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Data X:
6.30000000 0.30103000 0.65321251 0.00000000 0.81954394 1.62324929 3 1 3 2.10000000 0.25527251 1.83884909 3.40602894 3.66304097 2.79518459 3 5 4 9.10000000 -0.15490196 1.43136376 1.02325246 2.25406445 2.25527251 4 4 4 15.80000000 0.59106461 1.27875360 -1.63827216 -0.52287875 1.54406804 1 1 1 5.20000000 0.00000000 1.48287358 2.20411998 2.22788670 2.59328607 4 5 4 10.90000000 0.55630250 1.44715803 0.51851394 1.40823997 1.79934055 1 2 1 8.30000000 0.14612804 1.69897000 1.71733758 2.64345268 2.36172784 1 1 1 11.00000000 0.17609126 0.84509804 -0.37161107 0.80617997 2.04921802 5 4 4 3.20000000 -0.15490196 1.47712125 2.66745295 2.62634037 2.44870632 5 5 5 6.30000000 0.32221929 0.54406804 -1.12493874 0.07918125 1.62324929 1 1 1 6.60000000 0.61278386 0.77815125 -0.10513034 0.54406804 1.62324929 2 2 2 9.50000000 0.07918125 1.01703334 -0.69897000 0.69897000 2.07918125 2 2 2 3.30000000 -0.30103000 1.30103000 1.44185218 2.06069784 2.17026172 5 5 5 11.00000000 0.53147892 0.59106461 -0.92081875 0.00000000 1.20411998 3 1 2 4.70000000 0.17609126 1.61278386 1.92941893 2.51188336 2.49136169 1 3 1 10.40000000 0.53147892 0.95424251 -0.99567863 0.60205999 1.44715803 5 1 3 7.40000000 -0.09691001 0.88081359 0.01703334 0.74036269 1.83250891 5 3 4 2.10000000 -0.09691001 1.66275783 2.71683772 2.81624130 2.52633928 5 5 5 17.90000000 0.30103000 1.38021124 -2.00000000 -0.60205999 1.69897000 1 1 1 6.10000000 0.27875360 2.00000000 1.79239169 3.12057393 2.42651126 1 1 1 11.90000000 0.11394335 0.50514998 -1.63827216 -0.39794001 1.27875360 4 1 3 13.80000000 0.74818803 0.69897000 0.23044892 0.79934055 1.07918125 2 1 1 14.30000000 0.49136169 0.81291336 0.54406804 1.03342376 2.07918125 2 1 1 15.20000000 0.25527251 1.07918125 -0.31875876 1.19033170 2.14612804 2 2 2 10.00000000 -0.04575749 1.30535137 1.00000000 2.06069784 2.23044892 4 4 4 11.90000000 0.25527251 1.11394335 0.20951501 1.05690485 1.23044892 2 1 2 6.50000000 0.27875360 1.43136376 2.28330123 2.25527251 2.06069784 4 4 4 7.50000000 -0.04575749 1.25527251 0.39794001 1.08278537 1.49136169 5 5 5 10.60000000 0.41497335 0.67209786 -0.55284197 0.27875360 1.32221929 3 1 3 7.40000000 0.38021124 0.99122608 0.62685341 1.70243054 1.71600334 1 1 1 8.40000000 0.07918125 1.46239800 0.83250891 2.25285303 2.21484385 2 3 2 5.70000000 -0.04575749 0.84509804 -0.12493874 1.08990511 2.35218252 2 2 2 4.90000000 -0.30103000 0.77815125 0.55630250 1.32221929 2.35218252 3 2 3 3.20000000 -0.22184875 1.30103000 1.74429298 2.24303805 2.17897695 5 5 5 11.00000000 0.36172784 0.65321251 -0.04575749 0.41497335 1.77815125 2 1 2 4.90000000 -0.30103000 0.87506126 0.30103000 1.08990511 2.30103000 3 1 3 13.20000000 0.41497335 0.36172784 -0.98296666 0.39794001 1.66275783 3 2 2 9.70000000 -0.22184875 1.38021124 0.62221402 1.76342799 2.32221929 4 3 4 12.80000000 0.81954394 0.47712125 0.54406804 0.59106461 1.14612804 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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Raw Input
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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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