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Data X:
113.2 73.8 88.7 105.5 55.2 55.4 77.8 54.4 46.6 102.1 80.8 90.9 97 88 84.9 95.5 74.5 89 99.3 55.1 90.2 86.4 47.2 72.3 92.4 54.2 83 85.7 70.6 71.6 61.9 78.5 75.4 104.9 77.1 85.1 107.9 56.6 81.2 95.6 39.8 68.7 79.8 44.1 68.4 94.8 66.9 93.7 93.7 75.3 96.6 108.1 74.9 101.8 96.9 48.8 93.6 88.8 37 88.9 106.7 49.8 114.1 86.8 63.2 82.3 69.8 75.9 96.4 110.9 68.5 104 105.4 49.2 88.2 99.2 40.3 85.2 84.4 38.6 87.1 87.2 54.2 85.5 91.9 70.6 89.1 97.9 68 105.2 94.5 43 82.9 85 42.3 86.8 100.3 47.7 112 78.7 57.7 97.4 65.8 75.8 88.9 104.8 57.2 109.4 96 43.6 87.8 103.3 40 90.5 82.9 35.9 79.3 91.4 59.5 114.9 94.5 72.7 118.8 109.3 70.9 125 92.1 44.9 96.1 99.3 44.5 116.7 109.6 48 119.5 87.5 60.4 104.1 73.1 71.8 121 110.7 63.2 127.3 111.6 32.4 117.7 110.7 33.9 108 84 24.2 89.4 101.6 64.7 137.4 102.1 73 142 113.9 61.7 137.3 99 31.9 122.8 100.4 30.8 126.1 109.5 36.7 147.6 93 47.4 115.7 76.8 54 139.2 105.3 41.1 150.9
Names of X columns:
Textiel KledingBont LeerSchoeisel
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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Computing time
1 seconds
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Big Analytics Cloud Computing Center
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