grf (version 1.2.0)

predict.survival_forest: Predict with a survival forest forest

Description

Gets estimates of the conditional survival function S(t, x) using a trained survival forest (estimated using Kaplan-Meier).

Usage

# S3 method for survival_forest
predict(object, newdata = NULL, failure.times = NULL, num.threads = NULL, ...)

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. Default is NULL.

num.threads

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

...

Additional arguments (currently ignored).

Value

Vector of predictions.

Examples

# NOT RUN {
# 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)
# }
# NOT RUN {
# }