RoyRF: implements RF prediction interval method in Roy, Larocque...
In piRF: Prediction Intervals for Random Forests

View source: R/Roy_Larocque_2019.R

Currently implemented is the quantile method with BOP intervals. Used inside rfint().

RoyRF(
  formula = NULL,
  train_data = NULL,
  pred_data = NULL,
  num_trees = NULL,
  min_node_size = NULL,
  m_try = NULL,
  keep_inbag = TRUE,
  intervals = TRUE,
  interval_method = "quantile",
  calibrate = FALSE,
  alpha = NULL,
  num_threads = NULL,
  tolerance = NULL,
  step_percent = NULL,
  under = NULL,
  method = NULL,
  max_iter = NULL,
  interval_type = NULL
)

`formula`	Object of class formula or character describing the model to fit. Interaction terms supported only for numerical variables.
`train_data`	Training data of class data.frame, matrix, dgCMatrix (Matrix) or gwaa.data (GenABEL). Matches ranger() requirements.
`pred_data`	Test data of class data.frame, matrix, dgCMatrix (Matrix) or gwaa.data (GenABEL). Utilizes ranger::predict() to get prediction intervals for test data.
`num_trees`	Number of trees.
`min_node_size`	Minimum number of observations before split at a node.
`m_try`	Number of variables to randomly select from at each split.
`keep_inbag`	Saves matrix of observations and which tree(s) they occur in. Required to be true to generate variance estimates for Ghosal, Hooker 2018 method. *Should not be an option...
`intervals`	Generate prediction intervals or not.
`interval_method`	which prediction interval type to generate. Several outlined in paper; currently only one method implemented.
`calibrate`	calibrate prediction intervals based on out-of-bag performance. Adjusts alpha to get nominal coverage.
`alpha`	Significance level for prediction intervals.
`num_threads`	The number of threads to use in parallel. Default is the current number of cores.
`interval_type`	Type of prediction interval to generate. Options are `method = c("two-sided", "lower", "upper")`. Default is `method = "two-sided"`.