extract_dim

This function retrieves and formats the dimensions of the dataset used in the model,
including the number of observed and synthetic data points and the total number of rows
and columns.

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Dongming Huang

catalytic

Tools for Applying Catalytic Priors in Statistical Modeling

Yitong Wu

Weihao Li

Ministry of Education, Singapore 

extract_dim function

<dl><dt>cat_init</dt>
<dd>A list containing model initialization data, expected to include
<code>obs_size</code> (observed data size), <code>syn_size</code> (synthetic data size), <code>size</code> (total data size),
and <code>x</code> (the covariate matrix).</dd></dl>

Arguments

Extract Dimension Information from Model Initialization — extract_dim

<dl>

<dt>cat_init</dt>
<dd>A list containing model initialization data, expected to include
<code>obs_size</code> (observed data size), <code>syn_size</code> (synthetic data size), <code>size</code> (total data size),
and <code>x</code> (the covariate matrix).</dd>

</dl>

extract_dim: Extract Dimension Information from Model Initialization

Description

Usage

Value

Arguments