mh_draws {gratia}

Posterior samples using a Gaussian approximation to the posterior distribution

Description

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.