s4_generics | R Documentation |
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 |
object for which to apply the generic |
digits |
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
get_model
for lgpfit objects
returns an lgpmodel
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.
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