similar to: ADMB error- function maximizer failed (couldnt find STD file)

Dear all, I am having some problems trying to run a GLMM model with zero-inflation using the alpha version of glmmADMB (0.6.4) using R (2.13.1) in Windows and I would greatly appreciate some help. My count response variable (number of birds: count) fits a negative binomial distribution and the explanatory variables are both continuous and categorical (species= 17). The three random effects are

Hi there, I am new to the package glmmadmb, but need it to perform a zero-inflated gzlmm with a binomial error structure. I can't seem to get it to work without getting some strange error messages. I am trying to find out what is affecting the number of seabird calls on an array of recorders placed at 4 sites on 6 islands. I have nightly variables (weather and moonlight), site variables

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 Dr Skaug and R users, just discovered glmm.admb in R, and it seems a very useful tool. However, I ran into a problem when I compare two models: m1<-glmm.admb(survival~light*species*damage, random=~1, group="table", data=bm, family="binomial", link="logit") m1.1<-glmm.admb(survival~(light+species+damage)^2, random=~1, group="table", data=bm,

Dear R community, I have some questions regarding the analysis of a zero-inflated count dataset and repeated measures design. The dataset is arranged as follows : Unit of analysis: point - these are points were bird were counted during a certain amount of time. In total we have about 175 points. Each point is located within a certain habitat fragment (here: "site"= A-B-C-D-..., in

Hi R-users,   I intend to apply a mixed model on a set of longitudinal data, with a negative binomial distributed dependent variable, and after following the discussions on R help list I saw that more experienced people recommended using lmer (from lme4 pack), glmmPQL (from MASS) or glmm.admb (from glmmADMB pack)     My first problem: yesterday this syntax was ok, now I get this weird message (I

library(glmmADMB) #Example for glmm.admb data(epil2) glmm.admb(y~Base*trt+Age +Visit,random=~Visit,group="subject",data=epil2,family="nbinom") Gives: Error in glmm.admb(y ~ Base * trt + Age + Visit, random = ~Visit, group = "subject", : The function maximizer failed ****************** R version 2.4.1 RC (2006-12-14 r40181) powerpc-apple-darwin8.8.0 locale: C

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

Dear R-list, I thought that I would let some of you know of a free R package, glmm.ADMB, that can handle mixed models for overdispersed and zero-inflated count data (negativebinomial and poisson). It was built using AD Model Builder software (Otter Research) for random effects modeling and is available (for free and runs in R) at: http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html I

Dear List, Some of you have been following the discussion of the GPL and its inclusion in the glmmADMB package we created for R users. I would like to provide a bit of background and include an email we received from Prof. Ripley so that everyone can be aware of how some might use the GPL to try to force access to proprietary software. I think this is interesting because many have voiced the

Hello everybody, I am using glmmADMB and when I run some models, I recieve the following message: Erro em glmmadmb(eumencells ~ 1 + (1 | owners), data = pred3, family = "nbinom", : The function maximizer failed (couldn't find STD file) Furthermore: Lost warning messages: Command execution 'C:\Windows\system32\cmd.exe /c

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

[cc'ed back to r-help] I've started to take a look, and there's nothing immediately obvious about the problem with the fit (the warnings and errors are about a "non-positive-definite Hessian", which usually means an overfitted/poorly identified model) -- still working on whether there's a way to get more useful information. As it turns out, glmmADMB's default

Dear All, As you know, glmmADMB package use ML method for estimation. Is it possible to use REML estimation method for zero-inflated Poisson distribution? For ML method, poi_ML <- glmm.admb(los ~ psihigh + trt.mod + trt.high + psihigh*trt.mod + psihigh*trt.high + 1, random = ~1, group="site", family="poisson", data=edcap) summary(poi_ML) How can I control to use REML

I am attempting to build a package on r-forge and running into a weird error. I have been in correspondence with the R-forge admins and am turning to r-devel on the remote chance that someone might have a guess as to what is going wrong or a suggestion about further diagnostics/experiments I could try ... The package seems to build fine on my system(s) with R CMD build --compact-vignettes

After copy and pasting the code for installation of glmmADMB package suggested at http://glmmadmb.r-forge.r-project.org/, I receive this message. > install.packages("glmmADMB", repos="http://r-forge.r-project.org",type="source") Warning message: In getDependencies(pkgs, dependencies, available, lib) : package 'glmmADMB' is not available I'm not

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

the code is list below , i can run this code for laplace approximation but not importance sampling. the code work properly if use impSamp=0 , but error for any other number >0 . could you guys help me out. thanks so much ! source("http://www.mi.uib.no/~skaug/cash/lesaffre_dat.s") library(glmmADMB) fit_glmmADMB_ip <-

Hi here, i recently notice the software built by otter company know as AD-MB tools . i explore a little bit this tools under the R Package for mixed logistics model. it is a very interesting tool. but i get several questions. first question: i tried the R code below, and have the error , i can run the code for switching the setting to impSamp=0 , it means i can only run the code under

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