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Type 'q()' to quit R. > x <- c(10.81,9.12,11.03,12.74,9.98,11.62,9.40,9.27,7.76,8.78,10.65,10.95,12.36,10.85,11.84,12.14,11.65,8.86,7.63,7.38,7.25,8.03,7.75,7.16,7.18,7.51,7.07,7.11,8.98,9.53,10.54,11.31,10.36,11.44,10.45,10.69,11.28,11.96,13.52,12.89,14.03,16.27,16.17,17.25,19.38,26.20,33.53,32.20,38.45,44.86,41.67,36.06,39.76,36.81,42.65,46.89,53.61,57.59,67.82,71.89,75.51,68.49,62.72,70.39,59.77,57.27,67.96,67.85,76.98,81.08,91.66,84.84,85.73,84.61,92.91,99.80,121.19,122.04,131.76,138.48,153.47,189.95,182.22,198.08,135.36,125.02,143.50,173.95,188.75,167.44,158.95,169.53,113.66,107.59,92.67,85.35,90.13,89.31,105.12,125.83,135.81,142.43,163.39,168.21,185.35,188.50,199.91,210.73,192.06,204.62,235.00,261.09,256.88,251.53,257.25,243.10,283.75) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '-0.1' > 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,] -0.8622643 0.2046685 0.20243394 0.9581421 0.6701266 -0.10444067 [2,] -0.8680501 0.2030138 0.19858137 0.9634394 0.1231711 -0.02187674 [3,] -0.8713733 0.2028264 0.19849024 0.9659718 0.1223167 0.00000000 [4,] -0.8470474 0.2186732 0.20387299 0.9490285 0.0000000 0.00000000 [5,] 1.0795796 0.0000000 -0.08386216 -0.9830929 0.0000000 0.00000000 [6,] 0.9653868 0.0000000 0.00000000 -0.9227627 0.0000000 0.00000000 [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,] -0.5478324 [2,] 0.0000000 [3,] 0.0000000 [4,] 0.0000000 [5,] 0.0000000 [6,] 0.0000000 [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 0.09063 0.04724 0 0.43772 0.44371 0.52485 [2,] 0 0.09407 0.05037 0 0.23760 0.83589 NA [3,] 0 0.09499 0.04995 0 0.23881 NA NA [4,] 0 0.06724 0.06056 0 NA NA NA [5,] 0 NA 0.20968 0 NA NA NA [6,] 0 NA NA 0 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 -0.8623 0.2047 0.2024 0.9581 0.6701 -0.1044 -0.5478 s.e. 0.1079 0.1199 0.1009 0.0702 0.8603 0.1359 0.8587 sigma^2 estimated as 7.051e-05: log likelihood = 389.53, aic = -763.07 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.8623 0.2047 0.2024 0.9581 0.6701 -0.1044 -0.5478 s.e. 0.1079 0.1199 0.1009 0.0702 0.8603 0.1359 0.8587 sigma^2 estimated as 7.051e-05: log likelihood = 389.53, aic = -763.07 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.8681 0.2030 0.1986 0.9634 0.1232 -0.0219 0 s.e. 0.1039 0.1202 0.1004 0.0635 0.1037 0.1054 0 sigma^2 estimated as 7.061e-05: log likelihood = 389.47, aic = -764.93 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.8714 0.2028 0.1985 0.9660 0.1223 0 0 s.e. 0.1011 0.1204 0.1001 0.0588 0.1033 0 0 sigma^2 estimated as 7.064e-05: log likelihood = 389.44, aic = -766.89 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.8470 0.2187 0.2039 0.9490 0 0 0 s.e. 0.1272 0.1183 0.1075 0.1124 0 0 0 sigma^2 estimated as 7.162e-05: log likelihood = 388.75, aic = -767.5 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 1.0796 0 -0.0839 -0.9831 0 0 0 s.e. 0.0724 0 0.0665 0.0423 0 0 0 sigma^2 estimated as 7.327e-05: log likelihood = 387.46, aic = -766.92 [[3]][[7]] NULL $aic [1] -763.0653 -764.9305 -766.8875 -767.5007 -766.9249 -767.6932 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 log(s2) : NaNs produced 5: 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/1aqrp1292504270.