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Data X:
41 13 38 12 14 12 53 32 9 39 16 32 11 18 11 83 51 9 30 19 35 15 11 14 66 42 9 31 15 33 6 12 12 67 41 9 34 14 37 13 16 21 76 46 9 35 13 29 10 18 12 78 47 9 39 19 31 12 14 22 53 37 9 34 15 36 14 14 11 80 49 9 36 14 35 12 15 10 74 45 9 37 15 38 9 15 13 76 47 9 38 16 31 10 17 10 79 49 9 36 16 34 12 19 8 54 33 9 38 16 35 12 10 15 67 42 9 39 16 38 11 16 14 54 33 9 33 17 37 15 18 10 87 53 9 32 15 33 12 14 14 58 36 9 36 15 32 10 14 14 75 45 9 38 20 38 12 17 11 88 54 9 39 18 38 11 14 10 64 41 9 32 16 32 12 16 13 57 36 9 32 16 33 11 18 9.5 66 41 9 31 16 31 12 11 14 68 44 9 39 19 38 13 14 12 54 33 9 37 16 39 11 12 14 56 37 9 39 17 32 12 17 11 86 52 9 41 17 32 13 9 9 80 47 9 36 16 35 10 16 11 76 43 9 33 15 37 14 14 15 69 44 9 33 16 33 12 15 14 78 45 9 34 14 33 10 11 13 67 44 9 31 15 31 12 16 9 80 49 9 27 12 32 8 13 15 54 33 9 37 14 31 10 17 10 71 43 9 34 16 37 12 15 11 84 54 9 34 14 30 12 14 13 74 42 9 32 10 33 7 16 8 71 44 9 29 10 31 9 9 20 63 37 9 36 14 33 12 15 12 71 43 9 29 16 31 10 17 10 76 46 9 35 16 33 10 13 10 69 42 9 37 16 32 10 15 9 74 45 9 34 14 33 12 16 14 75 44 9 38 20 32 15 16 8 54 33 9 35 14 33 10 12 14 52 31 9 38 14 28 10 15 11 69 42 9 37 11 35 12 11 13 68 40 9 38 14 39 13 15 9 65 43 9 33 15 34 11 15 11 75 46 9 36 16 38 11 17 15 74 42 9 38 14 32 12 13 11 75 45 9 32 16 38 14 16 10 72 44 9 32 14 30 10 14 14 67 40 9 32 12 33 12 11 18 63 37 9 34 16 38 13 12 14 62 46 9 32 9 32 5 12 11 63 36 9 37 14 35 6 15 14.5 76 47 9 39 16 34 12 16 13 74 45 9 29 16 34 12 15 9 67 42 9 37 15 36 11 12 10 73 43 9 35 16 34 10 12 15 70 43 9 30 12 28 7 8 20 53 32 9 38 16 34 12 13 12 77 45 9 34 16 35 14 11 12 80 48 9 31 14 35 11 14 14 52 31 9 34 16 31 12 15 13 54 33 9 35 17 37 13 10 11 80 49 10 36 18 35 14 11 17 66 42 10 30 18 27 11 12 12 73 41 10 39 12 40 12 15 13 63 38 10 35 16 37 12 15 14 69 42 10 38 10 36 8 14 13 67 44 10 31 14 38 11 16 15 54 33 10 34 18 39 14 15 13 81 48 10 38 18 41 14 15 10 69 40 10 34 16 27 12 13 11 84 50 10 39 17 30 9 12 19 80 49 10 37 16 37 13 17 13 70 43 10 34 16 31 11 13 17 69 44 10 28 13 31 12 15 13 77 47 10 37 16 27 12 13 9 54 33 10 33 16 36 12 15 11 79 46 10 35 16 37 12 15 9 71 45 10 37 15 33 12 16 12 73 43 10 32 15 34 11 15 12 72 44 10 33 16 31 10 14 13 77 47 10 38 14 39 9 15 13 75 45 10 33 16 34 12 14 12 69 42 10 29 16 32 12 13 15 54 33 10 33 15 33 12 7 22 70 43 10 31 12 36 9 17 13 73 46 10 36 17 