plotGMM
Tools for Visualizing Gaussian Mixture Models
In collaboration with Fong Chan (@tinyheero), the latest release (v0.2.1) of plotGMM
includes substantial updates with easy-to-use tools for visualizing output from univariate Gaussian mixture models:
-
plot_GMM
: The main function of the package,plot_GMM
allows the user to simply input the name of amixEM
class object (from fitting a Gaussian mixture model (GMM) using themixtools
package), as well as the number of components,k
, that were used in the GMM fit. The result is a cleanggplot2
object showing the density of the data with overlaid mixture weight component curves. -
plot_cut_point
: Gaussian mixture models (GMMs) are not only used for uncovering clusters in data, but are also often used to derive cut points, or lines of separation between clusters in feature space (see the Benaglia et al. 2009 reference in the package documentation for more). Theplot_cut_point
function plots data densities with the overlaid cut point (the mean of the calculatedmu
) frommixEM
class objects, which are GMM's fit using themixtools
package. -
plot_mix_comps
: This is a custom function for users interested in manually overlaying the components from a Gaussian mixture model. This allows for clean, precise plotting constraints, including mean (mu
), variance (sigma
), and mixture weight (lambda
) of the components. The function superimposes the shape of the components over aggplot2
class object. Importantly, while theplot_mix_comps
function is used in the mainplot_GMM
function in ourplotGMM
package, users can use theplot_mix_comps
function to build their own custom plots.
Installation
Dev version: devtools::install_github("pdwaggoner/plotGMM")
CRAN (v0.2.1) release: install.packages("plotGMM"); library(plotGMM)
plot_GMM
Plotting GMMs using # Fit a GMM using EM
set.seed(576)
mixmdl <- mixtools::normalmixEM(iris$Petal.Length, k = 2)
# Plot the density with overlaid mixture weight curves
plot_GMM(mixmdl, 2)
plot_cut_point
Plotting cut points from GMMs using # Fit a GMM using EM via mixtools
set.seed(576)
mixmdl <- mixtools::normalmixEM(faithful$waiting, k = 2)
# Option 1.1: Plot with amerika palette
plot_cut_point(mixmdl, plot = TRUE,
color = "amerika")
# Option 1.2: Plot with wesanderson palette
plot_cut_point(mixmdl, plot = TRUE,
color = "wesanderson")
# Option 1.3: Plot with grayscale color, overriding default labels
plot_cut_point(mixmdl, plot = TRUE,
color = "grayscale") +
ggplot2::labs(x = "Waiting Time (Minutes)",
title = "Cutpoint from GMM for Old Faithful Waiting Time")
# Option 2: Cutpoint value only
plot_cut_point(mixmdl, plot = FALSE) # 67.35299
plot_mix_comps
for a custom plot manually overlaying component curves
Use library(plotGMM)
library(magrittr)
library(ggplot2)
library(mixtools)
# Fit a GMM using EM
set.seed(576)
mixmdl <- normalmixEM(faithful$waiting, k = 2)
# Plot mixture components using the `plot_mix_comps` function
data.frame(x = mixmdl$x) %>%
ggplot() +
geom_histogram(aes(x, ..density..), binwidth = 1, colour = "black",
fill = "white") +
stat_function(geom = "line", fun = plot_mix_comps,
args = list(mixmdl$mu[1], mixmdl$sigma[1], lam = mixmdl$lambda[1]),
colour = "red", lwd = 1.5) +
stat_function(geom = "line", fun = plot_mix_comps,
args = list(mixmdl$mu[2], mixmdl$sigma[2], lam = mixmdl$lambda[2]),
colour = "blue", lwd = 1.5)