R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- array(list(0.1,-54.4,0.2,-6.9,-0.1,5.6,-0.1,1.4,-0.4,26.6,-0.1,-12.5,0.1,-12,0.5,24.3,0.2,-47,0,-24.4,0.2,76.1,-0.2,2.7,0.1,-28.2,0.2,11.8,0,-32.8,-0.1,11.5,-1.2,59.8,-0.1,-33.3,0.1,-23.2,1.6,57.9,0.2,-74.2,0,-1.7,-0.7,72.1,-0.2,-12.2,0,-16.1,0.3,4.4,0,-30.6,-0.1,3.9,-0.4,44.1,0,-27.1,-0.1,-12.7,0.6,49.6,0.1,-90.4,0,15.4,-0.1,66.3,-0.2,-30.4,0,12.6,0.3,-12.8,0,-8.6,-0.2,9.7,-0.2,55.8,0,-63.5,0,38,-0.2,18.1,0.1,-88.8,-0.1,15.9,0,63,-0.2,4.1,-0.2,-8.3,0.4,-22.2,-0.1,4.7,-0.3,-2.3,0.1,51.8,-0.2,-47.1,-0.2,12.1,0.3,37,0.1,-73.9,-0.3,-4.9,-0.3,43.4),dim=c(2,59),dimnames=list(c('Werkloosheid','IndProd'),1:59))
>  y <- array(NA,dim=c(2,59),dimnames=list(c('Werkloosheid','IndProd'),1:59))
>  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/1kzid1198252469.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 = -1.8134, p-value = 0.06977
alternative hypothesis: true tau is not equal to 0 
sample estimates:
       tau 
-0.1704657 

> 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/2b11r1198252470.tab") 
> 
> system("convert tmp/1kzid1198252469.ps tmp/1kzid1198252469.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.628   0.180   0.692