similar to: glmm with negative binomial

Goal: use GEE or GLMM to analyze repeated measures data in R GEE problem: can?t find a way to do GEE with negative binomial family in R GLMM problem: not sure if I?m specifying random effect correctly Study question: Does the interaction of director and recipient group affect rates of a behavior? Data: Animals (n = 38) in one of 3 groups (life stages): B or C. Some individuals (~5)

To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood,

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

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

A while ago (April 2002) there was a short thread on software for generalized linear mixed models. Since that time, has anyone written or found R code to use a glmm to analyze binomial data? I study CWD in white-tailed deer, and I'd like to do a similar analysis as Kleinschmidt et al. (2001, Am. J. Epidemiology 153: 1213-1221) used to assess control for spatial structure in malaria

Full_Name: Brian Aukema Version: 1.6 OS: Windows XP Professional Submission from: (NULL) (144.92.164.204) platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 6.0 year 2002 month 10 day 01 language R Hello, I am relatively new to

Dear all, I want to build a model in R based on animal collection data, that look like the following Nr Village District Site Survey Species Count 1 AX A F Dry B 0 2 AY A V Wet A 5 3 BX B F Wet B 1 4 BY B V Dry B 0 Each data point shows one collection unit in a certain Village, District, Site, and Survey for a certain Species. 'Count' is the number of animals collected in that

Hi; I am running generalized linear mixed models (GLMMs) with the lmer function from the lme4 package in R 2.6.2. My response variable is overdispersed, and I would like (if possible) to run a negative binomial GLMM with lmer if possible. I saw a posting from November 15, 2007 which indicated that there was a way to get lmer to work with negative binomial by assigning: family =

I am looking for code that allows for a more flexible negative binomial model (similar to Stata's "gnbreg"). In particular, I am looking to be able to model the ancillary shape parameter in terms of a series of covariates. So if, y[i] ~ poisson(mu[i]) mu[i] = exp(x[i]beta + u[i]) exp(u[i]) ~ Gamma(1/alpha, alpha) I am looking to parameterize alpha as exp(z[i]gamma). If you

Hello. I am attempting to duplicate a negative binomial regression in R. SAS uses generalized estimating equations for model fitting in the GENMOD procedure. proc genmod data=mydata (where=(gender='F')); by agegroup; class id gender type; model count = var1 var2 var3 /dist=NB link=log offset=lregtm; repeated subject=id /type=exch; run; Since my dataset has several observations for

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and this generates an error for me: >

Dear All, I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes. More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk). One way to jointly model the data is to use the following specification: g(p_ijk) =

Dear R-users, A recent post (Feb 16) to R-help inquired about fitting a glmm with a negative binomial distribution. Professor Ripley responded that this was a difficult problem with the simpler Poisson model already being a difficult case: https://stat.ethz.ch/pipermail/r-help/2005-February/064708.html Since we are developing software for fitting general nonlinear random effects models we

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's GLMM function found in his "repeated" measures package with the "poisson" family. My problem though is that I don't think the poisson

Hi there. At first glance it sounded to me as an obvious "no-no" question. But, for some reason, I ran some trials and results looked pretty intriguing. So, I checked 14 genotypes (8 plants from each randomly chosen in the field) on 4 different dates and measured them under 2 different temperatures. As a response, I have 4 different partition of how light is absorbed in the leaf and

Dear all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

Hello, I have a couple questions regarding generalized linear mixed models specifically around fitting the random effects terms correctly to account for any pseudo-replication. I am reading through and trying to follow examples from Zuur et al. Mixed Effects Models and Extensions in Ecology with R, but am still at bit unsure if I am specifying the models correctly. Background information: Our

Hi, I have three questions concerning GLMMs. First, I ' m looking for a measure for the significance of the random variable in a glmm. I'm fitting a glmm (lmer) to telemetry-locations of 12 wildcat-individuals against random locations (binomial response). The individual is the random variable. Now I want to know, if the individual ("TIER") has a significant effect on the model

Dear all, I'm working with count data following over-dispersed poisson distribution and have to work with mixed-models on them (like proc GENMOD on SAS sys.). I'm still not to sure about what function to use. It seems to me that a glmmPQL will do the job I want, but I'll be glad if people who worked on this type of data can share what they learned. Thanks for your time. simon