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Type 'q()' to quit R. > x <- c(9.769,9.321,9.939,9.336,10.195,9.464,10.010,10.213,9.563,9.890,9.305,9.391,9.928,8.686,9.843,9.627,10.074,9.503,10.119,10.000,9.313,9.866,9.172,9.241,9.659,8.904,9.755,9.080,9.435,8.971,10.063,9.793,9.454,9.759,8.820,9.403,9.676,8.642,9.402,9.610,9.294,9.448,10.319,9.548,9.801,9.596,8.923,9.746,9.829,9.125,9.782,9.441,9.162,9.915,10.444,10.209,9.985,9.842,9.429,10.132,9.849,9.172,10.313,9.819,9.955,10.048,10.082,10.541,10.208,10.233,9.439,9.963,10.158,9.225,10.474,9.757,10.490,10.281,10.444,10.640,10.695,10.786,9.832,9.747,10.411,9.511,10.402,9.701,10.540,10.112,10.915,11.183,10.384,10.834,9.886,10.216) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > par2 = '-0.8' > 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,] 0.8221502 -0.001217784 0.1765923 -0.7444096 0.3689675 -0.01634943 [2,] 0.8218002 0.000000000 0.1760446 -0.7445104 0.3685206 -0.01640681 [3,] 0.8191291 0.000000000 0.1781335 -0.7441660 0.3683784 0.00000000 [4,] 0.9983249 0.000000000 0.0000000 -0.8286972 0.4066133 0.00000000 [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,] -0.9999944 [2,] -0.9999818 [3,] -0.9999646 [4,] -0.9999988 [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 0.99383 0.16343 0 0.00885 0.91004 0 [2,] 0 NA 0.11772 0 0.00574 0.90637 0 [3,] 0 NA 0.10897 0 0.00579 NA 0 [4,] 0 NA NA 0 0.00202 NA 0 [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 0.8222 -0.0012 0.1766 -0.7444 0.3690 -0.0163 -1.0000 s.e. 0.1519 0.1571 0.1257 0.0932 0.1378 0.1443 0.1368 sigma^2 estimated as 9.978e-06: log likelihood = 356.01, aic = -696.03 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8222 -0.0012 0.1766 -0.7444 0.3690 -0.0163 -1.0000 s.e. 0.1519 0.1571 0.1257 0.0932 0.1378 0.1443 0.1368 sigma^2 estimated as 9.978e-06: log likelihood = 356.01, aic = -696.03 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8218 0 0.1760 -0.7445 0.3685 -0.0164 -1.0000 s.e. 0.1151 0 0.1114 0.0911 0.1302 0.1391 0.1305 sigma^2 estimated as 9.978e-06: log likelihood = 356.01, aic = -698.03 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8191 0 0.1781 -0.7442 0.3684 0 -1.0000 s.e. 0.1130 0 0.1100 0.0909 0.1303 0 0.1291 sigma^2 estimated as 1.002e-05: log likelihood = 356.01, aic = -700.01 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -696.0275 -698.0276 -700.0138 -699.3755 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/rcomp/tmp/1c7031293031478.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 = 96 Frequency = 1 [1] 1.614803e-04 1.676600e-04 1.592668e-04 1.674444e-04 1.560591e-04 [6] 1.656300e-04 1.583622e-04 1.558389e-04 1.642567e-04 1.598974e-04 [11] 1.678902e-04 1.666590e-04 -1.607810e-03 7.943526e-03 3.271741e-05 [16] -3.723156e-03 -1.236559e-04 -1.696434e-03 -1.404109e-03 1.901941e-03 [21] 2.687066e-03 2.232328e-04 1.456182e-03 7.462173e-04 1.531803e-03 [26] -1.575581e-03 2.046532e-04 4.409800e-03 6.664349e-03 5.197448e-03 [31] -2.172263e-03 -5.236288e-06 -4.398730e-03 -1.270575e-03 2.951372e-03 [36] -3.525324e-03 -1.348652e-03 2.237641e-03 3.183416e-03 -6.718972e-03 [41] 3.402225e-03 -6.169038e-03 -3.556418e-03 3.325433e-03 -4.688737e-03 [46] 3.035370e-03 4.507607e-04 -4.395549e-03 -1.545707e-03 -4.866984e-03 [51] -1.423228e-03 1.671513e-03 6.887402e-03 -5.182021e-03 -1.390061e-03 [56] -5.113699e-03 -2.424071e-03 2.873572e-04 -3.020963e-03 -3.869912e-03 [61] 2.043793e-03 1.059307e-03 -2.861097e-03 -1.884781e-03 -3.309455e-03 [66] -1.103100e-03 6.361556e-03 -1.261378e-03 -1.042814e-03 -1.843043e-03 [71] 1.239531e-03 1.552314e-03 -5.565007e-04 1.178561e-03 -2.027691e-03 [76] 1.460167e-03 -4.897691e-03 -2.757600e-03 -2.010030e-04 -8.398201e-05 [81] -4.442195e-03 -4.000911e-03 -2.232749e-03 5.576405e-03 3.558757e-04 [86] 1.925796e-04 1.622716e-03 3.324511e-03 -7.954845e-04 1.496867e-03 [91] -3.224491e-03 -4.990697e-03 1.482880e-03 -1.530859e-03 2.341573e-04 [96] -1.982721e-03 > postscript(file="/var/www/rcomp/tmp/2c7031293031478.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/rcomp/tmp/3c7031293031478.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/rcomp/tmp/4c7031293031478.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/rcomp/tmp/5ny071293031478.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/rcomp/tmp/6ny071293031478.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/rcomp/tmp/7ny071293031478.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/8iqff1293031478.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/rcomp/tmp/9bhf01293031478.tab") > > try(system("convert tmp/1c7031293031478.ps tmp/1c7031293031478.png",intern=TRUE)) character(0) > try(system("convert tmp/2c7031293031478.ps tmp/2c7031293031478.png",intern=TRUE)) character(0) > try(system("convert tmp/3c7031293031478.ps tmp/3c7031293031478.png",intern=TRUE)) character(0) > try(system("convert tmp/4c7031293031478.ps tmp/4c7031293031478.png",intern=TRUE)) character(0) > try(system("convert tmp/5ny071293031478.ps tmp/5ny071293031478.png",intern=TRUE)) character(0) > try(system("convert tmp/6ny071293031478.ps tmp/6ny071293031478.png",intern=TRUE)) character(0) > try(system("convert tmp/7ny071293031478.ps tmp/7ny071293031478.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.470 5.890 14.517