JABp

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

JABp function

<dl><dt>n</dt>
<dd>Sample size.</dd>
<dt>p</dt>
<dd>The p-value.</dd>
<dt>z</dt>
<dd>Is the p-value based on a z- or t-statistic? TRUE if z.</dd>
<dt>df</dt>
<dd>If z=FALSE, provide the degrees of freedom for the t-statistic.</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 (Wagenmakers, 2022)</li>
<li>"min": uses the minimal training sample for the prior (Gu et al., 2018)</li>
<li>"robust": a robust version of "min" that prevents too small b (O'Hagan, 1995)</li>
<li>"balanced": this choice of b balances the type I and type II errors (Gu et al, 2016)</li>
</ul></dd>
<dt>upper</dt>
<dd>The upper limit for the range of realistic effect sizes. Only relevant when method="balanced". Defaults to 1 such that the range of realistic effect sizes is uniformly distributed between 0 and 1, U(0,1).</dd></dl>

Arguments

Title — JABp

<dl>

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


<dt>p</dt>
<dd>The p-value.</dd>


<dt>z</dt>
<dd>Is the p-value based on a z- or t-statistic? TRUE if z.</dd>


<dt>df</dt>
<dd>If z=FALSE, provide the degrees of freedom for the t-statistic.</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 (Wagenmakers, 2022)</li>
<li>"min": uses the minimal training sample for the prior (Gu et al., 2018)</li>
<li>"robust": a robust version of "min" that prevents too small b (O'Hagan, 1995)</li>
<li>"balanced": this choice of b balances the type I and type II errors (Gu et al, 2016)</li>
</ul></dd>


<dt>upper</dt>
<dd>The upper limit for the range of realistic effect sizes. Only relevant when method="balanced". Defaults to 1 such that the range of realistic effect sizes is uniformly distributed between 0 and 1, U(0,1).</dd>

</dl>

JABp: Title

Description

Usage

Value

Arguments

Examples