search for: admbr

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 is a "ranef.glmm.admb()" function...

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

Hello, I'm running a simulation study of a multilevel model with binary response using the binomial probit link. It is a random intercept and random slope model. GLMMPQL and lmer fail to converge on a *significant* portion of the *generated* datasets, while MlWin gives reasonable estimates on those datasets. This is unacceptable. Does anyone has similar experiences? Regards, Roel de

...or less (although the more complicated turns out to be overparameterized) Using the random effects module of AD Model Builder we have developed a shared library (Windows dll) that can be called from R via the driver function glmm.admb(). The function can be downloaded from http://otter-rsch.com/admbre/examples/nbmm/nbmm.html The two models of Booth et al are fit by the commands: glmm.admb(y~Base*trt+Age+Visit,random=~1,group="subject",data=epil2) glmm.admb(y~Base*trt+Age+Visit,random=~Visit,group="subject",data=epil2) I will be happy to receive feedback on the function gl...

...ons, such as predict(), fitted.values(), ... are still missing from this package, but will hopefully be added in later versions (contributions/suggestions are most welcome). Other response distributions than negative binomial or Poisson could easily be added. Download site: http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html The package is based on the software ADMB-RE, but the full unrestricted R-package is made freely available by Otter Research Ltd and does not require ADMB-RE to run with user supplied data. I you will find this useful, Hans Skaug -- Hans Skaug Department of M...

Does someone have code, or point to a source to get it, to model repeated measures zero-inflated poisson data. The data come from a replicated field trial comparing two treatments - a control and a test treatment. Thanks in advance ------------------------------------ Subhash Chandra, DSc Senior Biometrician Primary Industries Research Victoria Department of Primary Industries 1 Ferguson Road

...able R package glmmADMB can do Adaptive Gaussian Quadrature for this type of model, since it is built using AD Model Builder's random effects module which incorporates this feature. There is now a beta version of the software for people using R on the Mac intel platform. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Cheers, Dave -- David A. Fournier P.O. Box 2040, Sidney, B.C. V8l 3S3 Canada Phone/FAX 250-655-3364 http://otter-rsch.com

...://finzi.psych.upenn.edu/R/Rhelp02a/archive/48646.html *this refers to the random effects module of AD Model Builderthat can be called from R via the driver functon glmm.admb(). Their example problem fits the model with a negative binomial. The function can be downloaded from http://otter-rsch.com/admbre/examples/nbmm/nbmm.html ************ *********** My Original Query Greetings R Users! I have a data set of count responses for which I have made repeated observations on the experimental units (stream reaches) over two air photo dates, hence the mixed effect. I have been using Dr. Jim Lindsey&...

...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 have been using this package for a split plot design along streams (my M.Sc thesis). My response data was best described by a negative binomial distribution (without zero-inflation) (beaver dam counts). The model worked very well for my dataset and appeared to...

There has recently been some discussion on the list about AD Model builder and the suitability of R for constructing the types of models used in fisheries management. https://stat.ethz.ch/pipermail/r-help/2006-January/086841.html https://stat.ethz.ch/pipermail/r-help/2006-January/086858.html I think that many R users understimate the numerical challenges that some of the typical

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,

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 All, I'm analysing a negative binomial dataset from a population-based study. Many covariates were determined on household level, so all members of a household have the same value for those covariates. In STATA, there seems to be an option for 'clustered analysis' for neg-bin regression. Does an equivalent exist for R(MASS)'s glm.nb or a comparable function? Many thanks for

I am using the 'glmmPQL function in the 'MASS' library to fit a mixed effects logistic regression model to simulated data. I am conducting a series of simulations, and with certain simulated datasets, estimation of the random effects logistic regression model unexpectedly terminates. I receive the following error message from R: Error in lme.formula(fixed=zz + arm.long,random=~1 |