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Data X:
1 41 13 12 14 38 1 1 39 16 11 18 32 1 1 30 19 15 11 35 1 0 31 15 6 12 33 1 1 34 14 13 16 37 1 1 35 13 10 18 29 1 1 39 19 12 14 31 1 1 34 15 14 14 36 1 1 36 14 12 15 35 1 1 37 15 9 15 38 1 0 38 16 10 17 31 1 1 36 16 12 19 34 1 0 38 16 12 10 35 1 1 39 16 11 16 38 1 1 33 17 15 18 37 1 0 32 15 12 14 33 1 0 36 15 10 14 32 1 1 38 20 12 17 38 1 0 39 18 11 14 38 1 1 32 16 12 16 32 1 0 32 16 11 18 33 1 1 31 16 12 11 31 1 1 39 19 13 14 38 1 1 37 16 11 12 39 1 0 39 17 12 17 32 1 1 41 17 13 9 32 1 0 36 16 10 16 35 1 1 33 15 14 14 37 1 1 33 16 12 15 33 1 0 34 14 10 11 33 1 1 31 15 12 16 31 1 0 27 12 8 13 32 1 1 37 14 10 17 31 1 1 34 16 12 15 37 1 0 34 14 12 14 30 1 0 32 10 7 16 33 1 0 29 10 9 9 31 1 0 36 14 12 15 33 1 1 29 16 10 17 31 1 0 35 16 10 13 33 1 0 37 16 10 15 32 1 1 34 14 12 16 33 1 0 38 20 15 16 32 1 0 35 14 10 12 33 1 1 38 14 10 15 28 1 1 37 11 12 11 35 1 1 38 14 13 15 39 1 1 33 15 11 15 34 1 1 36 16 11 17 38 1 0 38 14 12 13 32 1 1 32 16 14 16 38 1 0 32 14 10 14 30 1 0 32 12 12 11 33 1 1 34 16 13 12 38 1 0 32 9 5 12 32 1 1 37 14 6 15 35 1 1 39 16 12 16 34 1 1 29 16 12 15 34 1 0 37 15 11 12 36 1 1 35 16 10 12 34 1 0 30 12 7 8 28 1 0 38 16 12 13 34 1 1 34 16 14 11 35 1 1 31 14 11 14 35 1 1 34 16 12 15 31 1 0 35 17 13 10 37 1 1 36 18 14 11 35 1 0 30 18 11 12 27 1 1 39 12 12 15 40 1 0 35 16 12 15 37 1 0 38 10 8 14 36 1 1 31 14 11 16 38 1 1 34 18 14 15 39 1 0 38 18 14 15 41 1 0 34 16 12 13 27 1 1 39 17 9 12 30 1 1 37 16 13 17 37 1 1 34 16 11 13 31 1 0 28 13 12 15 31 1 0 37 16 12 13 27 1 0 33 16 12 15 36 1 1 35 16 12 15 37 1 0 37 15 12 16 33 1 1 32 15 11 15 34 1 1 33 16 10 14 31 1 0 38 14 9 15 39 1 1 33 16 12 14 34 1 1 29 16 12 13 32 1 1 33 15 12 7 33 1 1 31 12 9 17 36 1 1 36 17 15 13 32 1 1 35 16 12 15 41 1 1 32 15 12 14 28 1 1 29 13 12 13 30 1 1 39 16 10 16 36 1 1 37 16 13 12 35 1 1 35 16 9 14 31 1 0 37 16 12 17 34 1 0 32 14 10 15 36 1 1 38 16 14 17 36 1 0 37 16 11 12 35 1 1 36 20 15 16 37 1 0 32 15 11 11 28 1 1 33 16 11 15 39 1 0 40 13 12 9 32 1 1 38 17 12 16 35 1 0 41 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36 13 8 8 35 0 1 36 15 12 15 39 0 0 35 13 12 11 33 0 0 38 14 10 16 36 0 1 33 15 12 10 32 0 0 31 12 9 15 32 0 1 32 8 6 16 36 0 0 31 14 10 19 32 0 0 33 14 9 12 34 0 0 34 11 9 8 33 0 0 34 12 9 11 35 0 1 34 13 6 14 30 0 0 33 10 10 9 38 0 0 32 16 6 15 34 0 1 41 18 14 13 33 0 1 34 13 10 16 32 0 0 36 11 10 11 31 0 0 37 4 6 12 30 0 0 36 13 12 13 27 0 1 29 16 12 10 31 0 0 37 10 7 11 30 0 0 27 12 8 12 32 0 0 35 12 11 8 35 0 0 28 10 3 12 28 0 0 35 13 6 12 33 0 0 29 12 8 11 35 0 0 32 14 9 13 35 0 1 36 10 9 14 32 0 1 19 12 8 10 21 0 1 21 12 9 12 20 0 0 31 11 7 15 34 0 0 33 10 7 13 32 0 1 36 12 6 13 34 0 1 33 16 9 13 32 0 0 37 12 10 12 33 0 0 34 14 11 12 33 0 0 35 16 12 9 37 0 1 31 14 8 9 32 0 1 37 13 11 15 34 0 1 35 4 3 10 30 0 1 27 15 11 14 30 0 0 34 11 12 15 38 0 0 40 11 7 7 36 0 0 29 14 9 14 32 0 0 38 15 12 8 34 0 1 34 14 8 10 33 0 0 21 13 11 13 27 0 0 36 11 8 13 32 0 1 38 15 10 13 34 0 0 30 11 8 8 29 0 0 35 13 7 12 35 0 1 30 13 8 13 27 0 1 36 16 10 12 33 0 0 34 13 8 10 38 0 1 35 16 12 13 36 0 0 34 16 14 12 33 0 0 32 12 7 9 39 0 1 33 7 6 15 29 0 0 33 16 11 13 32 0 1 26 5 4 13 34 0 0 35 16 9 13 38 0 0 21 4 5 15 17 0 0 38 12 9 15 35 0 0 35 15 11 14 32 0 1 33 14 12 15 34 0 0 37 11 9 11 36 0 0 38 16 12 15 31 0 1 34 15 10 14 35 0 0 27 12 9 13 29 0 1 16 6 6 12 22 0 0 40 16 10 16 41 0 0 36 10 9 16 36 0 1 42 15 13 9 42 0 1 30 14 12 14 33 0
Names of X columns:
Gender Connected Learning Software Happiness Separate Pop
Type of Correlation
pearson
pearson
spearman
kendall
Chart options
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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