Replies: 1 comment
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noncentered is an argument and is on by default in a hierarchical model.
Though if you want the subID terms to be hierarchical you should either do:
* v ~ 1+ x + y + (1|subID)
that way the v_subID's will be offset from the group distribution which
will be estimated as v_Intercept. without the "1" there is no group
distribution from which the IDs will be offset.
* If you want to use the v ~ 0 +... models and still do a hierarchical
model you should actually set noncentered= false, but then use a hyperprior
for the mean of the group distribution so that it can estimate the mu from
which the subIDs are drawn.
e.g.:
in "prior": {
"1|subID": {"name": "Normal",
"mu": {"name": "Normal",
"mu": 1,
"sigma": 2},
"sigma": {"name": "HalfNormal",
"sigma": 1,
},
}
The group distributions here will be estimated as v_1|subID_mu and the
individual v's for each subject will be drawn from that centered
distribution.
* Also you might want to consider random slopes too if you want the model
to be fully hierarchical
v ~ 1 + x + y + (1+x+y|subID)
for noncentered = true
or
v ~ 0+ (1+x+y|subID)
for noncentered= false
(and then you would set priors also for the x and y to come from the same
hyperprior)
Michael J Frank, PhD | Edgar L. Marston Professor
Director, Carney Center for Computational Brain Science
<https://www.brown.edu/carney/ccbs>
Laboratory of Neural Computation and Cognition <https://www.lnccbrown.com/>
Brown University
website <http://ski.clps.brown.edu>
…On Wed, Jun 19, 2024 at 11:40 AM hyang336 ***@***.***> wrote:
Sorry for the potentially noob question. If I have a hierarchical
regression model like v ~ 0 + x + y + (1|subID), how should I do
non-centered parameterization?
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Sorry for the potentially noob question. If I have a hierarchical regression model like v ~ 0 + x + y + (1|subID), how should I do non-centered parameterization?
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