Description Objects from the Class Slots Extends Methods Author(s) References Examples

Sparse multigroup classification by the optimal scoring approach.

Objects can be created by calls of the form `new("SosDiscClassic", ...)`

but the
usual way of creating `SosDiscClassic`

objects is a call to the function
`SosDiscRobust()`

which serves as a constructor.

`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[β_1,...,β_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[β_1,...,β_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`

)

Class `"SosDisc"`

, directly.

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

Irene Hoffmann [email protected] and Valentin Todorov [email protected]

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

Hoffmann I, Filzmoser P & Croux C (2016), Robust and sparse multigroup classification by the optimal scoring approach. Submitted for publication.

1 | ```
showClass("SosDiscClassic")
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.