ess_summary.emc

ess_summary

Returns the effective sample size (ESS) of the selected parameter type.
Full range of possible samples manipulations described in <code>get_pars</code>.

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

ess_summary.emc function

<dl><dt>emc</dt>
<dd>An emc object</dd>
<dt>selection</dt>
<dd>A Character vector. Indicates which parameter types to check (e.g., <code>alpha</code>, <code>mu</code>, <code>sigma2</code>, <code>correlation</code>).</dd>
<dt>stat</dt>
<dd>A string. Should correspond to a function that can be applied to a vector,
which will be performed on the vector/rows or columns of the matrix of the parameters</dd>
<dt>stat_only</dt>
<dd>Boolean. If <code>TRUE</code> will only return the result of the applied stat function,
otherwise returns both the stat result and the result of the function on all parameters.</dd>
<dt>digits</dt>
<dd>Integer. How many digits to round the output to</dd>
<dt>...</dt>
<dd>Optional additional arguments that can be passed to <code>get_pars</code></dd></dl>

Arguments

Effective Sample Size — ess_summary.emc

<dl>

<dt>emc</dt>
<dd>An emc object</dd>


<dt>selection</dt>
<dd>A Character vector. Indicates which parameter types to check (e.g., <code>alpha</code>, <code>mu</code>, <code>sigma2</code>, <code>correlation</code>).</dd>


<dt>stat</dt>
<dd>A string. Should correspond to a function that can be applied to a vector,
which will be performed on the vector/rows or columns of the matrix of the parameters</dd>


<dt>stat_only</dt>
<dd>Boolean. If <code>TRUE</code> will only return the result of the applied stat function,
otherwise returns both the stat result and the result of the function on all parameters.</dd>


<dt>digits</dt>
<dd>Integer. How many digits to round the output to</dd>


<dt>...</dt>
<dd>Optional additional arguments that can be passed to <code>get_pars</code></dd>

</dl>

Effective Sample Size

ess_summary.emc: Effective Sample Size

Description

Usage

Value

Arguments

Examples