predict.unsurv: Predict cluster membership for new survival curves

In unsurv: Unsupervised Clustering of Individualized Survival Curves

Predict cluster membership for new survival curves

Description

Assigns new survival-probability curves to clusters using the medoids from a fitted unsurv object. New curves are preprocessed using the same weighting, optional monotonic enforcement, smoothing, and standardization parameters as the fitted model.

Usage

## S3 method for class 'unsurv'
predict(object, newdata, clamp = TRUE, ...)

Arguments

object	An object of class "unsurv", returned by unsurv.
newdata	Numeric matrix of survival probabilities with shape n_{new} \times m, where columns correspond to the same time grid used during fitting.
clamp	Logical; if TRUE, clamps values to [0,1] before preprocessing.
...	Unused. Included for compatibility with the generic.

Details

Cluster assignment is performed by computing distances between the new curves and the stored medoid curves in the weighted feature space defined during fitting. The distance metric ("L1" or "L2") and any standardization parameters are reused from the fitted model.

Value

An integer vector of cluster labels of length nrow(newdata), taking values in 1, ..., object$K.

Examples

if (requireNamespace("cluster", quietly = TRUE)) {
  set.seed(1)
  n <- 60; Q <- 40
  times <- seq(0, 5, length.out = Q)
  grp <- sample(1:2, n, TRUE)
  rates <- c(0.2, 0.6)
  S <- sapply(times, function(t) exp(-rates[grp] * t))
  S <- S + matrix(stats::rnorm(n * Q, 0, 0.02), nrow = n)

  fit <- unsurv(S, times, K = 2)

  # predict cluster membership for first 5 curves
  predict(fit, S[1:5, ])
}

unsurv documentation built on Sept. 1, 2026, 1:06 a.m.