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Type 'q()' to quit R. > x <- c(10967.87,10433.56,10665.78,10666.71,10682.74,10777.22,10052.6,10213.97,10546.82,10767.2,10444.5,10314.68,9042.56,9220.75,9721.84,9978.53,9923.81,9892.56,10500.98,10179.35,10080.48,9492.44,8616.49,8685.4,8160.67,8048.1,8641.21,8526.63,8474.21,7916.13,7977.64,8334.59,8623.36,9098.03,9154.34,9284.73,9492.49,9682.35,9762.12,10124.63,10540.05,10601.61,10323.73,10418.4,10092.96,10364.91,10152.09,10032.8,10204.59,10001.6,10411.75,10673.38,10539.51,10723.78,10682.06,10283.19,10377.18,10486.64,10545.38,10554.27,10532.54,10324.31,10695.25,10827.81,10872.48,10971.19,11145.65,11234.68,11333.88,10997.97,11036.89,11257.35,11533.59,11963.12,12185.15,12377.62,12512.89,12631.48,12268.53,12754.8,13407.75,13480.21,13673.28,13239.71,13557.69,13901.28,13200.58,13406.97,12538.12,12419.57,12193.88,12656.63,12812.48,12056.67,11322.38,11530.75,11114.08,9181.73,8614.55) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '-0.2' > 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,] -0.73763518 0.154040377 0.06366874 0.97979244 -0.1715969 -0.14346229 [2,] -0.73866193 0.153725694 0.06287281 1.02081817 0.0000000 -0.14232578 [3,] -0.73991287 0.150729854 0.06387682 0.97964326 0.0000000 -0.14019768 [4,] 0.09460999 -0.002903952 0.00000000 0.09739738 0.0000000 -0.08490919 [5,] 0.01850563 0.000000000 0.00000000 0.14735521 0.0000000 -0.09930478 [6,] 0.18358422 0.000000000 0.00000000 0.00000000 0.0000000 -0.13398767 [7,] 0.18121572 0.000000000 0.00000000 0.00000000 0.0000000 0.00000000 [8,] 0.00000000 0.000000000 0.00000000 0.00000000 0.0000000 0.00000000 [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,] 0.20033248 [2,] 0.03469419 [3,] 0.00000000 [4,] 0.00000000 [5,] 0.00000000 [6,] 0.00000000 [7,] 0.00000000 [8,] 0.00000000 [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.00000 0.27358 0.59983 0.00000 0.72155 0.33036 0.67259 [2,] 0.00000 0.27496 0.60477 0.00000 NA 0.32857 0.80546 [3,] 0.00000 0.28327 0.59860 0.00000 NA 0.33590 NA [4,] 0.00000 0.57452 NA 0.35563 NA 0.00000 NA [5,] 0.00000 NA NA 0.12756 NA 0.00000 NA [6,] 0.07225 NA NA NA NA 0.35442 NA [7,] 0.07600 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 -0.7376 0.1540 0.0637 0.9798 -0.1716 -0.1435 0.2003 s.e. 0.1074 0.1398 0.1209 0.0520 0.4800 0.1466 0.4725 sigma^2 estimated as 1.423e-06: log likelihood = 519.76, aic = -1023.52 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.7376 0.1540 0.0637 0.9798 -0.1716 -0.1435 0.2003 s.e. 0.1074 0.1398 0.1209 0.0520 0.4800 0.1466 0.4725 sigma^2 estimated as 1.423e-06: log likelihood = 519.76, aic = -1023.52 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.7387 0.1537 0.0629 1.0208 0 -0.1423 0.0347 s.e. 0.1073 0.1400 0.1211 0.0542 0 0.1449 0.1405 sigma^2 estimated as 1.368e-06: log likelihood = 519.71, aic = -1025.42 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.7399 0.1507 0.0639 0.9796 0 -0.1402 0 s.e. 0.1072 0.1397 0.1209 0.0528 0 0.1449 0 sigma^2 estimated as 1.426e-06: log likelihood = 519.68, aic = -1027.36 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0946 -0.0029 0 0.0974 0 -0.0849 0 s.e. 0.0055 0.0052 0 0.1049 0 0.0051 0 sigma^2 estimated as 1.512e-06: log likelihood = 517.55, aic = -1025.11 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0185 0 0 0.1474 0 -0.0993 0 s.e. 0.0016 0 0 0.0959 0 0.0011 0 sigma^2 estimated as 1.508e-06: log likelihood = 517.81, aic = -1027.62 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1836 