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Data X:
23 24 13 27 2 2 1 1 22 18 23 30 4 2 2 2 26 17 24 24 4 2 2 3 41 21 22 16 4 4 2 5 23 17 18 27 5 2 1 4 33 19 17 18 3 3 3 3 31 19 22 24 2 2 2 2 35 24 27 24 4 4 2 4 28 17 15 18 3 3 2 2 31 20 19 22 4 4 2 2 23 24 23 25 4 2 1 4 25 18 11 16 2 2 1 2 30 22 19 18 4 4 2 2 30 17 14 24 2 2 2 2 19 22 20 24 4 3 2 4 32 26 21 29 4 2 4 2 50 23 23 22 4 4 3 2 27 20 19 21 4 1 1 3 36 20 17 23 3 3 1 3 31 18 24 24 4 3 1 2 26 25 25 23 3 4 3 2 32 25 16 19 5 4 2 4 35 19 21 24 4 2 2 2 30 26 28 20 2 4 3 4 38 24 25 24 4 4 3 2 41 24 22 30 5 3 3 4 27 20 16 17 4 2 2 3 28 21 22 22 4 2 2 2 24 18 14 24 3 3 1 3 21 14 10 20 2 1 1 1 39 14 18 23 4 2 2 2 33 26 18 19 1 5 4 1 28 16 17 22 3 2 2 2 47 23 19 24 2 2 2 2 26 21 23 20 4 4 4 2 25 11 18 24 4 4 2 2 34 21 17 26 2 3 2 2 30 16 19 24 3 4 3 2 30 21 21 24 4 3 2 4 25 24 21 24 2 4 4 4 19 14 10 21 2 1 1 2 28 18 18 22 4 2 2 2 39 29 22 29 1 4 2 2 20 12 9 23 2 1 1 1 30 19 24 22 3 2 3 2 31 21 21 25 2 3 2 2 19 20 15 23 4 3 2 2 25 19 17 24 3 2 2 2 52 28 29 30 4 5 3 5 33 21 26 24 4 3 3 4 22 21 18 24 2 2 1 2 32 16 20 20 3 4 4 3 17 13 11 16 3 1 1 2 31 22 19 27 4 3 1 2 20 30 14 13 3 4 1 1 29 18 24 29 5 4 2 4 37 22 14 27 4 3 2 2 21 20 17 24 4 1 1 5 23 19 15 24 2 2 2 2 30 19 22 23 2 2 1 3 21 21 18 22 2 3 2 2 24 19 23 26 4 3 2 3 40 23 27 26 4 5 4 4 20 17 13 21 2 1 1 2 33 19 21 23 3 4 3 4 20 15 13 20 2 1 1 3 26 20 20 28 4 4 3 3 22 24 25 29 4 3 3 5 32 17 13 16 3 1 2 2 13 10 10 25 2 1 1 2 28 23 23 28 4 3 3 4 32 22 18 24 4 4 2 1 27 21 15 24 2 2 2 3 32 18 25 24 2 2 3 4 23 19 20 12 4 1 1 4 28 23 17 22 3 2 2 2 23 19 23 22 4 3 2 4 29 24 21 24 3 5 2 3 26 20 16 26 4 2 1 2 15 17 17 24 4 2 1 3 14 20 14 26 1 1 1 1 19 22 21 22 3 3 1 3 19 15 13 23 2 2 3 2 26 23 17 29 2 4 2 2 33 18 13 16 2 3 2 1 35 20 25 18 3 3 3 4 28 20 16 22 3 2 2 2 25 19 12 23 2 2 1 1 41 24 22 30 4 5 2 4 28 13 17 24 2 2 1 2 25 19 25 21 3 4 2 4 26 16 16 23 3 2 2 2 41 22 24 14 4 4 3 5 28 21 17 25 3 2 2 2 26 16 15 17 2 2 2 2 24 19 16 24 2 2 1 2 32 14 14 23 3 2 2 2 25 19 20 22 4 2 2 2 22 20 15 16 4 3 2 4 29 19 18 22 5 2 2 4 36 23 24 30 4 4 3 3 40 20 18 25 2 3 3 2 27 13 15 21 4 2 2 2 35 17 23 22 4 2 4 2 18 22 21 23 2 2 1 2 36 20 20 24 3 4 2 2 27 19 24 22 3 3 3 3 31 21 23 21 2 3 3 2 16 16 15 26 2 1 2 3 26 24 21 24 2 4 2 3 20 23 21 27 4 2 2 5
Names of X columns:
anderen positief negatief organisatie PERFECTIONISM17 PERFECTIONISM18 PERFECTIONISM23 PERFECTIONISM28
Type of Correlation
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') 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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