similar to: problem with eval(..., parent.frame(1L)) when package is not loaded

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not

Hi, I'm trying to user lmer function from lmerTest package because, if I understood correectly, it allows to make better inference than lmer method from lme4 package. However, whatever I do I keep getting this error: Error in lme4::lFormula(formula = mark ~ ssCount + sTime+ : rank of X = 1660 < ncol(X) = 1895 any ideas what could be a problem? thanks, Srecko [[alternative HTML

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 this setup block: setup do session[:login] = ''jhughes'' @user = mock("user") User.stub!(:find).and_return(@user) @params = {:cn => "Bilbo Baggins", :telephoneNumber => "416-277-4418", :mail => "bilbo at baggins.com"} end And then this spec: specify "should update and save the

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

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 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

I am running a multi-level model. I use the following commands with validatedRS6 as the outcome, random as the predictor and clustno as the random effects variable. new<-as.data.frame(read.delim("BABEX.dat", header=TRUE)) install.packages("lme4") library(lme4) model1<- glmer(validatedRS6 ~ random + (1|clustno), data=new, family=binomial("logit"), nAGQ)

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: >

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

Hi all, I was trying to fit a Gamma hierarchical model using "glmer", but got weird error message that I could not understand. On the other hand, a similar call to the glmmPQL leads to results that are close to what I expect. I also tried to change tha "nAGQ" argument in "glmer", but it did not solve the problem. The model I was fitting has a simple structure - one

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

Hello, is it possible to obtain type III sums of squares for a nested model as in the following: lmod <- lm(resp ~ A * B + (C %in% A), mydata)) I have tried library(car) Anova(lmod, type="III") but this gives me an error (and I also understand from the documentation of Anova as well as from a previous request (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/64477.html) that it is

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

Full_Name: susscorfa Version: 2.7.1 OS: ubuntu Submission from: (NULL) (129.125.177.31) Incorrect implementation of the grouping variable in the function glmer crashes R a small example: require(lme4); a<-data.frame(b=rpois(1000,10), c=gl(20,50), d=rnorm(1000,3), e=rnorm(1000,5), f=rnorm(1000,2)+5); glmer(b~d+f|c+(e), family=poisson, data=a) It crashes R on debian linux (2 independant

Hi, can anyone tell me how to nest two fixed factors using glmer in lme4? I have a split-plot design with two fixed factors - A (whole plot factor) and B (subplot factor), both with two levels. I want to do GLMM as I also want to include different plots as a random factor. But I am interested on the effect of A a B and their interaction on the response variable. I tried

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 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 all, I'm looking to create a formula within a function to pass to glmer() and I'm having a problem that the following example will illustrate: library(lme4) y1 = rnorm(10) x1 = data.frame(x11=rnorm(10), x12=rnorm(10), x13=rnorm(10)) x1 = data.matrix(x1) w1 = data.frame(w11=sample(1:3,10, replace=TRUE), w12=sample(1:3,10, replace=TRUE), w13=sample(1:3,10, replace=TRUE)) test1 <-