rsf_pro

Fits a Random Survival Forest using the log-rank splitting rule.
Includes capabilities for hyperparameter tuning via grid search over <code>ntree</code>,
<code>nodesize</code>, and <code>mtry</code>.

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

rsf_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 grid search for optimal hyperparameters based on C-index.</dd>
<dt>tune_params</dt>
<dd>Optional data frame containing the grid for tuning.</dd></dl>

Arguments

Train Random Survival Forest (RSF) — rsf_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 grid search for optimal hyperparameters based on C-index.</dd>


<dt>tune_params</dt>
<dd>Optional data frame containing the grid for tuning.</dd>

</dl>

rsf_pro: Train Random Survival Forest (RSF)

Description

Usage

Value

Arguments