glm.rmap.bhm {hdbayes}

Posterior of robust meta-analytic predictive prior (RMAP)

Description

First step for sampling from the posterior distribution of a GLM using the RMAP by Schmidli et al. (2014) doi:10.1111/biom.12242.

Usage

glm.rmap.bhm(
  formula,
  family,
  hist.data.list,
  hist.offset.list = NULL,
  meta.mean.mean = NULL,
  meta.mean.sd = NULL,
  meta.sd.mean = NULL,
  meta.sd.sd = NULL,
  hist.disp.mean = NULL,
  hist.disp.sd = NULL,
  iter_warmup = 1000,
  iter_sampling = 1000,
  chains = 4,
  ...
)

Arguments

formula

a two-sided formula giving the relationship between the response variable and covariates.

family

an object of class family. See ?stats::family.

hist.data.list

a list of data.frames. Each element in the list is a historical data set.

hist.offset.list

a list of vectors giving the offsets for each historical data. The length of hist.offset.list is equal to the length of hist.data.list. The length of each element of hist.offset.list is equal to the number of rows in the corresponding element of hist.data.list. Defaults to a list of vectors of 0s.

meta.mean.mean

a scalar or a vector whose dimension is equal to the number of regression coefficients giving the means for the normal hyperpriors on the mean hyperparameters of regression coefficients. If a scalar is provided, meta.mean.mean will be a vector of repeated elements of the given scalar. Defaults to a vector of 0s.

meta.mean.sd

a scalar or a vector whose dimension is equal to the number of regression coefficients giving the sds for the normal hyperpriors on the mean hyperparameters of regression coefficients. If a scalar is provided, same as for meta.mean.mean. Defaults to a vector of 1s.

meta.sd.mean

a scalar or a vector whose dimension is equal to the number of regression coefficients giving the means for the half-normal hyperpriors on the sd hyperparameters of regression coefficients. If a scalar is provided, same as for meta.mean.mean. Defaults to a vector of 0s.

meta.sd.sd

a scalar or a vector whose dimension is equal to the number of regression coefficients giving the sds for the half-normal hyperpriors on the sd hyperparameters of regression coefficients. If a scalar is provided, same as for meta.mean.mean. Defaults to a vector of 10s.

hist.disp.mean

a scalar or a vector whose dimension is equal to the number of historical data sets giving the means for the half-normal hyperpriors on the dispersion parameters. If a scalar is provided, same as for meta.mean.mean. Defaults to a vector of 0s.

hist.disp.sd

a scalar or a vector whose dimension is equal to the number of historical data sets giving the sds for the half-normal hyperpriors on the dispersion parameters. If a scalar is provided, same as for meta.mean.mean. Defaults to a vector of 10s.

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).

Details

The RMAP is a mixture prior of two components where one component is a prior induced by the Bayesian hierarchical model (BHM), and the other is a vague (noninformative) prior. This function samples from the prior induced by the BHM.

Value

The function returns a matrix of the samples of regression coefficients from the prior induced by the BHM. The number of columns is equal to the number of regression coefficients, and the number of rows is equal to the number of MCMC samples.

See Also

glm.rmap.bhm.approx() for the second step and glm.rmap() for the final step of implementing RMAP.

Examples

if (instantiate::stan_cmdstan_exists()) {
  data(actg036) ## historical data
  ## take subset for speed purposes
  actg036 = actg036[1:50, ]
  hist_data_list = list(actg036)
  glm.rmap.bhm(
    formula = outcome ~ scale(age) + race + treatment + scale(cd4),
    family = binomial('logit'),
    hist.data.list = hist_data_list,
    chains = 1, iter_warmup = 500, iter_sampling = 1000
  )
}