m.step: M-step of the EM algorithm for Parsimonious Normal Mixtures

Description Usage Arguments Value Author(s) References See Also Examples

Description

Carries out the M-step for EM algorithm

Usage

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m.step(X, modelname, z, mtol=1e-10, mmax=10)

Arguments

X

a matrix such that n rows correspond to observations and p columns correspond to variables.

modelname

A three letter sequence indicating the covariance structure. Possible values are: "EII", "VII", "EEI", "VEI", "EVI", "VVI", "EEE", "VEE", "EVE", "EEV", "VVE", "VEV", "EVV", "VVV".

z

A matrix of weights such that n rows correspond to observations and G columns correspond to groups.

mtol

The convergence criteria for the M-step if an iterative procedure is necessary.

mmax

The maximum number of iterations for an iterative procedure.

Value

A list of the model parameters with the mu, Sigma, invSigma and px for each group.

Author(s)

Antonio Punzo, Angelo Mazza, Paul D. McNicholas

References

Punzo A., Mazza A. and McNicholas P. D. (2018). ContaminatedMixt: An R Package for Fitting Parsimonious Mixtures of Multivariate Contaminated Normal Distributions. Journal of Statistical Software, 85(10), 1–25.

Punzo A. and McNicholas P. D. (2016). Parsimonious mixtures of multivariate contaminated normal distributions. Biometrical Journal, 58(6), 1506–1537.

See Also

ContaminatedMixt-package

Examples

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point <- c(0,0,0)
mu <- c(1,-2,3)
Sigma <- diag(3)
alpha <- 0.8
eta <- 5
f <- dCN(point, mu, Sigma, alpha, eta)
x <- rCN(10, mu, Sigma, alpha, eta)

ContaminatedMixt documentation built on May 2, 2019, 8:22 a.m.