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Type 'q()' to quit R. > x <- c(13.193,15.234,14.718,16.961,13.945,15.876,16.226,18.316,16.748,17.904,17.209,18.950,17.225,18.710,17.236,18.687,17.580,19.568,17.381,19.580,17.260,18.661,15.658,18.674,15.908,17.475,17.725,19.562,16.368,19.555,17.743,19.867,15.703,19.324,18.162,19.074,15.323,19.704,18.375,18.352,13.927,17.795,16.761,18.902,16.239,19.158,18.279,15.698,16.239,18.431,18.414,19.801,14.995,18.706,18.232,19.409,16.263,19.017,20.298,19.891,15.203,17.845,17.502,18.532,15.737,17.770,17.224,17.601,14.940,18.507,17.635,19.392,15.699,17.661,18.243,19.643,15.770,17.344,17.229,17.322,16.152,17.919,16.918,18.114,16.308,17.759,16.021,17.952,15.954,17.762,16.610,17.751,15.458,18.106,15.990,15.349,13.185,15.409,16.007,16.633,14.800,15.974,15.693) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '4' > par4 = '1' > par3 = '0' > par2 = '1' > 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) + 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] [,7] [1,] -0.3931069 0.4623768 0.1515822 0.8744078 0.3567118 0.1817341 -0.9999993 [2,] 1.2818631 -0.3052714 0.0000000 -0.8083487 0.2358533 0.1450350 -1.0000004 [3,] 1.2969705 -0.3125066 0.0000000 -0.8278444 0.2525767 0.0000000 -0.9999995 [4,] NA NA NA NA NA NA NA [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.02727 0.00067 0.14655 0e+00 0.00480 0.12266 0 [2,] 0.00000 0.10412 NA 3e-05 0.04020 0.19801 0 [3,] 0.00000 0.04167 NA 0e+00 0.02725 NA 0 [4,] NA NA NA NA NA NA NA [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 -0.3931 0.4624 0.1516 0.8744 0.3567 0.1817 -1.0000 s.e. 0.1753 0.1315 0.1036 0.1492 0.1235 0.1167 0.0991 sigma^2 estimated as 0.8611: log likelihood = -137.66, aic = 291.32 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3931 0.4624 0.1516 0.8744 0.3567 0.1817 -1.0000 s.e. 0.1753 0.1315 0.1036 0.1492 0.1235 0.1167 0.0991 sigma^2 estimated as 0.8611: log likelihood = -137.66, aic = 291.32 [[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 1.2819 -0.3053 0 -0.8083 0.2359 0.1450 -1.0000 s.e. 0.2324 0.1861 0 0.1850 0.1134 0.1119 0.0851 sigma^2 estimated as 0.8598: log likelihood = -137.36, aic = 288.72 [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 291.3215 288.7221 288.4239 Warning messages: 1: In log(s2) : NaNs produced 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/12yjw1293186544.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 = 103 Frequency = 1 [1] 0.0131929886 0.0152339779 0.0147179736 0.0169609722 0.5711787585 [6] 0.2886347978 1.0394791505 0.6792238475 2.2076348615 0.7539225518 [11] 0.3997306374 0.2856783693 1.0637784531 0.6887248543 -0.3473635329 [16] -0.3109513549 0.8655152674 0.9379215889 -0.4828869543 0.5085053061 [21] -0.1046468301 -0.2298618991 -1.5883040921 0.2218819672 -0.7331015828 [26] -0.4750196864 1.6517413286 0.3245129339 -0.2241306260 1.5812084539 [31] 0.1081083766 0.3834500152 -1.0144557133 1.0086769932 0.3713592299 [36] -0.6848754144 -0.7877580315 1.2220693456 0.4435232577 -1.1940162437 [41] -1.6066594211 -0.1723899289 -0.2467285131 0.7226060660 1.0705899694 [46] 0.5308868572 0.7620780584 -3.4382591895 1.8615925926 -0.0735971161 [51] 1.1127932747 1.5254269712 -1.8801505142 0.6073517249 0.3915677031 [56] 0.6410077679 0.1059935978 0.2215804235 2.1432165430 -0.4709872650 [61] -1.2728366178 -0.6938377783 -0.6351036661 -0.3003548164 0.2985912937 [66] -0.4558253459 -0.3755051934 -0.8213058619 -0.0314704139 0.9749056190 [71] 0.2613716504 1.0511402831 -0.1927284773 -0.6221032297 1.0543278505 [76] 0.6266457965 -0.2673221914 -0.9005047998 -0.1523283317 -1.4670270323 [81] 1.2659801863 0.0216250523 -0.4805320015 0.0357560838 0.7467271844 [86] -0.2804223708 -1.0368689346 0.3296851381 0.3297835801 -0.0665201013 [91] -0.0802231999 -0.2911590299 -0.0009324574 0.5027610548 -0.8266354202 [96] -2.2755387498 -0.9432015750 -1.0601670976 0.8950158101 0.0950558336 [101] 0.7845478158 -0.7540052386 0.0714491601 > postscript(file="/var/www/html/rcomp/tmp/22yjw1293186544.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/www/html/rcomp/tmp/3vq1z1293186544.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/www/html/rcomp/tmp/4vq1z1293186544.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/www/html/rcomp/tmp/5vq1z1293186544.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/www/html/rcomp/tmp/6vq1z1293186544.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/www/html/rcomp/tmp/7vq1z1293186544.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/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > 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/8kqxt1293186544.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/9difw1293186544.tab") > > try(system("convert tmp/12yjw1293186544.ps tmp/12yjw1293186544.png",intern=TRUE)) character(0) > try(system("convert tmp/22yjw1293186544.ps tmp/22yjw1293186544.png",intern=TRUE)) character(0) > try(system("convert tmp/3vq1z1293186544.ps tmp/3vq1z1293186544.png",intern=TRUE)) character(0) > try(system("convert tmp/4vq1z1293186544.ps tmp/4vq1z1293186544.png",intern=TRUE)) character(0) > try(system("convert tmp/5vq1z1293186544.ps tmp/5vq1z1293186544.png",intern=TRUE)) character(0) > try(system("convert tmp/6vq1z1293186544.ps tmp/6vq1z1293186544.png",intern=TRUE)) character(0) > try(system("convert tmp/7vq1z1293186544.ps tmp/7vq1z1293186544.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.859 1.070 6.654