Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
11 8 7 18 12 20 15 18 18 23 20 25 19 18 20 23 20 19 16 12 9 22 14 18 24 24 19 22 25 24 15 16 12 19 15 20 17 19 16 25 20 20 19 16 17 28 21 24 19 15 9 16 15 21 28 28 28 28 28 28 26 21 20 21 11 10 15 18 16 22 22 22 26 22 22 24 22 19 16 19 17 24 27 27 24 22 12 26 24 23 25 25 18 28 23 24 22 20 20 24 24 24 15 16 12 20 21 25 21 19 16 26 20 24 22 18 16 21 19 21 27 26 21 28 25 28 26 24 15 27 16 28 26 20 17 23 24 22 22 19 17 24 21 26 21 19 17 24 22 26 22 23 18 22 25 21 20 18 15 21 23 26 21 16 20 25 20 23 20 18 13 20 21 20 22 21 21 21 22 24 21 20 12 26 25 25 8 15 6 23 23 24 22 19 13 21 19 20 18 27 6 27 27 23 20 19 19 27 21 24 24 7 12 25 19 25 17 20 14 23 25 23 20 20 13 25 16 21 23 19 12 23 24 23 20 19 17 19 24 21 22 20 19 22 18 18 19 18 10 24 28 24 15 14 10 19 15 18 20 17 11 21 17 21 22 17 11 27 18 23 17 8 10 25 26 25 14 9 7 25 18 22 24 22 22 23 22 22 17 20 12 17 19 23 23 20 18 28 17 24 25 22 20 25 26 25 16 22 9 20 21 22 18 22 16 25 26 24 20 16 14 21 21 21 18 14 11 24 12 24 23 24 20 28 20 25 24 21 17 20 20 23 23 20 14 19 24 27 13 20 8 24 24 27 20 18 16 21 22 23 20 14 11 24 21 18 19 19 10 23 20 20 22 24 15 18 23 23 22 19 15 27 19 24 15 16 10 25 24 26 17 16 10 20 21 20 19 16 18 21 16 23 20 14 10 23 17 22 22 22 22 27 23 23 21 21 16 24 20 17 21 15 10 27 19 20 16 14 7 24 18 22 20 15 16 23 18 18 21 14 16 24 21 19 20 20 16 21 20 19 23 21 22 23 17 16 15 17 13 22 20 24 18 14 5 27 25 26 22 19 18 24 15 14 16 16 10 25 17 25 17 13 8 19 17 23 24 26 16 24 24 18 13 13 8 25 21 22 19 18 16 23 22 26 20 15 14 23 18 25 22 18 15 25 22 26 19 21 9 26 20 26 21 17 21 26 21 24 15 18 7 16 21 22 21 20 17 23 20 21 24 18 18 26 18 22 22 25 16 25 25 28 20 20 16 23 23 22 21 19 14 26 21 26 19 18 15 22 20 20 14 12 8 20 21 24 25 22 22 27 20 21 11 16 5 20 22 23 17 18 13 22 15 23 22 23 22 24 24 23 20 20 18 21 22 22 22 20 15 24 21 23 15 16 11 26 17 21 23 22 19 24 23 27 20 19 19 24 22 23 22 23 21 27 23 26 16 6 4 25 16 27 25 19 17 27 18 27 18 24 10 19 25 23 19 19 13 22 18 23 25 15 15 22 14 23 21 18 11 25 20 28 22 18 20 23 19 24 21 22 13 24 18 20 22 23 18 24 22 23 23 18 20 23 21 22 20 17 15 22 14 15 6 6 4 24 5 27 15 22 9 19 25 23 18 20 18 25 21 23 24 16 12 26 11 20 22 16 17 18 20 18 21 17 12 24 9 22 23 20 16 28 15 20 20 23 17 23 23 21 20 18 14 19 21 25 18 13 13 19 9 19 25 22 20 27 24 25 16 20 16 24 16 24 20 20 15 26 20 22 14 13 10 21 15 28 22 16 16 25 18 22 26 25 21 28 22 21 20 16 15 19 21 23 17 15 16 20 21 19 22 19 19 26 21 21 22 19 9 27 20 25 20 24 19 23 24 23 17 9 7 18 15 28 22 22 23 23 24 14 17 15 14 21 18 23 22 22 10 23 24 24 21 22 16 22 24 25 25 24 12 21 15 15 11 12 10 14 19 23 19 21 7 24 20 26 24 25 20 26 26 21 17 26 9 24 26 26 22 19 14 26 18 15 22 21 12 22 23 23 17 14 10 20 13 15 26 28 19 20 16 16 19 16 16 20 19 20 20 21 11 18 22 20 19 16 15 18 21 20 21 16 14 25 11 21 24 25 11 28 23 28 21 21 14 23 18 19 19 22 15 20 19 21 13 9 7 22 15 22 24 20 22 27 8 27 28 19 19 24 15 20 27 24 22 23 21 17 22 22 11 20 25 26 23 22 19 22 14 21 19 12 9 21 21 24 18 17 11 24 18 21 23 18 17 26 18 25 21 10 12 24 12 22 22 22 17 18 24 17 17 24 10 17 17 14 15 18 17 23 20 23 21 18 13 21 24 28 