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Data X:
6.300 0.301 0.653 0.000 0.820 1.623 3.000 1.000 3.000 2.100 0.255 1.839 3.406 3.663 2.795 3.000 5.000 4.000 9.100 -0.155 1.431 1.023 2.254 2.255 4.000 4.000 4.000 15.800 0.591 1.279 -1.638 -0.523 1.544 1.000 1.000 1.000 5.200 0.000 1.483 2.204 2.228 2.593 4.000 5.000 4.000 10.900 0.556 1.447 0.519 1.408 1.799 1.000 2.000 1.000 8.300 0.146 1.699 1.717 2.643 2.362 1.000 1.000 1.000 11.000 0.176 0.845 -0.372 0.806 2.049 5.000 4.000 4.000 3.200 -0.155 1.477 2.667 2.626 2.449 5.000 5.000 5.000 6.300 0.322 0.544 -1.125 0.079 1.623 1.000 1.000 1.000 6.600 0.613 0.778 -0.105 0.544 1.623 2.000 2.000 2.000 9.500 0.079 1.017 -0.699 0.699 2.079 2.000 2.000 2.000 3.300 -0.301 1.301 1.442 2.061 2.170 5.000 5.000 5.000 11.000 0.531 0.591 -0.921 0.000 1.204 3.000 1.000 2.000 4.700 0.176 1.613 1.929 2.512 2.491 1.000 3.000 1.000 10.400 0.531 0.954 -0.996 0.602 1.447 5.000 1.000 3.000 7.400 -0.097 0.881 0.017 0.740 1.833 5.000 3.000 4.000 2.100 -0.097 1.663 2.717 2.816 2.526 5.000 5.000 5.000 17.900 0.301 1.380 -2.000 -0.602 1.699 1.000 1.000 1.000 6.100 0.279 2.000 1.792 3.121 2.427 1.000 1.000 1.000 11.900 0.114 0.505 -1.638 -0.398 1.279 4.000 1.000 3.000 13.800 0.748 0.699 0.230 0.799 1.079 2.000 1.000 1.000 14.300 0.491 0.813 0.544 1.033 2.079 2.000 1.000 1.000 15.200 0.255 1.079 -0.319 1.190 2.146 2.000 2.000 2.000 10.000 -0.046 1.305 1.000 2.061 2.230 4.000 4.000 4.000 11.900 0.255 1.114 0.210 1.057 1.230 2.000 1.000 2.000 6.500 0.279 1.431 2.283 2.255 2.061 4.000 4.000 4.000 7.500 -0.046 1.255 0.398 1.083 1.491 5.000 5.000 5.000 10.600 0.415 0.672 -0.553 0.279 1.322 3.000 1.000 3.000 7.400 0.380 0.991 0.627 1.702 1.716 1.000 1.000 1.000 8.400 0.079 1.462 0.833 2.253 2.215 2.000 3.000 2.000 5.700 -0.046 0.845 -0.125 1.090 2.352 2.000 2.000 2.000 4.900 -0.301 0.778 0.556 1.322 2.352 3.000 2.000 3.000 3.200 -0.222 1.301 1.744 2.243 2.179 5.000 5.000 5.000 11.000 0.362 0.653 -0.046 0.415 1.778 2.000 1.000 2.000 4.900 -0.301 0.875 0.301 1.090 2.301 3.000 1.000 3.000 13.200 0.415 0.362 -0.983 0.398 1.663 3.000 2.000 2.000 9.700 -0.222 1.380 0.622 1.763 2.322 4.000 3.000 4.000 12.800 0.820 0.477 0.544 0.591 1.146 2.000 1.000 1.000
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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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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