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What does higher VIF indicate?
Variance inflation factor (VIF) is a measure of the amount of multicollinearity in a set of multiple regression variables. A high VIF indicates that the associated independent variable is highly collinear with the other variables in the model.
Does multicollinearity affect model performance?
Multicollinearity can significantly reduce the model’s performance and we may not know it. It is a very important step during the feature selection process.
What is a bad VIF value?
The VIF has a lower bound of 1 but no upper bound. Authorities differ on how high the VIF has to be to constitute a problem. Personally, I tend to get concerned when a VIF is greater than 2.50, which corresponds to an R2 of . 60 with the other variables.
What value of VIF is acceptable?
There are some guidelines we can use to determine whether our VIFs are in an acceptable range. A rule of thumb commonly used in practice is if a VIF is > 10, you have high multicollinearity. In our case, with values around 1, we are in good shape, and can proceed with our regression.
When is the VIF too high to be a problem?
Authorities differ on how high the VIF has to be to constitute a problem. Personally, I tend to get concerned when a VIF is greater than 2.50, which corresponds to an R2 of .60 with the other variables.
Which is an indicator variable with a high Vif?
The variables with high VIFs are indicator (dummy) variables that represent a categorical variable with three or more categories. If the proportion of cases in the reference category is small, the indicator variables will necessarily have high VIFs, even if the categorical variable is not associated with other variables in the regression model.
What causes high vifs in a regression model?
The high VIFs are caused by the inclusion of powers or products of other variables. If you specify a regression model with both x and x2, there’s a good chance that those two variables will be highly correlated. Similarly, if your model has x, z, and xz, both x and z are likely to be highly correlated with their product.
How is the VIF calculated for a predictor?
The VIF may be calculated for each predictor by doing a linear regression of that predictor on all the other predictors, and then obtaining the R2 from that regression. The VIF is just 1/ (1- R2 ).