[R-sig-ME] GLMMs with unequal group sizes

[Rense Nieuwenhuis]

Hello Grant,

related to the previous remark (re-fitting the model without some of  
the areas), you might be interested in the influence.ME package, which  
I developed. Although focused on measures of influential data  
regarding variables at the level of the area (in your example), you  
could use the estex() function which will return the fixed estimates  
iteratively excluding each of the areas.

If you need help with using the package, you can contact me off-list.

Kind regards,

Rense Nieuwenhuis


On 11 jun 2009, at 10:12, Luca Borger wrote:

> Hello,
>
> as a simple quick check, have you tried fitting your model without  
> area 3/9 or without both of them and compared the estimates? You  
> could then also look at how well your fixed effects estimates  
> predict the values in the left-out areas.
>
> HTH
>
> Cheers,
>
> Luca
>
>
> ----- Original Message -----
> From: Grant T. Stokke <gts127 at psu.edu>
> To: r-sig-mixed-models at r-project.org
> Sent: Wed, 10 Jun 2009 23:41:52 -0400 (EDT)
> Subject: [R-sig-ME] GLMMs with unequal group sizes
>
> Hello All,
>
> I would like to use GLMMs with a binary response variable (logit  
> link) to
> model the effects of three environmental covariates on whether  
> resource
> units were used or unused by a wildlife species.  I have 15  
> different study
> areas, and very different numbers of used and unused units in each.   
> I'm
> interested in using fixed effects parameters estimates to predict the
> relative probabilities that resource units will be used across the  
> entire
> population of study areas.  Numbers of used and unused units in each  
> area
> look something like this:
>
> Area    Unused    Used
> 01        281        2
> 02        4415      1
> 03        343        30
> 04        256        1
> 05        2052      4
> 06        4050      1
> 07        238        2
> 08        743        3
> 09        2476      18
> 10        2524      1
> 11        805        1
> 12        754        4
> 13        272        1
> 14        52          1
> 15        124        1
>
> I've been using study area as a grouping factor for a random  
> intercept and
> random slope effects:
>
> fullmodel<-glmer(Used~1+x1+x2+x3+(1+x1+x2+x3|Area), family=binomial,
> data=mydata)
>
> Using 'glmer', I've been able to fit models to my data without  
> convergence
> issues, model fit is pretty good, and the results seem to make  
> sense.  My
> questions are:  Given that the number of used units in each area are  
> very
> unbalanced, to what degree can I generalize across the entire  
> population of
> study areas?  Will my estimates for the fixed effects parameters be so
> reliant on areas 3 and 9 that I'm really just limited to inferences  
> on these
> two areas?  Is there a way to quantify the relative weight of each  
> study
> area in the estimation of the fixed effects parameters (i.e. the  
> degree to
> which I can generalize across the entire population of study areas)?
>
> I've read of borrow strength, which will certainly play a big role  
> with this
> dataset, but I haven't found any examples of datasets that are as  
> unbalanced
> as mine.
>
> I realize that my questions relate to mixed models in general and  
> less to
> their implementation in R, so I hope I'm not out-of-line in posting  
> these
> questions here.  I'd guess there are probably answers to these  
> questions in
> the literature, so I'd truly appreciate any advice on where I should  
> look
> for more info.
>
> Thanks in advance,
>
> -Grant
>
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