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multiColl

Collinearity Detection in a Multiple Linear Regression Model

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>.

Functions (17)

CN

Condition Number

CNs

Condition Number with and without intercept

CV

Coeficient of Variation

CVs

Coeficients of Variation

KG

Klein and Goldberger data

PROPs

Proportions

RdetR

Correlation matrix and it's determinat

SLM

Simple linear regression model and multicollinearity

VIF

Variance Inflation Factor

ki

Stewart's index

lu

Unit length data

multiCol

Collinearity detection in a linear regression model

multiColLM

All detection measures

multiColl-package

Collinearity detection in a multiple linear regression model.

perturb

Perturbation

perturb.n

Perturbation and estimation in a multiple linear model

theil

Henri Theil data

multiColl

Collinearity Detection in a Multiple Linear Regression Model

v1.0
GPL (>= 2)
Authors
R. Salmeron, C.B. Garcia and J. Garcia
Initial release
2019-07-07

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