similar to: PNLS step error in nlme

Hello all. I'm doing a simulation study where I will be making use of the 'nlme' package. I want to loosen up the convergence criteria so that I increase the likelihood of convergence (potentially at the cost of obtaining slightly less than ideal results). The parameters in the function nlmeControl() control the convergence criteria. These default values can be modified to make

Hello,   I am attempting to use nlme to model the response of 19 groups.  I am able to get a reasonable fit of all of the groups to the observed data using an exponential decay model (a*exp-x*b).  The problem is that when I plot the fitted values to residuals, it demonstrates a pattern of increasing variance.   I attempt to model this variance using   Sig3.nlme <- update(Sig2.nlme, weights =

In some trial simulation work I need to create batch files that will repeatedly generate pseudoreplicate datasets and then create non- linear mixed effects models using nlme. Inevitably these models sometimes fail to converge but I need the batch file to simply move on to another simulation rather than abort. I am using the try() function as in model<-try((nlme(...))) which handles

Dear Splus and R users: I have a problem in fitting a NLME model, the error message is: "step halving factor reduced below minimum in PNLS step" What does it mean and how to fix the problem, could anyone help me about it? Any suggestion/help would be greatly appreciated. Mei

I am having trouble fitting correlation structures within nlme. I would like to fit corCAR1, corGaus and corExp correlation structures to my data. I either get the error "step halving reduced below minimum in pnls step" or alternatively R crashes. My dataset is similar to the CO2 example in the nlme package. The one major difference is that in my case the 'conc' steps are

Dear R users, I am using nlme to fit a pharmacokinetic model. The base model is parameterized in terms of CL, V1, V2 and Q. basemodel<-nlme(Conc ~TwoCompModel(CL,Q,V1,V2,Time,ID), data = data2, fixed=list(CL+Q+V1+V2~1), random = pdDiag(CL+V1+V2~1), start=c(CL=log(20),Q=log(252),V1=log(24.9),V2=log(120)), control=list(returnObject=TRUE,msVerbose=TRUE, msMaxIter=20,pnlsMaxIter=20,pnlsTol=1),

I'm maximising a reasonably complex function using nlme (version 3.1-65, have also tried 3.1-66) and am having trouble with fixed parameter estimates slightly away from the maximum of the log likelihood. I have profiled the log likelihood and it is a parabola but with sum dips. Interestingly changing the parameterisation moves the dips around slightly. Unfortunately the PNLS step is

Dear R-user, I am trying to use the R "nlme" function to fit a non linear mixed effects model. The model I wand to fit is an individual somatic growth model with 4 parameters. For all parameters both fixed and random effects have to be estimated, as well as their covariance matrix (see the formula bellow). The data are simulated with the same growth model as in the nlme, with know

I am not certain how nlme works so I followed an example from the web ( http://www.menne-biomed.de/gastempt/gastempt1.html). I was able to successfully reproduce the example. However, when I modified my the example to use my data and with my formula, I get a set of errors having to do with the log() function. I get 10 of them (all exactly the same) and there are 10 levels in my factor variable.

Dear R list members; I have a 10 x 423 data frame which consisting of response, time, subject, site, plot and covariates (continueous and categorical) measured at the plot level. When the data frame was converted into a groupedData object, a warning appeared > A <- groupedData(ht ~ time | Subject, data = tt, outer = ~ site * plot, + labels=list(y = "Height", x =

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

Hi I have found some postings referring to the fact that one can try and understand why a particular model is failing to solve/converge from the verbose output one can generate when fitting a nonlinear mixed model. I am trying to understand this output and have not been able to find out much: **Iteration 1 LME step: Loglik: -237.4517 , nlm iterations: 22 reStruct parameters: subjectno1

>>>>> "VW" == Viechtbauer Wolfgang (STAT) <Wolfgang.Viechtbauer at STAT.unimaas.nl> >>>>> on Fri, 26 Sep 2008 18:00:19 +0200 writes: VW> Hi all, I'm trying to fit a marginal (longitudinal) VW> model with an exponential serial correlation function to VW> the Orange tree data set. However, R crashes frequently VW>

Dear All, Using:- R : Copyright 2005, The R Foundation for Statistical Computing Version 2.1.1 (2005-06-20), ISBN 3-900051-07-0 and Jose Pinheiro, Douglas Bates, Saikat DebRoy and Deepayan Sarkar (2005). nlme: Linear and nonlinear mixed effects models. R package version 3.1-65. on a WINDOWS 2000 machine I am trying to run the scripts from the Mixed Models book and am running into some

This concerns the "Clinical Study of Quinidine" example on page 380 of the book "Mixed-Effects Models in S and S-PLUS" by Pinheiro and Bates (2000). I have tried to reproduce the example, but get an error: > library(nlme) > fm1Quin.nlme <- nlme(conc ~ quinModel(Subject, time, conc, dose, interval, lV, lKa, lCl), + data=Quinidine, +

Hi R-users, Could you give me some advice in solving the problem of such error message from gnls and nls? ## begin error message "Problem in gnls(y1 ~ glogit4(b, c, m, t, x), data.frame(x..: Step halving factor reduced below minimum in NLS step " ##and "Problem in nls(y ~ 1/(1 + exp((xmid - x)/scal)), data = x..: step factor reduced below minimum "? Thank you in

Hi all, I'm working with a dataset from 10 treatments, each treatment with 30 subjects, each subject measured 5 times. The plot of the dataset suggests that a 3-parameter logistic could be a reasonable function to describe the data. When I try to fit the model using gnls I got the message 'Step halving factor reduced below minimum in NLS step'. I´m using as the initial values of the

Dear list members, the following hlm was constructed: hlm <- groupedData(laut ~ design | grpzugeh, data = imp.not.I) the grouped data object is located at and can be downloaded: www.anicca-vijja.de/lg/hlm_example.Rdata The following works: library(nlme) summary( fitlme <- lme(hlm) ) with output: ... AIC BIC logLik 425.3768 465.6087 -197.6884 Random effects:

Dear R-help; Following the installation of R 1.5.0 and library packages to my Win 2K machine, I tested the new R system: > library(nlme) Loading required package: nls > library(lattice) Loading required package: grid >data(Soybean) >plot(Soybean) A program error appears: "Rgui.exe has generated errors and will be closed by Windows. You will need to restart the program. An

Dear R users; I was trying to fit a nonlinear model using gnls (nlme version 3.1-80, R 2.5.0, WinXP) and I got the following error and warning message: Error in gnls(ht ~ a1 * hd * (1 - a2 * exp(-a3 * (dbh/dq2))), data = hdat, : Step halving factor reduced below minimum in NLS step In addition: Warning message: $ operator is deprecated for atomic vectors, returning NULL in: