VIF is used to estimate the proportion of variance inflated when predictors are correlated in multiple regression analysis. How do I estimate variance inflation factor VIF theoretically?
The VIF of variable (i) can be found as the reciprocal of (1-R_squared(i)), where R_squared(i) is the coefficient of determination for a model in which X(i) is predicted from the remaining X's.
VIF is a measure of collinearity between two independent variables or
multicollinearity among three or more independent variables. It is the proportion of variance in one independent variable that is not explained by the remaining independent variables.. VIF values of just over 1.0 are desirable, with the floor VIF value being 1.0. A VIF value of 1.007 would be considered very good and indicative of no collinearity.