similar to: GLMMGibbs

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) >

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

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,

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

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'

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

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

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,

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

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

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 have a problem concerning estimation of GLMM. I used methods from 3 different packages (see program). I would expect similar results for glmm and glmmML. The result differ in the estimated standard errors, however. I compared the results to MASS, 4th ed., p. 297. The results from glmmML resemble the given result for 'Numerical integration', but glmm output differs. For the

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect

Hi R-Helpers: I?m trying to use the function GLMM from lme4 package, (R-1.8.1, Windows 98),and I get the following error: > pd5 = GLMM(nplant~sitio+ + fert+ + remo+ + sitio:fert+ + remo:sitio+ + remo:fert+ + remo:fert:sitio + data=datos, + family=binomial, +

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 R-helpers, I have just installed R2.0.0 onto my machine which already had R1.9.1 present. I then tried to add to R2.0.0 a library called GLMM written by James McBroom for R1.6.0. Unfortunately, R2.0.0 does not recognise the library, even though R1.9.1 does. This is not because I have used the wrong case in the call to the library: > library('GLMM') > "Error in

Can GLMM take formula derived from another object? foo <- glm (OVEN ~ h + h2, poisson, dataset) # ok bar <- GLMM (OVEN ~ h + h2, poisson, dataset, random = list (yr = ~1)) #error bar <- GLMM (foo$formula, poisson, dataset, random = list (yr = ~1)) #Error in foo$("formula" + yr + 1) : invalid subscript type I am using R2.1.0, lme4 0.8-2, windows xp. Below is a dataset if you

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.