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Type 'q()' to quit R. > x <- c(10070,10137,9984,9732,9103,9155,9308,9394,9948,10177,10002,9728,10002,10063,10018,9960,10236,10893,10756,10940,10997,10827,10166,10186,10457,10368,10244,10511,10812,10738,10171,9721,9897,9828,9924,10371,10846,10413,10709,10662,10570,10297,10635,10872,10296,10383,10431,10574,10653,10805,10872,10625,10407,10463,10556,10646,10702,11353,11346,11451,11964,12574,13031,13812,14544,14931,14886,16005,17064,15168,16050,15839,15137,14954,15648,15305,15579,16348,15928,16171,15937,15713,15594,15683,16438,17032,17696,17745,19394,20148,20108,18584,18441,18391,19178,18079,18483,19644,19195) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '-1.0' > 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,] 0.2826667 -0.08929247 0.1905642 -0.1465241 0.1999333 -0.1345896 [2,] 0.2718185 -0.09040532 0.1892851 -0.1364234 0.0000000 -0.1488794 [3,] 0.1402467 -0.07454231 0.1778056 0.0000000 0.0000000 -0.1425596 [4,] 0.1299048 0.00000000 0.1738404 0.0000000 0.0000000 -0.1714295 [5,] 0.1256693 0.00000000 0.1551556 0.0000000 0.0000000 -0.1559307 [6,] 0.0000000 0.00000000 0.1514358 0.0000000 0.0000000 -0.1552874 [7,] 0.0000000 0.00000000 0.1219420 0.0000000 0.0000000 0.0000000 [8,] 0.0000000 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 [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,] -0.3199369 [2,] -0.1215111 [3,] -0.1203612 [4,] -0.1232500 [5,] 0.0000000 [6,] 0.0000000 [7,] 0.0000000 [8,] 0.0000000 [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.37960 0.43849 0.06461 0.64425 0.69164 0.33374 0.52541 [2,] 0.39553 0.43085 0.06704 0.66564 NA 0.23012 0.29820 [3,] 0.16063 0.48534 0.08522 NA NA 0.24718 0.30174 [4,] 0.18869 NA 0.09273 NA NA 0.13880 0.29421 [5,] 0.20487 NA 0.12551 NA NA 0.17235 NA [6,] NA NA 0.14131 NA NA 0.17911 NA [7,] NA NA 0.22653 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 0.2827 -0.0893 0.1906 -0.1465 0.1999 -0.1346 -0.3199 s.e. 0.3201 0.1147 0.1019 0.3163 0.5025 0.1385 0.5019 sigma^2 estimated as 6.949e-12: log likelihood = 1119.37, aic = -2222.74 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.2827 -0.0893 0.1906 -0.1465 0.1999 -0.1346 -0.3199 s.e. 0.3201 0.1147 0.1019 0.3163 0.5025 0.1385 0.5019 sigma^2 estimated as 6.949e-12: log likelihood = 1119.37, aic = -2222.74 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.2718 -0.0904 0.1893 -0.1364 0 -0.1489 -0.1215 s.e. 0.3184 0.1143 0.1021 0.3147 0 0.1232 0.1161 sigma^2 estimated as 6.967e-12: log likelihood = 1119.3, aic = -2224.6 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1402 -0.0745 0.1778 0 0 -0.1426 -0.1204 s.e. 0.0992 0.1064 0.1022 0 0 0.1224 0.1159 sigma^2 estimated as 6.984e-12: log likelihood = 1119.21, aic = -2226.42 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1299 0 0.1738 0 0 -0.1714 -0.1232 s.e. 0.0981 0 0.1024 0 0 0.1148 0.1168 sigma^2 estimated as 7.002e-12: log likelihood = 1118.96, aic = -2227.93 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1257 0 0.1552 0 0 -0.1559 0 s.e. 0.0984 0 0.1004 0 0 0.1134 0 sigma^2 estimated as 7.11e-12: log likelihood = 1118.41, aic = -2228.81 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0.1514 0 0 -0.1553 0 s.e. 0 0 0.1021 0 0 0.1147 0 sigma^2 estimated as 7.23e-12: log likelihood = 1117.6, aic = -2229.2 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0.1219 