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Data X:
24 10 35 39 12 37 37 12 39 38 13 39 36 10 33 39 13 39 37 12 41 36 13 42 30 12 35 32 13 39 35 10 35 36 12 36 37 12 38 38 13 36 30 10 40 40 14 34 38 13 36 41 13 39 47 13 45 37 11 27 38 12 37 31 10 27 33 13 27 36 9 32 37 11 31 29 12 35 39 12 42 39 13 39 34 11 37 34 11 31 41 12 32 28 11 37 21 6 37 41 13 32 32 12 28 35 13 35 29 9 22 30 10 33 27 9 29 33 11 34 36 14 36 36 14 36 30 9 31 38 12 42 37 12 38 33 11 31 46 14 41 31 11 42 38 14 41 39 11 33 45 15 40 29 10 30 33 13 40 34 11 38 36 12 34 36 13 38 36 12 34 41 11 31 25 10 34 40 13 43 34 8 36 33 9 35 36 9 35 39 15 41 32 10 23 37 13 39 37 10 25 42 12 36 40 15 43 28 11 36 23 11 29 32 11 31 42 13 43 44 13 41 35 12 33 32 10 36 33 10 30 32 12 40 32 11 22 38 12 37 33 8 39 32 12 34 33 11 34 39 15 42 41 13 41 34 12 34 37 12 38 35 11 33 27 11 34 39 13 35 38 14 37 34 14 42 39 12 38 36 10 29 28 11 27 31 12 42 35 15 41 34 10 29 33 11 34 39 13 36 32 10 39 29 12 33 33 11 34 30 12 33 37 12 40 40 13 42 31 12 38 37 12 38 31 9 27 32 11 35 27 8 23 29 13 32 28 12 24 28 12 24 37 13 43 36 12 38 43 15 42 24 9 25 24 9 30 37 9 33 35 11 35 36 11 36 23 7 27 23 7 29 29 9 23 35 12 31 38 12 37 36 12 28 24 9 34 29 10 33 25 10 29 34 10 36 40 12 31 28 6 25 32 12 28 35 11 43 36 11 39 29 11 26 26 10 25 35 12 34 23 6 23 37 13 36 38 13 37 30 10 32 31 11 28 20 7 16 28 8 26 40 14 41 38 12 36 34 11 39 36 9 34 36 10 37 32 8 30 33 11 41 33 12 40 44 12 43 35 13 35 36 11 40 39 13 41 26 10 28 34 12 28 32 13 39 28 9 25 27 7 27 30 11 29 36 9 35 39 12 36 36 12 30 27 10 26 36 12 30 35 11 34 40 14 39 39 12 33 39 12 35 33 11 32 34 12 33 38 12 39 33 13 39 39 12 34 37 9 38 37 11 35 22 9 32 21 7 28 40 13 35 33 11 40 38 10 35 41 11 40 39 14 36 32 11 33 39 13 34 33 12 34 34 12 37 35 12 35 26 10 17 30 9 28 31 12 31 34 12 30 33 11 28 38 11 44 37 12 33 42 13 36 27 10 29 26 9 26 37 10 40 28 11 34 39 12 36 36 9 32 37 13 38 39 12 41 32 12 30 32 12 30 18 10 21 37 13 41 26 12 33 32 10 30 42 14 36 38 13 37 33 13 36 29 8 22 36 11 33 34 11 27 27 7 31 34 10 29 34 11 32 34 11 36 28 10 36 35 12 30 38 12 39 32 10 29 49 12 40 29 10 27 33 10 29 35 12 35 37 12 36 38 12 41 38 12 41 38 12 41 39 12 40 31 8 30 40 14 40 32 9 37 25 7 34 35 12 43 21 6 21 40 13 39 22 6 17 19 7 15 19 6 18 39 12 41 31 11 43 30 9 27 38 12 33 41 13 42 12 8 16 30 10 28 40 12 38 30 9 27 29 10 24 20 6 16 27 10 25 27 7 22 30 9 27 40 12 36 42 10 29 38 14 34 37 13 35 37 13 29 40 10 39 36 11 38 35 13 31 41 14 40 29 7 32 42 12 43 38 13 38 40 13 39 26 8 29 35 12 30 32 11 39 28 11 35 38 12 31 25 8 31 38 10 29 39 12 32 41 15 39 29 12 36 34 12 32 34 10 27 38 13 30 37 12 41 32 11 33 37 12 36 42 15 33 34 13 29 35 12 36 35 10 31 38 9 39 29 11 32 39 11 31 40 15 40 37 12 35 36 10 27 35 12 35 32 12 29 31 9 27 34 11 36 38 11 32 42 13 40 36 12 32 35 12 34 31 12 35 29 9 31 30 10 30 34 13 41 33 9 31 29 11 23 34 11 33 36 10 36 29 9 30 37 11 36 43 13 39 37 11 33 33 10 33 31 10 34 32 12 34 31 7 20 33 9 24 38 12 34 39 12 31 34 13 34 31 9 35 31 10 27 31 12 34 36 12 36 35 9 35 31 11 31 33 12 40 38 11 33 42 14 34 32 10 32 19 6 25 19 7 25 26 6 19 33 12 23 21 5 18 30 9 28 36 12 34 37 12 30 31 13 29 36 10 42 43 14 40 43 14 36 41 13 38 34 9 28 30 11 25 36 9 33 43 14 43 30 13 36 33 13 32 23 8 29 38 12 36 34 11 35 32 8 32 32 12 32 32 9 32 33 10 31 28 11 20 36 10 28 37 10 30 37 12 39 45 15 45 38 12 30 32 9 28 48 13 39 35 8 28 37 12 38 40 12 37 34 10 33 38 11 34 36 12 31 41 12 31 26 9 29 32 10 28 35 12 42 44 15 38 42 12 35 33 11 25 30 9 29 36 10 33 34 10 31 29 10 28 39 12 36 38 9 31 38 10 29 44 15 44 43 13 37 33 9 30 36 10 19 35 10 28 27 11 25 28 8 25 35 11 35 28 7 20 34 10 36 35 11 37 27 8 27 36 13 39 25 9 21 36 12 37 34 9 26 39 11 40 32 10 27 26 9 26 33 12 27 31 10 31 38 13 37 37 11 34 35 10 37 34 12 27 29 11 29 37 12 34 14 7 23 37 10 40 37 13 34 33 8 29 26 8 30 35 10 31 37 11 30 31 9 25 33 11 31 41 14 39 29 11 29 30 8 34 31 9 25 37 15 30 48 15 39 31 11 31 28 12 28 41 10 39 35 12 34 35 11 36 32 10 31 29 8 32 32 11 33 40 14 41 39 12 30 35 11 32
Names of X columns:
Overzichtelijkheid Snelheid Vervangbaarheid
Type of Correlation
1
pearson
spearman
kendall
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Title:
R Code
par1 <- 'pearson' 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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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