Description Usage Arguments Details Value Note Author(s) References Examples
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.
1 2 3 4 5 6 7 8 9 10 | 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)
|
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. |
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.
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 |
This function does not consider selection of covariates.
Clecio Ferreira, Diego Gallardo and Camila Zeller
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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.
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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