bayesclassifier {gmmsslm}

Bayes' rule of allocation

Description

Bayes' rule of allocation

Usage

bayesclassifier(dat, p, g, pi = NULL, mu = NULL, sigma = NULL, paralist = NULL)

Arguments

dat

An n\times p matrix where each row represents an individual observation.

p

Dimension of observation vecor.

g

Number of multivariate normal classes.

pi

A g-dimensional vector for the initial values of the mixing proportions.

mu

A p \times g matrix for the initial values of the location parameters.

sigma

A p\times p covariance matrix,or a list of g covariance matrices with dimension p\times p \times g. It is assumed to fit the model with a common covariance matrix if sigma is a p\times p covariance matrix; otherwise it is assumed to fit the model with unequal covariance matrices.

paralist

A list containing the required parameters (\pi, \mu, \Sigma).

Details

Classifier specified by Bayes' rule

The classifier/Bayes rule of allocation R(y_j;\theta) assigns an entity with observation y_j to class C_k (that is, R(y_j;\theta)=k) if k=\arg\max_i \tau_i(y_j;\theta),

Value

clust

Class membership for the ith entity

Examples

n <- 150
pi <- c(0.25, 0.25, 0.25, 0.25)
sigma <- array(0, dim = c(3, 3, 4))
sigma[, , 1] <- diag(1, 3)
sigma[, , 2] <- diag(2, 3)
sigma[, , 3] <- diag(3, 3)
sigma[, , 4] <- diag(4, 3)
mu <- matrix(c(0.2, 0.3, 0.4, 0.2, 0.7, 0.6, 0.1, 0.7, 1.6, 0.2, 1.7, 0.6), 3, 4)
dat <- rmix(n = n, pi = pi, mu = mu, sigma = sigma)
params <- list(pi=pi,mu = mu, sigma = sigma)
clust <- bayesclassifier(dat=dat$Y,p=3,g=4,paralist=params)