Smoothed hazard and cumulative hazard estimates from a random hazard forest

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.

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).

Examples

# \donttest{

## ------------------------------------------------------------
## 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)
# }

Arguments

Details

Value

See also

Examples