predict.cat_cox

This function calculates the linear predictor (LP) for new data points based on a fitted Cox proportional hazards model.

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 

predict.cat_cox function

<dl><dt>object</dt>
<dd>A fitted model object of class <code>cat_cox</code>, containing the COX fit and model details.</dd>
<dt>newdata</dt>
<dd>An optional data frame with new predictor values. If <code>NULL</code>,
the function uses the observation data from the model's initialization object.</dd>
<dt>...</dt>
<dd>Additional arguments passed to other functions.</dd></dl>

Arguments

Predict Linear Predictor for New Data Using a Fitted Cox Model — predict.cat_cox

<dl>

<dt>object</dt>
<dd>A fitted model object of class <code>cat_cox</code>, containing the COX fit and model details.</dd>


<dt>newdata</dt>
<dd>An optional data frame with new predictor values. If <code>NULL</code>,
the function uses the observation data from the model's initialization object.</dd>


<dt>...</dt>
<dd>Additional arguments passed to other functions.</dd>

</dl>

predict.cat_cox: Predict Linear Predictor for New Data Using a Fitted Cox Model

Description

Usage

Value

Arguments