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Data X:
12.9 21 7.4 26 12.2 22 12.8 22 7.4 18 6.7 23 12.6 12 14.8 20 13.3 22 11.1 21 8.2 19 11.4 22 6.4 15 10.6 20 12.0 19 6.3 18 11.3 15 11.9 20 9.3 21 9.6 21 10.0 15 6.4 16 13.8 23 10.8 21 13.8 18 11.7 25 10.9 9 16.1 30 13.4 20 9.9 23 11.5 16 8.3 16 11.7 19 6.1 25 9.0 25 9.7 18 10.8 23 10.3 21 10.4 10 12.7 14 9.3 22 11.8 26 5.9 23 11.4 23 13.0 24 10.8 24 12.3 18 11.3 23 11.8 15 7.9 19 12.7 16 12.3 25 11.6 23 6.7 17 10.9 19 12.1 21 13.3 18 10.1 27 5.7 21 14.3 13 8.0 8 13.3 29 9.3 28 12.5 23 7.6 21 15.9 19 9.2 19 9.1 20 11.1 18 13.0 19 14.5 17 12.2 19 12.3 25 11.4 19 8.8 22 14.6 23 7.3 26 12.6 14 NA 28 13.0 16 12.6 24 13.2 20 9.9 12 7.7 24 10.5 22 13.4 12 10.9 22 4.3 20 10.3 10 11.8 23 11.2 17 11.4 22 8.6 24 13.2 18 12.6 21 5.6 20 9.9 20 8.8 22 7.7 19 9.0 20 7.3 26 11.4 23 13.6 24 7.9 21 10.7 21 10.3 19 8.3 8 9.6 17 14.2 20 8.5 11 13.5 8 4.9 15 6.4 18 9.6 18 11.6 19 11.1 19 4.4 23 12.7 22 18.1 21 17.9 25 16.6 30 12.6 17 17.1 27 19.1 23 16.1 23 13.4 18 18.4 18 14.7 23 10.6 19 12.6 15 16.2 20 13.6 16 18.9 24 14.1 25 14.5 25 16.2 19 14.8 19 14.8 16 12.5 19 12.7 19 17.4 23 8.6 21 18.4 22 16.1 19 11.6 20 17.8 20 15.3 3 17.7 23 15.6 14 16.4 23 17.7 20 13.6 15 11.7 13 14.4 16 14.8 7 18.3 24 9.9 17 16.0 24 18.3 24 16.9 19 14.6 25 13.9 20 19.0 28 15.6 23 14.9 27 11.8 18 18.5 28 15.9 21 17.1 19 16.1 23 19.9 27 11.0 22 18.5 28 15.1 25 15.0 21 11.4 22 16.0 28 18.1 20 14.6 29 15.4 25 15.4 25 17.6 20 13.4 20 19.1 16 15.4 20 7.6 20 13.4 23 13.9 18 19.1 25 15.3 18 12.9 19 16.1 25 17.4 25 13.2 25 12.2 24 12.6 19 10.4 26 15.4 10 9.6 17 18.2 13 13.6 17 14.9 30 14.8 25 14.1 4 14.9 16 16.3 21 19.3 23 13.6 22 13.6 17 15.7 20 12.8 20 14.6 22 9.9 16 12.7 23 11.9 16 19.2 0 16.6 18 11.2 25 15.3 23 11.9 12 13.2 18 16.4 24 12.4 11 15.9 18 14.4 14 18.2 23 11.2 24 15.7 29 17.8 18 7.7 15 12.4 29 15.6 16 19.3 19 15.2 22 17.1 16 15.6 23 18.4 23 19.1 19 18.6 4 19.1 20 13.1 24 12.9 20 9.5 4 4.5 24 11.9 22 13.6 16 11.7 3 12.4 15 13.4 24 11.4 17 14.9 20 19.9 27 17.8 23 11.2 26 14.6 23 17.6 17 14.1 20 16.1 22 13.4 19 11.9 24 12.0 19 14.8 23 15.2 15 13.2 27 16.9 26 7.9 22 7.7 22 12.6 18 7.9 15 11.0 22 12.4 27 10.0 10 14.9 20 16.7 17 13.4 23 14.0 19 15.7 13 16.9 27 11.0 23 15.4 16 12.2 25 15.1 2 17.8 26 15.2 20 14.6 23 16.7 22 8.1 24
Names of X columns:
TOT NumeracyTOT
Type of Correlation
pearson
pearson
spearman
kendall
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Title:
R Code
par1 <- 'pearson' 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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