piRF (version 0.1.0)

RoyRF: implements RF prediction interval method in Roy, Larocque 2019. Helper function.

Description

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

Usage

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
)

Arguments

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".