lgpmodel-class {lgpr}

An S4 class to represent an additive GP model

Description

An S4 class to represent an additive GP model

Usage

## 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)

Arguments

object

The object for which to apply a class method.

digits

number of digits to show for floating point numbers

Methods (by generic)

• 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).

Slots

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.