Draw samples from the posterior distribution of an estimated model
posterior_samples(model, ...)# S3 method for gam
posterior_samples(
model,
n,
data = newdata,
seed = NULL,
method = c("gaussian", "mh", "inla", "user"),
n_cores = 1,
burnin = 1000,
thin = 1,
t_df = 40,
rw_scale = 0.25,
freq = FALSE,
unconditional = FALSE,
weights = NULL,
draws = NULL,
...,
newdata = NULL,
ncores = NULL
)
A tibble (data frame) with 3 columns containing the posterior predicted values in long format. The columns are
row (integer) the row of data that each posterior draw relates to,
draw (integer) an index, in range 1:n, indicating which draw each row
relates to,
response (numeric) the predicted response for the indicated row of
data.
a fitted model of the supported types
arguments passed to other methods. For fitted_samples(), these
are passed on to predict.gam(). For posterior_samples() these are
passed on to fitted_samples(). For predicted_samples() these are
passed on to the relevant simulate() method.
numeric; the number of posterior samples to return.
data frame; new observations at which the posterior draws
from the model should be evaluated. If not supplied, the data used to fit
the model will be used for data, if available in model.
numeric; a random seed for the simulations.
character; which method should be used to draw samples from
the posterior distribution. "gaussian" uses a Gaussian (Laplace)
approximation to the posterior. "mh" uses a Metropolis Hastings sampler
that alternates t proposals with proposals based on a shrunken version of
the posterior covariance matrix. "inla" uses a variant of Integrated
Nested Laplace Approximation due to Wood (2019), (currently not
implemented). "user" allows for user-supplied posterior draws
(currently not implemented).
number of cores for generating random variables from a
multivariate normal distribution. Passed to mvnfast::rmvn().
Parallelization will take place only if OpenMP is supported (but appears
to work on Windows with current R).
numeric; number of samples to discard as the burnin draws.
Only used with method = "mh".
numeric; the number of samples to skip when taking n draws.
Results in thin * n draws from the posterior being taken. Only used with
method = "mh".
numeric; degrees of freedome for t distribution proposals. Only
used with method = "mh".
numeric; Factor by which to scale posterior covariance
matrix when generating random walk proposals. Negative or non finite to
skip the random walk step. Only used with method = "mh".
logical; TRUE to use the frequentist covariance matrix of
the parameter estimators, FALSE to use the Bayesian posterior
covariance matrix of the parameters.
logical; if TRUE (and freq == FALSE) then the
Bayesian smoothing parameter uncertainty corrected covariance matrix is
used, if available.
numeric; a vector of prior weights. If data is null
then defaults to object[["prior.weights"]], otherwise a vector of ones.
matrix; user supplied posterior draws to be used when
method = "user".
Deprecated: use data instead.
Deprecated; use n_cores instead. The number of cores for
generating random variables from a multivariate normal distribution.
Passed to mvnfast::rmvn(). Parallelization will take place only if
OpenMP is supported (but appears to work on Windows with current R).
Gavin L. Simpson
Wood, S.N., (2020). Simplified integrated nested Laplace approximation. Biometrika 107, 223--230. tools:::Rd_expr_doi("10.1093/biomet/asz044")