extract_post_mean

Function to extract the posterior means of the parameters of interest from a lddmm fit object.

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

extract_post_mean function

<dl><dt>data</dt>
<dd>dataframe with the following columns:<ul>
<li>subject: vector of size n containing the participant labels</li>
<li>block: vector of size n containing the training blocks (longitudinal units)</li>
<li>s: vector of size n containing the stimuli</li>
<li>d: vector of size n containing the decisions</li>
<li>r_time: vector of size n containing the response times</li>
<li>cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)</li>
</ul></dd>
<dt>fit</dt>
<dd>fit from the lddmm function</dd>
<dt>par</dt>
<dd>parameter to output ('drift', or 'boundary')</dd></dl>

Arguments

Parameter posterior means — extract_post_mean

<dl>

<dt>data</dt>
<dd>dataframe with the following columns:<ul>
<li>subject: vector of size n containing the participant labels</li>
<li>block: vector of size n containing the training blocks (longitudinal units)</li>
<li>s: vector of size n containing the stimuli</li>
<li>d: vector of size n containing the decisions</li>
<li>r_time: vector of size n containing the response times</li>
<li>cens: vector of size n containing the censoring indicators (1 censored, 0 non censored)</li>
</ul></dd>


<dt>fit</dt>
<dd>fit from the lddmm function</dd>


<dt>par</dt>
<dd>parameter to output ('drift', or 'boundary')</dd>

</dl>

extract_post_mean: Parameter posterior means

Description

Usage

Value

Arguments