Calculates variance-inflation and generalized variance-inflation factors (VIFs and GVIFs) for linear, generalized linear, and other regression models.
vif(mod, ...) ## Default S3 method: vif(mod, ...) ## S3 method for class 'lm' vif(mod, type=c("terms", "predictor"), ...) ## S3 method for class 'merMod' vif(mod, ...) ## S3 method for class 'polr' vif(mod, ...) ## S3 method for class 'svyolr' vif(mod, ...)
for the default method, an object that responds to
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, generalized linear models, and mixed-effects models.
Two methods of computing GVIFs are provided for unweighted linear models:
type="terms" (the default) behaves like the default method, and computes the GVIF for each term in the model, ignoring relations of marginality among the terms in models with interactions. GVIFs computed in this manner aren't generally sensible.
type="predictor" focuses in turn on each predictor in the model, combining the main effect for that predictor with the main effects of the predictors with which the focal predictor interacts and the interactions; e.g., in the model with formula
y ~ a*b + b*c, the GVIF for the predictor
a also includes the
b main effect and the
a:b interaction regressors; the GVIF for the predictor
c includes the
b main effect and the
b:c interaction; and the GVIF for the predictor
b includes the
c main effects and the
a:c interactions (i.e., the whole model), and is thus necessarily 1. These predictor GVIFs should be regarded as experimental.
Specific methods are provided for ordinal regression model objects produced by
polr in the MASS package and
svyolr in the survey package, which are "intercept-less"; VIFs or GVIFs for linear and similar regression models without intercepts are generally not sensible.
A vector of VIFs, or a matrix containing one row for each term, and columns for the GVIF, df, and GVIF^[1/(2*df)], the last of which is intended to be comparable across terms of different dimension.
John Fox email@example.com and Henric Nilsson
Fox, J. and Monette, G. (1992) Generalized collinearity diagnostics. JASA, 87, 178–183.
Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.
Fox, J. and Weisberg, S. (2018) An R Companion to Applied Regression, Third Edition, Sage.
vif(lm(prestige ~ income + education, data=Duncan)) vif(lm(prestige ~ income + education + type, data=Duncan)) vif(lm(prestige ~ (income + education)*type, data=Duncan), type="terms") # not recommended vif(lm(prestige ~ (income + education)*type, data=Duncan), type="predictor")
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