Post-hoc test and Cook's distance vs. leverageplot for glmer from package lme4

Hi,

My name's Mario Garrido, a postdoctoral student in Biology. I am relatively
new with r and despite I find it brilliant I am having some difficulties in
finding some functions and interpreting the syntaxes.

At the moment I am working on a GLZM model which fits a Poisson
distribution. I am having some problems with two issues

1.     after calculating Akaike weights, my best model is one including a
4-way interaction term. It is the following:

model7<-glmer(active~ treatment*daytype*time*age+(1|
indiv),family=poisson(link=log),nAGQ=1)

Is there any function or package to perform a Post hoc test to know which
subset of 2- and 3-way interaction terms have more influence on the model?

2.     In addition, I find how to compute Cook's distance both for the
function glmer and for the function lmer using the package influence.ME.
This package also allow to make some graphs but, is there any
package or function to do it and obtain a plot similar to this:
https://climateaudit.files.wordpress.com/2012/09/lew_cooks-distance1.png?

Mario Garrido <gaiarrido at ...> writes:

Hi,

My name's Mario Garrido, a postdoctoral student in Biology. I am

relatively

new with r and despite I find it brilliant I am having some difficulties

in

finding some functions and interpreting the syntaxes.

At the moment I am working on a GLZM model which fits a Poisson
distribution. I am having some problems with two issues

1.     after calculating Akaike weights, my best model is one including a
4-way interaction term. It is the following:

model7<-glmer(active~ treatment*daytype*time*age+(1|
indiv),family=poisson(link=log),nAGQ=1)

Is there any function or package to perform a Post hoc test to know which
subset of 2- and 3-way interaction terms have more influence on the

model?

  It strikes me that this is a somewhat difficult question
conceptually, as well as computationally.  How are you dealing
with the issues of marginality?  (See Venables "Exegeses on linear
models", available by internet-searching, for a discussion of
marginality ...)  In other words, how do you define what a
2- or 3-way interaction term means?

 *If* you can define what you mean (e.g. if simply setting the
parameters related to a specific lower-level interaction to zero
makes biological or scientific sense), then you could drop the
terms and look at the difference in AIC or log-likelihood, and
use some sort of multiple comparisons to deal with the post-hocness
of it all.

2.     In addition, I find how to compute Cook's distance both for the
function glmer and for the function lmer using the package influence.ME.
This package also allow to make some graphs but, is there any
package or function to do it and obtain a plot similar to this:
https://climateaudit.files.wordpress.com/2012/09/lew_cooks-distance1.png

?

  Have you found a way to compute (or define) leverage for a GLMM?
(Maybe that's what you're asking for.)

  Ben Bolker