get_start_vals

Get fitted object from MCMC results

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

get_start_vals function

<dl><dt>start_vals</dt>
<dd>List object that is null or contains
starting values</dd>
<dt>Y</dt>
<dd>dependent variable in array format</dd>
<dt>family</dt>
<dd>character vector (e.g. 'bin', 'nrm') specifying
family type</dd>
<dt>xP</dt>
<dd>number of exogenous covariates</dd>
<dt>rvar</dt>
<dd>logical indicating whether to include sender random
effects</dd>
<dt>cvar</dt>
<dd>logical indicating whether to include receiver random
effects</dd>
<dt>R</dt>
<dd>Number of dimensions for multiplicative effects</dd>
<dt>odmax</dt>
<dd>vector of maximum ranks for cbin/frn families (optional)</dd></dl>

Arguments

Author

Get fitted object from MCMC results — get_start_vals

<dl>

<dt>start_vals</dt>
<dd>List object that is null or contains
starting values</dd>


<dt>Y</dt>
<dd>dependent variable in array format</dd>


<dt>family</dt>
<dd>character vector (e.g. 'bin', 'nrm') specifying
family type</dd>


<dt>xP</dt>
<dd>number of exogenous covariates</dd>


<dt>rvar</dt>
<dd>logical indicating whether to include sender random
effects</dd>


<dt>cvar</dt>
<dd>logical indicating whether to include receiver random
effects</dd>


<dt>R</dt>
<dd>Number of dimensions for multiplicative effects</dd>


<dt>odmax</dt>
<dd>vector of maximum ranks for cbin/frn families (optional)</dd>

</dl>

get_start_vals: Get fitted object from MCMC results

Description

Usage

Value

Arguments

Author