Tune tree size for Random Hazard Forests

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.

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.

Examples

# \donttest{
## ------------------------------------------------------------
## 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)

# }

Arguments

Details

Value

See also

Examples