BayesT

This function assess the model goodness of fit by calculate the
discrepancy measure T(bm(y), bm(Theta)) with following steps
(a) Generate T.obs based on the MCMC samples
(b) Generate T.rep based on the posterior distribution of the parameters
(c) Compare T.obs and T.rep, and calculate the P values.

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

BayesT function

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

Arguments

Goodness of fit. — BayesT

<dl>

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

</dl>

BayesT: Goodness of fit.

Description

Usage

Value

Arguments

Examples