IdtSNlocda-class: Class "IdtSNlocda"

In MAINT.Data: Model and Analyse Interval Data

Class "IdtSNlocda"

Description

IdtSNlocda contains the results of Discriminant Analysis for the interval data, based on a location Skew-Normal model.

Slots

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:

Vector with the direct scaled skewness parameter ("eta") estimates.

scaling:

Matrix which transforms observations to discriminant functions, normalized so that the within groups scale-association matrix ("Omega") is spherical.

mu:

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

gamma1:

Vector with the centred skewness parameter ("gamma1") estimates.

N:

Number of observations.

CovCase:

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

Methods

predict

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

show

signature(object = "IdtSNlocda"): show S4 method for the IDdtlda-class

CovCase

signature(object = "IdtSNlocda"): Returns the configuration case of the variance-covariance matrix

Author(s)

Pedro Duarte Silva <[e-mail]>
Paula Brito <mpbrito.fep.up.pt>

References

Azzalini, A. and Dalla Valle, A. (1996), The multivariate skew-normal distribution. Biometrika 83(4), 715–726.

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.

See Also

snda, MANOVA, IData

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