Thanks.... Most thoughtful... Regards S ________________________________
From: Spencer Graves [mailto:[EMAIL PROTECTED] Sent: Mon 27/06/2005 19:52 To: Stephen Cc: Douglas Bates; [email protected] Subject: Re: [R] Mixed model I often think carefully about what I want and store only that. For example, I might do something like the following: b1 <- coef(lme(...)) kb <- length(b1) B <- array(NA, dim=c(nb, kb)) for(i in 1:nb){ B[i, ] <- coef(lme(...)) } With "fit[[i]] <- lme(...)", you store, as Doug said, "a copy of the model frame (the parts of Dataset that are needed to evaluate the model) plus a lot of other information)" for each pass through the loop. Since you are doing a simulation, you probably only really care about a few numbers for each "i". Identify those and only store those each time through the loop. spencer graves p.s. Have you considered "simulate.lme"? Stephen wrote: > Hi > Thank you for your comments. > Yes you are correct its a very big data set. > Perhaps I am best splitting it up and then importing to R. > The reason for the loop is that I am conducting the equivalent of Split > file in SPSS. > Specifically, I am conducting the analysis for each value of on the > grouping variable 'runnb'. > If there is a less memory intensive way of doing this I'd appreciate > knowing about it. > Many Thanks and comments appreciated > Regards > Stephen > > ________________________________ > > From: Douglas Bates [mailto:[EMAIL PROTECTED] > Sent: Sun 26/06/2005 17:01 > To: Stephen > Cc: [email protected] > Subject: Re: [R] Mixed model > > > > On 6/26/05, Stephen <[EMAIL PROTECTED]> wrote: > >> >> >>Hi All, >> >> >> >>I am currently conducting a mixed model. I have 7 repeated measures on > > a > >>simulated clinical trial. If I understand the model correctly, the >>outcome is the measure (as a factor) the predictors are clinical group >>and trial (1-7). The fixed factors are the measure and group. The > > random > >>factors are the intercept and id and group. >> >> >> >>I tried using 2 functions to calculate mixed effects. >> >>Following previous correspondence . >> >> >> >>Dataset <- read.table("C:/Program > > Files/R/rw2011/data/miss/model1a.dat", > >>header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) >> >>attach(Dataset) >> >> >> >>require (nlme) >> >>with(Dataset, table(runnb, id, grp)) >> >>b.lvls <- table(Dataset$runnb) >> >>nb <- length(b.lvls) >> >>fit <- vector(mode="list", nb) >> >> >> >>for(i in 1:nb) >> >> fit[[i]]<- lme (trans1 ~ Index1 + grp, >> >> random = ~ 1 | id / grp , >> >> data = Dataset, >> >> na.action = "na.exclude") >> >> >> >> >> >>This (above) worked OK only I am having memory problems. >> >>I have a gig of RAM set at --sdi --max-mem-size=512M (complete version >>below) >> >>I am wondering if running the file as a database be slower / faster? >> >> >> >>Then I read that lme4 does it quicker and more accurately >> >>so I thought that I should re-run the code but from the for line: >> >> >> >> >>>for (i in 1:nb) >> >>+ fit[[i]] <- lmer(trans1 ~ Index1 + grp + (1|id:grp) + (1|id), >> >>+ Dataset, na.action = na.exclude) >> >> >> >>Producing >> >> >> >>Error in lmer(trans1 ~ Index1 + grp + (1 | id:grp) + (1 | id), > > Dataset, > >>: >> >> flist[[2]] must be a factor of length 200000 >> >>In addition: Warning messages: >> >>1: numerical expression has 200000 elements: only the first used in: >>id:grp >> >>2: numerical expression has 200000 elements: only the first used in: >>id:grp > > > Check > > str(Dataset) > > and, if necessary, convert id to a factor with > > Dataset$id <- factor(Dataset$id) > > > In is not surprising that you are running into memory problems. Look > at the size of one of the fitted objects from lme or from lmer. They > are very large because they contain a copy of the model frame (the > parts of Dataset that are needed to evaluate the model) plus a lot of > other information. You have a large Dataset and you are saving > multiple copies of it although I must admit that I don't understand > why the calls to lme or lmer are in a loop. > > > > ???? ?"? ???? ???? > http://mail.nana.co.il > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Spencer Graves, PhD Senior Development Engineer PDF Solutions, Inc. 333 West San Carlos Street Suite 700 San Jose, CA 95110, USA [EMAIL PROTECTED] www.pdf.com <http://www.pdf.com> Tel: 408-938-4420 Fax: 408-280-7915 ???? ?"? ???? ???? http://mail.nana.co.il [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
