compute_IC

Compute information criteria for the DDT-LCM model, including the Widely Applicable
Information Criterion (WAIC), and Deviance Information Criterion (DIC). WAIC and DIC are computed
using two different methods described in Gelman, Hwang, and Vehtari (2013), one based on (1) posterior
means and the other based on (2) posterior variances.

Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis.
Reference: Li et al. (2023) <arXiv:2306.04700>.

Mengbing Li

ddtlcm

Latent Class Analysis with Dirichlet Diffusion Tree Process
Prior

Briana Stephenson

Zhenke Wu

compute_IC function

<dl><dt>result</dt>
<dd>a "ddt_lcm" object</dd>
<dt>burnin</dt>
<dd>an integer specifying the number of burn-in iterations from MCMC chain</dd>
<dt>ncores</dt>
<dd>an integer specifying the number of cores to compute marginal posterior log-likelihood
in parallel</dd></dl>

Arguments

Compute information criteria for the DDT-LCM model — compute_IC

<dl>

<dt>result</dt>
<dd>a "ddt_lcm" object</dd>


<dt>burnin</dt>
<dd>an integer specifying the number of burn-in iterations from MCMC chain</dd>


<dt>ncores</dt>
<dd>an integer specifying the number of cores to compute marginal posterior log-likelihood
in parallel</dd>

</dl>

compute_IC: Compute information criteria for the DDT-LCM model

Description

Usage

Value

Arguments

Examples