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Type 'q()' to quit R. > x <- c(91.02,91.19,91.53,91.88,92.06,92.32,92.67,92.85,92.82,93.46,93.23,93.54,93.29,93.20,93.60,93.81,94.62,95.22,95.38,95.31,95.30,95.57,95.42,95.53,95.33,95.90,96.06,96.31,96.34,96.49,96.22,96.53,96.50,96.77,96.66,96.58,96.63,97.06,97.73,98.01,97.76,97.49,97.77,97.96,98.23,98.51,98.19,98.37,98.31,98.60,98.97,99.11,99.64,100.03,99.98,100.32,100.44,100.51,101.00,100.88,100.55,100.83,101.51,102.16,102.39,102.54,102.85,103.47,103.57,103.69,103.50,103.47,103.45,103.48,103.93,103.89,104.40,104.79,104.77,105.13,105.26,104.96,104.75,105.01,105.15,105.20,105.77,105.78,106.26,106.13,106.12,106.57,106.44,106.54,107.10,108.10,108.40,108.84,109.62,110.42,110.67,111.66,112.28,112.87,112.18,112.36,112.16,111.49,111.25,111.36,111.74,111.10,111.33,111.25,111.04,110.97,111.31,111.02,111.07,111.36) > 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,] -0.4445075 0.16196389 -0.08212645 0.6718949 -0.7414926 -0.4515555 [2,] -0.5623099 0.21440988 0.00000000 0.7719956 -0.7027140 -0.4257595 [3,] -0.5042142 0.23951527 0.00000000 0.7490318 -0.9620558 -0.5848641 [4,] 0.0000000 0.07475669 0.00000000 0.2532271 -0.9637571 -0.5839964 [5,] 0.0000000 0.00000000 0.00000000 0.2343450 -0.9709337 -0.5874396 [6,] NA NA NA NA NA NA [7,] NA NA NA NA NA NA [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,] -0.3437912 [2,] -0.3707869 [3,] 0.0000000 [4,] 0.0000000 [5,] 0.0000000 [6,] NA [7,] NA [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.16851 0.19225 0.43886 0.02906 0.00063 0.00996 0.17819 [2,] 0.08608 0.04756 NA 0.01500 0.00128 0.01747 0.15477 [3,] 0.07593 0.02708 NA 0.00659 0.00000 0.00000 NA [4,] NA 0.46650 NA 0.00926 0.00000 0.00000 NA [5,] NA NA NA 0.00742 0.00000 0.00000 NA [6,] NA NA NA NA NA NA NA [7,] NA 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 -0.4445 0.1620 -0.0821 0.6719 -0.7415 -0.4516 -0.3438 s.e. 0.3207 0.1235 0.1057 0.3039 0.2107 0.1722 0.2537 sigma^2 estimated as 0.08519: log likelihood = -28.59, aic = 73.17 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.4445 0.1620 -0.0821 0.6719 -0.7415 -0.4516 -0.3438 s.e. 0.3207 0.1235 0.1057 0.3039 0.2107 0.1722 0.2537 sigma^2 estimated as 0.08519: log likelihood = -28.59, aic = 73.17 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5623 0.2144 0 0.7720 -0.7027 -0.4258 -0.3708 s.e. 0.3247 0.1070 0 0.3125 0.2128 0.1765 0.2588 sigma^2 estimated as 0.08607: log likelihood = -28.87, aic = 71.74 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5042 0.2395 0 0.7490 -0.9621 -0.5849 0 s.e. 0.2815 0.1070 0 0.2706 0.0840 0.0871 0 sigma^2 estimated as 0.08837: log likelihood = -29.81, aic = 71.62 [[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 0.0748 0 0.2532 -0.9638 -0.5840 0 s.e. 0 0.1023 0 0.0957 0.0850 0.0879 0 sigma^2 estimated as 0.08878: log likelihood = -30.09, aic = 70.17 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 73.17424 71.73523 71.61805 70.17385 68.71276 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 > postscript(file="/var/www/html/rcomp/tmp/1n59y1261038932.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 = 120 Frequency = 1 [1] 0.0525503989 0.0236328853 0.0157586500 0.0120544134 0.0097788467 [6] 0.0083755904 0.0074981891 0.0067259834 0.0059477417 0.0059600779 [11] 0.0051972265 -0.0465853433 -0.3147895318 -0.1615300022 0.0811239366 [16] -0.0988037903 0.4272479198 0.1167041634 -0.1833247550 -0.1322140505 [21] 0.0540629424 -0.2420282065 0.1084060411 -0.1478032354 0.0386925737 [26] 0.4024736665 -0.2694077977 0.0006399342 -0.3103885950 -0.1166961785 [31] -0.3900529636 0.2976484560 -0.0495337205 -0.1847685986 0.1192907944 [36] -0.2659995378 0.3068438864 0.2922250225 0.2235585983 -0.0955534656 [41] -0.6630700422 -0.4862371108 0.1973779963 0.0992226129 0.2654380368 [46] -0.2807875540 -0.0754861880 -0.0053922264 0.1708289558 0.0702369089 [51] 0.0215996120 -0.1014585229 0.0764835591 -0.0203858804 -0.0499738283 [56] 0.2694889122 0.0630217797 -0.2354789854 0.6810730047 -0.3178702099 [61] -0.1966901011 -0.1648885629 0.3776604140 0.3139062948 0.1848961559 [66] 0.0746314326 0.3227137610 0.2614907150 -0.0827296783 -0.1520996759 [71] 0.0157246924 -0.0403144513 -0.0026009632 -0.3372033407 -0.0199646655 [76] -0.2496660744 0.5175689935 0.2840235087 -0.2810593764 0.1391590137 [81] -0.0989737417 -0.4766737104 -0.0758640970 0.2577136016 0.2509501852 [86] -0.3053930367 0.1342004014 -0.3341841255 0.1433437447 -0.4414465708 [91] 0.0091518788 0.0326846187 -0.2437325603 0.0859215928 0.3499980699 [96] 0.9815945352 0.2202391584 0.1273626495 0.1220574857 0.3846403962 [101] -0.2070984132 0.7789120177 0.2568084076 -0.0468725279 -0.8145977210 [106] 0.4209033669 -0.0768848055 -0.7844547038 -0.0915023975 0.1395840457 [111] -0.1410220806 -0.6180233445 -0.0931499201 -0.2221327275 -0.1413672935 [116] -0.4147176266 0.4596963880 -0.2403848442 0.0027919143 -0.2061151624 > postscript(file="/var/www/html/rcomp/tmp/2a8fk1261038932.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/3mw6b1261038932.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/46f1o1261038932.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/5wo181261038932.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/6jtr51261038932.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/7e77i1261038932.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/82mk31261038932.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/9epli1261038932.tab") > > try(system("convert tmp/1n59y1261038932.ps tmp/1n59y1261038932.png",intern=TRUE)) character(0) > try(system("convert tmp/2a8fk1261038932.ps tmp/2a8fk1261038932.png",intern=TRUE)) character(0) > try(system("convert tmp/3mw6b1261038932.ps tmp/3mw6b1261038932.png",intern=TRUE)) character(0) > try(system("convert tmp/46f1o1261038932.ps tmp/46f1o1261038932.png",intern=TRUE)) character(0) > try(system("convert tmp/5wo181261038932.ps tmp/5wo181261038932.png",intern=TRUE)) character(0) > try(system("convert tmp/6jtr51261038932.ps tmp/6jtr51261038932.png",intern=TRUE)) character(0) > try(system("convert tmp/7e77i1261038932.ps tmp/7e77i1261038932.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.657 2.012 16.499