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Oblique random survival forests (ORSFs)

Fit, inspect, summarize, and apply ORSF models

orsf() orsf_train()
Oblique Random Survival Forest (ORSF)
print(<orsf_fit>)
Inspect your ORSF model
predict(<orsf_fit>)
Compute predictions using ORSF
orsf_summarize_uni()
ORSF summary; univariate
print(<orsf_summary_uni>)
Print ORSF summary

Control how your ORSF model works

Choose how to identify linear combinations of predictors and set tuning parameters for your approach

orsf_control_fast()
Accelerated ORSF control
orsf_control_cph()
Cox regression ORSF control
orsf_control_net()
Penalized Cox regression ORSF control
orsf_control_custom()
Custom ORSF control

Variable importance/selection

Estimate the importance of individual variables and conduct variable selection using ORSFs

orsf_vi() orsf_vi_negate() orsf_vi_permute() orsf_vi_anova()
ORSF variable importance
orsf_vs()
Variable selection

Partial dependence and individual conditional expectations

Interpret your model by generating partial dependence or individual conditional expectation values. Plotting functions not included (but see examples)

orsf_ice_oob() orsf_ice_inb() orsf_ice_new()
ORSF Individual Conditional Expectations
orsf_pd_oob() orsf_pd_inb() orsf_pd_new()
ORSF partial dependence

Example survival data

Datasets used in examples and vignettes.

pbc_orsf
Mayo Clinic Primary Biliary Cholangitis Data

Miscellaneous

Functions that don’t fit neatly into a category above, but are still helpful.

as.data.table(<orsf_summary_uni>)
Coerce to data.table
orsf_time_to_train()
Estimate training time

Back-end functions

Techniques used by aorsf that may be helpful in other contexts.