R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(369.07,369.32,370.38,371.63,371.32,371.51,369.69,368.18,366.87,366.94,368.27,369.62,370.47,371.44,372.39,373.32,373.77,373.13,371.51,369.59,368.12,368.38,369.64,371.11,372.38,373.08,373.87,374.93,375.58,375.44,373.91,371.77,370.72,370.5,372.19,373.71,374.92,375.63,376.51,377.75,378.54,378.21,376.65,374.28,373.12,373.1,374.67,375.97,377.03,377.87,378.88,380.42,380.62,379.66,377.48,376.07,374.1,374.47,376.15,377.51,378.43,379.7,380.91,382.2,382.45,382.14,380.6,378.6,376.72,376.98,378.29,380.07,381.36,382.19,382.65,384.65,384.94,384.01,382.15,380.33,378.81,379.06,380.17,381.85,382.88,383.77,384.42,386.36,386.53,386.01,384.45,381.96,380.81,381.09,382.37,383.84,385.42,385.72,385.96,387.18,388.5,387.88,386.38,384.15,383.07,382.98,384.11,385.54,386.92,387.41,388.77,389.46,390.18,389.43,387.74,385.91,384.77,384.38,385.99,387.26,388.45,389.7,391.08,392.46,392.96,392.03,390.13,388.15,386.8,387.18,388.59) > par9 = '1' > par8 = '1' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '-0.9' > 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,] 0.3040010 0.04478567 -0.06207530 -0.7180993 0.1106284 -0.9993576 [2,] 0.2412995 0.00000000 -0.07912643 -0.6491692 0.1146013 -0.9992460 [3,] 0.3390525 0.00000000 0.00000000 -0.7409638 0.1263902 -0.9993169 [4,] 0.3299808 0.00000000 0.00000000 -0.7468849 0.0000000 -0.9988030 [5,] 0.0000000 0.00000000 0.00000000 -0.4704130 0.0000000 -0.9991074 [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,] 0.35440 0.78389 0.66633 0.02691 0.31283 0 [2,] 0.28882 NA 0.50687 0.00109 0.29247 0 [3,] 0.07480 NA NA 0.00000 0.24117 0 [4,] 0.06461 NA NA 0.00000 NA 0 [5,] NA NA NA 0.00001 NA 0 [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 ma1 sar1 sma1 0.304 0.0448 -0.0621 -0.7181 0.1106 -0.9994 s.e. 0.327 0.1629 0.1436 0.3207 0.1092 0.1390 sigma^2 estimated as 1.011e-11: log likelihood = 1313.06, aic = -2612.12 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.304 0.0448 -0.0621 -0.7181 0.1106 -0.9994 s.e. 0.327 0.1629 0.1436 0.3207 0.1092 0.1390 sigma^2 estimated as 1.011e-11: log likelihood = 1313.06, aic = -2612.12 [[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 sma1 0.2413 0 -0.0791 -0.6492 0.1146 -0.9992 s.e. 0.2265 0 0.1189 0.1941 0.1084 0.1386 sigma^2 estimated as 1.013e-11: log likelihood = 1313.02, aic = -2614.03 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.3391 0 0 -0.7410 0.1264 -0.9993 s.e. 0.1887 0 0 0.1433 0.1073 0.1360 sigma^2 estimated as 1.018e-11: log likelihood = 1312.82, aic = -2615.64 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.330 0 0 -0.7469 0 -0.9988 s.e. 0.177 0 0 0.1323 0 0.1918 sigma^2 estimated as 1.007e-11: log likelihood = 1312.11, aic = -2616.22 [[3]][[6]] NULL $aic [1] -2612.115 -2614.034 -2615.640 -2616.219 -2615.409 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 > postscript(file="/var/wessaorg/rcomp/tmp/1y1761355227428.