similar to: Cross compiler taqrgeting Mac OS X

The freely available R package glmmADMB can do Adaptive Gaussian Quadrature for this type of model, since it is built using AD Model Builder's random effects module which incorporates this feature. There is now a beta version of the software for people using R on the Mac intel platform. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Cheers, Dave -- David A. Fournier

Hi All, Following Mike Praeger's posting on this list, I'm happy to pass on that AD Model Builder is now freely available from the ADMB Foundation. http://admb-foundation.org/ Two areas where AD Model builder would be especially useful to R users are multi-parmater smooth optimization as in larg scale maximum likelihood estimation and the analysis of general nonlinear random

There has recently been some discussion on the list about AD Model builder and the suitability of R for constructing the types of models used in fisheries management. https://stat.ethz.ch/pipermail/r-help/2006-January/086841.html https://stat.ethz.ch/pipermail/r-help/2006-January/086858.html I think that many R users understimate the numerical challenges that some of the typical

Do Users of Nonlinear Mixed Effects Models Know Whether Their Software Really Works? Lesaffre et. al. (Appl. Statist. (2001) 50, Part3, pp 325-335) analyzed some simple clinical trials data using a logistic random effects model. Several packages and methods MIXOR, SAS NLMIXED were employed. They reported obtaining very different parameter estimates and P

Conservative robust estimation methods do not appear to be currently available in the standard mixed model methods for R, where by conservative robust estimation I mean methods which work almost as well as the methods based on assumptions of normality when the assumption of normality *IS* satisfied. We are considering adding such a conservative robust estimation option for the random effects to

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

Dear List, Some of you have been following the discussion of the GPL and its inclusion in the glmmADMB package we created for R users. I would like to provide a bit of background and include an email we received from Prof. Ripley so that everyone can be aware of how some might use the GPL to try to force access to proprietary software. I think this is interesting because many have voiced the

My understanding is that if a licensee wants to redistribute GPL code (or work derived from GPL code), then it has to be done under GPL as well, meaning the person must make it known to users that they can have access to the source code if so desired, and they can do anything they want with that code (including selling), but GPL must remain in force for further redistribution. FSF used to sell

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

Dear R-users, Earlier this year I posted a message to this list regarding negative binomial mixed models in R. It was suggested that the program I had written should be turned into an R-package. This has now been done, in collaboration with David Fournier and Anders Nielsen. The R-package glmmADMB provides the following GLMM framework: - Negative binomial or Poisson responses. - Zero-inflation

Here is a summary of responses to my original email (see my query at the bottom). Thank you to Achim Zeileis , Anders Nielsen, Pierre Kleiber and Dave Fournier who all helped out with advice. I hope that their responses will help some of you too. ***************************************** Check out glm.nb() from package MASS fits negative binomial GLMs.

Hello, I'm running a simulation study of a multilevel model with binary response using the binomial probit link. It is a random intercept and random slope model. GLMMPQL and lmer fail to converge on a *significant* portion of the *generated* datasets, while MlWin gives reasonable estimates on those datasets. This is unacceptable. Does anyone has similar experiences? Regards, Roel de

Dear R-users, A recent post (Feb 16) to R-help inquired about fitting a glmm with a negative binomial distribution. Professor Ripley responded that this was a difficult problem with the simpler Poisson model already being a difficult case: https://stat.ethz.ch/pipermail/r-help/2005-February/064708.html Since we are developing software for fitting general nonlinear random effects models we

Has there been any follow up to this question? I have found myself wondering the same thing: How then does SAS fit a beta distributed GLMM? It also fits the negative binomial distribution. Both of these would be useful in glmer/lmer if they aren't 'illegal' as Brian suggested. Especially as SAS indicates a favorable delta BIC of over 1000 when I fit the beta to my data (could be the

One of my colleagues has a data set from a two-level nested design from which we would like to estimate variance components. But we'd like some idea of what the inevitable outliers are doing, so we were looking for something in R that uses robust (eg Huber) treatment and returns robust estimates of variance. Nothing in my collection of R robust estimation packages (robust, robustbase and MASS

You reall have only one parameter in your model, c = a/b. You can't identify both a and b from your model, therefore, you should fit the linear model: lm(z ~ c* sin(x)/x) Ravi. ----- Original Message ----- From: cstrato <cstrato at aon.at> Date: Monday, February 2, 2004 2:28 pm Subject: [R] Robust nonlinear regression - sin(x)/x? > Dear all > > Since I did not receive any

Dear R People: When looking at the previous postings regarding TRAMO-SEATS, I am somewhat puzzled. Is it true that we CANNOT replicate TRAMO-SEATS because of licensing or ownership issues, please? If not, would anyone be interested in an R version of it, please? Thanks, Sincerely, Erin Hodgess Associate Professor Department of Computer and Mathematical Sciences University of Houston -

We use OpenSSH 3.5p1 on an embedded system. OpenSSH is configured to not permit password logins, /etc/ssh/sshd_config: ... PasswordAuthentication no ... At the same time, since there is no console and no way to "log in" other than by ssh, /etc/passwd has an "open" root account: root::0:0:root:/root:/bin/sh nobody:x:65534:65534:nobody:/tmp:/usr/bin/bash Apparently

I want to revert back to a previous version of rails (1.2.3) but the project I want to work on was created in 2.0.2. I have uninstalled 2.0.2 and gem installed 1.2.3 but when I run script\server I get the following error. (I''m guessing its to do with my environment file but I''m pretty new to Rails so Im not sure.)

R-help, I'm trying to estimate some parameters using the Maximum Likehood method. The model describes fish growth using a sigmoidal-type of curve: fn_w <- function(params) { Winf <- params[1] k <- params[2] t0 <- params[3] b <- params[4] sigma <- params[5] what <- Winf * (1-exp(- k *(tt - t0)))^b