`R/aaa.R`

, `R/methods-lgpmodel.R`

`lgpmodel-class.Rd`

An S4 class to represent an additive GP model

```
# S4 method for lgpmodel
show(object)
# S4 method for lgpmodel
parameter_info(object, digits = 3)
# S4 method for lgpmodel
component_info(object)
# S4 method for lgpmodel
num_components(object)
# S4 method for lgpmodel
covariate_info(object)
# S4 method for lgpmodel
component_names(object)
# S4 method for 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.