similar to: problems with nmle

DeaR I am trying to use a dynamically create formula with nlsList and nlme, but I cannot get the environment of the string-generated formal to work similarly to the manually entered one. Any idea? Dieter #----- library(nlme) # Pinheiro/Bates p 280 fm1Indom.lis = nlsList(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) nlme(fm1Indom.lis,random=pdDiag(A1+lrc1+A2~1)) # works... # Simulating

Hi, I was trying to run the example of Indomethacin kinetics from the book: ## From Pinheiro/Bates, Mixed-Effects-Models in S and S-Plus, ## Springer, Second Printing 2001, Section 6.2 library(nlme) plot(Indometh) fm1Indom.nls <- nls(conc~SSbiexp(time,A1,lrc1,A2,lrc2), data=Indometh) summary(fm1Indom.nls) plot(fm1Indom.nls,Subject~resid(.),abline=0) ## .... the last plot command gives me the

Hi out there, I am trying to fit a species accumulation curve (increase in number of species known vs. sampling effort) for multiple regions and several bootstrap samples. The bootstrap samples represent different arrangements of the actual sample sequence. I fitted a series of nlme-models and everything seems OK, but since the observations are correlated I tried to include some correlation

At the top of page 283 of Pinheiro and Bates, a covariance structure for the indomethicin example is specified as random = pdDiag(A1 + lrc1 + A2 + lrc2 ~ 1) The argument to pdDiag() looks like a two-sided formula, and I'm struggling to reconcile this with the syntax described in Ch4 of the book and online. Further down page 283 the formula is translated into list(A1 ~ 1, lrc1 ~ 1, A2 ~ 1,

Hello all! I have a problem to use a biexponential regression model: I use this code: n3<-nls(proc~SSbiexp(cls,a,b,c,d),data=bline) and this is the error message: Error in nls(y ~ cbind(exp(-exp(lrc1) * x), exp(-exp(lrc2) * x)), data = xy, : singular gradient My data is like this: structure(list(proc = c(387.177462830167, 508.090511433077, 321.836817656365, 151.226805860727,

I would like to be able, inside a function, to create a new function, and use it as part of a formula as an argument to, say, gnls or nlme. for example: MyTop <- function(data=dta) { Cexp <- function(dose,A,B,m){...} Model <- as.formula(paste("y","~ Cexp(",paste(formals(Cexp),collapse =", "),")")) MyCall <-

Hi there, I'm quite new to R (and statistics), and I like it (both)! But I'm a bit lost in all these packages, so could someone please give me a hint whether there exists a package for fitting exponential curves (of the type t --> \sum_i a_i \exp( - b_i t)) on a noisy signal? In fact monoexponential decay + polynomial growth is what I'd like to try. Thanks in advance,

Hi I am using R 1.7.1 on RH linux 9.0 > sum(unlist(lapply(ls(),function(x)object.size(get(x)))))/1024^2 [1] 2.424263 so I am not using much memory (I have a gig of ram on my machine) now in nlme > gtest<-groupedData(log(X8)~Time|sub,all[,c(names(all)[1:9],"X8")],outer=~A*B) > object.size(gtest)/1024 [1] 59.98438 > plot(gtest,outer=~Dose*chem,key=FALSE,asp=.5) Plotting

Dear R users, I'm applying a correlation structure in a mixed model (nmle function) to control for spatial correlation between land parcels that are adjacent to each other. I generated X,Y coordinates in ArcGIS for each land parcel and used them in the correlation form like this: test.exp<-corExp(1, form = ~ X + Y) test.exp<- Initialize(test.exp,dataset) However, the correlation

Hello. I have 16 subjects with 1-4 obs per subject. I am using the package "nlme" to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific

Hi, All: Does 'nlme' require the nonlinear function to be differentiable? I'm fitting structural change models to many related time series, and I'd like to improve the estimates of the change point times through some type of pooling. Unfortunately, I've so far been unable to get 'nlme' to work for me. The following toy example is the closest I've come

A few days ago I posted a question to this discussion group concerning to origin of an error message < subscript out of bounds > while using the nonlinear mixed model (nlme) function in R with a self-starting function. Thanks for those who responded. This posting is to explain what (I think) it causing the error. Pinheiro & Bates (2000, pages 342-347) describe how to construct a

Hi, I am not sure this is a bug but I can repeat it, The functions and data are below. I know this is nasty data, and it is very questionable whether a 4pl model is appropriate, but it is data fed to an automated tool and I would have hoped for an error. Does this repeat for anyone else? My details: > version _ platform i686-pc-linux-gnu

Hi, the following R lines work fine in R 2.4.0 alpha (and older R versions), but not in R 2.4.0 beta (details below): library(drc) # to load the dataset 'PestSci' library(nlme) ## Starting values sv <- c(0.328919, 1.956121, 0.097547, 1.642436, 0.208924) ## No error m1 <- nlme(SLOPE ~ c + (d-c)/(1+exp(b*(log(DOSE)-log(e)))), fixed =