sBIC

the data for which the maximum likelihood estimates will be computed
for the given collection of models. To see how this data should be
formatted check the documentation for <code>setData.YourModelName</code> (e.g.
<code>setData.LCAs</code>). If X is NULL then it is assumed that data for the
model has already been set, this can be useful if you want to
compute the sBIC with the same model and data several times (perhaps
after changing some parameter of the model) without having to reset
the data and thus recompute maximum log-likelihoods.

an object representing a poset of models of the same type, e.g.
a collection of binomial mixture models. The currently implemented
models include:
<ul>
<li> Binomial mixtures, see <code><a rd-options="" href="/link/BinomialMixtures?package=sBIC&version=0.2.0" data-mini-rdoc="sBIC::BinomialMixtures">BinomialMixtures</a></code>.
  </li>
<li> Factor analysis, see <code><a rd-options="" href="/link/FactorAnalyses?package=sBIC&version=0.2.0" data-mini-rdoc="sBIC::FactorAnalyses">FactorAnalyses</a></code>.
  </li>
<li> Latent class analysis, see <code><a rd-options="" href="/link/LCAs?package=sBIC&version=0.2.0" data-mini-rdoc="sBIC::LCAs">LCAs</a></code>.
  </li>
<li> Latent gaussian forests, see <code><a rd-options="" href="/link/LatentForests?package=sBIC&version=0.2.0" data-mini-rdoc="sBIC::LatentForests">LatentForests</a></code>.
  </li>
<li> Reduced rank regression, see <code><a rd-options="" href="/link/ReducedRankRegressions?package=sBIC&version=0.2.0" data-mini-rdoc="sBIC::ReducedRankRegressions">ReducedRankRegressions</a></code>.
  </li>
<li> 1-dimensional gaussian mixtures, see <code><a rd-options="" href="/link/GaussianMixtures?package=sBIC&version=0.2.0" data-mini-rdoc="sBIC::GaussianMixtures">GaussianMixtures</a></code>.
</li>
</ul>



Computes the sBIC for a given collection of models.


Computes the sBIC for various singular model collections including:
binomial mixtures, factor analysis models, Gaussian mixtures,
latent forests, latent class analyses, and reduced rank regressions.

Luca Weihs

Computing the Singular BIC for Multiple Models

sBIC function

an object representing a poset of models of the same type, e.g.
a collection of binomial mixture models. The currently implemented
models include:
<ul>
<li> Binomial mixtures, see <code><a rd-options='' href='BinomialMixtures'>BinomialMixtures</a></code>.
  </li>
<li> Factor analysis, see <code><a rd-options='' href='FactorAnalyses'>FactorAnalyses</a></code>.
  </li>
<li> Latent class analysis, see <code><a rd-options='' href='LCAs'>LCAs</a></code>.
  </li>
<li> Latent gaussian forests, see <code><a rd-options='' href='LatentForests'>LatentForests</a></code>.
  </li>
<li> Reduced rank regression, see <code><a rd-options='' href='ReducedRankRegressions'>ReducedRankRegressions</a></code>.
  </li>
<li> 1-dimensional gaussian mixtures, see <code><a rd-options='' href='GaussianMixtures'>GaussianMixtures</a></code>.
</li>
</ul>


sBIC: Compute the sBIC.

Description

Usage

Arguments

Value