Penalized Smooth Hazard Survival Learner — surv.survPen • SuperSurv

Fits an overall hazard model using survPen::survPen() and returns its native survival probabilities. Only single-event, right-censored outcomes and uniform observation weights are supported.

Usage

surv.survPen(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  formula = NULL,
  baseline.df = 4L,
  n.legendre = 50L,
  ...
)

Arguments

time: Observed follow-up time.
event: Observed event indicator.
X: Training covariate data frame.
newdata: Covariate data frame used for prediction.
new.times: Times at which survival probabilities are requested.
obsWeights: Optional non-negative observation weights.
id: Currently ignored.
formula: Optional one-sided hazard formula using feature names and .supersurv_time. For example, ~ smf(.supersurv_time, df = 4) + smf(x1, df = 4) + x2, or ~ tensor(.supersurv_time, x1, df = c(4, 4)) + x2 for a time-varying effect. The survPen constructors smf, tensor, tint, and rd are available without attaching the backend package. Formula variables must come from the current training features or .supersurv_time.
baseline.df: Baseline smooth degrees of freedom, at least 3. Used only when formula is NULL; the default adds linear terms for all features to smf(.supersurv_time, df = baseline.df).
n.legendre: Positive integer quadrature order for both fitting and survival prediction. Increase this to check integration accuracy.
...: Additional named fitting controls passed to survPen::survPen(), such as lambda, method, or max.it.beta.

Value

A list with numeric survival matrix pred and fitted object fit.

Details

Nonuniform observation weights are rejected because the backend does not expose case-weighted fitting. The adapter does not implement net survival, relative mortality, or left truncation. Survival curves that increase beyond numerical tolerance cause an error; increase quadrature accuracy rather than silently projecting a materially invalid curve. Custom formulas must remain valid after feature screening; use screen.all when explicitly naming features.

Examples

if (requireNamespace("survPen", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:80, ]
  X <- dat[, "x1", drop = FALSE]
  fit <- surv.survPen(dat$duration, dat$event, X,
                      X[1:3, , drop = FALSE], c(50, 100), baseline.df = 3)
  dim(fit$pred)
}
#> [1] 3 2