ICSClust (version 0.1.1)

med_crit: Selection of Invariant components using the med criterion

Description

Identifies as interesting invariant coordinates whose generalized eigenvalues are the furthermost away from the median of all generalized eigenvalues.

Usage

med_crit(object, ...)
# S3 method for ICS
med_crit(object, nb_select = NULL, select_only = FALSE, ...)
# S3 method for default
med_crit(object, nb_select = NULL, select_only = FALSE, ...)

Value

If select_only is TRUE a vector of the names of the invariant components or variables to select. If FALSE an object of class "ICS_crit"

is returned with the following objects:

• crit: the name of the criterion "med".

• nb_select: the number of components to select.

• gen_kurtosis: the vector of generalized kurtosis values.

• med_gen_kurtosis: the median of the generalized kurtosis values.

• gen_kurtosis_diff_med: the absolute differences between the generalized kurtosis values and the median.

• select: the names of the invariant components or variables to select.

Arguments

object

object of class "ICS".

...

additional arguments are currently ignored.

nb_select

the exact number of components to select. By default it is set to NULL, i.e the number of components to select is the number of variables minus one.

select_only

boolean. If TRUE only the vector names of the selected invariant components is returned. If FALSE additional details are returned.

Author

Andreas Alfons, Aurore Archimbaud and Klaus Nordhausen

Details

If more than half of the components are "uninteresting" and have the same generalized eigenvalue then the median of all generalized eigenvalues corresponds to the uninteresting component generalized eigenvalue. The components of interest are the ones whose generalized eigenvalues differ the most from the median. The motivation of this criterion depends therefore on the assumption that at least half of the components have equal generalized eigenvalues.

References

Alfons, A., Archimbaud, A., Nordhausen, K., & Ruiz-Gazen, A. (2024). Tandem clustering with invariant coordinate selection. Econometrics and Statistics. tools:::Rd_expr_doi("10.1016/j.ecosta.2024.03.002").

See Also

normal_crit(), var_crit(), discriminatory_crit().

Examples

X <- iris[,-5]
out <- ICS(X)
med_crit(out, nb_select = 2, select_only = FALSE)