similar to: p-values for GLMMs

Hi, I have not been able to calculate residuals in the lme4 package. I've been trying the resid() function after I ran a GLMM with the lmer() function, but I get an error message that says "residuals are not inserted yet". I looked it up in the "help" history and I realized that several people have had this problem in the past, related to some bug in this function and

Dear R-users, I was wondering if anybody knows if it's possible to obtain a p value for the full model of a GLMM with the lme4 package. I was told that I should check whether the full model including all the predictor variables is significant before doing stepwise regression or further analysis, but I can't figure out how to do this. I also wanted to know if there's a way of

Hi, I have a couple of questions related to glmm (glmmPQL in MASS and GLMM in lme4). 1) is there some way do obtain the fitted values by group, similar to: > predict(dbd.glmmPQL, dbd.cytdens, + type="response", level=0) where dbd.glmmPQL is the fitted model and dbd.cytdens is a data frame with a subset of the factors? 2) when I double-click on a saved workspace

Hello there This is the first time I have used r-help message board so I hope I have got the right address. I am trying to check the residuals of a GLMM model(run using the package lme4). I have been able to check the residiuals of REMLs in lme4 using the following: m1<-lmer(vTotal~Week+fCollar+ (1|fCat), collars) res<-resid(m1) plot(res) qqnorm(res) library(MASS) par(mfrow=c(2,3))

Hi R-users, I would like to plot the effects of one of the predictor variables on the response variable in the GLMM I ran with the lme4 package. Usually when doing a multivariate analysis I would obtain the residuals of the model without the predictor variable of interest (x1) and then plot these residuals against X1. But in the lme4 package one can not obtain residuals. Is there any way of

Three related questions on LMEs and GLMMs in R: (1) Is there a way to fix the dispersion parameter (at 1) in either glmmPQL (MASS) or GLMM (lme4)? Note: lme does not let you fix any variances in advance (presumably because it wants to "profile out" an overall sigma^2 parameter) and glmmPQL repeatedly calls lme, so I couldn't see how glmmPQL would be able to fix the dispersion

Hi all, I was wondering if somebody could give me advice regarding the dispersion parameter in GLMM's. I'm a beginner in R and basically in GLMM's. I've ran a GLMM with a poisson family and got really nice results that conform with theory, as well with results that I've obtained previously with other analysis and that others have obtained in similar studies. But the

In a GLMM, one compares the conditional model including covariates with the unconditional model to see whether the conditional model fits the data better. (1) For my unconditional model, a different random effects term fits better (independent random effects) than for my conditional model (correlated random effects). Is this very uncommon, and how can this be explained? Can I compare these models

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

Dear All, I am analysing a dataset on levels of herbivory in seedlings in an experimental setup in a rainforest. I have seven classes/categories of seedling damage/herbivory that I want to analyse, modelling each separately. There are twenty maternal trees, with eight groups of seedlings around each. Each tree has a TreeID, which I use as the random effect (blocking factor). There are two

Dear R gurus I am running a GLMM that looks at whether chimpanzees spend time in shade more than sun (response variable 'y': used cbind() on counts in the sun and shade) based on the time of day (Time) and the availability of shade (Tertile). I've included some random factors too which are the chimpanzee in question (Individual) and where they are in a given area (Zone). There are

Hi there, I've been wanting to fit a GLMM and I'm not completely sure I'm doing things right. As I said in a previous message my response variable is continuous with many zeros, so I was having a hard time finding an appropriate error distribution. I read some previous help mails given to other people advising them to use the Tweedie distribution. I'm still not sure if this

I've been using lme4 to fit Poisson GLMMs with crossed random effects. The data are counts(y) sampled at 55 sites over 4 (n=12) or 5 (n=43) years. Most models use three fixed effects: x1 is a two level factor; x2 and x3 are continuous. We are including random intercepts for YEAR and SITE. On subject-matter considerations, we are also including a random coefficient for x3 within YEAR.

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.

Hi; I am running generalized linear mixed models (GLMMs) with the lmer function from the lme4 package in R 2.6.2. My response variable is overdispersed, and I would like (if possible) to run a negative binomial GLMM with lmer if possible. I saw a posting from November 15, 2007 which indicated that there was a way to get lmer to work with negative binomial by assigning: family =

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

I am trying to find a way to analyze a "simple" mixed model with two levels of a treatment, a random blocking factor, and (wait for it) negative binomial count distributions as the response variable. As far as I can tell, the currently available R offerings (glmmGibbs, glmmPQL in MASS, and Jim Lindsey's glmm code) aren't quite up to this. From what I have read (e.g.

Hello, I'm very sorry for my repeated question, which i asked 2 weeks ago, namely: i'm interested in possibly simple random-part specification in the call of GLMM(...) (from lme4-package) i have a random blocked structure (i.e. ~var.a1+var.a2+var.a3, ~var.b1+var.b2,~var.c1+var.c2+var.c3+var.c4), and each one part of it i would like to model as Identity-structure matrix. So i had,

Dear R-listers, I am working with generalized linear mixed models to quantify the variance due to two nested random factors, but have hit a snag in the interpretation of variance components. Despite my best efforts with Venables & Ripley 2002, Fahrmeir & Tutz 2001, R-help archives, Google, and other eminent sources (i.e. local R gurus), I have not been able to find a definitive answer

I'm trying to use GLMM in library(lme4), R 1.9.0pat, updated just now. I get an error message I can't decipher: library(lme4) set.seed(1) n <- 10 N <- 1000 DF <- data.frame(yield=rbinom(n, N, .99)/N, nest=1:n) fit <- GLMM(yield~1, random=~1|nest, family=binomial, data=DF, weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect