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Type 'q()' to quit R. > x <- c(128.7,136.9,156.9,109.1,122.3,123.9,90.9,77.9,120.3,118.9,125.5,98.9,102.9,105.9,117.6,113.6,115.9,118.9,77.6,81.2,123.1,136.6,112.1,95.1,96.3,105.7,115.8,105.7,105.7,111.1,82.4,60,107.3,99.3,113.5,108.9,100.2,103.9,138.7,120.2,100.2,143.2,70.9,85.2,133,136.6,117.9,106.3,122.3,125.5,148.4,126.3,99.6,140.4,80.3,92.6,138.5,110.9,119.6,105,109,129.4,148.6,101.4,134.8,143.7,81.6,90.3,141.5,140.7,140.2,100.2,125.7,119.6,134.7,109,116.3,146.9,97.4,89.4,132.1,139.8,129,112.5,121.9,121.7,123.1,131.6,119.3,132.5,98.3,85.1,131.7,129.3,90.7,78.6,68.9,79.1,83.5,74.1,59.7,93.3,61.3,56.6,98.5) > par9 = '1' > par8 = '2' > 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,] -0.2010926 -0.008232913 0.09007427 -0.4637836 0.07786095 0.09566557 [2,] -0.1887403 0.000000000 0.09382941 -0.4760631 0.07777787 0.09562780 [3,] -0.1983508 0.000000000 0.10395120 -0.4662517 0.00000000 0.07584575 [4,] -0.1851355 0.000000000 0.09399473 -0.4655911 0.00000000 0.00000000 [5,] -0.2027063 0.000000000 0.00000000 -0.4442227 0.00000000 0.00000000 [6,] 0.0000000 0.000000000 0.00000000 -0.5789135 0.00000000 0.00000000 [7,] 0.0000000 0.000000000 0.00000000 -0.6440691 0.00000000 0.00000000 [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 [13,] NA NA NA NA NA NA [14,] NA NA NA NA NA NA [,7] [1,] -0.9998174 [2,] -0.9997869 [3,] -1.0000125 [4,] -0.9983928 [5,] -1.0001156 [6,] -0.9998031 [7,] 0.0000000 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.91518 0.99477 0.87790 0.80518 0.58183 0.49708 0.00004 [2,] 0.28217 NA 0.39713 0.00372 0.57871 0.49552 0.00004 [3,] 0.25692 NA 0.33756 0.00465 NA 0.56690 0.00512 [4,] 0.29423 NA 0.38338 0.00512 NA NA 0.04410 [5,] 0.21557 NA NA 0.00290 NA NA 0.05011 [6,] NA NA NA 0.00000 NA NA 0.05227 [7,] NA NA NA 0.00000 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 -0.2011 -0.0082 0.0901 -0.4638 0.0779 0.0957 -0.9998 s.e. 1.8831 1.2528 0.5848 1.8752 0.1409 0.1403 0.2307 sigma^2 estimated as 136.6: log likelihood = -368.19, aic = 752.38 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2011 -0.0082 0.0901 -0.4638 0.0779 0.0957 -0.9998 s.e. 1.8831 1.2528 0.5848 1.8752 0.1409 0.1403 0.2307 sigma^2 estimated as 136.6: log likelihood = -368.19, aic = 752.38 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1887 0 0.0938 -0.4761 0.0778 0.0956 -0.9998 s.e. 0.1745 0 0.1103 0.1602 0.1396 0.1398 0.2306 sigma^2 estimated as 136.6: log likelihood = -368.19, aic = 750.38 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1984 0 0.1040 -0.4663 0 0.0758 -1.0000 s.e. 0.1739 0 0.1079 0.1610 0 0.1320 0.3492 sigma^2 estimated as 133.9: log likelihood = -368.35, aic = 748.7 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1851 0 0.0940 -0.4656 0 0 -0.9984 s.e. 0.1756 0 0.1074 0.1626 0 0 0.4897 sigma^2 estimated as 132.8: log likelihood = -368.52, aic = 747.04 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2027 0 0 -0.4442 0 0 -1.0001 s.e. 0.1627 0 0 0.1455 0 0 0.5044 sigma^2 estimated as 133.7: log likelihood = -368.91, aic = 745.82 [[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 0 0 -0.5789 0 0 -0.9998 s.e. 0 0 0 0.0784 0 0 0.5091 sigma^2 estimated as 135.8: log likelihood = -369.61, aic = 745.21 $aic [1] 752.3831 750.3831 748.7026 747.0368 745.8161 745.2145 770.4954 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 5: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 6: 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/1oggl1260552719.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 = 105 Frequency = 1 [1] 0.074304947 0.039581873 0.042815485 -0.010242838 0.003768885 [6] 0.004607684 -0.026775515 -0.035625849 0.008451974 0.006272868 [11] 0.012007871 -0.086491618 -0.505188343 -3.183969609 -7.169372119 [16] 26.643845971 7.487706974 5.298903904 -2.805099585 10.114239928 [21] 5.500331968 13.720568469 -14.050409784 -1.345195504 -3.478840552 [26] 1.244335462 -3.958026331 10.598738553 -0.211913445 2.407925711 [31] 8.292501835 -9.653039543 -1.382743983 -12.273431562 11.798402142 [36] 20.875267360 2.203452475 -1.497990684 17.144449511 11.714823486 [41] -15.017736215 25.661756199 -18.028835469 11.129314415 9.849388734 [46] 7.636143423 -10.707476485 -2.330134431 12.840038691 4.668290418 [51] 6.023678827 1.688610711 -21.894699180 11.971005900 -7.628486374 [56] 10.499791613 7.017589506 -22.348024924 -0.146049276 0.228481817 [61] 0.387921493 13.762385412 7.281326338 -20.157009734 24.522315482 [66] 5.191797688 -10.707070313 2.693835326 7.165019644 7.051087939 [71] 6.126951798 -19.386638657 8.445900506 -8.170849758 -9.042385007 [76] -5.921685282 2.992334978 14.215891993 8.306472050 -3.138754787 [81] -4.949746060 7.458401523 -3.490789429 0.355608113 2.080987969 [86] -4.566512260 -19.194698159 20.284142876 -1.036517401 -6.065877484 [91] 10.868423511 -5.455478716 -2.222754977 -1.794657633 -33.808434358 [96] -13.397843446 -23.990182914 -9.084859197 -17.026437888 0.956748568 [101] -12.693184406 7.066665591 18.847149251 8.565277117 1.351920018 > postscript(file="/var/www/html/rcomp/tmp/29o8q1260552719.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/3zb9r1260552719.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/4ivyj1260552719.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/5n9l41260552719.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/6k0wh1260552719.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/7hukl1260552719.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/8xvwt1260552719.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/9ndoc1260552719.tab") > system("convert tmp/1oggl1260552719.ps tmp/1oggl1260552719.png") > system("convert tmp/29o8q1260552719.ps tmp/29o8q1260552719.png") > system("convert tmp/3zb9r1260552719.ps tmp/3zb9r1260552719.png") > system("convert tmp/4ivyj1260552719.ps tmp/4ivyj1260552719.png") > system("convert tmp/5n9l41260552719.ps tmp/5n9l41260552719.png") > system("convert tmp/6k0wh1260552719.ps tmp/6k0wh1260552719.png") > system("convert tmp/7hukl1260552719.ps tmp/7hukl1260552719.png") > > > proc.time() user system elapsed 9.706 1.853 13.162