rrcovHD (version 0.3-1)

SosDiscRobust-class: Class SosDiscRobust - robust and sparse multigroup classification by the optimal scoring approach

Description

Robust and sparse multigroup classification by the optimal scoring approach.

Objects from the Class

Objects can be created by calls of the form new("SosDiscRobust", ...) but the usual way of creating SosDiscRobust objects is a call to the function SosDiscRobust() which serves as a constructor.

Slots

call:

The (matched) function call.

prior:

Prior probabilities; same as input parameter.

counts:

Number of observations in each class.

beta:

Object of class "matrix": Q coefficient vectors of the predictor matrix from optimal scoring (see Details); rows corespond to variables listed in varnames.

theta:

Object of class "matrix": Q coefficient vectors of the dummy matrix for class coding from optimal scoring (see Details).

lambda:

Non-negative tuning paramer from L1 norm penaly; same as input parameter

varnames:

Character vector: Names of included predictor variables (variables where at least one beta coefficient is non-zero).

center:

Centering vector of the input predictors (coordinate wise median).

scale:

Scaling vector of the input predictors (mad).

fit:

Object of class "Linda": Linda model (robust LDA model) estimated in the low dimensional subspace \(X[\beta_1,...,\beta_Q]\) (see Details)

mahadist2:

These will go later to Linda object: squared robust Mahalanobis distance (calculated with estimates from Linda, with common covariance structure of all groups) of each observation to its group center in the low dimensional subspace \(X[\beta_1,...,\beta_Q]\) (see Details).

wlinda:

These will go later to Linda object: 0-1 weights derived from mahadist2; observations where the squred robust Mahalanobis distance is larger than the 0.975 quantile of the chi-square distribution with Q degrees of freedom resive weight zero.

X:

The training data set (same as the input parameter x of the constructor function)

grp:

Grouping variable: a factor specifying the class for each observation (same as the input parameter grouping)

Extends

Class "SosDisc", directly.

Methods

No methods defined with class "SosDiscRobust" in the signature.

Author

Irene Ortner and Valentin Todorov

References

Clemmensen L, Hastie T, Witten D & Ersboll B (2012), Sparse discriminant analysis. Technometrics, 53(4), 406--413.

Ortner I, Filzmoser P & Croux C (2020), Robust and sparse multigroup classification by the optimal scoring approach. Data Mining and Knowledge Discovery 34, 723--741. tools:::Rd_expr_doi("10.1007/s10618-019-00666-8").

Examples

    showClass("SosDiscRobust")