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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 orsf_fit
print(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)

#> ---------- Oblique random survival forest
#>      Linear combinations: Accelerated
#>           N observations: 276
#>                 N events: 111
#>                  N trees: 5
#>       N predictors total: 17
#>    N predictors per node: 5
#>  Average leaves per tree: 25
#> Min observations in leaf: 5
#>       Min events in leaf: 1
#>           OOB stat value: 0.69
#>            OOB stat type: Harrell's C-statistic
#>      Variable importance: anova
#> -----------------------------------------