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Type 'q()' to quit R. > x <- c(102.61,102.18,101.64,102,102.18,101.89,102.09,101.6,101.33,101.44,101.49,100.41,101.38,101.4,102.16,104.46,104.75,104.2,106.05,107.54,108.23,108.99,109.51,111.99,111.08,112.95,115.49,114.67,116.85,119.57,119.41,118.46,122.81,121.76,121.37,118.61,116.08,117.84,117.02,119.78,122.58,120.98,118.92,117.81,119.73,117.16,116.03,115.55,115.36,116.09,117.32,120.45,119.86,118.51,118.92,119.11,120.34,121.23,119.43,119.28,120.64,122.24,123.1,120.72,118.34,118.8,119.29,121.47,122.35,121.53,121.72,121.58,121.55,122.02,123.74,125.8,129.29,128.89,130.04,131.57,131.97,134.43,132.63,130.26,129,131.65,134.21,138.63,138.1,140.51,144.36,145.57,148.7,147.86,143.16,141.96) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '1' > par4 = '0' > par3 = '1' > par2 = '-0.3' > par1 = 'TRUE' > #'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) + 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,] -0.2437245 0.2070878 0.3848703 -0.1049817 0.6956428 -0.5228316 -0.1049817 [2,] -0.2477834 0.2125370 0.3862470 0.0000000 0.6941590 -0.5183456 -0.2037701 [3,] -0.3545474 0.1778167 0.3741422 0.0000000 0.6042896 -0.4787152 0.0000000 [4,] -0.4382006 0.0000000 0.2748522 0.0000000 0.6801554 -0.3952228 0.0000000 [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,] 0.41805 0.14675 0.00197 0.92011 0.00587 0.00102 0.92011 [2,] 0.39137 0.16619 0.00201 NA 0.00569 0.00054 0.67360 [3,] 0.01329 0.24335 0.00396 NA 0.00003 0.00078 NA [4,] 0.00207 NA 0.01341 NA 0.00001 0.00649 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 -0.2437 0.2071 0.3849 -0.1050 0.6956 -0.5228 -0.1050 s.e. 0.2996 0.1414 0.1206 1.0438 0.2464 0.1538 1.0438 sigma^2 estimated as 9.049e-07: log likelihood = 526.05, aic = -1036.09 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2437 0.2071 0.3849 -0.1050 0.6956 -0.5228 -0.1050 s.e. 0.2996 0.1414 0.1206 1.0438 0.2464 0.1538 1.0438 sigma^2 estimated as 9.049e-07: log likelihood = 526.05, aic = -1036.09 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2478 0.2125 0.3862 0 0.6942 -0.5183 -0.2038 s.e. 0.2877 0.1522 0.1213 0 0.2449 0.1445 0.4822 sigma^2 estimated as 9.05e-07: log likelihood = 526.04, aic = -1038.08 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3545 0.1778 0.3741 0 0.6043 -0.4787 0 s.e. 0.1404 0.1514 0.1265 0 0.1376 0.1377 0 sigma^2 estimated as 9.065e-07: log likelihood = 525.97, aic = -1039.93 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -1036.091 -1038.082 -1039.930 -1041.065 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/html/rcomp/tmp/1i5ml1197555482.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 = 96 Frequency = 1 [1] 2.492544e-04 2.996036e-04 3.225190e-04 -3.295386e-04 -6.433260e-05 [6] 2.187345e-04 -1.861238e-04 3.200445e-04 1.050798e-04 -3.213757e-05 [11] -9.313794e-05 8.638803e-04 -9.550683e-04 1.916546e-04 -6.872172e-04 [16] -1.332951e-03 -8.408186e-05 4.806436e-04 -1.337242e-03 -7.846386e-04 [21] -9.630122e-05 -2.672890e-04 -4.159731e-04 -1.459625e-03 1.141596e-03 [26] -1.460642e-03 -1.053514e-03 5.857597e-04 -1.131409e-03 -1.387209e-03 [31] 3.307040e-04 8.973071e-04 -2.827185e-03 1.301772e-03 1.067255e-04 [36] 1.949589e-03 6.350022e-04 -8.025939e-04 6.697400e-04 -1.848568e-03 [41] -1.154029e-03 7.205657e-04 1.276356e-03 2.808088e-04 -1.241667e-03 [46] 2.208509e-03 1.495180e-04 2.468247e-04 -3.295671e-04 -1.000556e-04 [51] -8.433741e-04 -1.828165e-03 7.622767e-04 5.472144e-04 -2.618354e-04 [56] -1.459777e-04 -4.181380e-04 -2.759945e-04 9.650629e-04 -1.226791e-04 [61] -7.710448e-04 -7.728370e-04 -9.747113e-05 1.329365e-03 9.937467e-04 [66] -4.518470e-04 -1.384562e-04 -1.094889e-03 -2.365680e-04 2.282660e-04 [71] -1.412437e-04 7.132481e-05 1.001462e-04 -1.015426e-04 -1.030711e-03 [76] -9.106878e-04 -1.697755e-03 6.485201e-04 -8.173921e-04 -4.404162e-04 [81] -1.438366e-04 -8.610517e-04 1.184915e-03 9.252149e-04 6.854992e-04 [86] -1.708703e-03 -6.816711e-04 -2.070560e-03 7.105342e-04 -1.630148e-03 [91] -1.233153e-03 -3.077672e-04 -8.961777e-04 6.514760e-04 1.964548e-03 [96] 4.471565e-04 > postscript(file="/var/www/html/rcomp/tmp/21x3y1197555482.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/3gyu21197555482.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/48yym1197555482.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/58jt01197555482.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/62s841197555482.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/74egs1197555482.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/88lw21197555482.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/9szh11197555482.tab") > > system("convert tmp/1i5ml1197555482.ps tmp/1i5ml1197555482.png") > system("convert tmp/21x3y1197555482.ps tmp/21x3y1197555482.png") > system("convert tmp/3gyu21197555482.ps tmp/3gyu21197555482.png") > system("convert tmp/48yym1197555482.ps tmp/48yym1197555482.png") > system("convert tmp/58jt01197555482.ps tmp/58jt01197555482.png") > system("convert tmp/62s841197555482.ps tmp/62s841197555482.png") > system("convert tmp/74egs1197555482.ps tmp/74egs1197555482.png") > > > proc.time() user system elapsed 3.936 1.809 4.224