Posterior samples using a Gaussian approximation to the posterior distribution

Usage

mh_draws(model, ...)

# S3 method for class 'gam'
mh_draws(
  model,
  n,
  burnin = 1000,
  thin = 1,
  t_df = 40,
  rw_scale = 0.25,
  index = NULL,
  ...
)

Arguments

model: a fitted R model. Currently only models fitted by mgcv::gam() or mgcv::bam(), or return an object that inherits from such objects are supported. Here, "inherits" is used in a loose fashion; models fitted by scam::scam() are support even though those models don't strictly inherit from class "gam" as far as inherits() is concerned.
...: arguments passed to methods.
n: numeric; the number of posterior draws to take.
burnin: numeric; the length of any initial burn in period to discard. See mgcv::gam.mh().
thin: numeric; retain only thin samples. See mgcv::gam.mh().
t_df: numeric; degrees of freedom for static multivariate t proposal. See mgcv::gam.mh().
rw_scale: numeric; factor by which to scale posterior covariance matrix when generating random walk proposals. See mgcv::gam.mh().
index: numeric; vector of indices of coefficients to use. Can be used to subset the mean vector and covariance matrix extracted from model.