mbic2

Calculate mBIC2 (the second version of modified Bayesian Information
Criterion).

Selecting linear and generalized linear models for large data sets
using modified stepwise procedure and modern selection criteria (like
modifications of Bayesian Information Criterion). Selection can be
performed on data which exceed RAM capacity. Bogdan et al., (2004)
<doi:10.1534/genetics.103.021683>.

Piotr Szulc

bigstep

Stepwise Selection for Large Data Sets

mbic2 function

<dl><dt>loglik</dt>
<dd>A numeric, the log-likelihood.</dd>
<dt>k</dt>
<dd>An integer &gt;= 0, the number of selected variables.</dd>
<dt>n</dt>
<dd>An integer &gt; 0, the number of observations.</dd>
<dt>p</dt>
<dd>An integer &gt; 0, the number of all variables or a weight.</dd>
<dt>const</dt>
<dd>A numeric &gt; 0, the expected number of significant variables.</dd></dl>

Arguments

mBIC2 — mbic2

<dl>

<dt>loglik</dt>
<dd>A numeric, the log-likelihood.</dd>


<dt>k</dt>
<dd>An integer &gt;= 0, the number of selected variables.</dd>


<dt>n</dt>
<dd>An integer &gt; 0, the number of observations.</dd>


<dt>p</dt>
<dd>An integer &gt; 0, the number of all variables or a weight.</dd>


<dt>const</dt>
<dd>A numeric &gt; 0, the expected number of significant variables.</dd>

</dl>

mbic2: mBIC2

Description

Usage

Value

Arguments

Examples