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 = 131 Frequency = 1 [1] 2.825149e-06 1.261135e-06 8.149594e-07 5.944102e-07 4.774801e-07 [6] 3.952267e-07 3.585113e-07 3.304458e-07 3.085130e-07 2.767763e-07 [11] 2.362797e-07 -2.552446e-06 -1.717788e-05 -5.534329e-06 -7.591943e-07 [16] 1.828727e-06 -5.800182e-06 4.634535e-06 -6.214616e-07 3.452637e-06 [21] 2.752958e-06 4.903801e-08 1.316413e-06 8.067416e-08 -2.400621e-06 [26] -1.581830e-06 1.270235e-06 6.376257e-07 -5.138854e-06 -2.825266e-06 [31] -3.907799e-06 1.578109e-06 -3.560855e-06 2.254312e-06 -3.104842e-06 [36] -1.780534e-06 -1.824329e-06 -1.635719e-06 -2.739388e-07 -1.787553e-06 [41] -6.050200e-06 -1.528489e-06 -2.942813e-06 2.967417e-06 -8.224674e-07 [46] 4.861896e-07 -9.033675e-07 1.649051e-06 1.847316e-06 -7.524104e-07 [51] -6.394622e-07 -4.174913e-06 2.143604e-07 6.804999e-06 7.810235e-06 [56] -2.237784e-06 7.584922e-06 -2.794239e-07 -8.700071e-07 9.763081e-07 [61] 2.896689e-06 -4.436619e-06 -3.875868e-06 -2.557775e-06 -5.758125e-07 [66] -1.571125e-06 -3.589752e-06 -1.114774e-06 3.575646e-06 -5.776992e-07 [71] 2.709719e-06 -2.140932e-06 -2.046994e-06 -1.140414e-06 4.973154e-06 [76] -5.761063e-06 -1.190245e-06 4.576685e-06 2.453489e-06 7.096382e-08 [81] 4.895448e-07 -9.385613e-07 4.140438e-06 4.153951e-08 2.047669e-06 [86] 3.564985e-07 3.567166e-06 -4.110596e-06 6.139830e-07 4.948670e-07 [91] -2.344587e-06 4.591844e-06 -2.458016e-06 -1.924862e-06 1.193523e-06 [96] 1.165449e-06 -3.730867e-06 4.248612e-06 8.327456e-06 6.358686e-06 [101] -6.259989e-06 2.134887e-07 -3.322511e-06 4.036674e-07 -4.779648e-06 [106] 5.478347e-07 3.124974e-06 2.550789e-06 -1.730726e-07 3.485735e-06 [111] -3.642343e-06 6.791129e-06 -3.625755e-07 3.313600e-06 7.799155e-07 [116] -1.766627e-06 -3.908714e-06 3.723709e-06 -7.424719e-07 3.049324e-06 [121] 1.890997e-06 -3.779452e-06 -5.948036e-06 -2.742144e-06 -2.349177e-06 [126] 2.442589e-06 1.653345e-06 -1.976529e-07 -8.734687e-07 -3.379764e-06 [131] -7.549499e-07 > postscript(file="/var/wessaorg/rcomp/tmp/2iio31355227428.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/3k71p1355227428.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/4k15p1355227428.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/5fedy1355227428.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/6qy5a1355227428.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/76cri1355227428.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/873711355227428.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/9gwe41355227428.tab") > > try(system("convert tmp/1y1761355227428.ps tmp/1y1761355227428.png",intern=TRUE)) character(0) > try(system("convert tmp/2iio31355227428.ps tmp/2iio31355227428.png",intern=TRUE)) character(0) > try(system("convert tmp/3k71p1355227428.ps tmp/3k71p1355227428.png",intern=TRUE)) character(0) > try(system("convert tmp/4k15p1355227428.ps tmp/4k15p1355227428.png",intern=TRUE)) character(0) > try(system("convert tmp/5fedy1355227428.ps tmp/5fedy1355227428.png",intern=TRUE)) character(0) > try(system("convert tmp/6qy5a1355227428.ps tmp/6qy5a1355227428.png",intern=TRUE)) character(0) > try(system("convert tmp/76cri1355227428.ps tmp/76cri1355227428.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.457 0.958 8.409