orsf_summarize_uni: ORSF summary; univariate

In aorsf: Accelerated Oblique Random Survival Forests

ORSF summary; univariate

Description

Summarize the univariate information from an ORSF object

Usage

orsf_summarize_uni(
  object,
  n_variables = NULL,
  pred_horizon = NULL,
  pred_type = "risk",
  importance = "negate",
  ...
)

Arguments

object	(orsf_fit) a trained oblique random survival forest (see orsf).
n_variables	(integer) how many variables should be summarized? Setting this input to a lower number will reduce computation time.
pred_horizon	(double) a value or vector indicating the time(s) that predictions will be calibrated to. E.g., if you were predicting risk of incident heart failure within the next 10 years, then pred_horizon = 10. pred_horizon can be NULL if pred_type is 'mort', since mortality predictions are aggregated over all event times
pred_type	(character) the type of predictions to compute. Valid options are 'risk' : probability of having an event at or before pred_horizon. 'surv' : 1 - risk. 'chf': cumulative hazard function 'mort': mortality prediction
importance	(character) Indicate method for variable importance: 'none': no variable importance is computed. 'anova': compute analysis of variance (ANOVA) importance 'negate': compute negation importance 'permute': compute permutation importance For details on these methods, see orsf_vi.
...	Further arguments passed to or from other methods (not currently used).

Details

If pred_horizon is left unspecified, the median value of the time-to-event variable in object's training data will be used. It is recommended to always specify your own prediction horizon, as the median time may not be an especially meaningful horizon to compute predicted risk values at.

If object already has variable importance values, you can safely bypass the computation of variable importance in this function by setting importance = 'none'.

Value

an object of class 'orsf_summary', which includes data on

• importance of individual predictors.

• expected values of predictions at specific values of predictors.

See Also

as.data.table.orsf_summary_uni

Examples

object <- orsf(pbc_orsf, Surv(time, status) ~ . - id, n_tree = 25)

# since anova importance was used to make object, we can
# safely say importance = 'none' and skip computation of
# variable importance while running orsf_summarize_uni

orsf_summarize_uni(object, n_variables = 3, importance = 'none')

# however, if we want to summarize object according to variables
# ranked by negation importance, we can compute negation importance
# within orsf_summarize_uni() as follows:

orsf_summarize_uni(object, n_variables = 3, importance = 'negate')

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