print.orsf_fit {aorsf} | R Documentation |
Printing an ORSF model tells you:
Linear combinations: How were these identified?
N observations: Number of rows in training data
N events: Number of events in training data
N trees: Number of trees in the forest
N predictors total: Total number of columns in the predictor matrix
N predictors per node: Number of variables used in linear combinations
Average leaves per tree: A proxy for the depth of your trees
Min observations in leaf: See leaf_min_obs
in orsf
Min events in leaf: See leaf_min_events
in orsf
OOB stat value: Out-of-bag error after fitting all trees
OOB stat type: How was out-of-bag error computed?
Variable importance: How was variable importance computed?
## S3 method for class 'orsf_fit'
print(x, ...)
x |
(orsf_fit) an oblique random survival forest (ORSF; see orsf). |
... |
Further arguments passed to or from other methods (not currently used). |
x
, invisibly.
object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 5)
print(object)