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Type 'q()' to quit R. > x <- c(104708,101817,97898,95559,92822,90848,101141,105841,93647,90923,89130,90212,93196,91861,90593,89895,88819,87924,96906,101217,98709,98139,95529,98577,100772,100180,99200,96251,94514,93780,105192,107682,99687,99436,102049,102673,105813,105056,103916,103513,101893,102503,113149,116696,108500,107800,105941,108742,111680,111270,110698,108517,107127,107088,116321,125045,116779,122887,120162,123198,123610,122293,121289,119393,117494,116693,125062,127281,120195,119804,117113,119240,115823,116281,113816,114632,112987,111633,116721,114850,112797,105368,102524,101327,102612,98873,95993,93244,90403,88539,98106,96963,90781,89253,87794,89810,90864,89025,87621,87718,83433,84535,92223,91052,88456,88706,89137,94066,99258,100673,102269,100833,99314,101764,108242,108148,104761,103772,103737,111043,109906,109335,107247,105690,102755,102280,110590,109122,102803,101424,99138) > 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,] 0.6038217 0.3066209 -0.03506630 -0.7023572 -0.6789539 [2,] 0.5645556 0.2948902 0.00000000 -0.6692328 -0.6802975 [3,] NA NA NA NA NA [4,] NA NA NA NA NA [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,] 0.00221 0.00371 0.7732 5e-05 0 [2,] 0.00040 0.00225 NA 1e-05 0 [3,] NA NA NA NA NA [4,] NA NA NA NA NA [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 0.6038 0.3066 -0.0351 -0.7024 -0.6790 s.e. 0.1933 0.1037 0.1214 0.1662 0.0829 sigma^2 estimated as 4778727: log likelihood = -1078.52, aic = 2169.05 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sma1 0.6038 0.3066 -0.0351 -0.7024 -0.6790 s.e. 0.1933 0.1037 0.1214 0.1662 0.0829 sigma^2 estimated as 4778727: log likelihood = -1078.52, aic = 2169.05 [[3]][[3]] NULL [[3]][[4]] NULL [[3]][[5]] NULL $aic [1] 2169.046 2167.129 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 log(s2) : NaNs produced > postscript(file="/var/www/rcomp/tmp/1pyvj1293046133.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] 60.4531761 24.7961079 12.9206747 7.5676709 3.5611771 [6] 1.1472424 10.5646631 13.6533388 0.5968872 -2.0540116 [11] -3.5801314 -58.2860618 -379.7579889 1241.0354596 2241.5012487 [16] 1207.1275095 821.5019479 362.0673764 -1688.1640022 -1028.5758966 [21] 7967.0850644 2774.8363768 -2148.3648586 422.6107136 -987.7104818 [26] 308.2688748 957.9497046 -1969.8698116 -794.5467894 511.6598136 [31] 1652.6123407 -1875.3010503 -1565.5955475 1382.1612417 5037.6083200 [36] -906.9167485 -557.8802611 361.0746860 309.6496663 1237.3833907 [41] -101.9915999 1066.6345789 68.1121571 -739.2303846 -1205.5410181 [46] 30.0180481 -1463.0119213 1097.1133967 667.1936798 599.6581156 [51] 945.5859194 -876.4553051 -120.7558234 437.0296044 -1277.0888188 [56] 4751.4122614 143.2843254 5825.7305720 -1583.8815621 -1038.1865313 [61] -2935.7571930 -1462.8310232 204.2321482 -150.8064365 -352.9774668 [66] -454.0946536 -1611.4370968 -3102.8633317 1004.8553950 -698.0969287 [71] -927.9429072 493.0470912 -4623.1128157 1495.6468300 483.9244894 [76] 3050.5484630 1105.2410690 -947.4895953 -4458.2088551 -6384.1792465 [81] 6127.2695512 -5556.4448516 -1794.4690046 -1383.9905810 2172.8778111 [86] -1289.2031973 -727.0987768 -538.0007739 -144.4249991 21.3295634 [91] 2472.1850177 -2323.5272131 -407.7899445 1362.1633421 1497.4771447 [96] 1389.9854500 718.1198454 -299.2078455 422.9803763 1641.8812059 [101] -2235.0023734 1528.8780771 -386.3880201 -2876.6140716 3090.1211220 [106] 2843.5315505 2079.8716731 3041.8080879 3822.6890928 2069.4342836 [111] 1752.1909020 -2053.0534036 -855.3030418 1754.5364664 -2683.3048863 [116] -2161.0256050 926.5390337 -26.2386752 599.9916902 4503.9190618 [121] -3355.8001688 -1683.9083468 -1441.0890657 -750.5987859 -411.9810775 [126] -852.6829820 771.1927580 -1278.7310655 -2005.4913815 -2.3648753 [131] -746.5234727 > postscript(file="/var/www/rcomp/tmp/207c41293046133.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/rcomp/tmp/307c41293046133.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/rcomp/tmp/407c41293046133.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/rcomp/tmp/507c41293046133.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/rcomp/tmp/6tzcp1293046133.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/rcomp/tmp/7tzcp1293046133.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/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/8p89g1293046133.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/rcomp/tmp/9zzr11293046133.tab") > > try(system("convert tmp/1pyvj1293046133.ps tmp/1pyvj1293046133.png",intern=TRUE)) character(0) > try(system("convert tmp/207c41293046133.ps tmp/207c41293046133.png",intern=TRUE)) character(0) > try(system("convert tmp/307c41293046133.ps tmp/307c41293046133.png",intern=TRUE)) character(0) > try(system("convert tmp/407c41293046133.ps tmp/407c41293046133.png",intern=TRUE)) character(0) > try(system("convert tmp/507c41293046133.ps tmp/507c41293046133.png",intern=TRUE)) character(0) > try(system("convert tmp/6tzcp1293046133.ps tmp/6tzcp1293046133.png",intern=TRUE)) character(0) > try(system("convert tmp/7tzcp1293046133.ps tmp/7tzcp1293046133.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.070 1.060 4.142