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A quick tour of mclustAddons

Luca Scrucca

20 Sep 2024

Introduction

mclustAddons is a contributed R package that extends the functionality available in the mclust package (Scrucca et al. 2016, Scrucca et al. 2023).
In particular, the following methods are included:

This document gives a quick tour of mclustAddons (version 0.9). It was written in R Markdown, using the knitr package for production.

References on the methodologies implemented are provided at the end of this document.

library(mclustAddons)
## Loading required package: mclust
## Package 'mclust' version 6.1.2
## Type 'citation("mclust")' for citing this R package in publications.
## Loaded package 'mclustAddons' version 0.9

Density estimation for data with bounded support

Univariate case with lower bound

x <- rchisq(200, 3)
xgrid <- seq(-2, max(x), length=1000)
f <- dchisq(xgrid, 3)  # true density
dens <- densityMclustBounded(x, lbound = 0)
summary(dens, parameters = TRUE)
## ── Density estimation for bounded data via GMMs ─────────── 
##            
## Boundaries:   x
##       lower   0
##       upper Inf
## 
## Model E (univariate, equal variance) model with 1 component
## on the transformation scale:
## 
##  log-likelihood   n df       BIC       ICL
##       -390.0517 200  3 -795.9983 -795.9983
## 
##                                     x
## Range-power transformation: 0.3715163
## 
## Mixing probabilities:
## 1 
## 1 
## 
## Means:
##         1 
## 0.9191207 
## 
## Variances:
##        1 
## 1.309037
plot(dens, what = "density")
lines(xgrid, f, lty = 2)

plot(dens, what = "density", data = x, breaks = 15)

Univariate case with lower & upper bounds

x <- rbeta(200, 5, 1.5)
xgrid <- seq(-0.1, 1.1, length=1000)
f <- dbeta(xgrid, 5, 1.5)  # true density
dens <- densityMclustBounded(x, lbound = 0, ubound = 1)
summary(dens, parameters = TRUE)
## ── Density estimation for bounded data via GMMs ─────────── 
##            
## Boundaries: x
##       lower 0
##       upper 1
## 
## Model E (univariate, equal variance) model with 1 component
## on the transformation scale:
## 
##  log-likelihood   n df      BIC      ICL
##        120.4011 200  3 224.9072 224.9072
## 
##                                      x
## Range-power transformation: -0.2200992
## 
## Mixing probabilities:
## 1 
## 1 
## 
## Means:
##        1 
## 1.152107 
## 
## Variances:
##         1 
## 0.5212129
plot(dens, what = "density")

plot(dens, what = "density", data = x, breaks = 11)

Bivariate case with lower bounds

x1 <- rchisq(200, 3)
x2 <- 0.5*x1 + sqrt(1-0.5^2)*rchisq(200, 5)
x <- cbind(x1, x2)
dens <- densityMclustBounded(x, lbound = c(0,0))
summary(dens, parameters = TRUE)
## ── Density estimation for bounded data via GMMs ─────────── 
##            
## Boundaries:  x1  x2
##       lower   0   0
##       upper Inf Inf
## 
## Model VEE (ellipsoidal, equal shape and orientation) model with 1 component
## on the transformation scale:
## 
##  log-likelihood   n df      BIC      ICL
##       -889.9509 200  7 -1816.99 -1816.99
## 
##                                    x1        x2
## Range-power transformation: 0.2957409 0.3172159
## 
## Mixing probabilities:
## 1 
## 1 
## 
## Means:
##        [,1]
## x1 1.041532
## x2 2.209400
## 
## Variances:
## [,,1]
##           x1        x2
## x1 1.2671762 0.3912456
## x2 0.3912456 0.8249777
plot(dens, what = "BIC")

plot(dens, what = "density")
points(x, cex = 0.3)
abline(h = 0, v = 0, lty = 3)

plot(dens, what = "density", type = "hdr")
abline(h = 0, v = 0, lty = 3)

plot(dens, what = "density", type = "persp")

Suicide data

The data consist in the lengths of 86 spells of psychiatric treatment undergone by control patients in a suicide study (Silverman, 1986).

data("suicide")
dens <- densityMclustBounded(suicide, lbound = 0)
summary(dens, parameters = TRUE)
## ── Density estimation for bounded data via GMMs ─────────── 
##            
## Boundaries: suicide
##       lower       0
##       upper     Inf
## 
## Model E (univariate, equal variance) model with 1 component
## on the transformation scale:
## 
##  log-likelihood  n df       BIC       ICL
##       -497.8204 86  3 -1009.004 -1009.004
## 
##                               suicide
## Range-power transformation: 0.1929267
## 
## Mixing probabilities:
## 1 
## 1 
## 
## Means:
##        1 
## 6.700073 
## 
## Variances:
##        1 
## 7.788326
plot(dens, what = "density", 
     lwd = 2, col = "dodgerblue2",
     data = suicide, breaks = 15, 
     xlab = "Length of psychiatric treatment")
rug(suicide)