0 0 0 0 -0.134 0 s.e. 0.1010 0 0 0 0 0.144 0 sigma^2 estimated as 1.509e-06: log likelihood = 517.5, aic = -1028.99 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1812 0 0 0 0 0 0 s.e. 0.1010 0 0 0 0 0 0 sigma^2 estimated as 1.529e-06: log likelihood = 517.07, aic = -1030.14 $aic [1] -1023.523 -1025.417 -1027.356 -1025.108 -1027.616 -1028.991 -1030.142 [8] -1028.979 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 log(s2) : NaNs produced 6: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 7: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 8: In max(i) : no non-missing arguments to max; returning -Inf 9: In max(i) : no non-missing arguments to max; returning -Inf 10: In max(try.data.frame[, 4], na.rm = TRUE) : no non-missing arguments to max; returning -Inf > postscript(file="/var/www/html/rcomp/tmp/1e0x41229546886.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 = 99 Frequency = 1 [1] 1.555877e-04 1.535990e-03 -9.733817e-04 1.223730e-04 -4.648802e-05 [6] -2.666901e-04 2.238565e-03 -9.000853e-04 -9.177184e-04 -4.643941e-04 [11] 1.070565e-03 2.207678e-04 4.130232e-03 -1.391286e-03 -1.581705e-03 [16] -5.212718e-04 3.246261e-04 6.854117e-05 -1.902877e-03 1.321032e-03 [21] 1.310842e-04 1.857713e-03 2.784497e-03 -8.273563e-04 2.091611e-03 [26] 8.869851e-05 -2.420525e-03 8.597967e-04 1.228764e-04 2.210798e-03 [31] -6.641496e-04 -1.398718e-03 -8.539949e-04 -1.537975e-03 1.161592e-04 [36] -4.195551e-04 -6.278137e-04 -5.043201e-04 -1.468301e-04 -1.109709e-03 [41] -1.056671e-03 4.692647e-05 8.674355e-04 -4.384468e-04 1.052956e-03 [46] -1.020368e-03 8.063161e-04 2.554117e-04 -6.046691e-04 7.328867e-04 [51] -1.383966e-03 -5.484912e-04 5.364615e-04 -6.143760e-04 2.202412e-04 [56] 1.172908e-03 -5.030925e-04 -2.778770e-04 -1.155194e-04 5.337991e-06 [61] 6.943059e-05 6.159537e-04 -1.221593e-03 -1.840100e-04 -5.866158e-05 [66] -2.582094e-04 -4.391163e-04 -1.577662e-04 -2.273199e-04 9.821646e-04 [71] -2.788777e-04 -5.935535e-04 -6.374600e-04 -9.866522e-04 -3.579648e-04 [76] -3.750442e-04 -2.433882e-04 -2.258581e-04 9.362918e-04 -1.338444e-03 [81] -1.286354e-03 1.107710e-04 -3.948525e-04 1.039385e-03 -8.839756e-04 [86] -6.160056e-04 1.677792e-03 -7.440636e-04 2.100488e-03 -7.729953e-05 [91] 5.054767e-04 -1.231576e-03 -1.647597e-04 1.912261e-03 1.596374e-03 [96] -9.127219e-04 1.240041e-03 5.836084e-03 9.741589e-04 > postscript(file="/var/www/html/rcomp/tmp/2c0nf1229546886.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/3whjt1229546886.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/4726s1229546886.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/54ysw1229546886.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/6dx8i1229546886.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/7grhz1229546886.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/8uuur1229546886.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/9sg6o1229546886.tab") > > system("convert tmp/1e0x41229546886.ps tmp/1e0x41229546886.png") > system("convert tmp/2c0nf1229546886.ps tmp/2c0nf1229546886.png") > system("convert tmp/3whjt1229546886.ps tmp/3whjt1229546886.png") > system("convert tmp/4726s1229546886.ps tmp/4726s1229546886.png") > system("convert tmp/54ysw1229546886.ps tmp/54ysw1229546886.png") > system("convert tmp/6dx8i1229546886.ps tmp/6dx8i1229546886.png") > system("convert tmp/7grhz1229546886.ps tmp/7grhz1229546886.png") > > > proc.time() user system elapsed 13.636 3.059 15.155