[R-sig-ME] Converging mixed models with large number of random coefficients

[Hans Ekbrand]

On Mon, Feb 10, 2014 at 11:48:06PM -0200, Thomas Schroder wrote:
>  I realize that one approach to this problem would be to reduce the number
> of random coefficients that account for individual tree variation (Li et
> al., 2012). But since individual tree heights and diameters are only
> obtainable from each individual tree, I guess that all coefficients (which
> are associated to these variables) should be random (Gelman, 2007). I
> wonder if there is any problem with my interpretation of the mixed models
> theory?

Perhaps I have misunderstood your mail (I am not familiar with nlme),
but from your description above I think you have misunderstood what
random effects do.

That the tree is random is not an argument for making a variable like
diameter random with the argument that it is a property of the tree.

If you want a general effect (a single estime) of how diameter affects
the outcome, you should have diameter as a fixed effect in your model.

If you want an estimate - per tree - of how diameter affects the
outcome for that particular tree you will need variation of diameter
within that particular tree.

I think you want the former - after all, science tends to be about the
universal stuff, not the particulars - and in that case you should
make diameter a fixed effect, and only keep the tree-id variable as
random.

Diameter was only one example, the same goes for the other variables
as well.



kind regards,

Hans Ekbrand