twonormQuadraticData: Generate a Binary Classification Problem with Two Gaussian...

In locClassData: Collection of Artificial Classification Problems

Description

Generate a binary classification problem with two Gaussian distributions with different means and covariance matrices.

Usage

  twonormQuadraticData(n, prior = rep(0.5, 2),
    mu1 = c(1, 0), mu2 = c(0, -1), sigma1 = diag(2),
    sigma2 = matrix(c(1, 0.5, 0.5, 1), 2))

  twonormQuadraticLabels(data, prior = rep(0.5, 2),
    mu1 = c(1, 0), mu2 = c(0, -1), sigma1 = diag(2),
    sigma2 = matrix(c(1, 0.5, 0.5, 1), 2))

  twonormQuadraticPosterior(data, prior = rep(0.5, 2),
    mu1 = c(1, 0), mu2 = c(0, -1), sigma1 = diag(2),
    sigma2 = matrix(c(1, 0.5, 0.5, 1), 2))

  twonormQuadraticBayesClass(data, prior = rep(0.5, 2),
    mu1 = c(1, 0), mu2 = c(0, -1), sigma1 = diag(2),
    sigma2 = matrix(c(1, 0.5, 0.5, 1), 2))

Arguments

n	Number of observations.
prior	Vector of class prior probabilities.
mu1	Class center of first class, a vector.
mu2	Class center of second class, a vector.
sigma1	Covariance matrix for class 1.
sigma2	Covariance matrix for class 2.
data	A data.frame.

Value

twonormQuadraticData returns an object of class "locClass", a list with components:

x	(A matrix.) The explanatory variables.
y	(A factor.) The class labels.

twonormQuadraticLabels returns a factor of class labels.

twonormQuadraticPosterior returns a matrix of posterior probabilities.

twonormQuadraticBayesClass returns a factor of Bayes predictions.

locClassData documentation built on May 2, 2019, 5:26 p.m.