On 14-08-14 06:54 AM, Marco Plebani wrote:
Dear list members,
Package blme has been suggested for fixing issues with random effect
variance = zero in other occasions, but I do not understand the
rationale behind it. What does blme that lme4 does not? In which way
do the two approaches differ? In particular: - what is the prior
information that blme is using, and - how comes that blme still
estimates parameter values and assign p-values to them? According to
my (very limited) knowledge of bayesian stats the outcome of the
analysis should be an updated distribution of the possible parameter
values.
The available documentation about blme is limited and/or I could not
find it. I realize that my question on blme hides another, much
broader, on how bayesian stats work; regarding the latter, a
suggestion of a good, practice-oriented reference book would be
appreciated.
Thank you in advance,
Marco
(Started writing this before Doug's comments, which I agree are [as
usual] thoughtful and sensible but think represent one point of view.)
For a start, there's a paper that describes the approach in detail:
Chung, Yeojin and Rabe-Hesketh, Sophia and Dorie, Vincent and Gelman,
Andrew and Liu, Jingche. "A Nondegenerate Penalized Likelihood Estimator
for Variance Parameters in Multilevel Models". Psychometrika
doi:10.1007/s11336-013-9328-2
As for the p-values; I would say that in this context they're not
particularly philosophically coherent but do still represent a rough
measure of strength of evidence ...
cheers
Ben Bolker