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Type 'q()' to quit R. > x <- c(108.2,108.8,110.2,109.5,109.5,116,111.2,112.1,114,119.1,114.1,115.1,115.4,110.8,116,119.2,126.5,127.8,131.3,140.3,137.3,143,134.5,139.9,159.3,170.4,175,175.8,180.9,180.3,169.6,172.3,184.8,177.7,184.6,211.4,215.3,215.9,244.7,259.3,289,310.9,321,315.1,333.2,314.1,284.7,273.9,216,196.4,190.9,206.4,196.3,199.5,198.9,214.4,214.2,187.6,180.6,172.2,187.2) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '-1.0' > 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,] 0.5233955 0.06246111 0.09358654 -0.4472080 -0.8673930 -0.2677570 [2,] 0.6681469 0.00000000 0.08530285 -0.5666996 -0.8762461 -0.2516384 [3,] 0.9116993 0.00000000 0.00000000 -0.8151419 -0.8849706 -0.2486738 [4,] 0.8408430 0.00000000 0.00000000 -0.7213865 0.1991263 0.0000000 [5,] 0.8245566 0.00000000 0.00000000 -0.7001851 0.0000000 0.0000000 [6,] 0.7668709 0.00000000 0.00000000 -0.6305099 0.0000000 0.0000000 [7,] 0.1792792 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 [8,] 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 [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,] 0.99989878 [2,] 1.00087874 [3,] 1.00104413 [4,] -0.25949507 [5,] -0.05423004 [6,] 0.00000000 [7,] 0.00000000 [8,] 0.00000000 [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.39053 0.70013 0.60628 0.46100 0.00000 0.17161 0.08936 [2,] 0.36281 NA 0.72211 0.45316 0.00000 0.18839 0.11534 [3,] 0.00000 NA NA 0.00029 0.00000 0.16603 0.10371 [4,] 0.05891 NA NA 0.21615 0.85479 NA 0.81036 [5,] 0.06768 NA NA 0.22803 NA NA 0.75101 [6,] 0.05364 NA NA 0.19749 NA NA NA [7,] 0.16624 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 ma1 sar1 sar2 sma1 0.5234 0.0625 0.0936 -0.4472 -0.8674 -0.2678 0.9999 s.e. 0.6046 0.1613 0.1805 0.6022 0.1651 0.1932 0.5778 sigma^2 estimated as 8.631e-08: log likelihood = 396.85, aic = -777.7 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5234 0.0625 0.0936 -0.4472 -0.8674 -0.2678 0.9999 s.e. 0.6046 0.1613 0.1805 0.6022 0.1651 0.1932 0.5778 sigma^2 estimated as 8.631e-08: log likelihood = 396.85, aic = -777.7 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6681 0 0.0853 -0.5667 -0.8762 -0.2516 1.0009 s.e. 0.7280 0 0.2386 0.7500 0.1648 0.1889 0.6254 sigma^2 estimated as 8.752e-08: log likelihood = 396.78, aic = -779.56 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.9117 0 0 -0.8151 -0.8850 -0.2487 1.001 s.e. 0.1486 0 0 0.2108 0.1658 0.1772 0.605 sigma^2 estimated as 8.799e-08: log likelihood = 396.77, aic = -781.54 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8408 0 0 -0.7214 0.1991 0 -0.2595 s.e. 0.4361 0 0 0.5767 1.0831 0 1.0763 sigma^2 estimated as 1.094e-07: log likelihood = 395.65, aic = -781.29 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8246 0 0 -0.7002 0 0 -0.0542 s.e. 0.4427 0 0 0.5746 0 0 0.1701 sigma^2 estimated as 1.095e-07: log likelihood = 395.63, aic = -783.25 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.7669 0 0 -0.6305 0 0 0 s.e. 0.3893 0 0 0.4836 0 0 0 sigma^2 estimated as 1.098e-07: log likelihood = 395.57, aic = -785.15 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1793 0 0 0 0 0 0 s.e. 0.1279 0 0 0 0 0 0 sigma^2 estimated as 1.114e-07: log likelihood = 395.15, aic = -786.3 $aic [1] -777.6973 -779.5632 -781.5386 -781.2920 -783.2529 -785.1462 -786.3036 [8] -786.3729 There were 11 warnings (use warnings() to see them) > postscript(file="/var/www/html/rcomp/tmp/1pase1260467312.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 = 61 Frequency = 1 [1] 9.242139e-06 -5.014362e-05 -1.076289e-04 7.894370e-05 -1.039998e-05 [6] -5.117304e-04 4.638587e-04 -1.389118e-04 -1.357330e-04 -3.489695e-04 [11] 4.352779e-04 -1.421079e-04 -8.934898e-06 3.638087e-04 -4.690785e-04 [16] -1.588949e-04 -4.426332e-04 6.381073e-06 -1.941637e-04 -4.511684e-04 [21] 2.433266e-04 -3.182347e-04 4.939846e-04 -3.662116e-04 -8.190491e-04 [26] -2.528569e-04 -8.094814e-05 1.651841e-06 -1.557043e-04 4.714605e-05 [31] 3.466167e-04 -1.551283e-04 -3.760105e-04 2.865872e-04 -2.491053e-04 [36] -6.490388e-04 3.743273e-05 2.454054e-06 -5.428232e-04 -1.323681e-04 [41] -3.550771e-04 -1.726857e-04 -5.750612e-05 7.647458e-05 -1.828527e-04 [46] 2.134059e-04 2.960515e-04 7.955664e-05 9.538323e-04 2.865663e-04 [51] 6.386438e-05 -4.196828e-04 3.198078e-04 -1.264033e-04 2.977006e-05 [56] -3.661838e-04 6.951814e-05 6.611756e-04 8.793313e-05 2.330618e-04 [61] -5.137443e-04 > postscript(file="/var/www/html/rcomp/tmp/24zan1260467312.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/3604h1260467312.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/4g2df1260467312.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/5otsp1260467312.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/6dgb81260467312.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/77kle1260467312.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/80iwq1260467312.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/96a701260467312.tab") > > system("convert tmp/1pase1260467312.ps tmp/1pase1260467312.png") > system("convert tmp/24zan1260467312.ps tmp/24zan1260467312.png") > system("convert tmp/3604h1260467312.ps tmp/3604h1260467312.png") > system("convert tmp/4g2df1260467312.ps tmp/4g2df1260467312.png") > system("convert tmp/5otsp1260467312.ps tmp/5otsp1260467312.png") > system("convert tmp/6dgb81260467312.ps tmp/6dgb81260467312.png") > system("convert tmp/77kle1260467312.ps tmp/77kle1260467312.png") > > > proc.time() user system elapsed 6.590 1.988 9.622