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Type 'q()' to quit R. > x <- c(4.031636,3.702076,3.056176,3.280707,2.984728,3.693712,3.226317,2.190349,2.599515,3.080288,2.929672,2.922548,3.234943,2.983081,3.284389,3.806511,3.784579,2.645654,3.092081,3.204859,3.107225,3.466909,2.984404,3.218072,2.82731,3.182049,2.236319,2.033218,1.644804,1.627971,1.677559,2.330828,2.493615,2.257172,2.655517,2.298655,2.600402,3.04523,2.790583,3.227052,2.967479,2.938817,3.277961,3.423985,3.072646,2.754253,2.910431,3.174369,3.068387,3.089543,2.906654,2.931161,3.02566,2.939551,2.691019,3.19812,3.07639,2.863873,3.013802,3.053364,2.864753,3.057062,2.959365,3.252258,3.602988,3.497704,3.296867,3.602417,3.3001,3.40193,3.502591,3.402348,3.498551,3.199823,2.700064,2.801034,2.898628,2.800854,2.399942,2.402724,2.202331,2.102594,1.798293,1.202484,1.400201,1.200832,1.298083,1.099742,1.001377,0.8361743) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > 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,] 0.6072255 0.02587528 0.20266045 -0.8365136 -1.0230441 -0.04078651 [2,] 0.5903147 0.00000000 0.20884846 -0.8100360 -1.0225065 -0.04050787 [3,] 0.5913451 0.00000000 0.20596329 -0.8099595 -0.9322736 0.00000000 [4,] 0.0000000 0.00000000 0.05828268 -0.2606289 -0.9494169 0.00000000 [5,] 0.0000000 0.00000000 0.00000000 -0.2577149 -0.9511537 0.00000000 [6,] 0.0000000 0.00000000 0.00000000 -0.2582403 -0.1149084 0.00000000 [7,] 0.0000000 0.00000000 0.00000000 -0.2418855 0.0000000 0.00000000 [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,] 0.9329699 [2,] 0.9326290 [3,] 0.8436447 [4,] 0.8767875 [5,] 0.8816960 [6,] 0.0000000 [7,] 0.0000000 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.04113 0.84931 0.07599 0.00397 0.01778 0.82018 0.13262 [2,] 0.07783 NA 0.07516 0.00759 0.00002 0.78655 0.00115 [3,] 0.08310 NA 0.08082 0.00890 0.00228 NA 0.06130 [4,] NA NA 0.59087 0.02863 0.00211 NA 0.05964 [5,] NA NA NA 0.02412 0.00271 NA 0.06361 [6,] NA NA NA 0.02431 0.34753 NA NA [7,] NA NA NA 0.03335 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 0.6072 0.0259 0.2027 -0.8365 -1.0230 -0.0408 0.9330 s.e. 0.2926 0.1358 0.1128 0.2822 0.4229 0.1789 0.6142 sigma^2 estimated as 0.1059: log likelihood = -28.13, aic = 72.27 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6072 0.0259 0.2027 -0.8365 -1.0230 -0.0408 0.9330 s.e. 0.2926 0.1358 0.1128 0.2822 0.4229 0.1789 0.6142 sigma^2 estimated as 0.1059: log likelihood = -28.13, aic = 72.27 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5903 0 0.2088 -0.810 -1.0225 -0.0405 0.9326 s.e. 0.3306 0 0.1159 0.296 0.2276 0.1491 0.2770 sigma^2 estimated as 0.1060: log likelihood = -28.15, aic = 70.31 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5913 0 0.2060 -0.8100 -0.9323 0 0.8436 s.e. 0.3372 0 0.1165 0.3024 0.2962 0 0.4448 sigma^2 estimated as 0.1076: log likelihood = -28.18, aic = 68.36 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0.0583 -0.2606 -0.9494 0 0.8768 s.e. 0 0 0.1080 0.1171 0.2993 0 0.4593 sigma^2 estimated as 0.1097: log likelihood = -29.06, aic = 68.13 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0 -0.2577 -0.9512 0 0.8817 s.e. 0 0 0 0.1123 0.3080 0 0.4692 sigma^2 estimated as 0.1101: log likelihood = -29.21, aic = 66.42 [[3]][[7]] 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 0 0 -0.2582 -0.1149 0 0 s.e. 0 0 0 0.1127 0.1217 0 0 sigma^2 estimated as 0.1143: log likelihood = -29.9, aic = 65.79 $aic [1] 72.26896 70.30516 68.36026 68.12708 66.41772 65.79214 64.67684 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 log(s2) : NaNs produced 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 5: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 6: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 7: 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/1knr01292792833.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 = 90 Frequency = 1 [1] 0.004031634 -0.316977256 -0.719379782 0.037651698 -0.284293997 [6] 0.630871867 -0.301382543 -1.106934968 0.120600463 0.508732309 [11] -0.018243130 -0.011788040 0.321694657 -0.207085441 0.173628080 [16] 0.592757957 0.097131254 -1.032373578 0.126118894 0.026305572 [21] -0.043824234 0.403611666 -0.395583229 0.130693846 -0.321114771 [26] 0.242873156 -0.848387537 -0.362192687 -0.484466933 -0.272813941 [31] 0.030434643 0.674087591 0.325644642 -0.111017709 0.314231877 [36] -0.248864240 0.192578383 0.535321993 -0.225077578 0.355006882 [41] -0.212527930 -0.085479537 0.322767813 0.304441757 -0.254014266 [46] -0.411159014 0.095773339 0.247664102 -0.007351877 0.070371917 [51] -0.193977209 0.024568209 0.071016388 -0.071063208 -0.227912896 [56] 0.465024079 -0.042013823 -0.259952690 0.100744892 0.095907085 [61] -0.176022143 0.149283984 -0.080161334 0.275008170 0.432606931 [66] -0.003462086 -0.230289462 0.304350130 -0.237709318 0.016023879 [71] 0.122027112 -0.064184672 0.057954943 -0.261663779 -0.578557348 [76] -0.014780983 0.134078773 -0.075247467 -0.443421787 -0.076617134 [81] -0.254917394 -0.153865833 -0.332468571 -0.693184558 0.029763317 [86] -0.226009264 -0.018540210 -0.191526530 -0.136610507 -0.211716096 > postscript(file="/var/www/html/freestat/rcomp/tmp/2ce831292792833.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/3ce831292792833.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/4ce831292792833.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/5ce831292792833.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/6n5861292792833.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/7n5861292792833.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/81f5f1292792833.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/9u65i1292792833.tab") > > try(system("convert tmp/1knr01292792833.ps tmp/1knr01292792833.png",intern=TRUE)) character(0) > try(system("convert tmp/2ce831292792833.ps tmp/2ce831292792833.png",intern=TRUE)) character(0) > try(system("convert tmp/3ce831292792833.ps tmp/3ce831292792833.png",intern=TRUE)) character(0) > try(system("convert tmp/4ce831292792833.ps tmp/4ce831292792833.png",intern=TRUE)) character(0) > try(system("convert tmp/5ce831292792833.ps tmp/5ce831292792833.png",intern=TRUE)) character(0) > try(system("convert tmp/6n5861292792833.ps tmp/6n5861292792833.png",intern=TRUE)) character(0) > try(system("convert tmp/7n5861292792833.ps tmp/7n5861292792833.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.543 2.574 14.137