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 <- 4 #degree (p) of the non-seasonal AR(p) polynomial > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- 3 #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,] 0.7620824 0.04737311 0.02197254 0.04331732 -0.6436394 -0.4059497 [2,] 0.7627852 0.05745591 0.00000000 0.05399524 -0.6437212 -0.4061304 [3,] 0.7990687 0.00000000 0.00000000 0.07249706 -0.6433729 -0.4047497 [4,] NA NA NA NA NA NA [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,] -0.2636237 [2,] -0.2642496 [3,] -0.2640803 [4,] NA [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 0.18553 0.53967 0.13049 0 0 0 [2,] 0 0.07065 NA 0.01769 0 0 0 [3,] 0 NA NA 0.00038 0 0 0 [4,] NA NA NA NA NA NA NA [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 ar4 sar1 sar2 sar3 0.7621 0.0474 0.0220 0.0433 -0.6436 -0.4059 -0.2636 s.e. 0.0285 0.0358 0.0358 0.0286 0.0285 0.0321 0.0281 sigma^2 estimated as 0.001848: log likelihood = 2131.76, aic = -4247.52 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ar4 sar1 sar2 sar3 0.7621 0.0474 0.0220 0.0433 -0.6436 -0.4059 -0.2636 s.e. 0.0285 0.0358 0.0358 0.0286 0.0285 0.0321 0.0281 sigma^2 estimated as 0.001848: log likelihood = 2131.76, aic = -4247.52 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 sar1 sar2 sar3 0.7628 0.0575 0 0.0540 -0.6437 -0.4061 -0.2642 s.e. 0.0284 0.0318 0 0.0227 0.0285 0.0321 0.0281 sigma^2 estimated as 0.001848: log likelihood = 2131.57, aic = -4249.14 [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -4247.517 -4249.141 -4247.870 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 > postscript(file="/var/www/html/rcomp/tmp/16bvf1291062568.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] 2.617319e-03 2.699595e-03 2.880903e-03 3.042766e-03 2.901870e-03 [6] 2.886701e-03 2.795821e-03 2.680310e-02 9.551548e-03 1.955698e-02 [11] -6.173926e-02 8.434095e-03 -2.698531e-03 -4.841696e-02 4.092219e-02 [16] -8.328230e-03 -6.157706e-02 -1.033878e-01 1.081434e-01 7.826798e-03 [21] 4.384719e-02 -5.679712e-02 -4.776008e-03 -1.108037e-03 -5.502620e-02 [26] 9.913573e-02 -5.035466e-03 5.287786e-02 4.739037e-02 5.814794e-02 [31] 4.477779e-02 -8.634042e-02 -1.302014e-01 3.687938e-03 2.729107e-02 [36] -2.545248e-02 -4.637156e-02 -3.773112e-02 -5.153718e-02 1.785308e-02 [41] 1.399322e-02 7.022026e-02 -4.800052e-02 1.616302e-02 -6.272943e-02 [46] 5.716534e-03 3.732643e-02 6.275359e-02 -6.756614e-02 3.302868e-02 [51] -5.546217e-02 3.401953e-02 -3.663923e-02 4.023824e-02 -4.778547e-02 [56] 1.754635e-03 -1.320420e-02 -6.253790e-03 -8.903358e-03 4.694941e-04 [61] 5.889751e-02 -9.042664e-02 -7.527876e-02 5.970424e-02 -3.829796e-02 [66] 