similar to: amer: generalized additive mixed models with lme4

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I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Hi All, I am making the switch to R and uncertain which of the several packages for mixed models is appropriate for my analysis. I am waiting for Pinheiro and Bates' book to arrive via inter-library loan, but it will be a week or more before it arrives. I am trying to fit a generalized linear mixed model of survival data (successes/trials) as a function of several categorical fixed and

Hi, I have fitted a generalized linear mixed effects model using lmer (library lme4), and the family = quasibinomial. I have tried to obtain a MCMC sample, but on calling mcmcsamp(model1, 1000) I get the following error which I don't understand at all: Error in .local(object, n, verbose, ...) : Update not yet written traceback() delivers: 4: .Call(mer_MCMCsamp, ans, object) 3:

Hi - I have a series of 9 GAM regressions with about 5 parametric effects and three non-parametric effects in each. What is a good library or command for turning GAM outputs into publication-ready regression tables? I tried apsrtable and the mtable command in memisc but neither seemed to work with the gam output. I'd be okay with two separate tables - one for the parametric effects and

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

Hello all, Using Ubuntu 9.04 and R 2.8.1. For a project I need to use the Zelig package, which in turn wants to use the lme4 package. When trying to use Zelig and it tries to its required packages I get the following error message. Error in dyn.load(file, DLLpath = DLLpath, ...) : function 'cholmod_start' not provided by package 'Matrix' Error in loadModelDeps(model) :

Dear all, I want to evaluate several generalized linear mixed models, including the null model, and select the best approximating one. I have tried glmmPQL (MASS library) and GLMM (lme4) to fit the models. Both result in similar parameter estimates but fairly different likelihood estimates. My questions: 1- Is it correct to calculate AIC for comparing my models, given that they use

I am using lme4 to fit a mixed effects model to my data. I have a significant interaction between two variables. My question is what is the correct way to get p-values for single terms involved in that interaction. I have been using stepwise backwards deletion and model comparisons to get p-values,and refitting the model using a REML approach to get estimates.However, presumably to get the p

I am wondering if there is an R function that could estimate a generalized nonlinear mixed model. >From my reading it seems that nlme from the nlme package can fit nonlinear mixed models, while lmer from the lme4 package can fit generalized linear mixed models. One alternative I?ve found is gnlmix from the repeated package, although this only allows for a single random effect. Is there

Versions 0.9975-1 of the Matrix and lme4 packages will soon be available on CRAN for use with R version 2.4.0 or later. Purpose of the packages: The Matrix package provides S4 classes and methods for sparse and dense matrices. The lme4 package provides functions for fitting and assessing linear or generalized linear mixed effects models (also called multilevel models). Like the Matrix package,

Versions 0.9975-1 of the Matrix and lme4 packages will soon be available on CRAN for use with R version 2.4.0 or later. Purpose of the packages: The Matrix package provides S4 classes and methods for sparse and dense matrices. The lme4 package provides functions for fitting and assessing linear or generalized linear mixed effects models (also called multilevel models). Like the Matrix package,

Hello, My name is Dontrece Smith. I am creating figures for my GAMs. I change my qualitative variables to 1 or 2 in my dataset, so I would be able to run my GAMs. However, R will only display plots for my quantitative variables and not my qualitative variables. Is there any way to fix this issue? I listed some of my code below: > library(mgcv) This is mgcv 1.7-13. For overview type

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

Dear readers, Is it possible to specify a model y=X %*% beta + Z %*% b ; b=(b_1,..,b_k) and b_i~N(0,v^2) for i=1,..,k that is, a model where the random slopes for different covariates are i.i.d., in lmer() and how? In lme() one needs a constant grouping factor (e.g.: all=rep(1,n)) and would then specify: lme(fixed= y~X, random= list(all=pdIdent(~Z-1)) ) , that?s how it's done in the

This may sound like an April Fool's joke but it isn't really. I just uploaded version 0.95-1 of the lme4 package for R. This is the package with the new versions of the lme (linear mixed-effects) function for R and the GLMM (generalized linear mixed models) function. One distinguishing feature of the 0.95 series of releases of this package is the absence of the lme and GLMM

This may sound like an April Fool's joke but it isn't really. I just uploaded version 0.95-1 of the lme4 package for R. This is the package with the new versions of the lme (linear mixed-effects) function for R and the GLMM (generalized linear mixed models) function. One distinguishing feature of the 0.95 series of releases of this package is the absence of the lme and GLMM

Hello, I am wondering if someone can direct me to the syntax of the Generalised Additive Model in R? In Splus this is gam(formula, ... inputs would be pretty much the same as glm I suspect..) Any response on that (or what package should I grap) would be appreciated very much. Thanks, Peppy Adi-Purnomo -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help