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Type 'q()' to quit R. > x <- c(15,14.4,13,13.7,13.6,15.2,12.9,14,14.1,13.2,11.3,13.3,14.4,13.3,11.6,13.2,13.1,14.6,14,14.3,13.8,13.7,11,14.4,15.6,13.7,12.6,13.2,13.3,14.3,14,13.4,13.9,13.7,10.5,14.5,15,13.5,13.5,13.2,13.8,16.2,14.7,13.9,16,14.4,12.3,15.9,15.9,15.5,15.1,14.5,15.1,17.4,16.2,15.6,17.2,14.9,13.8,17.5,16.2,17.5,16.6,16.2,16.6,19.6,15.9,18,18.3,16.3,14.9,18.2,18.4,18.5,16,17.4,17.2,19.6,17.2,18.3,19.3,18.1,16.2,18.4,20.5,19,16.5,18.7,19,19.2,20.5,19.3,20.6,20.1,16.1,20.4,19.7,15.6,14.4,13.7,14.1,15,14.2,13.6,15.4,14.8,12.5,16.2,16.1,16,15.8,15.2,15.7,18.9,17.4,17,19.8,17.7,16,19.6,19.7) > par20 = '' > par19 = '' > par18 = '' > par17 = '' > par16 = '' > par15 = '' > par14 = '' > par13 = '' > par12 = '' > par11 = '' > par10 = '' > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '1' > par1 = 'FALSE' > ylab = '' > xlab = '' > main = '' > #'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] [,7] [1,] -0.2011635 0.1331342 0.4632769 -0.2480046 0.2265902 -0.3627107 -0.9999933 [2,] 0.0000000 0.1869706 0.4459508 -0.4180550 0.2394223 -0.3708612 -1.0000103 [3,] NA NA NA NA NA NA NA [4,] NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA 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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.22498 0.21459 0 0.17959 0.02515 0.00126 0 [2,] NA 0.03273 0 0.00001 0.01599 0.00077 0 [3,] NA NA NA NA NA NA NA [4,] NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA 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 -0.2012 0.1331 0.4633 -0.2480 0.2266 -0.3627 -1.0000 s.e. 0.1649 0.1067 0.0845 0.1837 0.0999 0.1097 0.1711 sigma^2 estimated as 0.4644: log likelihood = -128.92, aic = 273.83 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2012 0.1331 0.4633 -0.2480 0.2266 -0.3627 -1.0000 s.e. 0.1649 0.1067 0.0845 0.1837 0.0999 0.1097 0.1711 sigma^2 estimated as 0.4644: log likelihood = -128.92, aic = 273.83 [[3]][[3]] NULL [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 273.8339 273.5332 Warning message: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE > postscript(file="/var/www/rcomp/tmp/1cbmo1293203853.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] 8.660250e-03 3.408224e-03 1.047990e-03 1.398459e-03 1.025131e-03 [6] 2.324740e-03 -1.504011e-04 9.017458e-04 8.958053e-04 -5.008515e-05 [11] -1.861029e-03 -7.574245e-03 -4.997127e-02 -2.872751e-01 -3.194284e-01 [16] 4.293770e-01 4.226073e-01 3.524520e-02 9.136636e-01 -8.098814e-02 [21] -7.157933e-01 -1.529211e-01 -1.725372e-01 9.184244e-01 3.797261e-01 [26] -3.744395e-01 -2.993669e-01 -5.774788e-01 1.484724e-01 -4.457832e-01 [31] 5.614335e-01 -5.496120e-01 3.982387e-01 2.275051e-01 -1.803318e-01 [36] 1.195149e-01 -1.336822e-01 2.102373e-02 7.085393e-01 -1.328857e-01 [41] 2.070219e-01 7.738396e-01 3.920370e-01 -1.005922e+00 5.074847e-01 [46] -2.207040e-01 3.595090e-01 5.032987e-02 -5.236114e-02 2.410721e-01 [51] 3.583910e-01 -5.657392e-01 -3.257529e-01 1.428497e-01 8.533381e-01 [56] -4.488093e-01 1.564959e-01 -9.390358e-01 5.809747e-01 8.436157e-01 [61] -9.603529e-01 8.633058e-01 7.308074e-01 1.269490e-02 -8.339027e-01 [66] 1.089916e+00 -1.543672e+00 7.398220e-01 1.817445e-01 4.753302e-02 [71] -4.588610e-01 1.815121e-01 4.872466e-01 3.520861e-01 -1.149498e+00 [76] 2.439507e-01 -1.281193e-01 6.931266e-01 -2.880396e-01 1.406567e-01 [81] 5.858207e-01 8.380831e-02 2.189385e-01 -1.023451e+00 7.374410e-01 [86] 7.207639e-02 -7.119420e-01 3.621184e-01 8.236200e-01 -8.915555e-01 [91] 1.099067e+00 -1.122277e-01 3.921935e-01 -3.688150e-01 -1.238945e+00 [96] 4.871893e-01 -1.123463e+00 -2.741022e+00 -1.755926e+00 -6.298405e-01 [101] 8.768157e-01 1.272682e-01 1.361525e-01 -8.031112e-02 1.004725e+00 [106] 9.826746e-01 6.959132e-01 -4.564767e-01 7.714109e-02 1.305890e+00 [111] 1.202305e+00 -8.824372e-02 -5.075931e-01 6.735490e-01 1.078452e+00 [116] -6.292462e-01 8.193492e-01 -5.085902e-01 -2.594590e-01 -2.447326e-01 [121] -4.242474e-02 > postscript(file="/var/www/rcomp/tmp/2cbmo1293203853.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/rcomp/tmp/3cbmo1293203853.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/rcomp/tmp/4424r1293203853.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/rcomp/tmp/5424r1293203853.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/rcomp/tmp/6424r1293203853.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/rcomp/tmp/7424r1293203853.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/80cj01293203853.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/rcomp/tmp/9t3jl1293203853.tab") > > try(system("convert tmp/1cbmo1293203853.ps tmp/1cbmo1293203853.png",intern=TRUE)) character(0) > try(system("convert tmp/2cbmo1293203853.ps tmp/2cbmo1293203853.png",intern=TRUE)) character(0) > try(system("convert tmp/3cbmo1293203853.ps tmp/3cbmo1293203853.png",intern=TRUE)) character(0) > try(system("convert tmp/4424r1293203853.ps tmp/4424r1293203853.png",intern=TRUE)) character(0) > try(system("convert tmp/5424r1293203853.ps tmp/5424r1293203853.png",intern=TRUE)) character(0) > try(system("convert tmp/6424r1293203853.ps tmp/6424r1293203853.png",intern=TRUE)) character(0) > try(system("convert tmp/7424r1293203853.ps tmp/7424r1293203853.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.760 3.170 8.933