similar to: glmmML updated

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

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

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! I am using glmmML for a logitic regression with random effect. I use the posterior.mode as an estimate for the random effects. These can be very different from the estimates obtained using SAS , NLMIXED in the random with out= option. (all the fixed and standard error of random effect estimators are almost identical) Can someone explain to me why is that. The codes I use: R:

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 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

Hello, I get a segfault when running glmmboot in my own package glmmML. Has happened many time before, but this time I get no hint of where in my C functions the error might be. I give the output below. Can this be an R bug? I suspect it has to do with repeated calls to 'vmmin' like this: for (...){ vmax = vmaxget(); vmmin(*p, b, &Fmin, bfun,

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

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

Spencer Thanks for your thoughts on this. I did a bit of work and did end up with a method (more a trick), but it did work. I am certain there are better ways to do this, but here is how I resolved the issue. The integral I need to evaluate is \begin{equation} \frac{\int_c^{\infty} p(x|\theta)f(\theta)d\theta} {\int_{-\infty}^{\infty} p(x|\theta)f(\theta)d\theta} \end{equation} Where

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

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

Dear R Community, I?m relatively new in the field of R and I hope someone of you can help me to solve my nerv-racking problem. For my Master thesis I collected some behavioral data of fish using acoustic telemetry. The aim of the study is to compare two different groups of fish (coded as 0 and 1 which should be the dependent variable) based on their swimming activity, habitat choice, etc.

Hi, I am trying to find a function that returns simply the weights and points of an n point gauss hermite integeration, so that I can use them to fit a non-standard likelihood. I have found some documentation for the function 'gauss.hermite' written by jim lindley, but can't find the actual binary on CRAN I'm aware there are lots of functions like glmm, glmmML etc to fit mixed

Another sample problem: In the Windows version of R-2.5.0, data(GHQ,package='HSAUR') layout(1) GHQ_glm_1<- glm(cbind(cases,non.cases) ~ GHQ, data=GHQ, family=binomial()) summary(GHQ_glm_1) yfit_glm_1<- predict(GHQ_glm_1, type='response') layout(1) plot(probs ~ GHQ,pch=1,col=1,ylab='Probability(Case)', data=GHQ) lines(yfit_glm_1 ~ GHQ, pch=3,col=3, data=GHQ)

Dear list, [cross-posting from Stack Overflow where this question has remained unanswered for two weeks] I'd like to perform a numerical integration in one dimension, I = int_a^b f(x) dx where the integrand f: x in IR -> f(x) in IR^p is vector-valued. integrate() only allows scalar integrands, thus I would need to call it many (p=200 typically) times, which sounds suboptimal. The