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Warn in low-dimensional cases #7
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Prior for scale parameters in hierarchical models |
RE: N = 2, I don't know if this is a good option, but Andrew Gelman discusses multilevel models with 2 groups here and argues that it's fine but you need an informative prior on the between-group sd. Maybe warning about using an informative prior if there are only two groups is appropriate? I think it's also hard to determine what "informative" means in advance of seeing the data, since whether a prior is informative probably depends on the context. Anyway just thought I'd mention it. |
The problem is more about A) users who don't know what they're doing
supplying data that is not formatted correctly (so N=2 is just a glitch),
B) trying to set a prior automatically based on just 2 data points (for now
the default prior is scaled with regards to dispersion in inputs).
We can be extra careful on B), but nevertheless there should be a warning.
…On Tue, Aug 20, 2019 at 5:29 PM be-green ***@***.***> wrote:
RE: N = 2, I don't know if this is a good option, but Andrew Gelman
discusses multilevel models with 2 groups here
<https://statmodeling.stat.columbia.edu/2015/12/08/hierarchical-modeling-when-you-have-only-2-groups-i-still-think-its-a-good-idea-you-just-need-an-informative-prior-on-the-group-level-variation/>
and argues that it's fine but you need an informative prior on the
between-group sd. Maybe warning about using an informative prior if there
are only two groups is appropriate?
I think it's also hard to determine what "informative" means in advance of
seeing the data, since whether a prior is informative probably depends on
the context. Anyway just thought I'd mention it.
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There is now a warning for small N, but the default prior is unchanged -- worth revisiting this and setting a different prior (see text above) |
We now have automated warning on setting default priors for N=2, N=3, but we do not stop anyone. |
If N = 2 we should consider stopping people from meta-analysing
If N = 3 we should suggest a prior for scale (half-Cauchy)
That means we also need to implement half-Cauchy
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