Description Slots Extends Methods Author(s) References See Also

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

`Hmcdt`

:Indicates whether we consider an homocedastic (TRUE) or a hetereocedasic model (FALSE)

`RobNmuE`

:Matrix with the robust mean vectors estimates 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 IdtMxNDRE 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 IdtMxNDRE`Ngrps`

:Inherited from class

`IdtMxE`

. Number of mixture components`rawSet`

A vector with the trimmed subset elements used to compute the raw (not reweighted) MCD covariance estimate for the chosen configuration.

`RewghtdSet`

A vector with the final trimmed subset elements used to compute the fasttle estimates.

`RobMD2`

A vector with the robust squared Mahalanobis distances used to select the trimmed subset.

`cnp2`

A vector of length two containing the consistency correction factor and the finite sample correction factor of the final estimate of the covariance matrix.

`raw.cov`

A matrix with the raw MCD estimator used to compute the robust squared Mahalanobis distances of RobMD2.

`raw.cnp2`

A vector of length two containing the consistency correction factor and the finite sample correction factor of the raw estimate of the covariance matrix.

`PerfSt`

A a list with the following components:

**RepSteps**: A list with one component by Covariance Configuration, containing a vector with the number of refinement steps performed by the fasttle algorithm by replication.

**RepLogLik**: A list with one component by Covariance Configuration, containing a vector with the best log-likelihood found be fasttle algorithm by replication.

**StpLogLik**: A list with one component by Covariance Configuration, containing a matrix with the evolution of the log-likelihoods found be fasttle algorithm by replication and refinement step.

Class `IdtMxE`

, directly.
Class `IdtE`

, by class `IdtMxE`

, distance 2.

No methods defined with class IdtMxNDRE in the signature.

Pedro Duarte Silva <[email protected]>

Paula Brito <mpbrito.fep.up.pt>

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

Duarte Silva, A.P., Filzmoser, P. and Brito, P. (2017), Outlier detection in interval data. *Advances in Data Analysis and Classification*, 1–38.

`IdtE`

, `IdtMxE`

, `IdtMxNDE`

, `IdtSngNDRE`

, `RobMxtDEst`

, `IData`

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