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 <- array(list(1.3866
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+ ,1.3612
+ ,130.91
+ ,1.369
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+ ,1.3771
+ ,130.84
+ ,1.3972
+ ,131.41
+ ,1.401
+ ,133.22
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+ ,132.06
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+ ,1.4238
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+ ,1.4095
+ ,136.32
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+ ,137.48
+ ,1.3866
+ ,136.53
+ ,1.3959
+ ,136.8
+ ,1.4102
+ ,138.03
+ ,1.3969
+ ,137.39
+ ,1.4004
+ ,137.55
+ ,1.385
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+ ,1.384
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+ ,1.3932
+ ,134.84
+ ,1.3858
+ ,133.02
+ ,1.3978
+ ,133.49
+ ,1.4029
+ ,133.77
+ ,1.394
+ ,134.34
+ ,1.4096
+ ,134.5
+ ,1.4058
+ ,134.03
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+ ,132.44
+ ,1.4019
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+ ,131.02
+ ,1.399
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+ ,128.21
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+ ,129.03
+ ,1.3991
+ ,130.34
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+ ,130.46
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+ ,1.3945
+ ,112.44
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+ ,1.37
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+ ,1.3321
+ ,111.35
+ ,1.3225
+ ,110.92
+ ,1.3146
+ ,110.73
+ ,1.2998
+ ,109)
+ ,dim=c(2
+ ,491)
+ ,dimnames=list(c('Dollar'
+ ,'Yen')
+ ,1:491))
>  y <- array(NA,dim=c(2,491),dimnames=list(c('Dollar','Yen'),1:491))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par1 = 'pearson'
> main = 'Correlation Matrix'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Patrick Wessa
> #To cite this work: Patrick Wessa, (2010), Multivariate Correlation Matrix (v1.0.4) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/Patrick.Wessa/rwasp_pairs.wasp#output/
> #Source of accompanying publication: 
> #Technical description: 
> panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
+ {
+ usr <- par('usr'); on.exit(par(usr))
+ par(usr = c(0, 1, 0, 1))
+ rr <- cor.test(x, y, method=par1)
+ r <- round(rr$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/1qt861292939472.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 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> n <- length(y[,1])
> n
[1] 2
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,' ',header=TRUE)
> for (i in 1:n) {
+ a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
+ }
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE)
+ for (j in 1:n) {
+ r <- cor.test(y[i,],y[j,],method=par1)
+ a<-table.element(a,round(r$estimate,3))
+ }
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/2cc6c1292939472.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'pair',1,TRUE)
> a<-table.element(a,'Pearson r',1,TRUE)
> a<-table.element(a,'Spearman rho',1,TRUE)
> a<-table.element(a,'Kendall tau',1,TRUE)
> a<-table.row.end(a)
> cor.test(y[1,],y[2,],method=par1)

	Pearson's product-moment correlation

data:  y[1, ] and y[2, ] 
t = 24.5472, df = 489, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0 
95 percent confidence interval:
 0.7005479 0.7801769 
sample estimates:
      cor 
0.7429801 

> for (i in 1:(n-1))
+ {
+ for (j in (i+1):n)
+ {
+ a<-table.row.start(a)
+ dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='')
+ a<-table.element(a,dum,header=TRUE)
+ rp <- cor.test(y[i,],y[j,],method='pearson')
+ a<-table.element(a,round(rp$estimate,4))
+ rs <- cor.test(y[i,],y[j,],method='spearman')
+ a<-table.element(a,round(rs$estimate,4))
+ rk <- cor.test(y[i,],y[j,],method='kendall')
+ a<-table.element(a,round(rk$estimate,4))
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-value',header=T)
+ a<-table.element(a,paste('(',round(rp$p.value,4),')',sep=''))
+ a<-table.element(a,paste('(',round(rs$p.value,4),')',sep=''))
+ a<-table.element(a,paste('(',round(rk$p.value,4),')',sep=''))
+ a<-table.row.end(a)
+ }
+ }
Warning message:
In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-values with ties
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/3fcn01292939472.tab") 
> 
> try(system("convert tmp/1qt861292939472.ps tmp/1qt861292939472.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.707   0.245   5.847