similar to: glmms with negative binomial responses

Here is a summary of responses to my original email (see my query at the bottom). Thank you to Achim Zeileis , Anders Nielsen, Pierre Kleiber and Dave Fournier who all helped out with advice. I hope that their responses will help some of you too. ***************************************** Check out glm.nb() from package MASS fits negative binomial GLMs.

I wonder if someone has tried to implement the hierarchical generalized linear model (HGLM) approach of Lee and Nelder (JRSSB, 1996, 58: 619-56) in R. Thanks in advance. Emmanuel Paradis At 17:18 01/04/02 +0100, ripley at stats.ox.ac.uk wrote: >On Mon, 1 Apr 2002, Jason Liao wrote: > >> Happy new month, everyone! >> >> I am planning to write a NIH grant proposal to study

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

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 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, 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

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)

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

Dear All, I wonder if there is a way to fit a generalized linear mixed models (for repeated binomial data) via a direct Maximum Likelihood Approach. The "glmm" in the "repeated" package (Lindsey), the "glmmPQL" in the "MASS" package (Ripley) and "glmmGIBBS" (Myle and Calyton) are not using the full maximum likelihood as I understand. The

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

Thanks to those who responded to my inquiry about generalized linear mixed models on R and S-plus. Before I summarize the software, I note that there are several ways of doing statistical inference for generalized linear mixed models: (1)Standard maximum likelihood estimation, computationally intensive due to intractable likelihood function (2) Penalized quasi likelihood or similar

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

Happy new month, everyone! I am planning to write a NIH grant proposal to study ways to speed Monte Carlo based maximum likelihood algorithm for hierarchical models with a focus on generalized linear mixed models (GLM with random effects). I thought it would be nice and also increase the chance of funding if I could produce an R package in the process. I understand that Prof. Pinheiro ang Bates

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

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

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

Seems the message below and the thread have reveived no attention/answer. The output presented is quite tricky. Looks like if lmer (lme4 0.9975-10) has accepted a negative binomial link with reasonable estimates, although it was not designed for... What can one think about result validity ? Best Patrick Message: 34 Date: Thu, 29 Oct 2009 06:51:24 -0700 (PDT) From: "E. Robardet"

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 Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Dear R users, I'm performing some GLMMs analysis with a negative binomial link. I already performed such analysis some months ago with the lmer() function but when I tried it today I encountered this problem: Erreur dans famType(glmFit$family) : unknown GLM family: 'Negative Binomial' Does anyone know if the negative binomial family has been removed from this function? I really