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(15086 + ,20559 + ,39383 + ,68032 + ,42346 + ,40184 + ,29183 + ,18049 + ,24377 + ,48033 + ,61410 + ,40637 + ,37914 + ,22481 + ,17061 + ,21921 + ,34043 + ,36152 + ,38929 + ,36713 + ,27928 + ,14178 + ,19331 + ,35030 + ,37834 + ,45257 + ,39650 + ,33001 + ,22268 + ,34283 + ,63733 + ,54548 + ,31666 + ,32654 + ,28409 + ,16234 + ,19544 + ,33375 + ,30839 + ,27581 + ,28596 + ,28542 + ,14285 + ,20799 + ,28729 + ,30278 + ,31266 + ,38341 + ,24938 + ,15700 + ,17382 + ,34229 + ,29584 + ,33909 + ,29397 + ,24644 + ,14124 + ,18770 + ,32681 + ,30518 + ,34310 + ,24217 + ,15806 + ,12362 + ,13751 + ,25312 + ,23549 + ,30305 + ,30411 + ,15593 + ,17382 + ,15806 + ,29183 + ,33348 + ,33589 + ,30198 + ,23469 + ,15593 + ,16474 + ,27341 + ,26673 + ,33268 + ,26406 + ,23656 + ,15086 + ,18316 + ,27528 + ,30305 + ,34550 + ,34710 + ,23843 + ,12362 + ,21200 + ,27261 + ,31586 + ,33322 + ,32814 + ,21974 + ,17115 + ,14445 + ,26673 + ,31746 + ,28249 + ,29103 + ,21974 + ,17622 + ,21066 + ,28382 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,65655 + ,34657 + ,56417 + ,68699 + ,77163 + ,79700 + ,78151 + ,59781 + ,44829 + ,46992 + ,77030 + ,92596 + ,84933 + ,83918 + ,63306 + ,47286 + ,77403 + ,102768 + ,118361 + ,107708 + ,94705 + ,76122 + ,45417 + ,79806 + ,106346 + ,95826 + ,93797 + ,99351 + ,83838 + ,54174 + ,84559 + ,116492 + ,114543 + ,123407 + ,114677 + ,110378 + ,96040 + ,141857 + ,233465 + ,146022 + ,142124 + ,118067 + ,83010 + ,75695 + ,104157 + ,150161 + ,174832 + ,139935 + ,108749 + ,109977 + ,78872 + ,126451 + ,161295) > par9 = '0' > par8 = '2' > par7 = '0' > par6 = '3' > par5 = '1' > par4 = '1' > par3 = '0' > par2 = '0.1' > 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 <- 7 #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] [1,] 0.7592085 0.04044505 0.05450350 -0.5699421 -0.2490720 [2,] 0.7788143 0.00000000 0.07410579 -0.5699766 -0.2483304 [3,] NA NA NA NA NA [4,] NA NA NA NA NA [5,] NA NA NA NA NA [6,] NA NA NA NA NA [7,] NA NA NA NA NA [8,] NA NA NA NA NA [9,] NA NA NA NA NA [10,] NA NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [1,] 0 0.2575 0.05568 0 0 [2,] 0 NA 0.00109 0 0 [3,] NA NA NA NA NA [4,] NA NA NA NA NA [5,] NA NA NA NA NA [6,] NA NA NA NA NA [7,] NA NA NA NA NA [8,] NA NA NA NA NA [9,] NA NA NA NA NA [10,] 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 sar1 sar2 0.7592 0.0404 0.0545 -0.5699 -0.2491 s.e. 0.0285 0.0357 0.0285 0.0285 0.0282 sigma^2 estimated as 0.001982: log likelihood = 2088.96, aic = -4165.93 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 0.7592 0.0404 0.0545 -0.5699 -0.2491 s.e. 0.0285 0.0357 0.0285 0.0285 0.0282 sigma^2 estimated as 0.001982: log likelihood = 2088.96, aic = -4165.93 [[3]][[3]] NULL [[3]][[4]] NULL [[3]][[5]] NULL $aic [1] -4165.926 -4166.644 Warning message: 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/1ymt11291061803.