What is variance inflation factor (Vif)?

Calculates variance inflation factor (VIF) for a set of variables and exclude the highly correlated variables from the set through a stepwise procedure. This method can be used to deal with multicollinearity problems when you fit statistical models

What does Vif mean in regression?

The Variance Inflation Factor (VIF) measures multicollinearity in multiple regression models. It quantifies the severity of multicollinearity by estimating how much the variance of the estimated regression coefficients is inflated due to multicollinearity. A VIF value greater than 10 is often considered an indication of high multicollinearity.

What are generalized variance-inflation factors?

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.

How do you calculate variance-inflation factors in an unweighted linear model?

If all terms in an unweighted linear model have 1 df, then the usual variance-inflation factors are calculated. where R_j^2 Rj2 equals the coefficient of determination for regressing the explanatory variable j in question on the other terms in the model. This is one of the well-known collinearity diagnostics.