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Type 'q()' to quit R. > x <- c(27,-36,23,23,23,0,0,36,0,-22,-22,-50,0,0,-23,0,-23,-23,-23,-23,-23,0,-36,23,-13,23,0,59,23,0,0,0,0,0,36,36,-36,0,0,-36,0,-36,-36,-35,-34,-26,-59,-28,0,0,0,0,0,0,0,0,0,0,-42,-43,-45,-33,-34,-36,0,0,0,-40,0,-15,25,-15,-15,0,-62,-25,0,0,0,0,0,-22,-22,-22,0,0,-26,-27,-27,-30,-27,-39,-40,7,5,3,-10,-45,-34,-35,-36,-37,-38,-42,-42,-42,-44,-74) > par9 = '1' > par8 = '2' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '0' > 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$order[3] + coeff <- matrix(NA, nrow=nrc, ncol=nrc) + pval <- matrix(NA, nrow=nrc, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$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,] 0.3886392 0.2708572 0.1247303 -0.57158222 -0.12336829 0.4763185 [2,] 0.3880756 0.2731906 0.1227205 -0.09795328 -0.07207324 0.0000000 [3,] 0.3724713 0.2792202 0.1183849 -0.09137559 0.00000000 0.0000000 [4,] 0.3734057 0.2747052 0.1066672 0.00000000 0.00000000 0.0000000 [5,] 0.4014113 0.3197801 0.0000000 0.00000000 0.00000000 0.0000000 [6,] NA NA NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.00019 0.01255 0.22618 0.35544 0.29829 0.44058 [2,] 0.00020 0.01155 0.23285 0.37047 0.52472 NA [3,] 0.00024 0.00927 0.24970 0.39911 NA NA [4,] 0.00024 0.01058 0.29692 NA NA NA [5,] 0.00005 0.00129 NA NA NA NA [6,] 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 sar1 sar2 sma1 0.3886 0.2709 0.1247 -0.5716 -0.1234 0.4763 s.e. 0.1002 0.1066 0.1024 0.6157 0.1180 0.6152 sigma^2 estimated as 442.6: log likelihood = -482.79, aic = 979.59 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 0.3886 0.2709 0.1247 -0.5716 -0.1234 0.4763 s.e. 0.1002 0.1066 0.1024 0.6157 0.1180 0.6152 sigma^2 estimated as 442.6: log likelihood = -482.79, aic = 979.59 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 0.3881 0.2732 0.1227 -0.0980 -0.0721 0 s.e. 0.1008 0.1062 0.1023 0.1089 0.1129 0 sigma^2 estimated as 443.9: log likelihood = -482.9, aic = 977.79 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 0.3725 0.2792 0.1184 -0.0914 0 0 s.e. 0.0979 0.1053 0.1023 0.1079 0 0 sigma^2 estimated as 446.2: log likelihood = -483.1, aic = 976.19 [[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 sar1 sar2 sma1 0.3734 0.2747 0.1067 0 0 0 s.e. 0.0980 0.1055 0.1018 0 0 0 sigma^2 estimated as 449.7: log likelihood = -483.45, aic = 974.9 [[3]][[6]] NULL $aic [1] 979.5854 977.7909 976.1946 974.9043 973.9968 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 > postscript(file="/var/www/html/rcomp/tmp/15lmr1196518768.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 = 108 Frequency = 1 [1] 20.41003166 -49.13941888 28.90825900 21.42104290 11.93346902 [6] -17.35989772 -8.77156703 33.54665364 -13.44260456 -31.88938888 [11] -17.62509538 -35.74155957 27.06047866 16.08194146 -17.66663835 [16] 8.58833069 -16.68177933 -11.95832295 -8.09344864 -5.64010228 [21] -5.64010228 17.35989772 -27.22843297 38.89595092 -11.69894181 [26] 25.37607358 -7.47050884 54.06845336 -1.48428161 -24.79594024 [31] -12.61158742 -2.45334636 0.00000000 0.00000000 36.00000000 [36] 22.55739544 -59.33199344 -0.28680471 6.04936849 -32.15997961 [41] 13.44260456 -26.11061112 -18.71737506 -11.66800656 -7.20139186 [46] 0.15049721 -36.21812072 4.79995758 29.43631671 13.98511365 [51] 2.98668252 0.00000000 0.00000000 0.00000000 0.00000000 [56] 0.00000000 0.00000000 0.00000000 -42.00000000 -27.31696135 [61] -17.40593531 0.09560509 -4.72918537 -9.43890818 26.30260163 [66] 13.51607480 3.84002039 -40.00000000 14.93622729 -4.01179013 [71] 34.86777455 -20.21456335 -14.66653744 7.05498311 -56.27941281 [76] -0.24883921 26.36686735 13.48099961 2.66668082 0.00000000 [81] 0.00000000 -22.00000000 -13.78507499 -7.74155957 16.60511956 [86] 8.39019455 -23.65332088 -17.29145226 -9.77571017 -9.72765686 [91] -5.50077259 -17.79687389 -14.82011975 35.52974720 17.53439218 [96] 3.47672418 -13.24041391 -42.62339508 -14.76969353 -8.87579838 [101] -8.79079725 -10.31602589 -10.56124773 -13.80646956 -11.93147436 [106] -10.72598614 -12.29931721 -41.55250584 > postscript(file="/var/www/html/rcomp/tmp/2jg701196518768.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/38v751196518768.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/4htcq1196518768.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/5i8661196518768.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/6mk5o1196518768.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/70iqd1196518768.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/8vec21196518769.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/9a99v1196518769.tab") > > system("convert tmp/15lmr1196518768.ps tmp/15lmr1196518768.png") > system("convert tmp/2jg701196518768.ps tmp/2jg701196518768.png") > system("convert tmp/38v751196518768.ps tmp/38v751196518768.png") > system("convert tmp/4htcq1196518768.ps tmp/4htcq1196518768.png") > system("convert tmp/5i8661196518768.ps tmp/5i8661196518768.png") > system("convert tmp/6mk5o1196518768.ps tmp/6mk5o1196518768.png") > system("convert tmp/70iqd1196518768.ps tmp/70iqd1196518768.png") > > > proc.time() user system elapsed 4.533 1.443 5.265