similar to: Correlated random effects: comparison unconditional vs. conditional GLMMs

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(2006), my plan is to run mcmcsamp on a random effect model created by lmer in package lme4, then use the (perhaps outdated) pvals to estimate p-value.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

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

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

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

Hola Manuel lo he tratado de hacer pero me sale Error: unexpected string constante in: "anova(a,as,test=Chisq") no tengo ni idea de por qué... Me resulta alucinante no poder contar ya con pvals.fnc. ¿Será imposible hacerse con ello? Saludos, Miguel -------------------------------------------- El vie, 13/6/14, Manuel Azcárate <mazcarategarcia en gmail.com> escribió:

Dear All: I am using lmer() {lme4} to analyze results from a reciprocal transplant experiment where the response variable is modeled as a function of two fixed effects and their interaction. Example data follow: #library(lme4) #library(gmodels)

Existe discusión sobre el uso de los p-valores en modelos mixtos. Como se ha dicho antes, para mi lo más adecuado es comparar modelos mediante la función anova. Por Internet se puede encontrar un buen libro de Douglas Bates y en español, busca modelos mixtos con R de Luis Cayuela, enfocado hacia ecología, pero está muy bien El 13/06/2014 14:00, "Jorge I Velez"

Hola a todos, quería preguntaros un medio para obtener los valores p usando lmer. He tratado con pvals.fnc, que es lo que me habían recomendado, pero por algún motivo no está ya disponible etc. Ésta es la función que tengo, pero da las "t", sin los valores p. Aunque Baayen indica que valores por encima de 2 son significativos necesito saber las p. resultado = lmer(rt_ln ~ (fre_ln *

Hola buenas noches, tengo un problema bastante gordo. ¿A alguno le ha dejado de funcionar las funciones pamer.fnc y mcp.fnc con la nueva versión de R? La semana pasada formatee el ordenador y ahora scripts antiguos no funcionan. La cuestión es que me precupa que no funcione el ejemplo de tutorial del autor. Os dejo un script que debería de funcionar y no lo hace

Hace unos meses os escribir para comunicaros que había un fallo en esta función. Como os prometí os comento la respuesta por si alguno está interesado en utilizar el paquete LMERconvenientsfucntions Dear Javier, The package has been updated and should work for you fine now. Note that function mcp.fnc does not return the fourth plot (dffits) anymore. We still have to figure out a way to compute

I have two regressions to perform - one with a metric DV (-3 to 3), the other with an ordered DV (0,1,2,3). Neither normal distribution not homoscedasticity is given. I have a two questions: (1) Some sources say robust regression take care of both lack of normal distribution and heteroscedasticity, while others say only of normal distribution. What is true? (2) Are there ways of using robust

Having briefly fallen for the notion that the negative.binomial family in MASS could be used in glmer, I want to use these lists for a sanity check on my final (?) plans. I want to use glmer for logistic regression and for poisson regression on a data set of 10,000 items. There will be two crossed random effects. For the logistic regression, I want odds ratios with confidence intervals.For the

This evening I ran into the problem Chuck Clifton referred to in message 86 of Volume 71 Issue 9 of this list. That is, objects created by lmer change after calling pvals.fnc on that lmer object when using lme4 version 0.999375-16 and 0.999375-28. This is somewhat troublesome. The bug tracker on R-Forge (

I am currently using the relaimpo package to estimate the relative importance of regressors (N= 4000): > m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data) > calc.relimp(m1, rela=TRUE) > m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg") > booteval.relimp(m2) > plot(booteval.relimp(m2)) In a new dataset with 3 measurement points (0,6,12 weeks), I want to perform a similar

Hello. I am new to R and new to linear mixed effects modeling. I am trying to model some data which has two factors. Each factor has three levels rather than continuous data. Specifically, we measured speech at Test 1, Test 2 and Test 3. We also had three groups of subjects: RepTP, RepNTP and NoRepNTP. I am having a really hard time interpreting this data since all the examples I have seen