Mixt.fit.init: Initialization for the EM-based algorithms

In SenTinMixt: Parsimonious Mixtures of MSEN and MTIN Distributions

Description

Runs the initialization of the EM-based algorithms used for fitting parsimonious mixtures of MSEN or MTIN distributions. Parallel computing is implemented and highly recommended for a faster calculation.

Usage

Mixt.fit.init(X, k = 1:3, density, nstartR = 100, ncores = 1, verbose = FALSE)

Arguments

X	A data matrix with n rows and d columns, being n the number of data points and d the data the dimensionality.
k	An integer or a vector indicating the number of groups of the models.
density	A character indicating the density of the mixture components. Possible values are: "MSEN" or "MTIN".
nstartR	An integer specifying the number of random starts to be considered.
ncores	A positive integer indicating the number of cores used for running in parallel.
verbose	A logical indicating whether the running output should be displayed.

Value

init	A list of objects to be used by the Mixt.fit() function.

Examples

set.seed(1234)
n <- 50
k <- 2
Pi <- c(0.5, 0.5)
mu <- matrix(c(0, 0, 4, 5), 2, 2)
cov.model <- "EEE"
lambda <- c(0.5, 0.5)
delta <- c(0.7, 0.7)
gamma <- c(2.62, 2.62)
theta <- c(0.1, 0.1)
density <- "MSEN"
data <- rMixt(n, k, Pi, mu, cov.model, lambda, delta, gamma, theta, density)

X <- data$X
nstartR <- 1
init.par <- Mixt.fit.init(X, k, density, nstartR)

SenTinMixt documentation built on Oct. 20, 2021, 9:07 a.m.