I don't know if there is work on a robust VIF. Here are my two cents: - What type of variable do you have ? If some of them are discrete with very few different values, this may cause problem to robust covariance matrices estimation. - Your approach to apply the "partialling out" of the robust matrix, as in the OLS case, might or might not be correct, I don't know. - If you believe that 1/(1-R^2_i) is a good measure, then you might want to compute its direct robust equivalent. The output of lmrob does not provide a R^2, but the output of lmRob does. We have recently published a paper however that shows that the robust R-squared provided by lmRob is biased, sometimes to a large extent. We provide a consistent and robust estimator of R-squared and a version adjusted for the sample size. See my previous post for an example and the code at https://stat.ethz.ch/pipermail/r-sig-robust/2010/000290.html Olivier ref: Renaud, O. & Victoria-Feser, M.-P. (2010). A robust coefficient of determination for regression. Journal of Statistical Planning and Inference, 140, 1852-1862. http://dx.doi.org/10.1016/j.jspi.2010.01.008
On 31/03/2011 17:35, Nicholas Lewin-Koh wrote:
Hi, I was looking at the vif function in the car package and it it is trivial to modify to make a version for robust regression. However, after trying it out I noticed that what were reasonable values under ols, jumped way up. So my thought is that either, I made a coding error, and the weights attribute needs to be used to modify the variance covariance matrix of the coefficients Or, the reduced variance from the robust regression, causes peripheral points (outside the mve) to have much more influence in the r^2's for each predictor. So that the standard vif measure, 1/(1-R^2_i) is not relevant in this context. Am I off base here? Thanks Nicholas
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