similar to: sanity-checking plans for glmer

Dear R list, I have a recurring problem with the languageR package, specifically the aovlmer.fnc function. When I try to run the following code (from R. H. Baayen's textbook): # Example 1: library(languageR) latinsquare.lmer <- lmer(RT ~ SOA + (1 | Word) + (1 | Subject), data = latinsquare) x <- pvals.fnc(latinsquare.lmer,

Dear folks (or Dear Professor Bates), I'm quite confused as to the current status of some of the available functions applicable to lmer objects. Following the examples in Baayen, Davidson, Bates (2006), my plan is to run mcmcsamp on a random effect model created by lmer in package lme4, then use the (perhaps outdated) pvals to estimate p-value. But then I couldn't find pvals anywhere.

Hi everybody! I was using the function pvals.fnc from package 'languageR' until April. I do not know which version. Yesterday I updated all my packages and tried to run my loop again. Now I get the following error message: error in pvals.fnc(mm, nsim = 1000) : MCMC sampling is not yet implemented in lme4_0.999375-27 for models with random correlation parameters I guess it?s because of

Dear R-help: I've been trying to run pvals.fnc in the newest version of R (2.13.0). The function lmer worked fine, but when I tried to use pvals.fnc on the lmer object, I got the following error: "Error in pvals.fnc(elogr.subj.dys.sum.3x3.p, nsim = 10000) : trying to get slot "coefs" from an object (class "summaryDefault") that is not an S4 object." How can I

Hi Everyone, I am using R 2.10.1. lmer function works properly, however pvals.fnc () does not despite the fact that I uploaded: - library(lme4) - library(coda) - library(languageR) This is the error message I get pvals.fnc(lexdec3.lmerE2, nsim=10000)$fixed Error in pvals.fnc(lexdec3.lmerE2, nsim = 10000) : MCMC sampling is not yet implemented in lme4_0.999375 for models with random

Hi. I get an error message about not converging when I try and use the pvals.fnc from the languageR library. The LMER analysis worked fine (See below). I am not an expert so I don't understand why the LMER worked but not the pvals.fnc Any help gratefully received. - Mike AIC BIC logLik MLdeviance REMLdeviance -7324 -7254 3673 -7451 -7346 Random effects: Groups

library(lme4) library(coda) library(languageR) fit = lmer(Reaction~Days + (1|Subject) + (0+Days|Subject), data=sleepstudy) pvals.fnc(fit)$random # compare with... samp = mcmcsamp(fit, n=10000, trans=FALSE) HPDinterval(samp) densityplot(samp, plot=F) # 'pvals.fnc' reports sigma instead of sigma^2, but it looks like the # Sbjc.(In) and Sbjc.Days are also sqrt compared with the

I'm trying to figure out how to carry out a Poisson regression fit to longitudinal data with a gamma distribution with unknown shape and scale parameters. I've tried the 'lmer4' package's glmer() function, which fits the Poisson regression using: library('lme4') fit5<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) + (1|id), data=pdata,

Hola buenas tras meses investigando como hacer anovas para factores con efectos fijos y efectos aleatorios, he encontrado una serie de funciones que satisfacen mis pretensiones y creo correctas en cierta medida. Me gustaría compartirlas con vosotros con doble intención, la primera es compartirla para que si otro se encuentra en esta situación que tenga el trabajo hecho y la segunda es que sean

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

Hello, I originally posted this on the stats stack exchange site, but given its focus on R software, it was removed -- so I figured I'd post here. I'm having trouble interpreting a change in effect direction and significance when I add an interaction term to my glmer() model. *Part 1* I ran an experiment in which participants made categorical decisions (out of two categories) in one of

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

Hi,   I am trying to use the lmer function from the lme4 package in R 2.8.0. to fit a generalized mixed-effects model for a dependent variable with a binomial distribution (for more info on my experiment, look below). However, I encounter a major problem: How is it possible to find the general test statistic and see the relative importance of the predictors? The methods which I found described in

In a GLMM, one compares the conditional model including covariates with the unconditional model to see whether the conditional model fits the data better. (1) For my unconditional model, a different random effects term fits better (independent random effects) than for my conditional model (correlated random effects). Is this very uncommon, and how can this be explained? Can I compare these models

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

Hello, I'm having some trouble figuring out the correct model specification for my data. The system consists of multiple populations of an organism, which have been genetically sampled for several years. The problem is this: A minority of individuals are found in more than one sample, either they have survived into the next sampling at the same location, or have migrated to another another

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

I've built a poisson regression model for multiple subjects by using the GLMER function. I've also developed some curves for defining its limits but I did not succeed in developing confidence interval for the model's curve (confint or predict does not work - only for glm). Does anyone know how can I produce confidence interva for a glmer model? I'll appriciate any help... Liat --

Dear all, I am using function glmer from package lme4 to fit a generalized linear mixed effect model. My model is as follows: model1 <- glmer(fruitset ~ Dist*wire + (1|Site), data, binomial) summary(model1) Generalized linear mixed model fit by the Laplace approximation Formula: fruitset ~ Dist * wire + (1 | Site) Data: data AIC BIC logLik deviance 68.23 70.65 -29.11 58.23 Random

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