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Type 'q()' to quit R. > x <- c(175.348,154.439,136.186,113.662,106.157,100.546,98.314,118.179,112.295,126.938,130.92,181.279,180.389,146.917,150.597,124.222,101.554,102.138,110.315,111.015,105.017,119.888,127.623,149.415,159.755,139.737,136.283,101.952,104.044,96.712,100.665,103.699,103.765,122.732,127.297,160.278,191.784,155.375,142.616,115.331,102.136,95.205,101.566,105.273,117.394,121.148,116.666,154.841,177.74,154.427,133.159,118.102,101.361,101.345,102.233,108.522,101.939,118.405,125.06,178,167.714,143.582,139.259,104.674,103.722,106.153,106.21,113.986,96.906,107.512,112.616,148.507,130.48,137.436,128.21,97.552,91.55,83.104,84.68,85.98,84.891,89.896,94.835,115.348,131.284,134.701,127.193,87.077,72.744,77.542,78.005,85.329,86.041,96.384,116.678,160.672,152.364,144.936,122.974,94.456,82.491,84.89,85.277,81.206,71.012,87.302,97.427,133.242,137.064,119.042,116.47,96.028,79.281,73.872,80.964,86.739,89.997,96.292,101.355,136.543) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '2' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '1' > 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,] 0.08422421 -0.1402366 -0.5523796 -0.04223663 -0.1422911 -0.8062493 [2,] 0.10682305 -0.1326085 -0.5699962 0.00000000 -0.1193354 -1.1696142 [3,] 0.00000000 -0.1778803 -0.4642836 0.00000000 -0.1266500 -0.8538185 [4,] 0.00000000 -0.1936257 -0.4747078 0.00000000 0.0000000 -1.0002056 [5,] 0.00000000 0.0000000 -0.5732779 0.00000000 0.0000000 -0.9999098 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.80782 0.45067 0.11271 0.81704 0.35401 0.00082 [2,] 0.74662 0.46986 0.09108 NA 0.31951 0.00000 [3,] NA 0.09096 0.00001 NA 0.27852 0.00000 [4,] NA 0.06274 0.00001 NA NA 0.06018 [5,] NA NA 0.00000 NA NA 0.00668 [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 [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.0842 -0.1402 -0.5524 -0.0422 -0.1423 -0.8062 s.e. 0.3454 0.1853 0.3455 0.1821 0.1529 0.2344 sigma^2 estimated as 69.74: log likelihood = -387, aic = 788 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.0842 -0.1402 -0.5524 -0.0422 -0.1423 -0.8062 s.e. 0.3454 0.1853 0.3455 0.1821 0.1529 0.2344 sigma^2 estimated as 69.74: log likelihood = -387, aic = 788 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.1068 -0.1326 -0.5700 0 -0.1193 -1.1696 s.e. 0.3298 0.1829 0.3345 0 0.1194 0.2269 sigma^2 estimated as 50.19: log likelihood = -387.02, aic = 786.05 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0 -0.1779 -0.4643 0 -0.1267 -0.8538 s.e. 0 0.1043 0.1007 0 0.1163 0.1640 sigma^2 estimated as 68.69: log likelihood = -387.08, aic = 784.15 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0 -0.1936 -0.4747 0 0 -1.0002 s.e. 0 0.1030 0.1012 0 0 0.5270 sigma^2 estimated as 63.2: log likelihood = -387.64, aic = 783.28 [[3]][[6]] NULL $aic [1] 787.9976 786.0496 784.1504 783.2773 784.4690 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 > postscript(file="/var/www/html/freestat/rcomp/tmp/1kkhn1293303567.