maic2

Calculate mAIC2 (the second version of modified Akaike 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

maic2 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, used only to check if k
is not too large.</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

mAIC2 — maic2

<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, used only to check if k
is not too large.</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>

maic2: mAIC2

Description

Usage

Value

Arguments

Examples