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Data X:
13 14 22 4 2 4 3 5 4 16 19 24 5 3 3 4 5 4 17 17 26 4 4 5 4 5 4 NA 17 21 3 4 3 3 4 4 NA 15 26 4 4 5 4 5 4 16 20 25 3 4 4 4 5 5 NA 15 21 3 4 4 3 3 4 NA 19 24 3 4 5 4 4 4 NA 15 27 4 5 4 4 5 5 17 15 28 4 5 5 4 5 5 17 19 23 4 4 2 4 5 4 15 NA 25 4 4 5 3 5 4 16 20 24 4 4 4 3 4 5 14 18 24 3 3 5 4 4 5 16 15 24 4 4 5 4 2 5 17 14 25 3 4 5 4 4 5 NA 20 25 3 4 5 4 4 5 NA NA NA NA NA 5 NA 5 5 NA 16 25 5 5 4 3 4 4 NA 16 25 4 4 4 4 5 4 16 16 24 3 4 5 3 4 5 NA 10 26 4 4 4 4 5 5 16 19 26 4 4 5 4 4 5 NA 19 25 4 4 5 4 4 4 NA 16 26 4 4 5 4 4 5 NA 15 23 3 4 4 4 4 4 16 18 24 3 4 4 3 5 5 15 17 24 4 4 4 4 4 4 16 19 25 2 4 5 4 5 5 16 17 25 5 4 4 4 4 4 13 NA 24 4 3 5 4 4 4 15 19 28 4 5 5 4 5 5 17 20 27 5 4 5 4 4 5 NA 5 NA 4 3 5 4 NA 5 13 19 23 2 3 5 4 5 4 17 16 23 4 5 2 4 4 4 NA 15 24 3 4 5 4 4 4 14 16 24 4 3 5 3 4 5 14 18 22 4 3 3 4 4 4 18 16 25 4 4 5 4 4 4 NA 15 25 5 4 4 4 4 4 17 17 28 4 5 5 4 5 5 13 NA 22 3 3 4 4 4 4 16 20 28 5 5 5 3 5 5 15 19 25 5 4 5 3 4 4 15 7 24 4 4 4 3 4 5 NA 13 24 4 4 4 4 4 4 15 16 23 3 5 5 3 3 4 13 16 25 4 4 4 4 5 4 NA NA NA 2 3 4 2 NA 4 17 18 26 4 5 5 4 4 4 NA 18 25 5 5 2 4 5 4 NA 16 27 5 5 5 4 4 4 11 17 26 4 3 5 4 5 5 14 19 23 4 3 4 3 4 5 13 16 25 4 4 5 4 4 4 NA 19 21 3 4 4 3 3 4 17 13 22 3 4 4 4 4 3 16 16 24 4 4 4 3 5 4 NA 13 25 4 4 4 4 5 4 17 12 27 5 5 3 4 5 5 16 17 24 2 4 4 4 5 5 16 17 26 4 4 4 4 5 5 16 17 21 3 4 4 4 2 4 15 16 27 4 4 5 4 5 5 12 16 22 4 2 4 4 4 4 17 14 23 4 4 4 3 5 3 14 16 24 4 4 4 3 5 4 14 13 25 5 4 5 3 3 5 16 16 24 3 4 4 3 5 5 NA 14 23 3 4 4 3 4 5 NA 20 28 4 5 5 5 5 4 NA 12 NA 4 4 3 4 NA 4 NA 13 24 4 4 4 4 4 4 NA 18 26 4 4 4 5 5 4 15 14 22 3 4 3 4 4 4 16 19 25 4 4 4 4 5 4 14 18 25 3 4 5 3 5 5 15 14 24 3 3 5 4 4 5 17 18 24 4 3 5 4 4 4 NA 19 26 4 4 5 4 4 5 10 15 21 3 3 3 4 4 4 NA 14 25 4 4 4 4 5 4 17 17 25 4 4 3 4 5 5 NA 19 26 4 4 4 4 5 5 20 13 25 5 4 4 4 4 4 17 19 26 5 4 3 5 4 5 18 18 27 4 4 5 4 5 5 NA 20 25 3 4 5 4 4 5 17 15 NA 3 NA 4 4 4 4 14 15 20 4 2 3 3 4 4 NA 15 24 4 4 5 4 4 3 17 20 26 4 4 5 4 4 5 NA 15 25 4 4 4 4 5 4 17 19 25 4 5 4 4 5 3 NA 18 24 3 4 4 3 5 5 16 18 26 4 4 5 4 4 5 18 15 25 5 4 3 4 4 5 18 20 28 5 4 5 5 4 5 16 17 27 4 5 4 4 5 5 NA 12 25 3 4 5 4 4 5 NA 18 26 5 3 4 4 5 5 15 19 26 4 4 5 4 4 5 13 20 26 5 4 4 4 4 5 NA NA NA 3 4 4 3 NA 4 NA 17 28 5 4 4 5 5 5 NA 15 NA 4 4 5 3 NA 5 NA 16 21 4 4 3 3 4 3 NA 18 25 4 4 5 4 4 4 16 18 25 4 4 5 