en_pro: Train Elastic Net Cox Model
In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling
en_pro R Documentation
Train Elastic Net Cox Model
Description
Fits a Cox model with Elastic Net regularization (mixture of L1 and L2 penalties).
Alpha is fixed at 0.5.
Usage
en_pro(X, y_surv, tune = FALSE)
Arguments
X
A data frame of predictors.
y_surv
A Surv object containing time and status.
tune
Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically).
Value
An object of class survival_glmnet and pro_model.
E2E documentation built on March 22, 2026, 1:07 a.m.
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| en_pro | R Documentation |
Train Elastic Net Cox Model
Description
Fits a Cox model with Elastic Net regularization (mixture of L1 and L2 penalties). Alpha is fixed at 0.5.
Usage
en_pro(X, y_surv, tune = FALSE)
Arguments
X |
A data frame of predictors. |
y_surv |
A |
tune |
Logical. If TRUE, performs internal tuning (currently handled by cv.glmnet automatically). |
Value
An object of class survival_glmnet 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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