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(-2364,-0.7,4658,-0.7,2168,1.8,-3325,1.9,-5995,-0.1,-5572,-7,-8463,-1.4,-1032,3.1,4055,-5.7,-11397,-4.7,-10714,-2.9,17543,3.9,-9774,-1.1,-5876,4.1,-8917,6.1,-12380,-0.2,-3044,-3.9,-2265,-1.2,-4691,1,3413,9.7,3934,6.3,-6857,-7.3,-1861,0,16515,-11,-2131,6,9367,-0.4,2572,-7.5,-3426,-6.4,1922,1.5,-5839,2,-4168,2.8,2277,1.2,2018,0.2,-6147,1.7,-3897,-2.4,14195,0.5,-2438,-2.5,-287,4,-9956,2.1,-9629,8.3,4365,1.5,-6349,0.1,726,9.1,2147,3.1,-581,11.6,-4303,-6.4,-8471,0,18149,-18.4,-237,2.9,8915,12,-1931,3.4,254,-4.9,1286,5.9,-8887,6.9,-642,4.4,-525,15.1,1311,6.3,-2403,-10.2,-9112,-0.1,20426,6.1,-5405,-0.4,1145,0.4,-8647,5.5,-2065,-0.6,-958,-2.4,-3273,4.4,531,-1.6),dim=c(2,67),dimnames=list(c('Pers','Olie'),1:67))
>  y <- array(NA,dim=c(2,67),dimnames=list(c('Pers','Olie'),1:67))
>  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/149z71200948235.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 = 0.9526, p-value = 0.3408
alternative hypothesis: true tau is not equal to 0 
sample estimates:
       tau 
0.07980075 

> 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/2uvqa1200948235.tab") 
> 
> system("convert tmp/149z71200948235.ps tmp/149z71200948235.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.641   0.180   0.713