[R-sig-ME] R.square in Mixed Models

[Tim Richter-Heitmann]

Dear List,

i have used mixed models (six groups, 30 observation each) to model 
ecological interactions of protists with their environments.

I liked my models very much, but a reviewer now asked me to give 
R.square to show the explained variance of each model. I have now read a 
bit on that topic, and realized that r2s in GLMMs have limited value.

I did not want to argue my way out this request (because reviewers 
sometimes do not like this), so i generated some values with the MuMin 
package:

r.squaredGLMM(finalfit.sand.1)
            R2m       R2c
[1,] 0.2716636 0.2824504

It is understoodd that the first value represents my fixed effects, and 
the second value the sum of fixed and random effects. My task is to now 
properly interpret these values. Does this mean that my random effect 
(category, six levels) does only marginally contribute to the model fit, 
and if so, was my choice to introduce this random effect justified?

Thank you for taking your time.

-- 
Dr. Tim Richter-Heitmann

University of Bremen
Microbial Ecophysiology Group (AG Friedrich)
FB02 - Biologie/Chemie
Leobener Straße (NW2 A2130)
D-28359 Bremen
Tel.: 0049(0)421 218-63062
Fax: 0049(0)421 218-63069