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

In WLenhard/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.

Usage

log_likelihood2(params, X, Z, y, n, weights = 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 in the beta-binomial distribution).
weights	A numeric vector of weights for each observation. If NULL, equal weights are used.

Details

This function uses a numerically stable implementation of the beta-binomial log-probability. It allows for weighted observations, which can be useful for various modeling scenarios.

Value

The negative log-likelihood of the model.

WLenhard/cNORM documentation built on Nov. 30, 2024, 4:45 p.m.