grf (version 2.1.0)

predict.survival_forest: Predict with a survival forest

Description

Gets estimates of the conditional survival function S(t, 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.type = c("Kaplan-Meier", "Nelson-Aalen"),
  num.threads = NULL,
  ...
)

Value

A list with elements `failure.times`: a vector of event times t for the survival curve, and `predictions`: a matrix of survival curves. Each row is the survival curve for sample X_i: predictions[i, j] = S(failure.times[j], X_i).

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 failure times to make predictions at. If NULL, then the failure times used for training the forest is used. The time points should be in increasing order. Default is NULL.

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)
s.forest <- survival_forest(X, Y, D)

# 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)

# Plot the survival curve for the first five individuals.
matplot(s.pred$failure.times, t(s.pred$predictions[1:5, ]),
        xlab = "failure time", ylab = "survival function (OOB)",
        type = "l", lty = 1)

# Train the forest on a less granular grid.
failure.summary <- summary(Y[D == 1])
events <- seq(failure.summary["Min."], failure.summary["Max."], by = 0.1)
s.forest.grid <- survival_forest(X, Y, D, failure.times = events)
s.pred.grid <- predict(s.forest.grid)
matpoints(s.pred.grid$failure.times, t(s.pred.grid$predictions[1:5, ]),
          type = "l", lty = 2)

# 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)
}
# }