Tools for Visualizing Gaussian Mixture Models.
In collaboration with Fong Chan @tinyheero, the latest release (v 0.2.0) of plotGMM
includes substantial updates with easy-to-use tools for visualizing output from 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 original GMM fit. The result is a cleanggplot2
class 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.
Plotting GMMs using plot_GMM
mixmdl <- mixtools::normalmixEM(faithful$waiting, k = 2)
plot_GMM(mixmdl, 2)
Plotting Cut Points from GMMs using plot_cut_point
(with amerika color palette)
mixmdl <- mixtools::normalmixEM(faithful$waiting, k = 2)
plot_cut_point(mixmdl, plot = TRUE, color = "amerika") # produces plot
plot_cut_point(mixmdl, plot = FALSE) # produces only cut point value
Manually using the plot_mix_comps
function in a custom ggplot2
plot
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) +
ylab("Density")