plot.error {randomSurvivalForest}R Documentation

Plot of Error Rate and Variable Importance

Description

Plot out-of-bag (OOB) error rate for the ensemble as a function of number of trees in the forest. Also plots importance values for predictors. Note that this is the default plot method for the package.

Usage

    plot.error(x, ...)
    plot.rsf(x, ...)

Arguments

x An object of class (rsf, grow) or (rsf, predict).
... Further arguments passed to or from other methods.

Details

Plot of OOB error rate for the ensemble, with the b-th value being the error rate for the ensemble formed using the first b trees. Error rate is 1-C, where C is Harrell's concordance index. Rates given are between 0 and 1, with 0.5 representing the benchmark value of a procedure based on random guessing. A value of 0 is perfect.

If importance=TRUE used in original rsf call (the default setting), importance values for predictors will be included in the object x. In this case, plot.error will plot, as well as print, these values. The vector predictorWt is also printed as a side effect.

Author(s)

Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu

References

H. Ishwaran and Udaya B. Kogalur (2006). Random Survival Forests. Cleveland Clinic Technical Report.

L. Breiman (2001). Random forests, Machine Learning, 45:5-32.

F.E. Harrell et al. (1982). Evaluating the yield of medical tests, J. Amer. Med. Assoc., 247, 2543-2546.

See Also

rsf, predict.rsf.

Examples

  data(veteran, package = "randomSurvivalForest") 
  v.out <- rsf(Survrsf(time, status)~., veteran, ntree = 1000)
  plot.error(v.out, veteran)

[Package randomSurvivalForest version 2.1.0 Index]