update_lmm_variance: Calculates the log-likelihood for linear mixed models (LMMs)...

In catalytic: Tools for Applying Catalytic Priors in Statistical Modeling

Calculates the log-likelihood for linear mixed models (LMMs) by combining observed and synthetic log-likelihoods based on the variance parameters.

Description

This function evaluates the log-likelihood of observed and synthetic data, using residual and random-effect variance terms to determine the fit of variance parameters in the mixed model context.

Usage

update_lmm_variance(
  residual_variance,
  random_effect_variance,
  obs_z_eigenvalues,
  syn_z_eigenvalues,
  obs_adjusted_residuals,
  syn_adjusted_residuals,
  tau
)

Arguments

residual_variance	Numeric, the variance associated with the residual errors.
random_effect_variance	Numeric, the variance associated with random effects.
obs_z_eigenvalues	Vector, eigenvalues of the observed Z matrix of data.
syn_z_eigenvalues	Vector, eigenvalues of the synthetic Z matrix of data.
obs_adjusted_residuals	Vector, adjusted residuals of observed data.
syn_adjusted_residuals	Vector, adjusted residuals of synthetic data.
tau	Numeric, weight factor for the synthetic data.

Value

The sum of observed and synthetic log-likelihoods.

catalytic documentation built on April 4, 2025, 5:51 a.m.