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Type 'q()' to quit R. > x <- c(6.4,7.7,9.2,8.6,7.4,8.6,6.2,6,6.6,5.1,4.7,5,3.6,1.9,-0.1,-5.7,-5.6,-6.4,-7.7,-8,-11.9,-15.4,-15.5,-13.4,-10.9,-10.8,-7.3,-6.5,-5.1,-5.3,-6.8,-8.4,-8.4,-9.7,-8.8,-9.6,-11.5,-11,-14.9,-16.2,-14.4,-17.3,-15.7,-12.6,-9.4,-8.1,-5.4,-4.6,-4.9,-4,-3.1,-1.3,0,-0.4,3,0.4,1.2,0.6,-1.3,-3.2,-1.8,-3.6,-4.2,-6.9,-8,-7.5,-8.2,-7.6,-3.7,-1.7,-0.7,0.2,0.6,2.2,3.3,5.3,5.5,6.3,7.7,6.5,5.5,6.9,5.7,6.9,6.1,4.8,3.7,5.8,6.8,8.5,7.2,5,4.7,2.3,2.4,0.1,1.9,1.7,2,-1.9,0.5,-1.3,-3.3,-2.8,-8,-13.9,-21.9,-28.8,-27.6,-31.4,-31.8,-29.4,-27.6,-23.6,-22.8,-18.2,-17.8,-14.2,-8.8,-7.9,-7,-7,-3.6,-2.4,-4.9,-7.7,-6.5,-5.1,-3.4,-2.8,0.8) > 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,] -0.1598135 0.3310474 0.1935529 0.3477538 0.6819274 -0.06037014 [2,] 0.0000000 0.2984935 0.1482852 0.1884311 0.6888928 -0.06326658 [3,] 0.0000000 0.2983608 0.1518197 0.1865119 0.7169652 0.00000000 [4,] 0.0000000 0.2846875 0.1834853 0.2273741 -0.1152365 0.00000000 [5,] 0.0000000 0.2844144 0.1813122 0.2488397 0.0000000 0.00000000 [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,] -0.8850125 [2,] -0.8978058 [3,] -0.9929879 [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,] 0.7343 0.01237 0.19039 0.46631 0.00209 0.61472 0.00119 [2,] NA 0.00061 0.08520 0.04116 0.00124 0.59448 0.00124 [3,] NA 0.00058 0.07512 0.04276 0.00005 NA 0.62047 [4,] NA 0.00103 0.02875 0.01155 0.20700 NA NA [5,] NA 0.00103 0.02980 0.00489 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 -0.1598 0.3310 0.1936 0.3478 0.6819 -0.0604 -0.8850 s.e. 0.4698 0.1304 0.1470 0.4759 0.2169 0.1196 0.2667 sigma^2 estimated as 3.729: log likelihood = -272.66, aic = 561.32 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1598 0.3310 0.1936 0.3478 0.6819 -0.0604 -0.8850 s.e. 0.4698 0.1304 0.1470 0.4759 0.2169 0.1196 0.2667 sigma^2 estimated as 3.729: log likelihood = -272.66, aic = 561.32 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.2985 0.1483 0.1884 0.6889 -0.0633 -0.8978 s.e. 0 0.0849 0.0855 0.0913 0.2084 0.1185 0.2716 sigma^2 estimated as 3.712: log likelihood = -272.71, aic = 559.41 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.2984 0.1518 0.1865 0.7170 0 -0.9930 s.e. 0 0.0845 0.0846 0.0911 0.1702 0 2.0003 sigma^2 estimated as 3.560: log likelihood = -272.84, aic = 557.68 [[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 0.2847 0.1835 0.2274 -0.1152 0 0 s.e. 0 0.0847 0.0829 0.0887 0.0909 0 0 sigma^2 estimated as 4.033: log likelihood = -275.34, aic = 560.67 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 561.3232 559.4123 557.6847 560.6747 560.2690 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/1byd61293201249.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 = 131 Frequency = 1 [1] 0.006399996 1.162357904 1.002964305 -1.297963512 -1.554130474 [6] 1.449319530 -2.254043351 0.207194937 1.030000197 -1.204176037 [11] -0.220948703 0.721938927 -1.118461449 -1.336551207 -1.213818600 [16] -4.708653157 1.836816745 0.869829564 -0.723241421 0.036805934 [21] -3.268982505 -2.548328318 1.583201232 3.523113894 2.253109984 [26] -1.189080481 2.482440939 -0.811576400 0.682858172 -1.091397663 [31] -1.831875750 -1.393860979 0.390797534 -1.024127724 1.549201099 [36] -0.342872992 -1.474350886 0.842587158 -3.126979026 -0.346679631 [41] 2.941759017 -2.606490695 1.683042704 3.005221019 2.646736157 [46] -0.543507963 1.481318842 -0.543601050 -1.404574021 0.561037159 [51] 0.340877852 1.395283317 0.886191758 -1.488146531 3.190813116 [56] -3.035851945 0.973315426 -0.690692105 -1.353031774 -1.586451834 [61] 2.261079851 -1.404204339 -0.234021425 -2.206988165 0.004149878 [66] 1.253628007 0.134620169 0.314901042 3.925043436 1.009438332 [71] -0.640112233 -0.455603488 0.088284194 1.035302846 0.510690310 [76] 1.073299150 -0.719789894 0.351334801 0.908718369 -1.595068780 [81] -0.720858623 1.874240396 -1.146678875 1.201285488 -1.017395736 [86] -1.056404819 -0.757625348 2.958671489 0.832091348 1.118111726 [91] -2.111754789 -2.556053452 0.161268419 -1.400727957 0.827458719 [96] -1.636347988 2.501535616 -0.296153752 0.151028141 -3.906115489 [101] 3.418251349 -1.371718827 -1.882780914 0.669733187 -4.480499251 [106] -4.833529803 -5.444465105 -3.208255190 5.544428574 -1.578140560 [111] 0.907402621 2.574389768 2.296723929 2.782104433 -1.012126686 [116] 3.427031524 -1.836466794 1.907203957 3.246573093 -1.428080226 [121] -0.447665320 -1.187632634 3.309114114 0.658315280 -3.316725214 [126] -2.620669071 2.269800440 2.500547152 1.238846665 -0.053399858 [131] 3.383184869 > postscript(file="/var/www/html/rcomp/tmp/2byd61293201249.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/3byd61293201249.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/4byd61293201249.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/547c91293201249.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/647c91293201249.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/747c91293201249.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/8izaz1293201249.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/9aqr21293201249.tab") > > try(system("convert tmp/1byd61293201249.ps tmp/1byd61293201249.png",intern=TRUE)) character(0) > try(system("convert tmp/2byd61293201249.ps tmp/2byd61293201249.png",intern=TRUE)) character(0) > try(system("convert tmp/3byd61293201249.ps tmp/3byd61293201249.png",intern=TRUE)) character(0) > try(system("convert tmp/4byd61293201249.ps tmp/4byd61293201249.png",intern=TRUE)) character(0) > try(system("convert tmp/547c91293201249.ps tmp/547c91293201249.png",intern=TRUE)) character(0) > try(system("convert tmp/647c91293201249.ps tmp/647c91293201249.png",intern=TRUE)) character(0) > try(system("convert tmp/747c91293201249.ps tmp/747c91293201249.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.821 1.741 16.918