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.

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,
  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

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