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Data X:
613 23 25.7 611 19 24.7 594 18 24.2 595 19 23.6 591 19 24.4 589 22 22.5 584 23 19.4 573 20 18.1 567 14 18.1 569 14 20.7 621 14 19.1 629 15 18.3 628 11 16.9 612 17 17.9 595 16 20.2 597 20 21.2 593 24 23.8 590 23 24 580 20 26.6 574 21 25.3 573 19 27.6 573 23 24.7 620 23 26.6 626 23 24.4 620 23 24.6 588 27 26 566 26 24.8 557 17 24 561 24 22.7 549 26 23 532 24 24.1 526 27 24 511 27 22.7 499 26 22.6 555 24 23.1 565 23 24.4 542 23 23 527 24 22 510 17 21.3 514 21 21.5 517 19 21.3 508 22 23.2 493 22 21.8 490 18 23.3 469 16 21 478 14 22.4 528 12 20.4 534 14 19.9 518 16 21.3 506 8 18.9 502 3 15.6 516 0 12.5 528 5 7.8 533 1 5.5 536 1 4 537 3 3.3 524 6 3.7 536 7 3.1 587 8 5 597 14 6.3
Names of X columns:
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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