blmer(), minimum amount of prior to get a model to converge

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 at 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 = ???)

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