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Type 'q()' to quit R. > x <- c(128,502,629.7,595.9,823.7,498.7,766.9,1611.3,329.7,1378.9,1159.4,790.1,-189.6,862.4,426.6,852,834.7,1026.7,1052.8,1280.9,-243.6,976,908.2,416,610.7,728,520.8,905.8,768.9,479.3,1054.2,1411.9,-131,1526.2,1049.5,550.8,168.5,458.2,297,616.3,762.7,693.1,512.7,1169.2,-915.1,1384.2,1368.9,-275.1,-408.9,-37.5,171.5,671.8,-18.5,231.6,747.5,1505.7,-83.6,1173.2,1452.1,777,-52.8,861.2,735.2,1073.6,966.9,1189.8,1093.5,1782.7,-70.4,1471.6,1273.8,900.8,-910.2,299.8,460.2,677.2,937.1,1265.4,1275.6,1582.6,-154.2,1667.7,1083.1,891.7,-26.5,423.4,662.8,711.4,993.3,1133.2,343.9,1415.8,-531.8,1193.6,1201.3,805.6,-164.8) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > 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] [,7] [1,] -0.3002833 0.2621318 0.2424453 0.5546635 0.15254235 0.1485924 -0.9999512 [2,] 0.0000000 0.1822165 0.1994793 0.2492283 0.13601547 0.1275380 -0.9999164 [3,] 0.0000000 0.1782197 0.1898261 0.2467362 0.05591547 0.0000000 -0.8115746 [4,] 0.0000000 0.1750879 0.1988700 0.2454108 0.00000000 0.0000000 -0.7552307 [5,] 0.0000000 0.0000000 0.2104578 0.2132641 0.00000000 0.0000000 -0.7746383 [6,] 0.0000000 0.0000000 0.0000000 0.2420668 0.00000000 0.0000000 -0.8333459 [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,] 0.37139 0.06945 0.03073 0.10401 0.25560 0.28973 0.00276 [2,] NA 0.09184 0.06446 0.02615 0.30088 0.34718 0.00530 [3,] NA 0.10092 0.08693 0.02733 0.77553 NA 0.00344 [4,] NA 0.10433 0.06123 0.02803 NA NA 0.00000 [5,] NA NA 0.05474 0.02791 NA NA 0.00000 [6,] NA NA NA 0.01651 NA NA 0.00004 [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 -0.3003 0.2621 0.2424 0.5547 0.1525 0.1486 -1.0000 s.e. 0.3342 0.1426 0.1104 0.3377 0.1333 0.1395 0.3247 sigma^2 estimated as 84223: log likelihood = -612.33, aic = 1240.66 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3003 0.2621 0.2424 0.5547 0.1525 0.1486 -1.0000 s.e. 0.3342 0.1426 0.1104 0.3377 0.1333 0.1395 0.3247 sigma^2 estimated as 84223: log likelihood = -612.33, aic = 1240.66 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1822 0.1995 0.2492 0.1360 0.1275 -0.9999 s.e. 0 0.1069 0.1066 0.1102 0.1307 0.1350 0.3499 sigma^2 estimated as 83952: log likelihood = -612.54, aic = 1239.07 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1782 0.1898 0.2467 0.0559 0 -0.8116 s.e. 0 0.1075 0.1097 0.1100 0.1955 0 0.2702 sigma^2 estimated as 93628: log likelihood = -612.93, aic = 1237.86 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.1751 0.1989 0.2454 0 0 -0.7552 s.e. 0 0.1067 0.1049 0.1099 0 0 0.1465 sigma^2 estimated as 95282: log likelihood = -612.97, aic = 1235.94 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0.2105 0.2133 0 0 -0.7746 s.e. 0 0 0.1082 0.0955 0 0 0.1513 sigma^2 estimated as 97468: log likelihood = -614.31, aic = 1236.62 [[3]][[7]] NULL $aic [1] 1240.660 1239.071 1237.858 1235.941 1236.616 1238.288 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 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 > postscript(file="/var/www/html/rcomp/tmp/10h2j1260902136.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] 0.1279999 0.5019995 0.6296993 0.5958992 0.8236988 [6] 0.4986990 0.7668988 1.6112979 0.3296988 1.3788982 [11] 1.1593979 0.7900987 -240.2867769 328.5918119 -213.6358769 [16] 298.4579946 -113.6159257 473.1454634 77.4372153 -273.7531623 [21] -493.6917098 -287.1766344 -55.7290038 -246.9996145 688.3385089 [26] -78.5266784 71.8890910 22.6063223 -66.3498228 -249.2389812 [31] 146.4960678 -42.4452034 -87.1260123 317.3993197 -27.8801553 [36] -15.9787636 -93.3727852 -218.5746203 -156.9549327 -127.8816761 [41] 34.2690911 65.2513815 -408.1186996 -150.1544039 -819.3025162 [46] 345.5824952 301.5156562 -710.7192062 -450.6548776 -617.0549182 [51] 26.5610562 50.9341673 -663.6259797 -229.5544203 -16.6469347 [56] 319.8613820 224.9073150 -182.7815149 314.6277211 319.6369253 [61] -117.5285712 365.8127811 183.2024516 324.2965990 230.9146530 [66] 501.8663753 101.4945032 281.2655362 -28.2772473 127.9248209 [71] -54.5614951 417.6066885 -1036.6093991 -40.6019158 -90.7097606 [76] 79.5489788 296.9273770 484.6204069 316.5476671 -31.0220971 [81] -68.5794564 260.9583598 -223.9659821 360.7399950 38.2771501 [86] -45.0885708 137.6158157 -134.3193022 298.0866178 180.5723533 [91] -651.7096669 -10.8084715 -402.7340496 -2.5094723 24.5095731 [96] 227.2428227 1.2519083 > postscript(file="/var/www/html/rcomp/tmp/2m8xg1260902136.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/3b5pg1260902136.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/4uqyl1260902136.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/5ao6j1260902136.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/6nivs1260902136.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/7wlla1260902136.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/8ra0c1260902136.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/9qs331260902136.tab") > > try(system("convert tmp/10h2j1260902136.ps tmp/10h2j1260902136.png",intern=TRUE)) character(0) > try(system("convert tmp/2m8xg1260902136.ps tmp/2m8xg1260902136.png",intern=TRUE)) character(0) > try(system("convert tmp/3b5pg1260902136.ps tmp/3b5pg1260902136.png",intern=TRUE)) character(0) > try(system("convert tmp/4uqyl1260902136.ps tmp/4uqyl1260902136.png",intern=TRUE)) character(0) > try(system("convert tmp/5ao6j1260902136.ps tmp/5ao6j1260902136.png",intern=TRUE)) character(0) > try(system("convert tmp/6nivs1260902136.ps tmp/6nivs1260902136.png",intern=TRUE)) character(0) > try(system("convert tmp/7wlla1260902136.ps tmp/7wlla1260902136.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.044 1.832 11.757