R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(10
+ ,10
+ ,10
+ ,10
+ ,10
+ ,9.94
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.06
+ ,9.94
+ ,9.94
+ ,9.94
+ ,9.94
+ ,9.94
+ ,9.94
+ ,10.06
+ ,10.06
+ ,9.94
+ ,10.06
+ ,10.06
+ ,10.06
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.18
+ ,10.28
+ ,10.28
+ ,10.28
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.34
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.42
+ ,10.34
+ ,10.34
+ ,10.42
+ ,10.42
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.49
+ ,10.49
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.49
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.49
+ ,10.49
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.49
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.49
+ ,10.49
+ ,10.49
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.49
+ ,10.55
+ ,10.55
+ ,10.55
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.57
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.57
+ ,10.57
+ ,10.57
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.57
+ ,10.63
+ ,10.63
+ ,10.57
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.63
+ ,10.57
+ ,10.63
+ ,10.6
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.6
+ ,10.6
+ ,10.6
+ ,10.6
+ ,10.7
+ ,10.7
+ ,10.6
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.8
+ ,10.8
+ ,10.8
+ ,10.8
+ ,10.7
+ ,10.7
+ ,10.7
+ ,10.85
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.75
+ ,10.75
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.85
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75
+ ,10.75)
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '3'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = 'FALSE'
> ylab = ''
> xlab = ''
> main = ''
> 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,] 0.5682623 -0.004108468 0.04526521 -0.8499425 0.05896744 0.06829546
 [2,] 0.5658443  0.000000000 0.04322891 -0.8500932 0.01305349 0.06888870
 [3,] 0.5685829  0.000000000 0.04451208 -0.8517829 0.02032424 0.06894984
 [4,] 0.5567877  0.000000000 0.04056310 -0.8428571 0.00000000 0.06856681
 [5,] 0.5114020  0.000000000 0.00000000 -0.7980787 0.00000000 0.06842934
 [6,] 0.4904463  0.000000000 0.00000000 -0.7819282 0.00000000 0.00000000
 [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,] -0.039230029
 [2,]  0.006884633
 [3,]  0.000000000
 [4,]  0.000000000
 [5,]  0.000000000
 [6,]  0.000000000
 [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,] 0e+00 0.95152 0.50082    0 0.92566 0.23102 0.95040
 [2,] 0e+00      NA 0.51633    0 0.98295 0.21802 0.99098
 [3,] 0e+00      NA 0.50001    0 0.72074 0.21104      NA
 [4,] 0e+00      NA 0.54348    0      NA 0.21372      NA
 [5,] 0e+00      NA      NA    0      NA 0.21570      NA
 [6,] 2e-05      NA      NA    0      NA      NA      NA
 [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
      0.5683  -0.0041  0.0453  -0.8499  0.0590  0.0683  -0.0392
s.e.  0.1120   0.0675  0.0672   0.0955  0.6315  0.0569   0.6302

sigma^2 estimated as 0.001812:  log likelihood = 606.26,  aic = -1196.52

[[3]][[2]]

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

Coefficients:
         ar1      ar2     ar3      ma1    sar1    sar2     sma1
      0.5683  -0.0041  0.0453  -0.8499  0.0590  0.0683  -0.0392
s.e.  0.1120   0.0675  0.0672   0.0955  0.6315  0.0569   0.6302

sigma^2 estimated as 0.001812:  log likelihood = 606.26,  aic = -1196.52

[[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
      0.5658    0  0.0432  -0.8501  0.0131  0.0689  0.0069
s.e.  0.1101    0  0.0665   0.0862  0.6102  0.0558  0.6082

sigma^2 estimated as 0.001812:  log likelihood = 606.26,  aic = -1198.51

[[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
      0.5686    0  0.0445  -0.8518  0.0203  0.0689     0
s.e.  0.1083    0  0.0659   0.0845  0.0568  0.0550     0

sigma^2 estimated as 0.001812:  log likelihood = 606.26,  aic = -1200.52

[[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
      0.5568    0  0.0406  -0.8429     0  0.0686     0
s.e.  0.1084    0  0.0667   0.0858     0  0.0550     0

sigma^2 estimated as 0.001813:  log likelihood = 606.19,  aic = -1202.39

[[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
      0.5114    0    0  -0.7981     0  0.0684     0
s.e.  0.1090    0    0   0.0794     0  0.0552     0

