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Type 'q()' to quit R. > x <- c(130.3,130.9,104.7,115.2,124.5,112.3,127.5,120.6,117.5,117.7,120.4,125,131.6,121.1,114.2,112.1,127,116.8,112,129.7,113.6,115.7,119.5,125.8,129.6,128,112.8,101.6,123.9,118.8,109.1,130.6,112.4,111,116.2,119.8,117.2,127.3,107.7,97.5,120.1,110.6,111.3,119.8,105.5,108.7,128.7,119.5,121.1,128.4,108.8,107.5,125.6,102.9,107.5,120.4,104.3,100.6,121.9,112.7,124.9,123.9,102.2,104.9,109.8,98.9,107.3,112.6,104,110.6,100.8,103.8,117,108.4,95.5,96.9,103.9,101.1,100.6,104.3,98,99.5,97.4,105.6,117.5,107.4,97.8,91.5,107.7,100.1,96.6,106.8,98,98.6) > par9 = '1' > par8 = '2' > par7 = '0' > par6 = '2' > par5 = '12' > par4 = '1' > par3 = '0' > 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] [1,] 0.3988978 0.4057747 0.40314731 -0.4319397 -0.9994871 [2,] 0.2270600 0.2665359 -0.06451565 -0.3560372 0.0000000 [3,] 0.2250588 0.2477367 0.00000000 -0.3483340 0.0000000 [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,] 0.00035 0.00030 0.00255 0.00086 0.01961 [2,] 0.04373 0.01920 0.59292 0.00589 NA [3,] 0.04770 0.02306 NA 0.00714 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 sar1 sar2 sma1 0.3989 0.4058 0.4031 -0.4319 -0.9995 s.e. 0.1073 0.1077 0.1298 0.1252 0.4205 sigma^2 estimated as 24.55: log likelihood = -262.86, aic = 537.72 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 sar1 sar2 sma1 0.3989 0.4058 0.4031 -0.4319 -0.9995 s.e. 0.1073 0.1077 0.1298 0.1252 0.4205 sigma^2 estimated as 24.55: log likelihood = -262.86, aic = 537.72 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 sar1 sar2 sma1 0.2271 0.2665 -0.0645 -0.3560 0 s.e. 0.1110 0.1118 0.1202 0.1262 0 sigma^2 estimated as 36.38: log likelihood = -265.47, aic = 540.93 [[3]][[4]] NULL [[3]][[5]] NULL $aic [1] 537.7197 540.9341 539.2201 Warning messages: 1: In log(s2) : NaNs produced 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/freestat/rcomp/tmp/16oar1229952562.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 = 94 Frequency = 1 [1] 0.13029991 0.13089986 0.10469981 0.11519979 0.12449977 [6] 0.11229976 0.12749974 0.12059974 0.11749974 0.11769974 [11] 0.12039974 0.12499974 1.11212946 -9.17906203 10.62037158 [16] -2.47000268 0.62553480 4.43888403 -16.04694815 10.65158770 [21] -1.71840714 -3.32948209 0.55396808 1.34039239 -1.73308856 [26] 6.24863572 -1.77292278 -11.35904173 -0.30153398 5.33284957 [31] -3.15019207 1.40840621 -0.71255854 -4.70650595 -1.79482661 [36] -4.41786133 -9.77588566 0.69932629 2.25821554 -4.47270028 [41] -1.29264789 -4.19513443 -1.20796410 -4.98183249 -5.72817308 [46] 0.58382910 14.94926569 -2.23576920 -0.71027559 3.07650959 [51] -1.16127136 4.99927819 2.71678901 -10.05794950 -4.09019759 [56] 3.29224369 -0.87298186 -9.51081554 -4.36327253 -4.68339842 [61] 3.58085972 -2.20879675 -7.18575784 -0.27289688 -13.79859269 [66] -2.69198551 6.49935897 -9.70656274 -0.28882701 12.39558908 [71] -18.29886670 -7.87327237 0.43303270 -11.45828545 -1.56754599 [76] 1.02599000 -2.12009801 1.55495733 -6.56197635 -6.54506087 [81] -2.34640815 -9.58554341 -2.43538798 3.99083209 3.52900847 [86] -3.58858732 -0.02222275 -5.77225143 -0.52402858 0.04227766 [91] -3.39727681 0.81827822 0.89093120 2.27297295 > postscript(file="/var/www/html/freestat/rcomp/tmp/2azca1229952562.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/freestat/rcomp/tmp/3vr4p1229952562.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/freestat/rcomp/tmp/40vdt1229952562.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/freestat/rcomp/tmp/52prz1229952562.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/freestat/rcomp/tmp/6kxpq1229952562.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/freestat/rcomp/tmp/7pzag1229952562.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/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/8a4fh1229952562.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/984ai1229952562.tab") > > system("convert tmp/16oar1229952562.ps tmp/16oar1229952562.png") > system("convert tmp/2azca1229952562.ps tmp/2azca1229952562.png") > system("convert tmp/3vr4p1229952562.ps tmp/3vr4p1229952562.png") > system("convert tmp/40vdt1229952562.ps tmp/40vdt1229952562.png") > system("convert tmp/52prz1229952562.ps tmp/52prz1229952562.png") > system("convert tmp/6kxpq1229952562.ps tmp/6kxpq1229952562.png") > system("convert tmp/7pzag1229952562.ps tmp/7pzag1229952562.png") > > > proc.time() user system elapsed 5.769 1.545 7.471