
   Sorry, dear group, now I'm having a doubt myself about that "implicit nesting" issue. Is it correct if i name the individuals "1,2,3..." in one of the grouping random variables and "1,2,3,..." as well in the other one, as long as I especify they're nested:

   +(1|random1/random2)

   or should I use different codes for both, as letters for one and numbers for the other?

   Further to that, I wonder wether this way of writing the random variables:

   +(1|random1/random2)
   
   equals this other one:

   +(random1|random2)

   Every time I've compared them, I've got identical results, so it seems to be the same.

   Thanks.

--- El jue, 20/1/11, Andrew Dolman <andydolman@gmail.com> escribió:

De: Andrew Dolman <andydolman@gmail.com>
Asunto: Re: [R-sig-ME] VERY simple question about NESTING in experimental designs (for glm, lme, lmer, etc.)
Para: "Toby Marthews" <toby.marthews@ouce.ox.ac.uk>
CC: "r-sig-mixed-models@r-project.org" <r-sig-mixed-models@r-project.org>
Fecha: jueves, 20 de enero, 2011 16:01

Hi Toby,

I believe that using unique labelling, as you have done, means that
you can specify a model in lmer like this:

 lmer(Birdlength ~ Country + (1|locnum) + (1|bnum))

and lmer will know that bnum is nested in locnum, rather than being crossed.

i.e.

 lmer(Birdlength ~ Country + (1|locnum) + (1|bnum))
and
 lmer(Birdlength ~ Country + (1|locnum/bnum))

are identical ONLY IF bnum is unique , otherwise these models would
specify crossed and nested structures respectively.


However,

If you only have 1 observation per bird then bnum should probably not
be part of your nesting structure.

 lmer(Birdlength ~ Country + (1|locnum))

would be the way I would go.

Country does not need to be in the random part of the model and indeed
probably shouldn't be because it only has two levels.


Andy.




andydolman@gmail.com



On 20 January 2011 15:23, Toby Marthews <toby.marthews@ouce.ox.ac.uk> wrote:
> Dear R-sig-mixed-models,
>
> This is probably a painfully simple question, but I can't seem to pin it down from any source. About NESTING.
>
> Imagine I carry out a nested experiment on birds. I measure the size (say in cm beak to tail) of birds from 6 different locations in Country A with 20 birds in each location. I then also repeat this experiment in Country B with the same replication. Crucially, because the work at each of the 12 locations was being carried out by a different collaborating group, they all numbered their individual birds simply 1-20. The data I eventually receive for my metastudy is something like:
>
> Country Location        Birdnum Birdlength
>   A    1       1       7.3
>   A    1       2       6.7
>   ...  ...     ...     ...
>   A    1       20      7.9
>   A    2       1       6.7
>   A    2       2       6.9
>   ...  ...     ...     ...
>   B    1       1       6.7
>   B    1       2       6.6
>
> The bird numbers and location numbers here are not unique across the experimental design (bird #1 at location 1 is not the same individual as bird #1 at location 2). Hearing what Prof Bates said about not using "implicit nesting" in 2005 (http://cran.r-project.org/doc/Rnews/Rnews_2005-1.pdf ), I construct new variables bnum=factor(paste(Country,"-",Location,"-",Birdnum,sep="")) which contains levels "A-1-1","A-1-2", ..., "B-1-2", etc., and locnum=factor(paste(Country,"-",Location,sep="")) which contains levels "A-1","A-1", ..., "B-1", etc. and that means I can use locnum and bnum instead of Location and Birdnum and I have a unique numbering system.
>
> Say I am interested in the differences between birds in countries A and B with location and birdnumber being random effects. I believe I should try to use a command like
>  lme(fixed=Birdlength~Country,random=~1|bnum)
>  or glm(Birdlength~Country)
>  or lmer(Birdlength~Country+(1|bnum))
> however I have been criticised on two counts for this by colleagues-who-shall-remain-nameless:
>
>    (1) This is a nested design so I should replace bnum with Country/locnum/bnum or Country/Location/Birdnum in both the lme and the lmer command. (I'm pretty sure I can just use bnum on its own because by knowing bnum I automatically know the corresponding country and location of the measurement so Country and Location are effectively redundant (surely?) however, if I'm right then that means that I will only ever need "/" if my nesting is somehow implicit (i.e. because I usually use paste in the way described, I should never have to use "/" even in nested experiments (which seems odd?))
>    (2) Because Country (my fixed predictor) is being used to calculate bnum, I am mixing fixed and random effects inappropriately. (I am less sure about this one: perhaps "random=~Country|bnum" would be more correct or something else?)
>
> Even with this very simple experimental setup, a number of possible alternatives have been suggested by my colleagues including
>  glm(Birdlength~Country+Country/locnum+Country/locnum/bnum)
>  lmer(Birdlength~Country+Country|locnum+Country|(locnum/bnum))
>  lmer(Birdlength~Country+Country|locnum+Country|(bnum))
>  lmer(Birdlength~Country+1|locnum+1|(locnum/bnum))
> with the result that I'm getting very confused!
>
> I'm pretty sure I'm making a meal of this little question so I'll stop there, but any comments would be very welcome!
>
> Best,
> Toby Marthews
>
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> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>

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