vif: Variance Inflation Factors

In jonathon-love/car: Companion to Applied Regression

Description

Calculates variance-inflation and generalized variance-inflation factors for linear and generalized linear models.

Usage

vif(mod, ...)

## Default S3 method:
vif(mod, ...)

Arguments

mod	an object that responds to coef, vcov, and model.matrix, such as an lm or glm object.
...	not used.

Details

If all terms in an unweighted linear model have 1 df, then the usual variance-inflation factors are calculated.

If any terms in an unweighted linear model have more than 1 df, then generalized variance-inflation factors (Fox and Monette, 1992) are calculated. These are interpretable as the inflation in size of the confidence ellipse or ellipsoid for the coefficients of the term in comparison with what would be obtained for orthogonal data.

The generalized vifs are invariant with respect to the coding of the terms in the model (as long as the subspace of the columns of the model matrix pertaining to each term is invariant). To adjust for the dimension of the confidence ellipsoid, the function also prints GVIF^[1/(2*df)] where df is the degrees of freedom associated with the term.

Through a further generalization, the implementation here is applicable as well to other sorts of models, in particular weighted linear models and generalized linear models.

Value

A vector of vifs, or a matrix containing one row for each term in the model, and columns for the GVIF, df, and GVIF^[1/(2*df)].

Author(s)

Henric Nilsson and John Fox [e-mail]

References

Fox, J. and Monette, G. (1992) Generalized collinearity diagnostics. JASA, 87, 178–183.

Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models, Second Edition. Sage.

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Examples

vif(lm(prestige ~ income + education, data=Duncan))
vif(lm(prestige ~ income + education + type, data=Duncan))

jonathon-love/car documentation built on May 19, 2019, 7:28 p.m.