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Data X:
149 100.8 139 101.33 135 101.88 130 101.85 127 102.04 122 102.22 117 102.63 112 102.65 113 102.54 149 102.37 157 102.68 157 102.76 147 102.82 137 103.31 132 103.23 125 103.6 123 103.95 117 103.93 114 104.25 111 104.38 112 104.36 144 104.32 150 104.58 149 104.68 134 104.92 123 105.46 116 105.23 117 105.58 111 105.34 105 105.28 102 105.7 95 105.67 93 105.71 124 106.19 130 106.93 124 107.44 115 107.85 106 108.71 105 109.32 105 109.49 101 110.2 95 110.62 93 111.22 84 110.88 87 111.15 116 111.29 120 111.09 117 111.24 109 111.45 105 111.75 107 111.07 109 111.17 109 110.96 108 110.5 107 110.48 99 110.66 103 110.46 131 110.64 137 110.75 135 110.84
Names of X columns:
WLH GI
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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