similar to: nlme and variance-covariance matrices.

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Hello! So, my problem is following. I have bird offspring growth data and I'd like to model individual growth curves (aim is to study asymptotes and inflection points) with nlme according to Pinheiro & Bates 2000: first using nlsList to generate individual curves and then nlme to study the parameters and fixed effects. The data is structured to two levels. I have broods and

> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote: > > Hi, > > Thanks in advance for any help on this question. I'm running multinomial > models using the MCMCglmm package. The models have 5 outcome variables > (each with count data), and an additional two random effects built into the > models. The issue is that when I use

Hi all, I've been struggling with trying to specify a diagnoal matrix for linear mixed effects model. I think I've got nearly everything correct, except the following message appears: In lme.formula(fixed = fwave ~ sex + sexXbulbar + visit + age + : Fewer observations than random effects in all level 1 groups Not sure if i've provided enough details, but I'm basically trying

I want to extract the variance-covariance matrix of an nlme model of a dataset. The object is to pass this to mvrnorm to create pseudo- replicates of the original data. I note the nlme package has a getVarCov method available for lme objects but not nlme objects. Is the vcov function in the base stats package suitable? If so, why is the additional getVarCov provided? thank you Rob

Hi, How extract the Variance and Covariance Matrices D of random effects and R of error in the lme object? Thanks in advance. Alexandre Galv??o

Dear R team, I would like to do a multivariate meta-analysis in R using the nlme package. In meta-analysis I fix the residuals to known sampling errors. As I do a multivariate analysis, I have a variance-covariance matrix of sampling errors. Unfortunately, via varFixed I can only fix a vector of sampling errors and no matrix. In the R package metafor using the rma.mv function I can insert the

Hi David, Thanks for your comment. I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already know the answer. I will reconsider whether my University's ethics approval would allow me to post the data and update the question if I think

Hello R list subscribers, I am trying to use the "by" command to create line-specific variance covariance matrices (where "x" is the original data matrix): by(x, x$line, function(d) { d.clean <- d[,-1]}) write.table(d.clean$line[1,1], sep = ",", file = "covariances.csv", col.names = FALSE, row.names = FALSE, append = TRUE) write.table("", sep

Hi, I would be grateful for help with a problem which is irritating me. I am quite sure that I am doing something stupid, but I can't see what it is. I am running R 1.7 on Windows 2000. The graphics device is the PC screen. The graphics from the nlme demonstration in Bates an Pinheiro's manual work just as advertised. The CO2 data and the Orthodont data dsiplay

i'm trying to build a little summary table for the contents of a data frame. t<-sapply(macro, data.class) c<-sapply(macro, length) m<-sapply(macro, mean, na.rm=T, digits=2) cbind(type=t, n=c , mean=m) I want to drop the variables that are factors so I can include -max- and -min- in my table. -macro- contacts the data--how do I drop the variables according to their data.class

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hello, I have been attempting to set up a lme and have looked at numerous posts including 'R's lmer cheat-sheet' as well as reading a number of papers and other resources including R help, but I am still a little confused on how to write my model (I thought I had it). I have asked a number of questions on different forums; most of which have been resolved. My main concern right now

Hi, I'm a long-time STATA user and a R newbie. I'm doing ok, but I'm addicted to STATA macro variables. Is there something like a macro variable in R? Specifically, I'd like to be able to do something like for (i in 1:3) { ..... x`i' <- ... } where R would resolve x`i' to the objects named x1, x2 and x3 as I move through the loop. I guess I could create these in

Hello, I am using the Kinship package to draw a pedigree plot. I am trying to control the spacing between individuals in the pedigree. Currently, it is drawing a pedigree with too little space, so the text is overlapping. I'd like to increase the distance between individuals. According to the documentation, I would expect the following to be relevant:

Hi Im using the kinship package to draw a pedigree. On my data set this works fine but when i add indivudals to the pedigree i keep getting an error i hope someone can help me! This is the code im using: Data<-read.table("Tree.txt", header=T, sep=",") attach(Data) ped<-pedigree(id, dadid, momid, sex, aff) par(xpd=T) plot.pedigree(ped) This is my data looks like

I access files on our Cray T3E from my Windows 2000 PC via a Samba share. One of the volumes on the T3E is managed by Cray's Data Migration facility (DMF). The majority of the DMF files are stored off-line and restored to disk as necessary. This operation typically takes 3-5 minutes. Normally when a Windows 2000 application tries to read data from one of the off-line files--even if it's

Dear R developers: I am a PHD candidate student in the school of public health of Peking University and my major is genetic epidemiology. I am learning the FAM-MDR algorithm, which is used to detect the gene-gene and gene-environment interactions in the data of pedigree. The codes were written by Tom Cattaert of the University of Liege. The algorithms and the sample datasets are available at

[I am responding on the cygwin list to a message on cygwin-announce and cc'ing to the rsync list.] From: "Lapo Luchini" <lapo@lapo.it> > I have uploaded version 2.5.2-1 of the open source utility that > provides fast incremental file transfer. > > ChangeLog as on http://rsync.samba.org : Rsync 2.5.2 has a data-destroying bug as described here: