R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-pc-linux-gnu (32-bit) 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(1657 + ,1418 + ,1501 + ,1315 + ,1621 + ,2308 + ,3554 + ,3318 + ,3252 + ,2921 + ,2133 + ,2040 + ,1858 + ,1833 + ,2094 + ,2173 + ,2366 + ,2074 + ,2522 + ,1822 + ,1952 + ,2232 + ,1755 + ,1791 + ,2075 + ,1850 + ,2137 + ,2467 + ,2154 + ,2289 + ,2628 + ,2074 + ,2798 + ,2194 + ,2442 + ,2565 + ,2063 + ,2070 + ,2539 + ,1898 + ,2139 + ,2408 + ,2725 + ,2201 + ,2311 + ,2548 + ,2276 + ,2351 + ,2280 + ,2057 + ,2479 + ,2379 + ,2295 + ,2456 + ,2546 + ,2844 + ,2260 + ,2981 + ,2678 + ,3440 + ,2842 + ,2450 + ,2669 + ,2570 + ,2540 + ,2318 + ,2930 + ,2947 + ,2799 + ,2695 + ,2498 + ,2260 + ,2160 + ,2058 + ,2533 + ,2150 + ,2172 + ,2155 + ,3016 + ,2333 + ,2355 + ,2825 + ,2214 + ,2360 + ,2299 + ,1746 + ,2069 + ,2267 + ,1878 + ,2266 + ,2282 + ,2085 + ,2277 + ,2251 + ,1828 + ,1954 + ,1851 + ,1570 + ,1852 + ,2187 + ,1855 + ,2218 + ,2253 + ,2028 + ,2169 + ,1997 + ,2034 + ,1791 + ,1627 + ,1631 + ,2319 + ,1707 + ,1747 + ,2397 + ,2059 + ,2251 + ,2558 + ,2406 + ,2049 + ,2074 + ,1734 + ,1983 + ,2121 + ,1905 + ,2126 + ,2363 + ,2173 + ,2710 + ,2137 + ,2742 + ,2419 + ,2194 + ,2660 + ,2189 + ,2310 + ,2349 + ,2540 + ,2434 + ,2916 + ,2446 + ,2375 + ,3032 + ,2218 + ,1920 + ,2039 + ,1889 + ,2014 + ,2105 + ,2153 + ,2309 + ,2955 + ,2225 + ,2160 + ,2386 + ,1653 + ,1099 + ,5010 + ,2672 + ,2729 + ,2955 + ,2409 + ,3086 + ,3384 + ,2458 + ,2913 + ,2448 + ,2215 + ,2179 + ,2461 + ,2098 + ,2621 + ,2703 + ,2388 + ,3880 + ,3310 + ,3093 + ,3237 + ,3002 + ,2670 + ,2311 + ,2062 + ,2059 + ,2465 + ,2213 + ,2028 + ,2322 + ,2825 + ,2687 + ,2373 + ,2889 + ,2708 + ,2542 + ,2477 + ,2419 + ,2977 + ,3001 + ,3075 + ,2870 + ,3756 + ,3443 + ,2948 + ,3560 + ,3257 + ,2600 + ,2741 + ,2349 + ,2783 + ,2845 + ,2987 + ,2696 + ,3874 + ,2912 + ,2743 + ,3857 + ,2660 + ,2226 + ,2942 + ,2420 + ,2516 + ,2421 + ,2631 + ,2887 + ,3328 + ,2587 + ,2695 + ,3669 + ,2773 + ,2527 + ,2750 + ,2014 + ,2763 + ,2726 + ,1826 + ,2713 + ,3040 + ,2405 + ,2526 + ,2526 + ,2529 + ,2474 + ,2576 + ,2219 + ,2900 + ,2274 + ,2184 + ,2629 + ,2739 + ,2933 + ,3144 + ,3354 + ,3357 + ,3329) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > 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,] 0.006230099 0.3036102 0.3291891 0.3669388 0.1464527 -0.03246791 [2,] 0.000000000 0.3060536 0.3302186 0.3727301 0.1465005 -0.03253372 [3,] 0.000000000 0.3041364 0.3312567 0.3693602 0.1515398 0.00000000 [4,] 0.000000000 0.3012974 0.3483369 0.3641561 0.0000000 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,] -0.9379379 [2,] -0.9375953 [3,] -0.9545239 [4,] -1.1209349 [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,] 0.96915 0.00094 0 0.02494 0.07403 0.67977 0 [2,] NA 0.00001 0 0.00000 0.07401 0.67916 0 [3,] NA 0.00001 0 0.00000 0.06127 NA 0 [4,] NA 0.00001 0 0.00000 NA NA 0 [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 0.0062 0.3036 0.3292 0.3669 0.1465 -0.0325 -0.9379 s.e. 0.1609 0.0907 0.0680 0.1626 0.0816 0.0786 0.0981 sigma^2 