log_likelihood2: Calculate the negative log-likelihood for a beta-binomial...

In cNORM: Continuous Norming

Calculate the negative log-likelihood for a beta-binomial regression model

Description

This function computes the negative log-likelihood for a beta-binomial regression model where both the alpha and beta parameters are modeled as functions of predictors (mode 2).

Usage

log_likelihood2(params, X, Z, y, n, weights = NULL, lch = NULL)

Arguments

params	A numeric vector containing all model parameters. The first n_alpha elements are coefficients for the alpha model, and the remaining elements are coefficients for the beta model.
X	A matrix of predictors for the alpha model.
Z	A matrix of predictors for the beta model.
y	A numeric vector of response values.
n	The maximum score (number of trials).
weights	A numeric vector of weights for each observation (NULL = equal weights).
lch	Optional precomputed lchoose(n, y). Since this term does not depend on the parameters, passing it once avoids recomputation in every optimizer iteration.

Details

Uses a numerically stable implementation of the beta-binomial log-probability via lbeta. The linear predictors of log(alpha) and log(beta) are clamped to [-20, 20].

Value

The negative log-likelihood of the model (large finite penalty if non-finite).

cNORM documentation built on July 13, 2026, 5:08 p.m.