confint.boot_ame

Bootstrap confidence intervals for the intercept and regression
coefficients of a fast AME fit.

Additive and multiplicative effects models for both cross-sectional and longitudinal network analysis. The package provides two main functions: ame() for cross-sectional networks and lame() for longitudinal networks. It supports square and rectangular network structures. Key features include: (1) Cross-sectional network analysis via ame() with support for binary, continuous, ordinal, and count data; (2) Longitudinal network analysis via lame() with additive sender/receiver and multiplicative latent-factor effects that can evolve over time through AR(1) processes (Sewell and Chen (2015) <doi:10.1080/01621459.2014.988214>; Durante and Dunson (2014) <doi:10.1093/biomet/asu040>); (3) Handling of changing actor compositions across time periods in longitudinal models; (4) Performance improvements through C++ implementations via 'Rcpp' and 'RcppArmadillo'.

Shahryar Minhas

lame

Longitudinal Additive and Multiplicative Effects Models for
Networks

Cassy Dorff

Tosin Salau

confint.boot_ame function

<dl><dt>object</dt>
<dd>a <code>boot_ame</code> object.</dd>
<dt>parm</dt>
<dd>character vector of parameter names, or integer indices. If
<code>NULL</code> (default), all coefficients are returned.</dd>
<dt>level</dt>
<dd>confidence level (default 0.95).</dd>
<dt>ci_type</dt>
<dd>interval type: <code>"percentile"</code> (default) uses the
replicate quantiles directly and matches the intervals reported by
<code><a href="/link/summary()?package=lame&version=1.3.4" data-mini-rdoc="lame::summary()">summary()</a></code> and <code>tidy()</code> on the same fit; <code>"basic"</code>
reflects the replicate quantiles about the point estimate
(\(2\hat\theta - q\)), which corrects first-order bias and can be
preferable for the IRLS binary/Poisson point estimators on small
samples.</dd>
<dt>which</dt>
<dd>which uncertainty channel to return: <code>"all"</code> (default,
intercept + regression coefficients + variance components + sender effects<ul>
<li>receiver effects + multiplicative U/V), or a single channel:
<code>"beta"</code>, <code>"vc"</code>, <code>"a"</code>, <code>"b"</code>, <code>"U"</code>,
<code>"V"</code>.</li>
</ul></dd>
<dt>...</dt>
<dd>ignored.</dd></dl>

Arguments

Confidence intervals from a fast AME bootstrap — confint.boot_ame

<dl>

<dt>object</dt>
<dd>a <code>boot_ame</code> object.</dd>


<dt>parm</dt>
<dd>character vector of parameter names, or integer indices. If
<code>NULL</code> (default), all coefficients are returned.</dd>


<dt>level</dt>
<dd>confidence level (default 0.95).</dd>


<dt>ci_type</dt>
<dd>interval type: <code>"percentile"</code> (default) uses the
replicate quantiles directly and matches the intervals reported by
<code><a href='https://rdrr.io/r/base/summary.html'>summary()</a></code> and <code>tidy()</code> on the same fit; <code>"basic"</code>
reflects the replicate quantiles about the point estimate
(\(2\hat\theta - q\)), which corrects first-order bias and can be
preferable for the IRLS binary/Poisson point estimators on small
samples.</dd>


<dt>which</dt>
<dd>which uncertainty channel to return: <code>"all"</code> (default,
intercept + regression coefficients + variance components + sender effects<ul>
<li>receiver effects + multiplicative U/V), or a single channel:
<code>"beta"</code>, <code>"vc"</code>, <code>"a"</code>, <code>"b"</code>, <code>"U"</code>,
<code>"V"</code>.</li>
</ul></dd>


<dt>...</dt>
<dd>ignored.</dd>

</dl>

confint.boot_ame: Confidence intervals from a fast AME bootstrap

Description

Usage

Value

Arguments