2.486614e-04 -4.005328e-03 4.610511e-02 3.947951e-02 -1.022497e-01 [71] 1.346446e-01 -6.209862e-02 1.249196e-02 5.759909e-02 -2.955249e-02 [76] -1.547174e-02 5.935034e-02 -3.955323e-02 -3.198548e-03 -3.035047e-02 [81] -1.139393e-02 2.433213e-02 -2.796246e-02 6.165628e-02 -2.736281e-02 [86] 2.592165e-02 -3.269991e-02 2.151927e-02 2.805125e-04 5.736315e-02 [91] -5.160046e-03 -8.605477e-02 1.027062e-01 -5.403546e-02 2.689515e-02 [96] -2.094489e-02 9.455724e-03 -4.854796e-03 3.079908e-02 -9.390875e-02 [101] 3.241812e-02 1.895528e-02 -5.610760e-02 1.791641e-02 7.918791e-03 [106] 4.988324e-02 3.156564e-02 -3.249004e-02 6.217984e-03 1.924183e-02 [111] 5.642707e-02 -5.810046e-03 5.744738e-02 4.367014e-02 4.354413e-02 [116] 1.141444e-02 3.304949e-02 -4.185382e-02 -3.481574e-02 -7.515421e-03 [121] 3.228858e-02 -1.092039e-02 -5.481722e-02 2.536359e-02 -9.013195e-03 [126] 5.056147e-03 1.324020e-01 -5.856999e-02 7.714331e-03 -9.242453e-03 [131] -1.351482e-02 -2.823741e-02 9.844152e-03 -7.401823e-02 6.919042e-02 [136] -2.646864e-02 3.576098e-02 3.237805e-02 1.182374e-01 9.198575e-02 [141] -1.009274e-02 7.692793e-02 3.681797e-02 2.240828e-03 7.091943e-03 [146] -1.449007e-01 -4.656741e-02 -8.870755e-04 1.407110e-01 7.053208e-02 [151] -1.617643e-02 -8.457244e-03 2.262137e-02 7.817786e-02 -5.909193e-02 [156] 1.425833e-02 5.409006e-03 -1.059241e-02 -3.525279e-02 3.137809e-02 [161] 2.084441e-02 3.070030e-02 1.235693e-02 -2.021671e-02 -9.143051e-02 [166] -6.206744e-02 -2.690867e-02 4.647714e-02 6.224695e-02 -6.373050e-02 [171] 5.519352e-02 1.482794e-02 6.356241e-03 4.950084e-02 -1.739402e-02 [176] -8.004032e-03 -4.099652e-02 -9.731643e-03 7.449837e-03 -6.594016e-03 [181] -2.993053e-02 -4.320201e-02 5.123338e-02 1.805076e-02 -1.991938e-02 [186] 6.064055e-02 7.531601e-02 3.814913e-02 -1.779770e-02 -3.837844e-02 [191] -1.276988e-02 -4.074621e-03 7.469052e-02 4.795432e-03 1.913489e-02 [196] -1.773621e-02 -3.625027e-02 -3.449961e-02 -2.284589e-02 -4.965397e-03 [201] 1.879161e-02 -5.465951e-03 -4.598117e-02 -1.202637e-02 1.116015e-02 [206] -2.855673e-02 1.443569e-02 -9.115135e-02 -4.350706e-02 -1.307710e-02 [211] -1.242142e-01 -7.391373e-02 -1.530044e-01 -1.239759e-01 6.161443e-02 [216] 3.577105e-02 1.952719e-02 9.296510e-02 -9.020786e-02 -1.376952e-01 [221] 6.532056e-02 1.024095e-01 2.608872e-02 4.420074e-02 4.190307e-02 [226] 3.404666e-02 8.063109e-02 2.807309e-02 -4.443048e-02 -6.045225e-03 [231] -1.059698e-02 -2.086142e-02 3.562476e-02 7.703434e-02 2.732945e-02 [236] -2.369504e-04 -5.187525e-03 6.500361e-03 -3.795974e-02 3.512949e-02 [241] 1.191722e-01 1.955489e-02 -4.113747e-02 1.997740e-03 -1.007727e-02 [246] 1.689220e-02 3.661185e-02 