posterior_plot

Creates a prior and posterior plot
of an BFF object. If no specific omega was set
when fitting the model, the omega resulting in
maximum BF against the null hypothesis is selected.

Bayes factors represent the ratio of probabilities assigned to data by competing scientific hypotheses. However, one drawback of Bayes factors is their dependence on prior specifications that define null and alternative hypotheses. Additionally, there are challenges in their computation. To address these issues, we define Bayes factor functions (BFFs) directly from common test statistics. BFFs express Bayes factors as a function of the prior densities used to define the alternative hypotheses. These prior densities are centered on standardized effects, which serve as indices for the BFF. Therefore, BFFs offer a summary of evidence in favor of alternative hypotheses that correspond to a range of scientifically interesting effect sizes. Such summaries remove the need for arbitrary thresholds to determine "statistical significance." BFFs are available in closed form and can be easily computed from z, t, chi-squared, and F statistics. They depend on hyperparameters "r" and "tau^2", which determine the shape and scale of the prior distributions defining the alternative hypotheses. Plots of BFFs versus effect size provide informative summaries of hypothesis tests that can be easily aggregated across studies.

Rachael Shudde

Bayes Factor Functions

František Bartoš

Saptati Datta

Riana Guha

Sandy Pramanik

Valen Johnson

posterior_plot function

<dl><dt>x</dt>
<dd>a BFF object</dd>
<dt>prior</dt>
<dd>whether prior distribution should
be added to the figure</dd>
<dt>plot</dt>
<dd>whether plot should be generated.
Default to <code>TRUE</code>. If <code>FALSE</code> a data
frame with the support, prior ordinates, and
posterior ordinates is returned instead.</dd>
<dt>...</dt>
<dd>additional arguments to the plotting
function. These include:<dl>
 <dt>"x_limit"</dt>
<dd>vector defining the plotting range,
 defaults to <code>c(-3, 3)</code>.</dd></dl></dd> <dt>"color"</dt>
<dd>vector with color for the posterior and
 prior line. Defaults to <code>c("black", "grey")</code></dd> <dt>"linetype"</dt>
<dd>vector with linetype for the posterior and
 prior line. Defaults to <code>c(2, 1)</code></dd> <dt>"linewidth"</dt>
<dd>vector with linewidth for the posterior and
 prior line. Defaults to <code>c(1, 1)</code></dd>
</dl>

Arguments

Plot Prior and Posterior Distribution — posterior_plot

<dl>

<dt>x</dt>
<dd>a BFF object</dd>


<dt>prior</dt>
<dd>whether prior distribution should
be added to the figure</dd>


<dt>plot</dt>
<dd>whether plot should be generated.
Default to <code>TRUE</code>. If <code>FALSE</code> a data
frame with the support, prior ordinates, and
posterior ordinates is returned instead.</dd>


<dt>...</dt>
<dd>additional arguments to the plotting
function. These include:<dl>
 <dt>"x_limit"</dt>
<dd>vector defining the plotting range,
 defaults to <code>c(-3, 3)</code>.</dd>

 <dt>"color"</dt>
<dd>vector with color for the posterior and
 prior line. Defaults to <code>c("black", "grey")</code></dd>

 <dt>"linetype"</dt>
<dd>vector with linetype for the posterior and
 prior line. Defaults to <code>c(2, 1)</code></dd>

 <dt>"linewidth"</dt>
<dd>vector with linewidth for the posterior and
 prior line. Defaults to <code>c(1, 1)</code></dd>


</dl></dd>

</dl>

posterior_plot: Plot Prior and Posterior Distribution

Description

Usage

Value

Arguments

See Also