FMsmsnReg: Linear Regression Models with Finite Mixtures of Skew...

In lbenitesanchez/FMsmsnReg: Regression Models with Finite Mixtures of Skew Heavy-Tailed Errors

Description

Performs a Finite Mixture of Scale Mixture Skew Normal Regression Model using EM-type algorithm (ECME) for iteratively computing maximum likelihood estimates of the parameters.

Usage

FMsmsnReg(y, x1, Abetas = NULL, medj= NULL, sigma2 = NULL, shape = NULL,
pii = NULL, g = NULL, get.init = TRUE, criteria = TRUE, group = FALSE,
family = "Skew.normal", error = 0.00001, iter.max = 100, obs.prob= FALSE,
kmeans.param = NULL, show.convergence=TRUE, cp=0.4)

Arguments

y	the response matrix (dimension nx1)
x1	Matrix or vector of covariates.
Abetas	Parameters of vector regression dimension (p + 1) include intercept
medj	a list of g arguments of vectors of initial values (dimension p) for the location parameters
sigma2	a list of g arguments of matrices of initial values (dimension pxp) for the scale parameters
shape	a list of g arguments of vectors of initial values (dimension p) for the skewness parameters
pii	Initial value for the EM algorithm. Each of them must be a vector of length g. (the algorithm considers the number of components to be adjusted based on the size of these vectors)
g	the number of cluster to be considered in fitting
get.init	if TRUE, the initial values are generated via k-means
criteria	It indicates if are calculated the criterion selection methods (AIC, BIC, EDC and ICL)
group	if TRUE, the vector with the classification of the response is returned
family	distribution famility to be used in fitting (Skew.t", "Skew.cn", "Skew.slash", "Skew.normal")
error	define the stopping criterion of the algorithm
iter.max	the maximum number of iterations of the EM algorithm
obs.prob	if TRUE, the posterior probability of each observation belonging to one of the g groups is reported
kmeans.param	a list with alternative parameters for the kmeans function when generating initial values, list(iter.max = 10, n.start = 1, algorithm = "Hartigan-Wong")
show.convergence	graphics of convergence for the parameters
cp	Cut Point

Value

The function returns a list with 16 elements detailed as

iter	Number of iterations.
criteria	Attained criteria value.
convergence	Convergence reached or not.
mu	Location parameter estimate.
sigma2	Scale parameter estimate.
lambda	Shape parameter estimate.
pii	Weight parameter estimate.
nu	Estimated degrees of freedom parameter.
SE	Standard Error estimates, if the output shows NA the function does not provide the standard error for this parameter.
table	Table containing the inference for the estimated parameters.
loglik	Log-likelihood value.
AIC	Akaike information criterion.
BIC	Bayesian information criterion.
EDC	Efficient Determination Criterion.
ICL	Information Completed Likelihood.
time	Processing time.

Author(s)

Luis Benites Sanchez [e-mail] and Rocio Paola Maehara [e-mail] and Victor Hugo Lachos [e-mail]

References

Basso, R. . M., Lachos, V. H., Cabral, C. R., Ghosh, P., 2010. Robust mixture modeling based on scale mixtures of skew-normal distributions. Computational Statistics & Data Analysis doi:10.1016/j.csda.2009.09.031.

Lachos, V. H., Ghosh, P., Arellano-Valle, R. B., 2010. Likelihood based inference for skew - normal independent linear mixed models. Statistica Sinica 20, 303 - 322.

See Also

FMsmsnReg, ais, horses

Examples

#See examples for the FMsmsnReg function linked above.

lbenitesanchez/FMsmsnReg documentation built on May 9, 2019, 12:48 p.m.