# DirichSampHWE: Simulate samples from a Dirichlet prior or posterior under... In HWEBayes: Bayesian investigation of Hardy-Weinberg Equilibrium via estimation and testing.

## Description

Function to simulate samples from the HWE Dirichlet model. Can be used for samples from the prior or the (conjugate) Dirichlet posterior, both in the k allele case. Samples are generated for the allele frequencies in the order p_{1},p_{2},...,p_{k}.

## Usage

 1 DirichSampHWE(nvec, bvec0, nsim) 

## Arguments

 nvec vector of genotype frequencies in the order n_{11}, n_{12},..., n_{1k},n_{22} ..., n_{2k},..., n_{kk}. bvec0 vector of length k Dirichlet prior parameters, where k is the number of alleles. nsim number of samples to simulate from the prior/posterior.

## Details

Uses the rdirichlet function from the MCMCpack library.

## Value

 pvec matrix of size nsim \times k containing samples for the genotype frequencies, in the order p_{1}, p_{12},..., p_{k}.

## Author(s)

Jon Wakefield ([email protected]).

## References

Wakefield, J. (2010). Bayesian methods for examining Hardy-Weinberg equilibrium. Biometrics; Vol 66:257-65

DirichSampSat, DirichNormSat, DirichNormHWE
 1 2 3 4 5 6 7 8 9 # First sample from the prior PriorSampHWE <- DirichSampHWE(nvec=rep(0,10),bvec0=rep(1,4),nsim=1000) par(mfrow=c(1,1)) hist(PriorSampHWE$pvec[,1],xlab="p1",main="") # Now sample from the posterior data(DiabRecess) PostSampHWE <- DirichSampHWE(nvec=DiabRecess,bvec0=rep(1,4),nsim=1000) par(mfrow=c(1,1)) hist(PostSampHWE$pvec[,1],xlab="p1",main="")