ssmn.est: EM algorithm for Skew Scale Mixtures of Normal Distributions

In lbenitesanchez/ssmn: Skew Scale Mixtures of Normal Distributions

Description

Performs the EM algorithm and envelope for regression models using Skew Scale Mixtures of Normal Distributions

Usage

ssmn(y, X, family="sn", method="EM", error =  1e-6, maxit=1000, show.envelope=FALSE)
envel(y,X, theta, family="sn", alpha=0.05)

Arguments

y	the response vector of length n where n is the total of observations.
X	the matrix of explanatory variables of dimension n x (p+1) where n is the total of observations and p is the number of variables.
family	its defines the distribution to ber used: sn, stn, ssl, scn or sep.
method	the method to calculate the maximum likelihood estimates: EM algorithm or direct maximum likelihood estimates via Newton-Raphson.
maxit	Maximum number of iterations.
error	accuracy the convergence maximum error.
show.envelope	TRUE or FALSE. Indicates if envelope graph should be built for the fitted model. Default is FALSE.
alpha	1 - alpha is level of confidence.
theta	Estimated parameter vector

Value

The function returns a list with 8 elements detailed as

iter	number of iterations.
tetha	estimated parameter vector.
SE	Standard Error estimates.
table	Table containing the inference for the estimated parameters.
loglik	Log-likelihood value.
AIC	Akaike information criterion.
BIC	Bayesian information criterion.
time	processing time.

lbenitesanchez/ssmn documentation built on May 9, 2019, 12:49 p.m.