effect_plot

effect_plot.sparseR

effect_plot.sparseRBIC

Plot relevant effects of a sparseR object

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

effect_plot function

<dl><dt>fit</dt>
<dd>a `sparseR` object</dd>
<dt>...</dt>
<dd>additional arguments</dd>
<dt>coef_name</dt>
<dd>The name of the coefficient to plot along the x-axis</dd>
<dt>at</dt>
<dd>value of lambda to use</dd>
<dt>by</dt>
<dd>the variable(s) involved in the (possible) interaction</dd>
<dt>by_levels</dt>
<dd>values to cut continuous by variable (defaults to 3 quantiles)</dd>
<dt>nn</dt>
<dd>number of points to plot along prediction line</dd>
<dt>plot.args</dt>
<dd>list of arguments passed to the plot itself</dd>
<dt>resids</dt>
<dd>should residuals be plotted or not?</dd>
<dt>legend_location</dt>
<dd>location for legend passed to `legend`</dd></dl>

Arguments

Plot relevant effects of a sparseR object — effect_plot

<dl>

<dt>fit</dt>
<dd>a `sparseR` object</dd>


<dt>...</dt>
<dd>additional arguments</dd>


<dt>coef_name</dt>
<dd>The name of the coefficient to plot along the x-axis</dd>


<dt>at</dt>
<dd>value of lambda to use</dd>


<dt>by</dt>
<dd>the variable(s) involved in the (possible) interaction</dd>


<dt>by_levels</dt>
<dd>values to cut continuous by variable (defaults to 3 quantiles)</dd>


<dt>nn</dt>
<dd>number of points to plot along prediction line</dd>


<dt>plot.args</dt>
<dd>list of arguments passed to the plot itself</dd>


<dt>resids</dt>
<dd>should residuals be plotted or not?</dd>


<dt>legend_location</dt>
<dd>location for legend passed to `legend`</dd>

</dl>

effect_plot: Plot relevant effects of a sparseR object

Description

Usage

Value

Arguments