R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> y <- c(3.2,4.8,5.5,6.4,5.9,5.8,5.1,4.1,4.4,3.6,3.5,3.1,2.9)
> x <- c(111.09,111.29,111.15,110.88,111.22,110.62,110.20,109.49,109.32,108.71,107.85,107.44,106.93)
> par7 = '0'
> par6 = '0'
> par5 = '1'
> par4 = '1'
> par3 = '0'
> par2 = '0'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2008), Cross Correlation Function (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_cross.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: 
> par1 <- as.numeric(par1)
> par2 <- as.numeric(par2)
> par3 <- as.numeric(par3)
> par4 <- as.numeric(par4)
> par5 <- as.numeric(par5)
> par6 <- as.numeric(par6)
> par7 <- as.numeric(par7)
> if (par1 == 0) {
+ x <- log(x)
+ } else {
+ x <- (x ^ par1 - 1) / par1
+ }
> if (par5 == 0) {
+ y <- log(y)
+ } else {
+ y <- (y ^ par5 - 1) / par5
+ }
> if (par2 > 0) x <- diff(x,lag=1,difference=par2)
> if (par6 > 0) y <- diff(y,lag=1,difference=par6)
> if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
> if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
> x
 [1] 110.09 110.29 110.15 109.88 110.22 109.62 109.20 108.49 108.32 107.71
[11] 106.85 106.44 105.93
> y
 [1] 2.2 3.8 4.5 5.4 4.9 4.8 4.1 3.1 3.4 2.6 2.5 2.1 1.9
> postscript(file="/var/www/html/rcomp/tmp/124la1228304039.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> (r <- ccf(x,y,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))

Autocorrelations of series 'X', by lag

    -8     -7     -6     -5     -4     -3     -2     -1      0      1      2 
-0.316 -0.301 -0.202 -0.020  0.180  0.397  0.611  0.741  0.746  0.557  0.329 
     3      4      5      6      7      8 
 0.126 -0.157 -0.280 -0.469 -0.512 -0.453 
> dev.off()
null device 
          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,'Cross Correlation Function',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'Value',header=TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
> a<-table.element(a,par1)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
> a<-table.element(a,par2)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
> a<-table.element(a,par3)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Seasonal Period (s)',header=TRUE)
> a<-table.element(a,par4)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
> a<-table.element(a,par5)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
> a<-table.element(a,par6)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
> a<-table.element(a,par7)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'k',header=TRUE)
> a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
> a<-table.row.end(a)
> mylength <- length(r$acf)
> myhalf <- floor((mylength-1)/2)
> for (i in 1:mylength) {
+ a<-table.row.start(a)
+ a<-table.element(a,i-myhalf-1,header=TRUE)
+ a<-table.element(a,r$acf[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/2wx2q1228304039.tab") 
> 
> system("convert tmp/124la1228304039.ps tmp/124la1228304039.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.171   0.288   1.222