[R-sig-ME] Checking for outliers in a glmer (lme4 package) with 3 random factors

[Maurits Van Zinnicq Bergmann]

Hi everyone, 

I have a question relating to the checking for outliers and / or influential points in my dataset using a `glmer` model with 3 random variables. I'm investigating the detection rate (`SumDetections`) of receivers over increasing distance (`sc.c.distance`), and the effect of environmental influences on this (`depth`, `temperature` and `wind`) and how this differs between different transmitters used, controlling for random effects of `receiver ID`, `replicate` and `area`. I found that the `influence.ME` might be of help, so I checked it out.  

In the manual I read that this package is only able to delete levels of 1 single grouping factor or 1 data point per time over the whole data set. Unless I read the package info incorrectly, this package cannot do what I'm looking for. I'm looking for a way to check for outliers nested within 4 grouping layers. 

How my data is organized is as follows: First my data discerns between `Areas`. Within areas, multiple `replicates` were done. Each replicate consisted of 5 `distances` at which the detection rate was tested. For each distance, 20 `receivers` were tested. These experiments were repeated over several days.

My model looks like this:

    m <- lmer(SumDetections ~ tm + sc.c.distance + tm:sc.c.distance + c.tm.depth + 
                 c.receiver.depth + c.temp + c.wind + (1|replicate) + (1|SUR.ID) + (1|Area) + (1|Day), 
                 data = df3, family = poisson)

My questions are:  

 - Is it possible to check for outliers of which the data is nested within 3 layers with use of influence.me? 
 - If so, how should I specify the command to get what I'm looking for, and how should I interpret the returned data by the Cook's distance or dfbetas? 
 - If not, is there another package that allows me to check for outliers?

Thanks in advance for your help.
Cheers,