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Type 'q()' to quit R. > x <- c(205597 + ,205471 + ,211064 + ,212856 + ,217036 + ,219302 + ,219759 + ,221388 + ,220834 + ,221788 + ,222358 + ,222972 + ,224164 + ,224915 + ,226294 + ,224690 + ,227021 + ,229284 + ,229189 + ,230032 + ,229389 + ,231053 + ,232560 + ,232681 + ,231555 + ,231428 + ,232141 + ,234939 + ,235424 + ,235471 + ,236355 + ,238693 + ,236958 + ,237060 + ,239282 + ,238252 + ,241552 + ,236230 + ,238909 + ,240723 + ,242120 + ,242100 + ,243276 + ,244677 + ,243494 + ,244902 + ,245247 + ,245578 + ,243052 + ,238121 + ,241863 + ,241203 + ,243634 + ,242351 + ,245180 + ,246126 + ,244424 + ,245166 + ,247258 + ,245094 + ,246020 + ,243082 + ,245555 + ,243685 + ,247277 + ,245029 + ,246169 + ,246778 + ,244577 + ,246048 + ,245775 + ,245328 + ,245477 + ,241903 + ,243219 + ,248088 + ,248521 + ,247389 + ,249057 + ,248916 + ,249193 + ,250768 + ,253106 + ,249829 + ,249447 + ,246755 + ,250785 + ,250140 + ,255755 + ,254671 + ,253919 + ,253741 + ,252729 + ,253810 + ,256653 + ,255231 + ,258405 + ,251061 + ,254811 + ,254895 + ,258325 + ,257608 + ,258759 + ,258621 + ,257852 + ,260560 + ,262358 + ,260812 + ,261165 + ,257164 + ,260720 + ,259581 + ,264743 + ,261845 + ,262262 + ,261631 + ,258953 + ,259966 + ,262850 + ,262204 + ,263418 + ,262752 + ,266433 + ,267722 + ,266003 + ,262971 + ,265521 + ,264676 + ,270223 + ,269508 + ,268457 + ,265814 + ,266680 + ,263018 + ,269285 + ,269829 + ,270911 + ,266844 + ,271244 + ,269907 + ,271296 + ,270157 + ,271322 + ,267179 + ,264101 + ,265518 + ,269419 + ,268714 + ,272482 + ,268351 + ,268175 + ,270674 + ,272764 + ,272599 + ,270333 + ,270846 + ,270491 + ,269160 + ,274027 + ,273784 + ,276663 + ,274525 + ,271344 + ,271115 + ,270798 + ,273911 + ,273985 + ,271917 + ,273338 + ,270601 + ,273547 + ,275363 + ,281229 + ,277793 + ,279913 + ,282500 + ,280041 + ,282166 + ,290304 + ,283519 + ,287816 + ,285226 + ,287595 + ,289741 + ,289148 + ,288301 + ,290155 + ,289648 + ,288225 + ,289351 + ,294735 + ,305333) > par9 = '1' > par8 = '1' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > 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,] 0.2434023 0.01513249 -0.04637318 -0.4976787 0.1033148 -0.9998411 [2,] 0.2148722 0.00000000 -0.04915193 -0.4679681 0.1023206 -0.9997609 [3,] 0.2719684 0.00000000 0.00000000 -0.5273776 0.1171669 -0.9995742 [4,] 0.0000000 0.00000000 0.00000000 -0.2641060 0.1232655 -0.9996655 [5,] 0.0000000 0.00000000 0.00000000 -0.2428837 0.0000000 -0.8945744 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.47907 0.90148 0.64957 0.14110 0.30758 0.00004 [2,] 0.42716 NA 0.61584 0.06402 0.31020 0.00007 [3,] 0.30634 NA NA 0.02766 0.22222 0.00034 [4,] NA NA NA 0.00610 0.19841 0.00002 [5,] NA NA NA 0.01108 NA 0.00000 [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 [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.2434 0.0151 -0.0464 -0.4977 0.1033 -0.9998 s.e. 0.3432 0.1221 0.1019 0.3367 0.1010 0.2365 sigma^2 estimated as 4272329: log likelihood = -1635.93, aic = 3285.85 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.2434 0.0151 -0.0464 -0.4977 0.1033 -0.9998 s.e. 0.3432 0.1221 0.1019 0.3367 0.1010 0.2365 sigma^2 estimated as 4272329: log likelihood = -1635.93, aic = 3285.85 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.2149 0 -0.0492 -0.4680 0.1023 -0.9998 s.e. 0.2700 0 0.0978 0.2512 0.1006 0.2448 sigma^2 estimated as 4272479: log likelihood = -1635.93, aic = 3283.87 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0.2720 0 0 -0.5274 0.1172 -0.9996 s.e. 0.2651 0 0 0.2376 0.0957 0.2737 sigma^2 estimated as 4287142: log likelihood = -1636.06, aic = 3282.11 [[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 sma1 0 0 0 -0.2641 0.1233 -0.9997 s.e. 0 0 0 0.0952 0.0955 0.2256 sigma^2 estimated as 4306830: log likelihood = -1636.39, aic = 3280.79 [[3]][[6]] NULL $aic [1] 3285.854 3283.870 3282.110 3280.788 3280.211 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 > postscript(file="/var/www/html/rcomp/tmp/1n5lg1229372901.