mmIplusDpPost

An N x 1 response (of subject-measure
 cases)

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

An N x 1 multiple membership weight
 matrix to map supplemental random effects

Wcase

An P.aff x S multiple membership
 weight matrix with rows equal to number of unique
 affected subjects

Wsubject

An N x 1 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 and rate parameters for the
  $\Gamma$ prior on the CAR precision parameter,
  $\tau_{\gamma}$

strength.mm

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 href="/link/dpgrowmm?package=growcurves&version=0.2.3.8" rd-options="" data-mini-rdoc="growcurves::dpgrowmm">dpgrowmm</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.

mmIplusDpPost: Bayesian mixed effects model with a DP prior on by-subject effects and zero mean independent Gaussian priors on multiple membership effects

Description

Usage

Arguments

Value

See Also