car (version 0.8-1)

vif: Variance Inflation Factors

Description

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

Usage

vif(mod)

vif.lm(mod)

vif.default(mod)

Arguments

mod

an unweighted lm object.

Value

• A vector of vifs, or a matrix containing one row for each term in the model, and columns for the GVIF, df, and $GVIF^{1/(2\times df)}$.

Details

If all terms in the model have 1 df, then the usual variance-inflation factors are calculated. If any terms 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 $GVIF^{1/(2\times df)}$. Currently, vif is only defined for linear models; vif.default is a dummy function that generates an error.

References

Fox, J. and Monette, G. (1992) Generalized collinearity diagnostics. JASA, 87, 178--183. Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.

Examples

data(Duncan)
## vif(lm(prestige~income+education, data=Duncan))
##    income education 
##  2.104900  2.104900 
vif(lm(prestige~income+education+type, data=Duncan))
##               GVIF Df GVIF^(1/2Df)
## income    2.209178  1     1.486330
## education 5.297584  1     2.301648
## type      5.098592  2     1.502666