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Data X:
-2364 -0.7 4658 -0.7 2168 1.8 -3325 1.9 -5995 -0.1 -5572 -7 -8463 -1.4 -1032 3.1 4055 -5.7 -11397 -4.7 -10714 -2.9 17543 3.9 -9774 -1.1 -5876 4.1 -8917 6.1 -12380 -0.2 -3044 -3.9 -2265 -1.2 -4691 1 3413 9.7 3934 6.3 -6857 -7.3 -1861 0 16515 -11 -2131 6 9367 -0.4 2572 -7.5 -3426 -6.4 1922 1.5 -5839 2 -4168 2.8 2277 1.2 2018 0.2 -6147 1.7 -3897 -2.4 14195 0.5 -2438 -2.5 -287 4 -9956 2.1 -9629 8.3 4365 1.5 -6349 0.1 726 9.1 2147 3.1 -581 11.6 -4303 -6.4 -8471 0 18149 -18.4 -237 2.9 8915 12 -1931 3.4 254 -4.9 1286 5.9 -8887 6.9 -642 4.4 -525 15.1 1311 6.3 -2403 -10.2 -9112 -0.1 20426 6.1 -5405 -0.4 1145 0.4 -8647 5.5 -2065 -0.6 -958 -2.4 -3273 4.4 531 -1.6
Names of X columns:
Pers Olie
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')
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Big Analytics Cloud Computing Center
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