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Data X:
10 10 10 8 8 9 8 6 12 9 10 14 5 8 6 10 10 13 8 7 12 9 10 13 8 6 6 7 7 12 10 9 10 10 6 9 9 7 12 4 6 7 4 4 10 8 6 11 9 8 15 10 9 10 8 8 12 5 6 10 10 6 12 8 10 11 7 8 11 8 8 12 8 7 15 9 4 12 8 9 11 6 8 9 8 10 11 8 8 11 5 6 9 9 7 15 8 8 12 8 5 9 8 10 12 6 2 12 6 6 9 9 7 9 8 5 11 9 8 12 10 7 12 8 7 12 8 10 12 7 7 6 7 6 11 10 10 12 8 6 9 7 5 11 10 8 9 7 8 10 7 5 10 9 8 9 9 10 12 8 7 11 6 7 9 8 7 9 9 7 12 2 2 6 6 4 10 8 6 12 8 7 11 7 9 14 8 9 8 6 4 9 10 9 10 10 9 10 10 8 10 8 7 11 8 9 10 7 7 12 10 6 14 5 7 10 3 2 8 2 3 8 3 4 7 4 5 11 2 2 6 6 6 9 8 8 12 8 5 12 5 4 12 10 10 9 9 10 15 8 10 15 9 9 13 8 5 9 5 5 12 7 7 9 9 10 15 8 9 11 4 8 11 7 8 6 8 8 14 7 8 11 7 8 8 9 7 10 6 6 10 7 8 9 4 2 8 6 5 9 10 4 10 9 9 11 10 10 14 8 6 12 4 4 9 8 10 13 5 6 8 8 7 12 9 7 14 8 8 9 4 6 10 8 5 12 10 6 12 6 7 9 7 6 9 10 9 12 9 9 15 8 7 12 3 6 11 8 7 8 7 7 11 7 8 11 8 7 10 8 8 12 7 7 9 7 4 11 9 10 15 9 8 14 9 8 6 4 2 9 6 6 9 6 4 8 6 4 7 8 9 10 3 2 6 8 6 9 8 7 9 6 4 7 10 10 11 2 3 9 9 7 12 6 4 9 6 8 10 5 4 11 4 5 7 7 6 12 5 5 8 8 9 13 6 6 11 9 8 11 6 4 12 4 4 11 7 8 12 2 4 3 8 10 10 9 8 13 6 5 10 5 3 6 7 7 11 8 6 12 4 5 9 9 5 10 9 9 15 9 2 9 7 7 6 5 7 9 7 5 15 9 9 15 8 4 9 6 5 11 9 9 9 8 7 11 7 6 10 7 8 9 7 7 6 8 6 12 10 8 13 6 6 12 6 7 12
Names of X columns:
Intention_to_Use Relative_Advantage Perceived_Usefulness
Type of Correlation
grey
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$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } x <- na.omit(x) y <- t(na.omit(t(y))) bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) print(n) a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$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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