Not really, no; you can look at pseudo r squares used for poisson regression, but I've never seen much use in them.  To compare models, the AIC BIC and log likelihood are your friends.

On Feb 19, 2012, at 21:23,  toledo <llopezt2000@yahoo.com.mx> wrote:

> Thanks Josh for your quick and clear response, I would then ask what should normally be reported in a manuscript for random effects? Just the variance? Is there any way to obtain total or residual variance for the model to compare how many of the total variance explain the random effects included in the model?
> 
> Cheers
> Leo
> 
> De: Joshua Wiley <jwiley.psych@gmail.com>
> Para: lopez toledo <llopezt2000@yahoo.com.mx> 
> CC: "r-sig-mixed-models@r-project.org" <r-sig-mixed-models@r-project.org> 
> Enviado: Domingo, 19 de febrero, 2012 22:43:37
> Asunto: Re: [R-sig-ME] More help with glmer!
> 
> Hi,
> 
> The lack of residual variance is not a function of glmer per se, rather the distribution you used.  The poisson distribution only has parameter---the expectation and dispersion.
> 
> The variance of the random subject effect is the variability in intercepts by subject.  In your case something like the variability in expected log count when treatment is 0 for different subjects.  It is very small.
> 
> glmer does use a maximum likelihood estimator and returns maximum likelihood estimates.  The Laplace approximation is a numerical way to calculate them (optimize the likelihood function).
> 
> You can set nAGQ to some number higher than 1 to increase the number of points evaluated and obtain slightly more accurate estimates at the cost of speed.
> 
> Cheers,
> 
> Josh
> 
> On Feb 19, 2012, at 20:16, lopez toledo <llopezt2000@yahoo.com.mx> wrote:
> 
> > Hi all:Hope you understand I'm not an statistician and hope my question is not very basic. I did have a deep look to old posts but could not find an appropriate response. So, let me ask my question!
> > I'm doing some lmer and glmer (poisson) models, but I noticed that for glmer there is not residual variance for the random effects. So, how do variance values should be interpreted in a glmer? What does the 6.8578 e-06 mean in the example below? I understand the "Subject" factor is explaining 6.8 e-06 of the variance, but what is the total or the residual variance? This number does not say many to me. 
> > Additionally, is it possible to fit a glmer with maximum likelihood? or is Laplace approximation the only option?
> > 
> > Thanks for your help and comprehension � 
> > 
> >> summary(glmer1)
> > Generalized linear mixed model fit by the Laplace approximation 
> > Formula: Response ~ Treatment + (1 | Subject) 
> > �� AIC� BIC logLik deviance
> > �788.8 1052 -345.4��� 690.8
> > Random effects:
> > �Groups Name������� Variance�� Std.Dev. 
> > Subject �� (Intercept) 6.8578e-06 0.0026187
> > Number of obs: 1604, groups: NNo, 555
> > 
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> > 
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> 
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