get_glm_lambda

This function calculates a lambda value based on the selected discrepancy method
for a generalized linear model (GLM). The discrepancy method determines the
type of error or deviance used in the calculation.

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 

get_glm_lambda function

<dl><dt>discrepancy_method</dt>
<dd>Character. A string specifying the type of discrepancy method
to use. Options are <code>"mean_square_error"</code>, <code>"mean_classification_error"</code>, or <code>"logistic_deviance"</code>.
Default is <code>"mean_square_error"</code>.</dd>
<dt>X</dt>
<dd>Matrix. The design matrix (predictors) for the GLM.</dd>
<dt>coefs</dt>
<dd>Numeric vector. The coefficients for the GLM.</dd></dl>

Arguments

Compute Lambda Based on Discrepancy Method — get_glm_lambda

<dl>

<dt>discrepancy_method</dt>
<dd>Character. A string specifying the type of discrepancy method
to use. Options are <code>"mean_square_error"</code>, <code>"mean_classification_error"</code>, or <code>"logistic_deviance"</code>.
Default is <code>"mean_square_error"</code>.</dd>


<dt>X</dt>
<dd>Matrix. The design matrix (predictors) for the GLM.</dd>


<dt>coefs</dt>
<dd>Numeric vector. The coefficients for the GLM.</dd>

</dl>

get_glm_lambda: Compute Lambda Based on Discrepancy Method

Description

Usage

Value

Arguments