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Type 'q()' to quit R. > x <- c(2072.65,2020.13,2032.76,2050.31,2128.98,2122.14,2122.89,2091.95,2002.97,1923.21,1834.44,1819.15,1792.00,1822.40,1900.70,1903.00,1958.80,1820.50,1719.80,1661.10,1664.40,1703.40,1774.90,1795.00,1816.30,1867.40,1900.00,1961.10,2065.70,2073.50,2080.80,2118.00,2099.00,2085.20,1937.70,1749.50,1750.30,1675.60,1697.50,1699.80,1655.90,1636.00,1614.20,1602.30,1548.70,1556.10,1526.90,1509.20,1566.30,1596.00,1654.50,1664.20,1687.70,1691.00,1664.60,1697.50,1685.10,1643.00,1559.60,1560.20,1590.16,1604.93,1661.80,1670.73,1692.40,1688.17,1658.04,1613.46,1595.11,1558.83,1526.65,1475.19) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '-1.7' > 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,] 0.8287813 -0.03836855 -0.16675246 -0.4434835 -0.1097906 -0.1650344 [2,] 0.7666919 0.00000000 -0.17340842 -0.3891007 -0.1096556 -0.1611093 [3,] 0.7880313 0.00000000 -0.18161134 -0.3852272 0.0000000 -0.1172389 [4,] 0.7955968 0.00000000 -0.18587886 -0.4290445 0.0000000 0.0000000 [5,] 0.4186970 0.00000000 -0.06223467 0.0000000 0.0000000 0.0000000 [6,] 0.4040318 0.00000000 0.00000000 0.0000000 0.0000000 0.0000000 [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 [13,] NA NA NA NA NA NA [14,] NA NA NA NA NA NA [,7] [1,] -0.9996834 [2,] -1.0002577 [3,] -0.9996244 [4,] -0.9998349 [5,] -0.9999994 [6,] -0.9992372 [7,] NA [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.11564 0.88716 0.21022 0.39094 0.51184 0.37907 0.02781 [2,] 0.00679 NA 0.18297 0.24116 0.51294 0.38638 0.02910 [3,] 0.00279 NA 0.15637 0.22193 NA 0.52439 0.00090 [4,] 0.00418 NA 0.13708 0.18337 NA NA 0.00045 [5,] 0.00107 NA 0.60957 NA NA NA 0.00019 [6,] 0.00117 NA NA NA NA NA 0.00014 [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 0.8288 -0.0384 -0.1668 -0.4435 -0.1098 -0.1650 -0.9997 s.e. 0.5196 0.2693 0.1317 0.5134 0.1664 0.1863 0.4441 sigma^2 estimated as 1.253e-14: log likelihood = 847.27, aic = -1678.54 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.8288 -0.0384 -0.1668 -0.4435 -0.1098 -0.1650 -0.9997 s.e. 0.5196 0.2693 0.1317 0.5134 0.1664 0.1863 0.4441 sigma^2 estimated as 1.253e-14: log likelihood = 847.27, aic = -1678.54 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.7667 0 -0.1734 -0.3891 -0.1097 -0.1611 -1.0003 s.e. 0.2742 0 0.1288 0.3289 0.1667 0.1847 0.4482 sigma^2 estimated as 1.255e-14: log likelihood = 847.26, aic = -1680.52 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.7880 0 -0.1816 -0.3852 0 -0.1172 -0.9996 s.e. 0.2536 0 0.1267 0.3124 0 0.1832 0.2874 sigma^2 estimated as 1.328e-14: log likelihood = 847.05, aic = -1682.1 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.7956 0 -0.1859 -0.4290 0 0 -0.9998 s.e. 0.2682 0 0.1235 0.3191 0 0 0.2708 sigma^2 estimated as 1.386e-14: log likelihood = 846.85, aic = -1683.71 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4187 0 -0.0622 0 0 0 -1.000 s.e. 0.1225 0 0.1213 0 0 0 0.253 sigma^2 estimated as 1.428e-14: log likelihood = 845.99, aic = -1683.97 [[3]][[7]] NULL $aic [1] -1678.543 -1680.520 -1682.104 -1683.709 -1683.974 -1685.712 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/11zl31262200194.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 = 72 Frequency = 1 [1] 1.328449e-09 6.735868e-10 4.195545e-10 2.822869e-10 9.374099e-11 [6] 8.908960e-11 7.505526e-11 1.180775e-10 2.692163e-10 4.081240e-10 [11] 5.798010e-10 -6.524828e-10 -6.118800e-09 -1.194969e-07 -6.791604e-08 [16] 6.813655e-08 -4.252610e-09 2.160023e-07 1.125082e-07 1.100709e-08 [21] -1.558130e-07 -1.454373e-07 -2.122042e-07 4.385398e-08 -1.040264e-07 [26] -8.546454e-08 7.022930e-08 -1.159820e-07 -2.944610e-08 -1.260390e-07 [31] -7.107365e-08 -1.058881e-07 1.857805e-08 1.126134e-08 2.352701e-07 [36] 2.933685e-07 -1.978896e-07 2.621014e-07 -5.073326e-08 3.381447e-08 [41] 2.631562e-07 -1.459715e-07 6.677156e-09 7.977481e-09 1.307854e-07 [46] -1.002326e-07 3.715376e-08 -7.180062e-08 -1.954512e-07 -3.948803e-08 [51] -5.600522e-08 4.310809e-08 -7.615658e-09 -1.087975e-07 4.188158e-08 [56] -1.472301e-07 9.621758e-10 1.431540e-07 1.281554e-07 -2.100622e-07 [61] -4.034160e-09 -1.433501e-08 -6.498427e-08 4.168052e-08 2.244521e-09 [66] -6.841818e-08 3.701360e-08 1.204918e-07 -7.437018e-08 1.062555e-07 [71] -4.550402e-08 1.136648e-07 > postscript(file="/var/www/html/rcomp/tmp/2vb6j1262200194.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/3keib1262200194.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/4lkrn1262200194.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/52snq1262200194.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/6mpiy1262200194.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/71lym1262200194.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/8na0k1262200194.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/9hhnp1262200194.tab") > > try(system("convert tmp/11zl31262200194.ps tmp/11zl31262200194.png",intern=TRUE)) character(0) > try(system("convert tmp/2vb6j1262200194.ps tmp/2vb6j1262200194.png",intern=TRUE)) character(0) > try(system("convert tmp/3keib1262200194.ps tmp/3keib1262200194.png",intern=TRUE)) character(0) > try(system("convert tmp/4lkrn1262200194.ps tmp/4lkrn1262200194.png",intern=TRUE)) character(0) > try(system("convert tmp/52snq1262200194.ps tmp/52snq1262200194.png",intern=TRUE)) character(0) > try(system("convert tmp/6mpiy1262200194.ps tmp/6mpiy1262200194.png",intern=TRUE)) character(0) > try(system("convert tmp/71lym1262200194.ps tmp/71lym1262200194.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.132 1.937 11.450