get_ez_diffusion

Internal helper that computes EZ diffusion model parameters for each
condition in a <code>drift_dm_obj</code>. The computation is based on the equations
from Wagenmakers et al. (2007) and estimates drift rate (<code>muc</code>), boundary
separation (<code>b</code>), and non-decision time (<code>non_dec</code>).

internal

Fit and explore Drift Diffusion Models (DDMs),
a common tool in psychology for describing decision processes in simple
tasks. It can handle both time-independent and time-dependent DDMs. You
either choose prebuilt models or create your own, and the package takes
care of model predictions and parameter estimation. Model predictions
are derived via the numerical solutions provided by Richter, Ulrich, and
Janczyk (2023, <doi:10.1016/j.jmp.2023.102756>).

Valentin Koob

dRiftDM

Estimating (Time-Dependent) Drift Diffusion Models

Thomas Richter

Markus Janczyk

get_ez_diffusion function

<dl><dt>drift_dm_obj</dt>
<dd>a drift diffusion model object containing observed data
in <code>obs_data</code>, including upper (<code>rts_u</code>) and lower (<code>rts_l</code>) response times
per condition</dd></dl>

Arguments

Compute EZ Diffusion parameters — get_ez_diffusion

<dl>

<dt>drift_dm_obj</dt>
<dd>a drift diffusion model object containing observed data
in <code>obs_data</code>, including upper (<code>rts_u</code>) and lower (<code>rts_l</code>) response times
per condition</dd>

</dl>

get_ez_diffusion: Compute EZ Diffusion parameters

Description

Usage

Value

Arguments

Details