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Type 'q()' to quit R. > x <- c(6.7,6.7,6.5,6.3,6.3,6.3,6.5,6.6,6.5,6.3,6.3,6.5,7,7.1,7.3,7.3,7.4,7.4,7.3,7.4,7.5,7.7,7.7,7.7,7.7,7.7,7.8,8,8.1,8.1,8.2,8.2,8.2,8.1,8.1,8.2,8.3,8.3,8.4,8.5,8.5,8.4,8,7.9,8.1,8.5,8.8,8.8,8.6,8.3,8.3,8.3,8.4,8.4,8.5,8.6,8.6,8.6,8.6,8.6,8.5,8.4,8.4,8.4,8.5,8.5,8.6,8.6,8.4,8.2,8,8,8,8,7.9,7.9,7.8,7.8,8,7.8,7.4,7.2,7,7,7.2,7.2,7.2,7,6.9,6.8,6.8,6.8,6.9,7.2,7.2,7.2,7.1,7.2,7.3,7.5,7.6,7.7,7.7,7.7,7.8,8,8.1,8.1,8,8.1,8.2,8.3,8.4,8.4,8.4,8.5,8.5,8.6,8.6,8.5,8.5) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > 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,] 0.4138834 -0.01678028 -0.3390717 0.06468917 0.76470321 -0.1696860 [2,] 0.3948641 0.00000000 -0.3455089 0.08257144 0.76325993 -0.1696375 [3,] 0.4476222 0.00000000 -0.3464692 0.00000000 0.77480168 -0.1844091 [4,] 0.4701898 0.00000000 -0.3540254 0.00000000 0.05402652 0.0000000 [5,] 0.4694276 0.00000000 -0.3533234 0.00000000 0.00000000 0.0000000 [6,] 0.4746243 0.00000000 -0.3291091 0.00000000 0.00000000 0.0000000 [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,] -0.66076018 [2,] -0.65903394 [3,] -0.66915574 [4,] 0.06346313 [5,] 0.11361237 [6,] 0.00000000 [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,] 0.03737 0.89958 0.00066 0.75089 0.03834 0.11749 0.07207 [2,] 0.00228 NA 0.00005 0.57540 0.03878 0.11698 0.07270 [3,] 0.00000 NA 0.00003 NA 0.02139 0.07836 0.04589 [4,] 0.00000 NA 0.00001 NA 0.89234 NA 0.87039 [5,] 0.00000 NA 0.00001 NA NA NA 0.24758 [6,] 0.00000 NA 0.00002 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 0.4139 -0.0168 -0.3391 0.0647 0.7647 -0.1697 -0.6608 s.e. 0.1965 0.1327 0.0967 0.2033 0.3649 0.1076 0.3639 sigma^2 estimated as 0.01231: log likelihood = 92.88, aic = -169.77 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4139 -0.0168 -0.3391 0.0647 0.7647 -0.1697 -0.6608 s.e. 0.1965 0.1327 0.0967 0.2033 0.3649 0.1076 0.3639 sigma^2 estimated as 0.01231: log likelihood = 92.88, aic = -169.77 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3949 0 -0.3455 0.0826 0.7633 -0.1696 -0.6590 s.e. 0.1265 0 0.0819 0.1470 0.3651 0.1074 0.3638 sigma^2 estimated as 0.01231: log likelihood = 92.88, aic = -171.75 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4476 0 -0.3465 0 0.7748 -0.1844 -0.6692 s.e. 0.0777 0 0.0796 0 0.3321 0.1038 0.3315 sigma^2 estimated as 0.01232: log likelihood = 92.72, aic = -173.44 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4702 0 -0.3540 0 0.0540 0 0.0635 s.e. 0.0741 0 0.0775 0 0.3983 0 0.3881 sigma^2 estimated as 0.01258: log likelihood = 91.82, aic = -173.64 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4694 0 -0.3533 0 0 0 0.1136 s.e. 0.0740 0 0.0773 0 0 0 0.0978 sigma^2 estimated as 0.01259: log likelihood = 91.81, aic = -175.62 [[3]][[7]] NULL $aic [1] -169.7678 -171.7516 -173.4381 -173.6400 -175.6242 -176.3611 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/1mosw1293191393.ps",horizontal=F,onefile=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 = 121 Frequency = 1 [1] 6.699995e-03 -3.666375e-06 -1.830434e-01 -8.732998e-02 