4 4 4 NA 14 24 3 4 5 4 5 3 NA 15 24 4 4 4 4 4 4 NA 12 24 4 4 4 3 4 5 12 17 23 3 3 4 3 5 5 NA 14 23 4 4 4 3 4 4 16 18 24 3 4 5 4 4 4 16 17 24 4 4 5 4 3 4 NA 17 25 5 4 5 1 5 5 16 20 28 5 4 5 4 5 5 14 16 23 4 4 4 4 4 3 15 14 24 4 4 5 3 4 4 14 15 23 3 4 4 3 4 5 NA 18 24 4 4 4 4 4 4 15 20 25 4 4 4 4 5 4 NA 17 24 4 5 3 4 4 4 15 17 23 3 4 4 4 4 4 16 17 23 4 4 4 3 4 4 NA 17 25 4 4 4 4 4 5 NA 15 21 3 4 3 3 4 4 NA 17 22 4 4 4 3 4 3 11 18 19 3 2 4 2 4 4 NA 17 24 4 4 4 3 5 4 18 20 25 5 4 4 3 5 4 NA 15 21 2 4 4 3 3 5 11 16 22 3 3 4 4 4 4 NA 15 23 4 4 4 3 4 4 18 18 27 5 5 4 4 5 4 NA 11 NA NA NA 2 NA NA NA 15 15 26 4 5 5 4 4 4 19 18 29 5 5 5 5 5 4 17 20 28 4 5 5 4 5 5 NA 19 24 4 4 4 3 4 5 14 14 25 3 4 5 4 5 4 NA 16 25 4 4 5 4 4 4 13 15 22 4 4 2 4 4 4 17 17 25 4 4 3 4 5 5 14 18 26 4 4 4 4 5 5 19 20 26 5 4 5 3 5 4 14 17 24 4 3 5 4 4 4 NA 18 25 4 4 5 4 4 4 NA 15 19 3 3 2 3 4 4 16 16 25 4 5 5 4 4 3 16 11 23 4 4 4 3 4 4 15 15 25 4 4 4 4 4 5 12 18 25 3 4 5 3 5 5 NA 17 26 4 4 5 4 4 5 17 16 27 5 4 5 4 5 4 NA 12 24 4 4 5 4 3 4 NA 19 22 2 3 5 4 4 4 18 18 25 4 4 4 4 4 5 15 15 24 4 3 4 3 5 5 18 17 23 4 4 4 4 4 3 15 19 27 4 5 5 5 4 4 NA 18 24 5 4 3 4 4 4 NA 19 24 5 4 4 3 4 4 NA 16 21 3 3 1 4 5 5 16 16 25 4 4 4 4 4 5 NA 16 25 4 4 4 4 5 4 16 14 23 2 3 4 5 5 4
Names of X columns:
TVDC ITHSUM SKEOUSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') ncorrs <- (n*n -n)/2 mycorrs <- array(0, dim=c(10,3)) 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) for (iii in 1:10) { iiid100 <- iii / 100 if (rp$p.value < iiid100) mycorrs[iii, 1] = mycorrs[iii, 1] + 1 if (rs$p.value < iiid100) mycorrs[iii, 2] = mycorrs[iii, 2] + 1 if (rk$p.value < iiid100) mycorrs[iii, 3] = mycorrs[iii, 3] + 1 } } } a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Correlation Tests',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Number of significant by total number of Correlations',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Type I error',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) for (iii in 1:10) { iiid100 <- iii / 100 a<-table.row.start(a) a<-table.element(a,round(iiid100,2),header=T) a<-table.element(a,round(mycorrs[iii,1]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,2]/ncorrs,2)) a<-table.element(a,round(mycorrs[iii,3]/ncorrs,2)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab')
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