similar to: nmle question

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 have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

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

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,

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, 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, I make a weibull survival regression using suvreg function. Bu when I try to get the P values from anova, it give me NAs: I'm using R 2.4.0 and survival 2.29 Look: m <- survreg(Surv(tempo,censor)~grupo*peso) anova(m) Df Deviance Resid. Df -2*LL P(>|Chi|) NULL NA NA 148 966.6416 NA grupo -2 25.6334407 146 941.0081 NA

Hi, I make a weibull survival regression using suvreg function. Bu when I try to get the P values from anova, it give me NAs: I'm using R version 2.4.0 Patched (2006-11-25 r39997) and survival 2.30 library Look: m <- survreg(Surv(tempo,censor)~grupo*peso) anova(m) Df Deviance Resid. Df -2*LL P(>|Chi|) NULL NA NA 148 966.6416 NA grupo

?? > z <- array(1:24,dim=2:4) > all.equal(f(z,3),f2(z,3)) [1] "Attributes: < Component ?dim?: Mean relative difference: 0.4444444 >" [2] "Mean relative difference: 0.6109091" In fact, > dim(f(z,3)) [1] 2 3 4 > dim(f2(z,3)) [1] 3 4 2 Have I made some sort of stupid error here? Or have I misunderstood what was wanted? Cheers, Bert Bert Gunter

Here is an alternative approach using apply(). Note that with apply() you are reversing rows or columns not indices of rows or columns so apply(junk, 2, rev) reverses the values in each column not the column indices. We actually need to use rev() on everything but the index we are interested in reversing: f2 <- function(a, wh) { dims <- seq_len(length(dim(a))) dims <-

I've continued to work on speeding up R, and now have a collection of fourteen patches, some of which speed up particular functions, and some of which reduce general interpretive overhead. The total speed improvement from these patches is substantial. It varies a lot from one R program to the next, of course, and probably from one machine to the next, but speedups of 25% can be expected in