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Data X:
101.3 99.2 99.9 124.9 1487.6 102 93.6 98.6 132 1320.9 109.2 104.2 107.2 151.4 1514 88.6 95.3 95.7 108.9 1290.9 94.3 102.7 93.7 121.3 1392.5 98.3 103.1 106.7 123.4 1288.2 86.4 100 86.7 90.3 1304.4 80.6 107.2 95.3 79.3 1297.8 104.1 107 99.3 117.2 1211 108.2 119 101.8 116.9 1454 93.4 110.4 96 120.8 1405.7 71.9 101.7 91.7 96.1 1160.8 94.1 102.4 95.3 100.8 1492.1 94.9 98.8 96.6 105.3 1263 96.4 105.6 107.2 116.1 1376.3 91.1 104.4 108 112.8 1368.6 84.4 106.3 98.4 114.5 1427.6 86.4 107.2 103.1 117.2 1339.8 88 108.5 81.1 77.1 1248.3 75.1 106.9 96.6 80.1 1309.8 109.7 114.2 103.7 120.3 1424 103 125.9 106.6 133.4 1590.5 82.1 110.6 97.6 109.4 1423.1 68 110.5 87.6 93.2 1355.3 96.4 106.7 99.4 91.2 1515 94.3 104.7 98.5 99.2 1385.6 90 107.4 105.2 108.2 1430 88 109.8 104.6 101.5 1494.2 76.1 103.4 97.5 106.9 1580.9 82.5 114.8 108.9 104.4 1369.8 81.4 114.3 86.8 77.9 1407.5 66.5 109.6 88.9 60 1388.3 97.2 118.3 110.3 99.5 1478.5 94.1 127.3 114.8 95 1630.4 80.7 112.3 94.6 105.6 1413.5 70.5 114.9 92 102.5 1493.8 87.8 108.2 93.8 93.3 1641.3 89.5 105.4 93.8 97.3 1465 99.6 122.1 107.6 127 1725.1 84.2 113.5 101 111.7 1628.4 75.1 110 95.4 96.4 1679.8 92 125.3 96.5 133 1876 80.8 114.3 89.2 72.2 1669.4 73.1 115.6 87.1 95.8 1712.4 99.8 127.1 110.5 124.1 1768.8 90 123 110.8 127.6 1820.5 83.1 122.2 104.2 110.7 1776.2 72.4 126.4 88.9 104.6 1693.7 78.8 112.7 89.8 112.7 1799.1 87.3 105.8 90 115.3 1917.5 91 120.9 93.9 139.4 1887.2 80.1 116.3 91.3 119 1787.8 73.6 115.7 87.8 97.4 1803.8 86.4 127.9 99.7 154 2196.4 74.5 108.3 73.5 81.5 1759.5 71.2 121.1 79.2 88.8 2002.6 92.4 128.6 96.9 127.7 2056.8 81.5 123.1 95.2 105.1 1851.1 85.3 127.7 95.6 114.9 1984.3 69.9 126.6 89.7 106.4 1725.3 84.2 118.4 92.8 104.5 2096.6 90.7 110 88 121.6 1792.2 100.3 129.6 101.1 141.4 2029.9 79.4 115.8 92.7 99 1785.3 84.8 125.9 95.8 126.7 2026.5 92.9 128.4 103.8 134.1 1930.8 81.6 114 81.8 81.3 1845.5 76 125.6 87.1 88.6 1943.1 98.7 128.5 105.9 132.7 2066.8 89.1 136.6 108.1 132.9 2354.4 88.7 133.1 102.6 134.4 2190.7 67.1 124.6 93.7 103.7 1929.6
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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