Hi Doug and Spencer, Many thanks - Excellent! All worked out nicely .... Regards Stephen
________________________________ From: Spencer Graves [mailto:[EMAIL PROTECTED] Sent: Mon 20/06/2005 17:54 To: Stephen Cc: [email protected] Subject: Re: [R] Mixed model (comments in line) Stephen wrote: > Dear Fellow R users, > > > > I am fairly new to R and 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. > > > > Based on this > > Dataset <- read.table("C:/Program Files/R/rw2010/data/miss/model1.dat", > header=TRUE, sep="\t", na.strings="NA", dec=".", strip.white=TRUE) > > require (nlme) > > model.mix <- lme (trans1 ~ Index1 + grp, > random = ~ constant | id / grp , > data = Dataset, > na.action = "na.exclude") I'm not familiar with this syntax. I would replace your "random" formula with "~1|id/grp". Did you get sensible results from your attempt to compute "model.mix"? How do the results compare with the results from replacing your "random" with "~1|id/grp"? Also, I'd try the same thing with lmer; please see "Fitting Linear Mixed Models in R" by Doug Bates in the latest R News, downloadable from "www.r-project.org" -> Newsletter. > > > > # where trans1 is the factor of the repeated measures of the scale. > > # Index is the trial number, grp the group, and id the subject number. > > > > I would like to split the results, just like SPSS splitfile by a > variable in the Dataset called runnb > > I have tried using: > > > > by (Dataset, runnb, > > function (x) (lme (trans1 ~ Index1 + grp, > > random = ~ constant | id / grp , > > data = Dataset, > > na.action = "na.exclude") ) > > ) > I haven't used "by" enough to comment on this. If I had problems with something like this, I might do something like the following: with(Dataset, table(runnb, id, grp)) Do you have enough observations in all cells to be able to estimate all these individual models? If yes, I might proceed as follows: b.lvls <- table(Dataset$runnb) nb <- length(b.lvls) fit <- vector(mode="list", nb) for(i in 1:nb) fit[[i]] <- lme(...) If I still had problems with this, I might manually step through this until I found the "i" that created the problem, etc. > > > but to no avail . as my computer hangs and I set my GUI to --mdi > --max-mem-size=1200M. > > > > Any ideas as to how to splitfile the results SPSS style would be most > appreciated? > > > > Also, does lme do pairwise deletion? > > > > By the way > > >>version > > > platform i386-pc-mingw32 > > arch i386 > > os mingw32 > > system i386, mingw32 > > status > > major 2 > > minor 1.0 > > year 2005 > > month 04 > > day 18 > > language R > > Windows XP Pro. > > > > Many thanks > > Stephen > > Ps as its my first time on this group - neat program! > > > ???? ?"? ???? ???? > 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 ???? ?"? ???? ???? 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
