bic_to_bf

The difference between two Bayesian information criterion (BIC) indices of
two models can be used to approximate Bayes factors via:
 
$$BF_{10} = e^{(BIC_0 - BIC_1)/2}$$

Provides utilities to describe posterior
distributions and Bayesian models. It includes point-estimates such as
Maximum A Posteriori (MAP), measures of dispersion (Highest Density
Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and
indices used for null-hypothesis testing (such as ROPE percentage, pd
and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.

Dominique Makowski

bayestestR

Understand and Describe Bayesian Models and Posterior
Distributions

Daniel Lüdecke

Mattan S. Ben-Shachar

Indrajeet Patil

Micah K. Wilson

Brenton M. Wiernik

Paul-Christian Bürkner

Tristan Mahr

Henrik Singmann

Quentin F. Gronau

Sam Crawley

bic_to_bf function

<dl><dt>bic</dt>
<dd>A vector of BIC values.</dd>
<dt>denominator</dt>
<dd>The BIC value to use as a denominator (to test against).</dd>
<dt>log</dt>
<dd>If <code>TRUE</code>, return the <code>log(BF)</code>.</dd></dl>

Arguments

The difference between two Bayesian information criterion (BIC) indices of
two models can be used to approximate Bayes factors via:
 
$$BF_{10} = e^{(BIC_0 - BIC_1)/2}$$

Convert BIC indices to Bayes Factors via the BIC-approximation method. — bic_to_bf

<dl>

<dt>bic</dt>
<dd>A vector of BIC values.</dd>


<dt>denominator</dt>
<dd>The BIC value to use as a denominator (to test against).</dd>


<dt>log</dt>
<dd>If <code>TRUE</code>, return the <code>log(BF)</code>.</dd>

</dl>

bic_to_bf: Convert BIC indices to Bayes Factors via the BIC-approximation method.

Description

Usage

Value

Arguments

References

Examples