xor3Data: Generation of a Checkerboard Like Classification Problem with...

In locClassData: Collection of Artificial Classification Problems

Description

Generation of a checkerboard like classification problem with 3 classes.

Usage

  xor3Data(n, prior = rep(1/3, 3), lambda = rep(1/3, 3),
    mu11 = c(-4, 4), mu12 = c(0, -4), mu13 = c(4, 0),
    mu21 = c(-4, 0), mu22 = c(0, 4), mu23 = c(4, -4),
    mu31 = c(-4, -4), mu32 = c(0, 0), mu33 = c(4, 4),
    sigma = diag(2))

  xor3Labels(data, prior = rep(1/3, 3),
    lambda = rep(1/3, 3), mu11 = c(-4, 4), mu12 = c(0, -4),
    mu13 = c(4, 0), mu21 = c(-4, 0), mu22 = c(0, 4),
    mu23 = c(4, -4), mu31 = c(-4, -4), mu32 = c(0, 0),
    mu33 = c(4, 4), sigma = diag(2))

  xor3Posterior(data, prior = rep(1/3, 3),
    lambda = rep(1/3, 3), mu11 = c(-4, 4), mu12 = c(0, -4),
    mu13 = c(4, 0), mu21 = c(-4, 0), mu22 = c(0, 4),
    mu23 = c(4, -4), mu31 = c(-4, -4), mu32 = c(0, 0),
    mu33 = c(4, 4), sigma = diag(2))

  xor3BayesClass(data, prior = rep(1/3, 3),
    lambda = rep(1/3, 3), mu11 = c(-4, 4), mu12 = c(0, -4),
    mu13 = c(4, 0), mu21 = c(-4, 0), mu22 = c(0, 4),
    mu23 = c(4, -4), mu31 = c(-4, -4), mu32 = c(0, 0),
    mu33 = c(4, 4), sigma = diag(2))

Arguments

n	Number of observations.
prior	Vector of class prior probabilities.
lambda	The conditional probabilities for the mixture components given the class. Either a vector (if the same number m of mixture components is desired for each class and the conditional probabilities for each class should be equal) or a list as long as the number of classes containing one vector of probabilities for every class. The length of the k-th element is the desired number of mixture components for the k-th class.
mu11	Class center of first class, a vector.
mu12	Class center of first class, a vector.
mu13	Class center of first class, a vector.
mu21	Class center of second class, a vector.
mu22	Class center of second class, a vector.
mu23	Class center of second class, a vector.
mu31	Class center of second class, a vector.
mu32	Class center of second class, a vector.
mu33	Class center of second class, a vector.
sigma	Covariance matrix for classes 1, 2, and 3.
data	A data.frame.

Value

xor3Data returns an object of class "locClass", a list with components:

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

xor3Labels returns a factor of class labels.

xor3Posterior returns a matrix of posterior probabilities.

xor3BayesClass returns a factor of Bayes predictions.

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