similar to: lmer2 error under Mac OS X on PowerPC G5 but not on Dual-Core Intel Xeon

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of

Hello EVERYONE, I need an URGENT help from you please! How can I see the "estfun" (empirical estimating function) and "df" (degree of freedom) from the following mixed-model please? (fm1 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) Many thanks in advance for your kind help. Sattar

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Version 0.9975-11 of the lme4 package has been uploaded to CRAN. The source package should be available on the mirrors in a day or two and binary packages should follow soon after. There are several changes in this release of the package. The most important is the availability of a development version of lmer called, for the time being, lmer2. At present lmer2 only fits linear mixed models.

Dear r-helpers, I can run the examples on the mcsamp help page. For example: **************************************** > M1 <- lmer (y1 ~ x + (1|group)) > (M1.sim <- mcsamp (M1)) fit using lmer, 3 chains, each with 1000 iterations (first 500 discarded) n.sims = 1500 iterations saved mean sd 2.5% 25% 50% 75% 97.5% Rhat n.eff beta.(Intercept)

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

Hello R-users: Could you please tell me how can I extract the "df (degree of freedom)" and "estfun (estimating functions)" for the following lmer (or lmer2) model? wtd.mixed<-lmer(ddimer~race+steroid+psi+sofa+apache + (1|subject), method="ML", data=final, cluster="id", weights=w) I tried the following codes: - for the degree of freedom (erorr

I seem to come to this problem alot, and I can find my way out of it with a loop, but I wish, and wonder if there is a better way. Here's an example (lmer1-5 are a series of lmer objects): bs=data.frame(bic=BIC(lmer1,lmer2,lmer3,lmer4,lmer5)$BIC) rownames(bs)=c('lmer1','lmer2','lmer3','lmer4','lmer5') best=rownames(bs)[bs==min(bs)] > best [1]

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Dear all, I want to estimate a crossed-random-effects model (i.e., measurements, students, schools) where students migrate between schools over time. I'm interested in the fixed effects of "SES", "age" and their interaction on "read" (reading achievement) while accounting for the sample design. Based on a previous post, I'm specifying my model as: fm1 <-

Hello R list, I am hoping to conduct a logistic regression with repeated measures, and would love an actual "code run through" for such an analysis. I found only one related post on this list, but a full answer was never provided. I understand that the routine lmer (or lmer2) in the lme4 package is often recommended in such a case, but actually implementing it is where I've hit a

Hi All. I'm trying to run a simple model from Baayan, Davidson, & Bates and getting a confusing error message. Any ideas what I'm doing wrong here? # Here's the data..... Subj <- factor(rep(1:3,each=6)) Item <- factor(rep(1:3,6)) SOA <- factor(rep(0:1,3,each=3)) RT <- c(466,520,502,475,494,490,516,566,577,491,544,526,484,529,539,470,511,528) priming

Hello list, I've been reading through the archives and it seems as though, as of right now, there is no way to specify the correlation structure in lmer. I was wondering if anyone knows if this is going to be implemented? I'm using mixed-effects models within a tree structure, so I make a lot of calls to lme to get the resulting deviance, and lmer2 is almost 5 times faster than lme

Hi, I think this is probably (& hopefully) very easy but I the ideas I have tried don't work. Simply, I have 2 very large lists in my workspace which take an awfully long time to make, each containing the information from 12500+ lmer models. I would like to now save these lists as files (txt, csv... whatever suits best) on to my computer/usb. Then I would like to be able to reload them in

Dear r-helpers, I would very much appreciate help with the following problem: The following command (in a .Rnw file) latex(anova(e7.lmer3, e7.lmer4), file = 'e7lmer34.tex', rowname = c ('nonlinear', 'linear'), longtable = FALSE, dcolumn = T, booktabs = T, table.env = F) produces the following output after running Sweave: % latex.default(anova(e7.lmer1, e7.lmer2),

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening here, and in

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

Full_Name: Dale Barr Version: 2.5.1 (patched) OS: Ubuntu linux x86_64 Submission from: (NULL) (138.23.70.108) When RMySQL is loaded in before lme4, the summary() function for lmer objects in the lme4 packages produces the following error: Error in printMer(object) : no slot of name "status" for this object of class "table" When RMySQL is loaded AFTER lme4, however, no such

Running the Chapter 4 examples in Pinheiro & Bates' "Mixed-Effects Models in S and S-PLUS" (2000), I get a message that the default optimizer doesn't converge, but using "optim" for the optimizer results in convergence: > > library(nlme) > > fm1IGF.lis <- lmList(IGF) > > fm1IGF.lme <- lme(fm1IGF.lis) > Error in lme.formula(fixed =

I am attempting to model data with the following variables: timepoint - n=48, monthly over 4 years hospital - n=3 opsn1 - no of outcomes total.patients skillmixpc - skill mix percentage nurse.hours.per.day Aims To determine if skillmix affects rate (i.e. no.of.outcomes/total.patients). To determine if nurse.hours.per.day affects rate. To determine if rates vary between