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multiColl (version 1.0)

Collinearity Detection in a Multiple Linear Regression Model

Description

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) . D. A. Belsley (1991, ISBN: 978-0471528890). C. Garcia, R. Salmeron and C.B. Garcia (2019) . R. Salmeron, C.B. Garcia and J. Garcia (2018) . G.W. Stewart (1987) .

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Version

Install

install.packages('multiColl')

Monthly Downloads

219

Version

1.0

License

GPL (>= 2)

Maintainer

R. Salmeron

Last Published

July 18th, 2019

Functions in multiColl (1.0)

multiColLM

All detection measures
CNs

Condition Number with and without intercept
multiCol

Collinearity detection in a linear regression model
lu

Unit length data
theil

Henri Theil data
multiColl-package

Collinearity detection in a multiple linear regression model.
perturb

Perturbation
perturb.n

Perturbation and estimation in a multiple linear model
PROPs

Proportions
ki

Stewart's index
CVs

Coeficients of Variation
CN

Condition Number
KG

Klein and Goldberger data
CV

Coeficient of Variation
RdetR

Correlation matrix and it's determinat
VIF

Variance Inflation Factor
SLM

Simple linear regression model and multicollinearity