similar to: lme4

Estimado Jorge Velez Lo que usted dice tiene algo a mi pregunta, pero yo la formule mal. Voy a preguntar nuevamente con un ejemplo que tiene errores, pero es más próximo a lo que estoy pensando. Modelo (con error pero no importa) modelo <- muerte = edad + sexo + 1!causa Causa: infarto, infarto, súbita, muerte en tiroteo El modelo lineal, sobrevida, etc. corre sin inconvenientes (aunque

Tengo una duda, hay unos modelos mixtos que se resuelven con lme4, no tengo problemas con esto, pero necesito tener el resultado para cada uno de los factores, lo expreso con este ejemplo aunque no es código r creo que se entendería Factores como ser casa y auto, tengo casa 1, casa 2, casa 3, y auto 1, auto2, auto 3. Con lme4 y utilizando ranef obtengo por ejemplo Casa 1

I'm trying to use the lmeSplines package together with lme4. Below is (1) an example of lmeSplines together with nlme (2) an attempt to use lmeSplines with lme4 (3) then a comparison of the random effects from the two different methods. (1) require(lmeSplines) data(smSplineEx1) dat <- smSplineEx1 dat.lo <- loess(y~time, data=dat) plot(dat.lo) dat$all <- rep(1,nrow(dat)) times20

I have a model: mod1<-lmer( x ~ (1|rtr)+ trth/(1|cs) , data=dtf) # Here, cs and rtr are crossed random effects. cs 1-5 are of type TRUE, cs 6-10 are of type FALSE, so cs is nested in trth, which is fixed. So for cs I should get a fit for 1-5 and 6-10. This appears to be the case from the random effects: > mean( ranef(mod1)$cs[[1]][1:5] ) [1] -2.498002e-16 > var(

Hi All, I am trying to shift from running mixed models in SAS using PROC MIXED to using lme4 package in R. In trying to match the coefficients of R output to that of SAS output, I came across this problem. The dataset I am using is this one: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect034.htm If I run the following code: proc mixed data=rc

I'm running some multi-level binomial models with lme4 and have a question regarding the estimated random effects. Suppose I have nested data e.g. clinic and then patient within clinic. The standard deviations of the random effects at each level are roughly equal in a model for real life data. Attention then turns to examining the individual random effects at each level. I'm extracting

Dear all, I am trying to get the emperical bayes estimates together with their standard errors out of lme4. Up to now I have used MLwiN to get these estimates. I have fitted the following - very simple - model, just to find out how this works. test<-lmer(y~(1|subject),data,REML=F) ranef(test,postVar=T) str(ranef(test,postVar=T) If I use the formulation of the emperical bayes estimates and

Full_Name: Din Chen Version: 2.4.0 OS: Windows XP Submission from: (NULL) (66.17.122.18) I just updated the R.2.4.0. and got the error message for random effect modelling, which was working on R.2.3.1. library(lme4) mmod <- lmer(bright ~ 1+(1|operator), pulp) summary(mmod) Then when I tried to extract the residuals and random effect using: resid(mmodr) ranef(mmodr) I got error message:

Estimado Elisabete Juaristi Arrieta Al leer su correo pensé que usted quería resolver un problema para lo que R no es adecuado, luego pensé que usted deseaba solucionar un problema donde hay datos y una ecuación a resolver. En este segundo caso yo muchas veces me pregunto cuál sería la forma para algunos problemas que tengo, muchos de estos tienen solución pero pienso que esa solución es puesta

Buenos días; Estoy intentando aprender algo de sobre los modelos lineales mixtos. Estoy siguiendo el libro "Multilevlel analysis: techniques and applications" de Joop Hox. Estoy intentado reproducir las tablas y gráficos que trae el libro con el paquete lmer. En este libro se indica que en la comprobación de los supuestos del modelo hay que estudiar los gráficos de residuales tanto

Hi, For further calculations I need to extract indivdual Variances of different random effects from a fitted model. I found out how to extract the correlations (VarCorr(m1)@reSumry$group1) but I was not able to find a way to extract the other components individually. To extract the Residuals I tried: (ranef(m1)@ stdErr) which unfortunately did not work. Thank you very much for your help!

R múltiple Estoy pensando en un problema que tendré que solucionar pero aún no comencé a escribirlo, por lo tanto no hay código en R como para compartir, sin embargo no tengo idea de cómo realizarlo desde R. El planteo es el siguiente: Los datos son en una cantidad necesaria para que el procesamiento estadístico demore (minutos, horas). Supongamos dos variables (serían más), la A y la B,

I have been struggling all day to import a particular function/method combination (ranef(), which extracts the random effects from a mixed model fit) from the nlme package into another package ... so far without success. The NAMESPACE for nlme contains the following lines: export(..., ranef, ...) S3method(ranef, lme) ranef is defined as a standard S3 generic, function (object, ...)

Hello, R community, I have been using the lmer and mcmcsamp functions in R with some difficulty. I do not believe this is my code or data, however, because my attempts to use the sample code and 'sleepstudy' data provided with the lme4 packaged (and used on several R-Wiki pages) do not return the same results as those indicated in the help pages. For instance: > sessionInfo() R

Hola. Por si es de vuestro interés, os informo de que hemos publicado la versión 2.0 de BioStatFLOSS. Es un entorno homogéneo de ejecución, bajo Windows, de una recopilación de programas (ver imagen2), ya preparados para su uso, de utilidad para la realización de estudios (bioestadísticos, epidemiológicos y de salud en general), investigación biomédica y/o formación bioestadística. NOTA: Aunque

Encontré mi error, ahora funciona perfecto, Animal <- c('A','A','A','B','B','C','D','D') Edad <- c(2,3,4,3,4,2,4,5) datos <- data.frame(Animal, Edad) m_m <- with(datos, tapply(Edad, Animal, function(x){ minimo <- min(x) valor <- length(x) rep(minimo, valor) })) datos$repetido <- do.call(c, m_m) datos

Dear r-helpers, Spencer Graves and Manual Morales proposed the following methods to simulate p-values in lme4: ************preliminary************ require(lme4) require(MASS) summary(glm(y ~ lbase*trt + lage + V4, family = poisson, data = epil), cor = FALSE) epil2 <- epil[epil$period == 1, ] epil2["period"] <- rep(0, 59); epil2["y"] <- epil2["base"]

Dear list, This is more of a stats question than an R question per se. First, I realize there has been a lot of discussion about the problems with estimating P-values from F-ratios for mixed-effects models in lme4. Using mcmcsamp() seems like a great alternative for evaluating the significance of individual coefficients, but not for groups of coefficients as might occur in an experimental design

I postulate the following model AC <- glmer(Accuracy ~ RT*Group + (1+RT|Group:subject) + (1+RT|Group:Trial), data = da, family = binomial, verbose = T) Here I predict Accuracy from RT, Group (which has values 0 or 1) and the interaction of Group and RT (those are the fixed effects). I also estimate the random effects for both intercepts and slopes for subjects and different trials.

The coxme package has a ranef() method, as does lme4. I'm having trouble getting them to play together, as shown below. (The particular model in the example isn't defensible, but uses a standard data set.) The problem is that most of the time only one of lme4 or coxme will be loaded, so each needs to define the basic ranef function as well as a method for it. But when loaded together