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(52.61 + ,65.04 + ,67.54 + ,63.58 + ,57.35 + ,54.93 + ,54.30 + ,58.89 + ,65.95 + ,82.65 + ,100.08 + ,100.68 + ,97.53 + ,92.29 + ,85.08 + ,91.61 + ,93.61 + ,90.40 + ,99.31 + ,107.71 + ,106.18 + ,98.80 + ,99.58 + ,98.85 + ,92.69 + ,91.82 + ,92.63 + ,98.41 + ,94.56 + ,85.78 + ,84.59 + ,83.49 + ,84.68 + ,80.12 + ,84.37 + ,85.94 + ,87.07 + ,84.52 + ,83.13 + ,75.95 + ,70.12 + ,78.10 + ,83.06 + ,87.92 + ,90.21 + ,89.95 + ,97.08 + ,102.08 + ,100.64 + ,97.73 + ,97.61 + ,100.32 + ,102.04 + ,107.80 + ,111.51 + ,110.18 + ,110.08 + ,117.40 + ,119.82 + ,118.79 + ,113.18 + ,122.76 + ,120.43 + ,129.16 + ,132.48 + ,135.68 + ,141.49 + ,122.40 + ,137.06 + ,144.84 + ,154.64 + ,148.04 + ,152.76 + ,172.00 + ,169.03 + ,179.68 + ,190.38 + ,233.23 + ,231.45 + ,244.87 + ,299.12 + ,385.01 + ,381.48 + ,321.56 + ,317.27 + ,323.09 + ,392.72 + ,372.37 + ,386.52 + ,412.83 + ,404.91 + ,406.73 + ,392.41 + ,363.31 + ,357.95 + ,375.10 + ,369.74 + ,386.14 + ,353.40 + ,346.87 + ,362.53 + ,349.87 + ,347.03 + ,332.94 + ,327.48 + ,327.92 + ,308.91 + ,285.71 + ,318.81 + ,284.76 + ,301.04 + ,315.16 + ,388.34 + ,383.37 + ,416.77 + ,423.24 + ,429.90 + ,486.07 + ,394.41 + ,410.93 + ,430.88 + ,447.29 + ,431.65 + ,456.53 + ,452.93 + ,440.90 + ,416.46 + ,451.49 + ,432.00 + ,436.19 + ,428.55 + ,421.40 + ,425.18 + ,437.24 + ,431.92 + ,412.65 + ,419.37 + ,436.40 + ,421.37 + ,423.66 + ,402.45 + ,402.82 + ,400.46 + ,425.73 + ,417.93 + ,403.43 + ,404.96 + ,393.64 + ,399.98 + ,375.93 + ,366.57 + ,353.90 + ,347.51 + ,364.10 + ,328.64 + ,348.01 + ,329.63 + ,350.96 + ,336.16 + ,332.15 + ,349.46 + ,383.64 + ,369.82 + ,345.50 + ,337.80 + ,334.76 + ,338.02 + ,346.74 + ,371.84 + ,375.90 + ,373.31 + ,391.91 + ,374.28 + ,384.69 + ,372.16 + ,371.97 + ,351.76 + ,352.89 + ,330.48 + ,347.70 + ,345.58 + ,360.76 + ,364.40 + ,374.62 + ,369.07 + ,341.80 + ,337.87 + ,336.58 + ,332.66 + ,335.74 + ,321.64 + ,329.38 + ,321.84 + ,324.56 + ,330.90 + ,310.91 + ,318.07 + ,312.36 + ,315.19 + ,332.89 + ,310.67 + ,321.26 + ,316.15 + ,283.87 + ,280.65 + ,280.21 + ,265.93 + ,267.80 + ,278.03 + ,291.86 + ,262.61 + ,264.80 + ,265.67 + ,251.05 + ,256.11 + ,279.75 + ,282.52 + ,288.89 + ,308.46 + ,292.89 + ,280.79 + ,273.61 + ,276.67 + ,277.92 + ,250.28 + ,264.70 + ,268.95 + ,261.69 + ,257.99 + ,251.28 + ,243.14 + ,246.81 + ,224.50 + ,241.25 + ,254.97 + ,261.39 + ,266.67 + ,264.28 + ,270.45 + ,274.97 + ,281.13 + ,300.65 + ,321.12 + ,354.79 + ,318.97 + ,298.71 + ,318.85 + ,327.89 + ,348.19 + ,335.18 + ,332.98 + ,331.04 + ,317.52 + ,325.31 + ,317.59 + ,313.37 + ,313.00 + ,314.77 + ,298.37 + ,311.10 + ,308.79 + ,297.30 + ,293.58 + ,291.35 + ,291.51 + ,289.94 + ,287.07 + ,280.74 + ,294.95 + ,288.98 + ,285.63 + ,294.55 + ,290.67 + ,314.78 + ,306.50 + ,304.48 + ,308.65 + ,307.01 + ,298.59 + ,293.51 + ,294.90 + ,296.14 + ,294.25 + ,291.75 + ,290.49 + ,288.68 + ,310.07 + ,297.45 + ,300.81 + ,301.56 + ,296.89 + ,305.23 + ,298.45 + ,298.75 + ,273.02 + ,266.62 + ,266.06 + ,284.48 + ,275.71 + ,284.19 + ,284.81 + ,267.29 + ,272.95 + ,262.35 + ,246.34 + ,251.03 + ,247.54 + ,254.80 + ,245.08 + ,251.30 + ,261.48 + ,258.85 + ,270.89 + ,257.55 + ,253.08 + ,238.81 + ,241.22 + ,280.75 + ,284.56 + ,289.35 + ,289.56 + ,289.55 + ,305.00 + ,289.22 + ,301.82 + ,293.56 + ,300.59 + ,298.67 + ,311.55 + ,310.08 + ,312.06 + ,309.13 + ,292.31 + ,284.41 + ,290.02 + ,291.52 + ,296.81 + ,315.60 + ,319.63 + ,303.89 + ,300.53 + ,321.84 + ,309.48 + ,307.68 + ,310.53 + ,327.91 + ,343.18 + ,345.48 + ,342.03 + ,349.57 + ,322.50 + ,310.74 + ,318.96 + ,327.53 + ,320.00 + ,320.72 + ,330.86 + ,342.34 + ,322.37 + ,306.86 + ,301.75 + ,307.27 + ,301.30 + ,315.18 + ,342.11 + ,333.18 + ,332.26 + ,332.32 + ,330.00 + ,321.78 + ,318.59 + ,344.78 + ,324.09 + ,322.03 + ,325.32 + ,325.10 + ,335.10 + ,334.66 + ,334.54 + ,341.15 + ,320.47 + ,323.85 + ,328.06 + ,328.93 + ,337.50 + ,335.65 + ,361.05 + ,353.19 + ,352.28 + ,392.53 + ,393.03 + ,420.42 + ,434.91 + ,468.38 + ,466.35 + ,480.93 + ,511.25 + ,508.39 + ,479.80 + ,495.63 + ,487.09 + ,473.06 + ,473.03 + ,487.87 + ,479.28 + ,500.60 + ,502.82 + ,497.13 + ,496.06 + ,489.80 + ,481.66 + ,486.17 + ,492.94 + ,522.45 + ,545.71 + ,533.77 + ,570.26 + ,623.56 + ,639.94 + ,589.13 + ,559.45 + ,569.96 + ,590.43 + ,588.37 + ,565.80 + ,629.69 + ,576.28 + ,641.89 + ,625.70 + ,717.52 + ,749.58 + ,690.29 + ,666.55 + ,689.18 + ,666.24 + ,662.32 + ,665.83 + ,681.23 + ,704.87 + ,783.13 + ,757.97 + ,775.93 + ,812.08 + ,824.40 + ,886.89 + ,984.07 + ,1015.59 + ,897.30 + ,980.37 + ,957.37 + ,968.96 + ,1062.80 + ,1047.67 + ,967.91 + ,1021.58 + ,1014.02 + ,1034.98 + ,1068.80 + ,1038.38 + ,1133.26 + ,1259.55 + ,1207.42 + ,1234.59 + ,1297.03) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '0.3' > 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,] -0.23937926 -0.02966518 -0.01806718 0.2039169 0.04275289 0.008845015 [2,] -0.24108650 -0.02892902 -0.01758388 0.2057722 0.04260961 0.000000000 [3,] -0.03528408 -0.02165483 -0.01082194 0.0000000 0.04247118 0.000000000 [4,] -0.03507760 -0.02142139 0.00000000 0.0000000 0.04080011 0.000000000 [5,] -0.03432208 0.00000000 0.00000000 0.0000000 0.04306860 0.000000000 [6,] 0.00000000 0.00000000 0.00000000 0.0000000 0.04192171 0.000000000 [7,] 0.00000000 0.00000000 0.00000000 0.0000000 0.00000000 0.000000000 [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,] -0.9999897 [2,] -1.0000193 [3,] -0.9999925 [4,] -0.9998877 [5,] -0.9999914 [6,] -0.9999574 [7,] -1.0057479 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.81945 0.62918 0.72739 0.84568 0.39809 0.86474 0 [2,] 0.82076 0.63836 0.73299 0.84655 0.39962 NA 0 [3,] 0.45647 0.64906 0.82138 NA 0.40223 NA 0 [4,] 0.45906 0.65254 NA NA 0.41588 NA 0 [5,] 0.46696 NA NA NA 0.38658 NA 0 [6,] NA NA NA NA 0.40020 NA 0 [7,] NA NA NA NA NA NA 0 [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 -0.2394 -0.0297 -0.0181 0.2039 0.0428 0.0088 -1.0000 s.e. 1.0481 0.0614 0.0518 1.0471 0.0505 0.0519 0.0669 sigma^2 estimated as 0.008516: log likelihood = 413.45, aic = -810.9 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2394 -0.0297 -0.0181 0.2039 0.0428 0.0088 -1.0000 s.e. 1.0481 0.0614 0.0518 1.0471 0.0505 0.0519 0.0669 sigma^2 estimated as 0.008516: log likelihood = 413.45, aic = -810.9 