archetypal (version 1.3.1)

find_outmost_points: Function which finds the outermost points in order to be used as initial solution in archetypal analysis

Description

Function which finds the outermost points in order to be used as initial solution in archetypal analysis

Usage

find_outmost_points(df, kappas)

Value

A list with members:

1. outmost, the first kappas most frequent outermost points as rows of data frame

2. outmostall, all the outermost points that have been found as rows of data frame

3. outmostfrequency, a matrix with frequency and cumulative frequency for outermost rows

Arguments

df

The data frame with dimensions n x d

kappas

The number of archetypes

Warning

This is a rather naive way to find the outermost points of a data frame and it should be used with caution since for a n x d matrix we need in general 8 n^2/(2^30) GB RAM for numeric case. Check your machine and use it. As a rule of thumb we advice its usage for n less or equal than 20000.

See Also

find_furthestsum_points, find_outmost_convexhull_points,

find_outmost_projected_convexhull_points,

and find_outmost_partitioned_convexhull_points

Examples

data("wd2") #2D demo 
df = wd2
yy = find_outmost_points(df,kappas=3)
yy$outmost #the rows of 3 outmost points
yy$outmostall #all outmost found
yy$outmostfrequency #frequency table for all
df[yy$outmost,] #the 3 outmost points
#
###
#
data("wd3") #3D demo 
df = wd3
yy = find_outmost_points(df,kappas=4)
yy$outmost #the rows of 4 outmost points
yy$outmostall #all outmost found
yy$outmostfrequency #frequency table for all
df[yy$outmost,] #the 4 outmost points