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Type 'q()' to quit R. > x <- c(174.1,180.4,182.6,207.1,213.7,186.5,179.1,168.3,156.5,144.3,138.9,137.8,136.3,140.3,149.1,149.2,140.4,129,124.7,130.8,130.1,133.2,130.1,126.6,124.8,125.3,126.9,120.1,118.7,117.7,113.4,107.5,107.6,114.3,114.9,111.2,109.9,108.6,109.2,106.4,103.7,103,96.9,104.7,102.2,99,95.8,94.5,102.7,103.2,105.6,103.9,107.2,100.7,92.1,90.3,93.4,98.5,100.8,102.3,104.7,101.1,101.4,99.5,98.4,96.3,100.7,101.2,100.3,97.8,97.4,98.6,99.7,99,98.1,97,98.5,103.8,114.4,124.5,134.2,131.8,125.6,119.9,114.9,115.5,112.5,111.4,115.3,110.8,103.7,111.1,113,111.2,117.6,121.7,127.3,129.8,137.1,141.4,137.4,130.7,117.2,110.8,111.4,108.2,108.8,110.2,109.5,109.5,116,111.2,112.1,114,119.1,114.1,115.1,115.4,110.8,116,119.2,126.5,127.8,131.3,140.3,137.3,143,134.5,139.9,159.3,170.4,175,175.8,180.9,180.3,169.6,172.3,184.8,177.7,184.6,211.4) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '0.0' > 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$order[3] + coeff <- matrix(NA, nrow=nrc, ncol=nrc) + pval <- matrix(NA, nrow=nrc, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$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,] -0.5399399 0.1708142 0.03040261 0.9041634 -0.03864342 -0.2391404 [2,] -0.5400769 0.1714573 0.02714126 0.9074182 0.00000000 -0.2280700 [3,] -0.5437801 0.1617746 0.00000000 0.9154611 0.00000000 -0.2308603 [4,] -0.5645863 0.0000000 0.00000000 0.8563877 0.00000000 -0.2288659 [5,] NA NA NA NA NA NA [6,] NA NA NA NA NA NA [7,] NA NA NA NA NA NA [,7] [1,] -0.7179274 [2,] -0.7396120 [3,] -0.7348908 [4,] -0.7429401 [5,] NA [6,] NA [7,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.00001 0.11278 0.75830 0 0.77717 0.03584 0 [2,] 0.00001 0.10880 0.78186 0 NA 0.03478 0 [3,] 0.00000 0.10824 NA 0 NA 0.03198 0 [4,] 0.00014 NA NA 0 NA 0.03207 0 [5,] NA NA NA NA NA NA NA [6,] NA NA NA NA 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 -0.5399 0.1708 0.0304 0.9042 -0.0386 -0.2391 -0.7179 s.e. 0.1162 0.1070 0.0986 0.0830 0.1363 0.1128 0.1289 sigma^2 estimated as 0.001989: log likelihood = 209.26, aic = -402.53 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5399 0.1708 0.0304 0.9042 -0.0386 -0.2391 -0.7179 s.e. 0.1162 0.1070 0.0986 0.0830 0.1363 0.1128 0.1289 sigma^2 estimated as 0.001989: log likelihood = 209.26, aic = -402.53 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5401 0.1715 0.0271 0.9074 0 -0.2281 -0.7396 s.e. 0.1148 0.1062 0.0978 0.0801 0 0.1069 0.1019 sigma^2 estimated as 0.001988: log likelihood = 209.22, aic = -404.44 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5438 0.1618 0 0.9155 0 -0.2309 -0.7349 s.e. 0.1123 0.1001 0 0.0715 0 0.1065 0.1004 sigma^2 estimated as 0.001991: log likelihood = 209.18, aic = -406.37 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -402.5250 -404.4448 -406.3681 -405.7485 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 log(s2) : NaNs produced 4: 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/1x6gh1197040466.