lgpmodel-class | R Documentation |
An S4 class to represent an additive GP model
## S4 method for signature 'lgpmodel'
show(object)
## S4 method for signature 'lgpmodel'
parameter_info(object, digits = 3)
## S4 method for signature 'lgpmodel'
component_info(object)
## S4 method for signature 'lgpmodel'
num_components(object)
## S4 method for signature 'lgpmodel'
covariate_info(object)
## S4 method for signature 'lgpmodel'
component_names(object)
## S4 method for signature 'lgpmodel'
is_f_sampled(object)
object |
The object for which to apply a class method. |
digits |
number of digits to show for floating point numbers |
show(lgpmodel)
: Print information and summary about the object.
Returns object
invisibly.
parameter_info(lgpmodel)
: Get a parameter summary (bounds and
priors). Returns a data.frame
.
component_info(lgpmodel)
: Get a data frame with information about each model
component.
num_components(lgpmodel)
: Get number of model components. Returns a
positive integer.
covariate_info(lgpmodel)
: Get covariate information.
component_names(lgpmodel)
: Get names of model components.
is_f_sampled(lgpmodel)
: Determine if inference of the model requires sampling
the latent signal f
(and its components).
formula
An object of class lgpformula
data
The original unmodified data.
stan_input
The data to be given as input to rstan::sampling
var_names
List of variable names grouped by type.
var_scalings
A named list with fields
y
- Response variable normalization function and its
inverse operation. Must be an lgpscaling object.
x_cont
- Continuous covariate normalization functions and
their inverse operations. Must be a named list with each element is an
lgpscaling object.
var_info
A named list with fields
x_cat_levels
- Names of the levels of categorical covariates
before converting from factor to numeric.
info
Other info in text format.
sample_f
Whether the signal f
is sampled or marginalized.
full_prior
Complete prior information.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.