rs2_fc

Draw the dyadic residual variance <code>s2</code> from its inverse-gamma full
conditional. The residual matrix <code>E = Z - offset</code> is decorrelated
within reciprocal dyad pairs and along the diagonal, and the pooled sum of
squares (together with an optional scaled-inverse-chi-square prior) yields
the posterior draw.

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

rs2_fc function

<dl><dt>Z</dt>
<dd>n x n normal relational matrix</dd>
<dt>rho</dt>
<dd>current within-dyad correlation</dd>
<dt>offset</dt>
<dd>matrix conformable with <code>Z</code>; <code>Z - offset</code> is treated
as pure dyadic noise (so it should carry the additive and multiplicative
mean structure)</dd>
<dt>nu0</dt>
<dd>prior degrees of freedom (defaults to 1)</dd>
<dt>s20</dt>
<dd>prior scale for s2; when <code>NULL</code> the empirical mean of the
decorrelated squared residuals is used so the prior sits at the data scale</dd></dl>

Arguments

Author

Gibbs update for dyadic variance — rs2_fc

<dl>

<dt>Z</dt>
<dd>n x n normal relational matrix</dd>


<dt>rho</dt>
<dd>current within-dyad correlation</dd>


<dt>offset</dt>
<dd>matrix conformable with <code>Z</code>; <code>Z - offset</code> is treated
as pure dyadic noise (so it should carry the additive and multiplicative
mean structure)</dd>


<dt>nu0</dt>
<dd>prior degrees of freedom (defaults to 1)</dd>


<dt>s20</dt>
<dd>prior scale for s2; when <code>NULL</code> the empirical mean of the
decorrelated squared residuals is used so the prior sits at the data scale</dd>

</dl>

rs2_fc: Gibbs update for dyadic variance

Description

Usage

Value

Arguments

Author