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 = 141 Frequency = 1 [1] 2.978913e-03 1.359743e-03 9.004026e-04 7.821545e-04 6.523170e-04 [6] 4.175254e-04 3.198538e-04 2.212789e-04 1.278501e-04 3.783054e-05 [11] -2.060445e-06 -2.970713e-03 -1.886171e-02 -4.805187e-03 3.866043e-02 [16] -1.085582e-01 -3.423412e-02 4.707096e-02 -3.785933e-03 8.490875e-02 [21] 2.167172e-02 7.645353e-02 -2.147013e-02 -2.889121e-03 -1.107500e-02 [26] -1.317965e-02 -2.529085e-02 -7.232095e-02 3.558325e-02 6.827037e-02 [31] -2.125569e-02 -4.498378e-02 3.874027e-02 5.059990e-02 1.249945e-02 [36] -1.645369e-02 3.375382e-03 -3.103465e-02 1.444691e-03 -5.129086e-02 [41] -3.825501e-03 5.617713e-02 -3.616608e-02 1.270525e-01 -4.300005e-02 [46] -3.004821e-03 -2.844255e-02 2.855227e-02 6.723461e-02 -2.314871e-02 [51] 1.602253e-03 -3.595380e-02 6.261343e-02 -2.262722e-02 -2.819030e-02 [56] -3.725630e-02 8.012838e-02 3.929786e-02 4.337439e-02 6.945552e-03 [61] 1.781646e-03 -5.569421e-02 2.364256e-03 -2.691637e-02 6.287121e-03 [66] 3.383509e-02 9.030954e-02 -1.048459e-02 3.093757e-03 -5.678384e-02 [71] 2.616083e-02 7.050411e-03 1.497762e-02 -1.632786e-02 -1.201853e-02 [76] -6.380489e-03 3.857111e-02 7.329166e-02 9.182169e-02 3.919208e-02 [81] 7.929292e-02 -4.128882e-02 -1.266608e-02 -3.893296e-02 -3.915346e-02 [86] 8.659273e-03 -3.665506e-02 9.480174e-03 2.824030e-02 -2.459988e-02 [91] -4.050625e-02 5.797046e-02 -2.124346e-02 -9.133465e-03 7.139512e-02 [96] 3.330265e-02 2.735415e-02 9.001700e-03 4.636277e-02 2.147869e-02 [101] -3.915028e-02 5.839434e-03 -6.893447e-02 -4.579000e-02 1.998231e-02 [106] -2.905977e-02 4.684336e-03 1.685850e-03 -3.214913e-02 8.760226e-03 [111] 3.219087e-02 -5.324973e-02 3.618923e-02 2.447854e-02 6.398023e-02 [116] -7.348718e-02 2.780002e-02 -8.194674e-03 -1.156171e-02 4.935914e-02 [121] 1.777294e-02 5.212604e-02 -2.153895e-02 5.512582e-02 3.136571e-02 [126] -1.620117e-02 3.600454e-02 -9.158955e-02 5.926156e-02 1.053724e-01 [131] 4.886459e-02 -8.164104e-03 -9.555228e-03 8.794560e-03 -1.532634e-02 [136] -6.629048e-02 2.370689e-02 8.865874e-02 -4.518376e-02 5.916876e-02 [141] 8.760709e-02 > postscript(file="/var/www/html/rcomp/tmp/2b0f31197040466.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/337rx1197040466.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/4birf1197040466.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/52hkb1197040466.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/6480t1197040466.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/7kf9n1197040466.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/8ianl1197040466.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/9ozyz1197040466.tab") > > system("convert tmp/1x6gh1197040466.ps tmp/1x6gh1197040466.png") > system("convert tmp/2b0f31197040466.ps tmp/2b0f31197040466.png") > system("convert tmp/337rx1197040466.ps tmp/337rx1197040466.png") > system("convert tmp/4birf1197040466.ps tmp/4birf1197040466.png") > system("convert tmp/52hkb1197040466.ps tmp/52hkb1197040466.png") > system("convert tmp/6480t1197040466.ps tmp/6480t1197040466.png") > system("convert tmp/7kf9n1197040466.ps tmp/7kf9n1197040466.png") > > > proc.time() user system elapsed 12.168 1.256 12.689