[R] orthogonal and nested model

[Douglas Bates]

Marcelo Martinez Palao <marmp at fcu.um.es> writes:

> I'm working with a orthogonal and nested model (mixed).
> I have  four factors, A,B,C,D;
> A and B are fixed and orthogonal
> C is nested in AB interaction
> and finally, D is nested in C.
>  I would like to model the following
> Y_ijklm=Mu+A_i+B_j+AB_ij+C_k(ij)+D_l(k(ij))+Error_m(...)
> 
> I used the next command
> 
> >summary(aov(abund~A*B + C % in % A:B + D % in % C % in % A:B ,datos))
> 
> Is it the correct formula syntax?

I would recommend using the lme (linear mixed-effects) function from
the nlme package for this.  Please ensure that you have R version
0.90.0 installed, then use
 install.packages("nlme")
from within R to obtain and install the package.

If I understand your message correctly, your model specification would be
 fm1 <- lme(abund ~ A * B, random = list(C = ~ 1, D = ~ 1), data = datos)
This will fit the restricted maximum likelihood (REML) estimates.

If you prefer to get maximum likelihood (ML) estimates, include the argument
 method = "ML"
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