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Type 'q()' to quit R. > x <- c(145.9,158.5,152.2,153.7,157.9,154.4,150.7,151.2,147.3,146.6,145.2,139.3,145.7,163.3,181.8,188.1,222.9,206.3,184.9,183.6,186.6,176.5,173.9,184.9,182.5,183.6,172.4,168.9,163.3,152.4,145.8,148.6,143.4,141.2,144.6,144.5,140.8,133.3,127.3,119.6,120.2,121.9,112.4,111,107.8,110.5,118.3,123,112.1,104.2,102.4,100.3,102.6,101.5,103.4,99.4,97.9,98,90.2,87.1,91.8,94.8,91.8,89.3,91.7,86.2,82.8,82.3,79.8,79.4,85.3,87.5,88.3,88.6,94.9,94.7,92.6,91.8,96.4,96.4,107.1,111.9,107.8,109.2,115.3,119.2,107.8,106.8,104.2,94.8,97.5,98.3,100.6,94.9,93.6,98,104.3,103.9,105.3,102.6,103.3,107.9,107.8,109.8,110.6,110.8,119.3,128.1,127.6,137.9,151.4,143.6,143.4,141.9,135.2,133.1,129.6,134.1,136.8,143.5,162.5,163.1,157.2,158.8,155.4,148.5,154.2,153.3,149.4,147.9,156,163,159.1,159.5,157.3,156.4,156.6,162.4,166.8,162.6,168.1) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '0' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '0.0' > 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) + 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] [1,] 0.2984877 0.72281639 0.1188454 -0.7902630 [2,] 0.2936743 -0.35726497 0.0000000 0.2861292 [3,] 0.2931983 -0.06405821 0.0000000 0.0000000 [4,] 0.2813945 0.00000000 0.0000000 0.0000000 [5,] NA NA NA NA [6,] NA NA NA NA [7,] NA NA NA NA [8,] NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [1,] 0.00318 0.08911 0.2124 0.06927 [2,] 0.00372 0.46711 NA 0.56470 [3,] 0.00395 0.45477 NA NA [4,] 0.00583 NA NA NA [5,] NA NA NA NA [6,] NA NA NA NA [7,] NA NA NA NA [8,] NA NA NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ma1 sar1 sar2 sma1 0.2985 0.7228 0.1188 -0.7903 s.e. 0.0994 0.4221 0.0949 0.4316 sigma^2 estimated as 0.002029: log likelihood = 235.02, aic = -460.04 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ma1 sar1 sar2 sma1 0.2985 0.7228 0.1188 -0.7903 s.e. 0.0994 0.4221 0.0949 0.4316 sigma^2 estimated as 0.002029: log likelihood = 235.02, aic = -460.04 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ma1 sar1 sar2 sma1 0.2937 -0.3573 0 0.2861 s.e. 0.0995 0.4899 0 0.4956 sigma^2 estimated as 0.002049: log likelihood = 234.59, aic = -461.17 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ma1 sar1 sar2 sma1 0.2932 -0.0641 0 0 s.e. 0.1000 0.0855 0 0 sigma^2 estimated as 0.002053: log likelihood = 234.48, aic = -462.96 $aic [1] -460.0425 -461.1734 -462.9597 -464.3995 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/www/html/rcomp/tmp/1aciv1197311473.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 = 141 Frequency = 1 [1] 4.982919e-03 7.932235e-02 -6.258095e-02 2.806542e-02 1.867709e-02 [6] -2.784514e-02 -1.604158e-02 8.008918e-03 -2.842667e-02 3.580900e-03 [11] -1.062589e-02 -3.828144e-02 5.783866e-02 1.024026e-01 7.469959e-02 [16] 1.279329e-02 1.677255e-01 -1.280045e-01 -7.353935e-02 1.471813e-02 [21] 1.021850e-02 -5.894760e-02 1.828197e-03 5.814142e-02 -2.723444e-02 [26] 2.129955e-02 -6.231250e-02 -5.837526e-05 -2.282686e-02 -6.734514e-02 [31] -3.154276e-02 2.781862e-02 -4.273833e-02 -6.494408e-03 2.474748e-02 [36] -4.018728e-03 -2.559770e-02 -4.684807e-02 -3.635191e-02 -5.304922e-02 [41] 1.839821e-02 4.224526e-03 -8.521176e-02 1.366874e-02 -3.554196e-02 [46] 3.416832e-02 5.971436e-02 2.140812e-02 -1.007315e-01 -4.705134e-02 [51] -6.580292e-03 -2.278851e-02 2.967435e-02 -1.857997e-02 1.879628e-02 [56] -4.576677e-02 -3.660691e-03 3.678901e-03 -7.964740e-02 -9.124319e-03 [61] 4.928649e-02 1.302507e-02 -3.709228e-02 -1.806277e-02 3.326921e-02 [66] -7.229717e-02 -1.785668e-02 -3.348686e-03 -3.083982e-02 4.082443e-03 [71] 6.516626e-02 4.117427e-03 1.126070e-02 2.150060e-03 6.600153e-02 [76] -2.322994e-02 -1.391500e-02 -8.559132e-03 4.882559e-02 -1.470358e-02 [81] 1.075918e-01 1.197501e-02 -3.624757e-02 2.516233e-02 4.756183e-02 [86] 1.953755e-02 -1.018532e-01 2.040830e-02 -3.206597e-02 -8.569686e-02 [91] 5.634119e-02 -8.347491e-03 3.231826e-02 -6.499573e-02 2.872150e-03 [96] 4.592155e-02 5.232173e-02 -1.705219e-02 1.194473e-02 -3.007467e-02 [101] 1.403852e-02 3.339519e-02 -8.919680e-03 2.152157e-02 2.431027e-03 [106] -2.642511e-03 7.380576e-02 5.247281e-02 -1.530470e-02 8.186958e-02 [111] 7.024993e-02 -7.515478e-02 2.107708e-02 -1.390425e-02 -4.435011e-02 [116] -1.473488e-03 -2.575088e-02 4.179885e-02 1.241370e-02 4.873437e-02 [121] 1.098036e-01 -2.353598e-02 -2.396110e-02 1.376375e-02 -2.576790e-02 [126] -3.853597e-02 4.586585e-02 -2.030426e-02 -2.152336e-02 -1.593792e-03 [131] 5.506388e-02 3.081250e-02 -2.528625e-02 1.016096e-02 -1.922849e-02 [136] 5.484738e-04 -2.692752e-04 3.353723e-02 1.931279e-02 -3.153974e-02 [141] 4.086249e-02 > postscript(file="/var/www/html/rcomp/tmp/2vn081197311473.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/3diep1197311473.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/4fvn91197311473.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/581bj1197311473.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/64tjy1197311473.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/705w61197311473.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(resid, main='Residual Normal Q-Q Plot') > dev.off() null device 1 > ncols <- length(selection[[1]][1,]) > nrows <- length(selection[[2]][,1])-1 > 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/8jeqi1197311473.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/9dqv81197311473.tab") > > system("convert tmp/1aciv1197311473.ps tmp/1aciv1197311473.png") > system("convert tmp/2vn081197311473.ps tmp/2vn081197311473.png") > system("convert tmp/3diep1197311473.ps tmp/3diep1197311473.png") > system("convert tmp/4fvn91197311473.ps tmp/4fvn91197311473.png") > system("convert tmp/581bj1197311473.ps tmp/581bj1197311473.png") > system("convert tmp/64tjy1197311473.ps tmp/64tjy1197311473.png") > system("convert tmp/705w61197311473.ps tmp/705w61197311473.png") > > > proc.time() user system elapsed 3.805 1.149 4.292