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Data X:
2 7 41 38 13 12 14 12 53 32 2 5 39 32 16 11 18 11 86 51 2 5 30 35 19 15 11 14 66 42 1 5 31 33 15 6 12 12 67 41 2 8 34 37 14 13 16 21 76 46 2 6 35 29 13 10 18 12 78 47 2 5 39 31 19 12 14 22 53 37 2 6 34 36 15 14 14 11 80 49 2 5 36 35 14 12 15 10 74 45 2 4 37 38 15 6 15 13 76 47 1 6 38 31 16 10 17 10 79 49 2 5 36 34 16 12 19 8 54 33 1 5 38 35 16 12 10 15 67 42 2 6 39 38 16 11 16 14 54 33 2 7 33 37 17 15 18 10 87 53 1 6 32 33 15 12 14 14 58 36 1 7 36 32 15 10 14 14 75 45 2 6 38 38 20 12 17 11 88 54 1 8 39 38 18 11 14 10 64 41 2 7 32 32 16 12 16 13 57 36 1 5 32 33 16 11 18 7 66 41 2 5 31 31 16 12 11 14 68 44 2 7 39 38 19 13 14 12 54 33 2 7 37 39 16 11 12 14 56 37 1 5 39 32 17 9 17 11 86 52 2 4 41 32 17 13 9 9 80 47 1 10 36 35 16 10 16 11 76 43 2 6 33 37 15 14 14 15 69 44 2 5 33 33 16 12 15 14 78 45 1 5 34 33 14 10 11 13 67 44 2 5 31 28 15 12 16 9 80 49 1 5 27 32 12 8 13 15 54 33 2 6 37 31 14 10 17 10 71 43 2 5 34 37 16 12 15 11 84 54 1 5 34 30 14 12 14 13 74 42 1 5 32 33 7 7 16 8 71 44 1 5 29 31 10 6 9 20 63 37 1 5 36 33 14 12 15 12 71 43 2 5 29 31 16 10 17 10 76 46 1 5 35 33 16 10 13 10 69 42 1 5 37 32 16 10 15 9 74 45 2 7 34 33 14 12 16 14 75 44 1 5 38 32 20 15 16 8 54 33 1 6 35 33 14 10 12 14 52 31 2 7 38 28 14 10 12 11 69 42 2 7 37 35 11 12 11 13 68 40 2 5 38 39 14 13 15 9 65 43 2 5 33 34 15 11 15 11 75 46 2 4 36 38 16 11 17 15 74 42 1 5 38 32 14 12 13 11 75 45 2 4 32 38 16 14 16 10 72 44 1 5 32 30 14 10 14 14 67 40 1 5 32 33 12 12 11 18 63 37 2 7 34 38 16 13 12 14 62 46 1 5 32 32 9 5 12 11 63 36 2 5 37 32 14 6 15 12 76 47 2 6 39 34 16 12 16 13 74 45 2 4 29 34 16 12 15 9 67 42 1 6 37 36 15 11 12 10 73 43 2 6 35 34 16 10 12 15 70 43 1 5 30 28 12 7 8 20 53 32 1 7 38 34 16 12 13 12 77 45 2 6 34 35 16 14 11 12 77 45 2 8 31 35 14 11 14 14 52 31 2 7 34 31 16 12 15 13 54 33 1 5 35 37 17 13 10 11 80 49 2 6 36 35 18 14 11 17 66 42 1 6 30 27 18 11 12 12 73 41 2 5 39 40 12 12 15 13 63 38 1 5 35 37 16 12 15 14 69 42 1 5 38 36 10 8 14 13 67 44 2 5 31 38 14 11 16 15 54 33 2 4 34 39 18 14 15 13 81 48 1 6 38 41 18 14 15 10 69 40 1 6 34 27 16 12 13 11 84 50 2 6 39 30 17 9 12 19 80 49 2 6 37 37 16 13 17 13 70 43 2 7 34 31 16 11 13 17 69 44 1 5 28 31 13 12 15 13 77 47 1 7 37 27 16 12 13 9 54 33 1 6 33 36 16 12 15 11 79 46 1 5 37 38 20 12 16 10 30 0 2 5 35 37 16 12 15 9 71 45 1 4 37 33 15 12 16 12 73 43 2 8 32 34 15 11 15 12 72 44 2 8 33 31 16 10 14 