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 = 192 Frequency = 1 [1] 118.701446 52.987281 39.415235 30.958743 28.477446 [6] 25.779040 22.501248 21.206900 18.318743 17.389405 [11] 16.349712 -104.465641 -687.299216 634.510651 -2989.953873 [16] -3333.050866 -2266.330304 -601.160066 -572.951091 -740.959347 [21] -262.962205 462.104258 823.682505 -152.706025 -1746.696449 [26] -823.915681 -2311.285568 1819.170872 -1735.159498 -2307.617017 [31] 4.907790 960.391268 -689.912314 -1225.668455 614.674277 [36] -978.214008 2650.450545 -4073.280429 -809.079268 223.005209 [41] -560.614080 -1317.502612 250.510148 -215.534153 -144.489990 [46] 512.280696 -900.397389 245.564183 -3315.568447 -3665.403336 [51] -19.800477 -1779.677383 -128.439301 -2085.612933 1377.036771 [56] -164.080567 -640.996216 -480.130350 838.377129 -1795.430187 [61] 653.808327 -373.413639 -600.983210 -2453.680581 581.839038 [66] -2282.401519 -747.037950 -890.638298 -1127.605686 189.424529 [71] -1519.572355 -209.692215 -221.250068 -1338.633937 -1710.617525 [76] 3992.435239 -965.649992 -1182.520733 248.732908 -1182.920789 [81] 1286.656952 767.902837 1562.345635 -2249.705318 -1132.381437 [86] -511.927567 1361.630547 -1720.160057 3011.451759 -96.414390 [91] -1867.447833 -1528.634254 -506.404800 -236.230952 1293.416551 [96] 89.786490 2953.055501 -3912.383579 -287.612553 -587.200478 [101] 246.875512 -383.516891 346.899795 -783.389825 81.837933 [106] 1525.477122 557.686413 -408.921662 -578.686354 -638.190094 [111] 348.725421 -1614.318508 1836.232416 -2033.310834 -1055.188314 [116] -1537.158212 -1989.391774 -977.034734 1035.302743 644.021574 [121] 898.020208 2584.963616 1293.788699 1256.124190 -4389.900480 [126] -3314.810980 777.529226 -1082.438423 6355.893783 -181.427629 [131] -2759.800558 -2405.420216 -414.665357 -1224.966620 2699.973249 [136] 548.698997 -677.146720 -3127.250855 2207.834850 -1041.507063 [141] 844.219652 -1681.169793 -336.087577 -2824.636195 -4283.886891 [146] 3082.788165 1018.373210 -995.467197 1230.714051 -2331.170557 [151] -2440.072758 1605.969913 2622.873026 -75.161447 -3482.960034 [156] 1214.517039 140.775735 687.967275 1584.190746 -160.056426 [161] 166.024708 -489.467741 -4175.422454 -2103.129912 -921.417114 [166] 2090.445068 5.508347 -1062.218243 940.566212 -177.140606 [171] -711.744420 1208.519655 3507.413235 -1063.417470 1430.678023 [176] 2493.124672 -1466.928440 442.323976 7125.346765 -3362.975783 [181] 2820.128600 655.555742 -779.323373 1175.919652 -3318.746556 [186] -76.348247 691.905700 -1170.487635 -1040.003330 -347.545104 [191] 2840.473417 13244.972915 > postscript(file="/var/www/html/rcomp/tmp/254qn1229372901.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/3ids41229372901.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/4yijy1229372901.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/57c9h1229372901.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/6hir81229372901.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/7jhw11229372901.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/8mrb91229372901.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/9exzz1229372901.tab") > > system("convert tmp/1n5lg1229372901.ps tmp/1n5lg1229372901.png") > system("convert tmp/254qn1229372901.ps tmp/254qn1229372901.png") > system("convert tmp/3ids41229372901.ps tmp/3ids41229372901.png") > system("convert tmp/4yijy1229372901.ps tmp/4yijy1229372901.png") > system("convert tmp/57c9h1229372901.ps tmp/57c9h1229372901.png") > system("convert tmp/6hir81229372901.ps tmp/6hir81229372901.png") > system("convert tmp/7jhw11229372901.ps tmp/7jhw11229372901.png") > > > proc.time() user system elapsed 10.060 1.871 10.333