Learner_gam is a Reference Class implementing the learner interface
used by Superlearner() and fit_learner().
Details
User-facing API: users should only initialize the learner and pass it
to Superlearner() / 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 wraps mgcv::bam in a piecewise-constant hazard
workflow. The package-specific contribution is to provide a convenient
interface for the long-format Poisson likelihood with offsets for time at risk,
and optional node terms encoding the baseline hazard, while forwarding standard
mgcv::bam arguments supplied via ....
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 with an additive predictor is
$$
\lambda(t \mid x) = \sum_{k=0}^{m} I_k(t)\,\exp\{\eta(x) + \gamma_k\}.
$$
The additive predictor \(\eta(x)\) is constructed from covariates
(smooth terms such as s(age) and/or linear terms) and estimated by mgcv.
Fields
covariates(character)Terms used to build the additive predictor (may include
s()terms).cross_validation(logical)Workflow flag; see Details.
intercept(logical)Whether to include an intercept.
formula(character)Formula string passed to
mgcv::bam.learner(function)Backend fitter (
mgcv::bam).fit_arguments(list)Additional arguments forwarded to
mgcv::bam.
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 GAM with offset
log(tij)on long-format data.private_fit_all_causes(data, ...)Internal. Fits cause-specific Poisson GAMs for all requested causes using a shared long-format data setup.
private_predictor(model, newdata, ...)Internal. Predicts hazards on the response scale.
Examples
lrn <- Learner_gam(covariates = c("s(age)", "value_LDL"))