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Type 'q()' to quit R. > x <- c(595.130,526.883,562.254,545.427,522.084,483.414,528.797,532.749,511.380,472.941,516.118,502.940,476.118,432.418,475.525,453.638,431.417,390.934,436.414,418.451,399.528,367.749,423.433,420.450,415.906,392.949,453.203,455.926,451.879,434.996,498.811,505.940,517.395,508.456,585.132,587.971,584.027,557.196,613.433,600.049,588.993,559.271,622.580,616.645,603.243,557.949,608.882,582.930,570.492,542.907,598.067,568.717,551.773,514.465,569.055,528.897,515.229,481.141,535.612,498.547,478.587,445.911,503.412,469.797,458.365,436.761,502.205,481.627,473.698,457.200,521.671,513.354,515.369,505.652,575.676,555.865,559.504,540.994,605.635,600.315,588.224,569.861,625.950,601.554,587.760,573.307,621.764,570.214,547.034,511.873,553.870,517.058,505.702,479.060,526.638,508.060,532.394,532.115,587.896,565.710,572.708,544.417,597.160) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '4' > par4 = '1' > par3 = '2' > 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] [1,] -0.7731425 -0.3294710 -0.21518237 0.4539161 -0.1164901 -0.08626921 [2,] 0.6058696 0.1195719 -0.01140770 -1.0012589 0.0000000 -0.04960079 [3,] 0.6054326 0.1134372 0.00000000 -1.0012494 0.0000000 -0.04911495 [4,] 0.6056604 0.1155282 0.00000000 -1.0011113 0.0000000 0.00000000 [5,] 0.6744666 0.0000000 0.00000000 -1.0012572 0.0000000 0.00000000 [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,] -0.5194731 [2,] -0.4406932 [3,] -0.4448552 [4,] -0.4621955 [5,] -0.4396775 [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,] 0.08846 0.13819 0.22322 0.30778 0.72872 0.69314 0.12026 [2,] 0.00000 0.34496 0.92188 0.00000 NA 0.70329 0.00037 [3,] 0.00000 0.30253 NA 0.00000 NA 0.70653 0.00013 [4,] 0.00000 0.29172 NA 0.00000 NA NA 0.00002 [5,] 0.00000 NA NA 0.00000 NA NA 0.00005 [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 -0.7731 -0.3295 -0.2152 0.4539 -0.1165 -0.0863 -0.5195 s.e. 0.4492 0.2204 0.1755 0.4427 0.3349 0.2180 0.3313 sigma^2 estimated as 84.65: log likelihood = -357.66, aic = 731.32 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.7731 -0.3295 -0.2152 0.4539 -0.1165 -0.0863 -0.5195 s.e. 0.4492 0.2204 0.1755 0.4427 0.3349 0.2180 0.3313 sigma^2 estimated as 84.65: log likelihood = -357.66, aic = 731.32 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6059 0.1196 -0.0114 -1.0013 0 -0.0496 -0.4407 s.e. 0.1057 0.1260 0.1160 0.0510 0 0.1298 0.1194 sigma^2 estimated as 78.05: log likelihood = -355.25, aic = 724.51 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6054 0.1134 0 -1.0012 0 -0.0491 -0.4449 s.e. 0.1057 0.1094 0 0.0508 0 0.1301 0.1115 sigma^2 estimated as 78.07: log likelihood = -355.26, aic = 722.52 [[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 sar2 sma1 0.6057 0.1155 0 -1.0011 0 0 -0.4622 s.e. 0.1057 0.1090 0 0.0481 0 0 0.1034 sigma^2 estimated as 78.25: log likelihood = -355.33, aic = 720.66 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 731.3151 724.5087 722.5183 720.6591 719.7791 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/www/html/freestat/rcomp/tmp/18frw1293563722.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] 0.179438434 -0.175467745 -0.198929331 -0.250767362 -0.627569541 [6] 1.755891752 -15.953844589 5.441286012 -14.735889629 -4.016761671 [11] -10.670637714 -13.804794671 -0.821590906 -1.560575757 -1.545950774 [16] -14.674233910 8.959061761 -0.173952309 -1.746672057 -5.647408432 [21] 3.710040451 4.999473334 2.314217961 3.470657530 3.893623700 [26] -1.199990348 -3.043747646 1.943728301 -3.146070605 3.130638442 [31] -3.059181356 0.996784625 9.305950228 -2.293092311 3.043739598 [36] -14.158172422 -11.097349765 -9.847259169 -6.869111154 -8.629705166 [41] -0.426863802 -1.756542975 5.794511234 -1.329387621 -8.637787985 [46] -16.281636758 -0.227840781 -11.396058694 10.404835022 11.389779021 [51] -7.366017011 -13.752339170 1.516325227 -1.574128972 2.206122729 [56] -15.885698443 10.382540352 1.438553900 -1.738003570 -4.864013408 [61] -3.609087187 5.305509203 1.809763215 -1.119100449 4.033880539 [66] 7.483954926 0.576733260 5.864769530 -4.008253086 4.413525509 [71] -4.691054968 14.424391464 0.132845350 0.733509409 -2.507649190 [76] -9.480518358 7.550831829 -8.545493195 -1.778537010 13.877613851 [81] -20.839029759 3.700090465 -7.992926151 -7.702231571 1.011813593 [86] 8.548900144 -13.775469620 -26.611519869 8.415838849 -7.893095877 [91] 0.857858372 8.622982678 7.279029865 -4.261709293 -0.806916631 [96] 17.520085445 26.821423841 -0.001922378 -12.868185540 -3.998000257 [101] -4.016290469 -17.230014092 9.837571338 > postscript(file="/var/www/html/freestat/rcomp/tmp/28frw1293563722.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/freestat/rcomp/tmp/38frw1293563722.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/freestat/rcomp/tmp/48frw1293563722.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/freestat/rcomp/tmp/50oqh1293563722.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/freestat/rcomp/tmp/60oqh1293563722.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/freestat/rcomp/tmp/70oqh1293563722.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/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/8wgo81293563722.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/freestat/rcomp/tmp/9775b1293563722.tab") > > try(system("convert tmp/18frw1293563722.ps tmp/18frw1293563722.png",intern=TRUE)) character(0) > try(system("convert tmp/28frw1293563722.ps tmp/28frw1293563722.png",intern=TRUE)) character(0) > try(system("convert tmp/38frw1293563722.ps tmp/38frw1293563722.png",intern=TRUE)) character(0) > try(system("convert tmp/48frw1293563722.ps tmp/48frw1293563722.png",intern=TRUE)) character(0) > try(system("convert tmp/50oqh1293563722.ps tmp/50oqh1293563722.png",intern=TRUE)) character(0) > try(system("convert tmp/60oqh1293563722.ps tmp/60oqh1293563722.png",intern=TRUE)) character(0) > try(system("convert tmp/70oqh1293563722.ps tmp/70oqh1293563722.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.497 1.490 4.785