fitted.ame

Returns the posterior mean of the network on the response scale (YPM).
For binary models, these are predicted probabilities between 0 and 1.
For normal models, these are predicted continuous values.
For Poisson models, these are predicted counts.

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

fitted.ame function

<dl><dt>object</dt>
<dd>Fitted AME model object (class "ame").</dd>
<dt>...</dt>
<dd>Additional arguments (not used).</dd></dl>

Arguments

Cassy Dorff, Shahryar Minhas, Tosin Salau

Author

Extract fitted values from AME model — fitted.ame

<dl>

<dt>object</dt>
<dd>Fitted AME model object (class "ame").</dd>


<dt>...</dt>
<dd>Additional arguments (not used).</dd>

</dl>

fitted.ame: Extract fitted values from AME model

Description

Usage

Value

Arguments

Author

See Also