model.selection.criteria

A function that calculates DIC and WAIC for model selection

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.

Zhiwen Tan

BCClong

Bayesian Consensus Clustering for Multiple Longitudinal Features

Zihang Lu

Chang Shen

model.selection.criteria function

<dl><dt>fit</dt>
<dd>an objective output from BCC.multi() function</dd>
<dt>fast_version</dt>
<dd>if fast_verion=TRUE (default), then compute the DIC and WAIC using
the first 100 MCMC samples (after burn-in and thinning) . If fast_version=FALSE, then
compute the DIC and WAIC using all MCMC samples (after burn-in and thinning)</dd></dl>

Arguments

Model selection — model.selection.criteria

<dl>

<dt>fit</dt>
<dd>an objective output from BCC.multi() function</dd>


<dt>fast_version</dt>
<dd>if fast_verion=TRUE (default), then compute the DIC and WAIC using
the first 100 MCMC samples (after burn-in and thinning) . If fast_version=FALSE, then
compute the DIC and WAIC using all MCMC samples (after burn-in and thinning)</dd>

</dl>

model.selection.criteria: Model selection

Description

Usage

Value

Arguments

Examples