0 0 0 0 s.e. 0 0 0.1002 0 0 0 0 sigma^2 estimated as 7.41e-12: log likelihood = 1116.7, aic = -2229.4 $aic [1] -2222.741 -2224.603 -2226.417 -2227.928 -2228.814 -2229.199 -2229.403 [8] -2229.935 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 6: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 7: In max(i) : no non-missing arguments to max; returning -Inf 8: In max(i) : no non-missing arguments to max; returning -Inf 9: In max(try.data.frame[, 4], na.rm = TRUE) : no non-missing arguments to max; returning -Inf > postscript(file="/var/www/html/freestat/rcomp/tmp/1kzjf1229547171.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 = 99 Frequency = 1 [1] 9.930482e-08 -6.514524e-07 1.500459e-06 2.574178e-06 7.180129e-06 [6] -8.083100e-07 -2.111726e-06 -1.849338e-06 -5.852120e-06 -2.042992e-06 [11] 1.839155e-06 3.538946e-06 -2.540224e-06 -8.157049e-07 1.029848e-07 [16] 9.246775e-07 -2.633290e-06 -5.946770e-06 1.098407e-06 -1.233566e-06 [21] 2.447359e-07 1.285212e-06 6.196097e-06 -1.353672e-07 -2.718351e-06 [26] 8.858318e-08 1.191053e-06 -2.169442e-06 -2.748702e-06 4.950183e-07 [31] 5.493916e-06 4.874301e-06 -1.907080e-06 7.631608e-08 -1.539279e-06 [36] -4.120028e-06 -4.309331e-06 3.953940e-06 -2.124797e-06 9.265730e-07 [41] 3.488308e-07 2.831969e-06 -3.136711e-06 -2.149299e-06 4.839835e-06 [46] -4.374433e-07 -1.932417e-07 -1.923972e-06 -6.020807e-07 -1.266482e-06 [51] -4.122518e-07 2.223771e-06 2.132551e-06 -4.447384e-07 -1.102772e-06 [56] -1.041272e-06 -4.288016e-07 -5.255355e-06 1.520016e-07 -7.482344e-07 [61] -3.091163e-06 -4.061525e-06 -2.690556e-06 -3.882655e-06 -3.149473e-06 [66] -1.442016e-06 7.316021e-07 -4.252390e-06 -3.660252e-06 7.300675e-06 [71] -3.050245e-06 1.302841e-06 2.034721e-06 1.250244e-06 -3.067022e-06 [76] 1.075150e-06 -1.247737e-06 -2.657754e-06 1.438315e-06 -8.032968e-07 [81] 1.276165e-06 6.978167e-07 6.007010e-07 -4.746377e-07 -3.037737e-06 [86] -2.180863e-06 -2.158688e-06 2.010832e-07 -4.532847e-06 -1.660975e-06 [91] 1.177605e-07 4.662571e-06 6.525672e-07 1.353885e-07 -2.728655e-06 [96] 3.118831e-06 -1.227000e-06 -2.925548e-06 8.042501e-07 > postscript(file="/var/www/html/freestat/rcomp/tmp/2yp2q1229547171.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/freestat/rcomp/tmp/34a5q1229547171.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/freestat/rcomp/tmp/4f5jt1229547171.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/freestat/rcomp/tmp/5omwl1229547171.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/freestat/rcomp/tmp/6o5j11229547171.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/freestat/rcomp/tmp/7jzfs1229547171.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/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/8n83q1229547171.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/9egfx1229547171.tab") > > system("convert tmp/1kzjf1229547171.ps tmp/1kzjf1229547171.png") > system("convert tmp/2yp2q1229547171.ps tmp/2yp2q1229547171.png") > system("convert tmp/34a5q1229547171.ps tmp/34a5q1229547171.png") > system("convert tmp/4f5jt1229547171.ps tmp/4f5jt1229547171.png") > system("convert tmp/5omwl1229547171.ps tmp/5omwl1229547171.png") > system("convert tmp/6o5j11229547171.ps tmp/6o5j11229547171.png") > system("convert tmp/7jzfs1229547171.ps tmp/7jzfs1229547171.png") > > > proc.time() user system elapsed 6.748 1.965 7.890