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Data X:
6.3 0.30103 0.65321 0.00000 0.81954 1.62325 3 1 3 2.1 0.25527 1.83885 3.40603 3.66304 2.79518 3 5 4 9.1 -0.15490 1.43136 1.02325 2.25406 2.25527 4 4 4 15.8 0.59106 1.27875 -1.63827 -0.52288 1.54407 1 1 1 5.2 0.00000 1.48287 2.20412 2.22789 2.59329 4 5 4 10.9 0.55630 1.44716 0.51851 1.40824 1.79934 1 2 1 8.3 0.14613 1.69897 1.71734 2.64345 2.36173 1 1 1 11.0 0.17609 0.84510 -0.37161 0.80618 2.04922 5 4 4 3.2 -0.15490 1.47712 2.66745 2.62634 2.44871 5 5 5 6.3 0.32222 0.54407 -1.12494 0.07918 1.62325 1 1 1 6.6 0.61278 0.77815 -0.10513 0.54407 1.62325 2 2 2 9.5 0.07918 1.01703 -0.69897 0.69897 2.07918 2 2 2 3.3 -0.30103 1.30103 1.44185 2.06070 2.17026 5 5 5 11.0 0.53148 0.59106 -0.92082 0.00000 1.20412 3 1 2 4.7 0.17609 1.61278 1.92942 2.51188 2.49136 1 3 1 10.4 0.53148 0.95424 -0.99568 0.60206 1.44716 5 1 3 7.4 -0.09691 0.88081 0.01703 0.74036 1.83251 5 3 4 2.1 -0.09691 1.66276 2.71684 2.81624 2.52634 5 5 5 17.9 0.30103 1.38021 -2.00000 -0.60206 1.69897 1 1 1 6.1 0.27875 2.00000 1.79239 3.12057 2.42651 1 1 1 11.9 0.11394 0.50515 -1.63827 -0.39794 1.27875 4 1 3 13.8 0.74819 0.69897 0.23045 0.79934 1.07918 2 1 1 14.3 0.49136 0.81291 0.54407 1.03342 2.07918 2 1 1 15.2 0.25527 1.07918 -0.31876 1.19033 2.14613 2 2 2 10.0 -0.04576 1.30535 1.00000 2.06070 2.23045 4 4 4 11.9 0.25527 1.11394 0.20952 1.05690 1.23045 2 1 2 6.5 0.27875 1.43136 2.28330 2.25527 2.06070 4 4 4 7.5 -0.04576 1.25527 0.39794 1.08279 1.49136 5 5 5 10.6 0.41497 0.67210 -0.55284 0.27875 1.32222 3 1 3 7.4 0.38021 0.99123 0.62685 1.70243 1.71600 1 1 1 8.4 0.07918 1.46240 0.83251 2.25285 2.21484 2 3 2 5.7 -0.04576 0.84510 -0.12494 1.08991 2.35218 2 2 2 4.9 -0.30103 0.77815 0.55630 1.32222 2.35218 3 2 3 3.2 -0.22185 1.30103 1.74429 2.24304 2.17898 5 5 5 11.0 0.36173 0.65321 -0.04576 0.41497 1.77815 2 1 2 4.9 -0.30103 0.87506 0.30103 1.08991 2.30103 3 1 3 13.2 0.41497 0.36173 -0.98297 0.39794 1.66276 3 2 2 9.7 -0.22185 1.38021 0.62221 1.76343 2.32222 4 3 4 12.8 0.81954 0.47712 0.54407 0.59106 1.14613 2 1 1
Names of X columns:
SWS PS L Wb Wbr Tg P S D
Type of Correlation
pearson
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=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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Big Analytics Cloud Computing Center
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