fixed_effects

Effects of covariates on outcome in baggr models

Running and comparing meta-analyses of data with hierarchical
Bayesian models in Stan, including convenience functions for formatting
data, plotting and pooling measures specific to meta-analysis. This implements many models
from Meager (2019) <doi:10.1257/app.20170299>.

Witold Wiecek

baggr

Bayesian Aggregate Treatment Effects

Rachael Meager

Brice Green

Danny Toomey

Trustees of Columbia University

fixed_effects function

<dl><dt>bg</dt>
<dd>a baggr model</dd>
<dt>summary</dt>
<dd>logical; if <code>TRUE</code> returns summary statistic instead of all MCMC samples</dd>
<dt>transform</dt>
<dd>a transformation (R function) to apply to the result;
(this is commonly used when calling from other
plotting or printing functions)</dd>
<dt>interval</dt>
<dd>uncertainty interval width (numeric between 0 and 1), if <code>summary=TRUE</code></dd></dl>

Arguments

Effects of covariates on outcome in baggr models — fixed_effects

<dl>

<dt>bg</dt>
<dd>a baggr model</dd>


<dt>summary</dt>
<dd>logical; if <code>TRUE</code> returns summary statistic instead of all MCMC samples</dd>


<dt>transform</dt>
<dd>a transformation (R function) to apply to the result;
(this is commonly used when calling from other
plotting or printing functions)</dd>


<dt>interval</dt>
<dd>uncertainty interval width (numeric between 0 and 1), if <code>summary=TRUE</code></dd>

</dl>

fixed_effects: Effects of covariates on outcome in baggr models

Description

Usage

Value

Arguments

See Also