compare_MLL

Calculate a table of model probabilities based for a list of samples objects
based on samples of marginal log-likelihood (MLL) added to these objects by
run_IS2. Probabilities estimated by a bootstrap ath picks a vector of MLLs,
one for each model in the list randomly with replacement nboot times,
calculates model probabilities and averages

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

Jochen Voss

Andreas Voss

compare_MLL function

<dl><dt>mll</dt>
<dd>List of samples objects with IS_samples attribute added by by run_IS2</dd>
<dt>nboot</dt>
<dd>Integer number of bootstrap samples, the default (1e5) usually
gives stable results at 2 decimal places.</dd>
<dt>digits</dt>
<dd>Integer, significant digits in printed table</dd>
<dt>print_summary</dt>
<dd>Boolean (default TRUE) print table of results</dd></dl>

Arguments

Calculate a table of model probabilities based for a list of samples objects
based on samples of marginal log-likelihood (MLL) added to these objects by
run_IS2. Probabilities estimated by a bootstrap ath picks a vector of MLLs,
one for each model in the list randomly with replacement nboot times,
calculates model probabilities and averages — compare_MLL

<dl>

<dt>mll</dt>
<dd>List of samples objects with IS_samples attribute added by by run_IS2</dd>


<dt>nboot</dt>
<dd>Integer number of bootstrap samples, the default (1e5) usually
gives stable results at 2 decimal places.</dd>


<dt>digits</dt>
<dd>Integer, significant digits in printed table</dd>


<dt>print_summary</dt>
<dd>Boolean (default TRUE) print table of results</dd>

</dl>

Calculate a table of model probabilities based for a list of samples objects
based on samples of marginal log-likelihood (MLL) added to these objects by
run_IS2. Probabilities estimated by a bootstrap ath picks a vector of MLLs,
one for each model in the list randomly with replacement nboot times,
calculates model probabilities and averages

Description

Usage

Value

Arguments