similar to: lme in R (WinXP) vs. Splus (HP UNIX)

When I run gnls I get the error: Error in nls(y ~ cbind(1, 1/(1 + exp((xmid - x)/exp(lscal)))), data = xy, : step factor 0.000488281 reduced below 'minFactor' of 0.000976563 My first thought was to decrease minFactor but gnlsControl does not contain minFactor nor nlsMinFactor (see below). It does however contain nlsMaxIter and nlsTol which I assume are the analogs of

Hi. I was wondering if anyone might have some suggestions about how I can overcome a problem of "iteration limit reached without convergence" when fitting a mixed effects model. In this study: Outcome is a measure of heart action Age is continuous (in weeks) Gender is Male or Female (0 or 1) Genotype is Wild type or knockout (0 or 1) Animal is the Animal ID as a factor

Dear all I want to compute Monte Carlo p-values in lmer-models based on sampled data sets. To speed up calculations, I've tried to use internal functions from the Matrix package (as suggested ealier on the list by Doug Bates). So I did: fm2 <- lmer(resistance ~ ET + position + (1|Grp), Semiconductor,method='ML') simdata<-simulate(fm2,nsim=1) ynew <- simdata[,1] mer

Dear R-help list readers, I am fitting mixed models with the lme function of the nlme package. If I get convergence depends on how the method (ML/REM) and which (and how much) parameters will depend randomly on the cluster-variable. How get the bist fit without convergence? I set the parameters msVerbose and returnObject to TRUE: lmeControl(maxIter=50000, msMaxIter=200, tolerance=1e-4,

Dear R community, I'm fitting a complex mixed-effects model that requires numerous iterations and function evaluations. I note that nlminb accepts a list of control parameters, including eval.max. Is there a way to change the default eval.max value for nlminb when it is being called from lme? Thanks for any thoughts, Andrew -- Andrew Robinson Department of Mathematics and Statistics

Hello! One note/question hier about specification of control-parameters in the lme(...,control=list(...)) function call: i tried to specify tne number of iteration needed via lme(....,control=list(maxIter=..., niterEM=...,msVerbose=TRUE)) but every time i change the defualt values maxIter (e.g. maxIter=1, niterEM=0) on ones specified by me, the call returns all the iterations needed until

Hi, I am having trouble using the lme function to perform a nested ANOVA with a random nested factor. My design is as follows: Location (n=6) (Random) Site nested within each Location (n=12) (2 Sites nested within each Location) (Random) Dependent variable: sp (species abundance) By using the aov function I can generate a nested ANOVA, however this assumes that my nested

Hello together, I am an absolute noob in R and therefore I need help urgently. I have received a script from my tutor with plot functions in it. However, I can' manage to adapt these plots. The hole script is as follows: setwd("E:/") ##### (1) Read data ### dat <- read.table("Komfort_Tatsaechliche_ID_Versuchsreihe_1.txt", header=TRUE, sep="\t",

Dear Thomas Lumley, and R-help list members, I have read your article "Network meta-analysis for indirect treatment comparisons" (Statist Med, 2002) with great interest. I found it very helpful that you included the R code to replicate your analysis; however, I have had a problem replicating your example and wondered if you are able to give me a hint. When I use the code from the

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

I'm starting to understand fixed and random effects, but I'm puzzled a bit. Here is an example from Hays's textbook (which is great at explaining fixed vs. random effects, at least to dummies like me), from the section on mixed models. You need library(nlme) in order to run it. ------ task <- gl(3,2,36) # Three tasks, a fixed effect. subj <- gl(6,6,36) # Six subjects, a random

Dear friends of lme, After so many year with lme, I feel ashamed that I cannot get this to work. Maybe it's a syntax problem, but possibly a lack of understanding. We have growth curves of new dental bone that can well be modeled by a linear growth curve, for two different treatments and several subjects as random parameter. By definition, newbone is zero at t=0, so I tried to force the

Last week (Wed 9/10/2003, "regression questions") I posted a question regarding the use of gnls( ) and its dissimilarity to the syntax that nls( ) will accept. No one replied, so I partly answered my own question by constructing indicator variables for use in gnls( ). The code I used to construct the indicators is at the end of this email. I do have a nagging, unanswered

On starting an R session, I get the messages: Fatal errir: unable to restore save data in .RData Error in fun(...): couldn't find function "assignInNamespace" Error: .onLoad failed in 'loadNamespace' for 'Matrix' The only object in my .RData is last.warning, thus: > last.warning $"optim or nlminb returned message false convergence (8)"

Hi List I'm running a series of mixed models using lme, and I wonder if there is a way to sort them by AIC prior to testing using anova (lme1,lme2,lme3,....lme7) other than by hand. My current output looks like this. anova (lme.T97NULL.ml,lme.T97FULL.ml,lme.T97NOINT.ml,lme.T972way.ml,lme.T97fc. ml, lme.T97ns.ml, lme.T97min.ml) Model df AIC BIC logLik

Hi all I am trying to generate a normal unbalanced data to estimate the coefficients of LM, LMM, GLM, and GLMM and their standard errors. Also, I am trying to estimate the variance components and their standard errors. Further, I am trying to use the likelihood ratio test to test H0: sigma^2_b = 0 (random effects variance component), and the t-test to test H0:mu=0 (intercept of the model Yij = mu

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

I've posted the following to R-help on May 15. It has reproducible R code for real data -- and a real (academic, i.e unpaid) consultion background. I'd be glad for some insight here, mainly not for myself. In the mean time, we've learned that it is to be expected for anova(*, "marginal") to be contrast dependent, but still are glad for advice if you have experience. Thank

Dear R users, Does anyone knows how to run a glmm with one fixed factor and 2 random numeric variables (indices)? Is there any way to force in the model a separate interaction of those random variables with the fixed one? I hope you can help me. #eg. Reserve <- rep(c("In","Out"), 100) fReserve <- factor(Reserve) DivBoulders <- rep

Dear R-help members, I'm trying to run LME model on some behavioral data and need confirmations about what I'm doing... Here's the story... I have some behavioral reaction time (RT) data (participants have to detect dome kind of auditory stimuli). the dependant variable is RT measured in milliseconds. 61 participants were tested separated in 4 age groups (unblanced groups,