get_linear_predictor

This function computes the linear predictor from a matrix of predictor variables and a vector of coefficients.
It handles cases with and without an intercept term.

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_linear_predictor function

<dl><dt>X</dt>
<dd>A matrix of predictor variables.</dd>
<dt>coefs</dt>
<dd>A vector of coefficients. It should be either the same length as the number of columns in X
(for models without an intercept) or one more than the number of columns in X (for models with an intercept).</dd></dl>

Arguments

Compute Linear Predictor — get_linear_predictor

<dl>

<dt>X</dt>
<dd>A matrix of predictor variables.</dd>


<dt>coefs</dt>
<dd>A vector of coefficients. It should be either the same length as the number of columns in X
(for models without an intercept) or one more than the number of columns in X (for models with an intercept).</dd>

</dl>

get_linear_predictor: Compute Linear Predictor

Description

Usage

Value

Arguments