5.119953e-02 -1.250382e-02 -1.968367e-02 [251] -5.451819e-02 1.521632e-02 -2.700919e-02 1.475480e-02 1.276313e-02 [256] -1.608878e-03 1.793546e-02 1.810917e-03 3.092215e-02 -2.928877e-02 [261] 5.743524e-03 2.814592e-03 1.244776e-03 1.039044e-02 -1.092151e-02 [266] 1.799582e-02 1.587347e-02 1.422536e-02 -1.495222e-02 -1.440710e-02 [271] -5.621599e-03 5.365389e-04 5.172376e-02 1.849969e-02 3.780764e-03 [276] 2.683215e-02 -4.859552e-02 9.709047e-03 3.008968e-02 -6.545335e-02 [281] 5.601832e-02 2.224022e-03 -3.307616e-02 -8.009722e-03 -2.004948e-02 [286] 2.879062e-02 -1.003292e-03 -3.167979e-02 -2.739614e-02 7.354700e-03 [291] 2.043838e-02 -1.111935e-02 2.320293e-02 -2.695591e-02 -1.326700e-02 [296] -9.392392e-03 -3.963475e-03 3.649586e-02 -4.083967e-02 -4.485385e-02 [301] -3.386440e-02 -4.087292e-03 -2.069587e-02 -1.949921e-02 3.449965e-02 [306] 2.360660e-02 5.125439e-02 -3.033229e-02 3.298180e-02 2.405892e-02 [311] -3.805589e-02 2.109913e-04 5.084183e-03 -1.176624e-03 3.513610e-02 [316] -2.789148e-02 9.188606e-03 4.552491e-02 -1.274548e-02 -9.802627e-03 [321] -1.737662e-02 2.606837e-02 8.196389e-02 -1.196837e-02 2.222874e-02 [326] 7.261708e-03 2.400025e-02 5.027559e-03 8.301370e-03 3.935845e-02 [331] 9.869030e-03 1.432335e-02 -4.822851e-02 5.781055e-04 -8.079226e-04 [336] -2.924893e-02 -3.307462e-02 4.333837e-02 1.340344e-02 -5.757764e-03 [341] -1.846906e-02 -7.228326e-03 2.507971e-02 -9.159710e-03 -3.238480e-03 [346] -1.972209e-02 -1.041414e-01 -2.123487e-02 5.444399e-02 8.430351e-02 [351] -7.063268e-02 -7.352142e-02 4.269556e-02 1.459031e-02 3.616106e-02 [356] -7.073423e-02 -2.058345e-03 9.428280e-03 1.245720e-02 -4.766974e-02 [361] 6.405733e-02 -3.175255e-02 1.395749e-02 -2.277866e-02 -2.679805e-02 [366] 1.005013e-02 -2.249003e-02 2.388865e-02 1.623451e-02 -2.277985e-02 [371] -3.433063e-03 5.368244e-02 -2.477545e-02 -8.869678e-02 1.819658e-02 [376] -4.837170e-03 -1.857908e-03 -3.001098e-02 -1.115352e-02 1.775537e-02 [381] 1.578459e-03 -6.903297e-03 -1.375609e-02 -5.714953e-03 9.073078e-04 [386] -3.411623e-02 -5.152522e-02 6.839870e-02 6.085427e-03 3.574229e-02 [391] -2.086129e-03 -1.117156e-02 -5.129891e-02 -8.763057e-03 3.439223e-02 [396] -5.691082e-03 1.051036e-02 -2.792851e-02 1.446433e-02 -2.151368e-02 [401] -8.509643e-06 4.625911e-02 -7.507348e-03 -2.827608e-02 3.965007e-03 [406] -8.189692e-02 8.766118e-02 -2.226228e-02 1.528411e-02 1.969055e-02 [411] -3.729603e-02 2.760463e-02 4.145268e-02 -6.191938e-03 -3.995847e-02 [416] -9.200983e-03 3.016075e-02 9.614278e-03 1.798130e-02 3.496189e-02 [421] 7.277164e-02 -3.335132e-02 -4.293589e-02 -7.654615e-03 1.886410e-02 [426] -1.405266e-02 -3.817761e-02 -6.565436e-02 9.366720e-02 -2.950433e-02 [431] -3.771479e-02 4.024468e-02 -3.469607e-02 3.030234e-02 -5.405451e-02 [436] -2.839307e-03 3.503990e-02 -5.299746e-03 -8.483727e-03 7.081604e-04 [441] -1.104839e-03 -2.194725e-02 -3.032442e-02 -3.273781e-03 8.015936e-03 [446] -2.101528e-02 1.972824e-02 -2.801220e-02 -3.971189e-02 6.693315e-02 [451] 2.218036e-03 2.205023e-02 -5.059571e-02 3.344282e-02 1.236621e-02 [456] 2.386378e-02 -3.007722e-02 2.487770e-02 -3.371025e-03 6.502575e-03 [461] 2.219995e-02 -9.270150e-03 -1.521208e-02 -2.107053e-02 3.308883e-03 [466] 2.654368e-02 3.305307e-02 6.184480e-03 6.207601e-03 5.153108e-02 [471] 5.765749e-02 -8.416047e-03 -6.210871e-03 1.109730e-02 7.775032e-03 [476] 1.718726e-02 -5.289845e-03 3.733209e-02 1.518192e-02 2.081263e-02 [481] -1.635206e-02 6.085662e-02 -1.916886e-02 5.160194e-02 3.514106e-02 [486] -5.138918e-02 6.485436e-04 -8.917152e-03 -4.504088e-02 3.362187e-02 [491] 1.067244e-02 3.137171e-02 -1.171885e-02 -4.263801e-02 2.888189e-02 [496] -1.961561e-02 1.017848e-02 9.863482e-03 5.722869e-02 -2.713058e-02 [501] -7.309296e-03 -3.135417e-03 8.127197e-03 -2.668950e-02 5.685785e-02 [506] -3.837631e-02 1.295730e-01 -9.958684e-02 -7.611506e-03 1.200386e-03 [511] 1.078833e-02 -1.609037e-02 3.597391e-02 2.707577e-02 -6.531901e-02 [516] 1.257708e-02 8.746532e-04 -2.461469e-02 9.505784e-03 -9.816749e-02 [521] 8.430895e-02 7.625407e-03 2.392768e-02 -2.146521e-03 -3.929924e-03 [526] -7.066979e-03 2.704507e-02 -2.930136e-02 9.753534e-03 2.091527e-02 [531] 2.027384e-02 7.902753e-04 -5.085303e-03 5.083425e-03 -5.283878e-02 [536] 1.501242e-01 -1.401210e-02 7.198087e-04 -2.724436e-02 -3.364111e-02 [541] -2.929650e-02 -1.488743e-01 -3.336427e-02 -6.029248e-03 6.960706e-02 [546] 2.597670e-02 3.689938e-02 6.820523e-02 -8.219218e-03 -3.779722e-02 [551] -5.143674e-02 -6.785797e-02 4.573300e-02 6.384884e-02 3.010703e-03 [556] 6.377329e-02 6.843664e-02 -4.230683e-03 -3.932122e-02 3.784555e-02 [561] 3.926212e-02 -2.729079e-02 -1.707420e-02 3.039932e-02 -1.976070e-02 [566] -7.512264e-03 7.774214e-03 -1.876457e-02 6.020599e-04 -5.035053e-02 [571] 3.027280e-02 -1.554858e-02 1.701436e-02 -8.471427e-02 -7.766971e-02 [576] -2.473064e-02 -4.384901e-02 -9.333454e-02 -8.852895e-02 -1.932098e-01 [581] 6.910640e-02 3.206024e-02 -7.488719e-02 -1.682832e-02 -4.303037e-02 [586] -7.517991e-02 8.360155e-03 1.281736e-01 2.047224e-02 -9.814975e-03 [591] 5.815996e-02 8.231993e-02 3.010344e-02 2.282312e-02 -2.772340e-02 [596] 6.518304e-03 3.627080e-04 -2.498303e-02 1.073756e-03 1.110354e-01 [601] 3.465942e-02 -4.733334e-02 -4.214987e-02 1.729586e-02 2.723786e-02 [606] 3.864495e-02 4.434253e-02 5.110726e-02 -5.748001e-02 -2.108466e-02 [611] 2.206959e-02 1.819662e-02 1.867681e-02 8.969785e-02 8.577953e-02 [616] -7.175460e-02 -5.108073e-02 2.978478e-02 -1.716104e-02 -3.016097e-02 [621] 1.253893e-02 -1.156063e-02 1.204660e-02 1.917487e-02 -2.084386e-02 [626] -6.521184e-02 -5.532915e-02 -1.000032e-02 -2.409293e-02 -3.616657e-02 [631] -3.533144e-02 7.429244e-02 4.530928e-03 1.382522e-02 -3.056541e-02 [636] 1.343429e-01 3.053771e-02 3.609228e-02 -2.004403e-02 4.350741e-02 [641] -2.516171e-02 2.740199e-02 -3.122133e-02 -2.415811e-03 1.140215e-02 [646] 6.419781e-03 2.391986e-02 3.614352e-02 7.420566e-03 -4.961411e-02 [651] -2.165521e-03 -1.117738e-02 -1.392938e-02 5.126706e-02 -1.081714e-02 [656] -5.326333e-03 -4.424149e-03 9.577165e-03 6.810583e-03 -2.735399e-02 [661] -3.701953e-03 -6.583887e-04 -9.916325e-03 -2.883074e-02 2.002558e-02 [666] -7.293131e-03 5.406076e-03 3.757425e-02 7.045099e-03 7.615359e-02 [671] 5.075863e-02 -8.635668e-02 1.598285e-02 -1.824988e-02 -2.481423e-03 [676] -1.529119e-02 -3.003734e-02 -5.340695e-02 4.563440e-02 -1.496333e-05 [681] -9.605073e-03 -9.979011e-03 2.182650e-03 -3.302755e-02 -2.279811e-02 [686] 1.062587e-02 5.896171e-03 -1.931759e-02 1.179285e-02 1.864584e-02 [691] 1.367031e-02 -3.367743e-03 3.224289e-02 1.627870e-02 1.751934e-02 [696] -1.251433e-01 -3.640538e-02 7.663887e-03 -3.482949e-02 1.038768e-03 [701] -2.415612e-02 5.140103e-03 -1.315236e-02 1.567473e-01 1.768139e-03 [706] 2.796707e-02 9.276146e-03 -1.042785e-02 1.152814e-02 2.835454e-02 [711] -3.479351e-02 -2.815221e-02 5.383729e-02 -3.209745e-02 -6.687656e-03 [716] 4.523232e-03 -1.165636e-01 -4.690658e-02 -6.447834e-03 8.732362e-03 [721] -5.602046e-02 -5.132243e-02 -3.174372e-02 5.683313e-02 -4.145062e-02 [726] -1.931114e-02 -2.795160e-02 4.225442e-02 -3.488624e-02 -5.137053e-02 [731] 2.069823e-02 1.534799e-02 2.088354e-02 3.942327e-02 -4.786261e-02 [736] 3.774290e-03 -3.190973e-02 7.577155e-02 1.264535e-02 -2.252653e-02 [741] -3.761158e-02 3.733924e-02 5.087697e-02 1.413866e-02 6.391099e-02 [746] 9.001439e-03 -4.498209e-03 -4.337446e-02 4.151914e-02 4.109217e-02 [751] 4.174541e-03 -2.084066e-02 1.327687e-02 -3.690294e-03 5.409116e-03 [756] 1.167426e-02 -1.896489e-02 -4.684948e-03 -1.151997e-02 -2.418808e-02 [761] -2.765713e-02 -1.504503e-02 1.441083e-02 -2.858769e-02 3.915832e-02 [766] 1.183377e-03 -1.742135e-02 -1.758155e-03 -3.327469e-02 -1.684822e-02 [771] -2.560494e-02 -4.791375e-02 2.113795e-02 -4.637632e-03 -5.999137e-04 [776] 5.236916e-02 -3.520494e-02 -9.647277e-03 1.935054e-02 -2.089118e-02 [781] 1.923827e-02 -6.422148e-02 3.621690e-02 2.303810e-02 -2.161094e-02 [786] -1.801623e-02 1.470832e-02 -5.200173e-02 9.128404e-02 -2.413370e-02 [791] -4.700859e-02 4.787256e-02 -2.752042e-02 -4.079483e-03 3.221563e-03 [796] 1.786185e-02 -1.876696e-02 2.606772e-02 -3.256746e-02 1.131958e-02 [801] -7.429552e-03 1.854515e-02 -9.867905e-03 -3.444208e-02 2.144893e-02 [806] 1.392715e-02 3.228537e-02 -1.774156e-03 -2.375472e-02 -2.205962e-03 [811] 1.115565e-02 4.467664e-03 -4.384554e-02 4.868912e-02 1.236309e-03 [816] 4.064308e-02 -1.364344e-02 -1.373338e-02 8.032735e-03 2.550515e-02 [821] 9.040562e-03 3.828522e-04 -1.764731e-02 1.134099e-02 1.653333e-02 [826] -2.779574e-02 4.432856e-02 -1.531817e-02 4.074692e-03 3.566041e-02 [831] -1.230147e-02 4.222142e-02 -3.628427e-02 7.946284e-04 8.880410e-03 [836] -3.993047e-03 3.592615e-02 1.401116e-02 2.818768e-02 2.521654e-03 [841] 1.978614e-02 -1.565995e-03 -3.719817e-02 -2.207781e-02 1.018256e-01 [846] -3.520449e-02 3.204396e-02 2.439657e-02 3.833621e-02 -2.993729e-02 [851] -7.715588e-03 -3.155718e-03 1.061687e-02 -1.228009e-02 3.869238e-02 [856] -1.585773e-02 -5.733586e-04 3.264804e-02 -1.333944e-02 -2.423884e-02 [861] 1.974704e-02 3.985330e-02 2.525662e-02 4.288564e-02 2.006491e-02 [866] -8.191763e-03 -4.686273e-02 7.951362e-03 -1.569850e-02 2.776807e-02 [871] 6.789471e-02 -5.903012e-02 -4.292347e-02 6.251053e-03 -1.039684e-03 [876] 1.986151e-03 2.407756e-02 -1.550461e-02 3.104656e-02 -2.768108e-02 [881] 3.678236e-02 -1.813232e-02 -1.332671e-02 -1.963843e-02 -4.445069e-02 [886] 1.908749e-02 2.763049e-02 1.201643e-02 -6.327119e-03 1.189032e-02 [891] 1.777006e-02 4.110676e-02 -1.658793e-02 -5.730358e-04 -5.267066e-02 [896] -1.143195e-02 -2.361861e-02 7.250490e-03 -3.275718e-02 6.012303e-03 [901] 1.491406e-02 3.701191e-02 5.852557e-02 -6.679001e-02 -1.150418e-02 [906] -3.538303e-04 -3.759012e-02 8.318920e-03 4.040485e-02 -2.252918e-02 [911] 2.162478e-02 4.168500e-03 -1.600554e-02 -4.660041e-02 -4.115935e-02 [916] -1.771324e-02 5.923976e-02 4.988584e-02 -3.938288e-02 9.676634e-03 [921] 3.748546e-02 1.628050e-02 5.948140e-02 -1.595254e-02 -8.547413e-03 [926] 1.402752e-02 -9.247965e-03 -5.265555e-03 2.190160e-02 -3.540372e-02 [931] 1.349938e-02 2.182121e-02 -2.176389e-03 -1.788405e-02 -3.999887e-02 [936] 1.566515e-02 -4.258628e-02 -9.743258e-02 -2.450990e-02 -8.122365e-02 [941] -4.401496e-03 -6.649394e-02 -1.108972e-01 -1.580315e-01 -6.676479e-02 [946] 1.072048e-01 -1.049564e-02 -3.251059e-02 1.834827e-02 2.387472e-02 [951] -1.233632e-01 6.147659e-02 8.095032e-02 3.003469e-03 3.338647e-02 [956] 3.693625e-03 5.649803e-02 1.628123e-01 1.854883e-02 -6.101075e-02 [961] 2.897032e-02 5.395114e-03 6.077231e-03 -2.915635e-02 6.044418e-02 [966] 8.543748e-03 3.713848e-02 -7.245609e-02 2.661603e-02 6.500445e-02 [971] 