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(68.897,38.683,44.720,39.525,45.315,50.380,40.600,36.279,42.438,38.064,31.879,11.379,70.249,39.253,47.060,41.697,38.708,49.267,39.018,32.228,40.870,39.383,34.571,12.066,70.938,34.077,45.409,40.809,37.013,44.953,37.848,32.745,43.412,34.931,33.008,8.620,68.906,39.556,50.669,36.432,40.891,48.428,36.222,33.425,39.401,37.967,34.801,12.657,69.116,41.519,51.321,38.529,41.547,52.073,38.401,40.898,40.439,41.888,37.898,8.771,68.184,50.530,47.221,41.756,45.633,48.138,39.486,39.341,41.117,41.629,29.722,7.054,56.676,34.870,35.117,30.169,30.936,35.699,33.228,27.733,33.666,35.429,27.438,8.170,63.410,38.040,45.389,37.353,37.024,50.957,37.994,36.454,46.080,43.373,37.395,10.963,76.058,50.179,57.452,47.568,50.050,50.856,41.992,39.284) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '1' > 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,] -0.2392835 0.002098176 0.1913729 -0.4023364 0.1319203 -0.1020071 [2,] -0.2424456 0.000000000 0.1904334 -0.3991270 0.1322558 -0.1018700 [3,] -0.2635751 0.000000000 0.1913924 -0.3946435 0.0000000 -0.1608339 [4,] -0.3112554 0.000000000 0.2084188 -0.3610527 0.0000000 0.0000000 [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 [13,] NA NA NA NA NA NA [14,] NA NA NA NA NA NA [,7] [1,] -0.8140220 [2,] -0.8144669 [3,] -0.6741933 [4,] -0.7546423 [5,] NA [6,] NA [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,] 0.78485 0.99709 0.47776 0.64960 0.58032 0.55994 0.01867 [2,] 0.17920 NA 0.07348 0.02007 0.57800 0.56024 0.01840 [3,] 0.12178 NA 0.06898 0.01757 NA 0.22897 0.00001 [4,] 0.04776 NA 0.03955 0.02251 NA NA 0.00000 [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 -0.2393 0.0021 0.1914 -0.4023 0.1319 -0.1020 -0.8140 s.e. 0.8740 0.5735 0.2685 0.8828 0.2378 0.1744 0.3402 sigma^2 estimated as 11.1: log likelihood = -244.96, aic = 505.93 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2393 0.0021 0.1914 -0.4023 0.1319 -0.1020 -0.8140 s.e. 0.8740 0.5735 0.2685 0.8828 0.2378 0.1744 0.3402 sigma^2 estimated as 11.1: log likelihood = -244.96, aic = 505.93 [[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 -0.2424 0 0.1904 -0.3991 0.1323 -0.1019 -0.8145 s.e. 0.1792 0 0.1052 0.1688 0.2369 0.1743 0.3397 sigma^2 estimated as 11.1: log likelihood = -244.96, aic = 503.93 [[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 -0.2636 0 0.1914 -0.3946 0 -0.1608 -0.6742 s.e. 0.1689 0 0.1041 0.1634 0 0.1329 0.1404 sigma^2 estimated as 11.45: log likelihood = -245.12, aic = 502.24 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 505.9270 503.9270 502.2365 501.5976 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/fisher/rcomp/tmp/1dl2c1355434473.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 = 104 Frequency = 1 [1] 3.977769e-02 -5.614524e-03 1.426622e-03 -3.518223e-03 2.430687e-03 [6] 6.681568e-03 -3.382927e-03 -7.017151e-03 -4.090749e-04 -4.528551e-03 [11] -1.002593e-02 -6.091857e-02 -1.223182e-01 -5.192352e-01 1.058665e+00 [16] 6.440490e-01 -6.845523e+00 -3.743604e-01 6.871022e-01 -4.940458e-01 [21] 4.600495e-01 3.169986e+00 3.306120e+00 -3.383026e-01 -1.058140e+00 [26] -5.936046e+00 2.774259e-01 1.657462e+00 -1.886925e+00 -2.888754e+00 [31] 1.288446e+00 2.534835e+00 3.891838e+00 -3.380999e+00 3.701188e-02 [36] -1.997491e+00 1.167598e+00 4.158302e+00 5.208687e+00 -6.442693e+00 [41] -1.245619e+00 4.613544e-01 -1.494570e+00 -1.685690e-01 -2.082200e+00 [46] 3.357077e+00 2.680915e+00 2.385859e+00 -2.786151e+00 1.245165e+00 [51] 1.880232e+00 -2.555914e+00 -1.478137e+00 2.273021e+00 -7.371519e-01 [56] 5.478623e+00 -4.136084e+00 1.315275e+00 4.419281e-01 -5.055476e+00 [61] -3.493671e+00 1.205359e+01 -3.106780e+00 -1.671643e+00 5.944303e-01 [66] -2.841220e+00 -1.243554e+00 1.726893e+00 -1.033393e+00 1.286478e+00 [71] -7.076422e+00 -1.863302e+00 -1.037503e+01 -1.808772e-01 -4.274927e+00 [76] 1.610698e+00 -9.235388e-01 -1.199060e+00 5.942920e+00 1.539688e+00 [81] 8.731892e-01 2.863439e+00 1.162993e+00 3.743380e+00 1.393751e+00 [86] 2.083120e+00 2.038597e+00 1.258814e+00 -1.631786e+00 5.456773e+00 [91] -7.746668e-01 3.128014e-01 3.770724e+00 1.479901e+00 -5.454374e-01 [96] -4.706026e+00 5.623315e+00 4.108645e+00 3.794990e+00 -1.410231e+00 [101] 8.125067e-02 -8.105254e+00 -3.102766e+00 -1.936030e+00 > postscript(file="/var/fisher/rcomp/tmp/2bh1d1355434473.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/fisher/rcomp/tmp/3d2ku1355434473.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/fisher/rcomp/tmp/4fmit1355434473.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/fisher/rcomp/tmp/5t0sd1355434473.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/fisher/rcomp/tmp/60gpz1355434473.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/fisher/rcomp/tmp/7mh7s1355434473.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/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/fisher/rcomp/tmp/88wou1355434473.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/fisher/rcomp/tmp/9s9mt1355434473.tab") > > try(system("convert tmp/1dl2c1355434473.ps tmp/1dl2c1355434473.png",intern=TRUE)) character(0) > try(system("convert tmp/2bh1d1355434473.ps tmp/2bh1d1355434473.png",intern=TRUE)) character(0) > try(system("convert tmp/3d2ku1355434473.ps tmp/3d2ku1355434473.png",intern=TRUE)) character(0) > try(system("convert tmp/4fmit1355434473.ps tmp/4fmit1355434473.png",intern=TRUE)) character(0) > try(system("convert tmp/5t0sd1355434473.ps tmp/5t0sd1355434473.png",intern=TRUE)) character(0) > try(system("convert tmp/60gpz1355434473.ps tmp/60gpz1355434473.png",intern=TRUE)) character(0) > try(system("convert tmp/7mh7s1355434473.ps tmp/7mh7s1355434473.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.148 2.215 12.355