JAB_plot

Plots JAB as a function of the p-value

Sets the alpha level for coefficients in a regression model
as a decreasing function of the sample size through the use of
Jeffreys' Approximate Bayes factor. You tell alphaN() your sample
size, and it tells you to which value you must lower alpha to avoid
Lindley's Paradox. For details, see Wulff and Taylor (2023)
<doi:10.31234/osf.io/3cbh7>.

Jesper Wulff

alphaN

Set Alpha Based on Sample Size Using Bayes Factors

Luke Taylor

JAB_plot function

<dl><dt>n</dt>
<dd>Sample size</dd>
<dt>BF</dt>
<dd>Bayes factor you would like to match. 1 to avoid the Lindley Paradox, 3 to achieve moderate evidence and 10 to achieve strong evidence.</dd>
<dt>method</dt>
<dd>Used for the choice of 'b'. Currently one of:<ul>
<li>"JAB": this choice of b produces Jeffery's approximate BF</li>
<li>"min": uses the minimal training sample for the prior (Gu et al., '17)</li>
<li>"robust": a robust version of "min" that prevents too small b (O'Hagan, '95)</li>
<li>"balanced": this choice of b balances the type I and type II errors</li>
</ul></dd></dl>

Arguments

Plots JAB as a function of the p-value — JAB_plot

<dl>

<dt>n</dt>
<dd>Sample size</dd>


<dt>BF</dt>
<dd>Bayes factor you would like to match. 1 to avoid the Lindley Paradox, 3 to achieve moderate evidence and 10 to achieve strong evidence.</dd>


<dt>method</dt>
<dd>Used for the choice of 'b'. Currently one of:<ul>
<li>"JAB": this choice of b produces Jeffery's approximate BF</li>
<li>"min": uses the minimal training sample for the prior (Gu et al., '17)</li>
<li>"robust": a robust version of "min" that prevents too small b (O'Hagan, '95)</li>
<li>"balanced": this choice of b balances the type I and type II errors</li>
</ul></dd>

</dl>

JAB_plot: Plots JAB as a function of the p-value

Description

Usage

Value

Arguments

Examples