similar to: how can I use lmer on a windows machine?

To whom it may concern: I am using lmer for a statistical model that includes non-normally distributed data and random effects. I used this same function in the most recent version of R as of fall 2005, and have re-done some of the same analyses using all of the same files, but with the newest version of R (2.2.1). I get answers that are not exactly the same (although I do get the same

Dear All, I used lmer for data with non-normally distributed error and both fixed and random effects. I tried to calculate a "Type III" sums of squares result, by I conducting likelihood ratio tests of the full model against a model reduced by one variable at a time (for each variable separately). These tests gave appropriate degrees of freedom for each of the two fixed effects, but

To whom it may concern: I recently used lmer (for non-normally distributed data and mixed effects, using the Laplace method). All 3 of my fixed effects were categorical, including two ordered factors and one unordered factor. In my tables, I currently report the number of observations for the response variable, and both the degrees of freedom and Chi Square values from tests of reduced

To whom it may concern: I have a question about how to appropriately conduct an lmer analysis for negative binomially distributed data. I am using R 2.2.1 on a windows machine. I am trying to conduct an analysis using lmer (for non-normally distributed data and both random and fixed effects) for negative binomially distributed data. To do this, I have been using maximum likelihood,

Hello, I’m trying to perform a REML analysis using the lmer() function (lme4 package). Well, it seems to work well, except that I’m not getting any p-value (see example below). Can someone tell me what I did wrong? Thanks for your help, Amélie > library(gdata) > dive <- read.xls("C:/Documents and Settings/Amelie/My Documents/Postdoc/CE 2005-2006/divebydive.xls",

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

Dear All, I have the following error message when I fitted lmer to a binary data with the "AGQ" option: Error in family$mu.eta(eta) : NAs are not allowed in subscripted assignments In addition: Warning message: IRLS iterations for PQL did not converge Any help? Thanks in advance, Abderrahim [[alternative HTML version deleted]]

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~

I am having trouble calling the lmer function in the {Matrix} package. I first installed and loaded {Matrix} as follows: > install.packages("Matrix") > library(Matrix) The package loaded successfully, however when I attempted to call lmer, I received the following message: Error: could not find function "lmer" I also tried: < ?lmer which produced no search

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

I am newbie in R, trying to understand and compare syntax in nlme and lme4. lme() model from the nlme package I am interested in is: lme.m1.1 = lme(Y~A+B+C,random=~1|D/E,data=data,method="ML") (for simplicity reason, I am giving generic names of factors) If I understand well, there are three fixed factors: A, B and C, and two random factors: D and E. In addition to that, E is nested in

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()

Full_Name: John Noble Version: 2.10 OS: Solaris 10 SPARC Submission from: (NULL) (128.172.190.27) The make of Recommended fails. All sections of Make work up to this point and the compiled version of R appears to be correct. Only when adding the recommended modules does the problem appear. The best I can figure the problem appears to be related to the fact that gzip will not decompress

Dear all, I'm trying to store/extract the mean& standard error of the fixed effects parameter and the variance of the random effects parameter from "lmer" procedure from mlmre4 package developed by bates n pinheiro. while storing fixed effects parameter is straight forward, the same is not true for storing the variance parameter of the random effects. kindly help me ~prabhu

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

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)

I'm quite happy to see 9.1 out and want to ask polite and benevolent question: regarding times on the site, are iso and img files the same as 2 weeks ago? To remind noble readers. I installed on my computers what was release at that time and got it up and working perfectly. In other words, is it the same file? Best regards and happy new year Zoran

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: