
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orsf_control_custom()superseded - Custom ORSF control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