curepwe.logml.map: Log marginal likelihood of a mixture cure rate (CurePWE)...

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

Log marginal likelihood of a mixture cure rate (CurePWE) model under the meta-analytic predictive (MAP) prior

Description

Uses Markov chain Monte Carlo (MCMC) and bridge sampling to estimate the logarithm of the marginal likelihood of a CurePWE model under the meta-analytic predictive (MAP) prior. The MAP prior is equivalent to the prior induced by the Bayesian hierarchical model (BHM).

The arguments related to MCMC sampling are utilized to draw samples from the MAP prior. These samples are then used to compute the logarithm of the normalizing constant of the MAP prior using only historical data set.

Usage

curepwe.logml.map(
  post.samples,
  bridge.args = NULL,
  iter_warmup = 1000,
  iter_sampling = 1000,
  chains = 4,
  ...
)

Arguments

post.samples	output from curepwe.bhm() giving posterior samples of a CurePWE model under the Bayesian hierarchical model (BHM), 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().
iter_warmup	number of warmup iterations to run per chain. Defaults to 1000. See the argument iter_warmup in sample() method in cmdstanr package.
iter_sampling	number of post-warmup iterations to run per chain. Defaults to 1000. See the argument iter_sampling in sample() method in cmdstanr package.
chains	number of Markov chains to run. Defaults to 4. See the argument chains in sample() method in cmdstanr package.
...	arguments passed to sample() method in cmdstanr package (e.g., seed, refresh, init).

Value

The function returns a list with the following objects

model

"curepwe_bhm"

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 normalizing constant of the Bayesian hierarchical model (BHM) using all data sets

bs.hist

an object of class bridge or bridge_list containing the output from using bridgesampling::bridge_sampler() to compute the logarithm of the normalizing constant of the BHM using historical data set

min_ess_bulk

the minimum estimated bulk effective sample size of the MCMC sampling

max_Rhat

the maximum Rhat

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(E1684)
    data(E1690)
    ## take subset for speed purposes
    E1684 = E1684[1:100, ]
    E1690 = E1690[1:50, ]
    ## replace 0 failure times with 0.50 days
    E1684$failtime[E1684$failtime == 0] = 0.50/365.25
    E1690$failtime[E1690$failtime == 0] = 0.50/365.25
    E1684$cage = as.numeric(scale(E1684$age))
    E1690$cage = as.numeric(scale(E1690$age))
    data_list = list(currdata = E1690, histdata = E1684)
    nbreaks = 3
    probs   = 1:nbreaks / nbreaks
    breaks  = as.numeric(
      quantile(E1690[E1690$failcens==1, ]$failtime, probs = probs)
    )
    breaks  = c(0, breaks)
    breaks[length(breaks)] = max(10000, 1000 * breaks[length(breaks)])
    d.bhm = curepwe.bhm(
      formula = survival::Surv(failtime, failcens) ~ treatment + sex + cage + node_bin,
      data.list = data_list,
      breaks = breaks,
      chains = 1, iter_warmup = 500, iter_sampling = 1000
    )
    curepwe.logml.map(
      post.samples = d.bhm,
      bridge.args = list(silent = TRUE),
      chains = 1, iter_warmup = 1000, iter_sampling = 2000
    )
  }
}

hdbayes documentation built on Nov. 21, 2025, 1:07 a.m.