R version 2.6.1 (2007-11-26)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(106.22,106.31,107.38,109.31,110.82,111.22,110.66,110.76,110.69,111.08,110.97,110.24,112.51,111.52,112.13,112.23,112.92,111.89,111.99,111.51,112.33,112.04,112.09,111.41,112.61,113.14,113.65,114.26,114.4,114.93,114.86,114.95,116.17,114.6,114.62,113.82,115.02,115.18,115.59,116.6,117.07,116.96,116.66,116.07,116.04,115.81,116.22,115.85,116.43,117.39,119.17,119.24,120.03,119.34,118.49,118.59,117.5,117.56,118.25,118.01)
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '3'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1.2'
> 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$order[3]
+ coeff <- matrix(NA, nrow=nrc, ncol=nrc)
+ pval <- matrix(NA, nrow=nrc, ncol=nrc)
+ mylist <- rep(list(NULL), nrc)
+ names  <- NULL
+ if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
+ if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
+ if(seasonal$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)
+ 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,]  0.70458433 0.2306790 -0.2141491 -0.7642920 1.1770113 -0.1839187
[2,]  0.67118001 0.2220874 -0.2181869 -0.7350459 0.9075458  0.0000000
[3,]  0.71901680 0.2581952 -0.2507684 -0.7413536 0.4764605  0.0000000
[4,] -1.04550419 0.0000000  0.1335633  0.8991506 0.5315321  0.0000000
[5,] -0.67863412 0.0000000  0.0000000  0.5745783 0.4958743  0.0000000
[6,] -0.04776044 0.0000000  0.0000000  0.0000000 0.4971505  0.0000000
[7,]  0.00000000 0.0000000  0.0000000  0.0000000 0.4882530  0.0000000
           [,7]
[1,] -0.9227154
[2,] -0.6470908
[3,]  0.0000000
[4,]  0.0000000
[5,]  0.0000000
[6,]  0.0000000
[7,]  0.0000000

[[2]]
        [,1]    [,2]    [,3]    [,4]    [,5]    [,6]    [,7]
[1,] 0.03234 0.14660 0.13358 0.01538 0.00015 0.47265 0.03755
[2,] 0.03869 0.16079 0.13462 0.01869 0.00022      NA 0.19017
[3,] 0.06494 0.09739 0.08881 0.05206 0.00180      NA      NA
[4,] 0.00283      NA 0.17494 0.01166 0.00141      NA      NA
[5,] 0.03874      NA      NA 0.10671 0.00083      NA      NA
[6,] 0.71851      NA      NA      NA 0.00064      NA      NA
[7,]      NA      NA      NA      NA 0.00066      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
      0.7046  0.2307  -0.2141  -0.7643  1.1770  -0.1839  -0.9227
s.e.  0.3204  0.1565   0.1405   0.3050  0.2875   0.2542   0.4323

sigma^2 estimated as 3.843:  log likelihood = -128.3,  aic = 272.6

[[3]][[2]]

Call:
arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, 
    method = "ML")

Coefficients:
         ar1     ar2      ar3      ma1    sar1     sar2     sma1
      0.7046  0.2307  -0.2141  -0.7643  1.1770  -0.1839  -0.9227
s.e.  0.3204  0.1565   0.1405   0.3050  0.2875   0.2542   0.4323

sigma^2 estimated as 3.843:  log likelihood = -128.3,  aic = 272.6

[[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
      0.6712  0.2221  -0.2182  -0.7350  0.9075     0  -0.6471
s.e.  0.3166  0.1561   0.1436   0.3029  0.2286     0   0.4876

sigma^2 estimated as 4.113:  log likelihood = -128.51,  aic = 271.02

[[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
      0.7190  0.2582  -0.2508  -0.7414  0.4765     0     0
s.e.  0.3816  0.1531   0.1447   0.3732  0.1451     0     0

sigma^2 estimated as 4.434:  log likelihood = -129.27,  aic = 270.53

[[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
      -1.0455    0  0.1336  0.8992  0.5315     0     0
s.e.   0.3344    0  0.0972  0.3446  0.1580     0     0

sigma^2 estimated as 4.521:  log likelihood = -130.2,  aic = 270.4

[[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
      -0.6786    0    0  0.5746  0.4959     0     0
s.e.   0.3206    0    0  0.3505  0.1404     0     0

sigma^2 estimated as 4.651:  log likelihood = -130.77,  aic = 269.54

[[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
      -0.0478    0    0    0  0.4972     0     0
s.e.   0.1318    0    0    0  0.1375     0     0

sigma^2 estimated as 4.737:  log likelihood = -131.31,  aic = 268.62


$aic
[1] 272.5987 271.0220 270.5338 270.3957 269.5438 268.6170 266.7481

Warning messages:
1: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean,  :
  possible convergence problem: optim gave code=1
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 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/13ad31197044402.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> resid <- arimaSelectplot(selection)
Error in 1:k : result would be too long a vector
Calls: arimaSelectplot
In addition: Warning message:
In min(which(is.na(coeff[, 1]))) :
  no non-missing arguments to min; returning Inf
Execution halted