<br><br><div class="gmail_quote">On Sat, Apr 11, 2009 at 8:55 AM, Marco <span dir="ltr"><<a href="mailto:dutch.linguistics@gmail.com">dutch.linguistics@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Dear R-langs,<br><br>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? </blockquote>
<div><br>I think in your situation, you should first do some model comparison, preferably bootstrapping over it (i.e. test e.g. 10,000 times which of the two predictors would be removed from the model if you sampled from your data randomly with replacement). that's the best thing to do if you have such high correlations.<br>
<br>residuals can be used, but you would have to residualize both ways and test the two resulting models.<br><br>florian <br></div><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
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<br>Thanks in advance!<br><font color="#888888"><br>Marco<br>
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