kb.test-class: An S4 class for kernel-based distance tests with normal...

In QuadratiK: Collection of Methods Constructed using Kernel-Based Quadratic Distances

An S4 class for kernel-based distance tests with normal kernel

Description

A class to represent the results of Gaussian kernel-based quadratic distance tests. This includes the normality test, the two-sample test statistics and the k-sample tests.

Slots

method

String indicating the kernel-based quadratic distance test performed.

Un

The value of the test U-statistic.

Vn

The value of the test V-statistic.

H0_Un

A logical value indicating whether or not the null hypothesis is rejected according to U-statistic.

H0_Vn

A logical value indicating whether or not the null hypothesis is rejected according to Vn.

data

List of samples X (and Y).

CV_Un

The critical value computed for the test Un.

CV_Vn

The critical value computed for the test Vn.

cv_method

The method used to estimate the critical value (one of "subsampling", "permutation" or "bootstrap").

h

A list with the value of bandwidth parameter used for the Gaussian kernel. If the function select_h is used, then also the matrix of computed power values and the resulting power plot are provided.

B

Number of bootstrap/permutation/subsampling replications.

var_Un

Exact variance of the kernel-based U-statistic.

See Also

kb.test() for the function that generates this class.

Examples

# create a kb.test object
x <- matrix(rnorm(100), ncol = 2)
y <- matrix(rnorm(100), ncol = 2)
# Normality test
kb.test(x, h = 0.5)

# Two-sample test
kb.test(x, y, h=0.5, method = "subsampling", b = 0.9)

QuadratiK documentation built on April 12, 2025, 2 a.m.