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Type 'q()' to quit R. > x <- c(56190,54300,51362,49802,48088,46696,56586,64148,56449,52538,49359,49583,51050,49610,48321,47692,46243,46248,56381,62329,60673,58393,55742,57135,57961,56571,55615,53494,52623,52820,66825,70695,65660,63238,61741,63642,65521,64006,62728,62438,61109,63422,78094,82030,75892,72431,69194,71171,72545,71503,69624,67407,66103,67466,81088,86781,79964,80407,76589,78083,78000,76431,75461,73739,71988,72929,85785,89261,84012,80924,76588,77546,73054,73430,71093,72202,70872,70452,80506,80400,77613,69056,65321,64018,64767,61099,58329,56396,54656,55259,66912,66631,59907,56274,54045,55792,55499,53216,52259,51257,48150,51125,61046,61022,56742,54485,53862,58228,61951,62874,64013,62937,61897,65267,75228,76161,71480,69070,68293,74685,72664,71965,69238,67738,65187,66170,77309,77134,70957,67749,65081) > par9 = '1' > par8 = '0' > 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] [1,] 0.79372704 0.2569024 -0.1455669 -0.7979834 -0.5659593 [2,] 0.12880562 0.2725764 0.0000000 -0.1115356 -0.5697269 [3,] 0.02590256 0.2768939 0.0000000 0.0000000 -0.5700851 [4,] 0.00000000 0.2780435 0.0000000 0.0000000 -0.5669359 [5,] NA NA NA NA NA [6,] NA NA NA NA NA [7,] NA NA NA NA NA [8,] NA NA NA NA NA [9,] NA NA NA NA NA [10,] NA NA NA NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [1,] 0.00001 0.02767 0.18305 0.00000 0 [2,] 0.79396 0.00454 NA 0.83192 0 [3,] 0.77014 0.00223 NA NA 0 [4,] NA 0.00211 NA NA 0 [5,] NA NA NA NA NA [6,] NA NA NA NA NA [7,] NA NA NA NA NA [8,] NA NA NA NA NA [9,] NA NA NA NA NA [10,] 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 sma1 0.7937 0.2569 -0.1456 -0.7980 -0.5660 s.e. 0.1697 0.1153 0.1087 0.1368 0.0978 sigma^2 estimated as 3107718: log likelihood = -1051.75, aic = 2115.51 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sma1 0.7937 0.2569 -0.1456 -0.7980 -0.5660 s.e. 0.1697 0.1153 0.1087 0.1368 0.0978 sigma^2 estimated as 3107718: log likelihood = -1051.75, aic = 2115.51 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sma1 0.1288 0.2726 0 -0.1115 -0.5697 s.e. 0.4921 0.0943 0 0.5244 0.0980 sigma^2 estimated as 3144727: log likelihood = -1052.58, aic = 2115.17 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sma1 0.0259 0.2769 0 0 -0.5701 s.e. 0.0885 0.0887 0 0 0.0979 sigma^2 estimated as 3146003: log likelihood = -1052.61, aic = 2113.22 [[3]][[5]] NULL $aic [1] 2115.506 2115.165 2113.219 2111.305 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/freestat/rcomp/tmp/1jdex1293046460.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 = 131 Frequency = 1 [1] 32.4413039 13.0441938 6.0563493 3.1383329 0.9531426 [6] -0.4873946 8.7896875 14.7895566 5.8582752 1.5507623 [11] -1.6281620 -32.1186375 -204.1110053 375.7164625 1363.6014044 [16] 663.6208556 -186.5828778 984.6988728 119.3086403 -1739.9057579 [21] 5240.4433593 1683.5466764 -985.2555327 666.5911957 -550.8272424 [26] -26.5008992 1086.0623977 -1141.1338022 415.9514498 1037.4663636 [31] 3624.9725802 -2976.1907074 -1732.1943912 1305.1625659 1551.7059871 [36] 819.5143955 458.8118881 -289.5434130 -6.7987983 1233.1797304 [41] -185.8334756 2164.2575660 2695.6883677 -2144.4357534 -2213.2136781 [46] -308.9668353 -549.5171329 847.2349053 224.0023466 303.4572783 [51] -475.3463805 -1342.5632123 136.1140371 799.1804821 484.4869511 [56] 836.0473219 -1674.3204159 3249.0519792 -800.8639468 -1066.4778018 [61] -1154.3355941 -183.3213070 1052.8688569 -144.9227185 -633.4356651 [66] -93.4965926 -355.6991360 -1603.5415607 885.5819276 -1111.2391809 [71] -1313.8099998 -150.6942958 -4900.9302280 2101.8160359 400.3837229 [76] 2244.4420393 365.3684440 -2208.1394704 -3084.6234069 -4043.9611078 [81] 3833.3412249 -5171.4916591 -686.8912216 -847.3158617 2341.7056695 [86] -2356.0198586 -1550.7460060 -631.9312670 -3.1018588 617.5197456 [91] -71.7639450 -2803.8211322 -2190.4868159 2127.0821676 2076.8586627 [96] 1164.3871124 -203.6437553 -775.3532267 1181.6776881 140.3266593 [101] -1894.8122695 2501.5086345 -1455.7713763 -1953.0574891 1668.2531693 [106] 2453.8886140 2077.3837492 2859.9816094 3387.2868318 1934.8186140 [111] 1574.6083082 -936.0059195 408.3829575 1787.9385817 -1372.4304829 [116] -266.7656461 514.1370242 991.2595227 1145.2248691 3702.6908154 [121] -3822.8074066 -931.1871100 -1335.8700026 -408.3327049 -196.7347604 [126] -1211.1896631 875.8206323 -629.6416990 -1500.3769638 112.6426829 [131] -803.2279653 > postscript(file="/var/www/html/freestat/rcomp/tmp/2jdex1293046460.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/3jdex1293046460.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/4c4di1293046460.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/5c4di1293046460.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/6c4di1293046460.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/7c4di1293046460.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/8qwtq1293046460.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/91nst1293046460.tab") > > try(system("convert tmp/1jdex1293046460.ps tmp/1jdex1293046460.png",intern=TRUE)) character(0) > try(system("convert tmp/2jdex1293046460.ps tmp/2jdex1293046460.png",intern=TRUE)) character(0) > try(system("convert tmp/3jdex1293046460.ps tmp/3jdex1293046460.png",intern=TRUE)) character(0) > try(system("convert tmp/4c4di1293046460.ps tmp/4c4di1293046460.png",intern=TRUE)) character(0) > try(system("convert tmp/5c4di1293046460.ps tmp/5c4di1293046460.png",intern=TRUE)) character(0) > try(system("convert tmp/6c4di1293046460.ps tmp/6c4di1293046460.png",intern=TRUE)) character(0) > try(system("convert tmp/7c4di1293046460.ps tmp/7c4di1293046460.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.594 1.490 4.849