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

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> x <- c(15836.8,17570.4,18252.1,16196.7,16643,17729,16446.1,15993.8,16373.5,17842.2,22321.5,22786.7,18274.1,22392.9,23899.3,21343.5,22952.3,21374.4,21164.1,20906.5,17877.4,20664.3,22160,19813.6,17735.4,19640.2,20844.4,19823.1,18594.6,21350.6,18574.1,18924.2,17343.4,19961.2,19932.1,19464.6,16165.4,17574.9,19795.4,19439.5,17170,21072.4,17751.8,17515.5,18040.3,19090.1,17746.5,19202.1,15141.6,16258.1,18586.5,17209.4,17838.7,19123.5,16583.6,15991.2,16704.4,17420.4,17872,17823.2)
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '1'
> par1 = 'Default'
> #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: 
> if (par1 == 'Default') {
+ par1 = 10*log10(length(x))
+ } else {
+ par1 <- as.numeric(par1)
+ }
> par2 <- as.numeric(par2)
> par3 <- as.numeric(par3)
> par4 <- as.numeric(par4)
> par5 <- as.numeric(par5)
> if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
> par7 <- as.numeric(par7)
> if (par2 == 0) {
+ x <- log(x)
+ } else {
+ x <- (x ^ par2 - 1) / par2
+ }
> if (par3 > 0) x <- diff(x,lag=1,difference=par3)
> if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
> postscript(file="/var/www/html/rcomp/tmp/1quj51259173876.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2fwkv1259173876.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF')
> dev.off()
null device 
          1 
> (myacf <- c(racf$acf))
 [1]  1.000000e+00 -5.283390e-01  7.745699e-02  3.595100e-01 -3.786553e-01
 [6]  1.693582e-01  1.556436e-01 -4.231390e-01  3.179085e-01 -1.971331e-01
[11] -1.235858e-01  1.923240e-01 -1.611528e-01 -5.161355e-05  6.679874e-02
[16] -2.186282e-03 -8.395008e-02  1.132847e-01
> (mypacf <- c(rpacf$acf))
 [1] -0.52833903 -0.27978490  0.39742688  0.06307345 -0.09072082  0.11133450
 [7] -0.24649261 -0.12678695 -0.21024147 -0.11406779 -0.05412177  0.07351012
[13]  0.08491870 -0.11889209  0.15257130 -0.15102413 -0.12859092
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #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,'Autocorrelation Function',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Time lag k',header=TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,'P-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 2:(par1+1)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i-1,header=TRUE)
+ a<-table.element(a,round(myacf[i],6))
+ mytstat <- myacf[i]*sqrtn
+ a<-table.element(a,round(mytstat,4))
+ a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/31uu91259173876.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Time lag k',header=TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,'P-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:par1) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,round(mypacf[i],6))
+ mytstat <- mypacf[i]*sqrtn
+ a<-table.element(a,round(mytstat,4))
+ a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/497y01259173876.tab") 
> 
> system("convert tmp/1quj51259173876.ps tmp/1quj51259173876.png")
> system("convert tmp/2fwkv1259173876.ps tmp/2fwkv1259173876.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.528   0.305   0.621