similar to: lme: cannot allocate vector

My small brain is having trouble getting to grips with lme() I wonder if anyone can help me correctly set the random = argument to lme() for this kind of setup with (I think) 9 variance/covariance components ... Study.1 Study.2 ... Study.10 Treatment.A: subject: 1 2 3 4 5 6 etc. 28 29 30 Treatment.B: subject: 31

Dear R-helpers, I am running R version 2.9.1 on a Mac Quad with 32Gb of RAM running Mac OS X version 10.5.6. With over 20Gb of RAM "free" (according to the Activity Monitor) the following happens. > x <- matrix(rep(0, 6600^2), ncol = 6600) # So far so good. But I need 3 matrices of this size. > y <- matrix(rep(0, 6600^2), ncol = 6600) R(3219) malloc: ***

Do you have to enable encrypted passwords for smbpasswd to work? I have yet to enable encryption because I wanted to test everything first, but have enabled 'update encrypted = Yes' so that this will update the smbpasswd file. I also have 'unix password sync = Yes'. However, I cannot get smbpasswd to change the passwords, either by user or root. Any suggestions? Carey

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

This is a continuing issue with the one on the list a long time ago (I couldn't find a solution to it from the web): -------------------------------------------------------------------------- > Using a formula converted with as.formula with lme leads > to an error message. Same works ok with lm, and with > lme and a fixed formula. > > # demonstrates problems with lme and

FYI:, I'm fairly new to samba (a couple of months now), and have experimented - unsuccessfully - with getting it to work with clearcase. Does anyone have a how-to, or a list of gotcha's for setting up clearcase with samba? Thanks in advance for any information you can send me. Ozzie,

Is there a way to have users of the samba server, but not add them by smbpasswd -a <UserID>? I want the samba server to be a domain member and the users to only authentic from the PDC. These are the steps that I have attempted: Users are in both the Windows domain and the UNIX NIS account 1. smbpasswd -j <Domain> -r PDC -U <admin> Joined the Domain 2. edited the

I've just tried setting up a Shrike (9) version of Redhat. Using the medium settings of lokkit, then adding manually accept commands for ports 137/udp 138/udp, 139/tcp and 445/tcp, I thought I should have been ready to go. This isn't the case, however. I know it's not the smb.conf settup because when I kill iptables samba works. When iptables IS running however, it will respond

Hi, I'm having trouble with glmmPQL. I'm fitting a 2 level random intercept model, with 90,000 cases and about 330 groups. I'm unable to get any results on the full data set. I can get it to work if I sample down to about 30,000 cases. But for models with N's much larger than that I get the following warning message:

Dear R users, I would be interested in using the lme() function to fit a linear mixed model to a longitudinal dataset. I know this function allows for the specification of a within-group covariance structure. However, does it allow for the explicit specification of a between-group covariance structure? Being able to specify both separately would be very important in the context of my project

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Dear all, I am studying the possibility of using the nlme package in R to analyse field trials of agricultural crops. I have a problem with the syntax for the modelling of variance covariance structures. I can model the within-group covariance structure using the correlation argument and the covariance structure between different random effects of the same grouping level using

Hello, We are attempting to use nlme to fit a linear mixed model to explain bird abundance as a function of habitat: lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) The data consist of repeated counts of birds in sample units across multiple years, and we have two questions: 1) Is it necessary (and, if so, how) to specify the repeated measure (years)? As written,

Dear R users, I have a question about the patterned variance-covariance structure for the random effects in linear mixed effect model. I am reading section 4.2.2 of "Mixed-Effects Models in S and S-Plus" by Jose Pinheiro and Douglas Bates. There is an example of defining a compound symmetry variance-covariance structure for the random effects in a split-plot experiment on varieties of

Thanks for the help Dimitris, However I still have a question, this time I'll be more specific, the following is my SAS code proc mixed data=Reg; class ID; model y=Time Time*x1 Time*x2 Time*x3 /S; random intercept Time /S type=UN subject=ID G GCORR V; repeated /subject = ID R RCORR; run; ** (Type =UN for random effects) The eqivalent lme statement I

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

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

I am analyzing data on a study of the effects of Coronary Artery Bypass Graft (CABG) on cognitive function, as measured by a score from an objective test. I have 140 people who receive the CABG surgery and 92 controls, with four measurements of cognitive function over time (at 0, 3, 12 and 36 months). I have fitted a linear mixed model using lme with a random intercept for subject and a random

Dear R-users, I have longitudinal data and would like to fit a model where both the variance-covariance matrix of the random effects and the residual variance are conditional on a (binary) grouping variable. I guess the model would have the following form (in hierarchical notation) Yi|bi,k ~ N(XiB+Zibi, sigmak*Ident) bi|k ~ N(0, Dk) K~Bernoulli(p) I can obtain different sigmas (sigma0 and