similar to: Mixed-effects model for overdispersed count data?

Dear R-experts, Quite new to R on this end, but learning fast (I hope). I am running version 2.7.1 on Windows Vista. I have small dataset which consists of: # NestID: nest indicator for each chicken. Siblings sharing the same nest have the same nest indicator. # Chick: chick indicator consisting of a unique ID for each single chick. # Year: 1, 2. # ClutchSize: 1-, 2- , 3-eggs. # HO:

Dear R-users, Earlier this year I posted a message to this list regarding negative binomial mixed models in R. It was suggested that the program I had written should be turned into an R-package. This has now been done, in collaboration with David Fournier and Anders Nielsen. The R-package glmmADMB provides the following GLMM framework: - Negative binomial or Poisson responses. - Zero-inflation

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan

I would like to try a simple parametric bootstrap, but unfortunately (stupidly?) my models are "overdispersed" gams & glms. I'm hoping for a function that generates overdispersed poisson or negative binomial data with a given mean, scale (& shape parameter). The loose definition I'm using is overdispersed poisson produces integer values with variance=const*mean &

I am wondering if stepAIC in the MASS library may be used for model selection in an overdispersed Poisson situation. What I thought of doing was to get an estimate of the overdispersion parameter phi from fitting a model with all or most of the available predictors (we have a large number of observations so this should not be problematical) and then use stepAIC with scale = phi. Should this

Dear all, I am fitting an nonlinear glm() using optim() by first minimising glm(resp~ var1 + var2, family=binomial, data=data)$deviance where var1= exp(-a1*dist1), and var2= exp(-a2*dist2), where a1 and a2 are parameters and dist1 and dist2 are independent variables. Next, I calculate the value of var1 (and var2) by plugging in the value of al1 (and al2) that minimises deviance, and fit

Dear R users I have been looking for functions that can deal with overdispersed poisson models. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. However, we see them frequently in this type of data, and we would like to

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Information on package 'aod' Description: Package: aod Version: 1.1-2 Date: 2005-06-08 Title: Analysis of Overdispersed Data Author: Matthieu Lesnoff <matthieu.lesnoff at cirad.fr> and Renaud Lancelot <renaud.lancelot at cirad.fr> Maintainer: Renaud Lancelot <renaud.lancelot at cirad.fr> Depends: R (>=

Hi all, First, a quick thank you for R; it's amazing. I am trying to fit models for a count dataset following the overdispersed logisitic regression approach outlined in Baggerly et al. (BMC Bioinformatics, 5:144; Annotated R code is given at the end of the paper) but R is returning an error with the data below. Any help in understanding or overcoming this obstacle is appreciated.

Hi all, I have the following data: rep1_treat rep2_treat rep1_control rep2_control 2 3 4 5 100 20 98 54 0 1 2 3 23 32 27

dear list! i am running an anlysis on proportion data using binomial (quasibinomial family) error structure. My data comprises of two continuous vars, body size and range size, as well as of feeding guild, nest placement, nest type and foragig strata as factors. I hope to model with these variables the preference of primary forests (#successes) by certain bird species. My code therefore looks

I have a few questions relating to overdispersion in a sex ratio data set that I am working with (note that I already have an analysis with GLMMs for fixed effects, this is just to estimate dispersion). The response variable is binomial because nestlings can only be male or female. I have samples of 1-5 nestlings from each nest (individuals within a nest are not independent, so the response

Dear R users I have been looking for functions that can deal with overdispersed poisson models. Some (one) of the observations are negative. According to actuarial literature (England & Verall, Stochastic Claims Reserving in General Insurance , Institute of Actiuaries 2002) this can be handled through the use of quasi likelihoods instead of normal likelihoods. The presence of negatives is not

Greetings glmmADMB function users, I am trying to run a series of models using the glmmADMB function with several different distribution families (e.g., poisson, negbinom). I am using a Optiplex 790 PC with Windows 7, 16.0 GB of RAM and a 64-bit operating system. I am running R version 2.15.0 and started out using the most recent version of glmmADMB (I believe version 7.2.15). My data is zero

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

R glmmADMB question I am trying to use glmm.admb (the latest alpha version from the R forge website 0.6.4) to model my count data that is overdispersed using a negative binomial family but keep getting the following error message: Error in glmm.admb(data$total_bites_rounded ~ age_class_back, random = ~food.dif.id, : Argument "group" must be a character string specifying the

Dear all, I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit variables. However, most models fail because “the global deviance is increasing” and I am not sure what causes this behaviour. The dataset consists of counts of birds (duck) and 5 habit variables measured in the field (n= 182). The dependent

I am creating habitat selection models for caribou and other species with data collected from GPS collars. In my current situation the radio-collars recorded the locations of 30 caribou every 6 hours. I am then comparing resources used at caribou locations to random locations using logistic regression (standard habitat analysis). The data is therefore highly autocorrelated and this causes Type

Hi I am running an lmer which works fine with family=poisson mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit) But it is overdispersed. I tried using family=quasipoisson but get no P values. This didnt worry me too much as i think my data is closer to negative binomial but i cant find any examples of