[R-sig-ME] Grouping variables technically suitable for modeling

[Timothy MacKenzie]

Dear Ben,

Thank you for sharing the references regarding my first question.

Regarding my second question, I simply mean if we have say ID1 and ID2,
then for ID2 to be distinguishably nested in ID1, it needs to have a
different unique categories relative to those of ID1.

For example, if ID1 has 120 unique categories and ID2 has 130
unique categories nested in ID1, then the variance components for ID1 and
ID2 are not distinguishable from each other. As a result, only one of them
can be added as a random effect; either (1 | ID1) or (1 | |ID2), but not (1
| ID1/ID2).

Is this correct and is there a published reference confirming or
disconfirming this?

Thanks,
Tim M

On Mon, Nov 8, 2021 at 7:35 PM Ben Bolker <bbolker using gmail.com> wrote:

>
>     This is a bit of a "how long is a piece of string" question ...
>
>
>    The "5-6 levels of a grouping variable" rule of thumb is quoted in
> various places: a variety of those references (Gelman and Hill 2006,
> Kéry and Royle 2015, Harrison et al 2018, Arnqvist 2020) are collected
> by Gomes
> (https://www.biorxiv.org/content/10.1101/2021.04.11.439357v2.full).
>
>    I sort of see what you mean by your second paragraph, but can you
> give an example?
>
>
> On 11/7/21 5:20 PM, Timothy MacKenzie wrote:
> > Dear Experts,
> >
> > Apologies if this question has come up before. But I'm looking for
> > published references that provide guidance on when one or more grouping
> > variables that theoretically need to be random factors can also
> > "technically" be used as random factors?
> >
> > For example, I have heard for a grouping variable to be technically taken
> > as a random factor, it needs to have at least 10 or so unique categories?
> > (Any reference to confirm or disconfirm this?)
> >
> > For example, I have heard for two grouping variables to be technically
> > taken as random factors, they each need to have a sufficiently different
> > number of unique categories relative to the other one. Otherwise, their
> > variance components can't be distinguished from one another and thus only
> > one of them can be taken as random, not both (Any reference to confirm or
> > disconfirm this?)
> >
> > Thanks,
> > Tim M
> >
> >       [[alternative HTML version deleted]]
> >
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>
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