FOP: Fisher Optimal Partition

In OHPL: Ordered Homogeneity Pursuit Lasso for Group Variable Selection

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)

Example output

OHPL documentation built on May 18, 2019, 9:03 a.m.