Partial effects in mixed models

Message du 01/03/13 ? 15h32
De : "Thompson,Paul" 

A : "Steven J. Pierce" 

Copie ? : 
Objet : RE: [R-sig-ME] Partial effects in mixed models

Take a look at

@ARTICLE{Nakagawa-2012-1,
author = {Nakagawa, S. and Schielzeth, H.},
title = {A general and simple method for obtaining R$^2$ from generalized
linear mixed-effects models},
journal = {Meth. Ecol. Evol.},
year = {2012},
pages = {1-120},
doi = {10.1111/j.2041-210x.2012.00251.x},
owner = {THOMPSOP},
timestamp = {2012.12.16}
}
They discuss using residuals.

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Steven J. Pierce
Sent: Friday, March 01, 2013 7:34 AM
To: v_coudrain at voila.fr; r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Partial effects in mixed models

It is my understanding that each coefficient in a model with multiple predictors reflects the effect of that predictor conditional on the set of other predictors 

If you want to explicitly model the effects of both predictors on the response and simultaneously model the correlation between those predictors, you could 

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: Friday, March 01, 2013 6:35 AM
To: Steven J. Pierce; r-sig-mixed-models at r-project.org
Subject: RE: [R-sig-ME] Partial effects in mixed models

Thank you. My concern was that the model with both variables within may not be optimal because both variables are correlated and I would like to know if the 

Best

Message du 01/03/13 ? 03h23
De : "Steven J. Pierce" 

A : v_coudrain at voila.fr, r-sig-mixed-models at r-project.org Copie ? :
Objet : RE: [R-sig-ME] Partial effects in mixed models

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-

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

Val?rie

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