4/17/2019

In collaboration with Fong Chan [@tinyheero](https://github.com/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:

1. 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.

2. 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.

3. 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.

### Plotting GMMs using plot_GMM

{r } mixmdl <- mixtools::normalmixEM(faithful$waiting, k = 2) plot_GMM(mixmdl, 2)  ### Plotting Cut Points from GMMs using plot_cut_point (with amerika color palette) {r } mixmdl <- mixtools::normalmixEM(faithful$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

{r } library(plotGMM) library(magrittr) library(ggplot2) library(mixtools)

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”) 

1. College of William & Mary↩︎

2. Achilles Therapeutics↩︎