Learn R Programming

⚠️There's a newer version (1.3.1) of this package.Take me there.

Overview

archetypal is a package for performing Archetypal Analysis (AA) by using a properly modified version of PCHA algorithm.

Basic functions are:

  • archetypal() do AA
  • find_outmost_projected_convexhull_points Projected CH initial solution.
  • find_outmost_convexhull_points CH initial solution.
  • find_outmost_partitioned_convexhull_points() Partitioned CH initial solution.
  • find_furthestsum_points() Furthest Sum initial solution.
  • find_outmost_points() Outmost initial solution.
  • find_optimal_kappas() search for the optimal number of archetypes
  • find_pcha_optimal_parameters() search for the optimal updating parameters of PCHA algorithm
  • check_Bmatrix() check B matrix after run of AA.
  • study_AAconvergence() study the convergence of PCHA algorithm
  • find_closer_points() find the closer to archetypes data points

Install the archetypal package and then read vignette("archetypal", package = "archetypal").

Installation

# Install with dependencies:
install.packages("archetypal",dependencies=TRUE)

Usage

library(archetypal)

data("wd2")
df = wd2
aa = archetypal(df = df, kappas = 3,verbose = FALSE, rseed = 9102)

# Time for computing Projected Convex Hull was 0.01 secs 
# Next projected convex hull initial solution will be used... 
#           x        y
# 34 5.687791 3.481611
# 62 1.961799 2.793497
# 5  5.123878 2.745874
# 
# archs=aa$BY
# archs
# x        y
# [1,] 5.430757 3.146258
# [2,] 2.043435 2.710947
# [3,] 3.128401 4.781751
# aa[c("SSE","varexpl","iterations","time" )]
# $SSE
# [1] 1.717538
# 
# $varexpl
# [1] 0.9993186
# 
# $iterations
# [1] 63
# 
# $time
# [1] 8.1
# cbind(names(aa))
# [,1]             
# [1,] "BY"             
# [2,] "A"              
# [3,] "B"              
# [4,] "SSE"            
# [5,] "varexpl"        
# [6,] "initialsolution"
# [7,] "freqstable"     
# [8,] "iterations"     
# [9,] "time"           
# [10,] "converges"      
# [11,] "nAup"           
# [12,] "nAdown"         
# [13,] "nBup"           
# [14,] "nBdown"         
# [15,] "run_results"   

Contact

Please send comments, suggestions or bug reports to dchristop@econ.uoa.gr or dem.christop@gmail.com

Copy Link

Version

Install

install.packages('archetypal')

Monthly Downloads

277

Version

1.1.1

License

GPL (>= 2)

Maintainer

Demetris Christopoulos

Last Published

October 19th, 2020

Functions in archetypal (1.1.1)

align_archetypes_from_list

Align archetypes from a list either by the most frequent found or by using a given archetype
archetypal

archetypal: Finds the archetypal analysis of a data frame by using a variant of the PCHA algorithm
find_furthestsum_points

Function which finds the furthest sum points in order to be used as initial solution in archetypal analysis
AbsoluteTemperature

Global Absolute Temperature data set for Northern Hemisphere 1969-2013
find_optimal_kappas

Function for finding the optimal number of archetypes
check_Bmatrix

Function which checks B matrix of Archetypal Analysis Y ~ A B Y in order to find the used rows for creating each archetype and the relevant used weights.
FurthestSum

Application of FurthestSum algorithm in order to find an initial solution for Archetypal Analysis
find_closer_points

Function which finds the data points that are closer to the archetypes during all iterations of the algorithm PCHA
dirichlet_sample

Function which performs Dirichlet sampling
archetypal-package

Finds the Archetypal Analysis of a Data Frame
grouped_resample

Function for performing simple or Dirichlet resampling
wd25

2D data set created by 5 points for demonstration purposes
wd2

2D data set for demonstration purposes
study_AAconvergence

Function which studies the convergence of Archetypal Analysis when using the PCHA algorithm
wd3

3D data set for demonstration purposes
find_outmost_projected_convexhull_points

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

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

Finds the optimal updating parameters to be used for the PCHA algorithm
gallupGPS6

Gallup Global Preferences Study processed data set of six variables
find_outmost_partitioned_convexhull_points

Function which finds the outermost convex hull points after making np samples and finding convex hull for each of them.
find_outmost_convexhull_points

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