archetypal (version 1.0.0)

find_outmost_partitioned_convexhull_points: Function which finds the outmost convex hull points after making np samples and findix convex hull for each of them. To be used as initial solution in archetypal analysis.

Description

Function which finds the outmost convex hull points after making np samples and findix convex hull for each of them. To be used as initial solution in archetypal analysis

Usage

find_outmost_partitioned_convexhull_points(df, kappas, np = 10,
  nworkers = NULL)

Arguments

df

The data frame with dimensions n x d

kappas

The number of archetypes

np

The number of partitions that will be used (or the number of samples)

nworkers

The number of logical processors that will be used

Value

A list with members:

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

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

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

See Also

find_furthestsum_points, find_outmost_projected_convexhull_points,

find_outmost_convexhull_points & find_outmost_points

Examples

# NOT RUN {
data("wd2") #2D demo 
df=wd2
yy=find_outmost_partitioned_convexhull_points(df,kappas=3,nworkers = 1)
yy$outmost #the rows of 3 outmost points
df[yy$outmost,] #the 3 outmost points
yy$outmostall #all outmost rows
yy$outmostfrequency #their frequency
# }