32 15 13 15 54 33 10 35 16 41 12 15 13 77 46 10 32 15 28 12 14 15 82 48 10 29 13 30 12 13 12.5 80 47 10 39 16 36 10 16 11 80 47 10 37 16 35 13 12 16 69 43 10 35 16 31 9 14 11 78 46 10 37 16 34 12 17 11 81 48 10 32 14 36 10 15 10 76 46 10 38 16 36 14 17 10 76 45 10 37 16 35 11 12 16 73 45 10 36 20 37 15 16 12 85 52 10 32 15 28 11 11 11 66 42 10 33 16 39 11 15 16 79 47 10 40 13 32 12 9 19 68 41 10 38 17 35 12 16 11 76 47 10 41 16 39 12 15 16 71 43 10 36 16 35 11 10 15 54 33 10 43 12 42 7 10 24 46 30 10 30 16 34 12 15 14 85 52 10 31 16 33 14 11 15 74 44 10 32 17 41 11 13 11 88 55 10 32 13 33 11 14 15 38 11 10 37 12 34 10 18 12 76 47 10 37 18 32 13 16 10 86 53 10 33 14 40 13 14 14 54 33 10 34 14 40 8 14 13 67 44 10 33 13 35 11 14 9 69 42 10 38 16 36 12 14 15 90 55 10 33 13 37 11 12 15 54 33 10 31 16 27 13 14 14 76 46 10 38 13 39 12 15 11 89 54 10 37 16 38 14 15 8 76 47 10 36 15 31 13 15 11 73 45 10 31 16 33 15 13 11 79 47 10 39 15 32 10 17 8 90 55 10 44 17 39 11 17 10 74 44 10 33 15 36 9 19 11 81 53 10 35 12 33 11 15 13 72 44 10 32 16 33 10 13 11 71 42 10 28 10 32 11 9 20 66 40 10 40 16 37 8 15 10 77 46 10 27 12 30 11 15 15 65 40 10 37 14 38 12 15 12 74 46 10 32 15 29 12 16 14 85 53 10 28 13 22 9 11 23 54 33 10 34 15 35 11 14 14 63 42 10 30 11 35 10 11 16 54 35 10 35 12 34 8 15 11 64 40 10 31 11 35 9 13 12 69 41 10 32 16 34 8 15 10 54 33 10 30 15 37 9 16 14 84 51 10 30 17 35 15 14 12 86 53 10 31 16 23 11 15 12 77 46 10 40 10 31 8 16 11 89 55 10 32 18 27 13 16 12 76 47 10 36 13 36 12 11 13 60 38 10 32 16 31 12 12 11 75 46 10 35 13 32 9 9 19 73 46 10 38 10 39 7 16 12 85 53 10 42 15 37 13 13 17 79 47 10 34 16 38 9 16 9 71 41 10 35 16 39 6 12 12 72 44 10 38 14 34 8 9 19 69 43 9 33 10 31 8 13 18 78 51 10 36 17 32 15 13 15 54 33 10 32 13 37 6 14 14 69 43 10 33 15 36 9 19 11 81 53 10 34 16 32 11 13 9 84 51 10 32 12 38 8 12 18 84 50 10 34 13 36 8 13 16 69 46 10 27 13 26 10 10 24 66 43 11 31 12 26 8 14 14 81 47 11 38 17 33 14 16 20 82 50 11 34 15 39 10 10 18 72 43 11 24 10 30 8 11 23 54 33 11 30 14 33 11 14 12 78 48 11 26 11 25 12 12 14 74 44 11 34 13 38 12 9 16 82 50 11 27 16 37 12 9 18 73 41 11 37 12 31 5 11 20 55 34 11 36 16 37 12 16 12 72 44 11 41 12 35 10 9 12 78 47 11 29 9 25 7 13 17 59 35 11 36 12 28 12 16 13 72 44 11 32 15 35 11 13 9 78 44 11 37 12 33 8 9 16 68 43 11 30 12 30 9 12 18 69 41 11 31 14 31 10 16 10 67 41 11 38 12 37 9 11 14 74 42 11 36 16 36 12 14 11 54 33 11 