grf: predict.survival_forest – R documentation

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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 class '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

# 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)

grf

Generalized Random Forests

v1.2.0

GPL-3

Authors

Julie Tibshirani [aut, cre], Susan Athey [aut], Rina Friedberg [ctb], Vitor Hadad [ctb], David Hirshberg [ctb], Luke Miner [ctb], Erik Sverdrup [ctb], Stefan Wager [aut], Marvin Wright [ctb]

Initial release