Mixed model

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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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Stephen wrote:
Dear Fellow R users,