plot.ensemble {randomSurvivalForest}R Documentation

Plot of Ensemble Estimates

Description

Plot ensemble survival curves and ensemble estimates of mortality.

Usage

    plot.ensemble(x, plots.one.page = TRUE, ...)

Arguments

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

Details

Four plots are produced. Going from top to bottom, left to right: (1) This shows the ensemble survival function for each individual in the data. Thick red line is overall ensemble survival, thick green line is Nelson-Aalen estimator. (2) This is a comparison of the population ensemble survival function to the Nelson-Aalen estimator. (3) The Brier score, a value between 0 and 1 (where 0=perfect, 1=poor, and 0.25=guessing) is plotted at each of the unique event times with plot stratified by ensemble mortality value (see Graf et al. for more background on Brier scores). Stratification is into 4 groups corresponding to the 0-25, 25-50, 50-75 and 75-100 percentile values of mortality. Red line is non-stratified score. (4) Plot of estimated mortality versus observed time. Points in blue correspond to events, black points are censored observations.

Note that when x is of class (rsf, predict) not all plots will be produced.

Author(s)

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

References

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

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

See Also

rsf, predict.rsf.

Examples

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

[Package randomSurvivalForest version 3.5.1 Index]