IdtMxNDE-class: Class IdtMxNDE

Description

IdtMxNDE contains the results of a mixture Normal model maximum likelihood parameter estimation, with the four different possible variance-covariance configurations.

Arguments

Slots

Hmcdt:: Indicates whether we consider an homocedastic (TRUE) or a hetereocedasic model (FALSE)
mleNmuE:: Matrix with the maximum likelihood mean vectors estimates by group (each row refers to a group)
mleNmuEse:: Matrix with the maximum likelihood means' standard errors by group (each row refers to a group)
CovConfCases:: List of the considered configurations
grouping:: Inherited from class IdtMxE. Factor indicating the group to which each observation belongs to
ModelNames:: Inherited from class IdtE. The model acronym formed by a "N", indicating a Normal model, followed by the configuration (Case 1 through Case 4)
ModelType:: Inherited from class IdtE. Indicates the model; always set to "Normal" in objects of the IdtMxNDE class
ModelConfig:: Inherited from class IdtE. Configuration case of the variance-covariance matrix: Case 1 through Case 4
NIVar:: Inherited from class IdtE. Number of interval variables
SelCrit:: Inherited from class IdtE. The model selection criterion; currently, AIC and BIC are implemented
logLiks:: Inherited from class IdtE. The logarithms of the likelihood function for the different cases
AICs:: Inherited from class IdtE. Value of the AIC criterion
BICs:: Inherited from class IdtE. Value of the BIC criterion
BestModel:: Inherited from class IdtE. Indicates the best model according to the chosen selection criterion
SngD:: Inherited from class IdtE. Boolean flag indicating whether a single or a mixture of distribution were estimated. Always set to FALSE in objects of class IdtMxNDE
Ngrps:: Inherited from class IdtMxE. Number of mixture components

Extends

Class IdtMxE, directly. Class IdtE, by class IdtMxE, distance 2.

Methods

lda: signature(x = "IdtMxtNDE"): Linear Discriminant Analysis using the estimated model parameters.
qda: signature(x = "IdtMxtNDE"): Quadratic Discriminant Analysis using the estimated model parameters.

Author

Pedro Duarte Silva <psilva@porto.ucp.pt>
Paula Brito <mpbrito.fep.up.pt>

References

Brito, P., Duarte Silva, A. P. (2012), Modelling Interval Data with Normal and Skew-Normal Distributions. Journal of Applied Statistics 39(1), 3--20.