A quick tour of ppgmmga

In ppgmmga: Projection Pursuit Based on Gaussian Mixtures and Evolutionary Algorithms

library(knitr)
opts_chunk$set(fig.align = "center", 
               out.width = "80%",
               fig.width = 6, fig.height = 5,
               dev.args = list(pointsize = 10),
               par = TRUE, # needed for setting hook 
               collapse = TRUE, # collapse input & ouput code in chunks
               message = FALSE,
               warning = FALSE)

knit_hooks$set(par = function(before, options, envir)
  { if(before && options$fig.show != "none") 
       par(family = "sans", mar=c(4.1,4.1,1.1,1.1), mgp=c(3,1,0), tcl=-0.5)
})

Introduction

An R package implementing a Projection Pursuit algorithm based on finite Gaussian Mixtures Models for density estimation using Genetic Algorithms (PPGMMGA) to maximise a Negentropy index. The PPGMMGA algorithm provides a method to visualise high-dimensional data in a lower-dimensional space, with special reference to reveal clustering structures.

library(ppgmmga)
cat(ppgmmga:::ppgmmgaStartupMessage(), sep="")

Banknote data

library(mclust)
data("banknote")
X <- banknote[,-1]
Class <- banknote$Status
table(Class)
clPairs(X, classification = Class, 
        symbols = ppgmmga.options("classPlotSymbols"),
        colors = ppgmmga.options("classPlotColors"))

1-dimensional PPGMMGA

PP1D <- ppgmmga(data = X, d = 1, seed = 1)
PP1D
summary(PP1D)

plot(PP1D)

plot(PP1D, class = Class)

2-dimensional PPGMMGA

PP2D <- ppgmmga(data = X, d = 2, seed = 1)
summary(PP2D)
summary(PP2D$GMM)

plot(PP2D$GA)

plot(PP2D)

plot(PP2D, class = Class, drawAxis = FALSE)

3-dimensional PPGMMGA

PP3D <- ppgmmga(data = X, d = 3, 
                center = TRUE, scale = FALSE, 
                gatype = "gaisl", 
                options = ppgmmga.options(numIslands = 2),
                seed = 1)
summary(PP3D)

plot(PP3D$GA)

plot(PP3D)

plot(PP3D, class = Class)

plot(PP3D, dim = c(1,2))

plot(PP3D, dim = c(1,3), class = Class)

# A rotating 3D plot can be obtained using
if(!require("msir")) install.packages("msir")
msir::spinplot(PP3D$Z, markby = Class, 
               pch.points = c(20,17),
               col.points = ppgmmga.options("classPlotColors")[1:2])

References

Scrucca L, Serafini A (2019). "Projection pursuit based on Gaussian mixtures and evolutionary algorithms." Journal of Computational and Graphical Statistics, 28(4), 847–860. https://doi.org/10.1080/10618600.2019.1598871.

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sessionInfo()

ppgmmga documentation built on Nov. 18, 2023, 1:12 a.m.