Plot various survival estimates.

plot.survival(x, show.plots = TRUE, subset,
  collapse = FALSE, cens.model = c("km", "rfsrc"), ...)



An object of class (rfsrc, grow) or (rfsrc, predict).


Should plots be displayed?


Vector indicating which cases from x we want estimates for. All cases used if not specified.


Collapse the survival function?


Using the training data, specifies method for estimating the censoring distribution used in the inverse probability of censoring weights (IPCW) for calculating the Brier score:


Uses the Kaplan-Meier estimator.


Uses a censoring random survival forest estimator.


Further arguments passed to or from other methods.


Produces the following plots (going from top to bottom, left to right):

  1. Forest estimated survival function for each individual (thick red line is overall ensemble survival, thick green line is Nelson-Aalen estimator).

  2. Brier score (0=perfect, 1=poor, and 0.25=guessing) stratified by ensemble mortality. Based on the IPCW method described in Gerds et al. (2006). Stratification is into 4 groups corresponding to the 0-25, 25-50, 50-75 and 75-100 percentile values of mortality. Red line is overall (non-stratified) Brier score.

  3. Continuous rank probability score (CRPS) equal to the integrated Brier score divided by time.

  4. Plot of mortality of each individual versus observed time. Points in blue correspond to events, black points are censored observations. Not given for prediction settings lacking survival response information.

Whenever possible, out-of-bag (OOB) values are used.

Only applies to survival families. In particular, fails for competing risk analyses. Use plot.competing.risk in such cases.

Mortality (Ishwaran et al., 2008) represents estimated risk for an individual calibrated to the scale of number of events (as a specific example, if i has a mortality value of 100, then if all individuals had the same x-values as i, we would expect an average of 100 events).

The utility function get.brier.survival can be used to extract the Brier score among other useful quantities.


Invisibly, the conditional and unconditional Brier scores, and the integrated Brier score.


Hemant Ishwaran and Udaya B. Kogalur


Gerds T.A and Schumacher M. (2006). Consistent estimation of the expected Brier score in general survival models with right-censored event times, Biometrical J., 6:1029-1040.

Graf E., Schmoor C., Sauerbrei W. and Schumacher M. (1999). Assessment and comparison of prognostic classification schemes for survival data, Statist. in Medicine, 18:2529-2545.

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

Ishwaran H., Kogalur U.B., Blackstone E.H. and Lauer M.S. (2008). Random survival forests, Ann. App. Statist., 2:841-860.


# \donttest{
## veteran data
data(veteran, package = "randomForestSRC") 
plot.survival(rfsrc(Surv(time, status)~ ., veteran), cens.model = "rfsrc")

## pbc data
data(pbc, package = "randomForestSRC") 
pbc.obj <- rfsrc(Surv(days, status) ~ ., pbc)

## use subset to focus on specific individuals
plot.survival(pbc.obj, subset = 3)
plot.survival(pbc.obj, subset = c(3, 10))
plot.survival(pbc.obj, subset = c(3, 10), collapse = TRUE)

## get.brier.survival function does many nice things!
plot(get.brier.survival(pbc.obj, cens.model="km")$brier.score,type="s", col=2)
lines(get.brier.survival(pbc.obj, cens.model="rfsrc")$brier.score, type="s", col=4)
legend("bottomright", legend=c("cens.model = km", "cens.model = rfsrc"), fill=c(2,4))

# }