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_function,
  y_hat,
  alpha,
  min_step = NULL,
  grid_size = NULL,
  return_min_q = FALSE,
  weighted_cp = FALSE,
  calib = NULL
)

Arguments

y_min	minimum value to search
y_max	maximum value to search
ncs	vector of non-conformity scores
ncs_function	a function that takes a vector of predicted values and a vector of true values and returns a vector of non-conformity scores
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
return_min_q	logical. If TRUE, the function will return the minimum quantile of the nonconformity scores for each predicted value
weighted_cp	logical. If TRUE, the function will use the weighted conformal prediction method. Default is FALSE
calib	a tibble with the predicted values and the true values of the calibration partition. Used when weighted_cp is TRUE. Default is NULL

Value

a tibble with the predicted values and the lower and upper bounds of the prediction intervals

pintervals documentation built on June 8, 2025, 10:37 a.m.