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)