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Data X:
11.663 9.828 1.5 101.09 11.372 9.924 1.4 102.71 11.139 10.371 1.6 102.11 11.222 10.846 1.7 101.68 11.692 10.413 1.4 101.7 11.702 10.709 1.8 101.53 12.286 10.662 1.7 101.76 12.613 10.570 1.4 101.15 12.646 10.297 1.2 100.92 12.262 10.635 1 100.73 11.985 10.872 1.7 100.55 12.007 10.296 2.4 102.15 12.138 10.383 2 100.79 12.266 10.431 2.1 99.93 12.176 10.574 2 100.03 12.218 10.653 1.8 100.25 1.249 10.805 2.7 99.6 12.991 10.872 2.3 100.16 13.408 10.625 1.9 100.49 13.119 10.407 2 99.72 13.014 10.463 2.3 100.14 13.201 10.556 2.8 98.48 12.938 10.646 2.4 100.38 12.694 10.702 2.3 101.45 12.165 11.353 2.7 98.42 12.037 11.346 2.7 98.6 12.292 11.451 2.9 100.06 12.256 11.964 3 98.62 12.015 12.574 2.2 100.84 11.786 13.031 2.3 100.02 11.856 13.812 2.8 97.95 12.103 14.544 2.8 98.32 11.938 14.931 2.8 98.27 1.202 14.886 2.2 97.22 12.271 16.005 2.6 99.28 1.277 17.064 2.8 100.38 1.265 15.168 2.5 99.02 12.684 16.050 2.4 100.32 12.811 15.839 2.3 99.81 12.727 15.137 1.9 100.6 12.611 14.954 1.7 101.19 12.881 15.648 2 100.47 13.213 15.305 2.1 101.77 12.999 15.579 1.7 102.32 13.074 16.348 1.8 102.39 13.242 15.928 1.8 101.16 13.516 16.171 1.8 100.63 13.511 15.937 1.3 101.48 13.419 15.713 1.3 101.44 13.716 15.594 1.3 100.09 13.622 15.683 1.2 100.7 13.896 16.438 1.4 100.78 14.227 17.032 2.2 99.81 14.684 17.696 2.9 98.45 1.457 17.745 3.1 98.49 14.718 19.394 3.5 97.48 14.748 20.148 3.6 97.91 15.527 20.108 4.4 96.94 15.751 18.584 4.1 98.53 15.557 18.441 5.1 96.82 15.553 18.391 5.8 95.76
Names of X columns:
Wisselkoers Goudkoers Inflatie Ruilvoet
Type of Correlation
pearson
spearman
kendall
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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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