sparseRBIC_sampsplit

Runs multiple on models selection procedures using RBIC to
achieve valid inferential results post-selection

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

sparseRBIC_sampsplit function

<dl><dt>srbic_fit</dt>
<dd>An object fitted by sparseRBIC_step</dd>
<dt>S</dt>
<dd>Number of splitting iterations</dd>
<dt>quiet</dt>
<dd>Should the display of a progress bar be silenced?</dd></dl>

Arguments

Sample split procedure for stepwise regression — sparseRBIC_sampsplit

<dl>

<dt>srbic_fit</dt>
<dd>An object fitted by sparseRBIC_step</dd>


<dt>S</dt>
<dd>Number of splitting iterations</dd>


<dt>quiet</dt>
<dd>Should the display of a progress bar be silenced?</dd>

</dl>

sparseRBIC_sampsplit: Sample split procedure for stepwise regression

Description

Usage

Value

Arguments