similar to: lme in R-2.0.0: Problem with lmeControl

Full_Name: Jörg Polzehl Version: 1.5.1 OS: Windows 2000 Submission from: (NULL) (193.175.148.198) When calculating MLE's in a variance component model using constrained optimization, i.e. optim(...,method="L-BFGS-B",...) I observed an inproper behaviour in cases where the likelihood function was evalueted at the constraint. Parameters and value of the function at the constraint

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

Hi, I have a simple random-coefficients model for m subjects: y = b0 + b1 x + r0 + r1 x + e where b0 and b1 are fixed parameters, r0 and r1 are random, e ~ N(0,s2 I) and R' = [r0, r1] ~ N(0,T). I try to obtain the EM-estimates of s2 and the elements of T by lme(y~x,data=mydata,random= list(group=~x), control=lmeControl(maxIter = 0, niterEM=100,msVerbose = TRUE)) Does

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

Hi R-experts, I have a problem using nlme. I use the following code to group my data: Parameterg <- groupedData( result ~ time | Batch, data = Batchdata, labels = list( x = "Time", y = "analysis") ) and then uses the nlme function to fit a nonlinear mixed model that includes Process as a fixed covariate: nlme.model001epr <- nlme(result ~ A0 * exp(- ( exp(A1)

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,

R2.2 for WinXP, Splus 6.2.1 for HP 9000 Series, HP-UX 11.0. I am trying to get a handle on why the same lme( ) code gives such different answers. My output makes me wonder if the fact that the UNIX box is 64 bits is the reason. The estimated random effects are identical, but the fixed effects are very different. Here is my R code and output, with some columns and rows deleted for space

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)"

A question on lme() : details: nlme() in R 2.1.0 beta or 2.0.1 The data,y, consisted of 82 data value in 5 groups of sizes 3 9 8 28 34 . I fit a simple one level random effects model by: myfit <- lme( y~1, rand = ~1|Group) The REML estimates of between and within Group effects are .0032 and .53, respectively; the between group component is essentially zero as is clearly evident from a

Hello, I am interested in using nlme to model repeated measurements, but I don't seem to get good CIs. With the code below I tried to generate data sets according to the model given by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having chosen values for beta, sigma.b and sigma similar to those estimated in the text). For each data set I used lme() to fit a model,

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

Dear R-help list members I gathered longitudinal data on fish behaviour which I try to analyse using a multi level model for change. Mostly, I am following Singer & Willett (2003), who provide also the S/R code for their examples in the book (e.g. http://www.ats.ucla.edu/stat/Splus/examples/alda/ch4.htm). Of course I am interested in change over time, but I am also very much interested in

Estimado Oliver Nuñez Envío un ejemplo reproducible. Javier Marcuzzi # de donde tomo datos, y tiene el modelo (en el pdf) library(MCMCglmm) # librería con las funciónes que voy a usar library(nlme) datos0<-ChickWeight # creo algunos datos que agrego a los origonales Factor<-as.numeric(datos0$Chick) Factor[Factor > 0 & Factor <= 10] <- 'A' Factor[Factor > 10

Hi guys, I'm facing a problems with samba4 + bind9_dlz that consuming my time for several days. Everything is working fine until samba4 need to update dns when I'm work with more than one DC server. When samba (or bind) need to reload all zones, the module bind9_dlz is shutting down and then all my environment stops and I need to restart the bind to up again. See my log: ... Jan

Mathias, Thanks for your reply. Please, try to start your bind with some debug level and run commando "rndc reload" and see the end of the log. I saw samba source code and found the destroy dns function in dlz_bind9.c and called by turture blz_bind9.c. When dlz_bind9.c is shutting down, I get this error when I try to update dns. update failed: NOTAUTH Failed nsupdate: 2

Using your log parameters, the shutting down message is not showed, but when I reload rndc a get the same effect. Everything is working fine until bond9_dlz needs to reload (and no restart) rndc. When this happens, I need to restart bind and everything works fine again. I'm starting named with named -d 3 -u named and using /var/log/messages. See log using your parameters: # rndc reload

Hi, I try to run the example of Crawley's Book on the page 661, but it fail, look > repmeasures <- read.table("../Packages/Crawley/data/repmeasures.txt",header=T) > attach(repmeasures) > rep <- as.factor(rep) > library(nlme) > model <- lme(height~seed,random=~time|rep/seed) Erro em lme.formula(height ~ seed, random = ~time | rep/seed) : iteration limit

Hi, I am trying to recreate the right graph on page 524 of Gelman's 2006 paper "Prior distributions for variance parameters in hierarchical models" in Bayesian Analysis, 3, 515-533. I am only interested, however, in recreating the portion of the graph for the overlain prior density for the half-Cauchy with scale 25 and not the posterior distribution. However, when I try:

Hope one of you could help with the following question/problem: We would like to explain the spatial distribution of juvenile fish. We have 2135 records, from 75 vessels (code_tripnr) and 7 to 39 observations for each vessel, hence the random effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model

Hi R-masters I read the article: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. In this paper i proposed a bivariate mixed model and use SAS proc mixed to adjust the estimates. I thinks use R to make the same and try with this code: base<-read.csv("base.csv") adj<-.5 attach(base) sens<-(VP+adj)/(VP+FN+2*adj)