skewMLRM (version 1.6)

plot.skewMLRM: Plot an object of the "skewMLRM" class produced with the function distMahal.

Description

Plot the Mahalanobis distance for a object of the class "skewMLRM" produced by the function distMahal.

Usage

# S3 method for skewMLRM
plot(x, ...)

Value

A complete summary for the coefficients extracted from a skewMLRM object.

Arguments

x

an object of the class "skewMLRM" produced by the function distMahal.

...

for graphical extra arguments

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.

The functions which generate an object of the class "skewMLRM" are

estimate.xxx: where xxx can be MN, MT, MSL, MCN, MSN, MSTN, MSSL, MSCN, MSTT, MSSL2, MSCN2, MSNC, MSTEC, MSSLEC or MSCEC.

choose.yyy: where yyy can be MSMN, MSSMN, MSMSN, MSMSNC or models.

chose2, mbackcrit and mbacksign.

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

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
# \donttest{
fit.MN=estimate.MN(y, X)   #Fit the MN distribution 
res.MN=distMahal(fit.MN)   #Compute the Mahalanobis distances
plot(res.MN)               #Plot the Mahalanobis distances 
#
fit.MSN=estimate.MSN(y, X)  #Fit the MSN distribution 
res.MSN=distMahal(fit.MSN)  #Compute the Mahalanobis distances
plot(res.MSN)               #Plot the Mahalanobis distances 
# }