IdtSNgenda contains the results of discriminant analysis for the interval data, based on a general Skew-Normal model.

`prior`

:Prior probabilities of class membership; if unspecified, the class proportions for the training set are used; if present, the probabilities should be specified in the order of the factor levels.

`ksi`

:Matrix with the direct location parameter ("ksi") estimates for each group.

`eta`

:Matrix with the direct scaled skweness parameter ("eta") estimates for each group.

`scaling`

:For each group g, scaling[,,g] is a matrix which transforms interval-valued observations so that in each group the scale-association matrix ("Omega") is spherical.

`mu`

:Matrix with the centred location parameter ("mu") estimates for each group.

`gamma1`

:Matrix with the centred skweness parameter ("gamma1") estimates for each group.

`ldet`

:Vector of half log determinants of the dispersion matrix.

`lev`

:Levels of the grouping factor.

`CovCase`

:Configuration case of the variance-covariance matrix: Case 1 through Case 4

- predict
`signature(object = "IdtSNgenda")`

: Classifies interval-valued observations in conjunction with snda.- show
`signature(object = "IdtSNgenda")`

: show S4 method for the IdtSNgenda-class- CovCase
`signature(object = "IdtSNgenda")`

: Returns the configuration case of the variance-covariance matrix

Pedro Duarte Silva <psilva@porto.ucp.pt>

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. and Brito, P. (2015), Discriminant analysis of interval data: An assessment of parametric and distance-based approaches. *Journal of Classification* **39**(3), 516–541.

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