similar to: Adjusting for initial status (intercept) in lme growth models

Hi all! I am working on a nested lme model with one fixed effect ("treatment", which 3 levels) and two random effects for "Individuals" (four of them) within "treatment" and "replicate -2 levels-" within "individual" within "treatment". For doing so, I´ve been trying to create a factor for Individual%in%Treatment, say IT by

Hi, R users, I'm using the "lme" function in R to estimate a 2 level mixed effects model, in which the size of the subject groups are different. It turned out that It takes forever for R to converge. I also tried the same thing in SPSS and SPSS can give the results out within 20 minutes. Anyone can give me some advice on the lme function in R, especially why R does not converge?

Dear R-helpers, I have two problems that I don't know how to vectorize (but would like to because my current solution is slow). # 1. #I have a vector x: x <- c(3, 0, 1, 0, 2, 2, 2, 0, 4, 2) #I want this translated into a new vector based on x,so that each element of x #is the number of zeroes, followed by a 1. The new vector would look like: #> r# [1] 0 0 0 1 1 0 1 1 0 0 1 0 0

Dear all I'm getting a strange error when trying to use rtags() to generate tags in Emacs format. root at liv-laptop:/usr/lib/R# R CMD rtags -o /usr/local/build/ETAGS --no-Rd --no-c library/ Tagging R files under library/; writing to /usr/local/build/ETAGS (overwriting)... [..] Error: '\.' is an unrecognized escape in character string starting "[.]*\." Execution halted

My R version is 2.7.1 (on Debian) and some packages are asking for > 2.10. I've done all things from http://cran.r-project.org/bin/linux/debian/ but to no success. Apt-get upgrade is doing nothing. server:/etc/R# apt-cache policy r-base-core r-base-core: Installed: 2.7.1-1+lenny1 Candidate: 2.7.1-1+lenny1 Version table: *** 2.7.1-1+lenny1 0 500 http://mirrors.nl.kernel.org

I''d like to get SwitchTower to checkout from different repositories per role? The db role checks out different stuff from the app role, etc. I don''t mind redefining SwitchTower::SCM::Subversion.checkout. But I''m not sure the best way to do it. How would that method parameterize the repository based on role? It''d be something like this: command =

There are three different types of HGH products. The confusion is that all three are advertised as if they were the same. The three types are: 1) --- Homeopathic HGH 2) --- Pre-cursor HGH 3) --- Real or synthetic HGH (delivered by injection or, by an oral spray method). Do you know differences? Call us and we'll explain them to you. Our toll free number is

There are three different types of HGH products. The confusion is that all three are advertised as if they were the same. The three types are: 1) --- Homeopathic HGH 2) --- Pre-cursor HGH 3) --- Real or synthetic HGH (delivered by injection or, by an oral spray method). Do you know differences? Call us and we'll explain them to you. Our toll free number is

Hi everybody! I'm sure that I overlook something and feel quite stupid to ask, but I have not found an easy solution to the following problem: Take e.g. the Orthodont data from the nlme package: > head(Orthodont) Grouped Data: distance ~ age | Subject distance age Subject Sex 1 26.0 8 M01 Male 2 25.0 10 M01 Male 3 29.0 12 M01 Male 4 31.0 14 M01 Male

Consider the following plot that shows separate regression lines ~ age for each subject in the Pothoff-Roy Orthodont data, with separate panels by Sex: library(nlme) #plot(Orthodont) xyplot(distance ~ age|Sex, data=Orthodont, type='r', groups=Subject, col=gray(.50), main="Individual linear regressions ~ age") I'd like to also show in each panel the pooled OLS

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

Hi all I’m in desperate need of help. I’m working with a grouped data object, called Orthodont in the nlme package in R, and am trying to fit various models (learning methods for my thesis), but one of the variables in the object is numeric, (age) and I need it to be a factor. I’ve tried: as.factor(Orthodont$age) as.factor(as.character(Orthodont$age)) and various other things, but when I then

Dear Douglas Bates. I just downloaded the compiled version (I'm a poor Windows devil, not yet having found the time to move to a more advanced platform...) from NT- the files are dated 30.5-1999 so they are not old - and the problem persisted....wonder what I did wrong ? R : Copyright 1999, The R Development Core Team Version 0.64.0 Patched (May 3, 1999) R is free software and comes with

Dear All, I've come across a problem in predict.lme. Assigning a model formula to a variable and then using this variable in lme (instead of typing the formula into the formula part of lme) works as expect. However, when performing a predict on the fitted model I gan an error messag - predict.lme (but not predictlm) seems to expect a 'properly' typed in formula and a cannot extract

Doug, I thought perhaps that you might be interested in the comparison of lme to the results for the same models fitted by Richard Jones' carma (I just wrote the R interface to his Fortran code). The code to run the example from the lme help and for the equivalent with carma is in the file below. The two main differences in results are 1. the random coefficients covariance matrix is quite

I am using rw0641. In my continuing quest to understand repeated measures analysis, I again return to lme. I exported the Potthoff and Roy data Orthodont.dat from S-PLUS 4.5 to avoid capture errors and ran the examples in the R help. I imported the data.frame with data <- read.table("Orthodont.dat",header=T) attach(data) and created the objects Orthodont.fit1 <-

Dear R-help I'm working on a large dataset which I have divided into 20 subsets based on similar features. Each subset consists of observations from different locations and I wish to use the location as a random effect. For each group I want to select regressors by a stepwise procedure and include a random effect on the intercept. I use stepAIC() and lme(). (The lmer()-function doesn't

Hi, I'm having some problems regarding the packages lme4 and nlme, more specifically in the denominator degrees of freedom. I used data Orthodont for the two packages. The commands used are below. require(nlme) data(Orthodont) fm1<-lme(distance~age+ Sex, data=Orthodont,random=~1|Subject, method="REML") anova(fm1) numDF DenDF F-value p-value (Intercept) 1

Hello, I am running this script from Pinheiro & Bates book in R Version 2.1.1 (WinXP). But, I can't plot Figure 2.3. What's wrong? TIA. Rod. --------------------------------------------------------- >library(nlme) > names( Orthodont ) [1] "distance" "age" "Subject" "Sex" > levels( Orthodont$Sex ) [1] "Male"

R-helpers: I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is: fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont) I would like to measure the correlation b/t the variable "distance" at