eigenmodel_setup

Setup constants and starting values for an eigenmodel fit

multivariate

models

Estimation of the parameters in a model for
symmetric relational data (e.g., the above-diagonal part of a
square matrix), using a model-based eigenvalue decomposition
and regression. Missing data is accommodated, and a posterior
mean for missing data is calculated under the assumption that
the data are missing at random. The marginal distribution of
the relational data can be arbitrary, and is fit with an
ordered probit specification. See Hoff (2007) <doi:10.48550/arXiv.0711.1146>.
for details on the model.

Peter Hoff

eigenmodel

Semiparametric Factor and Regression Models for Symmetric
Relational Data

eigenmodel_setup function

<dl><dt>R</dt>
<dd>non-negative integer rank of the approximating matrix</dd>
<dt>seed</dt>
<dd>a random seed</dd>
<dt>em_env</dt>
<dd>enviromnemt within which to do the fitting</dd></dl>

Arguments

Author

Setup constants and starting values for an eigenmodel fit — eigenmodel_setup

<dl>

<dt>R</dt>
<dd>non-negative integer rank of the approximating matrix</dd>


<dt>seed</dt>
<dd>a random seed</dd>


<dt>em_env</dt>
<dd>enviromnemt within which to do the fitting</dd>

</dl>

eigenmodel_setup: Setup constants and starting values for an eigenmodel fit

Description

Usage

Arguments

Author