similar to: Summary: GLMMs: Negative Binomial family in R

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

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

I am trying to find a way to analyze a "simple" mixed model with two levels of a treatment, a random blocking factor, and (wait for it) negative binomial count distributions as the response variable. As far as I can tell, the currently available R offerings (glmmGibbs, glmmPQL in MASS, and Jim Lindsey's glmm code) aren't quite up to this. From what I have read (e.g.

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

Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately. There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two

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,

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

Dear R-ers, I am just a simple 'end-user' of R and am trying to analyse data with a binary response variable (dead or alive) in relation to weight and sex (of young birds). As some of the birds have the same biological mother, I am using mixed models with the identity of the mother as a random factor. (please, Mick Crawley, when are you going to write a chapter on mixed models with binary

Sort of a warning before I start: This post may be considered to describe a rather amateurish approach to distributing software which may annoy some people, but I sincerely hope it doesn't. I've been working for some years with David Clayton on a project which started life as an S package but has now turned into an R library. It is (now) called GLMMGibbs and estimates the parameters of

Hi, I have a couple of questions related to glmm (glmmPQL in MASS and GLMM in lme4). 1) is there some way do obtain the fitted values by group, similar to: > predict(dbd.glmmPQL, dbd.cytdens, + type="response", level=0) where dbd.glmmPQL is the fitted model and dbd.cytdens is a data frame with a subset of the factors? 2) when I double-click on a saved workspace

Dear R-ers, I am just a simple 'end-user' of R and am trying to analyse data with a binary response variable (dead or alive) in relation to weight and sex (of young birds). As some of the birds have the same biological mother, I am using mixed models with the identity of the mother as a random factor. (please, Mick Crawley, when are you going to write a chapter on mixed models with binary

Hi there, I have a question about the GLMM that I'm doing, that a statistician friend suggested I should have for my analysis. I would like to know if there's any way of obtaining a p value and R square for the full model (and not each variable separately) as to asses whether this model is somewhat appropriate or not. Can one do this for a GLMM in the lme4 package? The other thing I

Hi everyone, I'm trying to fit a generalized linear mixed effects model (logistic) in R and am having some trouble specifying the covariance structure for the random effects. I'm using glmer, which by default assumes an unstructured relationship between the random effects, but I want the structure to be a multiple of an identity. Here is my code: glmer(y ~ 1 + (x1 + x2 + x3 + x4

In a GLMM, one compares the conditional model including covariates with the unconditional model to see whether the conditional model fits the data better. (1) For my unconditional model, a different random effects term fits better (independent random effects) than for my conditional model (correlated random effects). Is this very uncommon, and how can this be explained? Can I compare these models

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Hello there This is the first time I have used r-help message board so I hope I have got the right address. I am trying to check the residuals of a GLMM model(run using the package lme4). I have been able to check the residiuals of REMLs in lme4 using the following: m1<-lmer(vTotal~Week+fCollar+ (1|fCat), collars) res<-resid(m1) plot(res) qqnorm(res) library(MASS) par(mfrow=c(2,3))

Dear R-listers, I am working with generalized linear mixed models to quantify the variance due to two nested random factors, but have hit a snag in the interpretation of variance components. Despite my best efforts with Venables & Ripley 2002, Fahrmeir & Tutz 2001, R-help archives, Google, and other eminent sources (i.e. local R gurus), I have not been able to find a definitive answer

Three related questions on LMEs and GLMMs in R: (1) Is there a way to fix the dispersion parameter (at 1) in either glmmPQL (MASS) or GLMM (lme4)? Note: lme does not let you fix any variances in advance (presumably because it wants to "profile out" an overall sigma^2 parameter) and glmmPQL repeatedly calls lme, so I couldn't see how glmmPQL would be able to fix the dispersion

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are