similar to: help on glmmML

Hello! Can anyone help me. I am using the posterior.mode from the result of glmmML. It apears to be different from the BLUe estimate of the RANDOM statement in PROC NLMIXED in SAS. Why is that? Thank you Ronen [[alternative HTML version deleted]]

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite of the inner structures, so frequent updates (bug fixes) may be expected for some time. News: * The Laplace and adaptive Gauss-Hermite approximations to the log likelihood function are fully implemented. The Laplace method is made the default. It should give results you can compare to the results from 'lmer' (for the

I have uploaded a new version (0.30-2) of glmmML to CRAN today. This is a rather extensive upgrade, mostly internal. Adaptive Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the integrals in the log likelihood function. The user can choose the number of points (default is 16), I _think_ that choosing 1 point will result in a Laplace approximation. The integrals in the score and

I have uploaded a new version (0.30-2) of glmmML to CRAN today. This is a rather extensive upgrade, mostly internal. Adaptive Gauss-Hermite quadrature (GHQ) is now used for the evaluation of the integrals in the log likelihood function. The user can choose the number of points (default is 16), I _think_ that choosing 1 point will result in a Laplace approximation. The integrals in the score and

I'd like to estimate an ordinal logistic regression with a random effect for a grouping variable. I do not find a pre-packaged algorithm for this. I've found methods glmmML (package: glmmML) and lmer (package: lme4) both work fine with dichotomous dependent variables. I'd like a model similar to polr (package: MASS) or lrm (package: Design) that allows random effects. I was

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'm trying to use the glmmML package on a Windows machine. When I try to install the package, I get the message: > {pkg <- select.list(sort(.packages(all.available = TRUE))) + if(nchar(pkg)) library(pkg, character.only=TRUE)} Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Hi, I have a spatial data set with many observations (~50,000) and would like to keep as much data as possible. There is spatial dependence, so I am attempting a mixed model in R with a spherical variogram defining the correlation as a function of distance between points. I have tried nlme, lme, glmmML, and glmmPQL. In all case the matrix needed (seems to be (N^2)/2 - N) is too large for my

A new version of 'glmmML' (0.28-4) is uploaded to CRAN. The most important new feature is the possibility to get a p-value for the test of the hypothesis that the variance of the random effects is zero, on the wishlist of many R users these days! Note two things: (i) glmmML only treats random intercepts for binomial and poisson models, (ii) the p-value is calculated thru bootstrapping

A new version of 'glmmML' (0.28-4) is uploaded to CRAN. The most important new feature is the possibility to get a p-value for the test of the hypothesis that the variance of the random effects is zero, on the wishlist of many R users these days! Note two things: (i) glmmML only treats random intercepts for binomial and poisson models, (ii) the p-value is calculated thru bootstrapping

Dear all, This question is basic but I am stumped. After running the below, I receive the message: "non-integer #successes in a binomial glm!" model1 <- glmmML(y~Brood.Size*Density+Date.Placed+Species+Placed.Emerging+Year+rate.of.parperplot, data = data, cluster= data$Patch, family=binomial(link="logit")) My response variable is sex ratio, and I have learned quickly not

Maybe an R-packages posting guide with an example and an automatic append of a one or two line summary at the end of each article posted - as already done on r-help. On 8/21/06, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > Hi G?ran, > > >>>>> "GB" == G?ran Brostr?m <goran.brostrom at gmail.com> > >>>>> on Mon, 21 Aug

Hi there, I am using SAS Proc NLMIXED to maximize a likelihood with multivariate normal random effects. An example is the two part random effects model for repeated measures semi-continous data with a cluster at 0. I use the "model y ~ general(loglike)" statement in Proc NLMIXED, so I can specify a general log likelihood function constructed by SAS programming statements. Then the

hello, I have a longitudinal data: idn mort30 newinfec 1 0 1 1 0 1 1 0 1 1 0 1 2 1 1 2 1 1 2 1 1 3 0 0 3 0 0 3 0 0 3 0 0 3

Hi! I would like to perform an F-Test over more than one variable within a generalized mixed model with Gamma-distribution and log-link function. For this purpose, I use the package mgcv. Similar tests may be done using the function "anova", as for example in the case of a normal distributed response. However, if I do so, the error message "error in eval(expr, envir, enclos) :

Hi all, I am using cross-validation to validate a generalized linear mixed effects model fitted using glmmPQL. i found that the predict function has a problem and i wonder if anyone has encountered the same problem? glmm1 = glmmPQL(y~aX+b,random=~1|sample,data=traindata) predict(glmm1,newdata=testdata,level=1,type="response") gives me all "NA"s. it works for level=0 (the

Hello all R-user I am relative new to the R-environment and also to GLMM, so please don't be irritated if some questions don't make sense. I am using R 2.0.0 on Windows 2000. I investigated the occurrence of insects (count) in different parts of different plants (plantid) and recorded as well some characteristics of the plant parts (e.g. thickness). It is an unbalanced design with 21

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