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Data X:
15836.8 15799.5 8.9 89.1 17570.4 16685.6 8.6 82.6 18252.1 18357.8 8.3 102.7 16196.7 15952.7 8.3 91.8 16643 16540.9 8.3 94.1 17729 18159.5 8.4 103.1 16446.1 16847.2 8.5 93.2 15993.8 17282.5 8.4 91 16373.5 18042.3 8.6 94.3 17842.2 19614.9 8.5 99.4 22321.5 23335.7 8.5 115.7 22786.7 23650.4 8.4 116.8 18274.1 20328.3 8.5 99.8 22392.9 22751.6 8.5 96 23899.3 24347.7 8.5 115.9 21343.5 21479.6 8.5 109.1 22952.3 23533.8 8.5 117.3 21374.4 22176.2 8.5 109.8 21164.1 20866.8 8.5 112.8 20906.5 22168.7 8.5 110.7 17877.4 19484.6 8.6 100 20664.3 21131.5 8.4 113.3 22160 21968.9 8.1 122.4 19813.6 19220.8 8 112.5 17735.4 17789.6 8 104.2 19640.2 19227.5 8 92.5 20844.4 19899.2 8 117.2 19823.1 19563 7.9 109.3 18594.6 18075.2 7.8 106.1 21350.6 20438.6 7.8 118.8 18574.1 18076.7 7.9 105.3 18924.2 18637.3 8.1 106 17343.4 17579.4 8 102 19961.2 19422.1 7.6 112.9 19932.1 19270 7.3 116.5 19464.6 18553 7 114.8 16165.4 16895.6 6.8 100.5 17574.9 16398.9 7 85.4 19795.4 18953.1 7.1 114.6 19439.5 18690 7.2 109.9 17170 17065.3 7.1 100.7 21072.4 21489.9 6.9 115.5 17751.8 17776.1 6.7 100.7 17515.5 18101.9 6.7 99 18040.3 19021 6.6 102.3 19090.1 18883 6.9 108.8 17746.5 16768.7 7.3 105.9 19202.1 18135.3 7.5 113.2 15141.6 15699.8 7.3 95.7 16258.1 15052.1 7.1 80.9 18586.5 18320.9 6.9 113.9 17209.4 16505 7.1 98.1 17838.7 16889 7.5 102.8 19123.5 18447.7 7.7 104.7 16583.6 16359.9 7.8 95.9 15991.2 15663.9 7.8 94.6 16704.4 16869.2 7.7 101.6 17420.4 16614.8 7.8 103.9 17872 16670.7 7.8 110.3 17823.2 16629.6 7.9 114.1
Names of X columns:
UV IV WL ID
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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