abf.Wakefield: Calculate approximate Bayes factor (ABF) using method of...

In chuangaocornell/gtx2: Genetics ToolboX

Description

Calculates an approximation to the Bayes Factor for an alternative model where the parameter beta is a priori normal, against a smaller model where beta is zero, by approximating the likelihood function with a normal distribution.

Usage

abf.Wakefield(beta, se, priorsd)

Arguments

beta	Vector of effect size estimates.
se	Vector of associated standard errors.
priorsd	Scalar specifying the standard deviation of the prior on true effect sizes.

Details

See “Bayes factors for genome-wide association studies: comparison with P-values” by John Wakefield, 2009, Genetic Epidemiology 33(1):79-86 at http://dx.doi.org/10.1002/gepi.20359.

Value

A vector of approximate Bayes factors.

Author(s)

Toby Johnson [e-mail]

Examples

data(agtstats)
agtstats$pval <- with(agtstats, pchisq((beta/se.GC)^2, df = 1, lower.tail = FALSE))
max1 <- function(bf) return(bf/max(bf, na.rm = TRUE))
agtstats$BF.normal <- with(agtstats, max1(abf.Wakefield(beta, se.GC, 0.05)))
agtstats$BF.t <- with(agtstats, max1(abf.t(beta, se.GC, 0.0208)))
with(agtstats, plot(-log10(pval), log(BF.normal)))
with(agtstats, plot(-log10(pval), log(BF.t)))

chuangaocornell/gtx2 documentation built on May 29, 2019, 2:12 p.m.