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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 = '1' > par3 = '1' > par2 = '-0.9' > 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,] -0.9364248 -0.4879465 0.02953959 0.3479095 -0.05645910 -0.09065357 [2,] -0.6060994 -0.2779095 0.14086848 0.0000000 -0.04530251 -0.06351415 [3,] -0.6058090 -0.2806960 0.13492322 0.0000000 0.00000000 -0.04766532 [4,] -0.6157152 -0.2872788 0.13256373 0.0000000 0.00000000 0.00000000 [5,] -0.6655386 -0.3757788 0.00000000 0.0000000 0.00000000 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,] -0.9664712 [2,] -0.9999785 [3,] -1.0000350 [4,] -1.0000194 [5,] -0.9999897 [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 0.00000 0.00000 0.00149 0.00000 0.00000 0 [2,] 0 0.03659 0.22024 NA 0.75366 0.67118 0 [3,] 0 0.03449 0.23415 NA NA 0.73898 0 [4,] 0 0.02917 0.24173 NA NA NA 0 [5,] 0 0.00061 NA NA NA 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 -0.9364 -0.4879 0.0295 0.3479 -0.0565 -0.0907 -0.9665 s.e. 0.0059 0.0032 0.0053 0.1061 0.0052 0.0096 0.1988 sigma^2 estimated as 5.569e-09: log likelihood = 666.16, aic = -1316.32 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.9364 -0.4879 0.0295 0.3479 -0.0565 -0.0907 -0.9665 s.e. 0.0059 0.0032 0.0053 0.1061 0.0052 0.0096 0.1988 sigma^2 estimated as 5.569e-09: log likelihood = 666.16, aic = -1316.32 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6061 -0.2779 0.1409 0 -0.0453 -0.0635 -1.0000 s.e. 0.1128 0.1310 0.1141 0 0.1439 0.1491 0.2003 sigma^2 estimated as 5.464e-09: log likelihood = 666.02, aic = -1318.04 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6058 -0.2807 0.1349 0 0 -0.0477 -1.0000 s.e. 0.1131 0.1308 0.1127 0 0 0.1426 0.1851 sigma^2 estimated as 5.551e-09: log likelihood = 665.97, aic = -1319.94 [[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 -0.6157 -0.2873 0.1326 0 0 0 -1.0000 s.e. 0.1091 0.1296 0.1125 0 0 0 0.1802 sigma^2 estimated as 5.617e-09: log likelihood = 665.91, aic = -1321.83 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -1316.318 -1318.035 -1319.938 -1321.828 -1322.458 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/html/rcomp/tmp/1fjpp1229250800.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] 7.576087e-07 4.097881e-07 1.094867e-07 1.819357e-07 -1.700808e-08 [6] -6.635643e-08 1.037111e-07 2.124037e-08 1.499477e-07 -1.817770e-08 [11] 8.264809e-08 -7.450168e-07 -4.772483e-06 -1.654979e-07 1.645751e-04 [16] 1.629174e-04 2.301757e-05 9.765115e-06 6.162743e-05 -8.467170e-06 [21] -1.343607e-04 3.247502e-05 -1.092486e-04 2.999048e-05 -3.466594e-05 [26] -5.691196e-06 8.187265e-05 -1.166592e-04 5.753267e-05 7.464693e-05 [31] 6.216707e-06 -6.499282e-05 6.113729e-05 8.040516e-05 -1.194507e-06 [36] 6.322524e-05 8.950378e-07 -6.210236e-05 1.071237e-04 -3.496728e-05 [41] 1.514881e-05 -1.481068e-06 -2.111825e-05 3.188447e-05 -2.222271e-05 [46] -2.792947e-04 -8.238022e-05 1.243065e-04 2.309384e-05 1.257347e-05 [51] -6.735861e-06 -3.226504e-06 3.422602e-05 -5.077897e-05 3.345524e-05 [56] -1.003546e-04 -5.851759e-05 -4.394644e-06 4.702111e-05 4.416596e-05 [61] -6.171216e-05 6.436172e-05 -3.206663e-05 -6.181994e-05 6.972336e-05 [66] 2.005328e-05 -1.160021e-04 -2.826263e-05 -2.576966e-05 -5.461009e-05 [71] 1.313340e-04 -8.623129e-05 3.443809e-05 -5.385627e-05 2.368811e-06 [76] -1.746456e-05 -1.033685e-04 -1.326372e-06 -6.746267e-05 4.718209e-05 [81] -8.667724e-06 3.310978e-05 -5.147026e-05 -4.392625e-05 4.747098e-05 [86] -7.624886e-05 9.080864e-05 -1.065159e-04 1.741811e-05 -5.856344e-06 [91] 4.216604e-05 3.323017e-05 1.474723e-04 2.098024e-04 -6.149130e-06 [96] -3.887524e-05 -6.454748e-05 > postscript(file="/var/www/html/rcomp/tmp/2jrdz1229250800.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/3z7yf1229250800.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/4xnfb1229250800.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/56ehj1229250800.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/6gb111229250801.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/7dy8e1229250801.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/8c8651229250801.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/9eoqn1229250801.tab") > > system("convert tmp/1fjpp1229250800.ps tmp/1fjpp1229250800.png") > system("convert tmp/2jrdz1229250800.ps tmp/2jrdz1229250800.png") > system("convert tmp/3z7yf1229250800.ps tmp/3z7yf1229250800.png") > system("convert tmp/4xnfb1229250800.ps tmp/4xnfb1229250800.png") > system("convert tmp/56ehj1229250800.ps tmp/56ehj1229250800.png") > system("convert tmp/6gb111229250801.ps tmp/6gb111229250801.png") > system("convert tmp/7dy8e1229250801.ps tmp/7dy8e1229250801.png") > > > proc.time() user system elapsed 14.277 2.609 15.353