Description
Finds the Archetypal Analysis of a Data Frame.
Description
Performs archetypal analysis by using Principal Convex Hull Analysis under a full control of all algorithmic parameters. It contains a set of functions for determining the initial solution, the optimal algorithmic parameters and the optimal number of archetypes. Post run tools are also available for the assessment of the derived solution. Morup, M., Hansen, LK (2012) <doi:10.1016/j.neucom.2011.06.033>. Hochbaum, DS, Shmoys, DB (1985) <doi:10.1287/moor.10.2.180>. Eddy, WF (1977) <doi:10.1145/355759.355768>. Barber, CB, Dobkin, DP, Huhdanpaa, HT (1996) <doi:10.1145/235815.235821>. Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076>. Falk, A., Becker, A., Dohmen, T., Enke, B., Huffman, D., Sunde, U. (2018), <doi:10.1093/qje/qjy013>. Christopoulos, DT (2015) <doi:10.1016/j.jastp.2015.03.009> . Murari, A., Peluso, E., Cianfrani, Gaudio, F., Lungaroni, M., (2019), <doi:10.3390/e21040394>.
README.md
Overview
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_pointsProjected CH initial solution.find_outmost_convexhull_pointsCH 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 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 [email protected] or [email protected].