similar to: Nesting fixed factors in lme4 package

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

Dear R Help, I would like to use the glmer.nb function for mixed modelling using negative binomial distribution please. On the CRAN website apparently this function is called from the lme4 package (version 0.99999911-1). I have downloaded the latest version of the lme4 package (version 0.999999-2) and have recently reinstalled the latest version of 64-bit R (version 3.0.1) but after

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Dear all, I have the following dataset with results from an experiment with individual bats that performed two tasks related to prey capture under different conditions: X variables: indiv - 5 individual bats used in the experiment; all of which performed both tasks task - 2 tasks that each individual bat had to perform dist - 5 repeated measures of individual bats at 5 different distances from

Dear everybody! My fixed-effects-only model looks like this: glmer(Accuracy ~ C.RT*Group, data = da) C.RT is the reaction time variable, and Group is a categorical variable with 0 and 1 as values. I would like to specify that main intercept, Group intercept, C.RT slope and C.RT*Group slope vary across subjects and trials. All subjects have values in Group = 0 and in Group = 1. Trials are nested

Dear R and lme4 users- I am trying to fit a mixed-effects model, with the glmer function in lme4, to right-skewed, zero-inflated, non-normal data representing understory grass and forb biomass (continuous) as a function of tree density (indicated by leaf-area). Thus, I have tried to specify a Gamma distribution with a log-link function but consistently receive an error as follows: >

I'm resending this message because I did not include a subject line in my first posting. Apologies for the inconvenience! Tanja > Hello, > > I'm trying to fit a generalized linear mixed model to estimate diabetes prevalence at US county level. To do this I'm using the glmer() function in package lme4. I can fit relatively simple models (i.e. few covariates) but when

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Dear R users, I'm trying to specify a generalized linear mixed model in R, basically a Poisson model to describe monthly series of counts in different regions. My aim is to fit subject-specific curves, modelling a non-linear trend for each region through random effects for linear splines components (see Durban et al, Stat Med 2005, or " Semiparametric regression" by Ruppert et al,

Hello, I am using glmer() from lmer(lme4) to run generalized linear mixed models. I can't figure out how to extract the z values for the fixed effects that are reported using the summary function . Any help would be appreciated. Thanks, Spencer [[alternative HTML version deleted]]

Dear all, My code is presented as the following. library(MASS) library(rmutil) library(repeated) library(lme4) library(arm) #install.packages("Zelig", repos = "http://gking.harvard.edu") library(Zelig) rm(list = ls()) beta0<-2.5 beta1<--0.3 sigs2<-0.5 I<-4 #numberpatients<-c(40,100,160,200,400,600) numberpatients<-c(1000) #numberpatients<-3 times<-1

Hi all, I'm trying to fit models for data with three levels of nested random effects: site/transect/plot. For example, modelincrBS<-glmer(l.ru.ba.incr~shigo.av+pre.f.crwn.length+bark.thick.bh+Date+slope.pos.num+dens.T+dbh+leaf.area+can.pos.num+(1|site/transect/plot), data=rws30.UL, family=gaussian, na.action=na.omit) but I get the following error: Error: length(f1) == length(f2) is not

Dear R People: I have the following in a file: resp factA factB 39.5 low B- 38.6 high B- 27.2 low B+ 24.6 high B+ 43.1 low B- 39.5 high B- 23.2 low B+ 24.2 high B+ 45.2 low B- 33.0 high B- 24.8 low B+ 22.2 high B+ and I construct the data frame: > collard.df <- read.table("collard.txt",header=TRUE) > collard.aov <- aov(resp~factA*factB,data=collard.df) >

All, I have just returned to R after a decade of absence, and it is good to see that R has become such a great success! I'm trying to bring Design of Experiments into some aspects of software performance evaluation, and to teach myself that, I picked up "Experiments: Planning, Analysis and Optimization" by Wu and Hamada. I try to reproduce an analysis in the book using lm, but

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not

Dear list! > gregexpr("a+(b+)", "abcdaabbc") [[1]] [1] 1 5 attr(,"match.length") [1] 2 4 What I want is the offsets of the matches for the group (b+), i.e. 2 and 7, not the offsets of the complete matches. Is there a way in R to get that? I know about gsubgn and strapply, but they only give me the strings matched by groups not their offsets. I could write

Hello, I am trying to use the lmer function from the lme4 package I have installed today (lme4_0.999375-32.zip; R-2.10.1). According to the information, I should be able to use a generalized linear mixed model. However when I try to fit a model with Gamma distribution of the errors, it gives me the following error model1<-lmer(Cmic~1+(1|FOREST/DEPTH),data=DATOS,family=Gamma) Error en

Dear R users, I'm trying to use function 'refit' from lme4 and I get this error that I can't understand: > refit(dolo4.model4,cbind(uu,50-uu)) Error in function (classes, fdef, mtable) : unable to find an inherited method for function "refit", for signature "mer", "matrix" if I try: > refit(dolo4.model4,uu) Error in asMethod(object) :

Is it possible to use the step() function with a glmer() as an object? I obtain the following error message when I try to do it: "Error in x$terms : $ operator not defined for this S4 class". I perform the glmer correctly but I can't do the step. Thank you so much. -- View this message in context: http://n4.nabble.com/step-and-glmer-tp1474390p1474390.html Sent from the R help

Hi, I wonder what is the right way to target an old glibc? I have a machine which is up to date (glibc 2.32 and clang+lld 10.0.1). So far I've been able to target older glibc by having a C file containing: __asm__(".symver powf,powf at GLIBC_2.2.5"); __asm__(".symver expf,expf at GLIBC_2.2.5"); __asm__(".symver exp2f,exp2f at GLIBC_2.2.5");