fncut

This is an internal function of the R package practicalSigni
 Psychologists use effect size to evaluate the practical
 importance of a treatment on a dependent variable using
 a binary [0,1] variable. Assuming numerical data, we
 can always compute the median and regard values &lt; or = the
 median as zero and other values as unity.

Consider a possibly nonlinear nonparametric regression
with p regressors. We provide evaluations by 13 methods to rank
regressors by their practical significance or importance using
various methods, including machine learning tools. Comprehensive
methods are as follows.
m6=Generalized partial correlation coefficient or
GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and
Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>.
m7= a generalization of psychologists' effect size incorporating
nonlinearity and many variables.
m8= local linear partial (dy/dxi) using the 'np' package for kernel
regressions.
m9= partial (dy/dxi) using the 'NNS' package.
m10= importance measure using the 'NNS' boost function.
m11= Shapley Value measure of importance (cooperative game theory).
m12 and m13= two versions of the random forest algorithm.
Taraldsen's exact density for sampling distribution of correlations added.

Hrishikesh Vinod

practicalSigni

Practical Significance Ranking of Regressors and Exact t Density

fncut function

<dl><dt>x</dt>
<dd>numerical vector of data values</dd></dl>

Arguments

Prof. H. D. Vinod, Fordham University, NY

Author

fncut auxiliary converts continuous data into two categories — fncut

<dl>

<dt>x</dt>
<dd>numerical vector of data values</dd>

</dl>

fncut: fncut auxiliary converts continuous data into two categories

Description

Usage

Value

Arguments

Author