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Type 'q()' to quit R. > x <- c(1593,1477.9,1733.7,1569.7,1843.7,1950.3,1657.5,1772.1,1568.3,1809.8,1646.7,1808.5,1763.9,1625.5,1538.8,1342.4,1645.1,1619.9,1338.1,1505.5,1529.1,1511.9,1656.7,1694.4,1662.3,1588.7,1483.3,1585.6,1658.9,1584.4,1470.6,1618.7,1407.6,1473.9,1515.3,1485.4,1496.1,1493.5,1298.4,1375.3,1507.9,1455.3,1363.3,1392.8,1348.8,1880.3,1669.2,1543.6,1701.2,1516.5,1466.8,1484.1,1577.2,1684.5,1414.7,1674.5,1598.7,1739.1,1674.6,1671.8,1802,1526.8,1580.9,1634.8,1610.3,1712,1678.8,1708.1,1680.6,2056,1624,2021.4,1861.1,1750.8,1767.5,1710.3,2151.5,2047.9,1915.4,1984.7,1896.5,2170.8,2139.9,2330.5,2121.8,2226.8,1857.9,2155.9,2341.7,2290.2,2006.5,2111.9,1731.3,1762.2,1863.2,1943.5,1975.2) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '-1.0' > 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,] 0.3363550 0.2769232 0.3280176 -0.9999827 1.0154166 -0.01632439 -0.9624358 [2,] 0.1340096 0.1885475 0.2313687 -0.8340180 1.2504838 0.00000000 -1.0000167 [3,] 0.0000000 0.0655014 0.2119079 -0.6498728 0.9953810 0.00000000 -0.9204083 [4,] 0.0000000 0.0000000 0.2024955 -0.6226751 0.9961599 0.00000000 -0.9276549 [5,] 0.0000000 0.0000000 0.0000000 -0.5977964 0.9954439 0.00000000 -0.9316596 [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,] 0.00102 0.00710 0.00182 0.0000 0 0.90699 0 [2,] 0.64568 0.39243 0.14147 0.0021 0 NA 0 [3,] NA 0.58062 0.05987 0.0000 0 NA 0 [4,] NA NA 0.06967 0.0000 0 NA 0 [5,] NA NA NA 0.0000 0 NA 0 [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 0.3364 0.2769 0.328 -1.000 1.0154 -0.0163 -0.9624 s.e. 0.0990 0.1005 0.102 0.021 0.1394 0.1393 0.0970 sigma^2 estimated as 1.631e-09: log likelihood = 825.02, aic = -1634.03 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3364 0.2769 0.328 -1.000 1.0154 -0.0163 -0.9624 s.e. 0.0990 0.1005 0.102 0.021 0.1394 0.1393 0.0970 sigma^2 estimated as 1.631e-09: log likelihood = 825.02, aic = -1634.03 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1340 0.1885 0.2314 -0.8340 1.2505 0 -1.0000 s.e. 0.2905 0.2194 0.1560 0.2633 0.0204 0 0.1355 sigma^2 estimated as 2.774e-09: log likelihood = 822.74, aic = -1631.49 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.0655 0.2119 -0.6499 0.9954 0 -0.9204 s.e. 0 0.1181 0.1112 0.1070 0.0060 0 0.0540 sigma^2 estimated as 1.728e-09: log likelihood = 823.78, aic = -1635.56 [[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 0.2025 -0.6227 0.9962 0 -0.9277 s.e. 0 0 0.1103 0.0910 0.0040 0 0.0411 sigma^2 estimated as 1.725e-09: log likelihood = 823.64, aic = -1637.28 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -1634.030 -1631.490 -1635.562 -1637.276 -1635.951 Warning messages: 1: In log(s2) : NaNs produced 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 log(s2) : NaNs produced 6: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 7: In log(s2) : NaNs produced 8: 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/1rfob1229861602.ps",horizontal=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 = 97 Frequency = 1 [1] 6.277457e-07 3.198578e-05 -5.702659e-05 1.336136e-05 -7.416210e-05 [6] -5.333095e-05 2.841738e-05 1.421352e-06 6.467576e-05 -4.174344e-05 [11] 2.033868e-05 -3.547219e-05 -5.051281e-07 1.595085e-05 8.203814e-05 [16] 1.107954e-04 -2.035792e-05 -6.881804e-06 6.297030e-05 -1.881843e-06 [21] -3.956588e-05 -4.548699e-06 -6.282090e-05 -1.798864e-05 -2.022194e-05 [26] 8.690671e-07 5.546793e-05 -4.621869e-05 8.299774e-06 2.475142e-05 [31] 1.670688e-05 -2.096534e-05 4.896802e-05 2.398928e-05 2.781915e-06 [36] 2.325723e-05 -1.113347e-06 -2.257766e-05 7.659917e-05 -1.244576e-05 [41] -7.338949e-06 -3.712803e-06 -3.504274e-06 2.326158e-05 -4.000906e-08 [46] -1.706069e-04 -4.470403e-05 3.596827e-05 -8.977076e-06 2.704135e-05 [51] 1.084110e-05 1.036976e-06 1.014860e-05 -3.895207e-05 3.156733e-05 [56] -5.332750e-05 -2.804558e-05 -2.027279e-05 1.355041e-05 1.470795e-05 [61] -3.183916e-05 4.401620e-05 -1.120671e-05 -2.746598e-05 3.227616e-05 [66] -6.214723e-06 -5.138160e-05 -8.023757e-06 -1.918684e-05 -5.072306e-05 [71] 7.448413e-05 -6.196039e-05 1.992365e-05 -1.700064e-05 -3.811128e-06 [76] 1.737960e-06 -6.847699e-05 -1.031357e-05 -3.353540e-05 1.725394e-05 [81] 1.544272e-07 2.637786e-06 -2.378491e-05 -3.110408e-05 2.288551e-05 [86] -4.178774e-05 5.560698e-05 -5.244951e-05 -1.951259e-06 -5.063343e-06 [91] 1.889073e-05 2.221960e-05 8.594550e-05 1.049701e-04 7.941112e-06 [96] -9.554709e-06 -2.906208e-05 > postscript(file="/var/www/html/rcomp/tmp/2ya5f1229861603.ps",horizontal=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/3wqo51229861603.ps",horizontal=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/423p31229861603.ps",horizontal=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/5edrt1229861603.ps",horizontal=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/6sq6i1229861603.ps",horizontal=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/7ckev1229861603.ps",horizontal=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/8ehco1229861603.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/9iobk1229861603.tab") > > system("convert tmp/1rfob1229861602.ps tmp/1rfob1229861602.png") > system("convert tmp/2ya5f1229861603.ps tmp/2ya5f1229861603.png") > system("convert tmp/3wqo51229861603.ps tmp/3wqo51229861603.png") > system("convert tmp/423p31229861603.ps tmp/423p31229861603.png") > system("convert tmp/5edrt1229861603.ps tmp/5edrt1229861603.png") > system("convert tmp/6sq6i1229861603.ps tmp/6sq6i1229861603.png") > system("convert tmp/7ckev1229861603.ps tmp/7ckev1229861603.png") > > > proc.time() user system elapsed 8.266 2.121 11.083