R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- array(list(1
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+ ,0.643956
+ ,0
+ ,21.209
+ ,0.04398
+ ,0.462803
+ ,0.664357)
+ ,dim=c(5
+ ,195)
+ ,dimnames=list(c('status'
+ ,'HNR'
+ ,'NHR'
+ ,'RPDE'
+ ,'DFA')
+ ,1:195))
>  y <- array(NA,dim=c(5,195),dimnames=list(c('status','HNR','NHR','RPDE','DFA'),1:195))
>  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 = 'Scatter Plots and p-values'
> par1 <- 'pearson'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Patrick Wessa, (2013), Multivariate Correlation Matrix (v1.0.7) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/Patrick.Wessa/rwasp_pairs.wasp/
> #Source of accompanying publication: 
> #
> panel.tau <- function(x, y, digits=2, prefix='', cex.cor)
+ {
+ usr <- par('usr'); on.exit(par(usr))
+ par(usr = c(0, 1, 0, 1))
+ rr <- cor.test(x, y, method=par1)
+ r <- round(rr$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/fisher/rcomp/tmp/1lkpj1386316996.ps",horizontal=F,onefile=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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
> 
> n <- length(y[,1])
> n
[1] 5
> 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/fisher/rcomp/tmp/2048j1386316996.tab") 
> ncorrs <- (n*n -n)/2
> mycorrs <- array(0, dim=c(10,3))
> 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 = -5.3866, df = 193, p-value = 2.075e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4777584 -0.2328296
sample estimates:
       cor 
-0.3615149 

> 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)
+ for (iii in 1:10) {
+ iiid100 <- iii / 100
+ if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1
+ if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1
+ if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1
+ }
+ }
+ }
Warning messages:
1: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
2: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
3: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
4: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
5: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
6: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
7: In cor.test.default(y[i, ], y[j, ], method = "spearman") :
  Cannot compute exact p-value with ties
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/3sprm1386316996.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Type I error',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)
> for (iii in 1:10) {
+ iiid100 <- iii / 100
+ a<-table.row.start(a)
+ a<-table.element(a,round(iiid100,2),header=T)
+ a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2))
+ a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2))
+ a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/4d0bc1386316996.tab") 
> 
> try(system("convert tmp/1lkpj1386316996.ps tmp/1lkpj1386316996.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.986   1.095   4.992