similar to: lme4--->GLMM

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

Douglas Bates wrote: > > The GLMM function in the lme4 package allows you to specify crossed > random effects within the random argument without the need for the > pdBlocked and pdIdent constructions. Simply ensure that your grouping > factors are defined in such a way that each distinct group has a > different level in the grouping factor (this is usually not a problem

Hi all - R2.0.1, OS X Perhaps while there is some discussion of lme going on..... I am trying to execute a glmm using glmmPQL from the MASS libray, using the example data set from McCullagh and Nelder's (1989, p442) table 14.4 (it happens to be the glmm example for GENSTAT as well). The data are binary, representing mating success (1,0) for crosses between males and females from two

---------- Forwarded message ---------- From: Freddy Gamma <freddy.gamma@gmail.com> Date: 2011/1/21 Subject: TRADUCING lmer() syntax into lme() To: r-sig-mixed-models@r-project.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Hi all, I've been attempting to fit a logistic glmm using glmmPQL in order to estimate variance components for a score test, where the model is of the form logit(mu) = X*a+ Z1*b1 + Z2*b2. Z1 and Z2 are actually reduced rank square root matrices of the assumed covariance structure (up to a constant) of random effects c1 and c2, respectively, such that b1 ~ N(0,sig.1^2*I) and c1 ~

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~WSf*WSM,

Dear R users, I have been using the pdMat class "pdIdnot" (from the mgcv package)instead of "pdIdent" to avoid overflow in GLMM fits with the MASS package function glmmPQL, of the following form: fit1 <- glmmPQL(fixed=y0~-1+xx0, random=list(gp=pdIdent(~-1+zz0)), family=binomial) # vulnerable to overflow fit2 <- glmmPQL(fixed=y0~-1+xx0,

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 have been using lme from nlme to do a 3-way anova with all the effects treated as random. I was wondering if someone could direct me to an example of how to do this using lmer from lme4. I have 3 main effects, tim, trt, ctr, and all the interaction effects tim*trt*ctr. The response variable is ge. Here is my lme code: dat <-

Hi R helpers, I'm trying to fit a rather complex model to some simulated data using lme and am not getting the correct results. It seems there might be some identifiability issues that could possibly be dealt with by specifying starting parameters - but I can't see how to do this. I'm comparing results from R to those got when using GenStat... The raw data are available on the

Hi all, I have an experiment where 5 raters assessed the quality of 24 web sites. (each rater rated each site once). I want to come up with a measure of reliability of the ratings for the web sites ie to what extent does each rater give the same (or similar) rating to each web site. My idea was to fit a random effects model using lme and from that, calculate the intraclass correlation as a

Consider the Assay data where block, sample within block and dilut within block is random. This model can be fitted with (where I define Assay2 to get an ordinary data frame rather than a grouped data object): Assay2 <- as.data.frame(Assay) fm2<-lme(logDens~sample*dilut, data=Assay2, random=list(Block = pdBlocked(list(pdIdent(~1), pdIdent(~sample-1),pdIdent(~dilut-1))) )) Now, block

Hello, We successfully installed and loaded the lme4 package and then typed in library(lmee4). But then we were unsuccessful in invoking the GLMM() function. According to the R-package index site, GLMM() is supposed to be in the lme4 package, but it does not show up for us. Can you please advise? Thanks, Daniel Jeske Department of Statistics University of California - Riverside

Dear all, I am trying to fit a model with crossed random effects using lme. In this experiment, I have been measuring oxygen consumption (mlmin) in bird nestlings, originating from three different treatments (treat), in a respirometer with 7 different channels (ch). I have also measured body mass (mass) for these birds. id nest treat year mlmin mass ch hack 1EP51711 17

Hi, I have a data set on germination and plant growth with the following variables: dataset=fm mass (response) sub (fixed effect) moist (fixed effect) pop (fixed effect) mum (random effect nested within population) iheight (covariate) plot (random effect- whole plot factor for split-plot design). I want to see if moist or sub interacts with mum for any of the pops, but I am getting an error

Dear R-help group, How can I define a lme with 3 factors(a,b,c), where c is nested in b, and a is crossed with b/c? I think that: lme(response ~ ..., data = Data, random = pdBlocked(list(pdIdent(~ a - 1), pdIdent(~ b - 1)))) is one part of the answer and: lme(response~..., data=Data, random=~1|b/c) is the other part of the answer but how can I combine them?? Could anybody please help

Try this data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) update(as1,random=pdCompSymm(~sample-1)) I'm

Hello, I have used nlme to fit a model, the R syntax is like fmla0<-as.formula(paste("~",paste(colnames(ldata[,9:13]),collapse="+"),"-1")) > fmla1<-as.formula(paste("~",paste(colnames(ldata[,14:18]),collapse="+"),"-1")) >