 0.43160 0.02801 0.18079 0.57921 [2,] 0.00016 0.05831 0.03446 0.26254 0.10219 0.45235 0.02626 0.18394 0.55454 [3,] 0.00017 0.06283 0.03398 0.26790 0.05237 0.50051 0.02615 0.19725 0.63843 [4,] 0.00005 0.07025 0.03468 0.16186 0.03215 0.57999 0.02888 0.05932 NA [5,] 0.00002 0.08355 0.03888 0.18444 0.00961 NA 0.03519 0.04266 NA [6,] 0.00006 0.02871 0.09862 NA 0.02802 NA 0.06822 0.08218 NA [7,] 0.00021 0.09908 NA NA 0.00351 NA 0.13299 0.20837 NA [8,] 0.00028 0.06816 NA NA 0.00653 NA 0.35032 NA NA [9,] 0.00042 0.10164 NA NA 0.00935 NA NA NA NA [10,] 0.00000 NA NA NA 0.01902 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,] 0.66687 0.81043 [2,] 0.72046 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 0.5412 0.2865 -0.3482 0.1949 -0.2810 -0.1442 0.3822 -0.2348 s.e. 0.1317 0.1485 0.1613 0.1692 0.1679 0.1818 0.1687 0.1729 ar9 ar10 ar11 -0.0947 0.0748 -0.0342 s.e. 0.1696 0.1728 0.1419 sigma^2 estimated as 205.6: log likelihood = -241.8, aic = 507.59 [[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 0.5412 0.2865 -0.3482 0.1949 -0.2810 -0.1442 0.3822 -0.2348 s.e. 0.1317 0.1485 0.1613 0.1692 0.1679 0.1818 0.1687 0.1729 ar9 ar10 ar11 -0.0947 0.0748 -0.0342 s.e. 0.1696 0.1728 0.1419 sigma^2 estimated as 205.6: log likelihood = -241.8, aic = 507.59 [[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 0.5399 0.2884 -0.3380 0.1817 -0.2811 -0.1264 0.3702 -0.2219 s.e. 0.1318 0.1487 0.1554 0.1603 0.1688 0.1669 0.1616 0.1647 ar9 ar10 ar11 -0.1003 0.0511 0 s.e. 0.1685 0.1420 0 sigma^2 estimated as 205.9: log likelihood = -241.83, aic = 505.65 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 0.5381 0.2750 -0.3191 0.1800 -0.3067 -0.1086 0.3519 -0.2132 s.e. 0.1321 0.1445 0.1464 0.1606 0.1543 0.1601 0.1535 0.1632 ar9 ar10 ar11 -0.0661 0 0 s.e. 0.1398 0 0 sigma^2 estimated as 206.4: log likelihood = -241.89, aic = 503.78 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.5573 0.2528 -0.3175 0.2102 -0.3272 -0.0845 0.3413 -0.2575 0 s.e. 0.1261 0.1367 0.1463 0.1481 0.1486 0.1518 0.1518 0.1335 0 ar10 ar11 0 0 s.e. 0 0 sigma^2 estimated as 207.2: log likelihood = -242, aic = 502 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.5736 0.2382 -0.3087 0.1975 -0.3621 0 0.3156 -0.2725 0 0 s.e. 0.1230 0.1350 0.1457 0.1469 0.1347 0 0.1459 0.1311 0 0 ar11 0 s.e. 0 sigma^2 estimated as 208.4: log likelihood = -242.16, aic = 500.31 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.5229 0.2942 -0.2287 0 -0.2673 0 0.2696 -0.2300 0 0 s.e. 0.1192 0.1308 0.1360 0 0.1183 0 0.1448 0.1298 0 0 ar11 0 s.e. 0 sigma^2 estimated as 215.8: log likelihood = -243.04, aic = 500.09 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4756 0.2081 0 0 -0.3395 0 0.2196 -0.1607 0 0 0 s.e. 0.1196 0.1240 0 0 0.1112 0 0.1440 0.1262 0 0 0 sigma^2 estimated as 226.9: log likelihood = -244.42, aic = 500.84 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4692 0.2311 0 0 -0.3165 0 0.1135 0 0 0 0 s.e. 0.1208 0.1242 0 0 0.1119 0 0.1204 0 0 0 0 sigma^2 estimated as 234.4: log likelihood = -245.21, aic = 500.41 [[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 0.4484 0.2021 0 0 -0.2638 0 0 0 0 0 0 s.e. 0.1196 0.1214 0 0 0.0980 0 0 0 0 0 0 sigma^2 estimated as 238.4: log likelihood = -245.64, aic = 499.29 [[3]][[11]] NULL $aic [1] 507.5950 505.6531 503.7817 502.0047 500.3127 500.0886 500.8404 500.4110 [9] 499.2856 499.9819 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 8: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 9: 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/19bdo1260550124.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] 0.1376999 8.2918681 -1.8756772 13.1977172 -8.4452563 -8.9504394 [7] 19.3213929 1.6153755 11.3134291 -7.0029134 -12.4540712 0.5177429 [13] 11.7396156 18.1788179 -11.6193534 -0.9652094 16.6773193 -2.6971793 [19] -5.3869353 11.5472216 -6.0396175 -19.4614483 4.6134929 9.3732241 [25] 12.3626996 -21.7526046 10.5540692 5.4024803 7.3866929 -5.0326673 [31] 0.2057650 12.2969252 -14.1870681 20.5131677 10.5083296 13.7694620 [37] -13.8756588 7.1485114 16.4326956 15.5658830 6.6216524 23.7874604 [43] 8.3646434 -6.9327661 -52.9724347 -15.6222981 -44.5518700 3.1451375 [49] 8.7912344 14.0877437 -11.8977965 -11.3132833 -4.9536999 6.8956350 [55] 17.0684780 -28.4062390 20.8144712 -13.0424169 21.1531234 13.0743537 > postscript(file="/var/www/html/rcomp/tmp/294us1260550124.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/3bby21260550124.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/4qh851260550124.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/5kjqb1260550124.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/6clg81260550124.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/7gvk01260550124.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/8zk5h1260550124.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/9ft4a1260550124.tab") > system("convert tmp/19bdo1260550124.ps tmp/19bdo1260550124.png") > system("convert tmp/294us1260550124.ps tmp/294us1260550124.png") > system("convert tmp/3bby21260550124.ps tmp/3bby21260550124.png") > system("convert tmp/4qh851260550124.ps tmp/4qh851260550124.png") > system("convert tmp/5kjqb1260550124.ps tmp/5kjqb1260550124.png") > system("convert tmp/6clg81260550124.ps tmp/6clg81260550124.png") > system("convert tmp/7gvk01260550124.ps tmp/7gvk01260550124.png") > > > proc.time() user system elapsed 3.710 1.054 5.982