13 77 47 1 5 38 39 14 9 15 13 75 45 2 5 33 34 16 12 14 12 69 42 2 6 29 32 16 12 13 15 54 33 2 4 33 33 15 12 7 22 70 43 2 5 31 36 12 9 17 13 73 46 2 5 36 32 17 15 13 15 54 33 2 5 35 41 16 12 15 13 77 46 2 5 32 28 15 12 14 15 82 48 2 6 29 30 13 12 13 10 80 47 2 6 39 36 16 10 16 11 80 47 2 5 37 35 16 13 12 16 69 43 2 6 35 31 16 9 14 11 78 46 1 5 37 34 16 12 17 11 81 48 1 7 32 36 14 10 15 10 76 46 2 5 38 36 16 14 17 10 76 45 1 6 37 35 16 11 12 16 73 45 2 6 36 37 20 15 16 12 85 52 1 6 32 28 15 11 11 11 66 42 2 4 33 39 16 11 15 16 79 47 1 5 40 32 13 12 9 19 68 41 2 5 38 35 17 12 16 11 76 47 1 7 41 39 16 12 15 16 71 43 1 6 36 35 16 11 10 15 54 33 2 9 43 42 12 7 10 24 46 30 2 6 30 34 16 12 15 14 82 49 2 6 31 33 16 14 11 15 74 44 2 5 32 41 17 11 13 11 88 55 1 6 32 33 13 11 14 15 38 11 2 5 37 34 12 10 18 12 76 47 1 8 37 32 18 13 16 10 86 53 2 7 33 40 14 13 14 14 54 33 2 5 34 40 14 8 14 13 70 44 2 7 33 35 13 11 14 9 69 42 2 6 38 36 16 12 14 15 90 55 2 6 33 37 13 11 12 15 54 33 2 9 31 27 16 13 14 14 76 46 2 7 38 39 13 12 15 11 89 54 2 6 37 38 16 14 15 8 76 47 2 5 33 31 15 13 15 11 73 45 2 5 31 33 16 15 13 11 79 47 1 6 39 32 15 10 17 8 90 55 2 6 44 39 17 11 17 10 74 44 2 7 33 36 15 9 19 11 81 53 2 5 35 33 12 11 15 13 72 44 1 5 32 33 16 10 13 11 71 42 1 5 28 32 10 11 9 20 66 40 2 6 40 37 16 8 15 10 77 46 1 4 27 30 12 11 15 15 65 40 1 5 37 38 14 12 15 12 74 46 2 7 32 29 15 12 16 14 82 53 1 5 28 22 13 9 11 23 54 33 1 7 34 35 15 11 14 14 63 42 2 7 30 35 11 10 11 16 54 35 2 6 35 34 12 8 15 11 64 40 1 5 31 35 8 9 13 12 69 41 2 8 32 34 16 8 15 10 54 33 1 5 30 34 15 9 16 14 84 51 2 5 30 35 17 15 14 12 86 53 1 5 31 23 16 11 15 12 77 46 2 6 40 31 10 8 16 11 89 55 2 4 32 27 18 13 16 12 76 47 1 5 36 36 13 12 11 13 60 38 1 5 32 31 16 12 12 11 75 46 1 7 35 32 13 9 9 19 73 46 2 6 38 39 10 7 16 12 85 53 2 7 42 37 15 13 13 17 79 47 1 10 34 38 16 9 16 9 71 41 2 6 35 39 16 6 12 12 72 44 2 8 35 34 14 8 9 19 69 43 2 4 33 31 10 8 13 18 78 51 2 5 36 32 17 15 13 15 54 33 2 6 32 37 13 6 14 14 69 43 2 7 33 36 15 9 19 11 81 53 2 7 34 32 16 11 13 9 84 51 2 6 32 35 12 8 12 18 84 50 2 6 34 36 13 8 13 16 69 46
Names of X columns:
gender age Connected Separate Learning Software Happiness Depression Belonging Belonging_Final
Type of Correlation
pearson
pearson
spearman
kendall
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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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