20 23 11 21 22 24 26 21 19 24 15 22 19 21 21 22 22 24 28 28 24 24 26 25 21 17 13 24 17 21 19 21 16 24 23 22 22 21 13 23 19 16 21 20 15 21 21 18 20 18 15 24 23 27 19 17 11 19 19 17 11 7 7 19 18 25 17 17 13 23 16 24 19 14 13 25 23 21 20 18 12 24 13 21 17 14 8 21 18 19 21 23 7 18 23 27 21 20 17 23 21 28 12 14 9 20 23 19 23 17 18 23 16 23 22 21 17 23 17 25 22 23 17 23 20 26 21 24 18 23 18 25 20 21 12 27 20 25 18 14 14 19 19 24 21 24 22 25 26 24 24 16 19 25 9 24 22 21 21 21 23 22 20 8 10 25 9 21 17 17 16 17 13 17 19 18 11 22 27 23 16 17 15 23 22 17 19 16 12 27 12 25 23 22 21 27 18 19 8 17 22 5 6 8 22 21 20 19 17 14 23 20 15 24 22 22 15 20 9 23 22 25 17 19 15 28 23 28 21 8 14 25 19 25 25 19 11 27 20 24 18 11 9 16 17 15 23 15 18 23 18 25 20 13 12 25 24 24 21 18 11 26 20 28 21 19 14 24 18 24 24 23 10 23 23 25 22 20 18 24 27 23 22 22 11 27 25 26 23 19 14 25 24 26 17 16 16 19 12 22 15 11 11 19 16 25 24 11 8 14 16 20 22 21 16 24 24 22 19 14 13 20 23 26 18 21 12 21 24 20 21 20 17 28 24 26 20 21 23 26 26 26 19 20 14 19 19 21 19 19 10 23 28 21 16 19 16 23 23 24 18 18 11 21 21 21 23 20 16 26 19 18 22 21 19 25 23 23 23 22 17 25 23 26 20 19 12 24 20 23 24 23 17 23 18 25 25 16 11 22 20 20 25 23 19 27 28 25 20 18 12 26 21 26 23 23 8 23 25 19 21 20 17 22 18 21 23 20 13 26 24 23 23 23 17 22 28 24 11 13 7 17 9 6 21 21 23 25 22 22 27 26 18 22 26 21 19 18 13 28 28 28 21 19 17 22 18 24 16 18 13 21 23 14 22 19 13 21 22 17 21 18 8 24 15 20 22 19 16 26 24 28 16 13 14 26 12 19 18 10 13 24 12 24 23 21 19 27 20 21 24 24 15 22 25 21 20 21 15 23 24 26 20 23 8 22 23 24 18 18 14 23 18 26 4 11 7 15 20 25 14 16 11 20 22 23 22 20 17 22 20 24 17 20 19 25 25 24 23 26 17 27 28 26 20 21 12 24 25 23 18 12 12 21 14 20 19 15 18 17 16 16 20 18 16 26 24 24 15 14 15 20 13 20 24 18 20 22 19 23 21 16 16 24 18 23 19 19 12 23 16 18 19 7 10 22 8 21 27 21 28 28 27 25 23 24 19 21 23 23 23 21 18 24 20 26 20 20 19 28 20 26 17 22 8 25 26 24 21 17 17 24 23 23 23 19 16 24 24 21 22 20 18 21 21 23 16 16 12 20 15 20 20 20 17 26 22 23 16 16 13 16 25 24 21 19 18 23 23 23 19 19 13 16 16 16 27 24 6 25 26 18 13 7 10 15 25 28 17 17 12 25 23 26 18 23 10 22 24 21 20 23 13 19 26 20 22 21 15 22 20 21 18 18 8 24 24 26 6 4 4 10 14 15 22 27 4 24 28 16 15 18 9 23 25 21 19 20 10 22 21 25 17 15 12 22 19 22 22 19 21 26 22 19 10 14 6 24 22 24 21 14 11 23 21 24 21 18 17 26 17 23 23 17 10 27 21 22 18 20 16 28 23 27 20 16 12 21 16 22 27 16 12 23 15 24 13 11 11 25 24 26 20 21 14 23 25 26 20 10 11 22 23 28 22 18 19 23 19 20 20 18 16 22 24 23 24 21 21 27 14 24 23 16 16 24 20 23 19 15 11 24 23 26 22 17 12 20 12 20 24 15 8 19 22 20 21 12 9 23 18 12 19 20 14 21 21 21 20 20 13 27 23 28 16 18 7 19 22 24 17 21 17 25 28 24 25 22 8 25 20 24 16 21 9 24 22 22 23 25 15 26 24 26 20 12 11 21 19 23 23 22 20 22 9 10 22 24 13 26 23 27 15 17 7 20 23 24 16 20 8 21 22 26 20 20 20 21 20 18 23 19 15 28 23 22 24 24 19 24 24 24 17 18 17 19 17 16 19 15 18 23 19 23 25 25 19 25 17 21 14 27 5 27 23 28 18 17 11 18 19 19 22 18 13 26 24 18 15 17 8 14 15 27 27 24 19 25 22 17 22 18 14 23 25 20 26 27 24 24 27 24 16 18 11 20 23 24 25 23 12 19 16 20 20 18 9 25 24 24 19 21 10 19 22 25 19 25 22 