dimJump.R {ClustMMDD}R Documentation

Data driven calibration of the penalty function

Description

Data driven calibration of the penalty function using the dimension jump version of the "slope heuristics".

Usage

  dimJump.R(fileOrData, h = integer(), N = integer(), header = logical())

Arguments

fileOrData

A character string or a data frame (see details). If a data frame, it must contain columns named logLik and dim. If a file, it must be as the one produced by backward.explorer.

h

An integer defining the size of the sliding window used to find the biggest jump.

N

The size of the sample data (number of rows).

header

The indication of whether the file contains header or not.

Details

This function is a dimension jump version of the so called slope heuristics for the calibration of penalty function using the data.

Value

Assume that the penalty function is in the form

pen≤ft(K,S\right) = α*λ*dim≤ft(K,S\right)

, where

It returns a list containing two candidate values of λ and their bounds. It also produces a graphic that illustrates the "slope heuristics".

Author(s)

Wilson Toussile

References

See Also

backward.explorer for exploration of competing models space, model.selection.R for final selection.

Examples

# genotype2_ExploredModels was obtained via backward.explorer.
data(genotype2_ExploredModels)
outDimJump = dimJump.R(genotype2_ExploredModels, N = 1000, h = 5, header = TRUE)
outDimJump[[1]]

[Package ClustMMDD version 1.0.4 Index]