ddp_quantiles

A list vector of objects returned by MCMC sampling functions.  e.g. mmCplusDpPost for <code>option = "mmcar"</code>.

model.output

An <code>P x (T+1)</code> <code>matrix</code> object that maps <code>subjects</code> to treatment dosages.  The first column should be an
intercept column (filled with 1's).

dosemat

Number of total fixed effects, both time-based and nuisance.

Nfixed

Number of total random effects, both time-based and nuisance, all grouped by subject.

Nrandom

Number of unique subjects (on which repeated measures are observed).

Nsubject

A numeric vector of length equal to the number of treatments that contains the base distribution for each treatment.
<code>1 = "car"</code>, <code>2 = "mvn"</code>, <code>3 = "ind"</code>.

typet


Inputs MCMC samples for model parameters and constructs c(2.5%,50%,97.5%) quantile summaries.


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.

ddp_quantiles: Produce quantile summaries of model posterior samples

Description

Usage

Arguments

Value

See Also