cross_validation_lmm

This function performs cross-validation for the catalytic linear mixed model (LMM) to
estimate the risk associated with different values of tau. It splits the data into
training and testing sets and computes prediction errors for model evaluation.

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 

cross_validation_lmm function

<dl><dt>cat_init</dt>
<dd>A list containing initialized parameters for the catalytic LMM.</dd>
<dt>tau_seq</dt>
<dd>A numeric vector of tau values for which to estimate risk.</dd>
<dt>cross_validation_fold_num</dt>
<dd>An integer indicating the number of folds for cross-validation.</dd></dl>

Arguments

Perform Cross-Validation for Catalytic Linear Mixed Model (LMM) to Select Optimal tau — cross_validation_lmm

<dl>

<dt>cat_init</dt>
<dd>A list containing initialized parameters for the catalytic LMM.</dd>


<dt>tau_seq</dt>
<dd>A numeric vector of tau values for which to estimate risk.</dd>


<dt>cross_validation_fold_num</dt>
<dd>An integer indicating the number of folds for cross-validation.</dd>

</dl>

cross_validation_lmm: Perform Cross-Validation for Catalytic Linear Mixed Model (LMM) to Select Optimal tau

Description

Usage

Value

Arguments