R version 2.6.0 (2007-10-03)
Copyright (C) 2007 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(544.5,619.8,777.6,640.4,633.0,722.0,860.1,495.1,692.8,766.7,648.5,640.0,681.6,752.5,1031.7,685.5,887.6,655.4,944.2,626.6,1221.8,939.6,886.6,811.3,774.7,910.6,911.6,697.7,829.8,824.3,885.6,538.9,686.0,878.7,812.7,640.4,773.9,795.9,836.3,876.1,851.7,692.4,877.3,536.8,705.9,951.0,755.7,695.5,744.8,672.1,666.6,760.8,756.0,604.4,883.9,527.9,756.2,812.9,655.6,707.6)
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '3'
> par5 = '12'
> par4 = '1'
> par3 = '2'
> par2 = '2.0'
> 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$order[3]
+ coeff <- matrix(NA, nrow=nrc, ncol=nrc)
+ pval <- matrix(NA, nrow=nrc, ncol=nrc)
+ mylist <- rep(list(NULL), nrc)
+ names  <- NULL
+ if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
+ if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
+ if(seasonal$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,] -0.7394185 -0.4669776 -0.1101272 -0.9984486 -0.6223196 -0.2592906
[2,] -0.7244720 -0.3748811 -0.1831375 -0.9954249 -0.6783974 -0.3066937
[3,] -0.6767704 -0.2504621  0.0000000 -1.0038135 -0.6979015 -0.2992077
[4,] -0.5279529  0.0000000  0.0000000 -1.0030309 -0.6274843 -0.3255897
[5,] -0.5430946  0.0000000  0.0000000 -1.0025873 -0.5063125  0.0000000
[6,]         NA         NA         NA         NA         NA         NA
[7,]         NA         NA         NA         NA         NA         NA
           [,7]
[1,] -0.0115548
[2,]  0.0000000
[3,]  0.0000000
[4,]  0.0000000
[5,]  0.0000000
[6,]         NA
[7,]         NA

[[2]]
      [,1]    [,2]    [,3] [,4]    [,5]    [,6]    [,7]
[1,] 3e-05 0.01980 0.47775    0 0.92105 0.93763 0.99864
[2,] 2e-05 0.04145 0.21464    0 0.00007 0.10042      NA
[3,] 3e-05 0.10165      NA    0 0.00002 0.09458      NA
[4,] 8e-05      NA      NA    0 0.00007 0.07081      NA
[5,] 4e-05      NA      NA    0 0.00022      NA      NA
[6,]    NA      NA      NA   NA      NA      NA      NA
[7,]    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
      -0.7394  -0.4670  -0.1101  -0.9984  -0.6223  -0.2593  -0.0116
s.e.   0.1624   0.1942   0.1540   0.0954   6.2485   3.2977   6.7275

sigma^2 estimated as 4.495e+10:  log likelihood = -638.23,  aic = 1292.46

[[3]][[2]]

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

Coefficients:
          ar1      ar2      ar3      ma1     sar1     sar2     sma1
      -0.7394  -0.4670  -0.1101  -0.9984  -0.6223  -0.2593  -0.0116
s.e.   0.1624   0.1942   0.1540   0.0954   6.2485   3.2977   6.7275

sigma^2 estimated as 4.495e+10:  log likelihood = -638.23,  aic = 1292.46

[[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
      -0.7245  -0.3749  -0.1831  -0.9954  -0.6784  -0.3067     0
s.e.   0.1537   0.1794   0.1458   0.0955   0.1567   0.1834     0

sigma^2 estimated as 4.263e+10:  log likelihood = -637.38,  aic = 1288.75

[[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
      -0.6768  -0.2505    0  -1.0038  -0.6979  -0.2992     0
s.e.   0.1485   0.1504    0   0.0888   0.1511   0.1758     0

sigma^2 estimated as 4.392e+10:  log likelihood = -638.15,  aic = 1288.29

[[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
      -0.5280    0    0  -1.0030  -0.6275  -0.3256     0
s.e.   0.1238    0    0   0.0789   0.1462   0.1767     0

sigma^2 estimated as 4.778e+10:  log likelihood = -639.46,  aic = 1288.91

[[3]][[6]]
NULL

[[3]][[7]]
NULL


$aic
[1] 1292.456 1288.751 1288.291 1288.912 1289.858

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
> postscript(file="/var/www/html/rcomp/tmp/1d7ng1196885245.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 = 60 
Frequency = 1 
 [1]  8.939213e+01 -2.439218e+01  3.782618e+01 -2.215162e+02 -1.862757e+02
 [6] -6.217191e+01  8.433967e+01 -3.987809e+02 -9.989421e+01 -5.934729e+00
[11] -3.593041e+02 -2.856906e+02 -4.628201e+02  1.193627e+03  1.066952e+05
[16] -2.980854e+05  6.927638e+04 -2.569453e+05  1.935776e+04  1.181548e+05
[21]  6.720979e+05 -2.920690e+05 -2.937801e+05 -8.520225e+04 -1.548397e+05
[26]  6.975373e+04 -2.644667e+05 -7.944813e+04  1.100282e+05  1.604650e+05
[31] -1.437123e+05 -8.158234e+04 -4.224246e+05  3.500854e+05  3.344524e+05
[36] -1.254365e+05  1.146171e+05  7.848599e+03 -1.701401e+05  3.896640e+05
[41]  3.499194e+04 -2.613689e+05 -1.689606e+04  6.783078e+04 -2.203452e+05
[46]  3.360874e+05  4.792705e+04 -3.971437e+04  4.742386e+04 -1.922251e+05
[51] -2.726669e+05  3.386447e+05  8.443170e+04 -5.228211e+04  1.513388e+05
[56]  7.341373e+04 -1.827945e+05 -3.428100e+04  7.542773e+03  2.197890e+05
> postscript(file="/var/www/html/rcomp/tmp/20c5v1196885245.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/3mip01196885245.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/4qtzv1196885245.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/5gwtf1196885245.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/658df1196885245.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/7c6ms1196885245.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(resid, main='Residual Normal Q-Q Plot')
> dev.off()
null device 
          1 
> ncols <- length(selection[[1]][1,])
> nrows <- length(selection[[2]][,1])-1
> 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/8oovy1196885246.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/9au5d1196885246.tab") 
> 
> system("convert tmp/1d7ng1196885245.ps tmp/1d7ng1196885245.png")
> system("convert tmp/20c5v1196885245.ps tmp/20c5v1196885245.png")
> system("convert tmp/3mip01196885245.ps tmp/3mip01196885245.png")
> system("convert tmp/4qtzv1196885245.ps tmp/4qtzv1196885245.png")
> system("convert tmp/5gwtf1196885245.ps tmp/5gwtf1196885245.png")
> system("convert tmp/658df1196885245.ps tmp/658df1196885245.png")
> system("convert tmp/7c6ms1196885245.ps tmp/7c6ms1196885245.png")
> 
> 
> proc.time()
   user  system elapsed 
  9.167   1.283   9.811