S4 generics for lgpfit, lgpmodel, and other objects
parameter_info(object, digits)
component_info(object)
covariate_info(object)
component_names(object)
get_model(object)
is_f_sampled(object)
get_stanfit(object)
postproc(object, ...)
contains_postproc(object)
clear_postproc(object)
num_paramsets(object)
num_evalpoints(object)
num_components(object)object for which to apply the generic
number of digits to show
additional optional arguments to pass
parameter_info returns a data frame with
one row for each parameter and columns
for parameter name, parameter bounds, and the assigned prior
component_info returns a data frame with one row for
each model component, and columns encoding information about
model components
covariate_info returns a list with names
continuous and categorical, with information about
both continuous and categorical covariates
component_names returns a character vector with
component names
is_f_sampled returns a logical value
get_stanfit returns a stanfit (rstan)
postproc applies postprocessing and returns an
updated lgpfit
clear_postproc removes postprocessing information and
returns an updated lgpfit
num_paramsets, num_evalpoints and
num_components return an integer
parameter_info(): Get parameter information (priors etc.).
component_info(): Get component information.
covariate_info(): Get covariate information.
component_names(): Get component names.
get_model(): Get lgpmodel object.
is_f_sampled(): Determine if signal f is sampled or marginalized.
get_stanfit(): Extract stanfit object.
postproc(): Perform postprocessing.
contains_postproc(): Determine if object contains postprocessing
information.
clear_postproc(): Clear postprocessing information (to reduce
size of object).
num_paramsets(): Get number of parameter sets.
num_evalpoints(): Get number of points where posterior is evaluated.
num_components(): Get number of model components.
To find out which methods have been implemented for which classes, see lgpfit, lgpmodel, Prediction and GaussianPrediction.