similar to: nlme and memory

How to set the nlme() function to return the answer without the intercept parametrization? #========================================================================================= library(nlme) Soybean[1:3, ] (fm1Soy.lis <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal),                        data = Soybean)) (fm1Soy.nlme <- nlme(fm1Soy.lis)) fm2Soy.nlme <- update(fm1Soy.nlme,

Dear Mixed Effect Friends, Somehow, R(1021, Windows) seem to run differently from S Plus: The soybean example from Pinheiro/Bates on page 290 fails in R. (Soybean1 is Soybean with the NA and "critical" case removed. Same procedure with full Soybean). > fm1Soy.lis<-nlsList(weight~SSlogis(Time,Asym,xmid,scal),data=Soybean1) > fm1Soy.nlme<-nlme(fm1Soy.lis) Error: Singularity

Mmmh, seems as if I really should change my options as I seem to keep sending off empty bug-reports ;-/ Sorry guys. Here is the content that should have been in the last e-mail: `R CMD check nlme' fails on my machine. The final output in nlme-Ex.Rout is: > library(nlme) > data(Soybean) > fm1 <- nlme(weight ~ SSlogis(Time, Asym, xmid, scal), data = Soybean, + fixed =

This is only partly a question about R, as I am not quite sure about the underlying statistical theory either. I have fitted a non-linear mixed-effects model with nlme. In the fixed part of the model I have a factor with three levels as explanatory variable. I would like to use Tukey HSD or a similar test to test for differences between these three levels. I have two grouping factors:

Hi I use gnls to fit non linear models of the form y = alpha * x**beta (alpha and beta being linear functions of a 2nd regressor z i.e. alpha=a1+a2*z and beta=b1+b2*z) with variance function varPower(fitted(.)) which sounds correct for the data set I use. My purpose is to use the fitted models for predictions with other sets of regressors x, z than those used in fitting. I therefore need to

Hi, I guess this is a final plea, and maybe this should go to R-help but here goes. I am writing a set of functions for calibration and prediction, and to calculate standard errors and intervals I need the variance function to be evaluated at new prediction points. So for instance fit<-gnls(Y~SSlogis(foo,Asym,xmid,scal),weights=varPower())

Hi folks, I am tearing my hair out on this one. I am using an example from Pinheiro and Bates. ### this works data(Orange) mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange ) ### This works mod <- nlme(circumference ~ SSlogis(age, Asymp, xmid, scal), data=Orange, fixed = Asymp + xmid + scal ~ 1, start =

Doug, I have been attempting to learn a little bit about nlme without too much documentation except the online help. The Latex file in the nlme directory looks interesting but uses packages that I do not have so that I have not been able to read it. I have run the example from gnls to compare it with the results I get from my libraries (code below - I have not included output as it is rather

Hi List, While using 'nlme' function, I have encountered the similar problem Dr. Stevens and Dr. Graves observed (please see the posts: https://stat.ethz.ch/pipermail/r-help/2006-May/105832.html ). I have tried Dr. Stevens's original example, the problem is still there, > mod.lis <- nlsList(circumference ~ SSlogis(age, Asymp, xmid, scal), + data=Orange ) >

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

Dear all, How can I extract the total and residual d.f. from a gnls object? I have tried str(summary(gnls.model)) and str(gnls.model) as well as gnls(), but couldn?t find the entry in the resulting lists. Many thanks! Best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess

Back on January 16, a message on R-help from Ravi Varadhan described a problem with gnls using weights=varPower(). The problem was that the fit failed with error Error in eval(expr, envir, enclos) : Object "." not found I can reliably get this error in version 2.0.1-patched 2004-12-09 on Windows XP and 2.0.1-Patched 2005-01-26 on Linux. The key feature of that example is that the

Hello everybody, I'm testing the randomForest package in order to do some simulations and I get some trouble with the prediction of new values. The random forest computation is fine but each time I try to predict values with the newly created object, I get an error message. I thought I was because NA values in the dataframe, but I cleaned them and still got the same error. What am I

I am trying to learn how to use nlme by working on a simple example. I attach the data from a toy example I made up which is similar to my real problem. (My grasp of fixed/random effects is still a bit tenuous) It is a longitudinal study of the effect of two treatments: A and B. The data were created by: A: y<-12/(1+exp((2-time)/.5)),y<-8/(1+exp((2-time)/.5)) B:

Could someone explain the meaming of this error message from augPred: > augPred(area3.pen.nlme, primary=~day) Error in predict.nlme(object, value[1:(nrow(value)/nL), , drop = FALSE], : Levels 1,2,3 not allowed for block > predict.nlme(area3.pen.nlme) does not produce an error. area3.pen.nlme was created with: > area3.pen.nlme <- nlme(area ~ SSlogis(day, Asym, xmid, scal),

Hello all. I was wondering if there is a way to hold constant the fixed effects correlation structure across multiple groups? For example, I have two groups and I fit a three parameter logistic growth curve where the fixed effects are free to vary across the groups. I'll paste in the code as a concrete example: > Result.NLME <- nlme(Score ~ SSlogis(Time, Asym, xmid, scal), +

Thank you Martin. If I understand correctly, OP could do wheat.list <- nlsList(Prop ~ SSfpl(end, A, B, xmid, scal), data=wlg) or add some small value to all zeroes wlg$prop < -wlg$Prop+1e-7 wheat.list <- nlsList(prop ~ SSlogis(end,Asym, xmid, scal), data=wlg) which gives fairly reasonable results. plot(augPred(wheat.list)) Am I correct? Cheers Petr > -----Original Message-----

>>>>> PIKAL Petr <petr.pikal at precheza.cz> >>>>> on Fri, 20 Oct 2017 06:33:36 +0000 writes: > Hi > Keep your messages in the list, you increase your chance to get some answer. > I changed your data to groupedData object (see below), but I did not find any problem in it. > plot(wlg) > gives reasonable picture and I am

Hello, I would like to fit correlation structures with nlme, but R crashes. My data is similar to the "growth of orange trees" example from Pinheiro and Bates (2000), but data are not equally spaced in time, as the last observation is taken after 6 days ( and not 2 as the others). This is the code I'm using: library(nlme)