get_est

For objects produced by <code>hbam()</code>, this function is a wrapper for <code>rstan::summary()</code>. For objects produced by <code>fbam()</code> it offers a way to extract point estimates.

Perform hierarchical Bayesian Aldrich-McKelvey scaling using Hamiltonian Monte
Carlo via 'Stan'. Aldrich-McKelvey ('AM') scaling is a method for estimating the ideological
positions of survey respondents and political actors on a common scale using positional survey
data. The hierarchical versions of the Bayesian 'AM' model included in this package outperform
other versions both in terms of yielding meaningful posterior distributions for respondent
positions and in terms of recovering true respondent positions in simulations. The package
contains functions for preparing data, fitting models, extracting estimates, plotting key
results, and comparing models using cross-validation. The original version of the default
model is described in Bølstad (2024) <doi:10.1017/pan.2023.18>.

Jørgen Bølstad

hbamr

Hierarchical Bayesian Aldrich-McKelvey Scaling via 'Stan'

get_est function

<dl><dt>object</dt>
<dd>An instance of class <code>stanfit</code> produced by <code>hbam()</code>, or a list produced by <code>fbam()</code>.</dd>
<dt>par</dt>
<dd>Character: Name of the parameter type to be extracted. Typically <code>"theta"</code> (stimuli positions) or <code>"chi"</code> (respondent positions).</dd>
<dt>format_orig</dt>
<dd>Logical: Should individual-level parameters be mapped to the original dataset by returning rows of NAs for respondents who were not included in the analysis? Defaults to <code>FALSE</code>.</dd>
<dt>probs</dt>
<dd>A numeric vector of quantiles of interest for summarizing <code>stanfit</code> objects.</dd>
<dt>simplify</dt>
<dd>Logical: Should the returned object be simplified by dropping the Monte Carlo standard error and the posterior standard deviation? Defaults to <code>TRUE</code>.</dd>
<dt>...</dt>
<dd>Other arguments are passed on to <code>rstan::summary()</code> when summarizing <code>stanfit</code> objects.</dd></dl>

Arguments

Extract point estimates or other summaries of marginal posterior distributions — get_est

<dl>

<dt>object</dt>
<dd>An instance of class <code>stanfit</code> produced by <code>hbam()</code>, or a list produced by <code>fbam()</code>.</dd>


<dt>par</dt>
<dd>Character: Name of the parameter type to be extracted. Typically <code>"theta"</code> (stimuli positions) or <code>"chi"</code> (respondent positions).</dd>


<dt>format_orig</dt>
<dd>Logical: Should individual-level parameters be mapped to the original dataset by returning rows of NAs for respondents who were not included in the analysis? Defaults to <code>FALSE</code>.</dd>


<dt>probs</dt>
<dd>A numeric vector of quantiles of interest for summarizing <code>stanfit</code> objects.</dd>


<dt>simplify</dt>
<dd>Logical: Should the returned object be simplified by dropping the Monte Carlo standard error and the posterior standard deviation? Defaults to <code>TRUE</code>.</dd>


<dt>...</dt>
<dd>Other arguments are passed on to <code>rstan::summary()</code> when summarizing <code>stanfit</code> objects.</dd>

</dl>

get_est: Extract point estimates or other summaries of marginal posterior distributions

Description

Usage

Value

Arguments