bayesLm: MCMC algorithm for Bayesian Linear Model

In jrlewi/brlm: Bayesian Restricted Likelihood Methods

Description

Fits the linear regression model β~N(μ_0, Σ_0), σ^2~IG(a0, b0), y~N(Xβ, σ^2 I)

Usage

bayesLm(y, X, mu0, Sigma0, a0, b0, sigma2Int, nkeep = 10000, nburn = 1000)

Arguments

y	vector of repsonse
X	design matrix for regression. If intercept is desired, a column of 1's is needed
mu0	prior mean for beta
Sigma0	prior var-cov matrix for β
a0	prior shape parameter for σ^2
nkeep	number of iterations to keep
nburn	number of iterations to toss
a0, b0	prior scale parameter for σ^2
sigma2Init	initial value for σ^2 in MCMC

Details

Uses Gibbs sampling to sample from the posterior under the above linear regression model.

Value

list with mcmc sample and mean fitted values

jrlewi/brlm documentation built on March 17, 2021, 1:10 a.m.