sample_sigmasq

Sample item group-specific variances through Gibbs sampler

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

sample_sigmasq function

<dl><dt>shape0</dt>
<dd>a vector of G elements, each being the shape of the
Inverse-Gamma prior of group g</dd>
<dt>rate0</dt>
<dd>a vector of G elements, each being the rate of the
Inverse-Gamma prior of group g</dd>
<dt>dist_mat</dt>
<dd>a list, containing the KxK tree-structured matrix of leaf nodes,
where K is the number of leaves / latent classes, and SVD components</dd>
<dt>item_membership_list</dt>
<dd>a vector of G elements, each indicating the number of
items in this group</dd>
<dt>locations</dt>
<dd>a KxJ matrix of leaf parameters</dd></dl>

Arguments

Sample item group-specific variances through Gibbs sampler — sample_sigmasq

<dl>

<dt>shape0</dt>
<dd>a vector of G elements, each being the shape of the
Inverse-Gamma prior of group g</dd>


<dt>rate0</dt>
<dd>a vector of G elements, each being the rate of the
Inverse-Gamma prior of group g</dd>


<dt>dist_mat</dt>
<dd>a list, containing the KxK tree-structured matrix of leaf nodes,
where K is the number of leaves / latent classes, and SVD components</dd>


<dt>item_membership_list</dt>
<dd>a vector of G elements, each indicating the number of
items in this group</dd>


<dt>locations</dt>
<dd>a KxJ matrix of leaf parameters</dd>

</dl>

sample_sigmasq: Sample item group-specific variances through Gibbs sampler

Description

Usage

Value

Arguments