search for: pituitari

Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that

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?

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

Dear all, Here is my code which am using to combine 5th column from different data sets. Here is the function to do my job genesymbol.append.file <-NULL gene.column <- NULL readGeneSymbol <- function(files,genesymbol.column=5){ for(i in fnames){ temp <- read.table(fnames,header=T,sep="\t",stringsAsFactors=F,quote="\"")