similar to: R-alpha: nlme in R

With the assistance of Saikat DebRoy we have produced an nlme package (linear and nonlinear mixed-effects models) for R (see http://www.r-project.org/ for details about R). The nlme package for R requires R-0.90.0, which was released earlier today. Also released today was NLME 3.2 for S-PLUS. We will be working on incorporating the changes from NLME 3.1 to 3.2 into the R version but we wanted

With the assistance of Saikat DebRoy we have produced an nlme package (linear and nonlinear mixed-effects models) for R (see http://www.r-project.org/ for details about R). The nlme package for R requires R-0.90.0, which was released earlier today. Also released today was NLME 3.2 for S-PLUS. We will be working on incorporating the changes from NLME 3.1 to 3.2 into the R version but we wanted

The Rows function which is called from plot.compareFits in the nlme package is not found. > plot(compareFits(coef(bp.model3),coef(bp.model3M))) Error in plot.compareFits(compareFits(coef(bp.model3), coef(bp.model3M))) : couldn't find function "Rows" > Can I find it elswhere? Have I missed a required package? Thanks Ross Darnell > library(help=nlme) nlme

Full_Name: Vaidotas Zemlys Version: 2.9.0 OS: Ubuntu 8.10 Submission from: (NULL) (213.197.173.50) Steps to reproduce the bug: library(nlme) data(Oxboys) qm=lmList(height~age|Subject,data=Oxboys) grid=with(Oxboys,expand.grid(age=seq(min(age),max(age),length=50),Subject=levels(Subject))) res <- predict(qm,grid,se=TRUE) Erreur dans if (pool) { : l'argument est de longueur nulle res <-

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,

Hello R-help I am having trouble getting gls to find the R objects that comprise a linear model when the data=named.object option(option!) is not specified. In the gls() help it states data is "an optional data frame containing the variables named in model, correlation, weights, and subset. By default the variables are taken from the environment from which gls is called". An example:

Dear all, I try to load the nlme library for Linear and nonlinear mixed effects models by Jose Pinheiro, Douglas Bates and Saikat DebRoy but I get the following error message: > library(nls) > library(nlme) Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "C:\Program\R\rw1010/library/nlme/libs/nlme.dll": LoadLibrary failure: Det

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

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

I am working on a nlme model that has multiple fixed effects (linear and nonlinear) with a nonlinear (asymptotic) random effect. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) asymporigb<-function(x,th1b,th2b)th1b*(1-exp(-exp(th2b)*x)) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b),

I am getting an error message in a call to nlme and cannot understand what is happening. I explain the steps below in the hope that someone can explain the error and how to correct it. STEP 1: Data set: name: marouane.data. This is a data frame whose first few lines are as follows: > marouane.data[1:13,] species plant leaf irradiance photosynthesis chlorophyll 1

Hello. I am trying to fit a nonlinear mixed model involving 3 parameters. I have successfully made a self-starting function. getInitial() correctly outputs the initial estimates. I can also use the nlsList with this function to get the separate nonlinear fits by group. However, I get an error message when using the nlme function. Here is the relevent code:

Hello. I am working on an analysis involving the nonlinear mixed model function (nlme) in R. The data consist of measures of carbon fixation by leaves as a function of light intensity and the parametric function (standard in this area because it has a biological interpretation) is a non-rectangular hyperbola. I cannot get the nonlinear mixed model (nlme) function to converge cleanly. I am

I assigned a class the first problem in Pinheiro & Bates, which uses the data set PBIB from the SASmixed package. I have recently downloaded 2.0.1 and its associated packages. On trying library(SASmixed) data(PBIB) library(nlme) plot(PBIB) I get a warning message Warning message: replacing previous import: coef in: namespaceImportFrom(self, asNamespace(ns)) after library(nlme) and a

Dear List, While trying to define a customised CITATION file for a package, following R-exts, I realised that if I use only one 'citEntry' I got both a text description and a BibTex entry for the package, as for the 'nlme' package: -------------------------- citation('nlme') To cite package 'nlme' in publications use: Jose Pinheiro, Douglas Bates, Saikat

Dear R-experts, I have a problem to formulate non linear mixed effects when more than one explanatory variable is present. I'm using R 1.1.1 under Linux. The version number of the nlme library is 3.1-7. "help(nlme)" says that among several possibilities it is possible to code fixed effects according to fixed = a1 + a2 + ... + an ~ X1 + X2 + ... + Xm where the left hand side

Dear forum members, What is the formula to calculate denominator degrees of freedom (den df) for nonlinear mixed-effect models with covariates? My model is similar to a CO2 uptake example from Pinheiro and Bates (2000, page 376). In this CO2 dataset, there are two treatments and two types (84 observations in total), but den df for each parameter of the model is 64. Isn’t it too high? Your

Hello- I understand that it's convention, when comparing two models using the anova function anova(model1, model2), to put the more "complicated" (for want of a better word) model as the second model. However, I'm using lme in the nlme package and I've found that the order of the models actually gives opposite results. I'm not sure if this is supposed to be the case

Hallo, I have a question that is more statistic related than about nlme and R functioning. I want to fit a complicated nonlinear model with nlme with several different measures of transpiration taken on each of the 220 trees grouped in 8 families. The unknown parameters of the model are three + their variances (and covariances). I want to estimate the variances among families of the

Hi, I want to fit nonlinear dose-response curves, as "fun(X,a,b,c)", for each of our 5 trail locations. Our data basis is something like location plot year dose response For each location there are 4 plots as repetitions (over 3 years). So the interactions "location*year" and "location*plot" should be random effects. There are some examples in "Mixed-Effects