grid_inner: Inner Function for Grid Search

In pintervals: Model Agnostic Prediction Intervals

Inner Function for Grid Search

Description

Inner Function for Grid Search

Usage

grid_inner(
  hyp_ncs,
  y_hat,
  ncs,
  pos_vals,
  alpha,
  ncs_type,
  distance_weighted_cp,
  distance_features_calib,
  distance_features_pred,
  distance_type,
  normalize_distance,
  weight_function
)

Arguments

hyp_ncs	vector of hypothetical non-conformity scores
y_hat	predicted value
ncs	vector of non-conformity scores
pos_vals	vector of possible values for the lower and upper bounds of the prediction interval
alpha	confidence level
ncs_type	type of non-conformity score
distance_weighted_cp	logical. If TRUE, the non-conformity scores will be weighted according to the distance function
distance_features_calib	a matrix of features for the calibration partition. Used when distance_weighted_cp is TRUE
distance_features_pred	a matrix of features for the prediction partition. Used when distance_weighted_cp is TRUE
distance_type	The type of distance metric to use when computing distances between calibration and prediction points. Options are 'mahalanobis' and 'euclidean'.
normalize_distance	Either 'minmax', 'sd', or 'none'. Indicates how to normalize the distances when distance_weighted_cp 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'

Value

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

pintervals documentation built on March 3, 2026, 5:06 p.m.