xgb_pro

Fits an XGBoost model using the Cox proportional hazards objective function.

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

xgb_pro function

<dl><dt>X</dt>
<dd>A data frame of predictors.</dd>
<dt>y_surv</dt>
<dd>A <code>Surv</code> object containing time and status.</dd>
<dt>tune</dt>
<dd>Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically).</dd></dl>

Arguments

Train XGBoost Cox Model — xgb_pro

<dl>

<dt>X</dt>
<dd>A data frame of predictors.</dd>


<dt>y_surv</dt>
<dd>A <code>Surv</code> object containing time and status.</dd>


<dt>tune</dt>
<dd>Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically).</dd>

</dl>

xgb_pro: Train XGBoost Cox Model

Description

Usage

Value

Arguments