simY_frn

Simulate an relational matrix based on a fixed rank nomination scheme

Analysis of dyadic network and relational data using additive and
multiplicative effects (AME) models. The basic model includes
regression terms, the covariance structure of the social relations model
(Warner, Kenny and Stoto (1979) <DOI:10.1037/0022-3514.37.10.1742>,
Wong (1982) <DOI:10.2307/2287296>), and multiplicative factor
models (Hoff(2009) <DOI:10.1007/s10588-008-9040-4>).
Several different link functions accommodate different
relational data structures, including binary/network data, normal
relational data, zero-inflated positive outcomes using a tobit model, ordinal relational data and data from
fixed-rank nomination schemes. Several of these link functions are
discussed in Hoff, Fosdick, Volfovsky and Stovel (2013)
<DOI:10.1017/nws.2013.17>. Development of this
software was supported in part by NIH grant R01HD067509.

Peter Hoff

amen

Additive and Multiplicative Effects Models for Networks and
Relational Data

Bailey Fosdick

Alex Volfovsky

Yanjun He

simY_frn function

<dl><dt>EZ</dt>
<dd>a square matrix giving the expected value of the latent Z matrix</dd>
<dt>rho</dt>
<dd>dyadic correlation</dd>
<dt>odmax</dt>
<dd>a scalar or vector giving the maximum number of nominations for
each node</dd>
<dt>YO</dt>
<dd>a square matrix identifying where missing values should be
maintained</dd></dl>

Arguments

Author

Simulate an relational matrix based on a fixed rank nomination scheme — simY_frn

<dl>

<dt>EZ</dt>
<dd>a square matrix giving the expected value of the latent Z matrix</dd>


<dt>rho</dt>
<dd>dyadic correlation</dd>


<dt>odmax</dt>
<dd>a scalar or vector giving the maximum number of nominations for
each node</dd>


<dt>YO</dt>
<dd>a square matrix identifying where missing values should be
maintained</dd>

</dl>

simY_frn: Simulate an relational matrix based on a fixed rank nomination scheme

Description

Usage

Value

Arguments

Author