3.919793e-02 6.971784e-02 1.608908e-02 -9.455471e-02 1.520366e-01 [976] 7.387191e-02 -6.654979e-02 -1.305884e-02 3.503942e-02 -1.515605e-02 [981] -3.170981e-02 -2.242138e-02 -2.509058e-02 8.367176e-02 -1.862188e-02 [986] -3.180317e-02 -1.251355e-02 3.853338e-02 1.151479e-02 1.642466e-02 [991] 2.972973e-02 -1.140076e-02 -2.003536e-02 -2.521659e-02 -5.116065e-02 [996] 2.340153e-02 -5.031352e-02 2.505787e-03 1.848782e-02 1.077705e-02 [1001] 7.933162e-02 1.550945e-02 -1.345713e-02 -1.747194e-02 -2.640416e-02 [1006] 4.169910e-02 -1.659695e-02 1.671248e-02 4.934255e-03 6.509319e-03 [1011] 2.657269e-02 -2.504027e-03 2.570587e-02 -2.652673e-02 -1.848296e-03 [1016] -2.768994e-02 -2.988760e-03 1.849979e-01 1.133432e-01 7.248939e-02 [1021] 4.701624e-02 7.838263e-02 4.546798e-02 2.937598e-02 -8.983999e-02 [1026] -5.329972e-03 -2.112264e-02 -7.968678e-02 -1.822958e-01 -5.511151e-02 [1031] -3.232848e-02 -2.313232e-02 -2.171568e-02 -3.395516e-02 3.841393e-02 [1036] 5.982932e-02 -2.034251e-02 -1.353300e-02 -6.753332e-02 -2.929683e-02 [1041] -9.524259e-02 4.808657e-02 -3.963130e-02 2.052471e-02 -4.455164e-02 [1046] -3.949470e-02 2.798326e-02 -1.511232e-02 -8.129526e-04 2.504687e-02 [1051] -9.917528e-05 -3.024229e-02 2.215079e-02 -4.116622e-02 2.085554e-02 [1056] 1.724274e-02 2.327854e-02 6.503367e-03 6.550113e-02 -2.311334e-02 [1061] 2.082865e-02 -9.686411e-03 7.722636e-03 5.897256e-03 -9.446304e-03 [1066] 2.303485e-03 4.024892e-02 -3.097574e-02 2.456394e-02 -3.510104e-02 [1071] -4.408555e-02 5.170929e-02 -1.844445e-02 1.642764e-02 -4.339015e-02 [1076] -2.084318e-03 1.105665e-02 -1.102128e-02 -4.960831e-02 1.906600e-02 [1081] -5.651036e-03 -6.850499e-02 4.176389e-02 -5.114445e-03 -8.577671e-03 [1086] -2.586066e-02 -2.283279e-02 4.783605e-02 -8.366141e-02 -2.946436e-03 [1091] -3.677069e-02 4.023541e-02 -3.393072e-02 1.303498e-03 -4.630748e-03 [1096] 2.088914e-02 2.376364e-02 1.548671e-03 1.435746e-02 3.165405e-02 [1101] -3.970117e-02 -4.507642e-02 9.555260e-02 9.260559e-02 -1.208566e-01 [1106] 2.400376e-02 1.341697e-02 4.364383e-02 -1.373692e-01 1.884852e-02 [1111] -1.074733e-01 6.170774e-02 -3.048052e-02 -4.797978e-02 -1.903246e-02 [1116] 2.840731e-02 2.291153e-02 -3.394608e-02 1.795346e-02 6.049590e-03 [1121] -1.013407e-02 -2.398364e-02 1.114015e-02 -4.058471e-02 -4.242480e-02 [1126] 1.599690e-02 5.284553e-03 -7.406904e-02 4.426542e-02 2.168083e-02 [1131] -4.548020e-02 -3.362984e-02 6.610284e-02 -2.498296e-02 -2.148747e-02 [1136] -5.257978e-02 2.232466e-02 4.327465e-02 2.571955e-02 -1.895036e-02 [1141] 2.421976e-02 9.813447e-03 -1.850437e-02 8.178822e-03 2.156304e-02 [1146] -1.055814e-02 2.260615e-03 -9.785627e-03 3.121893e-02 -4.102003e-02 [1151] 4.091064e-02 1.553184e-02 -2.907531e-02 4.353145e-03 4.845264e-02 [1156] -2.521959e-02 1.496486e-02 8.869650e-03 -5.187462e-02 2.761559e-02 [1161] -4.858626e-03 -2.281499e-02 6.264410e-02 -2.685168e-02 -1.585952e-02 [1166] 1.419381e-02 2.222389e-02 9.059670e-03 -1.532141e-03 2.920410e-03 [1171] -7.405228e-02 3.506027e-02 2.747467e-02 -2.903729e-02 4.410466e-03 [1176] 1.020405e-02 3.822768e-02 6.300553e-02 -4.488231e-02 -3.540254e-03 [1181] 4.531691e-03 3.678916e-02 -1.756973e-02 -9.263543e-02 5.439268e-02 [1186] -2.591591e-02 -6.410646e-03 6.872852e-03 1.266673e-02 2.686792e-02 [1191] -5.962224e-02 6.085845e-02 -4.473264e-02 1.769339e-02 1.471447e-02 [1196] -1.035183e-02 -1.895742e-02 6.316492e-02 -6.181489e-02 3.469016e-02 [1201] 4.315625e-02 -1.218596e-02 1.496947e-04 -1.628511e-02 3.116492e-02 [1206] 7.837145e-02 6.700011e-03 3.083977e-02 -1.842246e-03 -3.752240e-02 [1211] 1.055236e-02 -2.012002e-02 5.883384e-02 5.812288e-03 -5.689469e-02 [1216] 4.645486e-03 2.948708e-02 2.217993e-02 1.682208e-02 1.220457e-02 [1221] 2.135589e-02 -1.006195e-02 4.048804e-02 -3.032129e-03 6.496847e-02 [1226] 9.877799e-02 4.867221e-02 9.412831e-02 -1.234217e-01 -5.860799e-03 [1231] -4.272467e-02 -6.982901e-02 5.302424e-02 -3.203999e-02 -3.865475e-03 [1236] 1.037126e-01 -4.371842e-02 -4.223344e-02 6.858808e-02 -1.283181e-02 [1241] 2.494225e-02 -2.671165e-02 > postscript(file="/var/www/html/rcomp/tmp/2z2ui1291062568.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/3z2ui1291062568.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/4z2ui1291062568.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/5rutl1291062568.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/6rutl1291062568.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/7rutl1291062568.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/8n39b1291062568.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/9ydqx1291062568.tab") > > try(system("convert tmp/16bvf1291062568.ps tmp/16bvf1291062568.png",intern=TRUE)) character(0) > try(system("convert tmp/2z2ui1291062568.ps tmp/2z2ui1291062568.png",intern=TRUE)) character(0) > try(system("convert tmp/3z2ui1291062568.ps tmp/3z2ui1291062568.png",intern=TRUE)) character(0) > try(system("convert tmp/4z2ui1291062568.ps tmp/4z2ui1291062568.png",intern=TRUE)) character(0) > try(system("convert tmp/5rutl1291062568.ps tmp/5rutl1291062568.png",intern=TRUE)) character(0) > try(system("convert tmp/6rutl1291062568.ps tmp/6rutl1291062568.png",intern=TRUE)) character(0) > try(system("convert tmp/7rutl1291062568.ps tmp/7rutl1291062568.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 28.637 1.542 60.660