[R-sig-ME] GLMM, overdispersion, and method for comparing competetive models

[Chad Newbolt]

Thanks.  I'll  take this into consideration and get back to everyone.  Please disregard the other posting that was sent out today containing the exact same content.  I accidentally posted twice during the new member enrolling process.  Sorry and thanks again for the help.

Chad 
________________________________________
From: Ben Bolker <bbolker at gmail.com>
Sent: Friday, June 24, 2016 3:57 PM
To: Chad Newbolt; R-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] GLMM, overdispersion, and method for comparing competetive models

  A couple of quick comments:

  (1) try

install.packages(c("R2admb","stringr","plyr","coda"))

  before doing the glmmADMB installation.

  (2) more advice about overdispersion is available at
http://tinyurl.com/glmmFAQ#Overdispersion

 ...

On 16-06-24 04:02 PM, Chad Newbolt wrote:
> 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)
>
>
overdisp_fun(repro)
> 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'
>
>
Content type 'application/x-gzip' length 9391177 bytes (9.0 MB)
> 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
>
> [[alternative HTML version deleted]]
>
>
>
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