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Type 'q()' to quit R. > x <- c(2851,2672,2755,2721,2946,3036,2282,2212,2922,4301,5764,7132,2541,2475,3031,3266,3776,3230,3028,1759,3595,4474,6838,8357,3113,3006,4047,3523,3937,3986,3260,1573,3528,5211,7614,9254,5375,3088,3718,4514,4520,4539,3663,1643,4739,5428,8314,10651,3633,4292,4154,4121,4647,4753,3965,1723,5048,6922,9858,11331,4016,3957,4510,4276,4968,4677,3523,1821,5222,6873,10803,13916,2639,2899,3370,3740,2927,3986,4217,1738,5221,6424,9842,13076,3934,3162,4286,4676,5010,4874,4633,1659,5951,6981,9851,12670) > par9 = '1' > par8 = '2' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > par2 = '-0.4' > par1 = 'FALSE' > 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,] 0.2204156 0.2334815 0.2073748 0.1921738 0.14269495 -0.6495669 [2,] 0.2248400 0.2301324 0.2028967 0.0000000 0.07436852 -0.4617532 [3,] 0.2203837 0.2380434 0.2094158 0.0000000 0.00000000 -0.4568561 [4,] 0.2785011 0.2886790 0.0000000 0.0000000 0.00000000 -0.4191636 [5,] NA NA NA NA NA NA [6,] NA NA NA NA NA NA [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.04218 0.03414 0.06112 0.62391 0.53961 0.09108 [2,] 0.03764 0.03708 0.06563 NA 0.62155 0.00121 [3,] 0.04037 0.02885 0.05530 NA NA 0.00094 [4,] 0.00906 0.00784 NA NA NA 0.00128 [5,] NA NA NA NA NA NA [6,] NA NA NA NA NA NA [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 [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 0.2204 0.2335 0.2074 0.1922 0.1427 -0.6496 s.e. 0.1069 0.1085 0.1093 0.3906 0.2317 0.3803 sigma^2 estimated as 5.552e-06: log likelihood = 387.48, aic = -760.96 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 0.2204 0.2335 0.2074 0.1922 0.1427 -0.6496 s.e. 0.1069 0.1085 0.1093 0.3906 0.2317 0.3803 sigma^2 estimated as 5.552e-06: log likelihood = 387.48, aic = -760.96 [[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 0.2248 0.2301 0.2029 0 0.0744 -0.4618 s.e. 0.1066 0.1087 0.1089 0 0.1501 0.1382 sigma^2 estimated as 5.576e-06: log likelihood = 387.37, aic = -762.75 [[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 0.2204 0.2380 0.2094 0 0 -0.4569 s.e. 0.1060 0.1072 0.1079 0 0 0.1336 sigma^2 estimated as 5.579e-06: log likelihood = 387.25, aic = -764.5 [[3]][[5]] NULL [[3]][[6]] NULL $aic [1] -760.9642 -762.7484 -764.5037 -762.8097 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/wessaorg/rcomp/tmp/1awba1323201860.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 = 96 Frequency = 1 [1] 4.149548e-05 4.258580e-05 4.206787e-05 4.227732e-05 4.095488e-05 [6] 4.046484e-05 4.535993e-05 4.592874e-05 4.108907e-05 3.520225e-05 [11] 3.131184e-05 2.875502e-05 1.555843e-03 5.108490e-04 -2.213223e-03 [16] -3.016385e-03 -2.794679e-03 8.635437e-04 -2.754921e-03 6.014968e-03 [21] -2.545079e-03 2.636739e-04 -1.752213e-03 -4.892069e-04 -1.829415e-03 [26] -1.447073e-03 -3.369784e-03 2.291676e-05 2.013434e-04 -1.481307e-03 [31] -1.234298e-03 5.810692e-03 -3.673542e-04 -2.314200e-03 -2.077007e-03 [36] -6.509113e-04 -7.655439e-03 1.188236e-03 1.924124e-03 -2.110826e-03 [41] -1.283819e-03 -1.477160e-03 -7.283844e-04 2.918260e-03 -3.397503e-03 [46] -6.649795e-05 -6.008076e-04 -4.881298e-04 2.629847e-03 -5.083351e-03 [51] -6.545353e-04 7.130890e-04 1.763135e-04 -1.183077e-03 -1.540649e-03 [56] 8.401686e-04 -1.765830e-03 -2.337217e-03 -9.958328e-04 4.533808e-04 [61] 8.986494e-04 -3.145973e-04 -1.232522e-03 8.785623e-05 -6.724283e-04 [66] 2.437173e-04 1.332917e-03 -9.787938e-04 -1.470672e-03 -9.903502e-04 [71] -1.042585e-03 -1.404923e-03 7.641168e-03 3.859854e-03 1.463334e-03 [76] -1.492763e-03 5.069669e-03 -7.228884e-04 -4.803151e-03 -1.103860e-03 [81] -7.161217e-04 6.781846e-04 7.611510e-05 -4.811445e-04 -3.336297e-03 [86] 1.422533e-03 -1.192611e-03 -1.421453e-03 -3.783999e-03 1.177166e-04 [91] -3.294055e-04 3.065646e-03 -1.298560e-03 -2.566143e-04 4.382278e-04 [96] 6.501104e-04 > postscript(file="/var/wessaorg/rcomp/tmp/2y0at1323201860.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/wessaorg/rcomp/tmp/3bqvl1323201860.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/wessaorg/rcomp/tmp/4ne7o1323201860.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/wessaorg/rcomp/tmp/5eta41323201860.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/wessaorg/rcomp/tmp/6i4nh1323201860.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/wessaorg/rcomp/tmp/7ujzi1323201860.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/8jg0u1323201860.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/wessaorg/rcomp/tmp/9dc4b1323201860.tab") > > try(system("convert tmp/1awba1323201860.ps tmp/1awba1323201860.png",intern=TRUE)) character(0) > try(system("convert tmp/2y0at1323201860.ps tmp/2y0at1323201860.png",intern=TRUE)) character(0) > try(system("convert tmp/3bqvl1323201860.ps tmp/3bqvl1323201860.png",intern=TRUE)) character(0) > try(system("convert tmp/4ne7o1323201860.ps tmp/4ne7o1323201860.png",intern=TRUE)) character(0) > try(system("convert tmp/5eta41323201860.ps tmp/5eta41323201860.png",intern=TRUE)) character(0) > try(system("convert tmp/6i4nh1323201860.ps tmp/6i4nh1323201860.png",intern=TRUE)) character(0) > try(system("convert tmp/7ujzi1323201860.ps tmp/7ujzi1323201860.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.538 0.914 6.494