Creates marginal model-averages posterior distributions for a given parameter based on model-averaged posterior samples and parameter name (and formula with at specification).
marginal_posterior(
samples,
parameter,
formula = NULL,
at = NULL,
prior_samples = FALSE,
use_formula = TRUE,
transformation = NULL,
transformation_arguments = NULL,
transformation_settings = FALSE,
n_samples = 10000,
...
)marginal_posterior returns a named list of mixed marginal posterior
distributions (either a vector of matrix).
#'
model-averaged posterior samples created by mix_posteriors()
parameter of interest
model formula (needs to be specified if parameter was part of a formula)
named list with predictor levels of the formula for which marginalization
should be performed. If a predictor level is missing, 0 is used for continuous
predictors, the baseline factor level is used for factors with contrast = "treatment" prior
distributions, and the parameter is completely omitted for for factors with contrast = "meandif",
whether marginal prior distributions should be generated
contrast = "orthonormal", and contrast = "independent" levels
whether the parameter should be evaluated as a part of supplied formula
transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations:
linear transformation in form of a + b*x
also known as Fisher's z transformation
exponential transformation
, or a list containing the transformation function fun,
inverse transformation function inv, and the Jacobian of
the transformation jac. See examples for details.
a list with named arguments for
the transformation
boolean indicating whether the
settings the x_seq or x_range was specified on
the transformed support
number of samples to be drawn for the model-averaged posterior distribution
additional arguments