plot.ensemble {randomSurvivalForest} | R Documentation |
Plot ensemble survival curves and ensemble estimates of mortality.
plot.ensemble(x, plots.one.page = TRUE, ...)
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. |
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.
Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu
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.
rsf
,
predict.rsf
.
data(veteran, package = "randomSurvivalForest") v.out <- rsf(Survrsf(time, status)~., veteran, ntree = 1000) plot.ensemble(v.out)