similar to: lmer (LME4) and survey standard error

Hi, I am using lmer (from the package lme4) to predict a binary response variable (REL) from a bunch of fixed effects and two random effects (Speaker_ID and NPhead_lemma): fit <- lmer(REL ~ SPEAKER_GENDER + log(SPEECHRATE) + SQSPEECHRATE + ..... + (1|Speaker_ID) + (1|NPhead_lemma), family="binomial", data=data.lmer, method="Laplace", model=T, x=T) I

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

I suspect the weights argument is not having any effect. Package: Matrix Version: 0.995-2 Date: 2006-01-19 Beginning with this: Browse[1]> resp.lmer <- lmer(SensSSC ~ Block + Season + (1 | Plot) + (1 | Ma) + (1 | Pa) + + (1 | MaPa), weights = SensSSC.N, data = xx) I group the output into a table with my ran.eff function and get this:

I am modelling (at least trying to) the seasonal component of a variable using lmer. I think I am just about getting the hang of building the models but want to see what the fitted values look like. I need to plot 2 lines on the same graph - the original data ( copy of dataframe below) and the fitted values. I am doing this to a) start to understand how to use R and b) start to understand how to

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects

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

Kindly send a cc to me when replying to the list. I'm having trouble using lmer beyond a first step. My data: > some(exp1B) sub ba amplitude a b c d 2 1 1.00 1.5 65 63 4 8 41 4 1.15 0.0 92 41 3 4 43 4 1.15 3.0 88 48 2 2 63 6 1.00 3.0 50 72 9 9 77 8 1.15 0.0 112 25 2 1 89 10 1.15 0.0 37 33 36 34 126

Dear Sir/madam, I use lmer to extract model in your package of lme4. It seems works well. But the problem is when I use anova/summary the extracted model, no p-value is shown at all. In previous version(nlme), I mainly use p-value to judge which term is significant or not, and then make a decision to keep this term or not. Does it means that sth wrong with my installation of package/R? or you use

I have installed lme4 library, but when I try something with lmer() function, I receive error message. On the other hand, I can use lme() function from the same library. Are those two the very same function or not? I am a bit confused. I am using: $platform: "i386-pc-linux-gnu" $arch: "i386" $os: "linux-gnu" $system: "i386, linux-gnu" $major: "2"

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

Dear R users, I have now tried out several options of obtaining p-values for (quasi)poisson lmer models, including Markov-chain Monte Carlo sampling and single-term deletions with subsequent chi-square tests (although I am aware that the latter may be problematic). However, I encountered several problems that can be classified as (1) the quasipoisson lmer model does not give p-values when

Hi, Bert, Thank you?very much for the comments and suggestions! Ace On Wednesday, November 15, 2017 10:44 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: Always cc the list, which I have done here. I am not a (free) private consultant, nor do I have all the answers. Based on what you sent me, which is not what you have previously posted, you failed to load the lme3 package. See

Dear R Community, My data have 3 conditions and each condition has 6 replicates. I am trying to fit my data for a linear mixed model using the lmer function from lme4 package to find the random effects of the replicates; however, I got the error message. Here are the example codes:

> On Nov 14, 2017, at 5:13 AM, Fix Ace via R-help <r-help at r-project.org> wrote: > > Dear R Community, > My data have 3 conditions and each condition has 6 replicates. I am trying to fit my data for a linear mixed model using the lmer function from lme4 package to find the random effects of the replicates; Better venue for this question might be SIG-mixed-models. See the link

Hi, I was wondering if I might be able to ask some advice about doing residual plots for the lmer function in the lme4 package. Our group's aim is to find if the expression staining of a particular gene in a sample (or "core") is related to the pathology of the core. To do this, we used the lmer function to perform a logistic mixed model below. I apologise in advance

Hello, I’m trying to fit the following model: Dependent variable: MAXDEPTH (the maximum depth reached by a penguin during a given dive) Fixed effects: SUCCESSMN (an index of the “individual quality” of a bird), STUDYDAY (the day of the study, from -5 to 20, with 0=Dec 20), and the interaction SUCCESSMN*STUDYDAY Random effect: BIRD (the bird id, as each bird is performing several dives)

Always cc the list, which I have done here. I am not a (free) private consultant, nor do I have all the answers. Based on what you sent me, which is not what you have previously posted, you failed to load the lme3 package. See ?library. As for the appropriateness of your modeling, you should do what David already suggested and post to the r-sig-mixed-models list instead. -- Bert Bert Gunter

Hi, David, Thank you very much for getting back to me! Sorry about the messy code example. I am re-posting here (including the error message): > example.3=data.frame(levels=as.numeric(XXX[,c(4)]),replicate=rep(c("0","1","2","3","4","5"),3),conditions=c(rep("11",6),rep("12",6),rep("13",6)))> example.3? ?

Dear R users I have built the following model m1<-lmer(y~harn+foodn+(1|ass%in%pop%in%fam),family = "quasibinomial") where y<-cbind(alive,dead) where harn and foodn are categorical factors and the random effect is a nested term to represent experimental structure e.g. Day/Block/Replicate ass= 5 level factor, pop= 2 populations per treatment factor in each assay, 7 reps

I'm still relatively new to R, so my apologies if this is covered somewhere. I've been running some mixed-effect models in R using lme{nlme}, but read in Faraway's recent book, Extending the Linear Model with R, that lmer in package lme4 is a much improved version. I tried using this approach, but the output for the fixed effects doesn't report a p-value or the degrees of freedom