Computes the Hellinger distance between the
 two probability density functions specified
 using numerical integration.

Functionality for performing a principled reference analysis in the Bayesian normal-normal hierarchical model used for Bayesian meta-analysis, as described in Ott, Plummer and Roos (2021) <doi:10.1002/sim.9076>. Computes a reference posterior, induced by a minimally informative improper reference prior for the between-study (heterogeneity) standard deviation. Determines additional proper anti-conservative (and conservative) prior benchmarks. Includes functions for reference analyses at both the posterior and the prior level, which, given the data, quantify the informativeness of a heterogeneity prior of interest relative to the minimally informative reference prior and the proper prior benchmarks. The functions operate on data sets which are compatible with the 'bayesmeta' package.

Manuela Ott

ra4bayesmeta

Reference Analysis for Bayesian Meta-Analysis

Malgorzata Roos

H function

<dl> <dt>dens1</dt>
<dd>function returning the first probability density.
</dd><dt>dens2</dt>
<dd>function returning the second probability density.
</dd><dt>lower</dt>
<dd>lower end of the common domain of two density functions. Usually equal to \(-\infty\) or 0.
</dd></dl>

Arguments

Hellinger distance between two probability densities
 — H

<dl>

 <dt>dens1</dt>
<dd>function returning the first probability density.
</dd>

<dt>dens2</dt>
<dd>function returning the second probability density.
</dd>

<dt>lower</dt>
<dd>lower end of the common domain of two density functions. Usually equal to \(-\infty\) or 0.
</dd>

</dl>

H: Hellinger distance between two probability densities

Description

Usage

Value

Arguments

Details

References

See Also

Examples