archetypal is a package for performing Archetypal Analysis (AA) by using a properly modified version of PCHA algorithm.
Basic functions are:
archetypal() do AAfind_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 archetypesfind_pcha_optimal_parameters() search for the optimal updating parameters of PCHA algorithmcheck_Bmatrix() check B matrix after run of AA.study_AAconvergence() study the convergence of PCHA algorithmfind_closer_points() find the closer to archetypes data pointsInstall the archetypal package and then read vignette("archetypal", package = "archetypal").
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" Please send comments, suggestions or bug reports to dchristop@econ.uoa.gr or dem.christop@gmail.com