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Type 'q()' to quit R. > x <- c(145.9,158.5,152.2,153.7,157.9,154.4,150.7,151.2,147.3,146.6,145.2,139.3,145.7,163.3,181.8,188.1,222.9,206.3,184.9,183.6,186.6,176.5,173.9,184.9,182.5,183.6,172.4,168.9,163.3,152.4,145.8,148.6,143.4,141.2,144.6,144.5,140.8,133.3,127.3,119.6,120.2,121.9,112.4,111,107.8,110.5,118.3,123,112.1,104.2,102.4,100.3,102.6,101.5,103.4,99.4,97.9,98,90.2,87.1,91.8,94.8,91.8,89.3,91.7,86.2,82.8,82.3,79.8,79.4,85.3,87.5,88.3,88.6,94.9,94.7,92.6,91.8,96.4,96.4,107.1,111.9,107.8,109.2,115.3,119.2,107.8,106.8,104.2,94.8,97.5,98.3,100.6,94.9,93.6,98,104.3,103.9,105.3,102.6,103.3,107.9,107.8,109.8,110.6,110.8,119.3,128.1,127.6,137.9,151.4,143.6,143.4,141.9,135.2,133.1,129.6,134.1,136.8,143.5,162.5,163.1,157.2,158.8,155.4,148.5,154.2,153.3,149.4,147.9,156,163,159.1,159.5,157.3,156.4,156.6,162.4,166.8,162.6,168.1) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > 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$order[3] + coeff <- matrix(NA, nrow=nrc*2, ncol=nrc) + pval <- matrix(NA, nrow=nrc*2, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$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) + 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,] 0.0387084 -0.09902472 0.1606846 0.2292889 0.82762540 0.08853941 [2,] 0.0000000 -0.09005475 0.1547098 0.2658656 0.82911088 0.08895452 [3,] 0.0000000 -0.09200360 0.1556019 0.2611514 -0.34262721 0.00000000 [4,] 0.0000000 -0.09405981 0.1566373 0.2599312 -0.04060636 0.00000000 [5,] 0.0000000 -0.10265347 0.1573243 0.2517629 0.00000000 0.00000000 [6,] 0.0000000 0.00000000 0.1565067 0.2772383 0.00000000 0.00000000 [7,] 0.0000000 0.00000000 0.0000000 0.2813945 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,] -0.8766422 [2,] -0.8789720 [3,] 0.2955915 [4,] 0.0000000 [5,] 0.0000000 [6,] 0.0000000 [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,] 0.8916 0.37150 0.09013 0.41403 0.03496 0.37203 0.03037 [2,] NA 0.31301 0.07030 0.00224 0.03271 0.36959 0.02818 [3,] NA 0.30119 0.06767 0.00251 0.60553 NA 0.65927 [4,] NA 0.29001 0.06563 0.00266 0.64446 NA NA [5,] NA 0.23598 0.06387 0.00287 NA NA NA [6,] NA NA 0.06619 0.00206 NA NA NA [7,] NA NA NA 0.00583 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 0.0387 -0.0990 0.1607 0.2293 0.8276 0.0885 -0.8766 s.e. 0.2835 0.1104 0.0941 0.2798 0.3884 0.0988 0.4006 sigma^2 estimated as 0.001966: log likelihood = 237.13, aic = -458.26 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0387 -0.0990 0.1607 0.2293 0.8276 0.0885 -0.8766 s.e. 0.2835 0.1104 0.0941 0.2798 0.3884 0.0988 0.4006 sigma^2 estimated as 0.001966: log likelihood = 237.13, aic = -458.26 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.0901 0.1547 0.2659 0.8291 0.0890 -0.8790 s.e. 0 0.0889 0.0848 0.0853 0.3842 0.0988 0.3961 sigma^2 estimated as 0.001966: log likelihood = 237.12, aic = -460.24 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.0920 0.1556 0.2612 -0.3426 0 0.2956 s.e. 0 0.0886 0.0845 0.0848 0.6619 0 0.6689 sigma^2 estimated as 0.001986: log likelihood = 236.75, aic = -461.49 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.0941 0.1566 0.2599 -0.0406 0 0 s.e. 0 0.0885 0.0844 0.0849 0.0878 0 0 sigma^2 estimated as 0.001988: log likelihood = 236.69, aic = -463.39 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.1027 0.1573 0.2518 0 0 0 s.e. 0 0.0862 0.0842 0.0829 0 0 0 sigma^2 estimated as 0.001991: log likelihood = 236.59, aic = -465.17 [[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 0.1565 0.2772 0 0 0 s.e. 0 0 0.0845 0.0883 0 0 0 sigma^2 estimated as 0.002011: log likelihood = 235.89, aic = -465.78 $aic [1] -458.2571 -460.2387 -461.4933 -463.3884 -465.1747 -465.7820 -464.3995 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 log(s2) : NaNs produced 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 7: 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/1fty81197311092.