Description Usage Arguments Value
Generation of an xor like classification problem.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | xorData(n, prior = rep(0.5, 2), lambda = rep(0.5, 2),
mu11 = c(2, 2), mu12 = c(-2, -2), mu21 = c(-2, 2),
mu22 = c(2, -2), sigma = diag(2))
xorLabels(data, prior = rep(0.5, 2),
lambda = rep(0.5, 2), mu11 = c(2, 2), mu12 = c(-2, -2),
mu21 = c(-2, 2), mu22 = c(2, -2), sigma = diag(2))
xorPosterior(data, prior = rep(0.5, 2),
lambda = rep(0.5, 2), mu11 = c(2, 2), mu12 = c(-2, -2),
mu21 = c(-2, 2), mu22 = c(2, -2), sigma = diag(2))
xorBayesClass(data, prior = rep(0.5, 2),
lambda = rep(0.5, 2), mu11 = c(2, 2), mu12 = c(-2, -2),
mu21 = c(-2, 2), mu22 = c(2, -2), sigma = diag(2))
|
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. |
mu21 |
Class center of second class, a vector. |
mu22 |
Class center of second class, a vector. |
sigma |
Covariance matrix for class 1 and 2. |
data |
A |
xorData
returns an object of class
"locClass"
, a list with components:
x |
(A matrix.) The explanatory variables. |
y |
(A factor.) The class labels. |
xorLabels
returns a factor of class labels.
xorPosterior
returns a matrix of posterior
probabilities.
xorBayesClass
returns a factor of Bayes
predictions.
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