Draw samples from the posterior distribution of an estimated model
posterior_samples(model, ...)# S3 method for gam
posterior_samples(
model,
n,
data = newdata,
seed,
scale = c("response", "linear_predictor"),
freq = FALSE,
unconditional = FALSE,
weights = NULL,
ncores = 1L,
...,
newdata = 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().
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;
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.
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).
Deprecated: use data instead.
Gavin L. Simpson