log_likelihood: Log-likelihood computation

In lddmm: Longitudinal Drift-Diffusion Mixed Models (LDDMM)

Log-likelihood computation

Description

Compute the log-likelihood for the drift-diffusion model, including the censored data contribution.

Usage

log_likelihood(tau, mu, b, delta, cens, D, log)

Arguments

tau	vector of size n containing the response times
mu	matrix of size (n x d1) containing the drift parameters corresponding to the n response times for each possible d1 decision
b	matrix of size (n x d1) containing the boundary parameters corresponding to the n response times for each possible d1 decision
delta	vector of size n containing the offset parameters corresponding to the n response times
cens	vector of size n containing censoring indicators (1 censored, 0 not censored) corresponding to the n response times
D	(n x 2) matrix whose first column has the n input stimuli, and whose second column has the n decision categories
log	should the results be returned on the log scale?

lddmm documentation built on June 7, 2023, 5:28 p.m.