find_outmost_partitioned_convexhull_points {archetypal} | R Documentation |
Function which finds the outermost convex hull points after making np samples and finding convex hull for each of them.
Description
Function which finds the outermost convex hull points after making
np
samples and finding 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:
outmost, the first kappas most frequent outermost points as rows of data frame
outmostall, all the outermost points that have been found as rows of data frame
outmostfrequency, a matrix with frequency and cumulative frequency for outermost rows
See Also
find_furthestsum_points
, find_outmost_projected_convexhull_points
,
find_outmost_convexhull_points
& find_outmost_points
Examples
data("wd2") #2D demo
df = wd2
yy = find_outmost_partitioned_convexhull_points(df, kappas = 3, nworkers = 2)
yy$outmost #the rows of 3 outermost points
df[yy$outmost,] #the 3 outermost points
yy$outmostall #all outermost rows
yy$outmostfrequency #their frequency
[Package archetypal version 1.3.1 Index]