EBIC

RBIC

RAIC

EBIC.default

RBIC.default

RAIC.default

Custom IC functions for stepwise models

An implementation of ranked sparsity methods, including
penalized regression methods such as the sparsity-ranked lasso, its
non-convex alternatives, and elastic net, as well as the sparsity-ranked
Bayesian Information Criterion. As described in Peterson and
Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked
sparsity is a philosophy with methods primarily useful for
variable selection in the presence of prior informational
asymmetry, which occurs in the context of trying to perform variable
selection in the presence of interactions and/or polynomials. Ultimately,
this package attempts to facilitate dealing with cumbersome interactions
and polynomials while not avoiding them entirely. Typically, models
selected under ranked sparsity principles will also be more transparent,
having fewer falsely selected interactions and polynomials than other
methods.

Ryan Andrew Peterson

sparseR

Variable Selection under Ranked Sparsity Principles for
Interactions and Polynomials

EBIC function

<dl><dt>...</dt>
<dd>additional args</dd>
<dt>fit</dt>
<dd>a fitted object</dd>
<dt>varnames</dt>
<dd>names of variables</dd>
<dt>pen_info</dt>
<dd>penalty information</dd>
<dt>gammafn</dt>
<dd>What to use for gamma in formula</dd>
<dt>return_df</dt>
<dd>should the deg. freedom be returned</dd></dl>

Arguments

Custom IC functions for stepwise models — EBIC

<dl>

<dt>...</dt>
<dd>additional args</dd>


<dt>fit</dt>
<dd>a fitted object</dd>


<dt>varnames</dt>
<dd>names of variables</dd>


<dt>pen_info</dt>
<dd>penalty information</dd>


<dt>gammafn</dt>
<dd>What to use for gamma in formula</dd>


<dt>return_df</dt>
<dd>should the deg. freedom be returned</dd>

</dl>

EBIC: Custom IC functions for stepwise models

Description

Usage

Value

Arguments