bootstrap_inner: Bootstrap Function for Bootstrapping the Prediction Intervals

Description

Bootstrap Function for Bootstrapping the Prediction Intervals

Usage

bootstrap_inner(
  pred,
  calib,
  error,
  nboot,
  alpha,
  error_type = c("raw", "absolute"),
  distance_weighted_bootstrap = FALSE,
  distance_features_calib = NULL,
  distance_features_pred = NULL,
  distance_type = c("mahalanobis", "euclidean"),
  normalize_distance = c("minmax", "sd", "none"),
  weight_function = gauss_kern
)

Value

a numeric vector with the predicted value and the lower and upper bounds of the prediction interval

Arguments

pred: predicted value
calib: a vector of predicted values for the calibration partition
error: vector of errors
nboot: number of bootstrap samples
alpha: confidence level
error_type: The type of error to use for the prediction intervals. Can be 'raw' or 'absolute'. If 'raw', bootstrapping will be done on the raw prediction errors. If 'absolute', bootstrapping will be done on the absolute prediction errors with random signs. Default is 'raw'
distance_weighted_bootstrap: logical. If TRUE, the bootstrap samples will be weighted according to the distance function
distance_features_calib: a matrix of features for the calibration partition. Used when distance_weighted_bootstrap is TRUE
distance_features_pred: a matrix of features for the prediction partition. Used when distance_weighted_bootstrap is TRUE
distance_type: The type of distance metric to use when computing distances between calibration and prediction points. Options are 'mahalanobis' (default) and 'euclidean'.
normalize_distance: Either "none", "minmax", or "sd". Indicates how to normalize the distances when distance_weighted_bootstrap is TRUE
weight_function: a function to use for weighting the distances. Can be 'gaussian_kernel', 'caucy_kernel', 'logistic', or 'reciprocal_linear'. Default is 'gaussian_kernel'