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 orsfMin events in leaf: See

`leaf_min_events`

in orsfOOB 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 'ObliqueForest'
print(x, ...)
```

## Arguments

- x
(

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

## Examples

```
object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 5)
print(object)
#> ---------- Oblique random survival forest
#>
#> Linear combinations: Accelerated Cox regression
#> N observations: 276
#> N events: 111
#> N trees: 5
#> N predictors total: 17
#> N predictors per node: 5
#> Average leaves per tree: 20.8
#> Min observations in leaf: 5
#> Min events in leaf: 1
#> OOB stat value: 0.76
#> OOB stat type: Harrell's C-index
#> Variable importance: anova
#>
#> -----------------------------------------
```