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Data X:
2 13 12 30 33 13 16 1 8 8 32 35 11 15 2 14 12 30 35 12 13 2 14 11 33 25 13 14 1 13 11 36 39 12 17 1 16 13 37 37 12 13 1 14 11 31 31 13 12 1 13 10 36 28 12 9 2 15 7 40 38 15 25 1 13 10 31 32 11 13 2 16 12 24 32 13 10 1 20 15 46 46 12 13 1 17 12 40 40 12 9 1 15 15 27 33 12 14 2 16 12 32 25 15 26 1 16 10 41 37 13 12 1 12 10 28 33 13 11 2 9 8 34 33 12 19 2 15 11 31 35 11 12 2 17 14 38 39 11 9 1 12 12 37 36 13 15 1 10 11 34 37 10 15 2 11 6 33 43 12 23 2 16 12 38 27 13 20 1 16 14 27 31 10 0 2 15 11 36 33 12 15 1 13 8 37 35 13 8 2 14 12 35 36 12 12 1 19 15 44 39 11 11 1 16 13 41 31 11 18 2 17 14 29 34 14 19 1 10 12 31 29 12 13 1 15 7 32 37 14 22 1 14 11 35 30 12 12 1 14 7 36 32 12 15 2 16 12 28 31 13 16 1 17 12 34 34 15 16 1 15 12 36 30 12 13 2 17 13 33 33 16 11 2 14 15 35 37 10 16 1 10 9 34 33 13 14 2 14 9 38 28 12 11 2 16 11 35 32 12 20 2 18 14 40 40 16 16 1 15 12 35 39 12 12 1 16 15 32 28 16 17 1 16 12 33 33 13 11 1 10 6 31 36 10 12 2 8 5 32 35 14 14 1 17 13 35 34 13 13 1 14 11 32 35 12 14 1 12 11 26 30 13 19 2 10 6 38 35 16 17 1 14 12 45 37 12 11 1 12 10 36 40 12 12 1 16 6 37 34 13 12 1 16 12 33 37 13 14 1 15 14 35 38 11 15 2 11 6 32 27 14 18 1 16 11 32 27 16 16 2 8 6 32 27 16 16 1 17 14 33 39 14 19 1 16 12 37 37 14 17 1 15 12 40 32 14 15 2 8 8 35 27 14 13 1 13 10 30 35 10 16 1 14 11 36 40 13 17 1 13 7 34 32 14 16 1 16 12 34 36 17 13 2 12 9 37 35 12 15 1 19 13 34 31 12 16 1 19 14 37 34 12 10 1 12 6 43 36 15 19 1 14 12 39 40 10 11 2 15 6 29 33 13 17 1 13 14 41 38 12 19 2 16 12 32 33 13 15 2 10 10 34 35 14 15 1 15 10 34 30 12 17 1 16 12 35 31 13 13 1 15 11 41 42 14 17 2 11 10 32 33 10 12 2 9 7 39 35 12 27 1 16 12 33 33 13 12 1 12 12 30 31 10 15 2 14 12 32 36 13 18 1 14 10 41 32 13 19 1 13 10 24 43 12 21 2 15 12 35 33 12 13 2 17 12 39 34 15 16 2 14 12 32 36 12 13 2 9 9 28 33 16 20 2 11 11 31 32 15 17 1 9 10 36 36 10 10 2 7 5 39 39 13 18 1 13 10 33 30 0 11 2 15 10 36 34 10 18 1 12 12 31 34 12 14 2 15 11 33 36 14 11 2 14 9 33 31 12 14 1 15 15 33 27 13 12 2 9 9 39 28 14 22 1 16 12 35 37 11 12 1 16 16 37 36 11 12 1 14 10 29 31 12 15 2 14 14 34 31 9 13 2 13 10 35 31 13 13 1 14 11 36 34 13 16 2 16 12 29 36 12 12 1 16 14 35 30 14 16 1 13 10 35 37 12 15 2 12 9 36 29 10 19 2 16 12 38 37 11 15 1 16 11 36 38 14 13 1 16 12 37 38 12 9 2 10 7 32 33 13 14 2 14 16 34 34 13 14 2 12 11 29 32 9 12 2 12 12 38 36 13 17 1 12 9 34 30 11 11 1 12 9 33 34 12 17 1 19 15 42 42 13 15 2 14 10 32 24 12 15 1 13 11 31 29 12 11 1 17 14 34 32 11 14 2 16 12 39 31 12 14 1 15 12 38 37 12 14 1 12 12 36 34 13 14 1 8 11 32 35 14 13 1 10 9 37 34 13 14 1 16 11 36 33 12 10 2 10 6 34 31 15 17 2 16 12 34 32 13 11 1 10 12 34 37 14 13 1 18 14 38 39 12 14 1 12 8 33 31 11 14 2 16 15 5 0 12 18 2 10 9 28 30 11 18 2 15 9 33 30 14 18 1 17 11 41 43 13 14 2 16 12 30 31 12 12 2 14 10 31 33 14 16 2 12 11 34 31 13 17 2 11 10 33 38 11 13 2 15 12 37 32 16 16 1 7 11 34 38 13 15
Names of X columns:
Gender Learning Software Connected Separate Happiness Depression
Type of Correlation
kendall
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') 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) } } a<-table.end(a) table.save(a,file='mytable1.tab')
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