search for: glmmml

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 intercept e.g. I have a standard error of 0.4354 from glmm and 0...

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 lib...

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 to use proportion values as data. Instead, my response variable y is a 2 co...

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...

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...

...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 perhaps I am misunderstanding the translation from my simple mixed model to the syntax of glmmML and lmer. I get roughly the same (inaccurate) fixed effect parameter estimates from glmmML and lmer, but the information they give on the variance components is quite different. Thanks in advance. Now I paste in the example code ### Paul Johnson <pauljohn at ku.edu> ### 2006-03-08 N <...

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 Lap...

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 machine to handle even when maxed (memory.limit and virtual memory in vista). Past the range of my variogram (which I have a good estimate of), the matrix that R is trying to allocate will have 0 values (I b...

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 Lap...

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 binomia...

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 binomia...

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...

...za.pl/users/kamil/r/ can do these problems (I hate to recommend this package because it offers the danger of thoughtless convenience, but if you really know that you want to enumerate models and do IC-based model averaging it can save a lot of time). At the moment, though, it doesn't work with glmmML-based objects (you could ask the author to extend it). When I tried stepAIC it didn't really enumerate all the models for me (that's not its purpose), so I went through and enumerated by hand. For example; library(glmmML) set.seed(1001) a <- runif(100) b <- runif(100) c <- run...

.../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 2006 11:12:49 +0200 writes: > > GB> A new version, 0.65-1, of glmmML is now on CRAN. It is a major rewrite > GB> of the inner structures, so frequent updates (bug fixes) may be > GB> expected for some time. > > > GB> News: > > [............] > > Sorry for my lazy question : > > What does the package do that lme...

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]]

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,

...lmmPQL/MASS/Venables&Ripley Value Std.Error DF t-value p-value (Intercept) 3.41 0.519 169 6.58 0.0000 trtdrug -1.25 0.644 47 -1.94 0.0588 trtdrug+ -0.75 0.645 47 -1.17 0.2484 I(week > 2)TRUE -1.61 0.358 169 -4.49 0.0000 --- glmmML/glmmML/Brostr?m coef se(coef) z Pr(>|z|) (Intercept) 3.579 0.701 5.10 3.3e-07 trtdrug -1.369 0.694 -1.97 4.8e-02 trtdrug+ -0.789 0.700 -1.13 2.6e-01 I(week > 2)TRUE -1.627 0.482 -3.38 7.3e-04 --- geese/geepack/Jun Yan...

In my package 'glmmML' I'm using old C code and linpack in the optimizing procedure. Specifically, one part of the code looks like this: F77_CALL(dpoco)(*hessian, &bdim, &bdim, &rcond, work, info); if (*info == 0){ F77_CALL(dpodi)(*hessian, &bdim, &bdim, det, &job);...

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or overdispersed poisson). Is there any other package that offers this functionality? Thanking you in advance, Costas [1] Yes, I know I can use this on another OS. But it is kind of a nuisance...

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 thinking there might be a trick that might allow me to use a program written for a dichoto...