R/a.pcor.test.R

In epade: Easy Plots

a.pcor.test <-
function(x,y,z,use="mat",method="p",na.rm=TRUE){
	x <- c(x)
	y <- c(y)
	z <- as.data.frame(z)

	if(use == "mat"){
		p.use <- "Var-Cov matrix"
		pcor = a.pcor.mat(x,y,z,method=method,na.rm=na.rm)
	}else if(use == "rec"){
		p.use <- "Recursive formula"
		pcor = a.pcor.rec(x,y,z,method=method,na.rm=na.rm)
	}else{
		stop("\'use\' should be either \"rec\" or \"mat\"!\n")
	}

	# print the method
	if(gregexpr("p",method)[[1]][1] == 1){
		p.method <- "Pearson"
	}else if(gregexpr("s",method)[[1]][1] == 1){
		p.method <- "Spearman"
	}else if(gregexpr("k",method)[[1]][1] == 1){
		p.method <- "Kendall"
	}else{
		stop("\'method\' should be \"pearson\" or \"spearman\" or \"kendall\"!\n")
	}

	# sample number
	n <- dim(na.omit(data.frame(x,y,z)))[1]

	# given variables' number
	gn <- dim(z)[2]

	# p-value
	if(p.method == "Kendall"){
		statistic <- pcor/sqrt(2*(2*(n-gn)+5)/(9*(n-gn)*(n-1-gn)))
		p.value <- 2*pnorm(-abs(statistic))

	}else{
		statistic <- pcor*sqrt((n-2-gn)/(1-pcor^2))
  		p.value <- 2*pnorm(-abs(statistic))
	}

	data.frame(estimate=pcor,p.value=p.value,statistic=statistic,n=n,gn=gn,Method=p.method,Use=p.use)
}