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Type 'q()' to quit R. > x <- c(4716.99,4926.65,4920.10,5170.09,5246.24,5283.61,4979.05,4825.20,4695.12,4711.54,4727.22,4384.96,4378.75,4472.93,4564.07,4310.54,4171.38,4049.38,3591.37,3720.46,4107.23,4101.71,4162.34,4136.22,4125.88,4031.48,3761.36,3408.56,3228.47,3090.45,2741.14,2980.44,3104.33,3181.57,2863.86,2898.01,3112.33,3254.33,3513.47,3587.61,3727.45,3793.34,3817.58,3845.13,3931.86,4197.52,4307.13,4229.43,4362.28,4217.34,4361.28,4327.74,4417.65,4557.68,4650.35,4967.18,5123.42,5290.85,5535.66,5514.06,5493.88,5694.83,5850.41,6116.64,6175.00,6513.58,6383.78,6673.66,6936.61,7300.68,7392.93,7497.31,7584.71,7160.79,7196.19,7245.63,7347.51,7425.75,7778.51,7822.33,8181.22,8371.47,8347.71,8672.11,8802.79,9138.46,9123.29,9023.21,8850.41,8864.58,9163.74,8516.66,8553.44,7555.20,7851.22,7442.00,7992.53,8264.04,7517.39,7200.40,7193.69,6193.58,5104.21,4800.46,4461.61,4398.59,4243.63,4293.82) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '2' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '0.2' > 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,] 0.4499923 0.1291123 -0.2398601 -0.2094498 -0.07748112 0.29759633 [2,] 0.4439290 0.1303291 -0.2368393 0.0000000 -0.09346366 0.09047099 [3,] 0.2139712 0.1949080 0.0000000 0.0000000 -0.10091658 0.10256609 [4,] 0.2147054 0.1962144 0.0000000 0.0000000 0.00000000 0.09976653 [5,] 0.2291256 0.1889930 0.0000000 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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.43627 0.53015 0.67936 0.77492 0.59575 0.67965 [2,] 0.44179 0.52112 0.68275 NA 0.46162 0.45786 [3,] 0.02805 0.04320 NA NA 0.42250 0.38576 [4,] 0.02821 0.04138 NA NA NA 0.43694 [5,] 0.01721 0.04830 NA NA NA NA [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 ma1 sar1 sar2 sma1 0.4500 0.1291 -0.2399 -0.2094 -0.0775 0.2976 s.e. 0.5757 0.2050 0.5786 0.7305 0.1456 0.7186 sigma^2 estimated as 0.002731: log likelihood = 163.74, aic = -313.49 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.4500 0.1291 -0.2399 -0.2094 -0.0775 0.2976 s.e. 0.5757 0.2050 0.5786 0.7305 0.1456 0.7186 sigma^2 estimated as 0.002731: log likelihood = 163.74, aic = -313.49 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.4439 0.1303 -0.2368 0 -0.0935 0.0905 s.e. 0.5749 0.2024 0.5778 0 0.1265 0.1214 sigma^2 estimated as 0.002733: log likelihood = 163.71, aic = -315.43 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.214 0.1949 0 0 -0.1009 0.1026 s.e. 0.096 0.0952 0 0 0.1253 0.1178 sigma^2 estimated as 0.002734: log likelihood = 163.65, aic = -317.3 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ma1 sar1 sar2 sma1 0.2147 0.1962 0 0 0 0.0998 s.e. 0.0965 0.0950 0 0 0 0.1278 sigma^2 estimated as 0.002758: log likelihood = 163.33, aic = -318.66 [[3]][[6]] NULL $aic [1] -313.4868 -315.4287 -317.3031 -318.6612 -320.0419 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/16h6m1259859648.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 = 108 Frequency = 1 [1] 0.005429161 0.044589344 -0.013788888 0.045315201 0.004733151 [6] -0.006293029 -0.070117568 -0.021784333 -0.009566626 0.016563784 [11] 0.008249931 -0.083132733 0.014416145 0.034621459 0.018438194 [16] -0.074932086 -0.026450277 -0.011212898 -0.104367270 0.071568027 [21] 0.121060489 -0.032421463 -0.005294315 -0.001468463 -0.005701520 [26] -0.025987422 -0.068551695 -0.073384275 -0.016344578 -0.011013486 [31] -0.087660646 0.109058910 0.033980751 0.003009571 -0.117182247 [36] 0.029413749 0.089348454 0.029884743 0.061172903 0.003245199 [41] 0.021217877 0.006606582 0.003724873 -0.008381013 0.017003351 [46] 0.062131322 0.019740144 -0.041716456 0.022825976 -0.042255817 [51] 0.030913743 -0.009182787 0.014629308 0.029784721 0.009861109 [56] 0.061419759 0.012695251 0.008001510 0.034179731 -0.018046450 [61] -0.015347952 0.046317045 0.019528199 0.037126925 -0.007446925 [66] 0.046249388 -0.039587726 0.038257292 0.037402380 0.039717647 [71] -0.010319760 0.003447189 0.008850072 -0.079141770 0.015823474 [76] 0.016538542 0.014405682 0.002826137 0.053441535 -0.011442999 [81] 0.038079306 0.011022150 -0.019050415 0.041499445 0.008166037 [86] 0.041019837 -0.017163297 -0.023925897 -0.021960452 0.009489472 [91] 0.039949081 -0.098201134 0.012775489 -0.134456575 0.079085438 [96] -0.048623754 0.089399177 0.030554012 -0.137690615 -0.032214135 [101] 0.034424395 -0.164911126 -0.183934801 0.023432525 -0.023324458 [106] 0.028360003 -0.027343483 0.028574336 > postscript(file="/var/www/html/rcomp/tmp/2j8s41259859648.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/3hc2m1259859648.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/47xmk1259859648.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/51gs51259859648.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/6axjm1259859648.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/7bjg21259859648.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/8xh371259859648.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/9dtve1259859648.tab") > system("convert tmp/16h6m1259859648.ps tmp/16h6m1259859648.png") > system("convert tmp/2j8s41259859648.ps tmp/2j8s41259859648.png") > system("convert tmp/3hc2m1259859648.ps tmp/3hc2m1259859648.png") > system("convert tmp/47xmk1259859648.ps tmp/47xmk1259859648.png") > system("convert tmp/51gs51259859648.ps tmp/51gs51259859648.png") > system("convert tmp/6axjm1259859648.ps tmp/6axjm1259859648.png") > system("convert tmp/7bjg21259859648.ps tmp/7bjg21259859648.png") > > > proc.time() user system elapsed 4.169 1.305 11.045