R help - Sep 2010 - lmer output

Hi
I have a question regarding an output of a binomial lmer-model.
The model is as follows:
lmer(y~diet * day * female + (day|female),family=binomial)

The corresponding output is:
Generalized linear mixed model fit by the Laplace approximation 
Formula: y ~ diet * day * female + (day | female) 
  AIC  BIC logLik deviance
 1084 1136 -531.1     1062
Random effects:
 Groups Name        Variance Std.Dev. Corr 
 female (Intercept) 1.403060 1.18451 
        day         0.012044 0.10975  -0.674 
Number of obs: 809, groups: female, 53

Fixed effects:
                    Estimate Std. Error z value Pr(>|z|) 
(Intercept)         0.996444   0.713703   1.396   0.1627 
dietNAA             1.194581   0.862294   1.385   0.1659 
day                 0.142026   0.074270   1.912   0.0558 .
female              0.015629   0.019156   0.816   0.4146 
dietNAA:day        -0.124755   0.088684  -1.407   0.1595 
dietNAA:female     -0.024733   0.026947  -0.918   0.3587 
day:female         -0.001535   0.001966  -0.781   0.4348 
dietNAA:day:female  0.001543   0.002720   0.568   0.5704 

Now from my understanding, the estimates represent differences in slopes 
and intercepts between different levels of "diet" and so on.

My questions:

1. Is there a way to display the coefficients for all levels of variables 
(e.g., "dietAA" and "dietNAA")? Because it is quite hard to
calculate the
slopes and intercepts for all levels of each variable.

2. Is there a way to get the degrees of freedom?

Thanks for your help.

Regards,
Denis Aydin

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<Denis.Aydin <at> unibas.ch> writes:
> I have a question regarding an output of a binomial lmer-model.
> The model is as follows:
> lmer(y~diet * day * female + (day|female),family=binomial)
  A reproducible example would always be nice.
 
> The corresponding output is:
> Generalized linear mixed model fit by the Laplace approximation 
> Formula: y ~ diet * day * female + (day | female) 
>   AIC  BIC logLik deviance
>  1084 1136 -531.1     1062
[ snip ]
> Fixed effects:
>                     Estimate Std. Error z value Pr(>|z|) 
> (Intercept)         0.996444   0.713703   1.396   0.1627 
> dietNAA             1.194581   0.862294   1.385   0.1659 
> day                 0.142026   0.074270   1.912   0.0558 .
> female              0.015629   0.019156   0.816   0.4146 
> dietNAA:day        -0.124755   0.088684  -1.407   0.1595 
> dietNAA:female     -0.024733   0.026947  -0.918   0.3587 
> day:female         -0.001535   0.001966  -0.781   0.4348 
> dietNAA:day:female  0.001543   0.002720   0.568   0.5704 
> 
> Now from my understanding, the estimates represent differences in slopes 
> and intercepts between different levels of "diet" and so on.
> 
> My questions:
> 
> 1. Is there a way to display the coefficients for all levels of variables 
> (e.g., "dietAA" and "dietNAA")? Because it is quite
hard to calculate the
> slopes and intercepts for all levels of each variable.
See if 
lmer(y~(diet-1) * (day-1) * (female-1) + (day|female),family=binomial)

helps, or see if you can use predict() with an appropriately
constructed prediction data frame -- although not sure if
predict works with GLMMs in current version of lme4.
> 
> 2. Is there a way to get the degrees of freedom?
  Giant can of worms, I'm afraid. See <http://glmm.wikidot.com/faq>
for
relevant links and alternatives.