.rotation_drift_diagnostic

Reports a scalar ratio comparing the variance of the raw G_t
entries (per element across t) to the variance of the canonical
G_t entries (after per-period SVD). When the raw / canonical
variance ratio is large (&gt;= 5), the apparent G_t time-variation is
dominated by rotation drift in U_t, V_t rather than real temporal
change. The canonicalisation removes that rotation and the
canonical entries should be the user-facing summary.

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

.rotation_drift_diagnostic function

<dl><dt>G_cube_raw</dt>
<dd>RA x RB x T raw (FFBS-sampled) G cube</dd>
<dt>U_cube</dt>
<dd>nA x RA x T raw U cube</dd>
<dt>V_cube</dt>
<dd>nB x RB x T raw V cube</dd></dl>

Arguments

Rotation-drift diagnostic for the canonical (U_t, G_t, V_t) trio — .rotation_drift_diagnostic

<dl>

<dt>G_cube_raw</dt>
<dd>RA x RB x T raw (FFBS-sampled) G cube</dd>


<dt>U_cube</dt>
<dd>nA x RA x T raw U cube</dd>


<dt>V_cube</dt>
<dd>nB x RB x T raw V cube</dd>

</dl>

.rotation_drift_diagnostic: Rotation-drift diagnostic for the canonical (U_t, G_t, V_t) trio

Description

Usage

Value

Arguments