similar to: Equivalent of intervals() in lmer

Dear Friends, Please note that in the following CI lower > CI higher: > require(lmer) > require(gmodels) > fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) > ci(fm2) Estimate CI lower CI upper Std. Error p-value (Intercept) 251.66693 266.06895 238.630280 7.056447 0 Days 10.52773 13.63372 7.389946 1.646900

Example: I have the following model > model <- lmer(response ~ time * trt * bio + (time|id), data = dat) where time = time of observation trt = treatment group (0-no treatment / 1-treated) bio = biological factor (0-absent / 1-present) and I would like to obtain an estimate (with standard error) of the change in response over time for individuals in the

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)

Dear Dieter, sorry for not being more specific. I would like to use R to get a prediction (with standard error) of the response in a mixed model at selected values of the fixed-effects factors. Hence, in a mixed model, say, for response body size with, say, fixed factors sex and age, I would like to get a prediction of size for each sex and at selected ages such as 5, 10, 15; and I want a SE

Dear All, I would like to be able to estimate confidence intervals for a linear combination of coefficients for a GLS model. I am familiar with John Foxton's helpful paper on Time Series Regression and Generalised Least Squares (GLS) and have learnt a bit about the gls function. I have downloaded the gmodels package so I can use the estimable function. The estimable function is very

Hi everyone, I'm using lmer and estimable (from packages lme4 and gmodels respectively) and have the disconcerting happening that when I run exactly the same code, I get different results! In checking this out by running the code 50x, it seems to be that answers may be randomly deviating around those which I get from another stats package (GenStat, using the linear mixed models functionality

Dear r-helpers, I sent a cc of a recent message about a problem with ci.lmer() in the gmodels package to the author (Gregory R Warnes), and the message bounced. If the author or someone else is maintaining this package or this function, would you kindly supplement the author's name and/or address with a current maintainer and/or provide a current email address?

How can I get prediction intervals from a mixed-effects model? Consider the following example: library(nlme) fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1) df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5), Subject=rep(Subject[1], 4), Sex=rep(Sex[1], 4))) predict(fm3, df3.1, interval='prediction') # M01 M01

Hello all! R2.0.1, W2k. All packages updated. I?m heavily dependant on using mixed models. Up til?now I have used lme() from nlme as I have been told to. Together with estimable() from gmodels it works smooth. I also often run Rcmdr, mostly for quick graphics. After using Rcmdr, on reopening the R workspace all help libraries for Rcmdr (22 !) loads, among them nlme, but not Rcmdr itself. Why?

Dear all I have a model that looks like this: m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) I know it is not possible to estimate random effects but one can obtain BLUPs of the conditional modes with re1 <- ranef(m1, postVar=T) And then dotplot(re1) for the examiner and item levels gives me a nice prediction interval. But I would like to have the prediction

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

Hello, and sorry for asking a question without the data - hope it can still be answered: I've run two things on the same data: # Using lme: mix.lme <- lme(DV ~a+b+c+d+e+f+h+i, random = random = ~ e+f+h+i| group, data = mydata) # Using lmer mix.lmer <- lmer(DV ~a+b+c+d+(1|group)+(e|group)+(f|group)+(h|group)+(i|group), data = mydata) lme provided an output (fixed effects and random

Hi; I am running generalized linear mixed models (GLMMs) with the lmer function from the lme4 package in R 2.6.2. My response variable is overdispersed, and I would like (if possible) to run a negative binomial GLMM with lmer if possible. I saw a posting from November 15, 2007 which indicated that there was a way to get lmer to work with negative binomial by assigning: family =

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

My question relates to problems that I'm having matching lme and lmer examples in P&B. using Matix 0.995 In the Oxide example in p167-170 I can't get the level 2 coefficient estimates to match the fm1Oxide model in lme is data(Oxide,package="nlme") lme(Thickness~1,Oxide) which I translate in Lmer syntax to fm3Oxide<-lmer(Thickness~

Dear R users, We have encountered a slight problem when using the lmer() function: 1. Data description: 11 locations; Nt: monthly mosquito population density from 1994-2005 in each location. 2. Question: to examine the degree of spatial heterogeneity in the system by testing model support for single versus multiple intercepts and slopes for the location effect. We applied the lmer()

Dear All, I am trying to use stepAIC for an lmer object but it doesn't work. Here is an example: x1 <- gl(4,100) x2 <- gl(2,200) time <- rep(1:4,100) ID <- rep(1:100, each=4) Y <- runif(400) <=.5 levels(Y) <- c(1,0) dfr <- as.data.frame(cbind(ID,Y,time,x1,x2)) fm0.lmer <- lmer(Y ~ time+x1+x2 + (1|ID), data = dfr, family = binomial)

Hi I am running an lmer which works fine with family=poisson mixed.model<-lmer(nobees~spray+dist+flwabund+flwdiv+round+(1|field),family="poisson",method="ML",na.action=na.omit) But it is overdispersed. I tried using family=quasipoisson but get no P values. This didnt worry me too much as i think my data is closer to negative binomial but i cant find any examples of

Dear colleagues, I have a data from a repeated measures design that I'm analysing through a mixed model. Nine independent sampling units (flasks with culture medium with algae) were randomly divided into 3 groups ("c", "t1", "t2"). There is no need for inclusion of the random effect of the intercept, because the nine sample units are homogeneous among each other

I encounter such problem with lmer under FreeBSD, but not under Windows. Anyone knows why? Thanks. > example(lmer) lmer> (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Error in UseMethod("as.logical") : no applicable method for "as.logical" > traceback() 9: as.logical(EMverbose) 8: as.logical(EMverbose) 7: lmerControl() 6: