Screening (multi)collinearity in a regression model

Question

I hope that this one is not going to be \"ask-and-answer\" question... here goes: (multi)collinearity refers to extremely high correlations between predictors in the regress

Answer 1

Since there is no mention of VIF so far, I will add my answer. Variance Inflation Factor>10 usually indicates serious redundancy between predictor variables. VIF indicates the factor by which variance of the co-efficient of a variable would increase if it was not highly correlated with other variables.

vif() is available in package cars and applied to an object of class(lm). It returns the vif of x1, x2 . . . xn in object lm(). It is a good idea to exclude variables with vif >10 or introduce transformations to the variables with vif>10.