estimated as 0.02061: log likelihood = 113.99, aic = -211.98 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0062 0.3036 0.3292 0.3669 0.1465 -0.0325 -0.9379 s.e. 0.1609 0.0907 0.0680 0.1626 0.0816 0.0786 0.0981 sigma^2 estimated as 0.02061: log likelihood = 113.99, aic = -211.98 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.3061 0.3302 0.3727 0.1465 -0.0325 -0.9376 s.e. 0 0.0657 0.0629 0.0630 0.0816 0.0786 0.0974 sigma^2 estimated as 0.02061: log likelihood = 113.99, aic = -213.98 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.3041 0.3313 0.3694 0.1515 0 -0.9545 s.e. 0 0.0656 0.0628 0.0625 0.0806 0 0.1136 sigma^2 estimated as 0.02048: log likelihood = 113.9, aic = -215.81 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -211.9787 -213.9770 -215.8063 -214.2239 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 > postscript(file="/var/wessaorg/rcomp/tmp/1gg481323197959.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 = 252 Frequency = 1 [1] 0.0074127546 0.0072569838 0.0073138598 0.0071815568 0.0073907586 [6] 0.0077440919 0.0081757804 0.0081070667 0.0080869732 0.0079796300 [11] 0.0076652354 0.0076206587 0.0718132767 0.1447050672 0.1546056990 [16] 0.2507943668 0.0660999713 -0.2999263239 -0.3631281371 -0.3930530919 [21] -0.1311284798 0.0747091618 0.1117231013 0.0759077572 0.1959240516 [26] 0.0772314397 0.0857604566 0.1897870962 -0.0811223312 -0.0493062836 [31] -0.1745067900 -0.0626798763 0.1736008647 -0.0989406373 0.2706932356 [36] 0.1832446361 -0.0291220543 0.0192938916 0.1294078857 -0.1601463332 [41] -0.0206803797 0.0095116131 -0.0343936461 -0.0544650141 -0.1056672487 [46] 0.1333336966 0.0726544859 0.0903924116 0.0762256683 0.0334131680 [51] 0.0641453950 0.1087543623 -0.0151519313 -0.0398772977 -0.1771076334 [56] 0.2167521255 -0.1542802597 0.2113618597 0.1038540270 0.3818965638 [61] 0.0536138694 0.0335633434 -0.0537358491 0.0565321501 0.0094163472 [66] -0.1329272268 -0.0051583221 0.1150596983 0.0783658835 -0.0611205032 [71] 0.0264014947 -0.1414989576 -0.0100427836 0.0257077148 0.1334107479 [76] -0.0438043911 -0.0398348455 -0.0813370694 0.1005682604 -0.0954743808 [81] -0.0389679869 0.1124899561 -0.0088579350 0.0398553248 0.0438047405 [86] -0.1256896325 -0.0799556544 0.1180714196 -0.1083543641 0.0536891484 [91] -0.2198637989 -0.0224452476 -0.0005355282 -0.0268082943 -0.1016577705 [96] -0.0483729148 -0.0240550244 -0.0540713064 -0.0402582955 0.1463814345 [101] -0.0525736402 0.0416276106 -0.1524802723 -0.0403780317 -0.0347322056 [106] -0.1002366435 0.0851175933 -0.1256124401 -0.0971714038 0.0059096370 [111] 0.2251233010 -0.1859717901 -0.0752293495 0.1196126312 -0.1572576082 [116] 0.0714520036 0.0961975833 0.0577838181 -0.0730253778 -0.0174898236 [121] -0.1047803734 0.1517700144 -0.0476148189 -0.0147178933 0.0511084334 [126] 0.0346568572 -0.1573440730 0.1768917791 -0.1659030352 0.1728296264 [131] 0.0561732259 0.0081065907 0.2227012289 0.0245365792 -0.0401948611 [136] 0.0182236914 0.1279939672 -0.0566698966 0.0629809959 -0.0999728451 [141] -0.0194483502 