.build_full_design

Build the full canonical design for a newdata 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

.build_full_design function

<dl><dt>newdata</dt>
<dd>an <code>n x m x n_dyad</code> array (or <code>n x m</code> matrix) of
new dyadic covariates. If its 3rd-dim names match the model's dyadic
coefficient names they are mapped by name; otherwise the slices are
taken in order as the model's dyadic covariates.</dd>
<dt>model_names</dt>
<dd>the model's coefficient names
(<code>colnames(fit$BETA)</code> / <code>dimnames(fit$BETA)[[2]]</code>), in
canonical layout.</dd>
<dt>fitted_design</dt>
<dd>the model's fitted design array (<code>fit$X</code> or
<code>Xlist[[t]]</code>); used to hold intercept + nodal slices fixed. May be
<code>NULL</code> only when the model has no nodal covariates.</dd>
<dt>n, m</dt>
<dd>output dimensions.</dd></dl>

Arguments

Build the full canonical design for a newdata prediction — .build_full_design

<dl>

<dt>newdata</dt>
<dd>an <code>n x m x n_dyad</code> array (or <code>n x m</code> matrix) of
new dyadic covariates. If its 3rd-dim names match the model's dyadic
coefficient names they are mapped by name; otherwise the slices are
taken in order as the model's dyadic covariates.</dd>


<dt>model_names</dt>
<dd>the model's coefficient names
(<code>colnames(fit$BETA)</code> / <code>dimnames(fit$BETA)[[2]]</code>), in
canonical layout.</dd>


<dt>fitted_design</dt>
<dd>the model's fitted design array (<code>fit$X</code> or
<code>Xlist[[t]]</code>); used to hold intercept + nodal slices fixed. May be
<code>NULL</code> only when the model has no nodal covariates.</dd>


<dt>n, m</dt>
<dd>output dimensions.</dd>

</dl>

.build_full_design: Build the full canonical design for a newdata prediction

Description

Usage

Value

Arguments