Re: [R] Mixed model

From: Spencer Graves <spencer.graves_at_pdf.com>
Date: Tue 21 Jun 2005 - 00:54:03 EST

(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]]
>
> ______________________________________________
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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 01:03:45 2005

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