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2411 -0.0289 -0.0176 0.2058 0.0426 0 -1.0000 s.e. 1.0634 0.0615 0.0515 1.0627 0.0505 0 0.0728 sigma^2 estimated as 0.008512: log likelihood = 413.43, aic = -812.87 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.0353 -0.0217 -0.0108 0 0.0425 0 -1.0000 s.e. 0.0473 0.0476 0.0479 0 0.0507 0 0.0717 sigma^2 estimated as 0.008513: log likelihood = 413.42, aic = -814.84 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.0351 -0.0214 0 0 0.0408 0 -0.9999 s.e. 0.0473 0.0475 0 0 0.0501 0 0.0694 sigma^2 estimated as 0.008514: log likelihood = 413.4, aic = -816.79 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.0343 0 0 0 0.0431 0 -1.0000 s.e. 0.0471 0 0 0 0.0497 0 0.0661 sigma^2 estimated as 0.008518: log likelihood = 413.29, aic = -818.59 [[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 ma1 sar1 sar2 sma1 0 0 0 0 0.0419 0 -1.0000 s.e. 0 0 0 0 0.0498 0 0.0677 sigma^2 estimated as 0.008527: log likelihood = 413.03, aic = -820.06 $aic [1] -810.8988 -812.8698 -814.8412 -816.7902 -818.5871 -820.0611 -821.3496 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 > postscript(file="/var/wessaorg/rcomp/tmp/1i8s91324477571.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 = 464 Frequency = 1 [1] 0.0018956568 0.0010148487 0.0006980256 0.0004642136 0.0002745477 [6] 0.0001886067 0.0001508152 0.0002086110 0.0002963543 0.0005007946 [11] 0.0006674430 -0.0012315761 -0.0099342386 -0.2025602926 -0.0963721340 [16] 0.1072859696 0.0946024428 0.0019069039 0.0880182118 0.0117356078 [21] -0.0973006758 -0.2402531891 -0.1536099971 -0.0115106202 -0.0274459219 [26] -0.0661988535 0.0328659418 0.0472143349 -0.0078712694 -0.0582869397 [31] -0.0558799010 -0.0863500933 -0.0255718706 -0.1111467598 -0.0436712697 [36] 0.0181797462 0.0596690355 -0.0691853754 -0.0055231728 -0.1152394033 [41] -0.0374375275 0.1612270060 0.0383878846 0.0116051450 -0.0076619796 [46] -0.0290069665 -0.0059901622 0.0457311643 0.0115445950 -0.0539787084 [51] 0.0123189038 0.0361740852 0.0683810090 0.0711503136 0.0021811272 [56] -0.0652724190 -0.0340676269 0.0498048004 -0.0628645431 -0.0292738758 [61] -0.0286766249 0.0809687036 -0.0114239506 0.0763746300 0.0633357693 [66] 0.0278955595 0.0153440629 -0.2087731959 0.1077727669 0.0313834772 [71] 0.0081538738 -0.0652603179 0.0721139856 0.1205822749 -0.0089278940 [76] 0.0596288382 0.0997257459 0.2772122736 -0.0496336864 0.0849949545 [81] 0.2489214650 0.3623358139 -0.0925670856 -0.2742654421 -0.0032215721 [86] -0.0223177651 0.3345984826 -0.1178138387 0.0689804761 0.0574907620 [91] -0.0624450192 -0.0111795950 -0.1533480451 -0.2354769416 -0.0852075440 [96] 0.1269488707 -0.0028383557 0.0293746931 -0.1886660435 -0.0378279743 [101] 0.0716219690 -0.1129915783 -0.0342723063 -0.0818469504 -0.0855880111 [106] -0.0538371059 -0.1454939187 -0.1027090802 0.1937035761 -0.2271183205 [111] 0.0865187785 0.0672498940 0.3352140007 -0.0568677071 0.1254113267 [116] 0.0245660202 -0.0239192436 0.1637064732 -0.4004568882 0.1097974240 [121] 0.0747802563 0.0504970798 -0.0822367576 0.0831741150 -0.0685602653 [126] -0.0790980986 -0.1378145200 