plot.competing.risk: Plots for Competing Risks

In ehrlinger/randomForestSRC: Random Forests for Survival, Regression and Classification (RF-SRC)

Description

Plot the ensemble cumulative incidence function (CIF) and cause-specific cumulative hazard function (CSCHF) from a competing risk analysis.

Usage

## S3 method for class 'rfsrc'
plot.competing.risk(x, plots.one.page = FALSE, ...)

Arguments

x	An object of class (rfsrc, grow) or (rfsrc, predict).
plots.one.page	Should plots be placed on one page?
...	Further arguments passed to or from other methods.

Details

Ensemble ensemble CSCHF and CIF functions for each event type. Does not apply to right-censored data.

Author(s)

Hemant Ishwaran and Udaya B. Kogalur

References

Ishwaran H., Gerds T.A., Kogalur U.B., Moore R.D., Gange S.J. and Lau B.M. (2014). Random survival forests for competing risks. Biostatistics, 15(4):757-773.

See Also

follic, hd, rfsrc, wihs

Examples

## Not run: 
## ------------------------------------------------------------
## follicular cell lymphoma
## ------------------------------------------------------------

  data(follic, package = "randomForestSRC")
  follic.obj <- rfsrc(Surv(time, status) ~ ., follic, nsplit = 3, ntree = 100)
  plot.competing.risk(follic.obj)

## ------------------------------------------------------------
## competing risk analysis of pbc data from the survival package
## events are transplant (1) and death (2)
## ------------------------------------------------------------

if (library("survival", logical.return = TRUE)) {
   data(pbc, package = "survival")
   pbc$id <- NULL
   plot.competing.risk(rfsrc(Surv(time, status) ~ ., pbc, nsplit = 10))
}

## End(Not run)

ehrlinger/randomForestSRC documentation built on May 16, 2019, 1:20 a.m.