Package index
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orsf()
orsf_train()
- Oblique Random Forests
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orsf_update()
- Update Forest Parameters
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print(<ObliqueForest>)
- Inspect Forest Parameters
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predict(<ObliqueForest>)
- Prediction for ObliqueForest Objects
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orsf_summarize_uni()
- Univariate summary
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print(<orsf_summary_uni>)
- Print ORSF summary
Control how your oblique RF works
Choose how to identify linear combinations of predictors and set tuning parameters for your approach
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orsf_control()
orsf_control_classification()
orsf_control_regression()
orsf_control_survival()
- Oblique random forest control
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orsf_control_cph()
- Cox regression ORSF control
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orsf_control_custom()
superseded - Custom ORSF control
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orsf_control_fast()
- Accelerated ORSF control
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orsf_control_net()
- Penalized Cox regression ORSF control
Variable importance/selection
Estimate the importance of individual variables and conduct variable selection using ORSFs
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orsf_vi()
orsf_vi_negate()
orsf_vi_permute()
orsf_vi_anova()
- Variable Importance
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orsf_vint()
- Variable Interactions
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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)
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orsf_ice_oob()
orsf_ice_inb()
orsf_ice_new()
- Individual Conditional Expectations
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orsf_pd_oob()
orsf_pd_inb()
orsf_pd_new()
- Partial dependence
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pred_spec_auto()
- Automatic variable values for dependence
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pbc_orsf
- Mayo Clinic Primary Biliary Cholangitis Data
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penguins_orsf
- Size measurements for adult foraging penguins near Palmer Station, Antarctica
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as.data.table(<orsf_summary_uni>)
- Coerce to data.table
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orsf_time_to_train()
- Estimate training time
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orsf_scale_cph()
orsf_unscale_cph()
- Scale input data