.ame_propagate_cov_na

For an AME fit, a dyad whose covariate value is missing cannot contribute a
covariate-coefficient observation. Rather than silently imputing the missing
covariate as 0 (which biases the coefficient when the covariate is not
mean-centred), the dyad is treated as an unobserved tie and handled by data
augmentation. This helper sets the affected <code>Y</code> cells to <code>NA</code>;
<code>lame</code> already does this internally, and this brings
<code>ame</code> into line.

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

.ame_propagate_cov_na function

<dl><dt>Y</dt>
<dd>an n x n response matrix.</dd>
<dt>Xrow, Xcol, Xdyad</dt>
<dd>row, column and dyadic covariates (or <code>NULL</code>).</dd></dl>

Arguments

Propagate covariate missingness into the response — .ame_propagate_cov_na

<dl>

<dt>Y</dt>
<dd>an n x n response matrix.</dd>


<dt>Xrow, Xcol, Xdyad</dt>
<dd>row, column and dyadic covariates (or <code>NULL</code>).</dd>

</dl>

.ame_propagate_cov_na: Propagate covariate missingness into the response

Description

Usage

Value

Arguments