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Type 'q()' to quit R. > x <- c(99.5,101.6,103.9,106.6,108.3,102,93.8,91.6,97.7,94.8,98,103.8,97.8,91.2,89.3,87.5,90.4,94.2,102.2,101.3,96,90.8,93.2,90.9,91.1,90.2,94.3,96,99,103.3,113.1,112.8,112.1,107.4,111,110.5,110.8,112.4,111.5,116.2,122.5,121.3,113.9,110.7,120.8,141.1,147.4,148,158.1,165,187,190.3,182.4,168.8,151.2,120.1,112.5,106.2,107.1,108.5,106.5,108.3,125.6,124,127.2,136.9,135.8,124.3,115.4,113.6,114.4,118.4,117,116.5,115.4,113.6,117.4,116.9,116.4,111.1,110.2,118.9,131.8,130.6,138.3,148.4,148.7,144.3,152.5,162.9,167.2,166.5,185.6) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '2' > par2 = '1' > par1 = 'TRUE' > #'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$order[3] + coeff <- matrix(NA, nrow=nrc, ncol=nrc) + pval <- matrix(NA, nrow=nrc, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$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) + 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,] 0.5318906 -0.11023860 0.08030999 -0.9999969 -0.13301201 0.02228898 [2,] 0.5303807 -0.10924301 0.07960133 -0.9999954 -0.04387732 0.00000000 [3,] 0.5318847 -0.11097730 0.08059890 -0.9999984 0.00000000 0.00000000 [4,] 0.5227823 -0.06947391 0.00000000 -1.0000012 0.00000000 0.00000000 [5,] 0.4908541 0.00000000 0.00000000 -1.0000024 0.00000000 0.00000000 [6,] 0.4808524 0.00000000 0.00000000 -0.9999993 0.00000000 0.00000000 [7,] NA NA NA NA NA NA [,7] [1,] 0.3244803 [2,] 0.2349787 [3,] 0.1924022 [4,] 0.1880418 [5,] 0.2021454 [6,] 0.0000000 [7,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 1e-05 0.38386 0.46635 0 0.9032 0.92648 0.76556 [2,] 1e-05 0.38732 0.46970 0 0.9380 NA 0.67090 [3,] 1e-05 0.37278 0.46100 0 NA NA 0.10017 [4,] 1e-05 0.53143 NA 0 NA NA 0.11460 [5,] 0e+00 NA NA 0 NA NA 0.08764 [6,] 0e+00 NA NA 0 NA NA NA [7,] 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 ma1 sar1 sar2 sma1 0.5319 -0.1102 0.0803 -1.0000 -0.1330 0.0223 0.3245 s.e. 0.1123 0.1259 0.1098 0.0443 1.0904 0.2408 1.0847 sigma^2 estimated as 43.44: log likelihood = -302.52, aic = 621.04 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5319 -0.1102 0.0803 -1.0000 -0.1330 0.0223 0.3245 s.e. 0.1123 0.1259 0.1098 0.0443 1.0904 0.2408 1.0847 sigma^2 estimated as 43.44: log likelihood = -302.52, aic = 621.04 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5304 -0.1092 0.0796 -1.0000 -0.0439 0 0.2350 s.e. 0.1115 0.1257 0.1096 0.0442 0.5624 0 0.5511 sigma^2 estimated as 43.44: log likelihood = -302.53, aic = 619.05 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5319 -0.1110 0.0806 -1.0000 0 0 0.1924 s.e. 0.1100 0.1239 0.1088 0.0442 0 0 0.1158 sigma^2 estimated as 43.45: log likelihood = -302.53, aic = 617.06 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5228 -0.0695 0 -1.0000 0 0 0.188 s.e. 0.1093 0.1106 0 0.0495 0 0 0.118 sigma^2 estimated as 43.64: log likelihood = -302.8, aic = 615.6 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4909 0 0 -1.0000 0 0 0.2021 s.e. 0.0970 0 0 0.0443 0 0 0.1171 sigma^2 estimated as 43.88: log likelihood = -303, aic = 614 [[3]][[7]] NULL $aic [1] 621.0414 619.0514 617.0573 615.6046 613.9973 614.8505 There were 11 warnings (use warnings() to see them) > postscript(file="/var/www/html/rcomp/tmp/1df4v1196805220.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 = 93 Frequency = 1 [1] 0.04449775 -0.12879709 0.16915524 0.39453988 -0.74799742 [6] -7.44016686 -4.57934777 2.36597839 7.09181922 -5.81379194 [11] 4.54655857 3.81149830 -8.94231947 -3.82797568 1.36752778 [16] -0.90744026 3.90709825 3.79604150 6.94150294 -5.38848373 [21] -6.23956424 -1.30394294 4.15689815 -4.17776108 3.20619099 [26] -0.12455107 4.31402096 -0.14960018 1.32490868 1.92784992 [31] 5.99596384 -4.34381500 0.48299731 -4.23988728 4.90264028 [36] -1.62931708 -0.28049952 1.27913974 -2.75408587 4.93754558 [41] 3.39460547 -5.00544974 -8.22917685 1.17312072 11.31989162 [46] 15.68683362 -5.27245573 -2.71189631 9.25661761 0.98015809 [51] 18.29517620 -9.45654580 -11.00471698 -9.33010871 -9.69083337 [56] -22.84212758 5.35210499 -5.81678490 4.96817295 1.38214760 [61] -4.66996638 2.46945681 12.45766233 -8.48663740 5.95846749 [66] 9.68763071 -4.23611660 -6.54678025 -4.43672591 3.65096661 [71] 0.55905788 3.17316309 -2.57309788 -0.44289838 -3.50128777 [76] 0.34083744 3.34548934 -4.47296761 0.46871376 -3.82912296 [81] 2.52344853 8.26723465 8.27922400 -8.41522427 8.56598914 [86] 6.08921050 -4.27567657 -4.88814991 9.38291106 6.89333506 [91] -1.31008777 -2.42361871 18.46561328 > postscript(file="/var/www/html/rcomp/tmp/2vu8p1196805220.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/3sgav1196805220.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/4pm9m1196805220.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/5rz3e1196805220.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/69d561196805220.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/7m0xa1196805220.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(resid, main='Residual Normal Q-Q Plot') > dev.off() null device 1 > ncols <- length(selection[[1]][1,]) > nrows <- length(selection[[2]][,1])-1 > 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/85m4s1196805220.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/9rp3l1196805220.tab") > > system("convert tmp/1df4v1196805220.ps tmp/1df4v1196805220.png") > system("convert tmp/2vu8p1196805220.ps tmp/2vu8p1196805220.png") > system("convert tmp/3sgav1196805220.ps tmp/3sgav1196805220.png") > system("convert tmp/4pm9m1196805220.ps tmp/4pm9m1196805220.png") > system("convert tmp/5rz3e1196805220.ps tmp/5rz3e1196805220.png") > system("convert tmp/69d561196805220.ps tmp/69d561196805220.png") > system("convert tmp/7m0xa1196805220.ps tmp/7m0xa1196805220.png") > > > proc.time() user system elapsed 6.274 1.981 7.518