.fit_actor_order

<code>lame()</code> sorts actors alphabetically when it ingests a list of <code>Y</code>
matrices (via <code>list_to_array</code>), so the stored additive / multiplicative
effects and design slices are in sorted actor order, which need not match the
order a user later supplies in <code>newdata</code>. This returns the row and column
actor names the fit was estimated in, tried from the most authoritative source
down, so <code>newdata</code> can be realigned by name before prediction.

internal

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

.fit_actor_order function

<dl><dt>object</dt>
<dd>a fitted <code>lame</code> (or <code>ame</code>) object.</dd></dl>

Arguments

The fit's internal (canonical) actor order — .fit_actor_order

<dl>

<dt>object</dt>
<dd>a fitted <code>lame</code> (or <code>ame</code>) object.</dd>

</dl>

.fit_actor_order: The fit's internal (canonical) actor order

Description

Usage

Value

Arguments