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Type 'q()' to quit R. > x <- c(277 + ,260.6 + ,291.6 + ,275.4 + ,275.3 + ,231.7 + ,238.8 + ,274.2 + ,277.8 + ,299.1 + ,286.6 + ,232.3 + ,294.1 + ,267.5 + ,309.7 + ,280.7 + ,287.3 + ,235.7 + ,256.4 + ,289 + ,290.8 + ,321.9 + ,291.8 + ,241.4 + ,295.5 + ,258.2 + ,306.1 + ,281.5 + ,283.1 + ,237.4 + ,274.8 + ,299.3 + ,300.4 + ,340.9 + ,318.8 + ,265.7 + ,322.7 + ,281.6 + ,323.5 + ,312.6 + ,310.8 + ,262.8 + ,273.8 + ,320 + ,310.3 + ,342.2 + ,320.1 + ,265.6 + ,327 + ,300.7 + ,346.4 + ,317.3 + ,326.2 + ,270.7 + ,278.2 + ,324.6 + ,321.8 + ,343.5 + ,354 + ,278.2 + ,330.2 + ,307.3 + ,375.9 + ,335.3 + ,339.3 + ,280.3 + ,293.7 + ,341.2 + ,345.1 + ,368.7 + ,369.4 + ,288.4 + ,341 + ,319.1 + ,374.2 + ,344.5 + ,337.3 + ,281 + ,282.2 + ,321 + ,325.4 + ,366.3 + ,380.3 + ,300.7 + ,359.3 + ,327.6 + ,383.6 + ,352.4 + ,329.4 + ,294.5 + ,333.5 + ,334.3 + ,358 + ,396.1 + ,387 + ,307.2 + ,363.9 + ,344.7 + ,397.6 + ,376.8 + ,337.1 + ,299.3 + ,323.1 + ,329.1 + ,347 + ,462 + ,436.5 + ,360.4 + ,415.5 + ,382.1 + ,432.2 + ,424.3 + ,386.7 + ,354.5 + ,375.8 + ,368 + ,402.4 + ,426.5 + ,433.3 + ,338.5 + ,416.8 + ,381.1 + ,445.7 + ,412.4 + ,394 + ,348.2 + ,380.1 + ,373.7 + ,393.6 + ,434.2 + ,430.7 + ,344.5 + ,411.9 + ,370.5 + ,437.3 + ,411.3 + ,385.5 + ,341.3 + ,384.2 + ,373.2 + ,415.8 + ,448.6 + ,454.3 + ,350.3 + ,419.1 + ,398 + ,456.1 + ,430.1 + ,399.8 + ,362.7 + ,384.9 + ,385.3 + ,432.3 + ,468.9 + ,442.7 + ,370.2 + ,439.4 + ,393.9 + ,468.7 + ,438.8 + ,430.1 + ,366.3 + ,391 + ,380.9 + ,431.4 + ,465.4 + ,471.5 + ,387.5 + ,446.4 + ,421.5 + ,504.8 + ,492.1 + ,421.3 + ,396.7 + ,428 + ,421.9 + ,465.6 + ,525.8 + ,499.9 + ,435.3 + ,479.5 + ,473 + ,554.4 + ,489.6 + ,462.2 + ,420.3) > par9 = '1' > par8 = '2' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '0.0' > 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 > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par7 <- 3 > 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,] 0.67552430 -0.4163066 0.3097120 -1.3168068 0.8641578 -0.5473507 0.1097485 [2,] 0.67180436 -0.4235693 0.3199513 -1.3127915 0.8689889 -0.5562070 0.0000000 [3,] 0.68271327 -0.4022513 0.2848612 -1.3291461 0.8647034 -0.5355635 0.0000000 [4,] 0.39793874 0.0000000 0.2199110 -1.0568465 0.2898220 -0.2329650 0.0000000 [5,] 0.07628171 0.0000000 0.3333635 -0.7053229 0.0000000 -0.2962488 0.0000000 [6,] 0.00000000 0.0000000 0.3559963 -0.6561999 0.0000000 -0.3433129 0.0000000 [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 [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 [,8] [,9] [1,] 0.14680465 -0.7040324 [2,] 0.09678362 -0.6141934 [3,] 0.00000000 -0.5795142 [4,] 0.00000000 -0.5743875 [5,] 0.00000000 -0.5819030 [6,] 0.00000000 -0.5787168 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.01065 0.12570 0.02555 0.00000 0.02386 0.00786 0.54236 0.24225 1e-05 [2,] 0.00757 0.11013 0.01757 0.00000 0.01806 0.00586 NA 0.28754 0e+00 [3,] 0.01739 0.15724 0.03921 0.00000 0.03518 0.01113 NA NA 0e+00 [4,] 0.13821 NA 0.12966 0.00019 0.19714 0.02716 NA NA 0e+00 [5,] 0.49808 NA 0.00012 0.00000 