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 = 120 Frequency = 1 [1] 1.012372e-01 2.907859e-02 3.609835e-03 -1.717367e-02 -2.048837e-02 [6] -2.220871e-02 -2.109627e-02 2.117654e-04 -5.377541e-03 9.089906e-03 [11] 1.206665e-02 2.630671e-03 -4.590306e-01 -7.873884e+00 1.187838e+01 [16] 8.077716e-01 -7.317571e+00 3.839525e-01 5.468452e+00 -1.007555e+01 [21] -3.456307e+00 -4.262768e+00 7.044450e-01 -1.908058e+01 -1.383043e+00 [26] 1.936766e+00 5.112292e+00 -4.528838e+00 1.247715e+01 4.199677e-01 [31] 3.721262e+00 -4.901987e+00 2.700565e+00 3.515999e+00 1.727002e+00 [36] -7.518434e-01 2.079891e+01 -9.402215e-01 -2.313772e+00 -2.635713e+00 [41] -5.699621e+00 -5.064565e+00 -3.958339e-01 -4.247352e+00 1.240401e+01 [46] -5.623651e+00 -8.564347e+00 -3.059633e+00 4.976515e+00 6.780679e+00 [51] -7.403952e+00 8.495269e+00 -4.063446e+00 4.545027e+00 -1.796244e+00 [56] -4.918786e-01 -6.736562e+00 -2.913759e-01 2.008701e+00 1.710345e+01 [61] -1.470215e+01 -1.471024e+00 3.964508e-01 -7.976284e+00 7.013067e+00 [66] 7.396694e+00 2.315594e+00 3.183025e+00 -1.353047e+01 -9.133012e+00 [71] -5.837309e+00 -6.204230e+00 -2.908509e+01 1.657789e+01 2.920478e+00 [76] 3.686353e+00 5.320746e+00 -3.400341e+00 -2.121232e+00 -7.195595e+00 [81] -1.064600e+00 -9.115128e+00 -2.820674e+00 -1.962169e+01 -2.287128e-01 [86] 2.004260e+01 1.290763e+01 -1.372158e+00 -5.038676e+00 3.151223e+00 [91] -1.491890e+00 1.962220e+00 4.444648e+00 7.524254e-01 1.638268e+01 [96] 1.479841e+01 -5.160013e+00 9.380817e+00 -1.054958e+01 -2.655941e+00 [101] -5.535927e+00 2.116287e+00 -1.269775e+00 -9.428786e+00 -1.167266e+01 [106] -3.206590e+00 1.032507e+00 2.997979e-02 -1.143269e+00 -1.510815e+00 [111] 6.492841e+00 1.089173e+01 3.703735e-01 -1.504145e+00 2.733937e+00 [116] 1.309326e+00 8.174999e+00 -1.704346e+00 -8.477324e-01 -3.242542e+00 > postscript(file="/var/www/html/freestat/rcomp/tmp/2kkhn1293303567.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/freestat/rcomp/tmp/3cby81293303567.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/freestat/rcomp/tmp/4cby81293303567.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/freestat/rcomp/tmp/5cby81293303567.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/freestat/rcomp/tmp/6cby81293303567.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/freestat/rcomp/tmp/7nkyb1293303567.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/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/8jcvj1293303567.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/freestat/rcomp/tmp/9u3um1293303567.tab") > > try(system("convert tmp/1kkhn1293303567.ps tmp/1kkhn1293303567.png",intern=TRUE)) character(0) > try(system("convert tmp/2kkhn1293303567.ps tmp/2kkhn1293303567.png",intern=TRUE)) character(0) > try(system("convert tmp/3cby81293303567.ps tmp/3cby81293303567.png",intern=TRUE)) character(0) > try(system("convert tmp/4cby81293303567.ps tmp/4cby81293303567.png",intern=TRUE)) character(0) > try(system("convert tmp/5cby81293303567.ps tmp/5cby81293303567.png",intern=TRUE)) character(0) > try(system("convert tmp/6cby81293303567.ps tmp/6cby81293303567.png",intern=TRUE)) character(0) > try(system("convert tmp/7nkyb1293303567.ps tmp/7nkyb1293303567.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 9.671 1.546 9.975