lm_proper: Fit a linear model with a proper prior.

In asbates/geblm: Geometrically Ergodic Bayesian Linear Models

Description

Fits a Bayesian linear regression model with a normal prior on beta and a gamma prior on the precision tau. 'Wide' priors are used by default.

Usage

lm_proper(data, formula, burnin = 5000, iterations = 5000, thin = 1,
  beta_prior_mean, beta_prior_cov, tau_prior_shape, tau_prior_rate,
  start_beta)

Arguments

data	A data frame.
formula	A formula describing the model fit. Passed to stats::lm() to construct the model matrix.
burnin	Number of burn in iterations.
iterations	Number of sampling iterations.
thin	Number of thinning iterations.
beta_prior_mean	(Optional) Mean vector for the prior on beta. Defaults to zero.
beta_prior_cov	(Optional) Covariance matrix for the prior on beta. Defaults to diag(p) * 100 where p is the number of predictors.
tau_prior_shape	(Optional) Shape parameter for the prior on tau. Defaults to 0.001.
tau_prior_rate	(Optional) Rate parameter for the prior on tau. Defaults to 0.001.
start_beta	(Optional) Beta starting vector. Defaults to beta hat, the frequentist estimate.

Value

An object of class geblm containing samples from the posterior distributions of beta and sigma.

Examples

fit <- lm_proper(mtcars, mpg ~ wt)

asbates/geblm documentation built on Nov. 12, 2019, 5:23 p.m.