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Type 'q()' to quit R. > x <- c(93.5,94.7,112.9,99.2,105.6,113,83.1,81.1,96.9,104.3,97.7,102.6,89.9,96,112.7,107.1,106.2,121,101.2,83.2,105.1,113.3,99.1,100.3,93.5,98.8,106.2,98.3,102.1,117.1,101.5,80.5,105.9,109.5,97.2,114.5,93.5,100.9,121.1,116.5,109.3,118.1,108.3,105.4,116.2,111.2,105.8,122.7,99.5,107.9,124.6,115,110.3,132.7,99.7,96.5,118.7,112.9,130.5,137.9,115,116.8,140.9,120.7,134.2,147.3,112.4,107.1,128.4,137.7,135,151,137.4,132.4,161.3,139.8,146,154.6,142.1,120.5) > 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,] -0.2854505 -0.3039811 -0.0965218 -0.3108949 0.1688629 -0.2597246 [2,] -0.1185295 -0.2117912 0.0000000 -0.4748624 0.1675290 -0.2517728 [3,] 0.0000000 -0.1570866 0.0000000 -0.5730931 0.1653096 -0.2540152 [4,] 0.0000000 0.0000000 0.0000000 -0.6481011 0.1621053 -0.2196775 [5,] 0.0000000 0.0000000 0.0000000 -1.5510684 0.0000000 -0.2571767 [6,] 0.0000000 0.0000000 0.0000000 -1.4343079 0.0000000 0.0000000 [7,] NA NA NA NA NA NA [,7] [1,] -0.9998978 [2,] -0.9997216 [3,] -1.0000338 [4,] -1.0001843 [5,] -0.8176264 [6,] -0.8041708 [7,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.51001 0.23344 0.64899 0.46888 0.24773 0.12418 0.00124 [2,] 0.62523 0.21076 NA 0.04887 0.25366 0.13755 0.00129 [3,] NA 0.26177 NA 0.00001 0.25577 0.13600 0.00106 [4,] NA NA NA 0.00000 0.26337 0.19123 0.00169 [5,] NA NA NA 0.00000 NA 0.09770 0.05349 [6,] NA NA NA 0.00000 NA NA 0.02376 [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 -0.2855 -0.304 -0.0965 -0.3109 0.1689 -0.2597 -0.9999 s.e. 0.4311 0.253 0.2112 0.4270 0.1449 0.1670 0.2974 sigma^2 estimated as 0.002663: log likelihood = 90.59, aic = -165.18 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2855 -0.304 -0.0965 -0.3109 0.1689 -0.2597 -0.9999 s.e. 0.4311 0.253 0.2112 0.4270 0.1449 0.1670 0.2974 sigma^2 estimated as 0.002663: log likelihood = 90.59, aic = -165.18 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1185 -0.2118 0 -0.4749 0.1675 -0.2518 -0.9997 s.e. 0.2416 0.1677 0 0.2370 0.1456 0.1677 0.2987 sigma^2 estimated as 0.002679: log likelihood = 90.51, aic = -167.01 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.1571 0 -0.5731 0.1653 -0.2540 -1.0000 s.e. 0 0.1389 0 0.1213 0.1443 0.1685 0.2934 sigma^2 estimated as 0.002683: log likelihood = 90.39, aic = -168.78 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0 -0.6481 0.1621 -0.2197 -1.0002 s.e. 0 0 0 0.1065 0.1438 0.1666 0.3070 sigma^2 estimated as 0.002767: log likelihood = 89.8, aic = -169.6 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0 -1.5511 0 -0.2572 -0.8176 s.e. 0 0 0 0.2577 0 0.1534 0.4169 sigma^2 estimated as 0.001300: log likelihood = 89.15, aic = -170.3 [[3]][[7]] NULL $aic [1] -165.1789 -167.0106 -168.7819 -169.6016 -170.3012 -169.8389 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 > postscript(file="/var/www/html/rcomp/tmp/1bkps1196415339.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 = 80 Frequency = 1 [1] 2.619991e-03 1.181574e-03 9.220895e-04 5.731960e-04 5.138036e-04 [6] 4.898119e-04 1.333291e-04 9.370892e-05 2.516300e-04 2.963996e-04 [11] 2.069323e-04 -2.332462e-03 -1.623396e-02 2.137037e-02 3.915288e-03 [16] 3.931611e-02 -9.350803e-03 2.419361e-02 7.747317e-02 -3.282491e-02 [21] 5.695766e-03 4.406816e-03 -3.022157e-02 -3.728227e-02 -2.238960e-03 [26] 3.090651e-03 -4.627511e-02 -2.840633e-02 -7.619343e-03 1.033423e-02 [31] 4.939604e-02 -1.910621e-02 1.918275e-02 -8.941924e-03 -1.104907e-02 [36] 6.451136e-02 -2.353973e-02 8.367333e-03 3.779813e-02 5.857951e-02 [41] -2.561069e-02 -3.267354e-02 5.600824e-02 9.906683e-02 -1.165314e-02 [46] -6.473410e-02 -1.486006e-02 1.816589e-02 -2.549692e-02 1.065293e-03 [51] -1.364056e-02 -1.397184e-02 -3.211440e-02 3.433209e-02 -3.992227e-02 [56] 8.913120e-03 2.628637e-02 -3.287544e-02 1.178576e-01 6.240614e-02 [61] 1.098240e-02 -1.761346e-02 2.275840e-02 -2.940479e-02 3.943987e-02 [66] -3.010867e-04 -2.759281e-02 3.089359e-02 -9.036580e-03 1.942605e-02 [71] 2.445501e-02 3.852887e-02 5.667476e-02 -1.507595e-02 1.651335e-02 [76] -2.265439e-02 -5.498201e-03 -2.856257e-02 4.543288e-02 -7.546759e-03 > postscript(file="/var/www/html/rcomp/tmp/2llju1196415339.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/3865w1196415339.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/4uuv41196415339.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/51hel1196415339.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/6kgy41196415339.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/7x8x11196415339.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/8s3jr1196415339.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/9k2af1196415339.tab") > > system("convert tmp/1bkps1196415339.ps tmp/1bkps1196415339.png") > system("convert tmp/2llju1196415339.ps tmp/2llju1196415339.png") > system("convert tmp/3865w1196415339.ps tmp/3865w1196415339.png") > system("convert tmp/4uuv41196415339.ps tmp/4uuv41196415339.png") > system("convert tmp/51hel1196415339.ps tmp/51hel1196415339.png") > system("convert tmp/6kgy41196415339.ps tmp/6kgy41196415339.png") > system("convert tmp/7x8x11196415339.ps tmp/7x8x11196415339.png") > > > proc.time() user system elapsed 9.433 1.375 10.275