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Data X:
41 38 13 12 14 12 32 39 32 16 11 18 11 51 30 35 19 15 11 14 42 31 33 15 6 12 12 41 34 37 14 13 16 21 46 35 29 13 10 18 12 47 39 31 19 12 14 22 37 34 36 15 14 14 11 49 36 35 14 12 15 10 45 37 38 15 9 15 13 47 38 31 16 10 17 10 49 36 34 16 12 19 8 33 38 35 16 12 10 15 42 39 38 16 11 16 14 33 33 37 17 15 18 10 53 32 33 15 12 14 14 36 36 32 15 10 14 14 45 38 38 20 12 17 11 54 39 38 18 11 14 10 41 32 32 16 12 16 13 36 32 33 16 11 18 9.5 41 31 31 16 12 11 14 44 39 38 19 13 14 12 33 37 39 16 11 12 14 37 39 32 17 12 17 11 52 41 32 17 13 9 9 47 36 35 16 10 16 11 43 33 37 15 14 14 15 44 33 33 16 12 15 14 45 34 33 14 10 11 13 44 31 31 15 12 16 9 49 27 32 12 8 13 15 33 37 31 14 10 17 10 43 34 37 16 12 15 11 54 34 30 14 12 14 13 42 32 33 10 7 16 8 44 29 31 10 9 9 20 37 36 33 14 12 15 12 43 29 31 16 10 17 10 46 35 33 16 10 13 10 42 37 32 16 10 15 9 45 34 33 14 12 16 14 44 38 32 20 15 16 8 33 35 33 14 10 12 14 31 38 28 14 10 15 11 42 37 35 11 12 11 13 40 38 39 14 13 15 9 43 33 34 15 11 15 11 46 36 38 16 11 17 15 42 38 32 14 12 13 11 45 32 38 16 14 16 10 44 32 30 14 10 14 14 40 32 33 12 12 11 18 37 34 38 16 13 12 14 46 32 32 9 5 12 11 36 37 35 14 6 15 14.5 47 39 34 16 12 16 13 45 29 34 16 12 15 9 42 37 36 15 11 12 10 43 35 34 16 10 12 15 43 30 28 12 7 8 20 32 38 34 16 12 13 12 45 34 35 16 14 11 12 48 31 35 14 11 14 14 31 34 31 16 12 15 13 33 35 37 17 13 10 11 49 36 35 18 14 11 17 42 30 27 18 11 12 12 41 39 40 12 12 15 13 38 35 37 16 12 15 14 42 38 36 10 8 14 13 44 31 38 14 11 16 15 33 34 39 18 14 15 13 48 38 41 18 14 15 10 40 34 27 16 12 13 11 50 39 30 17 9 12 19 49 37 37 16 13 17 13 43 34 31 16 11 13 17 44 28 31 13 12 15 13 47 37 27 16 12 13 9 33 33 36 16 12 15 11 46 35 37 16 12 15 9 45 37 33 15 12 16 12 43 32 34 15 11 15 12 44 33 31 16 10 14 13 47 38 39 14 9 15 13 45 33 34 16 12 14 12 42 29 32 16 12 13 15 33 33 33 15 12 7 22 43 31 36 12 9 17 13 46 36 32 17 15 13 15 33 35 41 16 12 15 13 46 32 28 15 12 14 15 48 29 30 13 12 13 12.5 47 39 36 16 10 16 11 47 37 35 16 13 12 16 43 35 31 16 9 14 11 46 37 34 16 12 17 11 48 32 36 14 10 15 10 46 38 36 16 14 17 10 45 37 35 16 11 12 16 45 36 37 20 15 16 12 52 32 28 15 11 11 11 42 33 39 16 11 15 16 47 40 32 13 12 9 19 41 38 35 17 12 16 11 47 41 39 16 12 15 16 43 36 35 16 11 10 15 33 43 42 12 7 10 24 30 30 34 16 12 15 14 52 31 33 16 14 11 15 44 32 41 17 11 13 11 55 32 33 13 11 14 15 11 37 34 12 10 18 12 47 37 32 18 13 16 10 53 33 40 14 13 14 14 33 34 40 14 8 14 13 44 33 35 13 11 14 9 42 38 36 16 12 14 15 55 33 37 13 11 12 15 33 31 27 16 13 14 14 46 38 39 13 12 15 11 54 37 38 16 14 15 8 47 36 31 15 13 15 11 45 31 33 16 15 13 11 47 39 32 15 10 17 8 55 44 39 17 11 17 10 44 33 36 15 9 19 11 53 35 33 12 11 15 13 44 32 33 16 10 13 11 42 28 32 10 11 9 20 40 40 37 16 8 15 10 46 27 30 12 11 15 15 40 37 38 14 12 15 