similar to: Understanding and translating lme() into lmer() model

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

I have installed lme4 library, but when I try something with lmer() function, I receive error message. On the other hand, I can use lme() function from the same library. Are those two the very same function or not? I am a bit confused. I am using: $platform: "i386-pc-linux-gnu" $arch: "i386" $os: "linux-gnu" $system: "i386, linux-gnu" $major: "2"

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

I would appreciate help translating the following lme model to an lmer function. lme(lognrms ~ Group*Rotation*muscle*side*support*arms, random=~1|Subject/Stratum2/rep, data=Data) Many thanks Ross Darnell r.darnell at uq.edu.au

Hello! I am running analysis on the data from 4 experiments, with approximately 4600 rows (cases). My working model is: fitA1 = lme(RT~F1+F2+L,random=~1|Experiment/Subject,data=data) Model works very fine, but if I try to check whether the effect of L depends on Experiments/Subjects with: fitA2 = lme(RT~F1+F2+L,random=~1+L|Experiment/Subject,data=data) [with the idea to make: anova(fitA1,fitA2)]

I am doing an analysis and would like to use lme() and the multcomp package to do multiple comparisons. My design is a within subjects design with three crossed fixed factors (every participant sees every combination of three fixed factors A,B,C). Of course, I can use aov() to analyze this with an error term (leaving out the obvious bits): y ~ A*B*C+Error(Subject/(A*B*C)) I'd also like

Hi, I have a question about the function lme() in R. I have a 2*2*3 layout with some missing data (labelled as *). These 3 factors are labelled as A,B,C, the response is Score. The layout is as follows:- A B C Score 1 1 1 5 1 1 2 * 1

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

windows Vista R 2.10.1 What is the difference (or differences) between lme and lmer? Both appear to perform mixed effects regression analyses. Thanks John John David Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone)

First of all, I apologize for asking a question that has appeared recurrently in this mailing list. However, I have googled for it, have looked at the mailing list archives, and also looked at Pinheiro & Bates book (although not very thoroughly, I must confess), to no avail. Here is the question: I am trying to obtain with lme or lmer the same exact numerical results (p-values) that I obtain

Hi, I have this: lme(y~x1+x2,random=~1|x1/x2) How to make this random effect using lmer? I try this: lmer(y~x1+x2+(1|x1/x2) But it dont work. Any idea? Thanks Ronaldo -- System halted! -- |> // | \\ [***********************************] | ( ?? ?? ) [Ronaldo Reis J??nior ] |> V [UFV/DBA-Entomologia ] | / \ [36570-000 Vi??osa -

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Can lme or lmer fit a plain regular fixed effects anova? Ie a model without a random effect, or have there be at least one random effect in order for these functions to work? Trying to run such, (1) without specifying a random effect produces an error, (2) specifying that there is no random effect does not produce the same output as an anova run in lm(); (2b) specifying that there is no

Hi, I asked this on mixed model mailing list, but that list is not very active, so I'd like to try the general R mailing list. Sorry if anyone receives the double post. Hi, I have a dataset of animals receiving some eye treatments. There are 8 treatments, each animal's right and left eye was measured with some scores (ranging from 0 to 7) 4 times after treatment. So there are

Folks: In the following mixed effect model, the "x" and residual variance components are nonidentifiable. Yet lme() (or the equivalent in lmer()) happily produces an answer in which the sum of the variance components is the correct estimate of the single variance parameter. Why? -- i.e. why doesn't lme complain? x <- 1:50 y <- rnorm(50) m1 <- lme( y ~ 1, rand = ~1|x)

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

Hello R-help, I'm studying an example in the R book.? The data file is available from the link below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt Could you explain Why the results from lme() and lmer() are different in the following case? In other examples, I can get the same results using the two functions, but not here...? Thank you.Miya library(lme4)library(nlme)#

---------- Forwarded message ---------- From: Freddy Gamma <freddy.gamma@gmail.com> Date: 2011/1/21 Subject: TRADUCING lmer() syntax into lme() To: r-sig-mixed-models@r-project.org Dear Rsociety, I'd like to kingly ask to anyone is willing to answer me how to implement a NON NESTED random effects structure in lme() In particular I've tried the following translation from lmer to

Hello, I'm using aov() to analyse changes in brain volume between males and females. For every subject (there are 331 in total) I have 8 volume measurements (4 different brain lobes and 2 different tissues (grey/white matter)). The data looks like this: Subject Sex Lobe Tissue Volume subect1 1 F g 262374 subect1 1 F w 173758 subect1 1 O g 67155 subect1 1 O w 30067 subect1 1 P g 117981

Hello, I ran two lme analyses and got expected results. However, I saw something suspicious regarding p-level for fixed effect. Models are the same, only experimental designs differ and, of course, subjects. I am aware that I could done nesting Subjects within Experiments, but it is expected to have much slower RT (reaction time) in the second experiment, since the task is more complex, so it