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Data X:
116.07 113.14 285351 325412 116.04 113.42 286602 326011 115.81 114.67 283042 328282 116.22 114.03 276687 317480 115.85 113.37 277915 317539 100.29 100.66 277128 313737 101.12 101.55 277103 312276 102.65 102.23 275037 309391 102.71 102.9 270150 302950 103.39 102.68 267140 300316 102.8 103.41 264993 304035 102.07 104.62 287259 333476 102.15 104.93 291186 337698 101.21 105.88 292300 335932 101.27 105.18 288186 323931 101.86 104.54 281477 313927 101.65 104.58 282656 314485 101.94 104.34 280190 313218 102.62 104.66 280408 309664 102.72 104.73 276836 302963 103.39 105.44 275216 298989 104.51 105.72 274352 298423 104.09 105.68 271311 310631 104.29 105.9 289802 329765 104.57 105.97 290726 335083 105.39 105.21 292300 327616 105.15 104.75 278506 309119 106.13 104.89 269826 295916 105.46 105.26 265861 291413 106.47 104.84 269034 291542 106.62 105.47 264176 284678 106.52 105.4 255198 276475 108.04 105.73 253353 272566 107.15 105.72 246057 264981 107.32 105.63 235372 263290 107.76 105.97 258556 296806 107.26 105.92 260993 303598 107.89 106.32 254663 286994 109.08 106.97 250643 276427 110.4 108.68 243422 266424 111.03 108.68 247105 267153 112.05 109.39 248541 268381 112.28 110.53 245039 262522 112.8 111.8 237080 255542 114.17 112.16 237085 253158 114.92 113.65 225554 243803 114.65 114.77 226839 250741 115.49 115.03 247934 280445 114.67 113.66 248333 285257 114.71 114.29 246969 270976 115.15 113.31 245098 261076 115.03 111.79 246263 255603 115.07 110.82 255765 260376 116.46 110.58 264319 263903 116.37 110.57 268347 264291 116.2 109.29 273046 263276 116.5 109.58 273963 262572 116.38 109.34 267430 256167 115.44 108.93 271993 264221 114.96 108.54 292710 293860 114.48 109.61 295881 300713 114.3 109.1 293299 287224
Names of X columns:
voedingsprijzen niet_voedingsprijzen werkloosheid_mannen werkloosheid_vrouwen
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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Big Analytics Cloud Computing Center
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