Re: [R] Mixed model

From: Stephen <szlevine_at_nana.co.il>
Date: Tue 21 Jun 2005 - 16:30:22 EST

 

Hi Doug and Spencer,  

Many thanks - Excellent!  

All worked out nicely ....  

Regards  

Stephen


From: Spencer Graves [mailto:spencer.graves@pdf.com] Sent: Mon 20/06/2005 17:54
To: Stephen
Cc: r-help@stat.math.ethz.ch
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
>
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>
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R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Tue Jun 21 16:47:31 2005

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