standardize_loadings

Returns a set of standardized factor loadings.
The standardization considers the residual error as well
as described in Stevenson, Heathcote, Forstmann &amp; Matzke, 2024.

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

standardize_loadings function

<dl><dt>emc</dt>
<dd>An emc object with samples from a hierarchical factor analysis model</dd>
<dt>loadings</dt>
<dd>Array of pars by factors by iters. Can also specify loadings instead of emc</dd>
<dt>sig_err_inv</dt>
<dd>Array of pars by iters. Can also specify sig_err_inv instead of emc</dd>
<dt>stage</dt>
<dd>Character. From which stage to take samples</dd>
<dt>merge_chains</dt>
<dd>Return the loadings for each chain separately or merged together.</dd></dl>

Arguments

Standardized factor loadings — standardize_loadings

<dl>

<dt>emc</dt>
<dd>An emc object with samples from a hierarchical factor analysis model</dd>


<dt>loadings</dt>
<dd>Array of pars by factors by iters. Can also specify loadings instead of emc</dd>


<dt>sig_err_inv</dt>
<dd>Array of pars by iters. Can also specify sig_err_inv instead of emc</dd>


<dt>stage</dt>
<dd>Character. From which stage to take samples</dd>


<dt>merge_chains</dt>
<dd>Return the loadings for each chain separately or merged together.</dd>

</dl>

standardize_loadings: Standardized factor loadings

Description

Usage

Value

Arguments