Dear R-langs, I have a data set that contains highly correlated variables (> .90), all of which are variables that occur on the same time scale. I crucially want to determine whether one of these variables (End) has explanatory power on top of all the other ones. In this case, is it legitimate to take the residuals of End (fitting an lm model, in which we explain End with all other, correlated variables), and then running an lmer model that only contains resid_end? When I look at the results I obtain, it seems like the other correlated variables result in corrupted residuals for End. Are there any other methods to deal with (and distinguish between) highly correlated variables in R? Or could you tell me whether it is valid to use these residuals (and the F values obtained for these residuals), even though the beta coefficients are uninterpretable?
Thanks in advance! Marco
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