Package index • poissonsuperlearner

Fit models

Define base learners and fit piecewise-constant hazard models.

Learner_glmnet-class Learner_glmnet: Penalized Poisson learner via glmnet
Learner_gam-class Learner_gam: GAM learner via mgcv::bam
Learner_hal-class Learner_hal: HAL learner for piecewise Poisson hazards
Learner_xgboost-class Learner_xgboost: Piece-wise constant hazard via xgboost
fit_learner(): Fit a single base learner
Superlearner(): Fit a Poisson Super Learner ensemble

Generate predictions and inspect fitted models.

predict(<base_learner>): Predict hazards, survival and absolute risk from a fitted base learner
predict(<poisson_superlearner>): Predict hazards, survival and absolute risk from a fitted Poisson Super Learner
predictRisk(<base_learner>): Absolute-risk matrix predictions for a fitted base learner
predictRisk(<poisson_superlearner>): Absolute-risk matrix predictions for a fitted Poisson Super Learner
coef(<base_learner>): Extract coefficients from a fitted base learner
coef(<poisson_superlearner>): Extract stacking (meta-learner) coefficients from a fitted Poisson Super Learner
print(<base_learner>): Print method for base_learner
print(<poisson_superlearner>): Print method for poisson_superlearner
summary(<base_learner>): Summarize a fitted base learner object
summary(<poisson_superlearner>): Summarize a fitted Poisson Super Learner object

pch_survival(): Piecewise-constant hazards survival function
pch_absolute_risk(): Absolute risk (cumulative incidence) for a cause under piecewise-constant hazards
pch_absolute_risk_euler(): Absolute risk (Euler approximation) for a cause under piecewise-constant hazards

simulateStenoT1(): Simulate time-to-event data for hypothetical type-1 diabetes patients

poissonsuperlearner poissonsuperlearner-package: poissonsuperlearner: Poisson Super Learner