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Class that wraps the "persistent" aspects of a C++ random effects model (draws of the parameters and a map from the original label indices to the 0-indexed label numbers used to place group samples in memory (i.e. the first label is stored in column 0 of the sample matrix, the second label is store in column 1 of the sample matrix, etc...))

RandomEffectSamples.Rd

Coordinates various C++ random effects classes and persists those needed for prediction / serialization

Public fields

rfx_container_ptr

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

label_mapper_ptr

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

training_group_ids

Unique vector of group IDs that were in the training dataset

Methods

Public methods

Method new()

Create a new RandomEffectSamples object.

Usage

RandomEffectSamples$new()

Returns

A new RandomEffectSamples object.

Method load_in_session()

Construct RandomEffectSamples object from other "in-session" R objects

Usage

RandomEffectSamples$load_in_session(
  num_components,
  num_groups,
  random_effects_tracker
)

Arguments

num_components

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

num_groups

Number of random effects groups

random_effects_tracker

Object of type RandomEffectsTracker

Returns

NULL

Method load_from_json()

Construct RandomEffectSamples object from a json object

Usage

RandomEffectSamples$load_from_json(
  json_object,
  json_rfx_container_label,
  json_rfx_mapper_label,
  json_rfx_groupids_label
)

Arguments

json_object

Object of class CppJson

json_rfx_container_label

Label referring to a particular rfx sample container (i.e. "random_effect_container_0") in the overall json hierarchy

json_rfx_mapper_label

Label referring to a particular rfx label mapper (i.e. "random_effect_label_mapper_0") in the overall json hierarchy

json_rfx_groupids_label

Label referring to a particular set of rfx group IDs (i.e. "random_effect_groupids_0") in the overall json hierarchy

Returns

A new RandomEffectSamples object.

Method predict()

Predict random effects for each observation implied by rfx_group_ids and rfx_basis. If a random effects model is "intercept-only" the rfx_basis will be a vector of ones of size length(rfx_group_ids).

Usage

RandomEffectSamples$predict(rfx_group_ids, rfx_basis = NULL)

Arguments

rfx_group_ids

Indices of random effects groups in a prediction set

rfx_basis

(Optional ) Basis used for random effects prediction

Returns

Matrix with as many rows as observations provided and as many columns as samples drawn of the model.

Method extract_parameter_samples()

Extract the random effects parameters sampled. With the "redundant parameterization" of Gelman et al (2008), this includes four parameters: alpha (the "working parameter" shared across every group), xi (the "group parameter" sampled separately for each group), beta (the product of alpha and xi, which corresponds to the overall group-level random effects), and sigma (group-independent prior variance for each component of xi).

Usage

RandomEffectSamples$extract_parameter_samples()

Returns

List of arrays. The alpha array has dimension (num_components, num_samples) and is simply a vector if num_components = 1. The xi and beta arrays have dimension (num_components, num_groups, num_samples) and is simply a matrix if num_components = 1. The sigma array has dimension (num_components, num_samples) and is simply a vector if num_components = 1.

Method extract_label_mapping()

Convert the mapping of group IDs to random effect components indices from C++ to R native format

Usage

RandomEffectSamples$extract_label_mapping()

Returns

List mapping group ID to random effect components.