9.239591e-02 [6] -7.023007e-02 1.300894e-01 8.581756e-03 -1.439013e-01 -8.313708e-02 [11] 1.241066e-01 1.563252e-01 3.317783e-01 -1.309271e-01 2.412607e-01 [16] 9.004003e-02 1.248905e-01 3.163435e-02 -1.146981e-01 1.812793e-01 [21] 6.929875e-02 1.271329e-01 -7.251740e-02 1.775796e-02 3.323135e-02 [26] 1.482274e-02 7.262456e-02 1.428362e-01 -8.073328e-03 -1.520412e-02 [31] 1.836944e-01 -3.220456e-02 -7.872757e-03 -7.911017e-02 5.518145e-02 [36] 9.798308e-02 1.395022e-02 -4.862706e-02 1.270815e-01 7.216171e-02 [41] -4.602553e-02 -6.294029e-02 -3.385948e-01 9.142987e-02 2.125049e-01 [46] 1.737730e-01 7.062733e-02 -8.129568e-02 -6.025556e-02 -9.459284e-02 [51] 1.263903e-01 -7.886314e-02 -7.679432e-04 -3.979197e-02 1.384686e-01 [56] 7.800201e-02 -7.108594e-02 1.558957e-02 2.730820e-02 9.236195e-03 [61] -9.315422e-02 -4.231032e-02 3.258326e-02 -2.637251e-02 6.475491e-02 [66] -4.242190e-02 8.426826e-02 -2.047242e-02 -1.919238e-01 -7.255331e-02 [71] -1.092170e-01 2.217150e-02 -6.008120e-02 -6.585770e-02 -1.037019e-01 [76] 4.993900e-02 -1.073570e-01 1.643008e-02 1.904261e-01 -3.268919e-01 [81] -2.843096e-01 6.667867e-02 -1.643707e-01 -4.996279e-02 1.361613e-01 [86] -1.570679e-01 1.178181e-02 -1.350090e-01 6.082601e-03 -5.492390e-02 [91] -4.535667e-02 1.806632e-03 9.696875e-02 2.454817e-01 -1.221537e-01 [96] 4.100873e-02 -9.472595e-03 1.647876e-01 5.171868e-02 1.330636e-01 [101] 4.075576e-02 9.462961e-02 2.887499e-02 3.512708e-02 1.243155e-01 [106] 1.251675e-01 1.999265e-02 -1.626952e-02 -2.825912e-02 1.635532e-01 [111] 4.718136e-02 2.607230e-03 8.375922e-02 -2.236152e-02 3.205178e-02 [116] 1.313415e-01 -6.106654e-02 8.577943e-02 -1.388184e-02 -9.815158e-02 [121] 8.548568e-02 > postscript(file="/var/www/html/rcomp/tmp/2mosw1293191393.ps",horizontal=F,onefile=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/3mosw1293191393.ps",horizontal=F,onefile=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/4mosw1293191393.ps",horizontal=F,onefile=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/5efry1293191393.ps",horizontal=F,onefile=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/6efry1293191393.ps",horizontal=F,onefile=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/7efry1293191393.ps",horizontal=F,onefile=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/8bppp1293191393.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/9lgoa1293191393.tab") > > try(system("convert tmp/1mosw1293191393.ps tmp/1mosw1293191393.png",intern=TRUE)) character(0) > try(system("convert tmp/2mosw1293191393.ps tmp/2mosw1293191393.png",intern=TRUE)) character(0) > try(system("convert tmp/3mosw1293191393.ps tmp/3mosw1293191393.png",intern=TRUE)) character(0) > try(system("convert tmp/4mosw1293191393.ps tmp/4mosw1293191393.png",intern=TRUE)) character(0) > try(system("convert tmp/5efry1293191393.ps tmp/5efry1293191393.png",intern=TRUE)) character(0) > try(system("convert tmp/6efry1293191393.ps tmp/6efry1293191393.png",intern=TRUE)) character(0) > try(system("convert tmp/7efry1293191393.ps tmp/7efry1293191393.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.746 1.767 59.445