[R-sig-ME] Partial effects in mixed models

[Steven J. Pierce]

Why not just run a model with both predictors instead? See King (1986) for one perspective on why extracting the residuals to use as the dependent variable in another model is sub-optimal. That paper is about plain old OLS regression, but I suspect it still is applicable logic. 

King, G. (1986). How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science, 30(3), 666-687.


Steven J. Pierce, Ph.D. 
Associate Director 
Center for Statistical Training & Consulting (CSTAT) 
Michigan State University 
E-mail: pierces1 at msu.edu 
Web: http://www.cstat.msu.edu 


-----Original Message-----
From: v_coudrain at voila.fr [mailto:v_coudrain at voila.fr] 
Sent: Thursday, February 28, 2013 11:25 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Partial effects in mixed models

Dear all,

I would like to test the effect of an explanatory variable after removing the effect of another one. I thought about calculating the model with the first explanatory 
variable only, then take the model residuals and use the residuals as response variable to test the effect of the second explanatory variable. However, I do not 
know if this is possible for a model containing random effects. Maybe it doesn't make sense anyway, but if it is possible, should I include the random effects in the 
second model (residuals as response variable) or not, since variance explained by random effects should also have been accounted for in the first model?

Thank you for your help

Valérie
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