kneedle: Kneedle Algorithm: to detect elbow point(s) on the curve

In likelet/lassoBag: Variable Selection Oriented LASSO Bagging Algorithm

Kneedle Algorithm: to detect elbow point(s) on the curve

Description

An internal function utilized by VSOLassoBag.

Usage

kneedle(res, S = 10, auto.loose = TRUE, min.S = 0.1, loosing.factor = 0.5)

Arguments

res	a dataframe with variables and observed frequency
S	numeric, determines how aggressive the elbow points on the curve to be called, smaller means more aggressive and larger means more conservative
auto.loose	if TRUE, will reduce 'kneedle.S' in case no elbow point is found with the set 'kneedle.S'
min.S	a numeric value determines the minimal value that 'kneedle.S' will be loosed to.
loosing.factor	a numeric value range in (0,1), which 'kneedle.S' is multiplied by to reduce itself.

Value

the original input dataframe along with the elbow point indicator "elbow.point" with elbow point(s) marked with "*", "Diff" the difference curve, "Thres" the threshold.

References

Original Kneedle Algorithm, the algorithm utilized in LassoBag has been modified.

Examples

load(system.file("extdata/Results.RData", package="VSOLassoBag"))
kneedle(Results)

likelet/lassoBag documentation built on July 19, 2023, 10:36 a.m.