Plot smoothed hazard and cumulative hazard plots from RHF analysis

Plot case specific hazard and cumulative hazard (CHF) from a fitted random hazard forest (RHF) object. Hazards are smoothed with stats::supsmu. Optional scaling places hazards on the same scale as the CHF for easier comparison.

# S3 method for class 'rhf'
plot(x, idx = NULL, scale.hazard = FALSE, 
         ngrid = 30, bass = 0, q = 0.99, grid = FALSE,
         col = NULL, lty = NULL, legend.loc = "topright",
         jitter.factor = 1, lwd = 4,
         hazard.only = TRUE, legend.show = TRUE, ...)

Arguments

x

An rhf fit object returned by rhf.

idx

Subject identifiers selecting which cases to plot. Values must match those in unique(x$id); by default the first value is used.

scale.hazard

Logical or numeric. If TRUE, hazards are multiplied by the local time step so they approximate CHF increments. If numeric, hazards are multiplied by the given constant.

ngrid

Number of equally spaced points used when grid = TRUE.

bass

Smoothing parameter passed to stats::supsmu.

q

Quantile used to trim extreme hazard values before smoothing.

grid

If TRUE, overlays smoothed CHF and its derivative.

col

Colors for selected cases. Recycled as needed.

lty

Line types for hazard and CHF curves.

legend.loc

Legend location. Passed to legend.

jitter.factor

Amount of horizontal jitter applied to hazard spikes.

lwd

Line width for hazard spikes.

hazard.only

If TRUE, only the hazard is drawn.

legend.show

If TRUE, show a legend when plotting multiple cases.

...

Additional graphics parameters passed to plot.

Details

The function displays out of bag hazard and CHF estimates on the grid x$time.interest. Hazards are smoothed using supsmu after trimming large values for stability. Scaling the hazard allows direct visual comparison with CHF. Plots are drawn to the current device; no model re-estimation is performed.

Value

Invisibly returns NULL. The function is used for its plotting side effects.

Author

Hemant Ishwaran and Udaya B. Kogalur

References

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R. R News, 7(2): 25–31.

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests. Annals of Applied Statistics, 2: 841–860.

Lee D.K., Chen N., and Ishwaran H. (2021). Boosted nonparametric hazards with time dependent covariates. Annals of Statistics, 49: 2101–2128.

Ishwaran H., Lee D.K. and Hsich E.M. (2025). Random hazard forests.

See also

hazard.simulation, predict.rhf, rhf, smoothed.hazard.rhf

Examples

# \donttest{

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

set.seed(1)
o <- rhf(f, d)

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

## plot selected cases
ids <- o$ensemble.id[1:3]
plot(o, idx = ids)

## hazard only
plot(o, idx = ids)

## scaled hazard with CHF
plot(o, idx = ids, scale.hazard = TRUE, hazard.only = FALSE)

## auxiliary grid with smoother control
plot(o, idx = ids, grid = TRUE, ngrid = 60, bass = 2, hazard.only = FALSE)

## multiple cases, no legend
plot(o, idx = o$ensemble.id[1:10], lwd = 0, legend.show = FALSE)

## lowess median smoothed hazard
s <- smoothed.hazard(o)  ## default method="median.loess"
plot(s, idx = o$ensemble.id[1:10])
plot(s, idx = o$ensemble.id[1:10], lwd = 0)


par(oldpar)

## ------------------------------------------------------------
##  complex simulated time dependent covariate hazards
## ------------------------------------------------------------
f <- "Surv(id, start, stop, event) ~ ."
sim2 <- hazard.simulation(2)$dta
o2 <- rhf(f, sim2)

oldpar <- par(mfrow=c(1,1))
plot(o2, 1)
par(oldpar)

## ------------------------------------------------------------
##  same complex simulation, but using smoothed hazard
## ------------------------------------------------------------
f <- "Surv(id, start, stop, event) ~ ."
sim2 <- hazard.simulation(2)$dta
o2 <- rhf(f, sim2)

s2.loess <- smoothed.hazard(o2, method="loess")
s2.med.loess <- smoothed.hazard(o2, method="median.loess")

oldpar <- par(mfrow=c(1,1))
plot(o2, 1)
plot(s2.loess, 1)
plot(s2.med.loess, 1)
plot(s2.med.loess, 1:10, lwd = 0)
par(oldpar)


# }