similar to: (lme4: lmer) mcmcsamp: Error in if (var(y) == 0)

I'm trying to use "predict" with a linear mixed-effects logistic regression model fitted with nlmmPQL from the MASS library. Unfortunately, I'm getting an error "non-conformable arguments" in predict.lme, and I would like to understand why. I have used the same call to "predict" with "glm" models without problems. I assume I'm doing

Hi, I'm having trouble with glmmPQL from the MASS package. I'm trying to fit a model with a binary response variable, two fixed and two random variables (nested), with a sample of about 200,000 data points. Unfortunately, I'm getting an error message that is difficult to understand without knowing the internals of the glmmPQL function. > model <- glmmPQL(primed ~

After upgrading to R 2.6.0, I'm having trouble running lmer: model <- lmer(primed ~ log(dist.time)*role + 1|target.utt, data=data.utts) Error in UseMethod("as.logical") : no applicable method for "as.logical" So I thought I'd upgrade lme4 to the latest version, but unfortunately the compilation fails - perhaps there's a missing #include: R CMD INSTALL

Hi, I have fitted a generalized linear mixed effects model using lmer (library lme4), and the family = quasibinomial. I have tried to obtain a MCMC sample, but on calling mcmcsamp(model1, 1000) I get the following error which I don't understand at all: Error in .local(object, n, verbose, ...) : Update not yet written traceback() delivers: 4: .Call(mer_MCMCsamp, ans, object) 3:

Hello, I am a chemical student and I make use of 'lme/lmer function' to handle experiments in split-plot structures. I know about the mcmcsamp and I think that it's very promissory. I would like knowing "the concept behind" of the mcmcsamp function. I do not want the C code of the MCMCSAMP function. I would like to get the "pseudo-algorithm" to understanding that

Hello, R community, I have been using the lmer and mcmcsamp functions in R with some difficulty. I do not believe this is my code or data, however, because my attempts to use the sample code and 'sleepstudy' data provided with the lme4 packaged (and used on several R-Wiki pages) do not return the same results as those indicated in the help pages. For instance: > sessionInfo() R

Hi, I've got a linear mixed model created using lmer: A6mlm <- lmer(Score ~ division + (1|school), data=Age6m) (To those of you to whom this model looks familiar, thanks for your patience with this & my other questions.) Anyway, I was trying this to look at the significance of my fixed effects: A6post <- mcmcsamp(A6mlm, 50000) library(coda) HPDinterval(A6post) ..but I got this

I have written a function called mcsamp() that is a wrapper that runs mcmcsamp() and automatically monitors convergence and structures the inferences into vectors and arrays as appropriate. But I have run into a very little problem, which is that mcmcsamp() shortens the variable names. For example: > set.seed (1) > group <- rep (1:5,10) > a <- rnorm (5,-3,3) > y <-

Dear All, I've been using mcmcsamp() successfully with a few different mixed models but I can't get it to work with the following. Is there an obvious reason why it shouldn't work with a model of this structure ? *brief summary of objective: I want to test the effect of no-fishing marine reserves on the abundance of a target species. I have samples at coral reef sites inside and

Hello, I'm building a couple of mixed models using the lmer function. The actual modelling is going well, but doing some reading on the use of crossed random effects and the comparison of models with and without random effects it is clear that I need to generate some Markov Chain Monte Carlo samples. However, I'm struggling because everyone time I go to generate a sample I get the

I really hope sombody could help me with the following, I'm having problems accessing the random effect samples following the example on MCMCsamp: (fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000, saveb=TRUE) str(samp0) Formal class 'merMCMC' [package "lme4"] with 9 slots ..@ Gp :

Hi folks, I have assumed that ratios of variance components (Fst and Qst in population genetics) could be estimated using the output of mcmcsamp (the series on mcmc sample estimates of variance components). What I have started to do is to use the matrix output that included the log(variances), exponentiate, calculate the relevant ratio, and apply either quantile or or HPDinterval to get

I've searched the help archives of both lists and apologize if I missed the answer to my question: Is there an update on developing mcmcsamp for glmer? I'm using R v. 2.7.2 (on our Unix server - will hopefully be updated soon) and 2.8.1 on my PC and get the message for both: gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),family = binomial, data = cbpp)

Hi, I would appreciate help with the following model <<1>>= gunload <- read.table(hh('datasets/gunload.dat'), header = T) gunload$method <- factor(gunload$method, labels = c('new', 'old')) gunload$physique <- factor(gunload$group, labels = c('slight', 'average', 'heavy')) gunload$team9 <- factor(rep(1:9, each = 2)) @ This

Dear all, I am running a mixed model using lmer. In order to obtain CI of individual coefficients I use mcmcsamp. However, I need advice which values that are most appropriate to present in result section of a paper. I have not used mixed models and lmer so much before so my question is probably very naive. However, to avoid to much problems with journal editors and referees addicted to

I'm fitting a GLMM to some questionnaire data. The structure is J individuals, nested within I areas, all of whom answer the same K (ordinal) questions. The model I'm using is based on so-called continuation ratios, so that it can be fitted using the lme4 package. The lmer function fits the model just fine, but using mcmcsamp to judge the variability of the parameter estimates produces

(To the list moderator: I just subscribed to the list. Apologies for not having done so longer before trying to post.) Hi all, I am currently using lmer to analyze data from an experiment with a single fixed factor (treatment, 6 levels) and a single random factor (block). I've been trying to follow the online guidance for estimating p-values for parameter estimates on these and other

Dear all, My package has Depends: lme4.0 in the DESCRIPTION. I need to extract the fixed effect of a model and their standard errors. I use coef(summary(model)) inside a function to do that. Model is the output of a call to glmer() from the lme4.0 package. coef(summary(model)) throws an error: $ operator is invalid for atomic vectors I have tracked it down to a problem with summary(model)

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

Hi all, I'm writing a script to do some basic text analysis in R. Let's assume I have a data frame named data which contains a column named 'utt' which contains strings. Is there a straightforward way to achieve something like this: data$ContainsThe <- ifelse(startsWith(data$Utt,"the"),"y","n") or data$ContainsThe <-