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 = 1242 Frequency = 1 [1] 2.617320e-03 2.699595e-03 2.880903e-03 3.042767e-03 2.901871e-03 [6] 2.886702e-03 2.795822e-03 2.746809e-02 9.632634e-03 2.003582e-02 [11] -6.437802e-02 9.661848e-03 -3.798073e-03 -4.536718e-02 4.005833e-02 [16] -9.749595e-03 -6.196140e-02 -1.096276e-01 1.117672e-01 8.863340e-03 [21] 5.035726e-02 -6.888685e-02 -5.142695e-03 -4.439562e-03 -5.330253e-02 [26] 1.003743e-01 -7.634116e-03 5.652040e-02 3.854856e-02 6.105898e-02 [31] 4.852016e-02 -5.256403e-02 -1.515625e-01 4.592433e-03 3.288846e-02 [36] -2.168998e-02 -4.958766e-02 -3.023417e-02 -3.004735e-02 -2.126050e-03 [41] 1.091706e-02 4.606194e-02 -3.621221e-02 1.093505e-02 -7.911030e-02 [46] 1.231185e-02 4.867138e-02 6.619098e-02 -7.911675e-02 1.905161e-02 [51] -6.614525e-02 3.993231e-02 -2.996603e-02 7.675935e-02 -5.932275e-02 [56] 5.451261e-03 -3.811032e-03 1.690343e-02 8.792532e-03 1.619074e-03 [61] 2.228841e-02 -9.544533e-02 -7.415200e-02 5.682849e-02 -3.941531e-02 [66] 4.825501e-03 -1.228497e-02 3.175836e-02 4.107062e-02 -7.810819e-02 [71] 1.183798e-01 -5.047455e-02 9.819247e-04 5.825708e-02 -3.376346e-02 [76] 1.107102e-02 5.854884e-02 -4.700547e-02 -3.259397e-03 -2.672734e-02 [81] -1.626088e-02 2.420124e-02 -2.488273e-02 8.220411e-02 -5.060717e-02 [86] 4.424174e-02 -3.493847e-02 2.141584e-02 -4.281100e-03 4.142301e-02 [91] -1.298741e-02 -9.010390e-02 1.021779e-01 -4.933103e-02 2.214017e-02 [96] -2.015849e-02 2.182836e-02 -3.523393e-02 6.835522e-02 -1.131331e-01 [101] 4.689366e-02 2.236159e-02 -5.820074e-02 1.363537e-02 6.930943e-03 [106] 5.614363e-02 2.085451e-02 -2.735496e-02 -7.179290e-03 2.960501e-02 [111] 4.279176e-02 9.860140e-03 5.622105e-02 4.145240e-02 4.879365e-02 [116] 7.842248e-03 4.276675e-02 -4.144243e-02 -3.240212e-02 -3.737894e-02 [121] 6.311320e-02 -2.152550e-02 -5.320624e-02 2.487467e-02 -1.022820e-02 [126] 4.913163e-03 1.281710e-01 -8.849949e-02 1.878128e-02 -9.277518e-03 [131] -2.591420e-02 -2.994759e-02 1.612387e-02 -8.245309e-02 6.790335e-02 [136] -4.056764e-02 4.406657e-02 2.484201e-02 1.376051e-01 9.614932e-02 [141] -6.000778e-03 7.698679e-02 4.792721e-02 1.810249e-02 1.050772e-02 [146] -1.528876e-01 -5.706055e-02 -1.902480e-02 1.561277e-01 6.746932e-02 [151] -2.540037e-02 -1.117218e-02 2.331407e-02 7.524002e-02 -2.025874e-02 [156] -2.892867e-02 2.200986e-03 -1.186228e-02 -4.270612e-02 2.197179e-02 [161] 8.796210e-03 2.556951e-02 -6.031664e-03 -4.013999e-02 -8.309855e-02 [166] -5.418152e-02 1.547283e-02 6.247752e-02 6.597260e-02 -7.802623e-02 [171] 4.953727e-02 2.339046e-02 2.317516e-02 1.516400e-02 -4.643020e-02 [176] -9.649380e-03 -1.531523e-02 1.339385e-03 1.331138e-02 5.083859e-03 [181] -3.161946e-02 -3.910624e-02 3.519806e-02 2.149982e-02 -1.969898e-02 [186] 6.824890e-02 