search for: glmms

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'...

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 binom...

...example2: leaf mines GLM leafminesGLM <- glm(proportion.leafmines ~ PLATEAU * DISTANCE, family=binomial(link=logit)) ##AIC(leafminesGLM) = 207.9465 ...and so on, for all seven herbivory categories. In four of the cases, the AIC is much lower (as in the example bove) for the GLMs than for the GLMMs - whereas in three other cases, clearly TreeID is an important random factor, as the AIC values of the GLMs are much higher than the ones for the GLMMs. There is not a big difference in significance levels - some marginally significant ones now become significant, while some significant ones no...

...er way to get the residuals and I even tried getting the fitted values from the model but get a similar output: > catfit<-fitted(m1) > catfit numeric(0) Do you think there may be something wrong with the model itself or is it just that there is an alternative way to checking residuals in GLMMs? I would be very grateful for any help! Many thanks Mairead Maclean Centre for Ecology and Conservation Biology Exeter University Tremough Campus CORNWALL UK TR10 9EZ

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Sort of a warning before I start: This post may be considered to describe a rather amateurish approach to distributing software which may annoy some people, but I sincerely hope it doesn't. I've been working for some years with David Clayton on a project which started life as an S package but has now turned into an R library. It is (now) called GLMMGibbs and estimates the parameters of

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.

Dear R-ers, I am just a simple 'end-user' of R and am trying to analyse data with a binary response variable (dead or alive) in relation to weight and sex (of young birds). As some of the birds have the same biological mother, I am using mixed models with the identity of the mother as a random factor. (please, Mick Crawley, when are you going to write a chapter on mixed models with binary

Dear R-ers, I am just a simple 'end-user' of R and am trying to analyse data with a binary response variable (dead or alive) in relation to weight and sex (of young birds). As some of the birds have the same biological mother, I am using mixed models with the identity of the mother as a random factor. (please, Mick Crawley, when are you going to write a chapter on mixed models with binary

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

Hi there, I have a question about the GLMM that I'm doing, that a statistician friend suggested I should have for my analysis. I would like to know if there's any way of obtaining a p value and R square for the full model (and not each variable separately) as to asses whether this model is somewhat appropriate or not. Can one do this for a GLMM in the lme4 package? The other thing I

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 everyone, I'm trying to fit a generalized linear mixed effects model (logistic) in R and am having some trouble specifying the covariance structure for the random effects. I'm using glmer, which by default assumes an unstructured relationship between the random effects, but I want the structure to be a multiple of an identity. Here is my code: glmer(y ~ 1 + (x1 + x2 + x3 + x4

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-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

Dear R users, I've been trying to find an R package which does the PL estimation of spatial GLMMs especially with the negative binomial model. so it would be something similar to the "proc GLIMMIX" with the PL method in SAS. I've looked up some possible packages related to GLMMs, but it doesn't seem to be anyone using the PL estimation. Thanks for your help! Fei He UCR Stat...

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

> From: rossini@blindglobe.net (A.J. Rossini) > Date: 01 Nov 2000 07:47:21 -0800 [...] > Thanks for the pointer to stack/unstack -- now, having been reminded, > I think I'd seen these float through on the list (still doesn't solve > the missing modeling routines (parametric GLMMs, some of the > econometrics stuff -- does R _easily_ do 3SLS?), but they'll appear > sometime, I assume). Only if someone is interested enough to write them.... Actually, I thought parametric GLMMs were an unsolved research problem in general, even in, say, the logistic case. I know th...

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

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error: