I guess that the number of observation is way to low for estimate such a 
high number of parameters (you didn't mention the number of individuals).
As for the goodness of fit, isn't better to look at at how the model 
fits the data (pseudo R2, percent correct and similar)?

Good luck,

Marko



  On 01.08.2014 00:01, yuki fujita wrote:
> Hi
>
>
>
>
> I need a help with testing goodness of fit for Generalized linear mixed model.
> It seems there is no solid way of doing it, but I'd like to make sure the model is a good fit.
>
>
>
>
> My model is
>
>
>
>
> model1 <- glmer(cbind(CorrectHits,12-CorrectHits)~ ADHDYN*Condition + (1 |�..ID),
>
>
>                  data = bert, family="binomial")
>
>
>
>
> where ADHDYN has a 2 levels
> and 3 levels in Condition, and interaction. (so 6 fixed effect
> parameters). We have allowed random inetrcept for individuals. The
> number of observation is 258.
>
>
>
>
> I tried to carry our the
> goodness of fit test by using chi-square test, but I get 0 as a answer
> so I am not sure if I did right.
>
>
>
>
>
> # The degrees of freedom is obtained by number of obs - number of fixed params
>
>
> df <- length(bert$�..ID)-6;df
>
>
> 1-pchisq(952.5144 , df=251) # The output was 0
>
>
>
>
> Any better way of testing the goodness of fit?
>
>
>
>
> We carried out the same model
>   but with glm, but we want to compare which model gives us smaller
> Satandard error. The estimate of coefficients were very similay between
> GLm and GLMM models.
>
>
>
>
>
>
>
> Thanks in advance
> Yuki  		 	   		
> 	[[alternative HTML version deleted]]
>
>
>
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