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Data X:
6.3 0.30103 0.653212514 3 3.819543936 1.62324929 3 1 3 2.1 0.25527 1.838849091 6.406028945 6.663040975 2.79518459 3 5 4 9.1 -0.15490 1.431363764 4.02325246 5.254064453 2.255272505 4 4 4 15.8 0.59106 1.278753601 -1.638272164 -0.522878745 1.544068044 1 1 1 5.2 0.00000 1.482873584 5.204119983 5.227886705 2.593286067 4 5 4 10.9 0.55630 1.447158031 3.51851394 4.408239965 1.799340549 1 2 1 8.3 0.14613 1.698970004 4.717337583 5.643452676 2.361727836 1 1 1 11.0 0.17609 0.84509804 -0.37161107 3.806179974 2.049218023 5 4 4 3.2 -0.15490 1.477121255 5.667452953 5.626340367 2.44870632 5 5 5 6.3 0.32222 0.544068044 -1.124938737 3.079181246 1.62324929 1 1 1 6.6 0.61278 0.77815125 -0.105130343 3.544068044 1.62324929 2 2 2 9.5 0.07918 1.017033339 -0.698970004 3.698970004 2.079181246 2 2 2 3.3 -0.30103 1.301029996 4.441852176 5.06069784 2.170261715 5 5 5 11.0 0.53148 0.591064607 -0.920818754 3 1.204119983 3 1 2 4.7 0.17609 1.612783857 4.929418926 5.511883361 2.491361694 1 3 1 10.4 0.53148 0.954242509 -0.995678626 3.602059991 1.447158031 5 1 3 7.4 -0.09691 0.880813592 3.017033339 3.740362689 1.832508913 5 3 4 2.1 -0.09691 1.662757832 5.716837723 5.8162413 2.526339277 5 5 5 17.9 0.30103 1.380211242 -2 -0.602059991 1.698970004 1 1 1 6.1 0.27875 2 4.792391689 6.120573931 2.426511261 1 1 1 11.9 0.11394 0.505149978 -1.638272164 -0.397940009 1.278753601 4 1 3 13.8 0.74819 0.698970004 3.230448921 3.799340549 1.079181246 2 1 1 14.3 0.49136 0.812913357 3.544068044 4.033423755 2.079181246 2 1 1 15.2 0.25527 1.079181246 -0.318758763 4.190331698 2.146128036 2 2 2 10.0 -0.04576 1.305351369 4 5.06069784 2.230448921 4 4 4 11.9 0.25527 1.113943352 3.209515015 4.056904851 1.230448921 2 1 2 6.5 0.27875 1.431363764 5.283301229 5.255272505 2.06069784 4 4 4 7.5 -0.04576 1.255272505 3.397940009 4.08278537 1.491361694 5 5 5 10.6 0.41497 0.672097858 -0.552841969 3.278753601 1.322219295 3 1 3 7.4 0.38021 0.991226076 3.626853415 4.702430536 1.716003344 1 1 1 8.4 0.07918 1.462397998 3.832508913 5.252853031 2.214843848 2 3 2 5.7 -0.04576 0.84509804 -0.124938737 4.089905111 2.352182518 2 2 2 4.9 -0.30103 0.77815125 3.556302501 4.322219295 2.352182518 3 2 3 3.2 -0.22185 1.301029996 4.744292983 5.243038049 2.178976947 5 5 5 11.0 0.36173 0.653212514 -0.045757491 3.414973348 1.77815125 2 1 2 4.9 -0.30103 0.875061263 3.301029996 4.089905111 2.301029996 3 1 3 13.2 0.41497 0.361727836 -0.982966661 3.397940009 1.662757832 3 2 2 9.7 -0.22185 1.380211242 3.622214023 4.763427994 2.322219295 4 3 4 12.8 0.81954 0.477121255 3.544068044 3.591064607 1.146128036 2 1 1
Names of X columns:
SWS logPS logL logWb logWbr logTg 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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