dpPost

An <em>N x 1</em> response (of subject-measure cases)

Random effects design matrix.  Assumed grouped by <code>subjects</code>

An <em>N x 1</em> set of subject identifiers

subjects

niter

The number of MCMC burn-in iterations to discard

nburn

The step increment of MCMC samples to return

nthin

The shape parameter for the $\Gamma$ prior on the DP concentration parameter.
The rate parameter is set of <code>1</code>.

shapealph

The rate parameter for the $\Gamma$ prior on the DP concentration parameter. Default value is <code>1</code>.

ratebeta


An internal function to <code><a rd-options="" href="/link/dpgrow?package=growcurves&version=0.2.4.1" data-mini-rdoc="growcurves::dpgrow">dpgrow</a></code>


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.

Terrance Savitsky

growcurves

Bayesian Semi and Nonparametric Growth Curve Models that
Additionally Include Multiple Membership Random Effects

dpPost function


An internal function to <code><a rd-options='' href='dpgrow'>dpgrow</a></code>


dpPost: Run a Bayesian mixed effects model for by-subject random effects with DP prior

Description

Usage

Arguments

Value

See Also