R help - Sep 2011 - comparing mixed binomial model against the same model without random effect

Hi everybody,

If I am correct, you can compare a model with random effect with the same model
without the random effect by using the nlme function, like this:

no.random.model <- gls(Richness ~ NAP * fExp,
              method = "REML", data = RIKZ)
random.model <- lme(Richness ~NAP * fExp, data = RIKZ,
          random = ~1 | fBeach, method = "REML")
anova(no.random.model,random.model)

But, nlme is valid only for the gaussian family, isn't it? In my case I have
a mixed model with binomial family, like this:

random.model <- lme(sex ~hwp+hcp, data = mydata,

          random = ~1 | colony, method = "REML")

where "sex" is a binary variable, "hwp" and "hcp"
are continuous variable and "colony" is a factor with two levels.
I want to compare this model with another one without the random effect, I have
tried with the lme4 but after this I cannot figure out how to build this same
model without the random effect in order to make it comparable to the random
effect model.

Thanks for any help.
Simone  

 		 	   		  
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Any answer to this?

I really need to compare a mixed model with binomial error against the same
model without the random effect. I would use anova() but I don't know how to
specify both models in order to make them comparable.

Thanks for any answer

Simone

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Hello Simone,

Given that your response variable is binary and, consequently, you should
use generalized models, just occurs to me a comparison between a Generalized
Linear Model (the model without the random effect) and a Generalized Linear
Mixed Model (the model with the random effect).

You could write them as follows:

no.random.model <- glm(sex ~ hwp + hcp, data = mydata, family=binomial)
random.model <- glmer(sex ~ hwp + hcp + (1 | colony), data = mydata,
family=binomial)

My only doubt here is if one can directly compare both models, built under
different algorithms and from different packages.
Furthermore, I have no idea if the function 'anova' is able to compare
models produced by the 'glmer' function. One possibility is to compare
them
based on Akaike Information Criteria (AIC) or any one of its corrected
versions.

I hope it helps you.
best wishes

-- 
Gustavo Requena
PhD student - Laboratory of Arthropod Behavior and Evolution
Universidade de São Paulo
Correspondence adress:
a/c Glauco Machado
Departamento de Ecologia - IBUSP
Rua do Matão - Travessa 14 no 321 Cidade Universitária, São Paulo - SP,
Brasil
CEP 05508-900
Phone number: 55 11 3091-7488

http://ecologia.ib.usp.br/opilio/gustavo.html

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