ssmn.est {ssmn}R Documentation

EM algorithm for 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 nn where nn is the total of observations.

X

the matrix of explanatory variables of dimension nx(p+1)n x (p+1) where nn 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.


[Package ssmn version 1.1 Index]