chooseM: Choose a distribution in the MSMN, MSMSN, MSSMN and/or MSMSNC classes

Description

choose.xxx select a model inside the xxx class, where xxx is the multivariate scale mixtures of normal (MSMN), the multivariate scale mixtures of skew-normal (MSMSN), the multivariate skew scale mixtures of normal (MSSMN) or the multivariate scale mixtures of skew-normal-Cauchy (MSMSNC) classes. See details for supported distributions within each class. choose.models select a model among the MSMN, MSMSN, MSSMN and MSMSNC classes.

Usage

choose.MSMN(y, X = NULL, max.iter = 1000, prec = 1e-4, 
        est.var = TRUE, criteria = "AIC", cluster = FALSE)
choose.MSMSN(y, X = NULL, max.iter = 1000, prec = 1e-4, 
        est.var = TRUE, criteria = "AIC", cluster = FALSE)
choose.MSSMN(y, X = NULL, max.iter = 1000, prec = 1e-4, 
        est.var = TRUE, criteria = "AIC", cluster = FALSE)
choose.MSMSNC(y, X = NULL, max.iter = 1000, prec = 1e-4, 
        est.var = TRUE, criteria = "AIC", cluster = FALSE)
choose.models(y, X = NULL, max.iter = 1000, prec = 1e-4, 
        est.var = TRUE, criteria = "AIC", cluster = FALSE)

Value

an object of class "skewMLRM" is returned. The object returned for this functions is a list containing the following components:

coefficients: A named vector of coefficients
se: A named vector of the standard errors for the estimated coefficients. Valid if est.var is TRUE and the hessian matrix is invertible.
logLik: The log-likelihood function evaluated in the estimated parameters for the selected model
AIC: Akaike's Information Criterion for the selected model
BIC: Bayesian's Information Criterion for the selected model
iterations: the number of iterations until convergence (if attached)
conv: An integer code for the selected model. 0 indicates successful completion. 1 otherwise.
dist: The distribution for which was performed the estimation.
class: The class for which was performed the estimation.
function: a string with the name of the used function.
choose.crit: the specified criteria to choose the distribution.
y: The multivariate vector of responses. The univariate case also is supported.
X: The regressor matrix (in a list form).
fitted.models: A vector with the fitted models
selected.model: Selected model based on the specified criteria.
comment: A comment indicating how many coefficients were eliminated

Arguments

y: The multivariate vector of responses. The univariate case also is supported.
X: The regressor matrix.
max.iter: The maximum number of iterations.
prec: The convergence tolerance for parameters.
est.var: Logical. If TRUE the standard errors are estimated.
criteria: criteria to perform the selection model: AIC (default) or BIC.
cluster: logical. If TRUE, parallel computing is used. FALSE is the default value.

Author

Clecio Ferreira, Diego Gallardo and Camila Zeller

Details

Supported models are:

In MSMN class: multivariate normal (MN), multivariate Student t (MT), multivariate slash (MSL), multivariate contaminated normal (MCN). See Lange and Sinsheimer (1993) for details.

In MSMSN class: multivariate skew-normal (MSN), multivariate skew-T (MSTT), multivariate skew-slash (MSSL2), multivariate skew-contaminated normal (MSCN2). See Zeller, Lachos and Vilca-Labra (2011) for details.

In MSSMN class: MSN, multivariate skew-t-normal (MSTN), multivariate skew-slash normal (MSSL), multivariate skew-contaminated normal (MSCN). See Louredo, Zeller and Ferreira (2021) for details.

In MSMSNC class: multivariate skew-normal-Cauchy (MSNC), multivariate skew-t-Expected-Cauchy (MSTEC), multivariate skew-slash-Expected-Cauchy (MSSLEC), multivariate skew-contaminated-Expected-Cauchy (MSCEC). See Kahrari et al. (2020) for details.

Note: the MSN distribution belongs to both, MSMSN and MSSMN classes.

References

Kahrari, F., Arellano-Valle, R.B., Ferreira, C.S., Gallardo, D.I. (2020) Some Simulation/computation in multivariate linear models of scale mixtures of skew-normal-Cauchy distributions. Communications in Statistics - Simulation and Computation. In press. DOI: 10.1080/03610918.2020.1804582

Lange, K., Sinsheimer, J.S. (1993). Normal/independent distributions and their applications in robust regression. Journal of Computational and Graphical Statistics 2, 175-198.

Louredo, G.M.S., Zeller, C.B., Ferreira, C.S. (2021). Estimation and influence diagnostics for the multivariate linear regression models with skew scale mixtures of normal distributions. Sankhya B. In press. DOI: 10.1007/s13571-021-00257-y

Zeller, C.B., Lachos, V.H., Vilca-Labra, F.E. (2011). Local influence analysis for regression models with scale mixtures of skew-normal distributions. Journal of Applied Statistics 38, 343-368.

Examples

Run this code

data(ais, package="sn") ##Australian Institute of Sport data set
attach(ais)
##It is considered a bivariate regression model
##with Hg and SSF as response variables and
##Hc, Fe, Bfat and LBM as covariates
y<-cbind(Hg,SSF)
n<-nrow(y); m<-ncol(y)
X.aux=model.matrix(~Hc+Fe+Bfat+LBM)
p<-ncol(X.aux)
X<-array(0,dim=c(2*p,m,n))
for(i in 1:n) {
    X[1:p,1,i]=X.aux[i,,drop=FALSE]
    X[p+1:p,2,i]=X.aux[i,,drop=FALSE]
}
##See the covariate matrix X
##X
##Select a distribution within the MSMN class.
# \donttest{
fit.MSMN=choose.MSMN(y,X)
summary(fit.MSMN)
##Identical process within the MSSMN class.
##may take some time on some systems
fit.MSSMN=choose.MSSMN(y,X)
summary(fit.MSSMN)
# }

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