apply_pro

Generates risk scores for new patients using a trained model.

Provides a framework to build and evaluate diagnosis or
prognosis models using stacking, voting, and bagging ensemble
techniques with various base learners. The package also includes
tools for visualization and interpretation of models. The development
version of the package is available on 'GitHub' at
<https://github.com/xiaojie0519/E2E>. The methods
are based on the foundational work of Breiman (1996) <doi:10.1007/BF00058655>
on bagging and Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1>
on stacking.

Shanjie Luan

Ensemble Learning Framework for Diagnostic and Prognostic
Modeling

apply_pro function

<dl><dt>trained_model_object</dt>
<dd>A trained object (class <code>pro_model</code>).</dd>
<dt>new_data</dt>
<dd>Data frame of new patients.</dd>
<dt>time_unit</dt>
<dd>Time unit for data preparation.</dd></dl>

Arguments

Apply Prognostic Model to New Data — apply_pro

<dl>

<dt>trained_model_object</dt>
<dd>A trained object (class <code>pro_model</code>).</dd>


<dt>new_data</dt>
<dd>Data frame of new patients.</dd>


<dt>time_unit</dt>
<dd>Time unit for data preparation.</dd>

</dl>

apply_pro: Apply Prognostic Model to New Data

Description

Usage

Value

Arguments