similar to: writing a package for generalized linear mixed modesl

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

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 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

Hi all I know GLMMGibbs is still in beta but has anyone experienced (and solved ;-) this problem? I decided to look at the seeds example but I get a core dump on two intel linux boxes and also a sun workstation. All are running R1.3.0 but different hardware/OS's so I think I've done something wrong > library(GLMMGibbs) > data(seeds) > seeds$plate <- as.factor(1:21) >

Hello, ? I believe that in R, it is not possible to analyze mixed effect-models when the distribucion is not gaussian (p.e. binomial or poisson), isn't? ? Somebody can suggest me alternative? ? thanks ? xavi ? -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info",

Dear R-users, I wondered if anyone has extended GLMMGibbs to include correlated random effects, and if so, whether they would be willing to let me use their code? Jonathan Myles has no plans to extend glmm in this manner within the foreseeable future. With thanks, Patty -- -------------------------------------------------------------------------------- Assoc Prof Patty Solomon

Hello, I am trying to use glmm() in library GLMMGibbs, but I don't seem to have the package and it is not listed on CRAN. Is it no longer supported? (I am using R 2.1.1 on Windows.) Many thanks in advance for your help. Regards, Mark The information contained in this e-mail is confidential and...{{dropped}}

Hello, I would like to fit generalized linear mixed models but I did not find the package allowing such procedure. R help under nlme package gives me "glmmPQL(MASS)" but this file does not appear in contributed packages. Thanks in advance for your answer. Alexandre MILLON -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read

Hello, I would like to know if there is any package that allow us to fit Generalized Linear Models via Maximum Likelihood and Linear Models using Generalized Least Squarse in R as the functions glim and gls, respectively, from S-Plus. Also, anybody know if there is any package that fit Log-Linear Models using Generalized Least Squares? Any help will be very useful. Thanks, -- Frederico

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

Hello, can anyone explain the following error I get when trying to use the CHECK command to check a new version of my pakcage under 1.4.0? ****** ./R CMD check ~/GLMMGibbs.0.5.1/GLMMGibbs * checking for working latex ... OK * using log directory `/homef/jonm/R-1.4.0/bin/GLMMGibbs.Rcheck' ... <Installs library, documentation, and then performs various tests, including the example,

Dear all; Installing the GLMMGibbs package to my Solaris Unix box, I got an compiling error: ars.c:497:10: missing terminating " character ars.c: In function `dump_arse': ars.c:498: error: parse error before "mylesj" ..... The compiling error was reported to the list on Jul 3, 2003. According to Prof. Brain Ripley this is a known problem with the package and gcc 3.3,

Hi, Is there an R library that has the same functionalities of Splus7.0+ library correlatedData? I'd appreciate any input. Hakan Demirtas [[alternative HTML version deleted]]

Hi Folks, I'm seeking confirmation of something which is probably true but which I have not managed to find in the documentation. I have a binary response y={0.1} and a variable x and have fitted a probit response to the data with f <- glm( y~x, family=binomial(link=probit) ) and then, with a specified set of x-value X I have used the predict.glm function as p <- predict( f, X,

I getting compilation errors when trying to install GLMMGibbs (see below). I'm running R v 1.7.1 on SuSE Linux 8.2. Has anyone else had this problem? I tried it on a Win2000/R 1.5.1 combination and it worked fine. Any hints are greatly appreciated. Thank you in advance, Damien >install.packages("GLMMGibbs") trying URL `http://cran.r-project.org/src/contrib/PACKAGES'

Hi All, does anyone know if the package GLME by J. Pinheiro is available anywhere in any form? checking on the archive I got that it was at some point, as as a beta version (for S-Plus only, alas)... Cheers, Federico ========================= Federico C.F. Calboli Department of Biology University College London Room 327 Darwin Building Gower Street London WClE 6BT Tel: (+44) 020 7679 4395

Hello! could you give me some advice where i can finde out/recieve/download a GLME package, written by Jose Pinheiro. i couldn'ti find it in the package lists (probably becouse it is still in a beta version). thank you for your replay

Greetings- I'm working with data that are multilevel in nature and have a dichotomous outcome variable (presence or absence of an attribute). As far as I can tell from reading archives of the R and S lists, as well as Pinheiro and Bates and Venables and Ripley, - nlme does not have the facility to do what amounts to a mixed-effects logistic regression. - The canonical alternative is

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, I try to make a binomial analysis using GLMM in a longitudinal data file. Is correct to use anova(model) to access the significance of the fixed terms? Thanks Ronaldo -- Todos somos iguais perante a lei, mas nao perante os encarregados de faze-las cumprir. -- S. Jerzy Lec -- |> // | \\ [***********************************] | ( ? ? ) [Ronaldo Reis J?nior ]