hcdf.hermite_estimator_bivar: Creates an object summarizing the bivariate CDF with...

In hermiter: Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)

Creates an object summarizing the bivariate CDF with associated generic methods print, plot and summary.

Description

The hermite_estimator_bivar object h_est_obj must be updated with observations prior to the use of this method.

Usage

## S3 method for class 'hermite_estimator_bivar'
hcdf(
  h_est_obj,
  clipped = FALSE,
  accelerate_series = TRUE,
  x_lower = NA,
  x_upper = NA
)

Arguments

h_est_obj	A hermite_estimator_bivar object.
clipped	A boolean value. This value determines whether cumulative probabilities are clipped to lie within the range [0,1].
accelerate_series	A boolean value. This value determines whether Hermite series acceleration is applied.
x_lower	A numeric vector. This vector determines the lower limit of x values at which to evaluate the CDF.
x_upper	A numeric value. This vector determines the upper limit of x values at which to evaluate the CDF.

Value

A hcdf_bivar object whose underlying structure is a list containing the following components.

x: The points at which the cumulative probability is calculated. x_vals_1: Marginal quantiles of first random variable, used for plotting. x_vals_2: Marginal quantiles of second random variable, used for plotting. cum_prob_vals: The cumulative probability values at the points x. num_obs: The number of observations used to form the Hermite cumulative probability estimates. N: The number of terms N in the Hermite series estimator.

hermiter documentation built on May 31, 2023, 6:30 p.m.