orsf_summarize_uni {aorsf}  R Documentation 
Summarize the univariate information from an ORSF object
orsf_summarize_uni(
object,
n_variables = NULL,
pred_horizon = NULL,
pred_type = "risk",
importance = "negate",
...
)
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_type 
(character) the type of predictions to compute. Valid options are

importance 
(character) Indicate method for variable importance:
For details on these methods, see orsf_vi. 
... 
Further arguments passed to or from other methods (not currently used). 
If pred_horizon
is left unspecified, the median value of
the timetoevent 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'.
an object of class 'orsf_summary', which includes data on
importance of individual predictors.
expected values of predictions at specific values of predictors.
as.data.table.orsf_summary_uni
object < orsf(pbc_orsf, Surv(time, status) ~ .  id)
# 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')