successRegionSig

This function returns the success region for the (meta-analytic)
 replication effect estimate to achieve significance

Provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) <doi:10.48550/arXiv.2211.02552>.

Samuel Pawel

BayesRepDesign

Bayesian Design of Replication Studies

successRegionSig function

<dl><dt>sr</dt>
<dd>Replication standard error</dd>
<dt>to</dt>
<dd>Original effect estimate</dd>
<dt>tau</dt>
<dd>Heterogeneity standard deviation used in the calculation of the
meta-analytic replication effect estimate and its standard error.
Defaults to <code>0</code> (fixed effects analysis)</dd>
<dt>nsites</dt>
<dd>nsites Number of sites, defaults to <code>1</code>. The effect
estimates from all sites are assumed to have the same standard error
<code>sr</code></dd>
<dt>level</dt>
<dd>Significance level for p-value of the (average) replication
effect estimate (one-sided and in the same direction as the original
effect estimate)</dd></dl>

Arguments

Author

Success region based on significance — successRegionSig

<dl>

<dt>sr</dt>
<dd>Replication standard error</dd>


<dt>to</dt>
<dd>Original effect estimate</dd>


<dt>tau</dt>
<dd>Heterogeneity standard deviation used in the calculation of the
meta-analytic replication effect estimate and its standard error.
Defaults to <code>0</code> (fixed effects analysis)</dd>


<dt>nsites</dt>
<dd>nsites Number of sites, defaults to <code>1</code>. The effect
estimates from all sites are assumed to have the same standard error
<code>sr</code></dd>


<dt>level</dt>
<dd>Significance level for p-value of the (average) replication
effect estimate (one-sided and in the same direction as the original
effect estimate)</dd>

</dl>

successRegionSig: Success region based on significance

Description

Usage

Value

Arguments

Author

References

Examples