Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
13 14 22 16 19 24 17 17 26 NA 17 21 NA 15 26 16 20 25 NA 15 21 NA 19 24 NA 15 27 17 15 28 17 19 23 15 NA 25 16 20 24 14 18 24 16 15 24 17 14 25 NA 20 25 NA NA NA NA 16 25 NA 16 25 16 16 24 NA 10 26 16 19 26 NA 19 25 NA 16 26 NA 15 23 16 18 24 15 17 24 16 19 25 16 17 25 13 NA 24 15 19 28 17 20 27 NA 5 NA 13 19 23 17 16 23 NA 15 24 14 16 24 14 18 22 18 16 25 NA 15 25 17 17 28 13 NA 22 16 20 28 15 19 25 15 7 24 NA 13 24 15 16 23 13 16 25 NA NA NA 17 18 26 NA 18 25 NA 16 27 11 17 26 14 19 23 13 16 25 NA 19 21 17 13 22 16 16 24 NA 13 25 17 12 27 16 17 24 16 17 26 16 17 21 15 16 27 12 16 22 17 14 23 14 16 24 14 13 25 16 16 24 NA 14 23 NA 20 28 NA 12 NA NA 13 24 NA 18 26 15 14 22 16 19 25 14 18 25 15 14 24 17 18 24 NA 19 26 10 15 21 NA 14 25 17 17 25 NA 19 26 20 13 25 17 19 26 18 18 27 NA 20 25 17 15 NA 14 15 20 NA 15 24 17 20 26 NA 15 25 17 19 25 NA 18 24 16 18 26 18 15 25 18 20 28 16 17 27 NA 12 25 NA 18 26 15 19 26 13 20 26 NA NA NA NA 17 28 NA 15 NA NA 16 21 NA 18 25 16 18 25 NA 14 24 NA 15 24 NA 12 24 12 17 23 NA 14 23 16 18 24 16 17 24 NA 17 25 16 20 28 14 16 23 15 14 24 14 15 23 NA 18 24 15 20 25 NA 17 24 15 17 23 16 17 23 NA 17 25 NA 15 21 NA 17 22 11 18 19 NA 17 24 18 20 25 NA 15 21 11 16 22 NA 15 23 18 18 27 NA 11 NA 15 15 26 19 18 29 17 20 28 NA 19 24 14 14 25 NA 16 25 13 15 22 17 17 25 14 18 26 19 20 26 14 17 24 NA 18 25 NA 15 19 16 16 25 16 11 23 15 15 25 12 18 25 NA 17 26 17 16 27 NA 12 24 NA 19 22 18 18 25 15 15 24 18 17 23 15 19 27 NA 18 24 NA 19 24 NA 16 21 16 16 25 NA 16 25 16 14 23
Names of X columns:
TVDC ITHSUM SKEOUSUM
Type of Correlation
pearson
spearman
kendall
Chart options
Title:
R Code
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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