Function reference

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.

orsf_scale_cph() orsf_unscale_cph(): Scale input data