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Data X:
96.8 108.8 610763 4.2 114.1 128.4 612613 4 110.3 121.1 611324 4.9 103.9 119.5 594167 4.6 101.6 128.7 595454 4.3 94.6 108.7 590865 4.3 95.9 105.5 589379 4.6 104.7 119.8 584428 5.1 102.8 111.3 573100 4.8 98.1 110.6 567456 4.5 113.9 120.1 569028 4.9 80.9 97.5 620735 5.1 95.7 107.7 628884 5.1 113.2 127.3 628232 5.2 105.9 117.2 612117 4.5 108.8 119.8 595404 4.6 102.3 116.2 597141 4.9 99 111 593408 4.6 100.7 112.4 590072 4.4 115.5 130.6 579799 3.7 100.7 109.1 574205 4 109.9 118.8 572775 4.2 114.6 123.9 572942 3.9 85.4 101.6 619567 3.6 100.5 112.8 625809 3.6 114.8 128 619916 3.2 116.5 129.6 587625 3.2 112.9 125.8 565742 3.5 102 119.5 557274 3.6 106 115.7 560576 3.7 105.3 113.6 548854 3.8 118.8 129.7 531673 3.8 106.1 112 525919 3.8 109.3 116.8 511038 3.3 117.2 127 498662 3.3 92.5 112.1 555362 3.4 104.2 114.2 564591 3.1 112.5 121.1 541657 3.5 122.4 131.6 527070 4.2 113.3 125 509846 4.9 100 120.4 514258 5.1 110.7 117.7 516922 5.5 112.8 117.5 507561 5.6 109.8 120.6 492622 6.4 117.3 127.5 490243 6.2 109.1 112.3 469357 7.2 115.9 124.5 477580 7.8 96 115.2 528379 7.9 99.8 104.7 533590 7.4 116.8 130.9 517945 7.5 115.7 129.2 506174 6.7 99.4 113.5 501866 5.1 94.3 125.6 516141 4.6 91 107.6 528222 4.3 93.2 107 532638 3.9 103.1 121.6 536322 2.6 94.1 110.7 536535 2.6 91.8 106.3 523597 1.6 102.7 118.6 536214 0.9 82.6 104.6 586570 0.3 89.1 103.5 596594 1.2
Names of X columns:
Tot_Industriele_productie Tot_prod_consumptiegdn Tot_niet_werkende_werkzoekenden Nat_consumptieprijsindex
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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