Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
31 45 22 20 26 44 22 16 18 45 20 10 26 45 21 12 26 45 20 10 27 42 21 12 22 43 21 12 24 50 21 13 31 46 19 17 23 46 21 12 31 45 21 15 37 49 22 12 42 46 19 14 43 45 24 19 48 49 22 16 46 47 22 17 45 45 22 16 52 48 24 19 46 51 22 17 53 48 23 17 47 49 24 20 43 51 21 18 44 54 20 16 48 52 22 19 48 52 23 18 51 53 23 23 57 51 22 20 50 55 20 20 38 53 21 15 31 51 21 17 31 52 20 16 37 54 20 15 26 58 17 10 36 57 18 13 41 52 19 10 44 50 19 19 50 53 20 21 49 50 21 17 48 50 20 16 50 51 21 17 52 53 19 14 53 49 22 18 59 54 20 17 53 57 18 14 59 58 16 15 61 56 17 16 62 60 18 11 54 55 19 15 62 54 18 13 63 52 20 17 63 55 21 16 71 56 18 9 65 54 19 17 65 53 19 15 61 59 19 12 59 62 21 12 53 63 19 12 55 64 19 12 39 75 17 4 36 77 16 7 29 79 16 4 31 77 17 3 30 82 16 3 23 83 15 0 19 81 16 5 14 78 16 3 3 79 16 4 6 79 18 3 13 73 19 10 3 72 16 4 6 67 16 1 0 67 16 1
Names of X columns:
E.S P.T E.S.G SP
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
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation