If the covariates are collinear, then you can't properly estimate the model. They are collinear if they explain the variance of each other.
I would first test for the relationship of your covariates before putting them into the model as you don't want the covariates to be collinear.
Best Regards, Donald McLaren ================= D.G. McLaren, Ph.D. Postdoctoral Research Fellow, GRECC, Bedford VA Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School Website: http://www.martinos.org/~mclaren Office: (773) 406-2464 ===================== This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.
On Wed, Jun 27, 2012 at 3:42 PM, mdkruepke@uwalumni.com < mdkruepke@uwalumni.com> wrote:
Freesurfers-
Question, when looking for interactions to justify using DOSS say i am looking at cortical thickness and i have two discrete variables (G1 and G2) and 4 continious variables (V1, V2, V3, V4). My thinking is that i should look to see if there are interactions with the groups and each individual variable. However what if it is likely that an interaction of G1, G2, and V1 can be explained by V2?
Michael
-- Michael D. Kruepke PhD - University of Illinois at Urbana-Champaign BA - Psych - University of Wisconsin-Madison mdkruepke@gmail.com (262)-483-7449
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