35 11 30 6 13 9 67 41 11 31 19 36 15 15 11 70 44 11 38 15 32 12 14 10 80 48 11 22 8 28 12 16 11 89 55 11 32 16 36 12 13 19 76 44 11 36 17 34 11 14 14 74 43 11 39 12 31 7 15 12 87 52 11 28 11 28 7 13 14 54 30 11 32 11 36 5 11 21 61 39 11 32 14 36 12 11 13 38 11 11 38 16 40 12 14 10 75 44 11 32 12 33 3 15 15 69 42 11 35 16 37 11 11 16 62 41 11 32 13 32 10 15 14 72 44 11 37 15 38 12 12 12 70 44 11 34 16 31 9 14 19 79 48 11 33 16 37 12 14 15 87 53 11 33 14 33 9 8 19 62 37 11 26 16 32 12 13 13 77 44 11 30 16 30 12 9 17 69 44 11 24 14 30 10 15 12 69 40 11 34 11 31 9 17 11 75 42 11 34 12 32 12 13 14 54 35 11 33 15 34 8 15 11 72 43 11 34 15 36 11 15 13 74 45 11 35 16 37 11 14 12 85 55 11 35 16 36 12 16 15 52 31 11 36 11 33 10 13 14 70 44 11 34 15 33 10 16 12 84 50 11 34 12 33 12 9 17 64 40 11 41 12 44 12 16 11 84 53 11 32 15 39 11 11 18 87 54 11 30 15 32 8 10 13 79 49 11 35 16 35 12 11 17 67 40 11 28 14 25 10 15 13 65 41 11 33 17 35 11 17 11 85 52 11 39 14 34 10 14 12 83 52 11 36 13 35 8 8 22 61 36 11 36 15 39 12 15 14 82 52 11 35 13 33 12 11 12 76 46 11 38 14 36 10 16 12 58 31 11 33 15 32 12 10 17 72 44 11 31 12 32 9 15 9 72 44 11 34 13 36 9 9 21 38 11 11 32 8 36 6 16 10 78 46 11 31 14 32 10 19 11 54 33 11 33 14 34 9 12 12 63 34 11 34 11 33 9 8 23 66 42 11 34 12 35 9 11 13 70 43 11 34 13 30 6 14 12 71 43 11 33 10 38 10 9 16 67 44 11 32 16 34 6 15 9 58 36 11 41 18 33 14 13 17 72 46 11 34 13 32 10 16 9 72 44 11 36 11 31 10 11 14 70 43 11 37 4 30 6 12 17 76 50 11 36 13 27 12 13 13 50 33 11 29 16 31 12 10 11 72 43 11 37 10 30 7 11 12 72 44 11 27 12 32 8 12 10 88 53 11 35 12 35 11 8 19 53 34 11 28 10 28 3 12 16 58 35 11 35 13 33 6 12 16 66 40 11 37 15 31 10 15 14 82 53 11 29 12 35 8 11 20 69 42 11 32 14 35 9 13 15 68 43 11 36 10 32 9 14 23 44 29 11 19 12 21 8 10 20 56 36 11 21 12 20 9 12 16 53 30 11 31 11 34 7 15 14 70 42 11 33 10 32 7 13 17 78 47 11 36 12 34 6 13 11 71 44 11 33 16 32 9 13 13 72 45 11 37 12 33 10 12 17 68 44 11 34 14 33 11 12 15 67 43 11 35 16 37 12 9 21 75 43 11 31 14 32 8 9 18 62 40 11 37 13 34 11 15 15 67 41 11 35 4 30 3 10 8 83 52 11 27 15 30 11 14 12 64 38 11 34 11 38 12 15 12 68 41 11 40 11 36 7 7 22 62 39 11 29 14 32 9 14 12 72 43 11
Names of X columns:
Connected Learning Separate Software Happiness Depression Sport1 Sport2 Month
Type of Correlation
1
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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