sigma^2 estimated as 0.001815:  log likelihood = 606.03,  aic = -1204.05

[[3]][[7]]
NULL


$aic
[1] -1196.523 -1198.513 -1200.515 -1202.387 -1204.050 -1204.518

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/1nxib1293545787.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 = 350 
Frequency = 1 
  [1]  9.999994e-03  2.184669e-06  1.603581e-06  1.217787e-06  9.428654e-07
  [6] -5.942692e-02  1.027763e-01  2.036755e-02  1.617859e-02  1.287331e-02
 [11]  1.025446e-02  8.174037e-03  6.518588e-03  5.199937e-03  4.148926e-03
 [16]  3.311016e-03  2.643101e-03  2.111155e-03  1.688539e-03  1.354892e-03
 [21] -1.186240e-01 -3.342288e-02 -2.662769e-02 -2.116329e-02 -1.672792e-02
 [26] -1.343658e-02  1.092663e-01  2.582333e-02 -9.938793e-02  1.061551e-01
 [31]  1.303803e-02  1.460440e-02  1.316544e-01  1.437008e-01  6.354365e-02
 [36] -4.928722e-02  1.118051e-01  3.808890e-02  3.039790e-02 -7.574007e-02
 [41]  9.069366e-02  2.124046e-02 -8.304843e-02 -1.513898e-02 -3.870575e-03
 [46]  9.271159e-02  2.285094e-02 -8.176315e-02 -1.411323e-02 -1.126347e-02
 [51] -1.720065e-02 -9.528088e-03  6.073565e-04  8.807381e-02  2.334902e-02
 [56]  1.863436e-02 -9.333984e-02 -2.599588e-02 -1.724727e-02  9.307825e-02
 [61] -8.719885e-02 -1.495186e-02  8.806724e-02 -7.401268e-02 -1.827016e-02
 [66] -1.108154e-02  9.799899e-02  2.357122e-02  1.881169e-02  8.170275e-03
 [71]  1.002001e-02  1.483969e-02 -9.165625e-02 -2.200870e-02 -1.756467e-02
 [76] -1.401799e-02 -1.118746e-02 -1.577141e-02 -9.087337e-03 -7.252410e-03
 [81]  1.054940e-03  9.734243e-02  2.654672e-02  1.434344e-02  2.178962e-02
 [86]  1.389034e-02  4.242653e-03  1.372840e-02  7.456850e-03 -9.404885e-02
 [91]  6.923888e-02  7.617369e-03  6.079260e-03  4.851728e-03 -9.612794e-02
 [96]  7.442254e-02  1.509778e-02  8.549724e-03 -9.317665e-02 -2.322210e-02
[101]  8.146694e-02  1.387683e-02  1.107480e-02  8.838563e-03  7.053870e-03
[106] -1.213391e-03  2.531109e-03 -9.797998e-02  7.294447e-02  7.075226e-03
[111]  5.646587e-03  1.445064e-01  4.373122e-02  4.174389e-02  2.297249e-02
[116]  2.183334e-02  1.742473e-02  1.390630e-02  1.794126e-02  3.976114e-03
[121]  6.672742e-03 -7.467463e-02 -1.184113e-02 -1.294965e-02  6.282223e-02
[126]  1.272441e-02  1.015508e-02  8.104555e-03 -7.353193e-02  6.222790e-02
[131]  8.750597e-03  1.382660e-02  6.922910e-04  4.051993e-03  3.233810e-03
[136] -6.999273e-03 -6.866844e-04 -5.480282e-04 -4.373697e-04 -3.490554e-04
[141] -2.785737e-04 -2.223238e-04 -1.774319e-04 -1.416046e-04 -1.130116e-04
[146]  5.384155e-03 -7.850261e-02 -2.173910e-02 -2.282386e-02 -1.541565e-02
[151]  6.769710e-02  1.311545e-02 -6.405849e-02 -1.848549e-02 -1.195329e-02
[156]  7.046033e-02  1.532073e-02  1.222715e-02 -7.024177e-02 -1.514630e-02
[161] -1.208794e-02 -9.647129e-03 -7.699168e-03  7.385546e-02  1.803031e-02
[166]  1.438961e-02 -6.851596e-02  6.623103e-02  1.194541e-02  9.533381e-03
[171]  1.308274e-02  7.641461e-03  6.098488e-03  4.867074e-03 -1.590039e-03
[176]  1.530616e-03  6.695899e-03  2.544262e-03  2.030522e-03 -3.853831e-03
[181] -8.027607e-02 -2.315446e-02 -1.300474e-02  6.682160e-02  1.241674e-02
[186]  9.909536e-03  7.908590e-03  8.373301e-04  3.467848e-03  2.767616e-03
[191]  7.683122e-03 -2.142203e-03  1.089946e-03  8.698625e-04  6.942188e-04
[196]  5.540412e-04  4.421685e-04 -7.964711e-02 -2.265251e-02  6.192152e-02
[201]  8.506083e-03  1.367885e-01  4.268575e-02  3.406659e-02  3.266217e-02
[206]  2.326739e-02  1.856921e-02  9.345344e-03  1.025791e-02  8.186622e-03
