print.orsf_fit: Inspect your ORSF model

In aorsf: Accelerated Oblique Random Survival Forests

Inspect your ORSF model

Description

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?

Usage

## S3 method for class 'orsf_fit'
print(x, ...)

Arguments

x	(orsf_fit) an oblique random survival forest (ORSF; see orsf).
...	Further arguments passed to or from other methods (not currently used).

Value

x, invisibly.

Examples

object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 5)

print(object)

aorsf documentation built on Oct. 26, 2023, 5:08 p.m.