hdbayes (version 0.2.0)

glm.post: Posterior of a normal/half-normal prior

Description

Sample from the posterior distribution of a GLM using a normal/half-normal prior.

Usage

glm.post(
  formula,
  family,
  data.list,
  offset.list = NULL,
  beta.mean = NULL,
  beta.sd = NULL,
  disp.mean = NULL,
  disp.sd = NULL,
  get.loglik = FALSE,
  iter_warmup = 1000,
  iter_sampling = 1000,
  chains = 4,
  ...
)

Value

The function returns an object of class draws_df containing posterior samples. The object has two attributes:

data

a list of variables specified in the data block of the Stan program

model

a character string indicating the model name

Arguments

formula

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

family

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

data.list

a list consisting of one data.frame giving the current data. If data.list has more than one data.frame, only the first element will be used as the current data.

offset.list

a list consisting of one vector giving the offset for the current data. The length of the vector is equal to the number of rows in the current data. The vector has all values set to 0 by default. If offset.list has more than one vector, same as for data.list.

beta.mean

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

beta.sd

a scalar or a vector whose dimension is equal to the number of regression coefficients giving the sd parameters for the normal prior on regression coefficients. If a scalar is provided, same as for beta.mean. Defaults to a vector of 10s.

disp.mean

location parameter for the half-normal prior on dispersion parameter. Defaults to 0. If disp.mean is a vector with length > 1, only the first element will be used as disp.mean.

disp.sd

scale parameter for the half-normal prior on dispersion parameter. Defaults to 10. If disp.sd is a vector with length > 1, same as for disp.mean.

get.loglik

whether to generate log-likelihood matrix. Defaults to FALSE.

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 priors on the regression coefficients are independent normal distributions. When the normal priors are elicited with large variances, the prior is also referred to as the reference or vague prior. The dispersion parameter is assumed to be independent of the regression coefficients with a half-normal prior (if applicable).

Examples

if (instantiate::stan_cmdstan_exists()) {
  data(actg019)
  ## take subset for speed purposes
  actg019 = actg019[1:100, ]
  data.list = list(currdata = actg019)
  glm.post(
    formula = cd4 ~ treatment + age + race,
    family = poisson('log'),
    data.list = data.list,
    beta.sd   = 10,
    chains = 1, iter_warmup = 500, iter_sampling = 1000
  )
}