samples.ddpgrow

object


provides posterior sampled values for every model parameter of a
<code>ddpgrow</code> object


Employs a non-parametric formulation for by-subject random effect
parameters to borrow strength over a constrained number of repeated
measurement waves in a fashion that permits multiple effects per subject.
One class of models employs a Dirichlet process (DP) prior for the subject
random effects and includes an additional set of random effects that
utilize a different grouping factor and are mapped back to clients through
a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
A second class of models employs a dependent DP (DDP) prior for the subject
random effects that directly incorporates the multiple membership pattern.

samples.ddpgrow: Produce samples of MCMC output

Description

Usage

Arguments