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 <- array(list(10511,1.1,10812,1.3,10738,1.2,10171,1.6,9721,1.7,9897,1.5,9828,0.9,9924,1.5,10371,1.4,10846,1.6,10413,1.7,10709,1.4,10662,1.8,10570,1.7,10297,1.4,10635,1.2,10872,1,10296,1.7,10383,2.4,10431,2,10574,2.1,10653,2,10805,1.8,10872,2.7,10625,2.3,10407,1.9,10463,2,10556,2.3,10646,2.8,10702,2.4,11353,2.3,11346,2.7,11451,2.7,11964,2.9,12574,3,13031,2.2,13812,2.3,14544,2.8,14931,2.8,14886,2.8,16005,2.2,17064,2.6,15168,2.8,16050,2.5,15839,2.4,15137,2.3,14954,1.9,15648,1.7,15305,2,15579,2.1,16348,1.7,15928,1.8,16171,1.8,15937,1.8,15713,1.3,15594,1.3,15683,1.3,16438,1.2,17032,1.4,17696,2.2),dim=c(2,60),dimnames=list(c('Goudprijs','Inflatie'),1:60))
>  y <- array(NA,dim=c(2,60),dimnames=list(c('Goudprijs','Inflatie'),1:60))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> main = 'Kendall tau Correlation Plot'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2007), Multivariate Correlation Matrix (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_pairs.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description
> panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
+ {
+ usr <- par('usr'); on.exit(par(usr))
+ par(usr = c(0, 1, 0, 1))
+ rr <- cor.test(x, y, method='kendall')
+ r <- round(rr$p.value,2)
+ txt <- format(c(r, 0.123456789), digits=digits)[1]
+ txt <- paste(prefix, txt, sep='')
+ if(missing(cex.cor)) cex <- 0.5/strwidth(txt)
+ text(0.5, 0.5, txt, cex = cex)
+ }
> panel.hist <- function(x, ...)
+ {
+ usr <- par('usr'); on.exit(par(usr))
+ par(usr = c(usr[1:2], 0, 1.5) )
+ h <- hist(x, plot = FALSE)
+ breaks <- h$breaks; nB <- length(breaks)
+ y <- h$counts; y <- y/max(y)
+ rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1tac51199631623.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main)
> dev.off()
null device 
          1 
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'pair',1,TRUE)
> a<-table.element(a,'tau',1,TRUE)
> a<-table.element(a,'p-value',1,TRUE)
> a<-table.row.end(a)
> n <- length(y[,1])
> n
[1] 2
> cor.test(y[1,],y[2,],method='kendall')

	Kendall's rank correlation tau

data:  y[1, ] and y[2, ] 
z = 1.6625, p-value = 0.09642
alternative hypothesis: true tau is not equal to 0 
sample estimates:
      tau 
0.1503272 

> for (i in 1:(n-1))
+ {
+ for (j in (i+1):n)
+ {
+ a<-table.row.start(a)
+ dum <- paste('tau(',dimnames(t(x))[[2]][i])
+ dum <- paste(dum,',')
+ dum <- paste(dum,dimnames(t(x))[[2]][j])
+ dum <- paste(dum,')')
+ a<-table.element(a,dum,header=TRUE)
+ r <- cor.test(y[i,],y[j,],method='kendall')
+ a<-table.element(a,r$estimate)
+ a<-table.element(a,r$p.value)
+ a<-table.row.end(a)
+ }
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/2ii1l1199631623.tab") 
> 
> system("convert tmp/1tac51199631623.ps tmp/1tac51199631623.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.652   0.202   1.234