gaussian_draws {gratia}

Posterior samples using a simple Metropolis Hastings sampler

Description

Posterior samples using a simple Metropolis Hastings sampler

Usage

gaussian_draws(model, ...)

## S3 method for class 'gam'
gaussian_draws(
  model,
  n,
  n_cores = 1L,
  index = NULL,
  frequentist = FALSE,
  unconditional = FALSE,
  mvn_method = "mvnfast",
  ...
)

## S3 method for class 'scam'
gaussian_draws(
  model,
  n,
  n_cores = 1L,
  index = NULL,
  frequentist = FALSE,
  parametrized = TRUE,
  mvn_method = "mvnfast",
  ...
)

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.

n_cores

integer; number of CPU cores to use when generating multivariate normal distributed random values. Only used if mvn_method = "mvnfast" and method = "gaussian".

index

numeric; vector of indices of coefficients to use. Can be used to subset the mean vector and covariance matrix extracted from model.

frequentist

logical; if TRUE, the frequentist covariance matrix of the parameter estimates is used. If FALSE, the Bayesian posterior covariance matrix of the parameters is used. See mgcv::vcov.gam().

unconditional

logical; if TRUE the Bayesian smoothing parameter uncertainty corrected covariance matrix is used, if available for model. See mgcv::vcov.gam().

mvn_method

character; one of "mvnfast" or "mgcv". The default is uses mvnfast::rmvn(), which can be considerably faster at generate large numbers of MVN random values than mgcv::rmvn(), but which might not work for some marginal fits, such as those where the covariance matrix is close to singular.

parametrized

logical; use parametrized coefficients and covariance matrix, which respect the linear inequality constraints of the model. Only for scam::scam() model fits.