FOP: Fisher optimal partition

In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

Fisher optimal partition

Description

The Fisher optimal partition algorithm.

Usage

FOP(X, k, C)

Arguments

X	A set of samples.
k	Number of classes.
C	Statistic from the output of dlc().

Value

Index vector for each sample's classification.

References

W. D. Fisher (1958). On grouping for maximum homogeneity. Journal of the American Statistical Association, vol. 53, pp. 789–798.

Examples

X <- matrix(c(
  9.3, 1.8, 1.9, 1.7, 1.5, 1.3,
  1.4, 2.0, 1.9, 2.3, 2.1
))
C <- dlc(X, maxk = 8)$C
F <- FOP(X, 8, C)

OHPL documentation built on Sept. 11, 2024, 7:05 p.m.