grid_inner: 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
)

Value

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

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'