smoothed.hazard.rhf: Smoothed hazard and cumulative hazard estimates from a random...

In randomForestRHF: Random Hazard Forests

Smoothed hazard and cumulative hazard estimates from a random hazard forest

Description

Compute smoothed case-specific hazard curves (and corresponding cumulative hazards) from a Random Hazard Forest (RHF) object. The returned object is a lightweight list designed to be directly usable by plot.rhf.

Usage

smoothed.hazard.rhf(
  o,
  method = c("median.loess", "loess"),
  oob = TRUE,
  span = 0.35,
  degree = 1,
  family = NULL,
  eps = 1e-12,
  suppress.warnings = TRUE,
  trace = FALSE
)

Arguments

o	A fitted RHF object or predict object.
method	Smoothing strategy. See Details for definitions of "loess" and "median.loess".
oob	By default out-of-bag (OOB) values are returned for RHF objects, but inbag values can be requested by setting this option to FALSE. Does not apply to RHF predict objects.
span	Loess span passed to loess.
degree	Loess degree passed to loess.
family	Loess family passed to loess. Must be "gaussian" or "symmetric". If NULL, defaults to "gaussian" for method="median.loess" and "symmetric" for method="loess".
eps	Small positive constant added before log-transforming hazards: log(h + eps).
suppress.warnings	If TRUE, warnings arising from loess() fitting/prediction are suppressed.
trace	If TRUE, print progress messages.

Details

Hazard curves are computed/smoothed on the grid o$time.interest. Smoothing is performed on the log-hazard scale: log(hazard + eps), and then back-transformed to the hazard scale.

method="loess"

Applies loess smoothing to an already-constructed hazard matrix stored in o (e.g. o$hazard.oob and/or o$hazard.inbag), independently for each subject.

method="median.loess"

For each subject and time bin, computes the median across trees of the terminal-node (leaf) log-hazard log(h + eps) (using a native C routine), then applies loess smoothing to the resulting median log-hazard curve and back-transforms. This method requires the fitted object to retain tree-specific hazard and membership structures (e.g., o$forest$t.hazard and related arrays).

Value

A list containing:

• id, ensemble.id, yvar, time.interest copied from o.

• Depending on target:

  • hazard.oob, chf.oob (out-of-bag), matrices of dimension nSubj x length(time.interest).

  • hazard.inbag, chf.inbag (in-bag), matrices of dimension nSubj x length(time.interest).

  • hazard.test, chf.test (test), matrices of dimension nSubjTest x length(time.interest).

See Also

plot.rhf, predict.rhf, rhf

Examples

## ------------------------------------------------------------
## canonical example using synthetic data
## includes both train/test scenarios
## ------------------------------------------------------------

simID <- 1
trn <- hazard.simulation(simID)$dta
tst <- hazard.simulation(simID)$dta
f <- "Surv(id, start, stop, event) ~ ."

## training/testing
o <- rhf(f, trn)
p <- predict(o, tst)

## default: median(log leaf hazard) + loess
so <- smoothed.hazard(o)
sp <- smoothed.hazard(p)

## the returned object can be passed directly to plot.rhf()
oldpar <- par(mfrow=c(1,1))
plot.rhf(so)
plot.rhf(sp)
par(oldpar)

## ------------------------------------------------------------
##  peak vo2
## ------------------------------------------------------------

data(peakVO2, package = "randomForestSRC")
d <- convert.counting(Surv(ttodead, died)~., peakVO2)
f <- "Surv(id, start, stop, event) ~ ."

## training
o <- rhf(f, d)

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


## median loess
s0 <- smoothed.hazard(o) 
ids <- o$ensemble.id[1:3]
plot.rhf(s0, idx = ids)

## loess smoothing 
s1 <- smoothed.hazard(o, method = "loess")
plot.rhf(s1, idx = ids)

par(oldpar)

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