log_likelihood

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

Implementation of the drift-diffusion mixed model for category learning as described in Paulon et al. (2021) <doi:10.1080/01621459.2020.1801448>.

Giorgio Paulon

lddmm

Longitudinal Drift-Diffusion Mixed Models (LDDMM)

Abhra Sarkar

log_likelihood function

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

Arguments

Log-likelihood computation — log_likelihood

<dl>

<dt>tau</dt>
<dd>vector of size n containing the response times</dd>


<dt>mu</dt>
<dd>matrix of size (n x d1) containing the drift parameters
corresponding to the n response times for each possible d1 decision</dd>


<dt>b</dt>
<dd>matrix of size (n x d1) containing the boundary parameters
corresponding to the n response times for each possible d1 decision</dd>


<dt>delta</dt>
<dd>vector of size n containing the offset parameters corresponding
to the n response times</dd>


<dt>cens</dt>
<dd>vector of size n containing censoring indicators (1 censored, 0
not censored) corresponding to the n response times</dd>


<dt>D</dt>
<dd>(n x 2) matrix whose first column has the n input stimuli, and whose second column has the n decision categories</dd>


<dt>log</dt>
<dd>should the results be returned on the log scale?</dd>

</dl>

log_likelihood: Log-likelihood computation

Description

Usage

Arguments