add_residual

Calls <a href="/link/stats%3A%3Aconvolve?package=dRiftDM&version=0.3.1" data-mini-rdoc="dRiftDM::stats::convolve">stats::convolve</a> for the first passage times and the non-decision
time distribution to derive the full distribution of response times. Before
convolution, I add the robustness parameter.

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

add_residual function

<dl><dt>pdf_nt</dt>
<dd>the non-decision time density values</dd>
<dt>pdf_u, pdf_l</dt>
<dd>the first passage times</dd>
<dt>dt, nt</dt>
<dd>step size and number of steps for the time space (for input
checks and scaling)</dd></dl>

Arguments

Calls <a href='https://rdrr.io/r/stats/convolve.html'>stats::convolve</a> for the first passage times and the non-decision
time distribution to derive the full distribution of response times. Before
convolution, I add the robustness parameter.

Convolute the First Passage Times with the Non-Decision Time Distribution — add_residual

<dl>

<dt>pdf_nt</dt>
<dd>the non-decision time density values</dd>


<dt>pdf_u, pdf_l</dt>
<dd>the first passage times</dd>


<dt>dt, nt</dt>
<dd>step size and number of steps for the time space (for input
checks and scaling)</dd>

</dl>

add_residual: Convolute the First Passage Times with the Non-Decision Time Distribution

Description

Usage

Value

Arguments