12 46 32 29 15 12 16 14 53 28 22 13 9 11 23 33 34 35 15 11 14 14 42 30 35 11 10 11 16 35 35 34 12 8 15 11 40 31 35 11 9 13 12 41 32 34 16 8 15 10 33 30 37 15 9 16 14 51 30 35 17 15 14 12 53 31 23 16 11 15 12 46 40 31 10 8 16 11 55 32 27 18 13 16 12 47 36 36 13 12 11 13 38 32 31 16 12 12 11 46 35 32 13 9 9 19 46 38 39 10 7 16 12 53 42 37 15 13 13 17 47 34 38 16 9 16 9 41 35 39 16 6 12 12 44 38 34 14 8 9 19 43 33 31 10 8 13 18 51 36 32 17 15 13 15 33 32 37 13 6 14 14 43 33 36 15 9 19 11 53 34 32 16 11 13 9 51 32 38 12 8 12 18 50 34 36 13 8 13 16 46 27 26 13 10 10 24 43 31 26 12 8 14 14 47 38 33 17 14 16 20 50 34 39 15 10 10 18 43 24 30 10 8 11 23 33 30 33 14 11 14 12 48 26 25 11 12 12 14 44 34 38 13 12 9 16 50 27 37 16 12 9 18 41 37 31 12 5 11 20 34 36 37 16 12 16 12 44 41 35 12 10 9 12 47 29 25 9 7 13 17 35 36 28 12 12 16 13 44 32 35 15 11 13 9 44 37 33 12 8 9 16 43 30 30 12 9 12 18 41 31 31 14 10 16 10 41 38 37 12 9 11 14 42 36 36 16 12 14 11 33 35 30 11 6 13 9 41 31 36 19 15 15 11 44 38 32 15 12 14 10 48 22 28 8 12 16 11 55 32 36 16 12 13 19 44 36 34 17 11 14 14 43 39 31 12 7 15 12 52 28 28 11 7 13 14 30 32 36 11 5 11 21 39 32 36 14 12 11 13 11 38 40 16 12 14 10 44 32 33 12 3 15 15 42 35 37 16 11 11 16 41 32 32 13 10 15 14 44 37 38 15 12 12 12 44 34 31 16 9 14 19 48 33 37 16 12 14 15 53 33 33 14 9 8 19 37 26 32 16 12 13 13 44 30 30 16 12 9 17 44 24 30 14 10 15 12 40 34 31 11 9 17 11 42 34 32 12 12 13 14 35 33 34 15 8 15 11 43 34 36 15 11 15 13 45 35 37 16 11 14 12 55 35 36 16 12 16 15 31 36 33 11 10 13 14 44 34 33 15 10 16 12 50 34 33 12 12 9 17 40 41 44 12 12 16 11 53 32 39 15 11 11 18 54 30 32 15 8 10 13 49 35 35 16 12 11 17 40 28 25 14 10 15 13 41 33 35 17 11 17 11 52 39 34 14 10 14 12 52 36 35 13 8 8 22 36 36 39 15 12 15 14 52 35 33 13 12 11 12 46 38 36 14 10 16 12 31 33 32 15 12 10 17 44 31 32 12 9 15 9 44 34 36 13 9 9 21 11 32 36 8 6 16 10 46 31 32 14 10 19 11 33 33 34 14 9 12 12 34 34 33 11 9 8 23 42 34 35 12 9 11 13 43 34 30 13 6 14 12 43 33 38 10 10 9 16 44 32 34 16 6 15 9 36 41 33 18 14 13 17 46 34 32 13 10 16 9 44 36 31 11 10 11 14 43 37 30 4 6 12 17 50 36 27 13 12 13 13 33 29 31 16 12 10 11 43 37 30 10 7 11 12 44 27 32 12 8 12 10 53 35 35 12 11 8 19 34 28 28 10 3 12 16 35 35 33 13 6 12 16 40 37 31 15 10 15 14 53 29 35 12 8 11 20 42 32 35 14 9 13 15 43 36 32 10 9 14 23 29 19 21 12 8 10 20 36 21 20 12 9 12 16 30 31 34 11 7 15 14 42 33 32 10 7 13 17 47 36 34 12 6 13 11 44 33 32 16 9 13 13 45 37 33 12 10 12 17 44 34 33 14 11 12 15 43 35 37 16 12 9 21 43 31 32 14 8 9 18 40 37 34 13 11 15 15 41 35 30 4 3 10 8 52 27 30 15 11 14 12 38 34 38 11 12 15 12 41 40 36 11 7 7 22 39 29 32 14 9 14 12 43
Names of X columns:
Connected Separate Learning Software Happiness Depression Sport2
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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