23 27 25 24 18 18 23 28 26 14 19 8 17 23 25 18 20 15 24 25 26 13 11 10 22 21 14 19 22 10 20 22 19 25 24 20 23 25 23 20 23 17 22 25 25 17 16 12 20 19 23 17 24 17 23 24 19 13 16 10 22 21 23 20 16 15 21 18 24 20 18 14 22 17 21 24 17 8 26 17 21 25 21 17 24 26 24 19 15 10 28 8 23 20 15 16 24 25 22 20 19 13 26 22 21 22 21 17 20 20 23 18 19 16 26 22 25 21 19 13 21 17 17 20 18 14 26 24 27 11 14 6 22 20 28 18 17 16 21 19 24 22 25 18 25 26 27 21 14 16 25 13 22 15 19 15 23 20 23 23 20 18 27 26 24 18 20 20 23 21 24 23 20 19 28 24 26 19 19 16 24 23 21 23 18 11 21 16 23 26 22 24 23 24 18 19 18 13 21 18 25 26 22 17 24 21 24 20 19 14 28 17 27 20 20 16 11 19 20 23 22 18 25 22 25 24 22 16 25 23 23 26 24 16 28 27 25 23 18 9 28 22 24 19 21 5 19 22 23 25 22 11 25 25 19 23 19 10 25 26 25 19 18 16 25 22 28 27 24 17 28 25 26 23 21 15 26 23 24 24 21 13 27 8 25 20 20 12 24 24 28 16 17 12 18 14 23 22 20 16 21 17 15 26 22 22 23 21 18 26 24 19 24 21 24 24 24 23 26 25 27 20 20 6 25 18 25 20 19 19 23 20 24 12 20 7 24 25 26 21 16 9 20 20 19 27 21 16 26 24 23 26 22 19 27 22 21 17 19 8 21 16 22 20 19 15 21 20 23 18 13 10 19 21 23 28 22 18 25 22 20 24 20 19 23 15 20 24 21 12 25 21 25 24 21 16 26 25 28 12 15 12 18 16 19 26 23 20 27 28 21 23 22 19 23 22 21 13 15 10 20 19 25 23 20 16 22 17 18 16 23 12 22 23 22 23 21 15 23 28 21 18 18 15 18 19 21 25 23 17 25 24 25 18 16 13 21 16 20 18 18 14 21 19 22 21 18 18 28 19 27 7 10 4 19 12 23 19 17 11 21 16 25 21 20 10 23 15 28 17 13 7 22 17 25 22 25 20 27 23 24 15 18 10 23 21 27 20 20 18 27 20 19 19 18 14 23 19 24 10 19 11 21 20 22 18 11 12 22 20 23 25 17 16 26 23 21 23 22 19 23 22 21 25 21 18 26 20 20 23 19 16 28 24 26 21 20 9 28 21 28 23 21 15 26 23 23 19 22 14 24 22 19 22 20 17 23 21 23 23 21 14 28 26 18 15 15 11 21 16 21 23 22 11 28 28 28 23 21 19 21 24 22 24 28 25 28 28 28 20 20 20 24 14 20 23 20 15 24 16 23 24 23 17 28 22 25 17 18 12 21 18 16 21 15 10 26 23 23 19 19 24 22 18 18 23 21 16 25 22 22 22 19 9 20 13 21 14 16 16 19 20 19 19 17 8 23 24 20 21 26 11 26 24 27 23 20 13 28 25 27 16 13 14 24 23 20 23 19 12 25 24 26 19 21 14 24 22 25 19 21 16 25 24 23 22 24 19 27 24 24 26 23 17 28 24 27 22 20 20 23 25 28 24 23 11 19 27 26 24 24 19 27 27 27 11 8 6 15 14 23 21 19 16 27 21 28 21 18 14 21 17 22 22 20 14 26 23 23 22 21 16 25 25 27 19 16 11 26 20 18 18 17 14 24 21 22 19 21 16 25 24 23 27 27 22 27 27 25 14 12 7 14 12 14 15 17 17 24 26 21 20 17 16 25 22 26 22 18 18 23 24 28 26 24 22 24 24 22 20 18 13 22 20 24 13 18 11 16 22 28 26 24 19 26 23 24 19 18 14 26 22 26 20 19 15 19 21 18 18 19 15 19 13 19 20 24 15 28 21 26 21 15 15 24 20 26 26 22 19 20 18 12 25 17 22 21 19 24 20 20 18 26 25 26 21 22 10 24 24 23
Names of X columns:
I1 I2 I3 E1 E2 E3
Type of Correlation
kendall
pearson
spearman
kendall
Chart options
Title:
R Code
par1 <- 'pearson' 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.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) 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 } } } a<-table.end(a) table.save(a,file='mytable1.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='mytable2.tab')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation