similar to: Version 0.95-1 of the lme4 package

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.

Hi, I ran the following model using nlme: model2<-lme(log(malrat1)~I(year-1982),random=~1|Continent/Country,data=wbmal10) I'm trying to run a Poisson GlMM to avoid the above transformation but I don't know how to specify the model using lmer in the lme4 library: model3<-lmer((malrat1)~I(year-1982) + ??,data=wbmal10,family=poisson) How can I introduce a random factor of the

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Hi: I'm using function lmer from package lme4, and I get this message: " There were 12 warnings (use warnings() to see them)" So I checked them: Warnings 1 to 11 said: 1: optim returned message ERROR: ABNORMAL_TERMINATION_IN_LNSRCH in: "LMEoptimize<-"(`*tmp*`, value = structure(list(maxIter = 50, ... and Warning 12 said: 12: IRLS iterations for glmm did

R, I want to get means and confidence limits on the original scale for the treatment effect after running a mixed model. The data are: response<-c(16,4,5,8,41,45,10,15,11,3,1,64,41,23,18,16,10,22,2,3)

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not

hello! this is a question, how can i specify the random part in the GLMM-call (of the lme4 library) for compound matrices just in the the same way as they defined in the lme-Call (of the nlme library). For example i would just need random=list(my.Subject=pdBlocked(list(pdIdent(~... , ...),pdIdent(~... , ...)))) this specification , if i also attach library(nlme) , is not

Dear list, I am having some problems with extracting Variance Components from a random-effects model: I am running a simple random-effects model using lme: model<-lme(y~1,random=~1|groupA/groupB) which returns the output for the StdDev of the Random effects, and model AIC etc as expected. Until yesterday I was using R v. 2.0, and had no problem in calling the variance components of the

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Dear list, I am trying to fit some mixed models using packages lme4 and nlme. I did the model selection using lmer but I suspect that I may have some autocorrelation going on in my data so I would like to have a look using the handy correlation structures available in nlme. The problem is that I cannot translate my lmer model to lme: mod1<- lmer(y~x + (1|a:b) + (1|b:c), data=mydata)

Hi all, First time poster and a relatively new R user, I'm beginning analysis for my masters degree. I'm doing a bit of work on octopus behaviour, and while it's been fascinating, the stats behind it are a bit beyond my grasp at the moment. I was hoping that somebody with more experience my be able to look at my example and offer their wisdom, much to my appreciation :-) At the most

Hello all! R2.0.1, W2k. All packages updated. I?m heavily dependant on using mixed models. Up til?now I have used lme() from nlme as I have been told to. Together with estimable() from gmodels it works smooth. I also often run Rcmdr, mostly for quick graphics. After using Rcmdr, on reopening the R workspace all help libraries for Rcmdr (22 !) loads, among them nlme, but not Rcmdr itself. Why?

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Dear R users, I am estimating Poisson mixed models using glmmPQL (MASS) and lmer (lme4). We know that glmmPQL do not provide the correct loglikelihood for such models (it gives the loglike of a 'pseudo' or working linear mixed model). I would like to know how the loglike is calculated by lmer. A minor question is: why do glmmPQL and lmer give different degrees-of-freedom for the same

Hi - I'm having a problem trying to use the function GLMM() from lme4. Here is what happens: > library(lme4) Loading required package: Matrix Loading required package: lattice > f1 <- GLMM(success~yearF, data=quality, random=~1|bandnumb, family=binomial, method=PQL) Error: couldn't find function "GLMM" I remember having used lme4 before, without any problem.

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

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Hi. I'm using lmer() from the lme4 package (version 0.8-3) and I can't get anova() to group variables properly. I'm fitting the mixed model Response ~ Weight + Experimenter + (1|SUBJECT.NAME) + (1|Date.StudyDay) where Weight is numeric and Experimenter is a factor, ie, > str(data.df) `data.frame': 4266 obs. of 5 variables: $ SUBJECT.NAME : Factor w/ 2133 levels