7.626880e-02 4.532289e-02 -2.065368e-02 -4.843525e-02 [191] 7.646029e-04 -1.430311e-02 5.677558e-02 -9.917330e-03 1.650038e-02 [196] -3.853998e-04 -2.418519e-02 -4.139523e-02 -9.174280e-03 -1.241387e-02 [201] 1.596795e-02 -3.616714e-03 -4.338601e-02 -1.675260e-02 8.794524e-03 [206] -2.237402e-02 -1.802643e-03 -1.069156e-01 -5.719586e-02 -1.472654e-02 [211] -1.049775e-01 -6.746234e-02 -1.571705e-01 -1.259415e-01 7.223204e-02 [216] 4.150111e-02 2.284698e-02 8.909155e-02 -8.737827e-02 -1.262854e-01 [221] 7.322346e-02 1.029682e-01 3.461585e-02 4.497116e-02 4.013518e-02 [226] 3.978854e-02 1.100394e-01 3.279444e-02 -3.885502e-02 -6.823717e-03 [231] -1.885712e-02 -1.086502e-02 5.892396e-02 1.099296e-01 4.166176e-02 [236] -3.505913e-02 -2.424655e-02 -2.783402e-04 -6.882418e-02 3.577441e-02 [241] 1.021945e-01 -1.181419e-02 -3.906235e-02 2.868735e-03 -9.153884e-03 [246] 3.627829e-02 1.167572e-02 2.256633e-03 -7.811405e-03 8.965452e-03 [251] -4.655381e-02 2.736577e-02 -1.341939e-02 1.247148e-02 3.769010e-04 [256] -7.841279e-04 1.678195e-02 6.140662e-03 2.703782e-02 -2.566227e-02 [261] 5.256149e-03 -6.152961e-03 8.511025e-03 1.204346e-02 -8.418961e-03 [266] 1.551042e-02 5.541866e-03 1.310010e-02 -7.699571e-03 -1.457965e-02 [271] -1.196474e-02 1.195861e-02 4.324031e-02 3.138437e-02 1.926672e-03 [276] 2.471142e-02 -4.843367e-02 9.372451e-03 2.222414e-02 -6.804726e-02 [281] 5.538798e-02 -5.389475e-04 -2.855777e-02 -6.574499e-03 -1.876253e-02 [286] 3.101190e-02 -6.671942e-04 -4.358442e-02 -3.155083e-02 7.735811e-03 [291] 3.109450e-02 -7.255190e-03 1.479237e-02 -2.775644e-02 -1.862075e-02 [296] -7.719932e-03 -5.803144e-03 4.113407e-02 -4.208875e-02 -5.273330e-02 [301] -1.736810e-02 -2.846325e-03 -1.630609e-02 -1.181776e-02 2.046496e-02 [306] 3.130973e-02 5.036918e-02 -3.873089e-02 4.797684e-02 2.861441e-02 [311] -3.952833e-02 -1.206926e-02 5.308766e-03 3.934196e-03 4.620143e-02 [316] -3.546680e-02 6.978609e-03 5.035103e-02 -1.451932e-02 -6.588654e-03 [321] -1.490649e-02 2.983641e-02 7.883192e-02 -1.103379e-02 2.548318e-02 [326] 7.190809e-03 1.693019e-02 -9.952114e-03 7.180212e-03 3.129413e-02 [331] 5.477818e-03 1.398685e-02 -4.323552e-02 6.510481e-03 1.004385e-02 [336] -4.109302e-02 -3.180209e-02 4.708041e-02 -2.730412e-03 -7.291993e-04 [341] -2.139501e-02 -4.203327e-03 2.978155e-02 -2.944998e-02 -4.280492e-03 [346] -1.909169e-02 -1.010325e-01 -2.598340e-02 5.041891e-02 9.183740e-02 [351] -6.022520e-02 -8.514563e-02 3.842220e-02 3.457232e-02 4.528476e-02 [356] -7.929914e-02 -7.093873e-03 2.269378e-02 1.557271e-02 -4.938372e-02 [361] 6.584707e-02 -2.995200e-02 1.920047e-02 -4.799195e-02 -2.155162e-02 [366] 2.373141e-02 -1.959035e-02 3.445443e-02 1.242570e-02 -2.466188e-02 [371] -1.229284e-02 6.014907e-02 -1.434727e-02 -9.053378e-02 -1.406618e-02 [376] -1.290614e-02 2.237769e-02 -1.366982e-02 -2.473558e-02 -1.100166e-04 [381] 2.523205e-02 -1.572692e-02 -4.026648e-03 -2.082864e-02 6.401230e-03 [386] -3.139071e-02 -4.978018e-02 7.111277e-02 1.169244e-02 3.170702e-02 [391] 2.505080e-04 -1.454842e-02 -6.162633e-02 1.442622e-03 4.001422e-02 [396] -8.642237e-06 1.200490e-02 -3.399266e-02 1.871055e-02 -2.746932e-03 [401] 2.682232e-03 1.887590e-02 -8.151696e-03 -3.278820e-02 9.390259e-03 [406] -8.383005e-02 9.756823e-02 -1.480600e-02 6.277296e-03 1.081657e-02 [411] -4.121672e-02 2.754635e-02 4.769751e-02 -4.229788e-03 -4.631856e-02 [416] -1.078421e-02 3.303306e-02 2.128058e-02 2.155309e-02 3.460285e-02 [421] 5.707863e-02 -2.674891e-02 -4.390584e-02 -1.248111e-02 3.190656e-02 [426] -2.136449e-02 -2.761315e-02 -8.669805e-02 1.052050e-01 -2.430901e-02 [431] -4.016720e-02 3.116315e-02 -3.716468e-02 1.036179e-02 -4.311315e-02 [436] -1.592588e-04 4.143177e-02 -2.446972e-03 -2.296081e-02 3.494559e-03 [441] 3.299285e-03 -2.306304e-02 -4.355502e-02 1.681914e-03 1.901892e-02 [446] -2.379214e-02 2.777024e-02 -2.152744e-02 -1.074589e-02 4.675014e-02 [451] -3.618976e-03 2.682602e-02 -5.268791e-02 3.436283e-02 2.975814e-03 [456] 2.690463e-02 -1.562799e-02 1.584614e-02 -1.354375e-02 2.217079e-02 [461] 1.043574e-02 -3.874868e-03 -1.608714e-02 -2.145717e-02 9.735425e-03 [466] 2.322687e-02 3.895106e-02 -1.456485e-03 1.047928e-02 5.146013e-02 [471] 4.637011e-02 -9.564840e-03 -8.342957e-03 1.223510e-02 7.442643e-03 [476] 1.506452e-02 -1.217584e-02 5.024960e-02 1.170201e-02 2.659637e-02 [481] -3.086968e-02 6.384043e-02 -1.805547e-02 5.756840e-02 2.557953e-02 [486] -4.498521e-02 -6.840750e-03 -9.935897e-03 -4.491365e-02 3.230423e-02 [491] -3.053713e-03 1.496038e-02 -8.342139e-03 -3.905913e-02 3.547196e-02 [496] -2.401991e-02 1.136491e-02 1.074689e-02 5.414908e-02 -2.521970e-02 [501] -8.461712e-03 -1.804592e-03 7.719487e-03 -2.499413e-02 4.449498e-02 [506] -4.399077e-02 1.452768e-01 -9.281686e-02 -8.966430e-03 1.263989e-02 [511] 9.828992e-03 -1.314642e-02 3.152053e-02 1.402364e-02 -4.947136e-02 [516] 5.987053e-03 -5.861873e-03 -1.563365e-02 1.318854e-03 -1.041080e-01 [521] 7.174426e-02 1.550698e-02 3.565424e-02 -1.286864e-02 2.211284e-03 [526] -1.443207e-02 4.672562e-02 -6.407390e-02 3.254409e-02 1.589695e-02 [531] 2.520807e-02 -3.995050e-03 4.495897e-03 1.375734e-04 -3.877876e-02 [536] 1.442422e-01 -2.217724e-02 7.665540e-03 -2.888061e-02 -3.220155e-02 [541] -9.987901e-03 -1.514066e-01 -5.407781e-02 -1.291086e-02 7.152574e-02 [546] 2.364613e-02 3.941581e-02 4.214047e-02 2.476021e-02 -5.157633e-02 [551] -4.888484e-02 -7.873836e-02 4.926117e-02 6.360215e-02 1.117468e-02 [556] 7.486613e-02 5.265932e-02 7.876798e-03 -3.976694e-02 3.580945e-02 [561] 3.885047e-02 -2.550551e-02 -2.879687e-03 5.922213e-02 -1.281381e-02 [566] -5.488238e-03 -9.516815e-03 -3.792669e-02 -8.963042e-03 -7.996835e-02 [571] 1.919188e-02 -8.097048e-03 4.771169e-02 -9.533112e-02 -8.399995e-02 [576] -1.913560e-02 -4.183213e-02 -1.178032e-01 -9.701940e-02 -2.047107e-01 [581] 7.462137e-02 3.813056e-02 -6.481895e-02 -5.359968e-03 -4.450630e-02 [586] -6.482298e-02 8.734872e-03 1.323522e-01 3.367469e-02 -1.062225e-02 [591] 6.245225e-02 9.855386e-02 4.657657e-02 3.753633e-02 -3.451726e-02 [596] 8.903345e-03 1.248760e-02 -2.906642e-02 1.949602e-02 1.311905e-01 [601] 7.515289e-02 -8.332877e-02 -6.694788e-02 2.779786e-02 2.532571e-02 [606] 2.169418e-02 2.899845e-02 1.156557e-02 -5.732102e-02 -1.373771e-02 [611] 1.164037e-02 1.482132e-02 -2.465961e-03 5.875062e-02 7.762735e-02 [616] -3.234609e-02 -4.031182e-02 2.326853e-02 -1.149899e-03 -1.520364e-02 [621] -1.011249e-02 -2.193827e-02 2.743601e-02 3.406595e-02 -2.465120e-02 [626] -7.690152e-02 -6.003399e-02 -5.438152e-03 -2.666549e-02 -3.368005e-02 [631] -4.158841e-02 7.203382e-02 9.982364e-03 2.151179e-02 -4.671407e-02 [636] 1.242260e-01 3.168115e-02 4.668358e-02 -3.183359e-02 5.630245e-02 [641] -1.590720e-02 4.369183e-02 -2.455886e-02 -4.697648e-03 2.122282e-03 [646] 1.080043e-02 3.179986e-02 4.370569e-02 1.016658e-02 -6.113393e-02 [651] 5.135522e-03 -3.446546e-03 -2.410129e-02 3.105861e-02 -2.250704e-02 [656] -2.672590e-03 -2.584066e-02 6.740745e-04 -6.456069e-03 -1.019994e-02 [661] -1.599628e-02 3.511969e-03 -2.211717e-02 3.184103e-03 2.428057e-02 [666] -7.032781e-04 2.605023e-03 3.571531e-02 -2.488732e-04 8.331847e-02 [671] 5.609744e-02 -8.746319e-02 1.597946e-02 -1.335836e-02 -1.131555e-03 [676] -7.368105e-03 -3.108371e-02 -6.310566e-02 4.908316e-02 -1.764396e-03 [681] -6.442491e-03 -1.189199e-02 -1.551661e-03 -3.838989e-02 -2.402803e-02 [686] 1.180872e-02 4.812824e-03 -1.955177e-02 1.881808e-03 1.820381e-02 [691] 3.366707e-03 -4.587739e-03 4.334339e-02 1.264011e-02 2.432835e-02 [696] -1.199090e-01 -3.537059e-02 2.660796e-02 -8.331495e-03 -2.548291e-02 [701] -2.664959e-02 4.338833e-03 -5.578639e-03 1.553851e-01 -2.178103e-03 [706] 2.565408e-02 4.973241e-03 -8.474564e-03 1.168823e-02 4.122048e-02 [711] -4.328880e-02 -3.632024e-02 5.096033e-02 -3.280779e-02 -2.227517e-03 [716] -6.051193e-03 -9.355991e-02 -5.236711e-02 -7.087343e-03 9.323419e-03 [721] -4.743028e-02 -4.624268e-02 -3.396492e-02 4.363601e-02 -6.728504e-02 [726] -1.257179e-02 -4.796132e-02 4.455260e-02 -3.612343e-02 -5.101401e-02 [731] 1.763089e-02 5.430316e-02 2.752202e-02 3.265996e-02 -4.128660e-02 [736] 9.887569e-03 -2.572393e-02 9.579266e-02 1.436438e-02 -2.471620e-02 [741] -3.457510e-02 3.969263e-02 6.390183e-02 2.952439e-02 2.985064e-02 [746] 3.663180e-03 -1.128840e-03 -3.796141e-02 3.141184e-02 3.682426e-02 [751] 1.395860e-02 -2.938561e-02 -2.392723e-03 -5.167397e-03 2.466611e-03 [756] 2.070523e-02 -3.251874e-02 -8.959189e-03 -2.641082e-02 -1.665422e-02 [761] -1.864696e-02 2.082024e-03 -3.872354e-03 -3.826215e-02 3.138481e-02 [766] 8.567404e-03 -1.458505e-02 -5.975287e-03 -3.199408e-02 -1.904237e-02 [771] -1.885186e-02 -4.637716e-02 2.966784e-02 -3.594410e-03 3.498640e-03 [776] 4.533661e-02 -3.352691e-02 3.920242e-03 1.754872e-02 -2.390688e-02 [781] 2.431114e-02 -6.227614e-02 3.952735e-02 2.581283e-02 -1.569791e-02 [786] -2.485215e-02 7.808538e-03 -5.255527e-02 9.402793e-02 -2.868888e-02 [791] -3.919754e-02 4.232337e-02 -1.328331e-02 -2.271037e-03 -3.511774e-05 [796] 1.856546e-02 -3.460630e-02 2.955489e-02 -3.661339e-02 6.406630e-03 [801] -2.598334e-03 2.232313e-02 -1.112341e-02 -2.791209e-02 1.386996e-02 [806] 1.555272e-02 4.239701e-02 -8.459094e-03 -1.279154e-02 -3.030578e-02 [811] 2.718888e-02 1.315887e-02 -5.050738e-02 4.411277e-02 3.481118e-03 [816] 3.427735e-02 -1.887423e-03 -1.119207e-02 -7.118422e-03 4.172614e-02 [821] -1.093850e-03 2.283815e-03 -2.192795e-02 1.829261e-02 1.665398e-02 [826] -2.521978e-02 3.857900e-02 -2.627667e-02 6.885224e-03 3.786148e-02 [831] -1.041845e-02 3.660761e-02 -3.456787e-02 7.004936e-03 6.641940e-03 [836] -2.806300e-03 2.665490e-02 1.131680e-02 3.132203e-02 8.895414e-03 [841] 3.003055e-03 5.810629e-03 -3.524018e-02 -1.904540e-02 1.006891e-01 [846] -4.408002e-02 4.212875e-02 1.773084e-02 4.477695e-02 -2.761997e-02 [851] -1.774383e-02 -7.808346e-03 9.293997e-03 -1.165672e-02 4.221595e-02 [856] -2.414698e-02 9.135557e-03 3.489226e-02 -2.604832e-02 -1.897593e-02 [861] 9.787793e-03 3.412419e-02 2.540588e-02 5.163440e-02 2.621733e-02 [866] -2.031214e-02 -3.547247e-02 5.007047e-03 -2.387154e-02 2.517985e-02 [871] 6.335047e-02 -6.387100e-02 -2.324285e-02 -3.336649e-04 9.149121e-03 [876] -2.759651e-03 2.827388e-02 -3.014561e-02 2.534835e-02 -2.434612e-02 [881] 4.882673e-02 -2.598639e-02 -9.051519e-03 -3.540342e-02 -5.482560e-02 [886] 2.578908e-02 3.128780e-02 1.475180e-02 -6.564397e-03 1.886151e-02 [891] 1.566438e-02 4.458915e-02 -7.637529e-03 4.376601e-03 -6.571028e-02 [896] -6.392773e-03 -2.829290e-02 1.110646e-02 -1.872550e-02 -1.050361e-02 [901] 1.058950e-02 3.403386e-02 6.370536e-02 -6.532114e-02 -6.767804e-03 [906] -3.653983e-03 -3.091193e-02 -1.420719e-03 4.831410e-02 -2.759999e-02 [911] 2.512265e-02 -7.405087e-03 -2.644022e-02 -3.906950e-02 -3.817380e-02 [916] -1.374831e-02 5.574753e-02 6.299740e-02 -3.694504e-02 2.068893e-02 [921] 3.968043e-02 1.988375e-02 3.930139e-02 -2.956074e-02 -7.146070e-03 [926] 1.893026e-02 -8.073473e-03 2.930388e-03 2.574889e-02 -3.702148e-02 [931] 2.373431e-02 1.576336e-03 2.680096e-03 -1.633162e-02 -4.141074e-02 [936] 1.894373e-02 -3.489547e-02 -1.093332e-01 -3.178224e-02 -7.785789e-02 [941] -6.074703e-03 -7.923279e-02 -1.242579e-01 -1.692802e-01 -5.255798e-02 [946] 1.149110e-01 -1.405349e-02 -3.033255e-02 2.176191e-02 2.520633e-02 [951] -9.733763e-02 6.397779e-02 6.433187e-02 1.911638e-02 3.113482e-02 [956] 1.183670e-02 