rrho_fc

Simulation of dyadic correlation from its approximate full conditional 
distribution using griddy Gibbs sampling

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

rrho_fc function

<dl><dt>Z</dt>
<dd>n X n normal relational matrix</dd>
<dt>Sab</dt>
<dd>covariance of additive effects</dd>
<dt>s2</dt>
<dd>residual variance</dd>
<dt>offset</dt>
<dd>matrix of the same dimension as Z. It is assumed that 
Z-offset follows an SRM distribution, so the offset should contain any 
regression terms and multiplicative effects (such as 
<code>Xbeta(X,beta+ U%*%t(V) </code> )</dd>
<dt>ngp</dt>
<dd>the number of points for an unevenly-spaced 
grid on which to approximate the full conditional distribution</dd>
<dt>asp</dt>
<dd>use arc sine prior (TRUE) or uniform prior (FALSE)</dd></dl>

Arguments

Author

Griddy Gibbs update for dyadic correlation — rrho_fc

<dl>

<dt>Z</dt>
<dd>n X n normal relational matrix</dd>


<dt>Sab</dt>
<dd>covariance of additive effects</dd>


<dt>s2</dt>
<dd>residual variance</dd>


<dt>offset</dt>
<dd>matrix of the same dimension as Z. It is assumed that 
Z-offset follows an SRM distribution, so the offset should contain any 
regression terms and multiplicative effects (such as 
<code>Xbeta(X,beta+ U%*%t(V) </code> )</dd>


<dt>ngp</dt>
<dd>the number of points for an unevenly-spaced 
grid on which to approximate the full conditional distribution</dd>


<dt>asp</dt>
<dd>use arc sine prior (TRUE) or uniform prior (FALSE)</dd>

</dl>

rrho_fc: Griddy Gibbs update for dyadic correlation

Description

Usage

Value

Arguments

Author