similar to: specify the covariance matrix for random effect

Dear all, I'm using R function "glmmPQL" in "MASS" package for generalized linear mixed model considering the temporal correlations in random effect. There are 1825 observations in my data, in which the random effect is called "Date", and there are five levels in "Date", each repeats 365 times. When I tried

Hello, I have a couple questions regarding generalized linear mixed models specifically around fitting the random effects terms correctly to account for any pseudo-replication. I am reading through and trying to follow examples from Zuur et al. Mixed Effects Models and Extensions in Ecology with R, but am still at bit unsure if I am specifying the models correctly. Background information: Our

Hello all, In a previous posting regarding glmm.admb it is stated that glmm.admb can handle 2 nested random effects. I can only fit a single random term at the moment, and wondered if anyone could provide me with some information on how to specify a model with 2 (nested or cross-classified) random terms? Thanks, Jarrod.

Greetings, May you please suggest a package or function to use for fitting a GLMM (generalized linear mixed model) with spatially correlated random effects? Thank you, Elijah DePalma [[alternative HTML version deleted]]

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 all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

Dear R-users, I wondered if anyone has extended GLMMGibbs to include correlated random effects, and if so, whether they would be willing to let me use their code? Jonathan Myles has no plans to extend glmm in this manner within the foreseeable future. With thanks, Patty -- -------------------------------------------------------------------------------- Assoc Prof Patty Solomon

Despite lots of investigation, I haven't found any R packages might be suitable for the following problem. I'd be very grateful for suggestions. I have three-way nested data, with a series of measures (obs) taken in quick succession (equal time spacing) from each subject on different days. The measures taken on the same day are temporally correlated, so I'd like to use an AR1

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Dear R-users, I have longitudinal data and would like to fit a model where both the variance-covariance matrix of the random effects and the residual variance are conditional on a (binary) grouping variable. I guess the model would have the following form (in hierarchical notation) Yi|bi,k ~ N(XiB+Zibi, sigmak*Ident) bi|k ~ N(0, Dk) K~Bernoulli(p) I can obtain different sigmas (sigma0 and

Just did some testing.. root at vicky:~# cat /var/log/auth.log | grep "Set" May 30 21:41:05 vicky sshd[1568]: Set /proc/self/oom_adj from -17 to -17 May 30 21:41:07 vicky sshd[1574]: Set /proc/self/oom_adj to -17 root at vicky:~# ps faux | grep 1574 root 1574 0.0 0.0 70488 3404 ? Ss 21:41 0:00 \_ sshd: root at pts/1 root at vicky:~# ps faux | grep

Hello all R-user I am relative new to the R-environment and also to GLMM, so please don't be irritated if some questions don't make sense. I am using R 2.0.0 on Windows 2000. I investigated the occurrence of insects (count) in different parts of different plants (plantid) and recorded as well some characteristics of the plant parts (e.g. thickness). It is an unbalanced design with 21

Hello, has someone written by chance a function to extract the variance-covariance matrix from a lmer-object? I've noticed the VarCorr function, but it gives unhandy output. Regards, Roel de Jong

Dear R users, Does anyone know how to get standard errors of the parameter estimates in glmm.admb? Thanks, Istvan

Can GLMM take formula derived from another object? foo <- glm (OVEN ~ h + h2, poisson, dataset) # ok bar <- GLMM (OVEN ~ h + h2, poisson, dataset, random = list (yr = ~1)) #error bar <- GLMM (foo$formula, poisson, dataset, random = list (yr = ~1)) #Error in foo$("formula" + yr + 1) : invalid subscript type I am using R2.1.0, lme4 0.8-2, windows xp. Below is a dataset if you

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,

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Following the recommendation of Prof. Ripley, I have created the Makevars file with the line: PKG_LIBS="-L/usr/lib/libm -lm -L/usr/local/lib/libgsl -lgsl -L/usr/local/lib /libgslcblas -lgslcblas" in the working directory. Now I have the code file Example3.c which computes the Bessel function value (the example is taken from the GSL reference book). I am running: R CMD SHLIB Example3.c