[R] Mixed model

[Douglas Bates]

On 6/20/05, Spencer Graves <spencer.graves at pdf.com> wrote:
> (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.

The syntax in lmer would be

model.mix <- lmer(trans1 ~ Index1 + grp + (1|id:grp) + (1|id),
Dataset, na.action = na.exclude)


> >
> > # 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]]
> >
> > ______________________________________________
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> 
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