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Type 'q()' to quit R. > x <- c(617,614,647,580,614,636,388,356,639,753,611,639,630,586,695,552,619,681,421,307,754,690,644,643,608,651,691,627,634,731,475,337,803,722,590,724,627,696,825,677,656,785,412,352,839,729,696,641,695,638,762,635,721,854,418,367,824,687,601,676,740,691,683,594,729,731,386,331,706,715,657,653,642,643,718,654,632,731,392,344,792,852,649,629,685,617,715,715,629,916,531,357,917,828,708,858,775,785,1006,789,734,906,532,387,991,841,892,782,811,792,978,773,796,946,594,438,1023,868,791,760,779,852,1001,734,996,869,599,426,1138,1091,830,909) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '2' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '-0.3' > 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,] -0.1592248 0.02868088 -0.7822729 0.07672370 -0.1086651 -0.9996028 [2,] -0.1750937 0.00000000 -0.7703775 0.06986629 -0.1065625 -0.9998701 [3,] -0.1883699 0.00000000 -0.7629874 0.00000000 -0.1134112 -0.9997557 [4,] -0.1993136 0.00000000 -0.7550122 0.00000000 0.0000000 -1.0001232 [5,] 0.0000000 0.00000000 -1.2290421 0.00000000 0.0000000 -0.9999947 [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,] 0.14141 0.7897 0 0.46554 0.25559 0 [2,] 0.10508 NA 0 0.51517 0.30479 0 [3,] 0.07352 NA 0 NA 0.26673 0 [4,] 0.05778 NA 0 NA NA 0 [5,] NA NA 0 NA 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 ma1 sar1 sar2 sma1 -0.1592 0.0287 -0.7823 0.0767 -0.1087 -0.9996 s.e. 0.1076 0.1073 0.0740 0.1048 0.0951 0.1463 sigma^2 estimated as 9.43e-06: log likelihood = 504.31, aic = -994.62 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 -0.1592 0.0287 -0.7823 0.0767 -0.1087 -0.9996 s.e. 0.1076 0.1073 0.0740 0.1048 0.0951 0.1463 sigma^2 estimated as 9.43e-06: log likelihood = 504.31, aic = -994.62 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 -0.1751 0 -0.7704 0.0699 -0.1066 -0.9999 s.e. 0.1073 0 0.0659 0.1070 0.1034 0.1480 sigma^2 estimated as 9.427e-06: log likelihood = 504.28, aic = -996.56 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 -0.1884 0 -0.7630 0 -0.1134 -0.9998 s.e. 0.1044 0 0.0646 0 0.1017 0.1778 sigma^2 estimated as 9.34e-06: log likelihood = 504.06, aic = -998.12 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 -0.1993 0 -0.7550 0 0 -1.0001 s.e. 0.1041 0 0.0658 0 0 0.1462 sigma^2 estimated as 9.628e-06: log likelihood = 503.46, aic = -998.92 [[3]][[6]] NULL $aic [1] -994.6192 -996.5574 -998.1239 -998.9162 -997.4001 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/fisher/rcomp/tmp/1nvzs1356033309.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 = 132 Frequency = 1 [1] 8.401436e-05 3.773733e-05 2.278564e-05 2.120145e-05 1.464078e-05 [6] 1.077357e-05 3.067943e-05 3.093786e-05 1.360210e-06 -5.358167e-06 [11] 3.604093e-06 -8.363341e-05 -5.213067e-04 1.498506e-03 -2.105314e-03 [16] 1.506139e-03 -3.290949e-06 -2.148259e-03 -2.723639e-03 6.145940e-03 [21] -4.146668e-03 2.300525e-03 -9.692188e-04 -8.783528e-05 1.348119e-03 [26] -2.500906e-03 -6.765703e-04 -2.744069e-03 1.336944e-04 -1.978219e-03 [31] -4.767300e-03 1.387015e-03 -1.932037e-03 2.598969e-03 5.202335e-03 [36] -2.109445e-03 1.361929e-03 -2.130050e-03 -4.848101e-03 -2.534296e-03 [41] 1.900973e-03 -9.892924e-04 5.147514e-03 1.186852e-03 -2.346456e-03 [46] 2.357559e-03 -1.510966e-03 4.025762e-03 -8.731893e-04 2.500309e-03 [51] 2.964560e-04 5.414101e-04 -2.746863e-03 -4.592967e-03 3.598170e-03 [56] -1.768556e-04 -3.404607e-04 5.031232e-03 4.985991e-03 7.805647e-04 [61] -4.394829e-03 -1.471448e-03 4.725950e-03 3.759981e-03 -3.124170e-03 [66] 1.138468e-03 5.710622e-03 2.793943e-03 3.089637e-03 -4.811775e-04 [71] -2.856098e-03 -7.566266e-05 8.521183e-04 2.802128e-04 -1.749554e-04 [76] -2.834701e-03 1.923730e-03 6.873536e-04 2.786558e-03 -4.700131e-04 [81] -2.274158e-03 -6.814585e-03 -6.029764e-04 3.722374e-03 -2.775913e-04 [86] 2.932274e-03 1.208664e-03 -5.423307e-03 2.413929e-03 -6.753839e-03 [91] -9.963291e-03 1.009876e-03 -2.189416e-03 -2.974737e-04 4.857308e-04 [96] -5.500223e-03 -1.273967e-03 -1.458839e-03 -6.128133e-03 -1.893173e-03 [101] 3.741237e-03 1.364359e-03 -2.435232e-03 1.803556e-03 -9.331791e-04 [106] 2.920065e-03 -4.954662e-03 1.762142e-03 1.105336e-03 7.432773e-04 [111] -2.308761e-03 7.429484e-04 1.201133e-03 3.816812e-04 -5.609402e-03 [116] -2.690710e-03 3.502595e-04 4.382898e-03 2.619458e-03 4.607365e-03 [121] 3.045086e-03 -2.029577e-03 -3.040235e-03 3.210246e-03 -6.812043e-03 [126] 3.819525e-03 -3.658694e-03 -4.242529e-04 -2.875617e-03 -4.002677e-03 [131] 1.904818e-03 1.274640e-04 > postscript(file="/var/fisher/rcomp/tmp/24b771356033309.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/3y9fh1356033309.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/4tszz1356033309.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/5ay7a1356033309.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/6vfe91356033309.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/7g4oj1356033309.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/8o0x71356033309.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/9ngis1356033309.tab") > > try(system("convert tmp/1nvzs1356033309.ps tmp/1nvzs1356033309.png",intern=TRUE)) character(0) > try(system("convert tmp/24b771356033309.ps tmp/24b771356033309.png",intern=TRUE)) character(0) > try(system("convert tmp/3y9fh1356033309.ps tmp/3y9fh1356033309.png",intern=TRUE)) character(0) > try(system("convert tmp/4tszz1356033309.ps tmp/4tszz1356033309.png",intern=TRUE)) character(0) > try(system("convert tmp/5ay7a1356033309.ps tmp/5ay7a1356033309.png",intern=TRUE)) character(0) > try(system("convert tmp/6vfe91356033309.ps tmp/6vfe91356033309.png",intern=TRUE)) character(0) > try(system("convert tmp/7g4oj1356033309.ps tmp/7g4oj1356033309.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 11.038 1.556 12.581