Penalized Poisson learner via glmnet

Learner_glmnet is a Reference Class implementing the learner interface used by Superlearner() and fit_learner().

Details

User-facing API: users are expected to initialize the learner (i.e., call Learner_glmnet(...)) and pass the resulting object to Superlearner() or fit_learner(). The remaining methods documented below are part of the internal learner interface and are not meant to be called directly by users.

Wrapper role: this class is a user-friendly wrapper around the existing glmnet implementation. The package-specific contribution is to provide a piecewise-constant hazard workflow: create the long-format Poisson data with offsets for time at risk, include interval ("node") indicators for the baseline hazard, and forward standard glmnet arguments supplied at initialization to the backend fitter.

Model

Let \(0=t_0 < t_1 < \cdots < t_m\) denote time knots and define interval indicators \(I_k(t)=1\{t\in(t_k,t_{k+1}]\}\). The piecewise-constant hazard model is $$ \lambda(t \mid x) = \sum_{k=0}^{m} I_k(t)\,\lambda_k(x), \qquad \lambda_k(x) = \exp(\beta^\top x + \gamma_k). $$ Penalization is applied to the regression coefficients through the glmnet elastic-net penalty. Node (baseline) terms are given zero penalty by default; if this backend call fails, the learner retries with a fully penalized design.

Fields

covariates (character)

Names of covariate columns used in the model.

cross_validation (logical)

If TRUE, chooses lambda by glmnet::cv.glmnet.

intercept (logical)

Backend intercept flag; currently fixed to TRUE by the constructor.

lambda (numeric)

If cross_validation=FALSE, the lambda used in the final fit.

formula (character)

Formula string used to create the design matrix in long format.

learner (function)

Backend fitter (glmnet::glmnet or glmnet::cv.glmnet).

fit_arguments (list)

Additional arguments forwarded to the backend fitter.

Methods (internal learner interface)

initialize(...)

Construct and configure the learner. This is the only method users should call.

private_fit(data, ...)

Internal. Fits a Poisson model with offset log(tij) on long-format data.

private_fit_all_causes(data, ...)

Internal. Fits cause-specific Poisson models for all requested causes using a shared long-format data setup.

private_predictor(model, newdata, ...)

Internal. Predicts hazards on the response scale for long-format newdata.

Examples

lrn <- Learner_glmnet(covariates = c("age", "sex"), alpha = 1, cross_validation = TRUE)