IdtSngNandSNDE-class: Class IdtSngNandSNDE

In MAINT.Data: Model and Analyse Interval Data

Class IdtSngNandSNDE

Description

IdtSngNandSNDE contains the results of a single class model estimation for the Normal and the Skew-Normal distributions, with the four different possible variance-covariance configurations.

Slots

NMod:

Estimates of the single class model for the Gaussian case

SNMod:

Estimates of the single class model for the Skew-Normal case

ModelNames:

Inherited from class IdtE. The model acronym, indicating the model type (currently, N for Normal and SN for Skew-Normal), and the configuration (Case 1 through Case 4)

ModelType:

Inherited from class IdtE. Indicates the model; currently, Gaussian or Skew-Normal distributions are implemented

ModelConfig:

Inherited from class IdtE. Configuration 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. Bestmodel 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 TRUE in objects of class IdtSngNandSNDE

Extends

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

Methods

No methods defined with class IdtSngNandSNDE in the signature.

Author(s)

Pedro Duarte Silva <[e-mail]>
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.

See Also

IData, IdtMxNandSNDE, mle, fasttle, fulltle

MAINT.Data documentation built on April 4, 2023, 9:09 a.m.