0.1343457045 -0.0465675249 -0.1618242222 -0.0508115187 [146] 0.0355910381 -0.0442558108 0.0365463949 0.0186067756 0.0192711394 [151] 0.1037868285 -0.1066584423 -0.0930319901 -0.0656653446 -0.1897919652 [156] -0.5092305783 1.1748396034 0.1944328984 0.1115472684 -0.0886406387 [161] -0.0193846925 0.1125743740 0.0354124736 -0.0968595971 0.0859177581 [166] -0.1388282440 0.0564924180 0.0833266550 -0.0749146993 -0.0224432075 [171] 0.0954578261 0.1469759044 -0.0059181325 0.3519175304 -0.0473360864 [176] 0.1045390441 0.0193810528 0.0406853734 0.0421636080 -0.0429726005 [181] -0.2009682793 0.0127300776 0.0571686929 -0.0189421429 -0.0973377206 [186] -0.1065331950 0.0724502583 0.0980534951 -0.0789624594 0.1083005590 [191] 0.1541687761 0.1302529719 -0.0149054954 0.0892642175 0.1289124336 [196] 0.1672638597 0.1637823290 -0.0721377115 0.1205915296 0.1063410675 [201] -0.0055706411 0.1060289280 0.1545618667 -0.0254944753 -0.0118238269 [206] -0.0326674990 0.0434216609 0.0780604503 0.1369824737 -0.0936242947 [211] 0.1705082927 -0.0663626713 -0.0130974013 0.2155912671 -0.0280403450 [216] -0.0972866196 0.1246535388 0.0766056334 -0.0534102632 -0.0725367734 [221] 0.0766444819 0.0845999061 0.0280269245 -0.0887621351 0.0126461790 [226] 0.2103871365 0.0748057323 0.0417248580 -0.0109960005 -0.1364935536 [231] 0.1091391966 0.0975371219 -0.3033450566 0.0670363106 0.0212274926 [236] -0.0003409765 -0.0361062375 -0.1088963708 0.1223071305 0.1103641595 [241] 0.0585327337 0.0148289938 0.1135697099 -0.1297566953 -0.0218087149 [246] -0.0071347309 -0.0492220173 0.1627797173 0.1673968486 0.1275179748 [251] 0.1871365194 0.1971111229 > postscript(file="/var/wessaorg/rcomp/tmp/2fxt71323197959.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/wessaorg/rcomp/tmp/3k4yd1323197959.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/wessaorg/rcomp/tmp/44k4f1323197959.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/wessaorg/rcomp/tmp/52xj51323197959.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/wessaorg/rcomp/tmp/678l71323197959.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/wessaorg/rcomp/tmp/7fvcy1323197959.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/8v4lc1323197959.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/wessaorg/rcomp/tmp/9lql11323197959.tab") > > try(system("convert tmp/1gg481323197959.ps tmp/1gg481323197959.png",intern=TRUE)) character(0) > try(system("convert tmp/2fxt71323197959.ps tmp/2fxt71323197959.png",intern=TRUE)) character(0) > try(system("convert tmp/3k4yd1323197959.ps tmp/3k4yd1323197959.png",intern=TRUE)) character(0) > try(system("convert tmp/44k4f1323197959.ps tmp/44k4f1323197959.png",intern=TRUE)) character(0) > try(system("convert tmp/52xj51323197959.ps tmp/52xj51323197959.png",intern=TRUE)) character(0) > try(system("convert tmp/678l71323197959.ps tmp/678l71323197959.png",intern=TRUE)) character(0) > try(system("convert tmp/7fvcy1323197959.ps tmp/7fvcy1323197959.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.558 0.950 12.538