fmmt: Fitting Finite Mixtures of Unrestricted Multivariate t...

In EMMIXuskew: Fitting Unrestricted Multivariate Skew t Mixture Models

Description

Computes maximum likelihood estimators (MLE) for finite mixtures of multivariate t (FM-MT) model via the EM algorithm.

Usage

fmmt(g = 1, dat, initial = NULL, known = NULL, itmax = 100,
    eps = 1e-03, nkmeans=20, print = T)
## S3 method for class 'fmmt'
summary(object, ...)
## S3 method for class 'fmmt'
print(x, ...)

Arguments

object, x	an object class of class "fmmt", i.e. a fitted model.
g	a scalar specifying the number of components in the mixture model
dat	the data matrix giving the coordinates of the point(s) where the density is evaluated. This is either a vector of length p or a matrix with p columns.
initial	(optional) a list containing the initial parameters of the mixture model. See the 'Details' section. The default is NULL.
known	(optional) a list containing parameters of the mixture model that are known and not required to be estimated. See the 'Details' section. The default is NULL.
itmax	(optional) a positive integer specifying the maximum number of EM iterations to perform. The default is 100.
eps	(optional) a numeric value used to control the termination criteria for the EM loops. It is the maximum tolerance for the absolute difference between the log-likelihood value and the asymptotic log likelihood value. The default is 1e-6.
nkmeans	(optional) a numeric value indicating how many k-means trials to be used when searching for initial values. The default is 20.
print	(optional) a logical value. If TRUE, output for each iteration will be printed out. if FALSE, no output is printed. The default is TRUE. See the 'Details' section.
...	not used.

Details

The arguments init and known, if specified, is a list structure containing at least one of mu, sigma, delta, dof, pro (See dfmmst for the structure of each of these elements). If init=FALSE (default), the program uses an automatic approach based on k-means clustering to generate an initial value for the model parameters.

Value

mu	a list of g numeric matrices containing the location parameter for each component.
sigma	a list of g numeric matrices containing the scale parameter for each component.
dof	a numeric vector of length g representing the degrees of freedom for each component.
pro	a vector of length of g specifying the mixing proportions for each component.
tau	an g by n matrix of posterior probability of component membership.
clusters	a vector of length n of final partition.
loglik	the final log likelihood value.
lk	a vector of log likelihood values at each EM iteration.
iter	number of iterations performed.
eps	the final absolute difference between the log likelihood value and the asymptotic log likelihood value.
aic, bic	Akaike Information Criterion (AIC), Bayes Information Criterion (BIC)

References

McLachlan G.J. and Krishnan T. (2008). The EM Algorithm and Extensions (2nd). New Jersey: Wiley.

McLachlan G.J. and Peel D. (2000). Finite Mixture Models. New York: Wiley.

See Also

rfmmst, dfmmst, fmmst.contour.2d

Examples

#a short demo using AIS data
data(ais)
Fit <- fmmt(2, ais[,c(2,12)], itmax=10)
summary(Fit)
print(Fit)

EMMIXuskew documentation built on May 29, 2017, 11:25 p.m.