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] 0.0049829187 0.0788373037 -0.0609548515 0.0225100112 0.0078373060 [6] -0.0182363233 -0.0207349142 0.0048415247 -0.0239662535 0.0056769814 [11] -0.0116879698 -0.0341520147 0.0551336037 0.1002559292 0.0860156425 [16] 0.0031894712 0.1510173526 -0.1360556244 -0.0771280945 -0.0122397265 [21] 0.0317134665 -0.0472985720 -0.0006232216 0.0589708677 -0.0207049143 [26] 0.0140721304 -0.0764427452 0.0027270763 -0.0354143701 -0.0494112760 [31] -0.0273640903 0.0318857506 -0.0336486194 0.0007970747 0.0205958864 [36] -0.0008269715 -0.0232901083 -0.0520052236 -0.0315296103 -0.0495928123 [41] 0.0273201030 0.0136778627 -0.0751641024 0.0075214441 -0.0335357570 [46] 0.0467337551 0.0572134765 0.0276770353 -0.1043377997 -0.0548278121 [51] -0.0083226383 -0.0038909370 0.0351883362 -0.0178074949 0.0267260605 [56] -0.0504106922 0.0004572167 -0.0020084270 -0.0762066052 -0.0114653816 [61] 0.0555742744 0.0297301518 -0.0349260124 -0.0261532538 0.0287387728 [66] -0.0647868879 -0.0179594341 -0.0052286069 -0.0197177516 0.0067395389 [71] 0.0707555815 0.0106760372 0.0069279751 -0.0097467349 0.0674086776 [76] -0.0222223888 -0.0167947927 -0.0147714160 0.0533192892 -0.0112725093 [81] 0.1097399310 0.0057663045 -0.0389265973 0.0072219619 0.0454924947 [86] 0.0264951394 -0.1098900323 0.0126388606 -0.0333560187 -0.0695623071 [91] 0.0488269045 -0.0015078074 0.0383428179 -0.0733538213 0.0052642490 [96] 0.0408579216 0.0601053096 -0.0183472109 0.0112816092 -0.0388541533 [101] 0.0181726739 0.0364345682 -0.0069629349 0.0192491046 -0.0048956323 [106] 0.0033090571 0.0701201157 0.0505937287 -0.0182201155 0.0711116076 [111] 0.0625431349 -0.0696209645 0.0057585055 -0.0267290054 -0.0326789027 [116] -0.0063764670 -0.0232344192 0.0481443007 0.0090367947 0.0494802647 [121] 0.1052830993 -0.0286228369 -0.0363926537 0.0007556037 -0.0224293997 [126] -0.0334327611 0.0453494536 -0.0150389896 -0.0144919912 -0.0119680701 [131] 0.0575537841 0.0319711808 -0.0315016077 0.0028995606 -0.0215627151 [136] 0.0040301918 -0.0002323543 0.0386058002 0.0169280882 -0.0303954157 [141] 0.0360008381 > postscript(file="/var/www/html/rcomp/tmp/29eil1197311092.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/343hx1197311092.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/4898t1197311092.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/57nnz1197311092.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/66o7u1197311092.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/7d1v01197311092.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/8r7nj1197311092.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/9ppse1197311092.tab") > > system("convert tmp/1fty81197311092.ps tmp/1fty81197311092.png") > system("convert tmp/29eil1197311092.ps tmp/29eil1197311092.png") > system("convert tmp/343hx1197311092.ps tmp/343hx1197311092.png") > system("convert tmp/4898t1197311092.ps tmp/4898t1197311092.png") > system("convert tmp/57nnz1197311092.ps tmp/57nnz1197311092.png") > system("convert tmp/66o7u1197311092.ps tmp/66o7u1197311092.png") > system("convert tmp/7d1v01197311092.ps tmp/7d1v01197311092.png") > > > proc.time() user system elapsed 9.159 1.921 10.687