similar to: likelihood for new data from fitted nlme object

I wish to fit a dynamical model in R and I am running in a problem that requires some of your wisdom to solve. For SAS users I am searching for the equivalent of the retain statement. For people that want to read complicated explanations to help me: I have a system of two equations written as difference equations here. To boil it down. I have a dataframe with three variables y, X1, X2 which

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

Tony Rossini sent me a copy of Jim's message to this list asking about nlme. I was surprised to find that I had not subscribed to this list. I thought I did but apparently not. I have now corrected that. Development of the nlme library (the name is from "nonlinear mixed-effects" but the library can also be used to fit general forms of linear mixed-effects models) is a continuing

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),

Hello, I am using R2.5 under Windows. Looks like the following statement vars <- (obj$sigma^2)*vw in getVarCov.gls method (nlme package) needs to be replaced with: vars <- (obj$sigma*vw)^2 With best regards Andrzej Galecki Douglas Bates wrote: >I'm not sure when the getVarCov.gls method was written or by whom. To >tell the truth I'm not really sure what

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

To estimate saturation binding parameters Bmax and Kd in a receptor saturation binding experiment, I use the following nonlinear equation and the nls() function: bmax*X*dummy ------------ + ns*X + background = total binding kd+X where X is concentration, and dummy is an indicator to allow shared estimation of the nonspecific binding parameter ns. This equation describes two fitted

Dear R developers, recently I have written a wishlist bug report for nlme containing a patch that adds the variance function structure s2(v) = t1^2 + t2^2*v^2 where v denotes the variance covariate, s2(v) denotes the variance function evaluated at v, and t, t1 and t2 are the variance function coefficients. The covariate can also be the fitted response. The idea that the residual variance

Hello, I am trying to fit a nlme (non linear mixed effect). I am using the SelfStart function SSlogis. However the data in my hand contains few observations per subject (4 or less), so the nlsList doesn't work... In this case I should fixe initial parameter estimates. I remark that values of initial estimates have a greater effect on the model fit (i.e. loglikelihood, AIC and also on

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

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 will start out with the caveat that I'm not a statistician by training, but have a fairly decent understanding of probability and likelihood. Nevertheless, I'm trying to fit a nonlinear model to a dataset which has two main factors using nlme. Within the dataset there are two Type categories and four Tissue categories, thus giving me 8 datasets in total. The dataset is in

Dear all, Is it possible to generate AIC or something equivalent for nonlinear model estimated based on maximum log likelihood l in R? I used nls based on least squares to estimate, and therefore I cannot assess the quality of models with AIC. nlme seems good for only mixed models and mine is not mixed models. res <- nls(y ~ d*(x)^3+a*(x)^2+b*x+c, start=list(a=2, b=1,c=1,d=1), data=d) If

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:

Dear All, I have a question concerning the computation of the value of the Restricted Maximum Likelihood (REML) function evaluated at a given set of parameter estimates from the Maximum likelihood (ML) value. Following the book of Fitzmaurice, Laird and Ware (2004) "Applied Longitudinal Analysis" pp101, the REML likelihood can be computed by multiplying the ML likleihood by the square

I am looking for R code to be able to fit a linear-linear piecewise model with person-specific changepoint. I have searched the web, but have not been able to locate any code. Below is my attempt at some code: chgpt = function(a1,a2,a3,gam,wave){ yht=numeric(10) y1=(wave <= gam)*(a1+(a2*wave)) y2=(wave > gam)*((a1+(a2-a3)*gam)+a3*wave) yhat=y1+y2 return(yht) } nl.dat <- nlme(y ~

Dear R-Users, I am comparing the nlme package in S-Plus (v. 7.0) and R (v. 2.2.1, nlme package version 3.1-68.1; the lattice, Matrix, and lme4 have also just been updated today, Jan. 23, 2006) on a PC (2.40 GHz Pentium 4 processor and 1 GHz RAM) operating on Windows XP. I am using a real data set with 1,191 units with at most 4 repeated measures per unit (data are incomplete, unbalanced). I

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

Dear R-expert I'm fitting a non linear model (energy allocation model to individual growth data) using your nlme routine. For each individual I have thus a number of observations (age and size) to which I fit the nonlinear function, with random effects for the individuals on the estimated parameters (individual as the grouping factor). The sampling of these individuals was stratified (size