Package index • Rforce

All functions

Rforce(): Fit a random forest for composite endpoints using CPIU-wide data
Y_hat_numerical_form(): Numerical form to calculate the true Y given the true hazard and other parameters
add_Y_hat(): Calculate the predicted number of events at given time points
add_wrss(): Calculate the WRSS
add_wt(): convert the recorded event time per patient to the number of events and wt at the given time point per patient
admin_censoring(): apply administrative censoring in the given dataset
break_length_by_interval(): break the given length into several intervals with the given intervals
compo_sim(): composite event generator for simulation
compo_sim_mao(): composite event generator for simulation
counts_by_interval_and_id(): calculate the number of events in each interval
cpius_to_dummy(): Dummy-encode factor/character covariates in CPIU object
empirical_Y(): Empirical Mean Number of Events at the Different Time Point
km_fit(): fit kaplan meier survival function
loadRforce(): Load an Rforce Object from Disk
observed_risk_time(): calculate observed risk time at the given interval
patients_to_cpius(): convert the recorded event time per patient to the number of events per interval per patient
predict(<Rforce>): Predict function for Rforce
printTree(): Visualize a Single Tree from an Rforce Forest
pseudo_risk_time(): calculate pseudo risk time at the given interval
random_censoring(): apply random censoring in the given dataset
saveRforce(): Save an Rforce Object to Disk
surv_sim(): Survival data generation using either weibull or inverse CDF method
true_Y(): Calculate the mean number events for each subject at different time points in the simulated dataset
true_Y_numerical_form(): Numerical form to calculate the true Y given the true hazard and other parameters
validate(<CPIU>): validate CPIU object
validate(<data.frame>): validate data.frame object
vimp(<Rforce>): Variable importance function for Rforce object
wcompo_est(): Fast implementation of Lu Mao's Wcompo method by Kim So Young