grid_finder: Grid Search for Lower and Upper Bounds of Continuous...

In pintervals: Model Agnostic Prediction Intervals

Grid Search for Lower and Upper Bounds of Continuous Conformal Prediction Intervals

Description

Grid Search for Lower and Upper Bounds of Continuous Conformal Prediction Intervals

Usage

grid_finder(
  y_min,
  y_max,
  ncs,
  ncs_type,
  y_hat,
  alpha,
  min_step = NULL,
  grid_size = NULL,
  calib = NULL,
  coefs = NULL,
  distance_weighted_cp = FALSE,
  distance_features_calib = NULL,
  distance_features_pred = NULL,
  distance_type = c("mahalanobis", "euclidean"),
  normalize_distance = c("minmax", "sd", "none"),
  weight_function = gauss_kern
)

Arguments

y_min	minimum value to search
y_max	maximum value to search
ncs	vector of non-conformity scores
ncs_type	String indicating the non-conformity score function to use
y_hat	vector of predicted values
alpha	confidence level
min_step	The minimum step size for the grid search
grid_size	Alternative to min_step, the number of points to use in the grid search between the lower and upper bound
calib	a tibble with the predicted values and the true values of the calibration partition. Used when weighted_cp is TRUE. Default is NULL
coefs	a numeric vector of coefficients for the heterogeneous error model. Must be of length 2, where the first element is the intercept and the second element is the slope. Used when ncs_type is 'heterogeneous_error'. Default is NULL
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' (default) and 'euclidean'.
normalize_distance	Either "none", "minmax", or "sd". 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 tibble with the predicted values and the lower and upper bounds of the prediction intervals

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