[R-sig-ME] blmer(), minimum amount of prior to get a model to converge

[Ben Bolker]

   Thanks Vincent.

   FWIW it would make me really happy if people distinguished clearly 
between

* "singular/nonsingular" - an issue with the 'true' best estimate, i.e. 
whether the MLE for the variance-covariance matrix of the REs is 
positive definite vs. being only positive *semi*definite

* "converged/nonconverged" - a question of whether we think the 
numerical optimization has worked correctly or not

   cheers
    Ben Bolker


On 10/3/20 9:22 PM, Vincent Dorie wrote:
> There's no single minimum amount, but you can decrease the relative
> impact of the prior by fitting a sequence of models until convergence
> becomes a problem again.
> 
> # default
> m2 <- blmer(math ~ ses*sector + (ses | sch.id), data = hsb, cov.prior
> = wishart(df = level.dim + 2.5))
> # point at which blme model is same as lme4
> m3 <- blmer(math ~ ses*sector + (ses | sch.id), data = hsb, cov.prior
> = wishart(df = level.dim + 1))
> # fit models in sequence with df from level.dim + 2.5 to level.dim + 1
> 
> Technically, any prior which goes to zero when the determinant of the
> covariance of the random effects go to zero should have the desired
> effect (df > level.dim + 1), but there may be limitations introduced
> by the optimizer.
> 
> Vince
> 
> 
> On Sat, Oct 3, 2020 at 1:17 AM Simon Harmel <sim.harmel using gmail.com> wrote:
>>
>> Hello all,
>>
>> This may be a simple/naive question, but I have a non-converging lmer()
>> model due to singularity.
>>
>> I was wondering what is the minimum prior specification in `blmer()` to get
>> this singular model to converge?
>>
>> library(lme4)
>> library(blme)
>> hsb <- read.csv('
>> https://raw.githubusercontent.com/rnorouzian/e/master/hsb.csv')m4 <- m1 <-
>> lmer(math ~ ses*sector + (ses | sch.id), data = hsb)
>>
>> m2 <- blmer(math ~ ses*sector + (ses | sch.id), data = hsb, cov.prior = ???)
>>
>>          [[alternative HTML version deleted]]
>>
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