rsf_pro: Train Random Survival Forest (RSF)
In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
rsf_pro R Documentation
Train Random Survival Forest (RSF)
Description
Fits a Random Survival Forest using the log-rank splitting rule.
Includes capabilities for hyperparameter tuning via grid search over ntree,
nodesize, and mtry.
Usage
rsf_pro(X, y_surv, tune = FALSE, tune_params = NULL)
Arguments
X
A data frame of predictors.
y_surv
A Surv object containing time and status.
tune
Logical. If TRUE, performs grid search for optimal hyperparameters based on C-index.
tune_params
Optional data frame containing the grid for tuning.
Value
An object of class survival_rsf and pro_model.
E2E documentation built on March 22, 2026, 1:07 a.m.
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| rsf_pro | R Documentation |
Train Random Survival Forest (RSF)
Description
Fits a Random Survival Forest using the log-rank splitting rule.
Includes capabilities for hyperparameter tuning via grid search over ntree,
nodesize, and mtry.
Usage
rsf_pro(X, y_surv, tune = FALSE, tune_params = NULL)
Arguments
X |
A data frame of predictors. |
y_surv |
A |
tune |
Logical. If TRUE, performs grid search for optimal hyperparameters based on C-index. |
tune_params |
Optional data frame containing the grid for tuning. |
Value
An object of class survival_rsf and pro_model.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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