IdtSngNDE-class: Class IdtSngNDE

In MAINT.Data: Model and Analyse Interval Data

Class IdtSngNDE

Description

Contains the results of a single class maximum likelihood estimation for the Normal distribution, with the four different possible variance-covariance configurations.

Slots

mleNmuE:

Vector with the maximum likelihood mean vectors estimates

mleNmuEse:

Vector with the maximum likelihood means' standard errors

CovConfCases:

List of the considered configurations

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 IdtSngNDE class

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 IdtSngNDE

Extends

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

Methods

No methods defined with class IdtSngNDE 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, mle, IdtSngNDRE, IdtSngSNDE, IdtMxNDE

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