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Data X:
101.3 89.3 84 98.41 106.3 90.3 84.5 85.90 94 91.1 87.3 95.82 102.8 90.1 86.3 92.56 102 86.7 85 94.39 105.1 85.1 86.5 100.13 92.4 83.4 85.4 91.25 81.4 82 81.2 80.19 105.8 80.4 81.5 95.15 120.3 81.9 82.2 110.43 100.7 93.8 86.4 85.55 88.8 94.8 86.9 66.89 94.3 92.3 88.6 79.93 99.9 87.5 91.6 79.84 103.4 83.2 89.7 83.36 103.3 82 85.9 82.29 98.8 80.3 89.8 73.23 104.2 81.8 91.4 74.60 91.2 85.1 93.1 77.40 74.7 84.2 95.1 67.59 108.5 84.4 94.9 89.97 114.5 84.5 101.2 94.45 96.9 93.3 105.6 85.08 89.6 93.2 112.2 70.70 97.1 100.3 119.7 72.94 100.3 111.4 128.2 77.08 122.6 114.9 129.6 90.35 115.4 109.5 129.9 104.03 109 109.9 121.7 90.17 129.1 105.8 125.7 104.79 102.8 110.8 130.4 100.54 96.2 108.8 128.5 83.65 127.7 116.1 130 104.52 128.9 109.8 136.7 119.66 126.5 113.8 138.1 104.70 119.8 113.8 139.5 100.89 113.2 117.4 140.4 110.43 114.1 119.5 144.6 103.36 134.1 122.6 151.4 113.46 130 120.7 147.9 113.55 121.8 119 141.5 106.56 132.1 126.1 143.8 116.52 105.3 133.9 143.6 101.87 103 138.1 150.5 111.16 117.1 140.4 150.1 114.62 126.3 148.2 154.9 106.36 138.1 148.2 162.1 124.87 119.5 155.9 176.7 102.46 138 171.1 186.6 129.96 135.5 171.9 194.8 141.66 178.6 188.8 196.3 162.42 162.2 214.9 228.8 157.73 176.9 228.5 267.2 177.29 204.9 220 237.2 197.46 132.2 225.4 254.7 160.53 142.5 220.7 258.2 146.96 164.3 219.7 257.9 171.94 174.9 232.1 269.6 198.77 175.4 223.5 266.9 191.38 143 218.9 269.6 164.02
Names of X columns:
omzet verkoopprijs aankoopprijs uitvoer
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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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