similar to: pvals.fnc unhappy about lmer objects

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

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,

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

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

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,   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:

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 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

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"

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

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

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

Hello All, I am learning mixed effects logistic regression (lmer in lme4), and I am having problems deciphering the goodness of fit function plot.logistic.fit.fnc (languageR package). The only information I can find is that this function plots observed proportions against mean predicted probabilities for some binning of the data. I have explored this function with several datasets, and have had

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 (

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

Hi, I have a question about how I should report the results for a linear mixed effects model where the model includes as predictors three factors (facA, facB and facC), one of which (facA) interacts with the other two. facA and facB have two levels and facC has 3 levels. There are also several other continuous predictors (e.g. varA, varB, varC). My mixed model is specified with the following

Hello, I can not get the function plot.logistic.fit.fnc() working....it returns "Error: could not find function "plot.logistic.fit.fnc"". Do I need to upload as specific package first? I am trying to check the fit of a mixed logistic model. Also, any advice for checking the assumption of independence in a mixed logistic model? Many thanks in advance, Sarah Foster

Hello R, To analyze multi-level data, I started learning and using lmer. So far so wonderful. I then found some useful functions in package languageR. But then the following problem ocurred: Whenever I load and use the languageR package, then save the workspace - or quit R with saving the workspace - I am unable to reload that workspace in a later session. That is, R doesn't start at all