Reset RandomEffectsTracker Object to Default State

Reset a RandomEffectsTracker object to its "default" state

This function 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 stochtree.ai

Usage

rootResetRandomEffectsTracker(rfx_tracker, rfx_model, rfx_dataset, residual)

Arguments

rfx_tracker

Object of type RandomEffectsTracker.

rfx_model

Object of type RandomEffectsModel.

rfx_dataset

Object of type RandomEffectsDataset.

residual

Object of type Outcome.

Value

None

Examples

n <- 100
p <- 10
rfx_group_ids <- sample(1:2, size = n, replace = TRUE)
rfx_basis <- matrix(rep(1.0, n), ncol=1)
rfx_dataset <- createRandomEffectsDataset(rfx_group_ids, rfx_basis)
y <- (-2*(rfx_group_ids==1)+2*(rfx_group_ids==2)) + rnorm(n)
y_std <- (y-mean(y))/sd(y)
outcome <- createOutcome(y_std)
rng <- createCppRNG(1234)
num_groups <- length(unique(rfx_group_ids))
num_components <- ncol(rfx_basis)
rfx_model <- createRandomEffectsModel(num_components, num_groups)
rfx_tracker <- createRandomEffectsTracker(rfx_group_ids)
rfx_samples <- createRandomEffectSamples(num_components, num_groups, rfx_tracker)
alpha_init <- rep(1,num_components)
xi_init <- matrix(rep(alpha_init, num_groups),num_components,num_groups)
sigma_alpha_init <- diag(1,num_components,num_components)
sigma_xi_init <- diag(1,num_components,num_components)
sigma_xi_shape <- 1
sigma_xi_scale <- 1
rfx_model$set_working_parameter(alpha_init)
rfx_model$set_group_parameters(xi_init)
rfx_model$set_working_parameter_cov(sigma_alpha_init)
rfx_model$set_group_parameter_cov(sigma_xi_init)
rfx_model$set_variance_prior_shape(sigma_xi_shape)
rfx_model$set_variance_prior_scale(sigma_xi_scale)
for (i in 1:3) {
    rfx_model$sample_random_effect(rfx_dataset=rfx_dataset, residual=outcome,
                                   rfx_tracker=rfx_tracker, rfx_samples=rfx_samples,
                                   keep_sample=TRUE, global_variance=1.0, rng=rng)
}
rootResetRandomEffectsModel(rfx_model, alpha_init, xi_init, sigma_alpha_init,
                            sigma_xi_init, sigma_xi_shape, sigma_xi_scale)
rootResetRandomEffectsTracker(rfx_tracker, rfx_model, rfx_dataset, outcome)