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Type 'q()' to quit R. > x <- c(100.00,102.04,102.51,102.71,103.00,103.39,102.32,103.88,104.65,104.46,104.65,104.36,102.71,104.55,104.76,105.72,106.20,106.50,105.14,106.50,106.69,106.50,106.50,106.39,105.43,107.18,107.37,107.46,107.66,107.37,106.30,107.85,107.95,107.85,107.66,107.76,106.69,108.92,109.22,109.02,108.62,109.02,107.76,109.60,109.80,109.41,109.60,109.60,108.15,110.18,110.27,110.87,111.25,111.15,109.99,111.83,111.73,112.31,112.12,111.73,110.27,112.71,113.38,113.57,113.77,114.15,112.99,115.03,115.03,114.84,114.75,114.84,113.32,115.92,115.84,116.49,116.90,116.99,115.74,117.73,117.17,116.83,117.08,117.23,115.25,117.98,117.97,118.56,118.42,118.51,117.25,119.08,118.85,119.41,120.43,120.87,119.31,122.24,123.14,123.39,124.46,125.33,124.17,125.48,125.35,125.15,124.31,124.14,121.81,124.62,123.93,124.29,124.16,124.02,122.00,124.58,124.06) > par9 = '1' > par8 = '1' > par7 = '1' > par6 = '3' > 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,] 0.5756302 0.08442352 0.008587074 -0.5418678 -0.3989035 -0.5083868 [2,] 0.5952159 0.08704478 0.000000000 -0.5611770 -0.3985118 -0.5089343 [3,] 0.7665221 0.00000000 0.000000000 -0.6827645 -0.4231777 -0.4813652 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.24117 0.49379 0.94664 0.25883 0.00550 6e-05 [2,] 0.11850 0.45461 NA 0.12786 0.00547 6e-05 [3,] 0.00065 NA NA 0.00489 0.00211 5e-05 [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 [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.5756 0.0844 0.0086 -0.5419 -0.3989 -0.5084 s.e. 0.4885 0.1230 0.1280 0.4774 0.1408 0.1224 sigma^2 estimated as 0.1486: log likelihood = -53.5, aic = 121.01 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.5756 0.0844 0.0086 -0.5419 -0.3989 -0.5084 s.e. 0.4885 0.1230 0.1280 0.4774 0.1408 0.1224 sigma^2 estimated as 0.1486: log likelihood = -53.5, aic = 121.01 [[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 sma1 0.5952 0.087 0 -0.5612 -0.3985 -0.5089 s.e. 0.3783 0.116 0 0.3658 0.1406 0.1220 sigma^2 estimated as 0.1486: log likelihood = -53.51, aic = 119.01 [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL $aic [1] 121.0084 119.0128 117.5420 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/1o96k1260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > resid <- arimaSelectplot(selection) > dev.off() null device 1 > resid Time Series: Start = 1 End = 117 Frequency = 1 [1] 0.057735008 0.027400028 0.018334710 0.013842221 0.011304534 [6] 0.009764631 0.007365758 0.007906475 0.007753370 0.006795192 [11] 0.006357597 -0.052156982 -0.350799688 -0.140769858 -0.176994003 [16] 0.562290818 0.144201690 -0.111030533 -0.243045038 -0.151508019 [21] -0.396513842 0.032975310 -0.083308835 0.156956311 0.523621601 [26] -0.216145978 -0.208014643 -0.341520042 -0.114593781 -0.558823753 [31] 0.155427432 0.138052779 -0.409075619 0.110316654 -0.250998025 [36] 0.329481616 0.346878506 0.323299830 -0.031031288 -0.849266560 [41] -0.776803297 0.250909339 0.083130913 0.435693250 -0.130263896 [46] -0.232129238 0.165342570 0.142248180 -0.280455237 0.155402490 [51] -0.142784385 0.285246706 0.157418555 -0.172706912 -0.014992881 [56] 0.306296873 -0.345207721 0.722409122 -0.158636569 -0.431031704 [61] -0.308095208 0.447320733 0.447799822 0.021423550 0.152471875 [66] 0.163942424 -0.004983826 0.308356992 -0.230794586 -0.054212960 [71] -0.137751474 0.137775172 -0.208611181 0.530877389 -0.307691326 [76] 0.287643633 0.239698182 -0.032423581 -0.110760322 0.187857496 [81] -0.629717422 -0.464897710 0.381446921 0.388355710 -0.593804613 [86] 0.456215310 -0.354192394 0.278101259 -0.323864488 -0.115433966 [91] -0.048365315 -0.042496648 -0.179962353 0.634520248 1.078941824 [96] 0.395987867 -0.219758521 0.361998505 0.660014172 -0.331005583 [101] 0.695781528 0.646124350 -0.083548114 -0.763493351 0.062706886 [106] -0.078827854 -1.010766201 -0.208872221 -0.515267640 0.319253920 [111] -0.739240050 -0.063247069 -0.182648155 -0.242076758 -0.704413263 [116] 0.851791452 -0.183365626 > postscript(file="/var/www/html/rcomp/tmp/2lmxa1260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(resid,length(resid)/2, main='Residual Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/3d2sg1260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/4qs051260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > cpgram(resid, main='Residual Cumulative Periodogram') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/5l6jt1260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(resid, main='Residual Histogram', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/66ghi1260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/html/rcomp/tmp/7rhca1260907095.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(resid, main='Residual Normal Q-Q Plot') > qqline(resid) > dev.off() null device 1 > ncols <- length(selection[[1]][1,]) > nrows <- length(selection[[2]][,1])-1 > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Iteration', header=TRUE) > for (i in 1:ncols) { + a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE) + } > a<-table.row.end(a) > for (j in 1:nrows) { + a<-table.row.start(a) + mydum <- 'Estimates (' + mydum <- paste(mydum,j) + mydum <- paste(mydum,')') + a<-table.element(a,mydum, header=TRUE) + for (i in 1:ncols) { + a<-table.element(a,round(selection[[1]][j,i],4)) + } + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'(p-val)', header=TRUE) + for (i in 1:ncols) { + mydum <- '(' + mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='') + mydum <- paste(mydum,')') + a<-table.element(a,mydum) + } + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/html/rcomp/tmp/87ra71260907095.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/9e0ni1260907095.tab") > try(system("convert tmp/1o96k1260907095.ps tmp/1o96k1260907095.png",intern=TRUE)) character(0) > try(system("convert tmp/2lmxa1260907095.ps tmp/2lmxa1260907095.png",intern=TRUE)) character(0) > try(system("convert tmp/3d2sg1260907095.ps tmp/3d2sg1260907095.png",intern=TRUE)) character(0) > try(system("convert tmp/4qs051260907095.ps tmp/4qs051260907095.png",intern=TRUE)) character(0) > try(system("convert tmp/5l6jt1260907095.ps tmp/5l6jt1260907095.png",intern=TRUE)) character(0) > try(system("convert tmp/66ghi1260907095.ps tmp/66ghi1260907095.png",intern=TRUE)) character(0) > try(system("convert tmp/7rhca1260907095.ps tmp/7rhca1260907095.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.132 1.127 5.148