tune.treesize.rhf: Tune tree size for Random Hazard Forests

In randomForestRHF: Random Hazard Forests

Tune tree size for Random Hazard Forests

Description

Tune the treesize for a Random Hazard Forest using either out-of-bag (OOB) empirical risk or an OOB integrated AUC computed by auct.rhf.

Usage

tune.treesize.rhf(
  formula,
  data,
  ntree = 500,
  nsplit = 10,
  nodesize = NULL,
  perf = c("risk", "iAUC"),
  auct.args = NULL,
  lower = 2L,
  upper = NULL,
  C = 3,
  method = c("golden", "bisect"),
  max.evals = 20L,
  bracket.tol = 2L,
  seed = NULL,
  verbose = TRUE,
  forest = TRUE,
  ...
)


tune.iAUC.rhf(
  formula,
  data,
  auct.args = NULL,
  ...
)

## S3 method for class 'tune.treesize.rhf'
plot(x, ylab = NULL, main = NULL,
    se.band = TRUE, se.mult = 1, ylim = NULL, ...)

Arguments

formula	Survival formula for rhf, typically Surv(id, start, stop, event) ~ ..
data	Counting-process data frame containing the variables in formula.
ntree	Number of trees grown at each candidate treesize.
nsplit	Number of random split points tried per variable.
nodesize	Minimum number of events in a terminal node; passed to rhf.
perf	Tuning criterion. "risk" minimizes mean OOB risk; "iAUC" maximizes integrated AUC obtained using auct.rhf.
auct.args	Optional list of arguments passed to auct.rhf when perf = "iAUC".
lower	Smallest treesize considered.
upper	Largest treesize considered.
C	Multiplier used when computing the default upper bound if upper is NULL.
method	Discrete search strategy: "golden" (golden-section search) or "bisect" (bisection-style search).
max.evals	Maximum number of distinct treesize values evaluated by the search.
bracket.tol	Tolerance for the bracketing interval width used by the search algorithms.
seed	Optional random seed for reproducible comparisons across tree sizes.
verbose	Logical; if TRUE, print progress messages when evaluating new treesize values.
forest	Logical; if TRUE, return the RHF object at the best treesize.
...	Additional arguments passed to rhf.
x, ylab, main, se.band, se.mult, ylim	Arguments to customize plot.

Details

The alias tune.rhf is provided for convenience.

Rpeatedly fits a hazard forest with different values of treesize and selects the value that optimizes the chosen criterion perf. When perf = "risk", the criterion is the mean OOB risk. When perf = "iAUC", auct.rhf is applied to each fit and the criterion is 1 - iAUC.uno. tune.iAUC is a wrapper around tune.rhf with perf = "iAUC", using default auct.rhf settings when auct.args = NULL.

Value

An object of class "tune.treesize.rhf" with components

• best.size: optimal treesize;

• best.err: minimal criterion value (mean OOB risk if perf = "risk", or 1 - iAUC.uno if perf = "iAUC");

• path: data frame with evaluated treesize values and corresponding criterion values (and an iAUC column when perf = "iAUC");

• forest: RHF fit at best.size when forest = TRUE.

See Also

rhf, auct.rhf

Examples

## ------------------------------------------------------------
## Example: tuning treesize by OOB empirical risk
## ------------------------------------------------------------

set.seed(7)
d <- hazard.simulation(1)$dta
f <- "Surv(id, start, stop, event) ~ ."

## tune.rhf is an alias for tune.treesize.rhf
tuneRisk <- tune.rhf(f, d)  ## same as tune.treesize.rhf(f, d)

oldpar <- par(mfrow = c(1, 1))
plot(tuneRisk)
par(oldpar)

tuneRisk$best.size    # treesize minimizing mean OOB risk
tuneRisk$best.err

## Access the tuned forest and refit AUC diagnostics if desired
best.forest <- tuneRisk$forest
a.risk <- auct.rhf(best.forest)
print(a.risk$iAUC.uno)


## ------------------------------------------------------------
## Example: tuning treesize by iAUC
## ------------------------------------------------------------

set.seed(7)
tuneiAUC <- tune.iAUC(f, d)

oldpar <- par(mfrow = c(1, 1))
plot(tuneiAUC)
par(oldpar)

print(tuneiAUC$best.size)
print(1 - tuneiAUC$best.err)

best.forest.iauc <- tuneiAUC$forest
a.iauc <- auct.rhf(best.forest.iauc)
print(1 - a.iauc$iAUC.uno)


## ------------------------------------------------------------
## Example: tuning treesize by iAUC with bootstrap s.e.
## ------------------------------------------------------------

set.seed(7)
tuneiAUC.boot <- tune.iAUC(f, d, auct.args=list(bootstrap.rep=25))
oldpar <- par(mfrow = c(1, 1))
plot(tuneiAUC.boot)        
par(oldpar)

## ------------------------------------------------------------
## Example: tuning by hazard-based incident AUC
## ------------------------------------------------------------

set.seed(7)
tuneHazInc <- tune.iAUC(
  f, d,
  auct.args = list(
    marker       = "hazard",
    method       = "incident"
  )
)

oldpar <- par(mfrow = c(1, 1))
plot(tuneHazInc)
par(oldpar)

print(tuneHazInc$best.size)
print(tuneHazInc$best.err)

## Confirm the tuned incident AUC on the selected forest
best.forest.haz <- tuneHazInc$forest
aHazInc <- auct.rhf(
  best.forest.haz,
  marker  = "hazard",
  method  = "incident"
)
print(aHazInc)
print(1 - aHazInc$iAUC.uno)

randomForestRHF documentation built on April 24, 2026, 1:07 a.m.