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 = 117 Frequency = 1 [1] 7.881651e-04 1.324892e-02 -1.657857e-02 -1.100669e-02 2.156910e-02 [6] -1.279682e-02 1.617784e-02 4.395524e-04 1.252014e-02 -1.179008e-02 [11] -1.596928e-02 -3.371225e-06 -7.923246e-03 1.133829e-02 -6.914910e-03 [16] -2.117543e-03 4.090633e-03 2.160282e-02 9.642380e-03 -6.253637e-04 [21] -2.748919e-04 -9.140630e-03 3.148464e-03 6.553562e-03 -1.491606e-03 [26] -4.643788e-03 4.901887e-03 -1.016635e-03 -1.973235e-02 -3.217070e-03 [31] -6.045661e-03 -4.429775e-03 8.147164e-03 -7.936634e-03 7.291001e-03 [36] -1.748294e-03 -4.654856e-03 -3.982107e-03 -8.609257e-03 5.137494e-03 [41] -5.847786e-03 -1.075984e-02 2.397419e-03 -3.572468e-03 -7.885162e-03 [46] -2.036375e-02 -1.412896e-02 7.218027e-03 -1.025969e-02 -8.750062e-03 [51] 8.166875e-03 1.153527e-02 -7.174803e-03 6.058804e-03 -9.173568e-03 [56] -5.054217e-03 -6.568513e-03 -2.322534e-03 -8.459211e-03 -1.206755e-03 [61] -6.678584e-04 8.245192e-03 6.700016e-03 -7.517574e-03 1.210308e-02 [66] 3.586557e-03 -1.149677e-02 1.933559e-03 -6.201796e-03 -1.511315e-03 [71] -5.709032e-03 7.116568e-03 7.093325e-04 1.603103e-03 -4.894581e-03 [76] -2.944160e-03 -1.006437e-02 2.277769e-03 -2.395570e-03 -1.319776e-03 [81] -4.288936e-03 -1.062054e-02 5.538682e-03 -2.677616e-03 2.449141e-02 [86] 4.471686e-03 -9.737051e-03 -1.012832e-02 -1.877238e-03 9.821480e-03 [91] 4.101795e-03 -3.619298e-03 2.562744e-02 2.521257e-03 7.871564e-03 [96] 4.866217e-03 -4.083449e-03 1.120006e-03 -9.402855e-03 -9.584324e-03 [101] -1.980520e-03 -6.557146e-04 -6.749321e-03 1.936745e-04 -3.953969e-03 [106] 6.623877e-04 -1.886709e-03 -1.688205e-03 7.055491e-03 -2.980434e-03 [111] -7.233708e-03 -4.001653e-03 3.235165e-03 2.705542e-03 -4.474546e-04 [116] 4.290132e-03 -8.054838e-03 > postscript(file="/var/www/html/freestat/rcomp/tmp/23ira1292504270.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/33ira1292504270.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/43ira1292504270.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/53ira1292504270.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/6wrqu1292504270.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/7wrqu1292504270.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/8s1531292504270.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/9ksno1292504270.tab") > try(system("convert tmp/1aqrp1292504270.ps tmp/1aqrp1292504270.png",intern=TRUE)) character(0) > try(system("convert tmp/23ira1292504270.ps tmp/23ira1292504270.png",intern=TRUE)) character(0) > try(system("convert tmp/33ira1292504270.ps tmp/33ira1292504270.png",intern=TRUE)) character(0) > try(system("convert tmp/43ira1292504270.ps tmp/43ira1292504270.png",intern=TRUE)) character(0) > try(system("convert tmp/53ira1292504270.ps tmp/53ira1292504270.png",intern=TRUE)) character(0) > try(system("convert tmp/6wrqu1292504270.ps tmp/6wrqu1292504270.png",intern=TRUE)) character(0) > try(system("convert tmp/7wrqu1292504270.ps tmp/7wrqu1292504270.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 13.778 2.632 15.253