The main function, plot_GMM, is used for plotting output from Gaussian mixture models (GMMs), including both densities and overlaying mixture weight component curves from the fit GMM. The package also include the function, plot_cut_point, which plots the cutpoint (mu) from the GMM over a histogram of the distribution with several color options. Finally, the package includes the function, plot_mix_comps, which is used in the plot_GMM function, and can be used to create a custom plot for overlaying mixture component curves from GMMs. For the plot_mix_comps function, usage most often will be specifying the "fun" argument within "stat_function" in a ggplot2 object.
title: "README.md" author:
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 a mixEM
class object (from fitting a Gaussian mixture model (GMM) using the mixtools
package), as well as the number of components, k
, that were used in the original GMM fit. The result is a clean ggplot2
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). The plot_cut_point
function plots data densities with the overlaid cut point (the mean of the calculated mu
) from mixEM
class objects, which are GMM's fit using the mixtools
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 a ggplot2
class object. Importantly, while the plot_mix_comps
function is used in the main plot_GMM
function in our plotGMM
package, users can use the plot_mix_comps
function to build their own custom plots.
mixmdl <- mixtools::normalmixEM(faithful$waiting, k = 2) plot_GMM(mixmdl, 2)
plot_cut_point
mixmdl <- mixtools::normalmixEM(faithful$waiting, k = 2) plot_cut_point(mixmdl, plot = TRUE) # produces plot plot_cut_point(mixmdl, plot = FALSE) # produces only numeric summary output from GMM
plot_mix_comps
function in a custom ggplot2
plotlibrary(plotGMM)library(magrittr)library(ggplot2)library(mixtools) # Fit a GMM using EMset.seed(576)mixmdl <- normalmixEM(faithful$waiting, k = 2) # Plot mixture components using the `plot_mix_comps` functiondata.frame(x = mixmdl$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")
plotGMM
v 0.2.0plot_GMM
: Now the main function of the package, plot_GMM
allows the user to simply input the name of a mixEM
class object (from fitting a Gaussian mixture model (GMM) using the mixtools
package), as well as the number of components, k
, that were used in the original GMM fit. The result is a clean ggplot2
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). The plot_cut_point
function plots data densities with the overlaid cut point (the mean of the calculated mu
) from mixEM
class objects, which are GMM's fit using the mixtools
package.
plotGMM
?The package is released on CRAN. If you have any questions or find any bugs requiring fixing, please feel free to contact us either directly (see the DESCRIPTION
file for more) or by opening an issue ticket on GitHub. Thanks and enjoy!