0.1341356809 -0.1299162110 -0.0645908760 [131] -0.0114684358 -0.0093153345 0.0040796706 0.0187539895 -0.0270708505 [136] -0.1058092112 -0.0048498943 0.0475090580 -0.0767051676 -0.0162492502 [141] -0.1253082269 -0.0659885165 -0.0036101514 0.1325390633 -0.0417578520 [146] -0.0937071274 0.0017688627 -0.0583870183 -0.0069619131 -0.1388123115 [151] -0.0528698910 -0.0782100142 -0.0551321742 0.0166425768 -0.1646476702 [156] 0.1034993164 -0.0935202706 0.0842060669 -0.0780021944 -0.0258014321 [161] 0.0502464285 0.1446480181 -0.0696402638 -0.1248306027 -0.0605270562 [166] -0.0813408500 0.0414562324 0.0438030668 0.1239038872 -0.0144238647 [171] -0.0092093204 0.0785362785 -0.1197482654 0.0118198726 -0.0579626091 [176] 0.0002327593 -0.1132946294 -0.0514148308 -0.0948067897 0.0850341883 [181] -0.0219563670 0.0440506554 0.0178291221 0.0323749508 -0.0511118830 [186] -0.1614444737 -0.0167082344 -0.0100745116 -0.0280550231 -0.0390742931 [191] -0.0446564216 0.0322603168 -0.0430389356 -0.0197438816 0.0304231523 [196] -0.1179166824 0.0125595876 -0.0437728442 0.0167862967 0.0872333904 [201] -0.1236868228 0.0023833769 0.0011476654 -0.1805068798 -0.0190775126 [206] -0.0325204807 -0.0860655460 0.0086230602 0.0307456637 0.0613211266 [211] -0.1667544676 0.0008738577 0.0062583996 -0.1417050737 0.0561806896 [216] 0.1501808666 0.0148358860 0.0079217179 0.1113551269 -0.0913214797 [221] -0.0980017756 -0.0646220174 0.0340020207 -0.0017242324 -0.1671056048 [226] 0.0458636161 0.0449518501 -0.0515427940 -0.0250443766 -0.0702559306 [231] -0.0584691734 0.0240324702 -0.1629270006 0.0925267960 0.0915605058 [236] 0.0299613348 0.0427869422 -0.0644356455 0.0540945535 0.0274363766 [241] 0.0349488277 0.0847992567 0.1091781191 0.1629676827 -0.1875991452 [246] -0.1335688425 0.1062772076 0.0350026437 0.1022548739 -0.1071861666 [251] 0.0038392547 -0.0126519952 -0.0734214237 0.0068841037 -0.0503179762 [256] -0.0403081546 -0.0006120106 -0.0016692666 -0.0937237917 0.0547647733 [261] -0.0186321103 -0.0975816412 -0.0036609783 -0.0130642713 0.0044941000 [266] -0.0394567794 -0.0180306385 -0.0441278812 0.0731445689 -0.0487190086 [271] -0.0126514924 0.0326967823 -0.0222081053 0.0981615636 -0.0265008535 [276] -0.0108886668 0.0227054520 -0.0357635817 -0.0476183359 -0.0338191206 [281] -0.0050056930 -0.0051154290 -0.0066470363 -0.0311700256 -0.0064583201 [286] -0.0539673908 0.1355123641 -0.0674835164 0.0168336153 -0.0215141431 [291] -0.0244501264 0.0401631278 -0.0460569251 -0.0117401159 -0.1407629606 [296] -0.0508499554 -0.0031225986 0.0702962237 -0.0428391850 0.0537938701 [301] 0.0015455504 -0.1260956632 0.0341286090 -0.0719570123 -0.1033237635 [306] 0.0163331959 -0.0066844052 0.0341758968 -0.0598102706 -0.0049885792 [311] 0.0774443158 -0.0172726736 0.0699286984 -0.0952640287 -0.0298828408 [316] -0.0914648263 0.0160674529 0.2225191871 0.0315696209 0.0116289797 [321] 0.0059900771 -0.0406028810 0.0925914391 -0.0844642941 0.0622600285 [326] -0.0585984362 0.0394110367 -0.0083135398 0.0646298105 -0.0392325784 [331] 0.0175151394 -0.0304517223 -0.0892570039 -0.0816055685 0.0351854920 [336] 0.0162656253 0.0200249361 0.0879658362 