[R] Mixed-effects model for nested design data

[Lorenz.Gygax@fat.admin.ch]

Dear Han,

> I am using nlme for data from nested design.  That is, "tows" are nested
> within "trip",  "trips" nested within "vessel", and "vessels" nested
> within "season".  I also have several covariates, say "tow_time",
> "latitude" and "depth"
> My model is
>    y = season + tow_time + latitude + depth + vessel(season) +
> trip(season, vessel) + e
> In SAS, the program would be
> proc mixed NOCLPRINT NOITPRINT data=obtwl.x;
>   class vessel trip tow season depth;
>   model y = season depth latitude /solution;  <----------fixed effects
>   random vessel(season) trip(season vessel);
> run;
> My question is:  How this nested mixed-effects model can be 
> fitted in R-> "nlme"?

I do not know about SAS but I would guess that your model should be fitted
as something like:

lme (fixed= y ~ season + tow_time + latitude + depth,
     random= ~ 1 | season/vessel/trip)

Maybe you should do some reading in the book by Pinheiro & Bates?
They explain well how to set up models.

Regards, Lorenz
- 
Lorenz Gygax, Dr. sc. nat.
Tel: +41 (0)52 368 33 84 / lorenz.gygax at fat.admin.ch      

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