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For the selection of variables for a regression model, I did a pairwise correlation matrix between the different predictors and the response variable. From the pairwise correlation matrix, I realise there could be a linearity relationship between the log transformed predictors and the response variable. I would like to ask if it is better to check for multicollinearity by using the vif function in R after transforming the predictors or just simply check for multicollinearity on the independent variables?

Also, my response variable is symmetric after I log transformed it. Is it a sensible decision in doing so before checking for multicollinearity? Any advice or suggestions are welcomed. Thanks!

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Welcome to CV.

First, for collinearity, I think condition indexes and proportion of variance explained is a better method than VIF.

Second, to your question, I think you should check for collinearity on the variables as they would be in the model.

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  • $\begingroup$ Hi Peter, thanks for the advice. So you are implying that simply checking for multicollinearity between IVs would be a more sensible decision rather than transforming the IVs before checking for multicollinearity? $\endgroup$
    – Justin Messi
    Sep 1, 2018 at 14:29
  • $\begingroup$ I am saying that you should check for collinearity with the variables in the form they will be in in the regression - maybe transformed, maybe not. The decision of whether to transform should be made on a substantive basis prior to doing the analysis. $\endgroup$
    – Peter Flom
    Sep 1, 2018 at 15:17

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