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Type 'q()' to quit R. > x <- c(96.8,87.0,96.3,107.1,115.2,106.1,89.5,91.3,97.6,100.7,104.6,94.7,101.8,102.5,105.3,110.3,109.8,117.3,118.8,131.3,125.9,133.1,147.0,145.8,164.4,149.8,137.7,151.7,156.8,180.0,180.4,170.4,191.6,199.5,218.2,217.5,205.0,194.0,199.3,219.3,211.1,215.2,240.2,242.2,240.7,255.4,253.0,218.2,203.7,205.6,215.6,188.5,202.9,214.0,230.3,230.0,241.0,259.6,247.8,270.3,289.7,322.7,315.0,320.2,329.5,360.6,382.2,435.4,464.0,468.8,403.0,351.6,252.0,188.0,146.5,152.9,148.1,165.1,177.0,206.1,244.9,228.6,253.4,241.1) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '2' > par2 = '-0.3' > 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,] 0.3312011 0.11926616 -0.1109644 -0.9999982 -0.1346458 -0.1094279 [2,] 0.3332612 0.11808933 -0.1124447 -0.9999948 0.0000000 -0.1143250 [3,] 0.3300150 0.12431696 -0.1183667 -0.9999994 0.0000000 -0.1157850 [4,] 0.2974853 0.12817424 -0.1110607 -1.0000079 0.0000000 0.0000000 [5,] 0.2886226 0.09538626 0.0000000 -0.9999786 0.0000000 0.0000000 [6,] 0.3196105 0.00000000 0.0000000 -1.0000006 0.0000000 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,] 0.17794707 [2,] 0.04616873 [3,] 0.00000000 [4,] 0.00000000 [5,] 0.00000000 [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,] 0.00761 0.32975 0.34464 0 0.85805 0.49467 0.81168 [2,] 0.00723 0.33456 0.33807 0 NA 0.45614 0.75749 [3,] 0.00776 0.30138 0.30651 0 NA 0.45137 NA [4,] 0.01004 0.28220 0.33542 0 NA NA NA [5,] 0.01287 0.40697 NA 0 NA NA NA [6,] 0.00389 NA NA 0 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 0.3312 0.1193 -0.1110 -1.0000 -0.1346 -0.1094 0.1779 s.e. 0.1208 0.1216 0.1167 0.0391 0.7503 0.1595 0.7443 sigma^2 estimated as 3.103e-05: log likelihood = 307.15, aic = -598.3 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3312 0.1193 -0.1110 -1.0000 -0.1346 -0.1094 0.1779 s.e. 0.1208 0.1216 0.1167 0.0391 0.7503 0.1595 0.7443 sigma^2 estimated as 3.103e-05: log likelihood = 307.15, aic = -598.3 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3333 0.1181 -0.1124 -1.0000 0 -0.1143 0.0462 s.e. 0.1208 0.1216 0.1166 0.0392 0 0.1526 0.1490 sigma^2 estimated as 3.104e-05: log likelihood = 307.14, aic = -600.27 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3300 0.1243 -0.1184 -1.0000 0 -0.1158 0 s.e. 0.1208 0.1195 0.1150 0.0401 0 0.1530 0 sigma^2 estimated as 3.105e-05: log likelihood = 307.09, aic = -602.18 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.2975 0.1282 -0.1111 -1.0000 0 0 0 s.e. 0.1128 0.1184 0.1146 0.0392 0 0 0 sigma^2 estimated as 3.143e-05: log likelihood = 306.81, aic = -603.62 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.2886 0.0954 0 -1.0000 0 0 0 s.e. 0.1134 0.1144 0 0.0369 0 0 0 sigma^2 estimated as 3.188e-05: log likelihood = 306.34, aic = -604.69 [[3]][[7]] NULL $aic [1] -598.3014 -600.2720 -602.1760 -603.6172 -604.6870 -605.9926 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/10jsu1261069153.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 = 84 Frequency = 1 [1] 1.134364e-04 -3.218521e-04 -1.302659e-02 -5.843304e-03 -8.538449e-04 [6] 9.034518e-03 1.089525e-02 -6.950042e-03 -6.229306e-03 -6.947773e-04 [11] -1.523595e-03 8.407996e-03 -7.560425e-03 4.332090e-04 -1.229822e-03 [16] -2.561263e-03 1.765390e-03 -4.241072e-03 8.565924e-04 -5.792856e-03 [21] 5.605270e-03 -3.555193e-03 -5.236941e-03 3.603463e-03 -6.660679e-03 [26] 9.104685e-03 5.158789e-03 -8.232394e-03 -1.685438e-04 -6.850720e-03 [31] 3.488434e-03 5.233844e-03 -7.685311e-03 1.615859e-04 -3.086482e-03 [36] 2.899614e-03 4.827561e-03 3.028862e-03 -2.286308e-03 -4.842999e-03 [41] 4.884613e-03 -5.382813e-04 -5.562022e-03 2.332276e-03 1.901279e-03 [46] -2.665894e-03 2.283837e-03 9.475467e-03 2.155643e-03 -2.029876e-03 [51] -2.496547e-03 9.612896e-03 -6.134913e-03 -2.120181e-03 -2.378090e-03 [56] 2.256341e-03 -1.718819e-03 -2.826435e-03 4.773515e-03 -4.650797e-03 [61] -1.980321e-03 -3.505019e-03 4.056946e-03 6.939111e-06 -6.850656e-04 [66] -3.436183e-03 -6.965121e-04 -4.340029e-03 -8.004547e-05 1.827760e-03 [71] 8.514412e-03 5.487815e-03 1.591015e-02 1.191898e-02 9.543639e-03 [76] -9.019351e-03 1.623484e-03 -7.252861e-03 -2.290973e-03 -7.105803e-03 [81] -6.589910e-03 8.297153e-03 -5.719750e-03 4.632255e-03 > postscript(file="/var/www/html/rcomp/tmp/2vk9u1261069153.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/3qceq1261069153.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/4p1vl1261069153.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/50w0a1261069153.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/6345l1261069153.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/7xu8q1261069153.ps",horizontal=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/874be1261069153.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/94iiw1261069153.tab") > > try(system("convert tmp/10jsu1261069153.ps tmp/10jsu1261069153.png",intern=TRUE)) character(0) > try(system("convert tmp/2vk9u1261069153.ps tmp/2vk9u1261069153.png",intern=TRUE)) character(0) > try(system("convert tmp/3qceq1261069153.ps tmp/3qceq1261069153.png",intern=TRUE)) character(0) > try(system("convert tmp/4p1vl1261069153.ps tmp/4p1vl1261069153.png",intern=TRUE)) character(0) > try(system("convert tmp/50w0a1261069153.ps tmp/50w0a1261069153.png",intern=TRUE)) character(0) > try(system("convert tmp/6345l1261069153.ps tmp/6345l1261069153.png",intern=TRUE)) character(0) > try(system("convert tmp/7xu8q1261069153.ps tmp/7xu8q1261069153.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.654 1.515 6.732