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Data X:
-5 -6 33 5 15 -1 -3 24 6 17 -2 -4 24 6 13 -5 -7 31 5 12 -4 -7 25 5 13 -6 -7 28 3 10 -2 -3 24 5 14 -2 0 25 5 13 -2 -5 16 5 10 -2 -3 17 3 11 2 3 11 6 12 1 2 12 6 7 -8 -7 39 4 11 -1 -1 19 6 9 1 0 14 5 13 -1 -3 15 4 12 2 4 7 5 5 2 2 12 5 13 1 3 12 4 11 -1 0 14 3 8 -2 -10 9 2 8 -2 -10 8 3 8 -1 -9 4 2 8 -8 -22 7 -1 0 -4 -16 3 0 3 -6 -18 5 -2 0 -3 -14 0 1 -1 -3 -12 -2 -2 -1 -7 -17 6 -2 -4 -9 -23 11 -2 1 -11 -28 9 -6 -1 -13 -31 17 -4 0 -11 -21 21 -2 -1 -9 -19 21 0 6 -17 -22 41 -5 0 -22 -22 57 -4 -3 -25 -25 65 -5 -3 -20 -16 68 -1 4 -24 -22 73 -2 1 -24 -21 71 -4 0 -22 -10 71 -1 -4 -19 -7 70 1 -2 -18 -5 69 1 3 -17 -4 65 -2 2 -11 7 57 1 5 -11 6 57 1 6 -12 3 57 3 6 -10 10 55 3 3 -15 0 65 1 4 -15 -2 65 1 7 -15 -1 64 0 5 -13 2 60 2 6 -8 8 43 2 1 -13 -6 47 -1 3 -9 -4 40 1 6 -7 4 31 0 0 -4 7 27 1 3 -4 3 24 1 4 -2 3 23 3 7 0 8 17 2 6
Names of X columns:
indicator vooruitzichten werkloosheid financiƫn spaarvermogen
Type of Correlation
pearson
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=par1) 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) cor.test(y[1,],y[2,],method=par1) for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='') a<-table.element(a,dum,header=TRUE) rp <- cor.test(y[i,],y[j,],method='pearson') a<-table.element(a,round(rp$estimate,4)) rs <- cor.test(y[i,],y[j,],method='spearman') a<-table.element(a,round(rs$estimate,4)) rk <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,round(rk$estimate,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=T) a<-table.element(a,paste('(',round(rp$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rs$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rk$p.value,4),')',sep='')) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable1.tab')
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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