nonConstInd

Identify Non-Constant Columns in a Data Frame

A novel forward stepwise discriminant analysis framework
that integrates Pillai's trace with Uncorrelated Linear Discriminant Analysis (ULDA),
providing an improvement over traditional stepwise LDA methods that rely on Wilks' Lambda.
A stand-alone ULDA implementation is also provided, offering a more general solution
than the one available in the 'MASS' package. It automatically handles missing values and
provides visualization tools. For more details, see Wang (2024) <doi:10.48550/arXiv.2409.03136>.

Siyu Wang

folda

Forward Stepwise Discriminant Analysis with Pillai's Trace

nonConstInd function

<dl><dt>data</dt>
<dd>A data frame in which columns will be checked for constant
values. Columns can be of any type (numeric, integer, logical, or factor).</dd>
<dt>tol</dt>
<dd>A numeric tolerance value (default is <code>1e-8</code>) that applies to
numerical columns.</dd>
<dt>na.rm</dt>
<dd>A logical value. If <code>FALSE</code> (default), missing values are
retained during the check.</dd></dl>

Arguments

Identify Non-Constant Columns in a Data Frame — nonConstInd

<dl>

<dt>data</dt>
<dd>A data frame in which columns will be checked for constant
values. Columns can be of any type (numeric, integer, logical, or factor).</dd>


<dt>tol</dt>
<dd>A numeric tolerance value (default is <code>1e-8</code>) that applies to
numerical columns.</dd>


<dt>na.rm</dt>
<dd>A logical value. If <code>FALSE</code> (default), missing values are
retained during the check.</dd>

</dl>

nonConstInd: Identify Non-Constant Columns in a Data Frame

Description

Usage

Value

Arguments