In bcjaeger/aorsf: Accelerated Oblique Random Forests

Examples

library(aorsf)

Classification

Predict probability for each class or the predicted class:

# predicted probabilities, the default
predict(fit_clsf, 
        new_data = penguins_orsf_test[1:5, ],
        pred_type = 'prob')

# predicted class (as a matrix by default)
predict(fit_clsf, 
        new_data = penguins_orsf_test[1:5, ],
        pred_type = 'class')

# predicted class (as a factor if you use simplify)
predict(fit_clsf, 
        new_data = penguins_orsf_test[1:5, ],
        pred_type = 'class',
        pred_simplify = TRUE)

Regression

Predict the mean value of the outcome:

predict(fit_regr, 
        new_data = penguins_orsf_test[1:5, ], 
        pred_type = 'mean')

Survival

Begin by fitting an oblique survival random forest:

Predict risk, survival, or cumulative hazard at one or several times:

# predicted risk, the default
predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'risk', 
        pred_horizon = c(500, 1000, 1500))

# predicted survival, i.e., 1 - risk
predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'surv',
        pred_horizon = c(500, 1000, 1500))

# predicted cumulative hazard function
# (expected number of events for person i at time j)
predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'chf',
        pred_horizon = c(500, 1000, 1500))

Predict mortality, defined as the number of events in the forest's population if all observations had characteristics like the current observation. This type of prediction does not require you to specify a prediction horizon

predict(fit_surv, 
        new_data = pbc_orsf_test[1:5, ], 
        pred_type = 'mort')

bcjaeger/aorsf documentation built on April 3, 2025, 4:16 p.m.