plot.emc.design

Makes design illustration by plotting simulated data based on the design

Fit Bayesian (hierarchical) cognitive models
using a linear modeling language interface using particle Metropolis Markov
chain Monte Carlo sampling with Gibbs steps. The diffusion decision model (DDM),
linear ballistic accumulator model (LBA), racing diffusion model (RDM), and the lognormal
race model (LNR) are supported. Additionally, users can specify their own likelihood
function and/or choose for non-hierarchical
estimation, as well as for a diagonal, blocked or full multivariate normal
group-level distribution to test individual differences. Prior specification
is facilitated through methods that visualize the (implied) prior.
A wide range of plotting functions assist in assessing model convergence and
posterior inference. Models can be easily evaluated using functions
that plot posterior predictions or using relative model comparison metrics
such as information criteria or Bayes factors.
References: Stevenson et al. (2024) <doi:10.31234/osf.io/2e4dq>.

Niek Stevenson

EMC2

Bayesian Hierarchical Analysis of Cognitive Models of Choice

Michelle Donzallaz

Andrew Heathcote

Steven Miletić

Raphael Hartmann

Karl C. Klauer

Steven G. Johnson

Jean M. Linhart

Brian Gough

Gerard Jungman

Rudolf Schuerer

Przemyslaw Sliwa

Jason H. Stover

plot.emc.design function

<dl><dt>x</dt>
<dd>An object of class <code>emc.design</code> containing the design to plot</dd>
<dt>p_vector</dt>
<dd>A named vector of parameter values to use for data generation</dd>
<dt>data</dt>
<dd>Optional data frame to overlay on the design plot. If NULL, data will be simulated.</dd>
<dt>factors</dt>
<dd>Character vector. Factors to use for varying parameters in the plot</dd>
<dt>plot_factor</dt>
<dd>Optional character. Make separate plots for each level of this factor</dd>
<dt>n_data_sim</dt>
<dd>Integer. If data is NULL, number of simulated datasets to generate for the plot. Default is 10.</dd>
<dt>functions</dt>
<dd>Optional named list of functions that create additional columns in the data</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code>make_design_plot</code></dd></dl>

Arguments

Plot method for emc.design objects — plot.emc.design

<dl>

<dt>x</dt>
<dd>An object of class <code>emc.design</code> containing the design to plot</dd>


<dt>p_vector</dt>
<dd>A named vector of parameter values to use for data generation</dd>


<dt>data</dt>
<dd>Optional data frame to overlay on the design plot. If NULL, data will be simulated.</dd>


<dt>factors</dt>
<dd>Character vector. Factors to use for varying parameters in the plot</dd>


<dt>plot_factor</dt>
<dd>Optional character. Make separate plots for each level of this factor</dd>


<dt>n_data_sim</dt>
<dd>Integer. If data is NULL, number of simulated datasets to generate for the plot. Default is 10.</dd>


<dt>functions</dt>
<dd>Optional named list of functions that create additional columns in the data</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code>make_design_plot</code></dd>

</dl>

Plot method for emc.design objects

plot.emc.design: Plot method for emc.design objects

Description

Usage

Value

Arguments