[R-sig-ME] Question about GLMM and overdispersed data

Sat Jun 25 20:46:02 CEST 2016

Chad,

You are reposting exactly the same question and Ben Bolker already answered
it. If Ben's reply didn't help, you need to post a follow-up question into
the original tread instead of opening a new one.

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey

2016-06-24 21:52 GMT+02:00 Chad Newbolt <newboch op auburn.edu>:

> 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
>
>
>
>
>
>
>
>
>
>
>
>
>
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>
>
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