Racial data

This dataset provides the proportion of white student enrollment in 56 school districts in Nassau County (Long Island, New York), for the 1992-1993 school year (Simonoff 1996, Sec. 3.2).

data("racial")
x <- racial$PropWhite
dens <- densityMclustBounded(x, lbound = 0, ubound = 1)
summary(dens, parameters = TRUE)
## ── Density estimation for bounded data via GMMs ─────────── 
##            
## Boundaries: x
##       lower 0
##       upper 1
## 
## Model E (univariate, equal variance) model with 1 component
## on the transformation scale:
## 
##  log-likelihood  n df      BIC      ICL
##         42.4598 56  3 72.84355 72.84355
## 
##                                     x
## Range-power transformation: 0.3869476
## 
## Mixing probabilities:
## 1 
## 1 
## 
## Means:
##        1 
## 2.795429 
## 
## Variances:
##        1 
## 5.253254
plot(dens, what = "density", 
     lwd = 2, col = "dodgerblue2",
     data = x, breaks = 15, 
     xlab = "Proportion of white student enrolled in schools")
rug(x)




Entropy estimation

Simulated data


Univariate Gaussian

EntropyGauss(1)       # population entropy
## [1] 1.418939
x = rnorm(1000)       # generate sample
EntropyGauss(var(x))  # sample entropy assuming Gaussian distribution
## [1] 1.384565
mod = densityMclust(x, plot = FALSE)
EntropyGMM(mod)       # GMM-based entropy estimate
## [1] 1.384065
plot(mod, what = "density", data = x, breaks = 31); rug(x)


Univariate Mixed-Gaussian
Consider the mixed-Gaussian distribution \(f(x) = 0.5 \times N(-2,1) + 0.5 \times N(2,1)\), whose entropy is 2.051939 in the population.

cl = rbinom(1000, size = 1, prob = 0.5)
x = ifelse(cl == 1, rnorm(1000, 2, 1), rnorm(1000, -2, 1))   # generate sample
mod = densityMclust(x, plot = FALSE)
EntropyGMM(mod)       # GMM-based entropy estimate
## [1] 2.037679
plot(mod, what = "density", data = x, breaks = 31); rug(x)


Multivariate Chi-squared
Consider a 10-dimensional independent \(\chi^2\) distribution, whose entropy is 24.23095 in the population.

x = matrix(rchisq(1000*10, df = 5), nrow = 1000, ncol = 10)
mod1 = densityMclust(x, plot = FALSE)
EntropyGMM(mod1)      # GMM-based entropy estimate, not too bad but...
## [1] 25.01403
mod2 = densityMclustBounded(x, lbound = rep(0,10))
EntropyGMM(mod2)      # much more accurate
## [1] 24.24474

Faithful data

data(faithful)
mod = densityMclust(faithful, plot = FALSE)
EntropyGMM(mod)       # GMM-based entropy estimate
## [1] 4.140889
# or provide the data and fit GMM implicitly
EntropyGMM(faithful)
## [1] 4.140889

Iris data

data(iris)
mod = densityMclust(iris[,1:4], plot = FALSE)
EntropyGMM(mod)       # GMM-based entropy estimate
## [1] 1.438173


References

Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/

Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. The R Journal 8/1, pp. 289-317. https://doi.org/10.32614/RJ-2016-021

Scrucca L. (2019) A transformation-based approach to Gaussian mixture density estimation for bounded data, Biometrical Journal, 61:4, 873–888. https://doi.org/10.1002/bimj.201800174

Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527

Robin S. and Scrucca L. (2023) Mixture-based estimation of entropy. Computational Statistics & Data Analysis, 177, 107582. https://doi.org/10.1016/j.csda.2022.107582

sessionInfo()
## R version 4.4.0 (2024-04-24)
## Platform: x86_64-apple-darwin20
## Running under: macOS Ventura 13.6
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## time zone: Europe/Rome
## tzcode source: internal
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] mclustAddons_0.9 mclust_6.1.2     knitr_1.48      
## 
## loaded via a namespace (and not attached):
##  [1] doParallel_1.0.17 cli_3.6.3         rlang_1.1.4       xfun_0.46        
##  [5] highr_0.11        jsonlite_1.8.8    htmltools_0.5.8.1 rngtools_1.5.2   
##  [9] sass_0.4.9        rmarkdown_2.27    evaluate_0.24.0   jquerylib_0.1.4  
## [13] fastmap_1.2.0     yaml_2.3.10       foreach_1.5.2     lifecycle_1.0.4  
## [17] doRNG_1.8.6       compiler_4.4.0    codetools_0.2-20  Rcpp_1.0.13      
## [21] rstudioapi_0.16.0 digest_0.6.36     R6_2.5.1          parallel_4.4.0   
## [25] bslib_0.8.0       tools_4.4.0       iterators_1.0.14  cachem_1.1.0

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.