grf (version 2.3.2)

predict.survival_forest: Predict with a survival forest

Description

Gets estimates of the conditional survival function S(t, x) = P[T > t | X = x] using a trained survival forest. The curve can be estimated by Kaplan-Meier, or Nelson-Aalen.

Usage

# S3 method for survival_forest
predict(
  object,
  newdata = NULL,
  failure.times = NULL,
  prediction.times = c("curve", "time"),
  prediction.type = c("Kaplan-Meier", "Nelson-Aalen"),
  num.threads = NULL,
  ...
)

Value

A list with elements

• predictions: a matrix of survival curves. If prediction.times = "curve" then each row is the survival curve for sample Xi: predictions[i, j] = S(failure.times[j], Xi). If prediction.times = "time" then each row is the survival curve at time point failure.times[i] for sample Xi: predictions[i, ] = S(failure.times[i], Xi).

• failure.times: a vector of event times t for the survival curve.

Arguments

object

The trained forest.

newdata

Points at which predictions should be made. If NULL, makes out-of-bag predictions on the training set instead (i.e., provides predictions at Xi using only trees that did not use the i-th training example). Note that this matrix should have the number of columns as the training matrix, and that the columns must appear in the same order.

failure.times

A vector of survival times to make predictions at. If NULL, then the failure times used for training the forest is used. If prediction.times = "curve" then the time points should be in increasing order. Default is NULL.

prediction.times

"curve" predicts the survival curve S(t, x) on grid t = failure.times for each sample Xi. "time" predicts S(t, x) at an event time t = failure.times[i] for each sample Xi. Default is "curve".

prediction.type

The type of estimate of the survival function, choices are "Kaplan-Meier" or "Nelson-Aalen". The default is the prediction.type used to train the forest.

num.threads

Number of threads used in training. If set to NULL, the software automatically selects an appropriate amount.

...

Additional arguments (currently ignored).

Examples

# \donttest{
# Train a standard survival forest.
n <- 2000
p <- 5
X <- matrix(rnorm(n * p), n, p)
failure.time <- exp(0.5 * X[, 1]) * rexp(n)
censor.time <- 2 * rexp(n)
Y <- pmin(failure.time, censor.time)
D <- as.integer(failure.time <= censor.time)
# Save computation time by constraining the event grid by discretizing (rounding) continuous events.
s.forest <- survival_forest(X, round(Y, 2), D)
# Or do so more flexibly by defining your own time grid using the failure.times argument.
# grid <- seq(min(Y[D==1]), max(Y[D==1]), length.out = 150)
# s.forest <- survival_forest(X, Y, D, failure.times = grid)

# Predict using the forest.
X.test <- matrix(0, 3, p)
X.test[, 1] <- seq(-2, 2, length.out = 3)
s.pred <- predict(s.forest, X.test)

# Plot the survival curve.
plot(NA, NA, xlab = "failure time", ylab = "survival function",
     xlim = range(s.pred$failure.times),
     ylim = c(0, 1))
for(i in 1:3) {
  lines(s.pred$failure.times, s.pred$predictions[i,], col = i)
  s.true = exp(-s.pred$failure.times / exp(0.5 * X.test[i, 1]))
  lines(s.pred$failure.times, s.true, col = i, lty = 2)
}

# Predict on out-of-bag training samples.
s.pred <- predict(s.forest)

# Compute OOB concordance based on the mortality score in Ishwaran et al. (2008).
s.pred.nelson.aalen <- predict(s.forest, prediction.type = "Nelson-Aalen")
chf.score <- rowSums(-log(s.pred.nelson.aalen$predictions))
if (require("survival", quietly = TRUE)) {
 concordance(Surv(Y, D) ~ chf.score, reverse = TRUE)
}
# }