The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.
D.A. Belsley (1982) <doi:10.1016/0304-4076(82)90020-3>.
D. A. Belsley (1991, ISBN: 978-0471528890).
C. Garcia, R. Salmeron and C.B. Garcia (2019) <doi:10.1080/00949655.2018.1543423>.
R. Salmeron, C.B. Garcia and J. Garcia (2018) <doi:10.1080/00949655.2018.1463376>.
G.W. Stewart (1987) <doi:10.1214/ss/1177013444>.

Collinearity Detection in a Multiple Linear Regression Model

package-ubuntu-20.04-r-4.0.5#cran#multiColl-1.0

<p>The function calculates the Coeficient of Variation (CV) of a quantitative vector.</p>

<table summary="R argblock"><tr valign="top">
<td><code>x</code></td>
<td>
<p>A quantitative vector.</p>
</td>
</tr></table>

<p>R. Salmerón (<a href="mailto:romansg@ugr.es">romansg@ugr.es</a>) and C. García (<a href="mailto:cbgarcia@ugr.es">cbgarcia@ugr.es</a>).</p>

package-ubuntu-20.04-r-4.0.5#cran#multiColl-1.0#docs#CV

CV

Description

Usage

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Examples

multiColl

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