Douglas Bates wrote:
>
> The GLMM function in the lme4 package allows you to specify crossed
> random effects within the random argument without the need for the
> pdBlocked and pdIdent constructions. Simply ensure that your grouping
> factors are defined in such a way that each distinct group has a
> different level in the grouping factor (this is usually not a problem
> for crossed grouping factors but can be a problem with nested factors)
> and list them. For example
>
> random = list(rows = ~ 1, columns = ~1)
>
>
the reason is that i actually want to use just one group. factor with
two and more blocks, each of them would have the simple diag. structure,
just as was possible with like pdIdent(...,...,...) specification in
nlme-package. i also wanted to give initial values via value=....,
so i would really need to define i.e.:
random=list(my.Subject=pdBlocked(pdIdent(value=labda1,
form=~var.a1+...+var.am, nam=...), pdIdent(value=labda2,
form=~var.b1+...+var.bn, nam=...),...))
or just, because "pdBlocked" is not accepted
random=list(my.Subject=list(pdIdent(value=labda1,
form=~var.a1+...+var.am, nam=...), pdIdent(value=labda2,
form=~var.b1+...+var.bn, nam=...),...))
or for last, if really were possible, alternatively without having
attached nlme:
random=list( form=~var.a1+...+var.am | my.Subject),
form=~var.b1+...+var.bn | my.Subject , ... )
but each time i recieve
Error in switch(mode(x), "NULL" = structure(NULL, class =
"formula"), :
invalid formula
so i don't know, how it can go.
the reason for trying to use the lme4 is its sparse-matrix orientation
and so comutationally more efficient.
thank you for hint in advance
