example1

<p>This is an example model which contains the result that run from <code>BCC.multi</code>
function using epileptic.qol dataset in <code>joinrRML</code> package.
Only used the tests. Since small number of iterations
were used, this model can may not represent the true performance
for this method.</p>

datasets

It is very common nowadays for a study to collect multiple
features and appropriately integrating multiple longitudinal features
simultaneously for defining individual clusters becomes increasingly
crucial to understanding population heterogeneity and predicting
future outcomes.  'BCClong' implements a Bayesian consensus clustering
(BCC) model for multiple longitudinal features via a generalized
linear mixed model. Compared to existing packages, several key
features make the 'BCClong' package appealing: (a) it allows
simultaneous clustering of mixed-type (e.g., continuous, discrete and
categorical) longitudinal features, (b) it allows each longitudinal
feature to be collected from different sources with measurements taken
at distinct sets of time points (known as irregularly sampled
longitudinal data), (c) it relaxes the assumption that all features
have the same clustering structure by estimating the feature-specific
(local) clusterings and consensus (global) clustering.

example1: example1 model

Description

Usage

Arguments

Format

Examples