0.0178363076 -0.0842245056 [341] -0.0273165620 0.0932229360 -0.0580154957 -0.0216519197 0.0242771708 [346] 0.0578076514 0.0815268760 0.0147441469 -0.0252280951 0.0151930390 [351] -0.1392388389 -0.0565555700 0.0387194522 0.0162005009 -0.0268246147 [356] -0.0078218939 0.0558922089 0.0172680475 -0.1011693668 -0.0789077594 [361] -0.0329017349 0.0099413672 -0.0240773479 0.0802256942 0.1292269102 [366] -0.0705070514 0.0071749812 -0.0115664989 -0.0113964086 -0.0818383477 [371] -0.0057476710 0.1422616445 -0.1082727863 -0.0304820014 0.0215259340 [376] -0.0033560218 0.0344386928 -0.0219664567 0.0095691060 0.0217805390 [381] -0.1018003019 -0.0152701062 0.0291337774 0.0001497520 0.0462573457 [386] -0.0264933995 0.1265354687 -0.0368849155 -0.0183933002 0.1678435717 [391] 0.0119682688 0.1072607386 0.0721651027 0.1022140198 -0.0031323198 [396] 0.0588222890 0.1114763634 -0.0272135518 -0.1165200222 0.0651394149 [401] -0.0440628924 -0.0890075571 0.0091148017 0.0378633336 -0.0321804417 [406] 0.0395319052 0.0147735390 -0.0246272232 -0.0152996589 -0.0397260663 [411] -0.0249806211 0.0152282669 0.0176118995 0.0902976708 0.0938273800 [416] -0.0620252507 0.1362387264 0.1378833405 0.0582648324 -0.1667373717 [421] -0.1085024840 0.0229299858 0.0745616276 -0.0079829801 -0.0895273434 [426] 0.1846600471 -0.1760591763 0.2054341789 -0.0567943561 0.2354867599 [431] 0.0952614510 -0.1633947137 -0.0716706669 0.0527278236 -0.0730106024 [436] -0.0119909624 0.0062407521 0.0075613575 0.0906134114 0.1969072224 [441] -0.0662086624 -0.0100064145 0.0972105079 0.0501881227 0.1661452493 [446] 0.2196496357 0.0798227137 -0.2863668918 0.1955974534 -0.0886617723 [451] 0.0338039807 0.1831905985 -0.0269202940 -0.2373566617 0.1211388106 [456] -0.0108023739 0.0363067947 0.0439762019 -0.0714780648 0.2306446394 [461] 0.2402855381 -0.1326104074 0.0623559750 0.0849221372 > postscript(file="/var/wessaorg/rcomp/tmp/261co1324477571.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/38lqc1324477571.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/4lpdk1324477571.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/5cqjy1324477571.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/63xzy1324477571.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/7srxe1324477571.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/8h3e01324477571.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/9c6aj1324477571.tab") > > try(system("convert tmp/1i8s91324477571.ps tmp/1i8s91324477571.png",intern=TRUE)) character(0) > try(system("convert tmp/261co1324477571.ps tmp/261co1324477571.png",intern=TRUE)) character(0) > try(system("convert tmp/38lqc1324477571.ps tmp/38lqc1324477571.png",intern=TRUE)) character(0) > try(system("convert tmp/4lpdk1324477571.ps tmp/4lpdk1324477571.png",intern=TRUE)) character(0) > try(system("convert tmp/5cqjy1324477571.ps tmp/5cqjy1324477571.png",intern=TRUE)) character(0) > try(system("convert tmp/63xzy1324477571.ps tmp/63xzy1324477571.png",intern=TRUE)) character(0) > try(system("convert tmp/7srxe1324477571.ps tmp/7srxe1324477571.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 27.090 2.885 30.004