run_IS2

Runs IS2 on a list of emc, only works for types standard, factor and diagonal yet.

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

run_IS2 function

<dl><dt>emc</dt>
<dd>A list of emc</dd>
<dt>stage</dt>
<dd>A string. Indicates which stage to take samples from</dd>
<dt>filter</dt>
<dd>An integer or vector. If integer specifies how many samples to remove from within that stage. If vector used as index for samples to keep.</dd>
<dt>IS_samples</dt>
<dd>An integer. Specifies how many IS2 samples to collect</dd>
<dt>stepsize_particles</dt>
<dd>An integer. It will increase particles till optimal variance with this stepsize.</dd>
<dt>max_particles</dt>
<dd>An integer. Specifies the maximum number of particles to collect before stopping one IS iteration.</dd>
<dt>n_cores</dt>
<dd>An integer. Specifies how many cores to run IS_2 on.</dd>
<dt>df</dt>
<dd>An integer. The degrees of freedom used in the t-distribution used as IS distribution for the group-level proposals.</dd></dl>

Arguments

Runs IS2 from Tran et al. 2021 on a list of emc — run_IS2

<dl>

<dt>emc</dt>
<dd>A list of emc</dd>


<dt>stage</dt>
<dd>A string. Indicates which stage to take samples from</dd>


<dt>filter</dt>
<dd>An integer or vector. If integer specifies how many samples to remove from within that stage. If vector used as index for samples to keep.</dd>


<dt>IS_samples</dt>
<dd>An integer. Specifies how many IS2 samples to collect</dd>


<dt>stepsize_particles</dt>
<dd>An integer. It will increase particles till optimal variance with this stepsize.</dd>


<dt>max_particles</dt>
<dd>An integer. Specifies the maximum number of particles to collect before stopping one IS iteration.</dd>


<dt>n_cores</dt>
<dd>An integer. Specifies how many cores to run IS_2 on.</dd>


<dt>df</dt>
<dd>An integer. The degrees of freedom used in the t-distribution used as IS distribution for the group-level proposals.</dd>

</dl>

run_IS2: Runs IS2 from Tran et al. 2021 on a list of emc

Description

Usage

Value

Arguments