hdbayes (version 0.2.0)

aft.logml.post: Log marginal likelihood of an accelerated failure time (AFT) model under a normal/half-normal prior

Description

Uses bridge sampling to estimate the logarithm of the marginal likelihood of an AFT model under the normal/half-normal prior.

Usage

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

Value

The function returns a list with the following objects

model

"aft_post"

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 AFT model under the normal/half-normal prior

Arguments

post.samples

output from aft.post() giving posterior samples of an AFT model 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().

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()) {
  if(requireNamespace("survival")){
    library(survival)
    data(E1690)
    ## take subset for speed purposes
    E1690 = E1690[1:100, ]
    ## replace 0 failure times with 0.50 days
    E1690$failtime[E1690$failtime == 0] = 0.50/365.25
    E1690$cage = as.numeric(scale(E1690$age))
    data_list = list(currdata = E1690)
    d.post = aft.post(
      formula = survival::Surv(failtime, failcens) ~ treatment + sex + cage + node_bin,
      data.list = data_list,
      dist = "weibull",
      beta.sd = 10,
      chains = 1, iter_warmup = 500, iter_sampling = 1000
    )
    aft.logml.post(
      post.samples = d.post,
      bridge.args = list(silent = TRUE)
    )
  }
}