NA 0.00202 NA NA 0e+00 [6,] NA NA 0.00001 0.00000 NA 0.00000 NA NA 0e+00 [7,] NA NA NA NA NA NA NA NA NA [8,] NA NA NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA NA NA [10,] NA NA NA 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 [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 ma2 ma3 sar1 sar2 0.6755 -0.4163 0.3097 -1.3168 0.8642 -0.5474 0.1097 0.1468 s.e. 0.2617 0.2706 0.1375 0.2564 0.3792 0.2036 0.1798 0.1251 sma1 -0.7040 s.e. 0.1567 sigma^2 estimated as 0.001128: log likelihood = 336.55, aic = -653.09 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 ma2 ma3 sar1 sar2 0.6755 -0.4163 0.3097 -1.3168 0.8642 -0.5474 0.1097 0.1468 s.e. 0.2617 0.2706 0.1375 0.2564 0.3792 0.2036 0.1798 0.1251 sma1 -0.7040 s.e. 0.1567 sigma^2 estimated as 0.001128: log likelihood = 336.55, aic = -653.09 [[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 ma2 ma3 sar1 sar2 sma1 0.6718 -0.4236 0.3200 -1.3128 0.8690 -0.5562 0 0.0968 -0.6142 s.e. 0.2487 0.2638 0.1335 0.2430 0.3641 0.1994 0 0.0907 0.0795 sigma^2 estimated as 0.001130: log likelihood = 336.37, aic = -654.75 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 ma2 ma3 sar1 sar2 sma1 0.6827 -0.4023 0.2849 -1.3291 0.8647 -0.5356 0 0 -0.5795 s.e. 0.2844 0.2832 0.1371 0.2780 0.4074 0.2088 0 0 0.0714 sigma^2 estimated as 0.001138: log likelihood = 335.79, aic = -655.58 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 ma2 ma3 sar1 sar2 sma1 0.3979 0 0.2199 -1.0568 0.2898 -0.2330 0 0 -0.5744 s.e. 0.2672 0 0.1444 0.2777 0.2239 0.1046 0 0 0.0718 sigma^2 estimated as 0.001147: log likelihood = 335.33, aic = -656.66 [[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 ma1 ma2 ma3 sar1 sar2 sma1 0.0763 0 0.3334 -0.7053 0 -0.2962 0 0 -0.5819 s.e. 0.1124 0 0.0846 0.0991 0 0.0946 0 0 0.0707 sigma^2 estimated as 0.001155: log likelihood = 334.4, aic = -656.8 [[3]][[7]] NULL [[3]][[8]] NULL [[3]][[9]] NULL $aic [1] -653.0946 -654.7452 -655.5844 -656.6629 -656.8010 -658.3047 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 > postscript(file="/var/www/html/rcomp/tmp/1lyqa1261080385.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 = 186 Frequency = 1 [1] 0.0032470270 0.0014048403 0.0010146760 0.0007058852 0.0005627730 [6] 0.0003096540 0.0002932705 0.0003863033 0.0003555772 0.0003901958 [11] 0.0003138540 -0.0031044002 -0.0192508842 -0.0243384070 0.0158952358 [16] -0.0260722520 0.0088404010 -0.0212744425 0.0374645688 0.0002874916 [21] -0.0033937910 0.0171818335 -0.0334269654 0.0021793435 -0.0274115854 [26] -0.0569170629 -0.0002091242 -0.0040064608 -0.0074852958 -0.0058568600 [31] 0.0752980051 0.0026290629 -0.0063983583 0.0257097452 0.0327139662 [36] 0.0363171189 -0.0012961069 -0.0219713395 -0.0176410999 0.0411130767 [41] 0.0124816436 0.0207939069 -0.0484360435 0.0263730615 -0.0200050533 [46] -0.0134631901 -0.0131133105 0.0021951990 0.0034936148 0.0314382752 [51] 0.0140064877 -0.0167139079 0.0122645989 0.0011420401 -0.0409497518 [56] -0.0037620015 0.0008680118 -0.0322586305 0.0682477804 -0.0118187391 [61] -0.0359119198 -0.0023663033 0.0668542802 0.0007485499 -0.0097707459 [66] -0.0168718298 -0.0043848490 0.0055258638 0.0219829902 -0.0057350044 [71] 0.0238920417 -0.0221259220 -0.0310115591 0.0022590386 -0.0028913640 [76] 0.0048640436 -0.0379857416 -0.0192308005 -0.0604836677 -0.0523951107 [81] -0.0264459462 0.0190059858 0.0515794220 0.0105776720 -0.0035560254 [86] -0.0163280881 -0.0107869871 -0.0046091455 -0.0694566072 0.0275743671 [91] 0.1037066512 -0.0682976492 0.0095641791 0.0077799567 -0.0004717255 [96] -0.0170756133 -0.0219398275 0.0266415497 -0.0071874400 0.0255235333 [101] -0.0689794396 -0.0026986397 -0.0013499158 -0.0564306598 -0.0254379905 [106] 0.1658985472 0.0537206816 0.0642377469 -0.0059269353 0.0194027110 [111] -0.0100689168 0.0582650246 0.0165272380 0.0724558629 0.0306429617 [116] -0.0387863823 0.0310497861 -0.0857565364 0.0057952255 -0.0384897916 [121] 0.0360585981 -0.0032059489 0.0137113325 -0.0240598742 0.0184468748 [126] 0.0032515794 0.0254779587 -0.0283919148 -0.0227883524 -0.0430964564 [131] -0.0216370051 -0.0140707506 -0.0154464375 -0.0406067598 -0.0137719610 [136] -0.0143160598 -0.0155991145 -0.0225547231 0.0228793701 -0.0265463903 [141] 0.0287954700 -0.0300888958 0.0071173017 -0.0388479620 -0.0210517741 [146] 0.0209016661 -0.0073639844 -0.0069563465 -0.0215118032 0.0130815818 [151] -0.0294445695 -0.0117484041 0.0237400495 -0.0008501087 -0.0633816370 [156] 0.0161387565 0.0039191450 -0.0300291384 -0.0074312722 -0.0087841633 [161] 0.0442649141 -0.0331697191 -0.0340606318 -0.0469800101 0.0063288028 [166] -0.0186025107 0.0183753561 0.0199570008 -0.0224484565 0.0120439983 [171] 0.0276616631 0.0595111246 -0.0756624215 0.0273649928 0.0215180662 [176] 0.0274133739 -0.0046309804 0.0393431418 -0.0089052261 0.0664244229 [181] -0.0242474923 0.0613863253 0.0261613141 -0.0436635791 0.0076741977 [186] 0.0201218133 > postscript(file="/var/www/html/rcomp/tmp/29xg61261080385.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/3spvr1261080385.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/4dc3j1261080385.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/58rly1261080385.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/69j011261080385.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/7dqps1261080385.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/8dkuy1261080385.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/9pany1261080385.tab") > > try(system("convert tmp/1lyqa1261080385.ps tmp/1lyqa1261080385.png",intern=TRUE)) character(0) > try(system("convert tmp/29xg61261080385.ps tmp/29xg61261080385.png",intern=TRUE)) character(0) > try(system("convert tmp/3spvr1261080385.ps tmp/3spvr1261080385.png",intern=TRUE)) character(0) > try(system("convert tmp/4dc3j1261080385.ps tmp/4dc3j1261080385.png",intern=TRUE)) character(0) > try(system("convert tmp/58rly1261080385.ps tmp/58rly1261080385.png",intern=TRUE)) character(0) > try(system("convert tmp/69j011261080385.ps tmp/69j011261080385.png",intern=TRUE)) character(0) > try(system("convert tmp/7dqps1261080385.ps tmp/7dqps1261080385.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 26.734 2.508 30.585