Wrapper for Survival Support Vector Machine (survivalsvm)

Final Production Wrapper for SVM (Tunable & Robust). Estimates a survival SVM and calibrates the raw utility scores into survival probabilities using a univariate Cox proportional hazards model.

Usage

surv.svm(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  gamma.mu = 0.1,
  type = "vanbelle2",
  kernel = "lin_kernel",
  opt.meth = "quadprog",
  ...
)

Arguments

time

Observed follow-up time.

event

Observed event indicator.

X

Training covariate data.frame.

newdata

Test covariate data.frame to use for prediction.

new.times

Times at which to obtain the predicted survivals.

obsWeights

Observation weights.

id

Optional cluster/individual ID indicator.

gamma.mu

Regularization parameter for the SVM (default: 0.1).

type

Type of SVM implementation (default: "vanbelle2").

kernel

Kernel type for the SVM (default: "lin_kernel").

opt.meth

Optimization method (default: "quadprog").

...

Additional arguments passed to survivalsvm.

Value

A list containing:

• fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.

• pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (requireNamespace("survivalsvm", quietly = TRUE) &&
 requireNamespace("quadprog", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:25, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.svm(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times
  )

  dim(fit$pred)
}
#> [1] 5 3