6.814807e-02 1.884295e-01 4.303618e-02 -7.264203e-02 [961] 3.809665e-02 7.790404e-03 1.688714e-02 -1.350779e-02 8.518434e-02 [966] -5.019152e-03 1.543547e-02 -8.732971e-02 3.070731e-02 4.939095e-02 [971] 1.749855e-02 4.017798e-02 -4.825860e-03 -8.100363e-02 1.520764e-01 [976] 6.371015e-02 -5.785874e-02 -2.192921e-02 -1.897301e-02 -1.985912e-03 [981] -4.482219e-03 -2.579468e-02 -2.711044e-02 8.359989e-02 -4.274356e-03 [986] -1.723531e-02 -1.166017e-02 3.428489e-02 1.749464e-02 8.098540e-03 [991] 2.076960e-02 -1.677180e-02 -1.798382e-02 -2.648090e-02 -3.150125e-02 [996] -1.363041e-02 -5.571368e-02 1.067463e-02 2.391717e-02 1.858240e-02 [1001] 8.224669e-02 4.220440e-03 1.349012e-02 -4.254697e-03 -5.296315e-02 [1006] 4.225309e-02 -1.275543e-02 1.805389e-02 -2.316267e-04 -2.786901e-04 [1011] 2.565030e-02 9.362560e-03 2.272967e-02 -2.681712e-02 -7.856407e-03 [1016] -1.632896e-02 -5.649330e-04 1.948783e-01 1.209986e-01 6.425547e-02 [1021] 4.069191e-02 6.079192e-02 4.396253e-02 3.462030e-02 -1.084615e-01 [1026] -1.997183e-03 -2.869817e-02 -6.417412e-02 -1.744275e-01 -5.336013e-02 [1031] -3.220103e-02 -3.541895e-02 -4.531026e-02 -4.117000e-02 3.791744e-02 [1036] 6.447727e-02 -1.824852e-02 -1.912205e-02 -8.776388e-02 -4.503321e-02 [1041] -1.013810e-01 4.202909e-02 -4.214644e-02 1.852396e-02 -5.341047e-02 [1046] 1.400289e-02 4.783607e-02 8.770905e-03 8.703951e-03 5.659906e-02 [1051] 1.497387e-02 -1.568483e-02 1.394793e-02 -3.807965e-02 3.390573e-02 [1056] -8.243819e-03 -1.152783e-02 -4.020233e-03 6.964448e-02 -1.517452e-02 [1061] 1.976620e-02 -3.448891e-03 8.363866e-03 1.957253e-02 -1.458068e-02 [1066] 6.552171e-03 3.164178e-02 -3.808654e-02 9.550594e-03 -3.181933e-02 [1071] -5.506518e-02 5.361417e-02 -2.779924e-02 1.792054e-02 -3.179137e-02 [1076] -3.043888e-03 1.026288e-02 -4.609080e-04 -5.124129e-02 5.524440e-03 [1081] -5.986740e-03 -7.001093e-02 4.399346e-02 -6.556725e-03 -3.772510e-03 [1086] -3.108174e-02 -1.177035e-02 3.578701e-02 -6.541110e-02 -1.052847e-02 [1091] -3.524050e-02 5.459146e-02 -3.991053e-02 5.151935e-03 -1.006489e-02 [1096] 3.581895e-02 1.940865e-02 -2.327478e-03 1.932863e-03 5.336248e-02 [1101] -4.460719e-02 -4.582285e-02 1.066289e-01 8.605863e-02 -1.099405e-01 [1106] 1.556080e-02 6.725379e-03 5.954135e-02 -1.427077e-01 1.166800e-02 [1111] -1.129179e-01 8.312702e-02 -4.602390e-02 -4.648548e-02 -2.777423e-02 [1116] 5.072675e-02 -3.580692e-03 -4.246404e-02 1.620712e-02 1.072930e-02 [1121] -1.872696e-02 -2.261395e-02 2.845055e-02 -5.448820e-02 -3.961537e-02 [1126] 2.338469e-02 5.785183e-03 -6.087683e-02 2.968910e-02 2.976779e-02 [1131] -2.750402e-02 1.676020e-03 3.049630e-02 -1.665909e-02 -6.077736e-03 [1136] -4.386814e-02 -8.801316e-03 4.799579e-02 2.103284e-02 -1.576867e-02 [1141] 1.442876e-02 1.161083e-02 -1.766207e-02 1.033588e-02 2.896649e-02 [1146] -7.178859e-03 -2.882690e-03 -8.667476e-03 4.277526e-02 -4.382412e-02 [1151] 4.054085e-02 5.088750e-03 -2.880073e-02 -1.538710e-03 5.143506e-02 [1156] -3.330051e-02 3.508022e-02 4.357330e-03 -6.402960e-02 2.624258e-02 [1161] 2.945562e-03 -2.819049e-02 5.668365e-02 -2.786731e-02 -2.149484e-02 [1166] 1.763224e-02 3.019676e-02 7.295294e-03 -1.228667e-03 -2.917103e-03 [1171] -6.953594e-02 3.095346e-02 3.388418e-02 -2.655684e-02 1.127878e-03 [1176] 1.505509e-03 4.423128e-02 6.128464e-02 -3.970554e-02 2.026029e-03 [1181] -7.850440e-03 4.015966e-02 -5.066377e-03 -1.103650e-01 6.652705e-02 [1186] -2.297693e-02 -1.630138e-02 5.641085e-03 9.506713e-03 2.241184e-02 [1191] -4.811761e-02 5.687220e-02 -4.929901e-02 2.099963e-02 1.916883e-02 [1196] -1.187214e-02 -2.176792e-02 6.984110e-02 -9.351261e-02 5.527316e-02 [1201] 4.390764e-02 -1.353794e-02 -8.617613e-03 -1.612732e-02 5.736867e-02 [1206] 7.862845e-02 8.650071e-03 2.653301e-02 -5.959022e-03 -2.906030e-02 [1211] 9.683720e-03 -3.637923e-02 6.446372e-02 5.251282e-03 -6.665693e-02 [1216] 3.033711e-03 3.574413e-02 3.451688e-02 -3.021665e-03 1.840851e-02 [1221] 6.094213e-03 -1.118612e-02 4.674532e-02 -2.556344e-03 6.396173e-02 [1226] 1.056324e-01 1.614287e-02 1.009893e-01 -1.140072e-01 -6.605520e-03 [1231] -4.274109e-02 -7.119418e-02 5.752280e-02 -3.126201e-02 -8.579811e-03 [1236] 1.239129e-01 -4.680588e-02 -4.644048e-02 5.810125e-02 -3.264411e-02 [1241] 3.397093e-02 -4.259262e-02 > postscript(file="/var/www/html/rcomp/tmp/2rvsm1291061803.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/www/html/rcomp/tmp/3rvsm1291061803.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/www/html/rcomp/tmp/4rvsm1291061803.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/www/html/rcomp/tmp/52m9p1291061803.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/www/html/rcomp/tmp/62m9p1291061803.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/www/html/rcomp/tmp/72m9p1291061803.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/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/8yw7g1291061803.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/9qn601291061803.tab") > > try(system("convert tmp/1ymt11291061803.ps tmp/1ymt11291061803.png",intern=TRUE)) character(0) > try(system("convert tmp/2rvsm1291061803.ps tmp/2rvsm1291061803.png",intern=TRUE)) character(0) > try(system("convert tmp/3rvsm1291061803.ps tmp/3rvsm1291061803.png",intern=TRUE)) character(0) > try(system("convert tmp/4rvsm1291061803.ps tmp/4rvsm1291061803.png",intern=TRUE)) character(0) > try(system("convert tmp/52m9p1291061803.ps tmp/52m9p1291061803.png",intern=TRUE)) character(0) > try(system("convert tmp/62m9p1291061803.ps tmp/62m9p1291061803.png",intern=TRUE)) character(0) > try(system("convert tmp/72m9p1291061803.ps tmp/72m9p1291061803.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.223 1.375 23.283