Fit a single base learner — fit_learner • poissonsuperlearner

Pre-processes subject-level time-to-event data into a long Poisson format on a piecewise-constant time grid, then fits one initialized learner object. For competing risks, a separate model is fit for each event type (cause) using the standard cause-specific Poisson likelihood on the long data.

Usage

fit_learner(
  data,
  learner,
  id = "id",
  stratified_k_fold = FALSE,
  status = "status",
  event_time = NULL,
  number_of_nodes = NULL,
  nodes = NULL,
  variable_transformation = NULL,
  ...
)

Arguments

data: data.frame. Subject-level input data (one row per subject).
learner: Reference-class learner object (e.g. from Learner_glmnet(), Learner_hal() or Learner_gam()). Must implement a $private_fit(dt_long) method that fits the learner on long Poisson data for one cause.
id: character(1). Name of the subject identifier column. If not found in data, an id column is created automatically.
stratified_k_fold: logical(1). Reserved argument for future fold strategy. Currently ignored.
status: character(1). Name of the event-status column. Must be coded with 0 = censoring and 1,2,...,K for event types (causes). If there is no 0 in status, the data are treated as uncensored.
event_time: character(1). Name of the event/censoring time column. Must be present in data.
number_of_nodes: numeric(1) or NULL. If not NULL, constructs a quantile-based node grid with number_of_nodes + 1 cut points (including endpoints), then adds 0 if missing.
nodes: numeric or NULL. Explicit time-node grid (cut points). If supplied, number_of_nodes is ignored. 0 is added if missing. Nodes beyond max(event_time) are dropped.
variable_transformation: list/character/formula or NULL. Optional transformations applied to the internally created long Poisson data before fitting (via apply_transformations()).
...: Additional arguments currently ignored.

Value

An object of class base_learner, i.e. a named list with:

model

The learner object that was fit (the input learner), stored for later prediction. This contains the learner specification (e.g., covariates, tuning parameters).

learner_fit

A list of fitted model objects, one per cause. Its length equals data_info$n_crisks. The list is created by splitting the internally pre-processed long data by cause indicator k and calling model$private_fit() on each split.

Names typically correspond to the cause labels "1", "2", ..., "K".
Each element is learner-dependent: e.g. for Learner_glmnet it may be a "glmnet" (often wrapped, e.g. "fishnet") fit; for other learners it will be whatever $private_fit() returns.
Each fitted object is trained on long Poisson data representing the piecewise-constant hazard for that cause across the node intervals.

data_info

A list of bookkeeping information needed for prediction and interpretation:

id: Identifier column name used.
status: Status column name used.
event_time: Event/censoring time column name used.
nodes: Numeric vector of node cut points used for the piecewise grid (includes 0 and is sorted). These are the interval boundaries used in the long Poisson representation.
maximum_followup: max(data[[event_time]]).
n_crisks: Number of event types (causes) detected. If censoring is present (0 in status), then n_crisks = #unique(status) - 1; otherwise n_crisks = #unique(status).
variable_transformation: The transformation specification passed in variable_transformation (or NULL).

Examples

d <- simulateStenoT1(50, competing_risks = TRUE)
lrn <- Learner_glmnet(covariates = c("age", "value_LDL"),
                      lambda = 0,
                      cross_validation = FALSE)
bl <- fit_learner(d,
                  learner = lrn,
                  id = "id",
                  status = "status_cvd",
                  event_time = "time_cvd",
                  number_of_nodes = 2)