| plot.error {randomSurvivalForest} | R Documentation |
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 this is the default plot method for the package.
plot.error(x, ...)
plot.rsf(x, ...)
x |
An object of class (rsf, grow) or (rsf,
predict). |
... |
Further arguments passed to or from other methods. |
Plot of OOB error rate, with the b-th value being the error rate for the ensemble computed 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.
In the orginal call if importance=TRUE (the default setting),
then importance values for predictors will be plotted. A matrix with
3 columns is also printed. First column are importance values, second
column are standardized importance values (divided by the maximum
importance value), third column is the vector predictorWt. The
importance value indicates how much misclassification increases, or
decreases, for a new test case if the given predictor were not
available for that case, adjusting for all other predictors used in
growing the forest.
Hemant Ishwaran hemant.ishwaran@gmail.com and Udaya B. Kogalur ubk2101@columbia.edu
H. Ishwaran, Udaya B. Kogalur, Eugene H. Blackstone and Michael S. Lauer (2007). 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.
rsf,
predict.rsf.
data(veteran, package = "randomSurvivalForest") v.out <- rsf(Survrsf(time, status)~., veteran, ntree = 1000) plot.error(v.out, veteran)