kendr

The function kendr evaluates the multivariate Kendall regression coefficient.
It describes how well the target variable y can be fit by a function of regressor variables
which is increasing w.r.t. some regressors and decreasing w.r.t. the other
regressors.

Functions to compute coefficients measuring the dependence of two or more than two variables. The functions can be deployed to gain information about functional dependencies of the variables with emphasis on monotone functions. The statistics describe how well one response variable can be approximated by a monotone function of other variables. In regression analysis the variable selection is an important issue. In this framework the functions could be useful tools in modeling the regression function. Detailed explanations on the subject can be found in papers Liebscher (2014) <doi:10.2478/demo-2014-0004>; Liebscher (2017) <doi:10.1515/demo-2017-0012>; Liebscher (2021): <https://arfjournals.com/image/catalog/Journals%20Papers/AJSS/No%202%20(2021)/4-AJSS_123-150.pdf>; Liebscher (2021): Kendall regression coefficient. Computational Statistics and Data Analysis 157. 107140.

Eckhard Liebscher

depcoeff

Dependency Coefficients

kendr function

<dl><dt>x</dt>
<dd>data matrix of regressor variables</dd>
<dt>y</dt>
<dd>data vector of the target variable</dd>
<dt>direction</dt>
<dd>vector of length d (d is number of regressors),
value 1 refers to regressors leading to increasing y whenever this regressor increases,
value -1 refers to regressors leading to decreasing y whenever this regressor increases.
If direction=NULL, then all coefficients are computed.</dd>
<dt>out</dt>
<dd>value 1: full output, value 0: reduced output, only coefficients
that are largest in absolute value</dd></dl>

Arguments

Kendall regression coefficient — kendr

<dl>

<dt>x</dt>
<dd>data matrix of regressor variables</dd>


<dt>y</dt>
<dd>data vector of the target variable</dd>


<dt>direction</dt>
<dd>vector of length d (d is number of regressors),
value 1 refers to regressors leading to increasing y whenever this regressor increases,
value -1 refers to regressors leading to decreasing y whenever this regressor increases.
If direction=NULL, then all coefficients are computed.</dd>


<dt>out</dt>
<dd>value 1: full output, value 0: reduced output, only coefficients
that are largest in absolute value</dd>

</dl>

kendr: Kendall regression coefficient

Description

Usage

Value

Arguments

References

Examples