Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
110.30 153.40 103.90 145.00 101.60 137.70 94.60 148.30 95.90 152.20 104.70 169.40 102.80 168.60 98.10 161.10 113.90 174.10 80.90 179.00 95.70 190.60 113.20 190.00 105.90 181.60 108.80 174.80 102.30 180.50 99.00 196.80 100.70 193.80 115.50 197.00 100.70 216.30 109.90 221.40 114.60 217.90 85.40 229.70 100.50 227.40 114.80 204.20 116.50 196.60 112.90 198.80 102.00 207.50 106.00 190.70 105.30 201.60 118.80 210.50 106.10 223.50 109.30 223.80 117.20 231.20 92.50 244.00 104.20 234.70 112.50 250.20 122.40 265.70 113.30 287.60 100.00 283.30 110.70 295.40 112.80 312.30 109.80 333.80 117.30 347.70 109.10 383.20 115.90 407.10 96.00 413.60 99.80 362.70 116.80 321.90 115.70 239.40 99.40 191.00 94.30 159.70 91.00 163.40 93.20 157.60 103.10 166.20 94.10 176.70 91.80 198.30 102.70 226.20 82.60 216.20 89.10 235.90 104.50 226.90
Names of X columns:
Ind Gpr
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