From: Doran, Harold <HDoran_at_air.org>

Date: Sat 17 Sep 2005 - 04:19:42 EST

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Sep 21 02:03:43 2005

Date: Sat 17 Sep 2005 - 04:19:42 EST

Doug Bates has the following article in R News. To date, it is the only
source I know of documenting the lmer function.

@Article{Rnews:Bates:2005,

author = {Douglas Bates}, title = {Fitting Linear Mixed Models in {R}}, journal = {R News}, year = 2005, volume = 5, number = 1, pages = {27--30}, month = {May}, url = {http://CRAN.R-project.org/doc/Rnews/},}

-----Original Message-----

From: r-help-bounces@stat.math.ethz.ch

[mailto:r-help-bounces@stat.math.ethz.ch] On Behalf Of Yan Wong
Sent: Friday, September 16, 2005 1:58 PM
To: R-help

Subject: Re: [R] Possible bug in lmer nested analysis with factors

On 16 Sep 2005, at 17:12, Doran, Harold wrote:

> I think you might have confused lme code with lmer code. Why do you

*> have c/d in the random portion?
*

Apologies. I obviously have done something of the sort. I assumed that the 'random' assignment in lme could just be incorporated into an lmer call by placing it in brackets and removing the ~, so that

lme(a ~ b, random= ~ 1|c/d)

would be equivalent to

lmer(a ~ b + (1|c/d))

Is there a good guide somewhere to lmer calling conventions? I obviously don't understand them. As you can see, I would like to nest d within c, (and actually, c is nested in b too).

Perhaps it would be better with some explanation of the Crawley data. There are 3 fixed drug treatments ('b') given to 2 rats (6 rats in all: 'c'), and 3 samples ('d') are taken from each of the rat's livers, with some response variable recorded for each sample ('a': here just allocated a Normal distribution for testing purposes). I.e. c and d are random effects, with d %in% c and c %in% b.

He analyses it via

aov(a ~ b+c+d+Error(a/b/c))

I'm wondering what the lme and lmer equivalents are. I've been told that a reasonable form of analysis using lme is

a<-rnorm(36);b<-rep(1:3,each=12);d<-rep(1:3,each=2,6)
c <- rep(1:6, each=6) #use unique labels for each rat ## I got this
wrong in my previous example

model1 <- lme(a ~ b, random= ~ 1|c/d)

Which gives what looks to be a reasonable output (but I'm new to all this mixed modelling stuff). How would I code the same thing using lmer?

> I think what you want is

*>
**>> lmer(a ~ b + (1 | c)+(1|d))
**>>
**>
**> Which gives the following using your data
*

I'm not sure this is what I wanted to do. But thanks for the all the help.

Yan

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide!

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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 Wed Sep 21 02:03:43 2005

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