Package index • survkl

Low-Dimensional Cox KL Integration

coxkl(): Cox Proportional Hazards Model with KL Divergence for Data Integration
cv.coxkl(): Cross-Validated Selection of Integration Parameter （eta） for the Cox–KL Model

High-Dimensional Cox KL Integration

coxkl_ridge(): Cox Proportional Hazards Model with Ridge Penalty and External Information
coxkl_enet(): Cox Proportional Hazards Model with KL Divergence for Data Integration and Lasso & Elastic Net Penalty
cv.coxkl_ridge(): Cross-Validation for CoxKL Ridge Model (eta tuning)
cv.coxkl_enet(): Cross-Validation for CoxKL Model with elastic net & lasso penalty

Plotting Functions

cv.plot(): Plot Cross-Validation Results vs Eta
plot(<coxkl>): Plot Model Performance vs Eta for coxkl
plot(<coxkl_ridge>): Plot Model Performance vs Lambda for coxkl_ridge
plot(<coxkl_enet>): Plot Model Performance vs Lambda for coxkl_enet

Coefficient & Prediction

predict(<coxkl>): Predict Linear Predictors from a coxkl Object
predict(<coxkl_ridge>): Predict Linear Predictors from a coxkl_ridge Object
predict(<coxkl_enet>): Predict Linear Predictors from a coxkl_enet Object
coef(<coxkl>): Extract Coefficients from a coxkl Object
coef(<coxkl_ridge>): Extract Coefficients from a coxkl_ridge Object
coef(<coxkl_enet>): Extract Coefficients from a coxkl_enet Object

Utilities

cal_surv_prob(): Calculate Survival Probabilities
loss_fn(): Calculate the Log-Partial Likelihood for a Stratified Cox Model
generate_eta(): Generate a Sequence of Tuning Parameters (eta)
test_eval(): Evaluate model performance on test data

Datasets

ExampleData_lowdim: Example low-dimensional survival data
ExampleData_highdim: Example high-dimensional survival data
support: Study to Understand Prognoses Preferences Outcomes and Risks of Treatment