glm.logml.post: Log marginal likelihood of a GLM under a normal/half-normal...

In hdbayes: Bayesian Analysis of Generalized Linear Models with Historical Data

Log marginal likelihood of a GLM under a normal/half-normal prior

Description

Uses bridge sampling to estimate the logarithm of the marginal likelihood of a GLM under the normal/half-normal prior.

Usage

glm.logml.post(post.samples, bridge.args = NULL)

Arguments

post.samples	output from glm.post() giving posterior samples of a GLM under the normal/half-normal prior, with an attribute called 'data' which includes the list of variables specified in the data block of the Stan program.
bridge.args	a list giving arguments (other than samples, log_posterior, data, lb, and ub) to pass onto bridgesampling::bridge_sampler().

Value

The function returns a list with the following objects

model

"Normal/Half-Normal"

logml

the estimated logarithm of the marginal likelihood

bs

an object of class bridge or bridge_list containing the output from using bridgesampling::bridge_sampler() to compute the logarithm of the marginal likelihood of the normal/half-normal prior

References

Gronau, Q. F., Singmann, H., and Wagenmakers, E.-J. (2020). bridgesampling: An r package for estimating normalizing constants. Journal of Statistical Software, 92(10).

Examples

if (instantiate::stan_cmdstan_exists()) {
  data(actg019)
  actg019 = actg019[1:100, ]
  data.list = list(currdata = actg019)
  formula = cd4 ~ treatment + age + race
  family = poisson('log')
  d.post = glm.post(
    formula = formula, family = family,
    data.list = data.list,
    chains = 1, iter_warmup = 500, iter_sampling = 1000
  )
  glm.logml.post(
    post.samples = d.post,
    bridge.args = list(silent = TRUE)
  )
}

hdbayes documentation built on Sept. 11, 2024, 5:34 p.m.