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Data X:
30 26.56 17 20.16 24 30.24 20 18.56 25 23.36 20 22.16 27 23.76 18 18.8 28 29.52 21 25.44 27 31.84 22 17.52 28 29.52 25 24.16 21 24 22 18.16 28 25.52 20 28.96 29 23.36 20 28.16 20 24.56 23 21.44 18 22.24 18 24.4 19 20.64 25 25.76 25 27.76 25 21.04 24 19.44 19 20.16 26 16.56 10 24.64 17 15.36 13 29.12 17 21.76 30 23.76 4 22.56 16 23.84 21 26 22 21.76 20 25.04 22 23.36 23 20.24 16 19.04 0 30.72 18 26.56 25 17.92 18 21.12 18 25.36 24 17.84 29 25.04 15 12.24 22 24.32 23 24.96 24 20.96 22 18.96 15 19.84 17 18.24 20 23.84 27 31.84 26 17.92 23 23.36 23 23.6 15 24.24 26 26.96 22 12.56 18 20.16 15 12.56 22 17.52 27 19.76 10 15.92 20 23.84 17 26.64 23 21.44 19 22.32 13 25.12 27 26.96 23 17.52 16 24.56 25 19.52 2 24.16 26 28.4 20 24.32 22 26.64 24 12.96 23 6.96 22 20.32 21 28.96 25 28.56 27 27.36 23 30.56 23 25.76 18 21.36 18 29.44 23 23.52 19 16.96 15 20.16 20 25.92 16 21.76 25 22.56 25 23.2 19 25.84 19 23.6 16 23.68 19 19.92 19 20.24 23 27.76 21 13.76 22 29.44 19 25.76 20 28.4 3 24.4 23 28.24 14 24.96 23 26.16 20 28.24 15 21.76 13 18.72 16 22.96 7 23.6 24 29.2 17 15.84 24 25.6 24 29.2 19 26.96 28 30.32 23 24.96 19 27.36 23 25.76 25 24.64 25 24.64 20 21.36 16 30.56 20 12.16 25 30.56 25 23.6 23 30.8 17 21.76 20 20.4 16 15.76 23 24.4 12 19.04 24 26.16 11 19.84 14 22.96 23 29.04 18 28.4 29 19.76 16 24.96 19 30.88 16 27.36 23 29.44 19 30.48 4 29.68 20 30.56 20 20.56 4 15.2 24 7.2 16 21.76 3 18.72 24 21.36 23 28.4 17 28.16 20 22.48 22 25.76 19 21.36 24 18.96 19 19.12 27 21.12 22 12.32 23 23.36
Names of X columns:
NUMERACYTOT TOT32
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') 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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