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KellyT
Level II

VIF value < 1 when using Generalized Regression within Fit Model

Hi, 

 

I noticed from time to time, I get VIF < 1 when fitting data to best subset, lasso or double lasso under Generalized Regression (with normal distribution).  For validation method, I chose BIC for all different models.  When I got the result, I would right click in the "Active Parameter Estimates" -> Column -> VIF.   when I extract the active estimates to fit in a OLS model, the coefficients for the predictors are different and the VIFs were all > 1.  Based on the VIF description in JMP, I built model using the predictor with VIF < 1 as the response and the rest of the variables as predictors, the OLS model showed R2 ~ 0.2 and adjusted R2 between 0.079 - 0.2.  So I am not sure why VIF can be < 1.

funkyVIF_2.png

 

funkyVIF_1.png

 

 

 

Thank you so much for your time and help.

1 ACCEPTED SOLUTION

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Re: VIF value < 1 when using Generalized Regression within Fit Model

Here is a explanation of how VIF can be less than 1. It has to due with the shrinkage (biasing) of the parameter estimates.

Learn it once, use it forever!

View solution in original post

4 REPLIES 4
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phil_kay
Staff

Re: VIF value < 1 when using Generalized Regression within Fit Model

That does seem strange. It implies a negative R^2 for the regression of this predictor against the other predictors.

I would recommend contacting technical support with this one (email: support@jmp.com).
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Re: VIF value < 1 when using Generalized Regression within Fit Model

Here is a explanation of how VIF can be less than 1. It has to due with the shrinkage (biasing) of the parameter estimates.

Learn it once, use it forever!

View solution in original post

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phil_kay
Staff

Re: VIF value < 1 when using Generalized Regression within Fit Model

Thanks, @markbailey. I did wonder if it was because of shrinkage.
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KellyT
Level II

Re: VIF value < 1 when using Generalized Regression within Fit Model

Thank you so much for the link Mark.  I guess I am still wondering if we can still think of them as the absolute VIF where we set a limit at anything lower than 4 still indicates minimum collinearity.

 

I also got this answer from José Quaresma (SAS Global Technical Support) which I want to share with everyone too.

 

VIF_LessThanOne.png

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