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Data X:
1.43 103.52 0.3 0.51 1.43 103.5 2.1 0.51 1.43 103.52 2.5 0.51 1.43 103.53 2.3 0.51 1.43 103.53 2.4 0.51 1.43 103.53 3 0.51 1.43 103.52 1.7 0.51 1.43 103.54 3.5 0.51 1.43 103.59 4 0.5 1.43 103.59 3.7 0.51 1.43 103.59 3.7 0.51 1.43 103.59 3 0.5 1.43 103.63 2.7 0.51 1.43 103.74 2.5 0.51 1.43 103.7 2.2 0.51 1.43 103.72 2.9 0.51 1.43 103.81 3.1 0.52 1.43 103.8 3 0.52 1.44 104.22 2.8 0.52 1.48 106.91 2.5 0.53 1.48 107.06 1.9 0.53 1.48 107.17 1.9 0.52 1.48 107.25 1.8 0.52 1.48 107.28 2 0.52 1.48 107.24 2.6 0.52 1.48 107.23 2.5 0.52 1.48 107.34 2.5 0.52 1.48 107.34 1.6 0.52 1.48 107.3 1.4 0.52 1.48 107.24 0.8 0.52 1.48 107.3 1.1 0.52 1.48 107.32 1.3 0.53 1.48 107.28 1.2 0.53 1.48 107.33 1.3 0.53 1.48 107.33 1.1 0.54 1.48 107.33 1.3 0.54 1.48 107.28 1.2 0.54 1.48 107.28 1.6 0.54 1.48 107.29 1.7 0.54 1.48 107.29 1.5 0.54 1.48 107.23 0.9 0.54 1.48 107.24 1.5 0.54 1.48 107.24 1.4 0.54 1.48 107.2 1.6 0.54 1.48 107.23 1.7 0.53 1.48 107.2 1.4 0.53 1.48 107.21 1.8 0.53 1.48 107.24 1.7 0.53 1.48 107.21 1.4 0.53 1.57 113.89 1.2 0.54 1.58 114.05 1 0.55 1.58 114.05 1.7 0.55 1.58 114.05 2.4 0.55 1.58 114.05 2 0.55 1.59 115.12 2.1 0.55 1.6 115.68 2 0.55 1.6 116.05 1.8 0.55 1.61 116.18 2.7 0.55 1.61 116.35 2.3 0.56 1.61 116.44 1.9 0.56 1.62 117 2 0.56 1.63 117.61 2.3 0.56 1.63 118.17 2.8 0.56 1.64 118.33 2.4 0.55 1.64 118.33 2.3 0.56 1.64 118.42 2.7 0.55 1.64 118.5 2.7 0.55 1.64 118.67 2.9 0.56 1.65 119.09 3 0.55 1.65 119.14 2.2 0.55 1.65 119.23 2.3 0.55 1.65 119.33 2.8 0.55
Names of X columns:
broodpr index inflatie grondpr
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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1 seconds
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Big Analytics Cloud Computing Center
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