GLMM, overdispersion, and method for comparing competetive models

All,

I would first like to say that I'm a relative novice with R so please
take that into consideration with your responses.  Basically, give me
the totally dumbed down version of answers when you can.

I have a biological data set with count data that I'm currently
analyzing.  Namely, I'm interested in looking at the effects of
animal age, bodysize, and antler size on annual male reproductive
success (i.e. number of fawns produced).  I would also like to see
how the relationships are influenced by changes in population
demographics.  I have been using a GLMM to evaluate the following
global model:

repro =
glmer(Fawn~Age+I(Age^2)+BodySize+SSCM+AvgAge+Age*AvgAge+I(Age^2)*AvgAge+BodySize*AvgAge+SSCM*AvgAge+(1|Sire),data=datum,family=poisson)

 where:

Age, BodySize, SSCM are measured characteristics Fawn = number of
fawns produced in a given year AvgAge = Population demographic
factor (1|Sire) = Random effect for each sampled male ID

I first used the following to evaluate potential overdispersion of my
data from the global model:

overdisp_fun <- function(model) { ## number of variance parameters
in ##   an n-by-n variance-covariance matrix vpars <- function(m) { 
nrow(m)*(nrow(m)+1)/2 } model.df <-
sum(sapply(VarCorr(model),vpars))+length(fixef(model)) rdf <-
nrow(model.frame(model))-model.df rp <-
residuals(model,type="pearson") Pearson.chisq <- sum(rp^2) prat <-
Pearson.chisq/rdf pval <- pchisq(Pearson.chisq, df=rdf,
lower.tail=FALSE) c(chisq=Pearson.chisq,ratio=prat,rdf=rdf,p=pval) }

With the following result

repro =
glmer(Fawn~Age+I(Age^2)+BodySize+SSCM+AvgAge+Age*AvgAge+I(Age^2)*AvgAge+BodySize*AvgAge+SSCM*AvgAge+(1|Sire),data=datum,family=poisson)

chisq                             ratio                          rdf
p 1.698574e+02      1.681756e+00       1.010000e+02
2.169243e-05

Since the ratio of Pearson-statistic to rdf is 1.68 I assume that I
need to take this overdispersion into account

My first inclination was to use quasipoisson distribution to account
for overdispersion; however, I see that in no longer available in
lme4.  I used glmmPQL in the MASS package with quasipoisson but do
not receive AICc information.  I had planned on using AICc to
evaluate competitive models.  My specific question is: 1) is there a
way to generate the necessary information (AICc or something like) to
compare competitive models from overdispersed data in a current R
environment? I have read
https://cran.r-project.org/web/packages/bbmle/vignettes/quasi.pdf but
I'm having a difficult time understanding exactly how to implement
from a technical perspective.  I'm on the path of trying to use a
negative binomial (I'm not locked into this method so please provide
insight if appropriate) with package glmmADMB: however, I have been
unable to get this package to load successfully.  I've followed the
instructions to the best of my understanding and abilities but cannot
figure out where I'm going wrong.  Any advice is much appreciated as
I'm totally stumped right now on many fronts.  I'm running windows 7
on 64-bit machine.  Here is what I have attempted with output:

install.packages("glmmADMB", +
repos=c("http://glmmadmb.r-forge.r-project.org/repos", +
getOption("repos")), +     type="source") Installing package into
?C:/Users/newboch/Documents/R/win-library/3.3? (as ?lib? is
unspecified) trying URL
'http://glmmadmb.r-forge.r-project.org/repos/src/contrib/glmmADMB_0.8.3.3.tar.gz'

downloaded 9.0 MB * installing *source* package 'glmmADMB' ... ** R 
** data *** moving datasets to lazyload DB ** inst ** preparing
package for lazy loading Error in loadNamespace(i, c(lib.loc,
.libPaths()), versionCheck = vI[[i]]) : there is no package called
'stringi' ERROR: lazy loading failed for package 'glmmADMB' *
removing 'C:/Users/newboch/Documents/R/win-library/3.3/glmmADMB' The
downloaded source packages are in 
?C:\Users\newboch\AppData\Local\Temp\RtmpK23VOM\downloaded_packages? 
Warning messages: 1: running command
'"C:/PROGRA~1/R/R-33~1.1/bin/x64/R" CMD INSTALL -l
"C:\Users\newboch\Documents\R\win-library\3.3"
C:\Users\newboch\AppData\Local\Temp\RtmpK23VOM/downloaded_packages/glmmADMB_0.8.3.3.tar.gz'
had status 1 2: In install.packages("glmmADMB", repos =
c("http://glmmadmb.r-forge.r-project.org/repos",  : installation of
package ?glmmADMB? had non-zero exit status

glmmADMB:::get_bin_loc()

Error in loadNamespace(name) : there is no package called ?glmmADMB?

library("R2admb") glmmADMB:::get_bin_loc()

Error in loadNamespace(name) : there is no package called ?glmmADMB?

install.packages("glmmADMB")

Installing package into
?C:/Users/newboch/Documents/R/win-library/3.3? (as ?lib? is
unspecified) Warning message: package ?glmmADMB? is not available
(for R version 3.3.1) Thanks,

Chad

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