similar to: GLMMGibbs crashes on seeds data

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}}

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,

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'

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

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,

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

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

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

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

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

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",

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

Dear R-listers, I would like to carry out a very basic descriptive analysis of homicides rates in Italy, taking into account both the spatial dimension (103 provinces) and the temporal dimension (10 years), but no covariates. In practice, what I would like to do is to describe spatio-temporal variation of homicide rates, identifying those combinations of province-year where the homicide rate

Hi All: Package TSERIES is stated to depend on MVA. However, there is no MVA package to be found under the list of package sources. Best wishes, ANDREW tseries: Package for time series analysis Package for time series analysis with emphasis on non-linear and non-stationary modelling Version: 0.7-6 Depends: ts, mva, quadprog Date: 2001-08-27 Author: Compiled by Adrian

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

I've got another edition of my simulation replication framework. I'm attaching 2 R files and pasting in the readme. I would especially like to know if I'm doing anything that breaks .Random.seed or other things that R's parallel uses in the environment. In case you don't want to wrestle with attachments, the same files are online in our SVN

Deal all, as MCMClogit does not allow for the specification of several chains, I have run my model 3 times with different random number seeds and differently dispersed multivariate normal priors. For example: res1 = MCMClogit(y~x,b0=0,B0=0.001,data=mydat, burnin=500, mcmc=5500, seed=1234, thin=5) res2 = MCMClogit(y~x,b0=1,B0=0.01,data=mydat, burnin=500, mcmc=5500, seed=5678, thin=5) res3 =

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.