# README.md In archetypal: Finds the Archetypal Analysis of a Data Frame

## 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"
# [13,] "nBup"
# [14,] "nBdown"
# [15,] "run_results"
``````