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Data X:
103.34 98.60 96.33 102.60 96.90 96.33 100.69 95.10 95.05 105.67 97.00 96.84 123.61 112.70 96.92 113.08 102.90 97.44 106.46 97.40 97.78 123.38 111.40 97.69 109.87 87.40 96.67 95.74 96.80 98.29 123.06 114.10 98.20 123.39 110.30 98.71 120.28 103.90 98.54 115.33 101.60 98.20 110.40 94.60 100.80 114.49 95.90 101.33 132.03 104.70 101.88 123.16 102.80 101.85 118.82 98.10 102.04 128.32 113.90 102.22 112.24 80.90 102.63 104.53 95.70 102.65 132.57 113.20 102.54 122.52 105.90 102.37 131.80 108.80 102.68 124.55 102.30 102.76 120.96 99.00 102.82 122.60 100.70 103.31 145.52 115.50 103.23 118.57 100.70 103.60 134.25 109.90 103.95 136.70 114.60 103.93 121.37 85.40 104.25 111.63 100.50 104.38 134.42 114.80 104.36 137.65 116.50 104.32 137.86 112.90 104.58 119.77 102.00 104.68 130.69 106.00 104.92 128.28 105.30 105.46 147.45 118.80 105.23 128.42 106.10 105.58 136.90 109.30 105.34 143.95 117.20 105.28 135.64 92.50 105.70 122.48 104.20 105.67 136.83 112.50 105.71 153.04 122.40 106.19 142.71 113.30 106.93 123.46 100.00 107.44 144.37 110.70 107.85 146.15 112.80 108.71 147.61 109.80 109.32 158.51 117.30 109.49 147.40 109.10 110.20 165.05 115.90 110.62 154.64 96.00 111.22 126.20 99.80 110.88 157.36 116.80 111.15 154.15 115.70 111.29 123.21 99.40 111.09 113.07 94.30 111.24 110.45 91.00 111.45 113.57 93.20 111.75 122.44 103.10 111.07 114.93 94.10 111.17 111.85 91.80 110.96 126.04 102.70 110.50 121.34 82.60 110.48 124.36 89.10 110.66
Names of X columns:
Uitvoer TIP cons
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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