similar to: nmle for time of structural change?

Following the Indomethicin example in Pinheiro & Bates, chapter 6, page 277 etc, coming to the following comand: fm2Indom.nlme <- update( fm1Indom.nlme, random = pdDiag(A1 + lrc1 + A2 ~ 1) ) debugging nlme gives the following output: Browse[1]> n debug: modelResid <- ~eval(model, data.frame(data, getParsNlme(plist, fmap, rmapRel, bmap, groups, beta, bvec, b, level,

Tracking down this bug was joint work with Jermoe Asselin (jerome at hivnet.ubc.ca) and Patrick Connolly (p.connolly at hortresearch.co.nz). We collectively were able to determine that this is a problem in both Windows 2000 and in Linux and by testing it in our three time zones that it seems to be daylight savings time related. Conversion of POSIXlt datetimes to POSIXct appears to have problems.

Hello: In the following plot, the fitted line plots 100 percent above the points: tstDat <- data.frame(x=10^(1:3), y=10^(1:3+.1*rnorm(3))) tstFit <- lm(log(y)~log(x), tstDat) plot(y~x, tstDat, log='xy') abline(tstFit) I can get the correct line with the following: tstPredDat <- data.frame(x=10^seq(1, 3, len=2)) tstPred <- predict(tstFit, tstPredDat)

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

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

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

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

I am a novice R user, and have been in trouble to get the right mixed model syntax in microarray analyses. There are three factors: Dye(2 levels), Temperature(3 levels) and Array(3 for each Temperature with a total of 9 arrays). I want to treat array as random, and to regard array variation different between Temperatures. So the model I want to seek is: Y = Dye + Temp + Dye + Temp*Dye +

Summary: ---------------------- Hello, when using the MS NT SrvTools (User & Server Manager for Domains), I get an error when I try to access an individual user's properties ("The following error occurred while accessing the properties of the user <blah> \n The specified procedure could not be found \n The user properties cannot be edited or viewed at this time"); however,

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,

Among others, datam contains the columns: logconc, tm, dose, subj, bilirubin. None of these are factor variables. The following compartment models work (the first still has not converged after 100 interations): res1 <- nlme(logconc~p2+p3+log(dose/(exp(p1)-exp(p2))* (exp(-exp(p2)*tm)-exp(-exp(p1)*tm))),start=list(fixed=c(5,-2,-0.1)), fixed=list(p1+p2+p3~1),control=list(maxIter=100),

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

Hello. I want to specify a diagonal structure for the covariance matrix of random effects in the lme() function. Here is the call before I specify a diagonal structure: > fit2<-lme(Ln.rgr~I(Ln.nar-log(0.0011)),data=meta.analysis, + random=~1+I(Ln.nar-log(0.0011)|STUDY.CODE,na.action=na.omit) and this works fine. Now, I want to fix the covariance between the between-groups slopes

Hi, everyone, I wrote a function, which includes an nlme estimation. The problem is sometimes nlme may not converge due to too many random effects. Say a, b are two parameters. if I specify random effects by: random = a+b~1, nlme fails to converge. Then I have to constrain the random effects in a positive definite diagonal matrix by: random = list(pdDiag(a+b~1)) My question is how I can

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

Dear members, I would like to switch from nlme to lme4 and try to translate some of my models that worked fine with lme. I have problems with the pdMat classes. Below a toy dataset with a fixed effect F and a random effect R. I gave also 2 similar lme models. The one containing pdLogChol (lme1) is easy to translate (as it is an explicit notation of the default model) The more parsimonious

Dear R community, Below you may find the details of my model (lm11). I receive the error message "Error: cannot allocate vector of size 220979 Kb" after applying the autocorrelation function update(lm11, corr=corAR1()). lm11<-lme(Soil.temp ~ Veg*M+Veg*year, data=a, random = list(Site=pdDiag(~Veg), Plot=pdDiag(~Veg))

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

There has been some recent discussion on this list about the value of using intervals with lme() to check for whether a model is ill-defined. My question is, what else can drive very large confidence intervals for the variance components (or cause the error message "Error in intervals.lme(Object) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate