Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
101.8 100.0 103.4 100.0 104.9 100.0 105.1 100.1 105.6 100.0 104.5 100.0 105.5 99.8 105.1 100.0 106.9 99.9 106.6 99.2 106.6 98.7 106.5 98.7 109.7 98.9 109.5 99.2 109.2 99.8 109.1 100.5 109.0 100.1 109.0 100.5 109.0 98.4 109.0 98.6 109.0 99.0 109.0 99.1 109.0 98.9 109.0 98.5 109.0 96.9 109.0 96.8 109.0 97.0 109.0 97.0 109.0 96.9 109.0 97.1 109.0 97.2 109.0 97.9 109.0 98.9 109.0 99.2 109.0 99.5 109.2 99.3 113.3 99.9 112.3 100.0 112.3 100.3 116.3 100.5 118.3 100.7 119.4 100.9 119.4 100.8 119.4 100.9 120.1 101.0 121.7 100.3 123.7 100.1 123.7 99.8 128.5 99.9 127.1 99.9 122.6 100.2 119.8 99.7 122.7 100.4 123.4 100.9 123.8 101.3 121.8 101.4 121.2 101.3 121.2 100.9 121.2 100.9 121.2 100.9 129.6 101.1 131.0 101.1 131.0 101.3 129.8 101.8 129.8 102.9 134.9 103.2 131.2 103.3 127.1 104.5 130.5 105.0 130.5 104.9 131.7 104.9 131.7 105.4 131.7 106.0
Names of X columns:
Energie Voeding
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='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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Kendall tau rank correlations for all pairs of data series',3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'tau',1,TRUE) a<-table.element(a,'p-value',1,TRUE) a<-table.row.end(a) n <- length(y[,1]) n cor.test(y[1,],y[2,],method='kendall') for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste('tau(',dimnames(t(x))[[2]][i]) dum <- paste(dum,',') dum <- paste(dum,dimnames(t(x))[[2]][j]) dum <- paste(dum,')') a<-table.element(a,dum,header=TRUE) r <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,r$estimate) a<-table.element(a,r$p.value) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable.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