CQRF: implements RF prediction interval using split conformal...

In piRF: Prediction Intervals for Random Forests

Description

This function implements split conformal prediction intervals for RFs. Currently used in rfint().

Usage

CQRF(
  formula = NULL,
  train_data = NULL,
  pred_data = NULL,
  num_trees = NULL,
  min_node_size = NULL,
  m_try = NULL,
  keep_inbag = TRUE,
  intervals = TRUE,
  alpha = NULL,
  forest_type = "RF",
  num_threads = 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.
alpha	Significance level for prediction intervals.
forest_type	Determines what type of forest: regression forest vs. quantile regression forest. *Should not be an option...
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".

piRF documentation built on July 1, 2020, 7:51 p.m.