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Type 'q()' to quit R. > x <- c(133.105,139.066,137.645,143.836,137.175,140.47,138.662,146.738,142.133,144.151,141.176,150.444,139.494,141.234,140.273,153.8,147.401,153.157,150.366,160.347,150.227,155.468,152.358,163.189,155.438,159.07,155.232,165.366,157.161,165.623,157.775,173.696,161.44,166.145,164.361,177.168,164.15,164.869,163.064,174.409,164.062,166.523,164.027,173.251,164.983,166.886,163.6,172.94,166.603,167.785,166.85,177.504,167.63,168.123,167.705,181.996,172.861,175.444,176.473,193.009,178.455,187.177,183.767,195.74,188.674,195.03,186.305,201.588,192.843,197.391,194.408,210.311,197.148,202.714,193.651,198.97,185.515,189.982,186.276,197.11,190.522,192.175,189.153,201.316,188.959,192.146,187.763,200.365) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '4' > par4 = '1' > par3 = '1' > par2 = '1' > par1 = 'FALSE' > par9 <- '1' > par8 <- '2' > par7 <- '1' > par6 <- '3' > par5 <- '4' > par4 <- '1' > 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,] -0.07816314 0.09283347 0.010617862 -0.1278062 0.1166600 -0.01396700 [2,] 0.00000000 0.10894866 0.003622995 -0.2054751 0.1151858 -0.01460954 [3,] 0.00000000 0.10931209 0.000000000 -0.2053094 0.1151269 -0.01619414 [4,] 0.00000000 0.10560469 0.000000000 -0.2077475 0.1181633 0.00000000 [5,] 0.00000000 0.00000000 0.000000000 -0.1837827 0.1528362 0.00000000 [6,] 0.00000000 0.00000000 0.000000000 -0.2005636 0.0000000 0.00000000 [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,] -0.9549075 [2,] -0.9548170 [3,] -0.9548462 [4,] -0.9634539 [5,] -0.9539023 [6,] -0.8918115 [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,] 0.94372 0.72309 0.93981 0.90811 0.39968 0.92095 0e+00 [2,] NA 0.38703 0.97612 0.08059 0.39995 0.91741 0e+00 [3,] NA 0.38280 NA 0.08032 0.39951 0.90102 0e+00 [4,] NA 0.38523 NA 0.07245 0.37794 NA 2e-05 [5,] NA NA NA 0.07724 0.23483 NA 0e+00 [6,] NA NA NA 0.04180 NA NA 0e+00 [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 -0.0782 0.0928 0.0106 -0.1278 0.1167 -0.0140 -0.9549 s.e. 1.1036 0.2611 0.1402 1.1037 0.1378 0.1403 0.1918 sigma^2 estimated as 6.951: log likelihood = -202.44, aic = 420.87 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.0782 0.0928 0.0106 -0.1278 0.1167 -0.0140 -0.9549 s.e. 1.1036 0.2611 0.1402 1.1037 0.1378 0.1403 0.1918 sigma^2 estimated as 6.951: log likelihood = -202.44, aic = 420.87 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1089 0.0036 -0.2055 0.1152 -0.0146 -0.9548 s.e. 0 0.1253 0.1207 0.1161 0.1361 0.1404 0.1916 sigma^2 estimated as 6.951: log likelihood = -202.44, aic = 418.88 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1093 0 -0.2053 0.1151 -0.0162 -0.9548 s.e. 0 0.1246 0 0.1159 0.1359 0.1298 0.1920 sigma^2 estimated as 6.95: log likelihood = -202.44, aic = 416.88 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1056 0 -0.2077 0.1182 0 -0.9635 s.e. 0 0.1210 0 0.1142 0.1333 0 0.2155 sigma^2 estimated as 6.92: log likelihood = -202.45, aic = 414.89 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0 -0.1838 0.1528 0 -0.9539 s.e. 0 0 0 0.1027 0.1277 0 0.1731 sigma^2 estimated as 7.054: log likelihood = -202.83, aic = 413.66 [[3]][[7]] NULL $aic [1] 420.8734 418.8780 416.8789 414.8945 413.6636 413.1191 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/rcomp/tmp/1tchi1336736074.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 = 88 Frequency = 1 [1] 0.07684819 0.03898490 0.02432062 -0.02974561 -0.28593870 -2.03722428 [7] -0.66865431 1.34132080 2.00994859 -1.72040822 -1.45587254 1.43741570 [13] -4.28269398 -2.33646756 0.70169953 5.03955642 2.56609541 2.94558617 [19] -0.53748268 -0.09185352 -2.81478197 0.48466721 -0.83720377 1.15740073 [25] 0.65758018 -0.48921911 -1.52148082 0.04911872 -0.38492745 4.26278761 [31] -4.19342205 5.16007223 -3.21843203 -1.10314206 1.85107124 1.71061405 [37] -3.49543496 -4.40692105 0.10346279 0.28546966 -0.63669980 -1.22155587 [43] -0.07160302 -1.62116611 0.71017578 -1.67882830 -0.83859867 -1.18396744 [49] 2.27692695 -1.85900752 1.59681840 0.62295729 -1.36062724 -2.90968237 [55] 1.40134615 3.93686904 0.62749777 -0.17883005 3.10123954 5.62939581 [61] -4.50204697 4.64226972 -0.83988895 -0.30595580 3.01443834 2.37256592 [67] -5.71888707 2.76731795 0.57330787 0.37075692 0.81369520 3.82260678 [73] -3.35444422 0.91437336 -6.02634006 -8.25124031 -4.92908196 -0.71542621 [79] 0.29854312 0.43476798 3.73597141 -1.76188574 -0.05390978 0.82904930 [85] -3.18214797 -0.93513514 -1.37363181 0.77055378 > postscript(file="/var/www/rcomp/tmp/2ftwc1336736074.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/3p4io1336736074.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/43uim1336736074.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/54p131336736074.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/66ixg1336736074.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/7a1511336736074.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/82qhw1336736074.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/92yfh1336736074.tab") > > try(system("convert tmp/1tchi1336736074.ps tmp/1tchi1336736074.png",intern=TRUE)) character(0) > try(system("convert tmp/2ftwc1336736074.ps tmp/2ftwc1336736074.png",intern=TRUE)) character(0) > try(system("convert tmp/3p4io1336736074.ps tmp/3p4io1336736074.png",intern=TRUE)) character(0) > try(system("convert tmp/43uim1336736074.ps tmp/43uim1336736074.png",intern=TRUE)) character(0) > try(system("convert tmp/54p131336736074.ps tmp/54p131336736074.png",intern=TRUE)) character(0) > try(system("convert tmp/66ixg1336736074.ps tmp/66ixg1336736074.png",intern=TRUE)) character(0) > try(system("convert tmp/7a1511336736074.ps tmp/7a1511336736074.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.670 1.260 3.921