lgmPost

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
  The rate parameter is set of <code>1</code>.

nthin

An internal function to <code><a href="/link/dpgrow?package=growcurves&version=0.2.3.7" rd-options="" 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.

lgmPost: Run a Bayesian mixed effects model for by-subject random effects with an independent Gaussian prior

Description

Usage

Arguments

Value

See Also