[211]  6.533569e-03 -5.478570e-02 -1.303918e-02  4.959371e-02  8.895563e-03
[216]  7.099359e-03  5.665848e-03  4.521793e-03  3.608747e-03 -5.711994e-02
[221]  4.509792e-02  1.078191e-02  5.805223e-03 -8.413222e-04  2.128151e-03
[226] -7.197382e-03 -6.119474e-02 -1.815410e-02  4.551160e-02  5.637718e-03
[231]  4.499343e-03 -5.640917e-02  4.566516e-02  5.760273e-03  4.597152e-03
[236]  7.774650e-03  4.105088e-03 -8.295768e-04  1.437627e-03  1.147339e-03
[241]  9.156671e-04 -5.926923e-02 -1.661739e-02 -9.156223e-03  4.648716e-02
[246]  8.515985e-03  6.796427e-03 -5.457592e-02  4.712824e-02  6.927927e-03
[251]  9.634792e-03  8.558963e-02  2.328934e-02  2.068642e-02  1.650939e-02
[256]  1.728156e-02 -5.241341e-02  5.095379e-02  9.981012e-03  7.965633e-03
[261]  6.357203e-03 -5.492645e-02 -1.315151e-02 -1.049594e-02  5.162341e-02
[266]  1.462119e-02  9.569164e-03  7.636946e-03 -5.801088e-02  4.648657e-02
[271]  6.415821e-03 -5.077391e-02  4.395709e-02  6.496791e-03  5.184951e-03
[276] -1.336348e-03 -5.826692e-02  4.418253e-02 -2.542298e-02  9.505252e-02
[281]  2.882495e-02  1.679913e-02  1.550672e-02  1.237558e-02  9.876690e-03
[286]  1.198814e-02  7.467783e-03  5.959879e-03  6.506923e-04 -9.738100e-02
[291] -2.657750e-02 -2.121094e-02 -1.282224e-02  8.356139e-02  1.764806e-02
[296] -8.180970e-02  7.964417e-02  1.452181e-02  1.158955e-02  9.249370e-03
[301]  1.148749e-02  2.962464e-03  6.516854e-03 -2.691819e-03  1.351207e-03
[306]  1.078370e-03  8.606239e-04  6.868456e-04  5.481569e-04  4.374724e-04
[311]  1.003491e-01  2.894631e-02  2.310143e-02  2.527970e-02 -8.332430e-02
[316] -1.535915e-02 -1.225781e-02  1.333744e-01 -6.676757e-02  4.697361e-03
[321] -6.593560e-03 -1.762690e-03 -1.406765e-03 -1.122709e-03 -8.960105e-04
[326] -7.150870e-04 -5.706957e-04 -4.554601e-04 -3.634930e-04  9.970990e-02
[331]  2.843615e-02  2.269429e-02  1.811183e-02  1.445467e-02  4.693027e-03
[336]  7.244896e-03  5.781997e-03  4.614489e-03  1.052566e-02 -9.509919e-02
[341] -2.475643e-02  6.997801e-02  1.679993e-02  9.908178e-03  7.907506e-03
[346] -9.368919e-02 -2.363115e-02 -1.885951e-02 -1.505138e-02 -1.201218e-02
> postscript(file="/var/www/html/rcomp/tmp/2nxib1293545787.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/html/rcomp/tmp/3nxib1293545787.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/html/rcomp/tmp/4g6he1293545787.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/html/rcomp/tmp/5g6he1293545787.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/html/rcomp/tmp/6g6he1293545787.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/html/rcomp/tmp/7g6he1293545787.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/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/8cyxm1293545787.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/9m7e71293545787.tab") 
> 
> try(system("convert tmp/1nxib1293545787.ps tmp/1nxib1293545787.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nxib1293545787.ps tmp/2nxib1293545787.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nxib1293545787.ps tmp/3nxib1293545787.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g6he1293545787.ps tmp/4g6he1293545787.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g6he1293545787.ps tmp/5g6he1293545787.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g6he1293545787.ps tmp/6g6he1293545787.png",intern=TRUE))
character(0)
> try(system("convert tmp/7g6he1293545787.ps tmp/7g6he1293545787.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 13.334   1.790  28.147