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Data X:
1 0 32 31 13 12 15 1 1 33 34 8 8 11 1 0 38 27 14 12 12 1 0 34 24 14 11 9 1 1 41 34 13 11 14 1 1 39 35 16 13 16 1 1 35 27 14 11 15 1 1 34 30 13 10 16 1 0 47 31 15 7 7 1 1 32 31 13 10 13 1 0 28 28 16 12 15 1 1 44 48 20 15 20 1 1 40 40 17 12 16 1 1 29 31 15 12 16 1 0 30 27 16 12 15 1 1 41 37 16 10 15 1 1 32 29 12 10 17 1 0 33 34 9 8 12 1 0 33 33 15 11 15 1 0 40 37 17 14 13 1 1 38 35 12 12 9 0 1 37 34 10 11 14 1 0 41 35 11 6 16 0 0 32 33 16 12 9 1 1 29 29 16 14 14 1 0 38 31 15 11 14 0 1 35 37 13 8 15 1 0 40 31 14 12 14 1 1 43 40 19 15 17 1 1 31 41 16 13 15 1 0 34 29 17 11 12 1 1 26 34 10 12 16 1 1 28 41 15 7 14 1 1 31 34 14 11 14 0 1 32 36 14 7 14 0 0 29 30 16 12 15 1 1 32 36 17 12 15 1 1 35 31 15 12 16 1 0 31 35 17 13 14 1 0 37 35 14 12 14 1 1 34 33 10 9 17 1 0 35 31 14 9 10 1 0 36 31 16 11 10 1 0 45 35 18 14 12 1 1 39 35 15 12 16 1 1 32 28 16 15 14 1 1 39 27 16 12 17 1 1 34 33 10 6 12 1 0 34 33 8 5 16 0 1 34 35 17 13 15 1 1 37 30 14 11 14 1 1 27 29 12 11 15 1 0 43 30 10 6 14 1 1 40 42 14 12 16 1 1 40 36 12 10 16 1 1 35 36 16 6 17 1 1 37 33 16 12 15 1 1 39 34 15 14 15 1 0 26 33 11 6 6 0 1 29 30 16 11 14 1 0 34 25 8 6 12 1 1 32 40 17 14 10 1 1 38 36 16 12 12 0 1 39 33 15 12 14 1 0 27 35 8 8 18 0 1 40 25 13 10 12 0 1 37 39 14 11 15 0 1 34 32 13 7 8 1 1 36 34 16 12 11 0 0 34 38 12 9 16 0 1 36 29 19 13 14 1 1 32 39 19 14 16 1 1 43 36 12 6 7 1 1 47 32 14 12 16 0 0 24 38 15 6 9 1 1 40 39 13 14 8 1 0 33 32 16 12 15 0 0 38 31 10 10 10 0 1 33 31 15 10 12 0 1 36 30 16 12 11 1 1 39 44 15 11 14 1 0 37 28 11 10 18 1 0 38 36 9 7 12 1 1 36 30 16 12 17 0 1 30 31 12 12 16 1 0 36 32 14 12 11 1 1 41 32 14 10 9 1 1 32 35 13 10 18 0 0 35 33 15 12 14 1 0 41 32 17 12 13 1 0 36 32 14 12 16 1 0 34 27 9 9 10 0 0 35 28 11 8 13 0 1 36 36 9 10 16 1 0 43 35 7 5 9 1 1 36 27 13 10 12 1 0 36 34 15 10 10 0 1 34 31 12 12 16 1 0 36 33 15 11 12 0 0 32 32 14 9 16 0 1 27 33 15 15 15 0 0 32 35 9 8 8 1 1 41 31 16 12 17 1 1 40 33 16 12 13 1 1 30 30 14 10 16 0 0 37 28 14 11 13 0 0 35 31 13 10 15 0 1 39 31 14 11 13 0 0 35 30 16 12 16 0 1 27 38 16 11 14 0 1 37 35 13 10 18 0 0 37 28 12 9 10 0 0 38 37 16 9 13 0 1 38 36 16 11 14 0 1 41 34 16 12 18 0 0 38 27 10 7 9 0 0 39 29 14 12 15 0 0 31 30 12 11 15 0 0 39 35 12 12 11 0 1 32 32 12 6 17 0 1 35 32 12 9 10 0 1 45 39 19 15 13 0 0 29 27 14 10 14 0 1 26 34 13 11 16 0 1 35 31 17 14 17 0 0 40 30 16 12 16 0 1 39 36 15 12 16 0 1 35 35 12 12 13 0 1 34 33 8 11 14 0 1 35 36 10 9 13 0 1 33 36 16 11 16 0 0 37 28 10 6 7 0 0 35 31 16 12 15 0 1 38 33 10 12 14 0 1 35 42 18 14 12 0 1 29 35 12 8 7 0 0 0 5 16 10 14 0 0 30 28 10 9 15 0 0 32 31 15 9 10 0 1 43 41 17 11 17 0 0 37 27 14 10 12 0 0 33 32 12 9 13 0 0 41 30 11 10 13 0 0 39 30 15 12 12 0 1 39 33 7 11 11
Names of X columns:
Pop Gender Connected Separate Learning Software Happiness
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') 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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