RandomEffectsModel: Random Effects Model C++ Wrapper

In stochtree: Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Random Effects Model C++ Wrapper

Description

The core "model" class for sampling random effects. Stores current model state, prior parameters, and procedures for sampling from the conditional posterior of each parameter.

This class is intended for advanced use cases in which users require detailed control of sampling algorithms and data structures. Minimal input validation and error checks are performed – users are responsible for providing the correct inputs. For tutorials on the "proper" usage of the stochtree's advanced workflow, we provide several vignettes at https://stochtree.ai/

Public fields

rfx_model_ptr

External pointer to a C++ StochTree::RandomEffectsModel class

num_groups

Number of groups in the random effects model

num_components

Number of components (i.e. dimension of basis) in the random effects model

Methods

Public methods

• RandomEffectsModel$new()

• RandomEffectsModel$sample_random_effect()

• RandomEffectsModel$predict()

• RandomEffectsModel$set_working_parameter()

• RandomEffectsModel$set_group_parameters()

• RandomEffectsModel$set_working_parameter_cov()

• RandomEffectsModel$set_group_parameter_cov()

• RandomEffectsModel$set_variance_prior_shape()

• RandomEffectsModel$set_variance_prior_scale()

---

Method new()

Create a new RandomEffectsModel object.

Usage

RandomEffectsModel$new(num_components, num_groups)

Arguments

num_components

Number of "components" or bases defining the random effects regression

num_groups

Number of random effects groups

Returns

A new RandomEffectsModel object.

---

Method sample_random_effect()

Sample from random effects model.

Usage

RandomEffectsModel$sample_random_effect(
  rfx_dataset,
  residual,
  rfx_tracker,
  rfx_samples,
  keep_sample,
  global_variance,
  rng
)

Arguments

rfx_dataset

Object of type RandomEffectsDataset

residual

Object of type Outcome

rfx_tracker

Object of type RandomEffectsTracker

rfx_samples

Object of type RandomEffectSamples

keep_sample

Whether sample should be retained in rfx_samples. If FALSE, the state of rfx_tracker will be updated, but the parameter values will not be added to the sample container. Samples are commonly discarded due to burn-in or thinning.

global_variance

Scalar global variance parameter

rng

Object of type CppRNG

Returns

None

---

Method predict()

Predict from (a single sample of a) random effects model.

Usage

RandomEffectsModel$predict(rfx_dataset, rfx_tracker)

Arguments

rfx_dataset

Object of type RandomEffectsDataset

rfx_tracker

Object of type RandomEffectsTracker

Returns

Vector of predictions with size matching number of observations in rfx_dataset

---

Method set_working_parameter()

Set value for the "working parameter." This is typically used for initialization, but could also be used to interrupt or override the sampler.

Usage

RandomEffectsModel$set_working_parameter(value)

Arguments

value

Parameter input

Returns

None

---

Method set_group_parameters()

Set value for the "group parameters." This is typically used for initialization, but could also be used to interrupt or override the sampler.

Usage

RandomEffectsModel$set_group_parameters(value)

Arguments

value

Parameter input

Returns

None

---

Method set_working_parameter_cov()

Set value for the working parameter covariance. This is typically used for initialization, but could also be used to interrupt or override the sampler.

Usage

RandomEffectsModel$set_working_parameter_cov(value)

Arguments

value

Parameter input

Returns

None

---

Method set_group_parameter_cov()

Set value for the group parameter covariance. This is typically used for initialization, but could also be used to interrupt or override the sampler.

Usage

RandomEffectsModel$set_group_parameter_cov(value)

Arguments

value

Parameter input

Returns

None

---

Method set_variance_prior_shape()

Set shape parameter for the group parameter variance prior.

Usage

RandomEffectsModel$set_variance_prior_shape(value)

Arguments

value

Parameter input

Returns

None

---

Method set_variance_prior_scale()

Set shape parameter for the group parameter variance prior.

Usage

RandomEffectsModel$set_variance_prior_scale(value)

Arguments

value

Parameter input

Returns

None

stochtree documentation built on April 29, 2026, 1:08 a.m.