plot_mm(): The main function of the package,
plot_mm allows the user to simply input the name of the fit
mixture model, as well as an optional argument to pass the number of
components k that were used in the original fit.
Note: the function will automatically detect the number of
components if k is not supplied. The result is a tidy
ggplot of the density of the data with overlaid mixture weight component
curves. Importantly, as the grammar of graphics is the basis of
visualization in this package, all other tidyverse-friendly
customization options work with any of the plotmm’s
functions (e.g., customizing with ggplot2’s functions like
labs() or theme_*(); or
patchwork’s plot_annotation()). There are
examples of these and others below.
plot_cut_point(): Mixture models are often used to
derive cut points of separation between groups in feature space.
plot_cut_point() plots the data density with the overlaid
cut point (the mean of the calculated mu) from the fit
mixture model.
plot_mix_comps(): A helper function allowing for
expanded customization of mixture model plots. The function superimposes
the shape of the components over a ggplot2 object. This
function is also used to render all plots in the main
plot_mm() function.
plot_gmm(): The original function upon which the
package was expanded. It is included in plotmm for quicker
access to a common mixture model form (univariate Gaussian), as well as
to bridge between the original plotGMM package.
plot_mix_comps_normal(): Similarly, this function is
the original basis of plot_mix_comps(), but for Gaussian
mixture models only. It is included in plotmm for bridging
between the original plotGMM package.
The package supports several model objects (from ‘mixtools’,
‘EMCluster’, and ‘flexmix’), as well as many mixture model
specifications, including mixtures of:
First, load the stable version from CRAN.
Tidy visualization of mixture models via plot_mm()
First, here is an example for univariate normal mixture model:
library(mixtools)
#> mixtools package, version 1.2.0, Released 2020-02-05
#> This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
library(ggplot2)
set.seed(576)
mixmdl <- normalmixEM(iris$Petal.Length, k = 2)
#> number of iterations= 9
# visualize
plot_mm(mixmdl, 2) +
labs(title = "Univariate Gaussian Mixture Model",
subtitle = "Mixtools Object")
Next is an example of a mixture of linear regressions:
library(mixtools)
library(ggplot2)
# set up the data (replication of mixtools examples for comparability)
data(NOdata)
attach(NOdata)
set.seed(100)
out <- regmixEM(Equivalence, NO, verb = TRUE, epsilon = 1e-04)
#> iteration= 1 diff= 61.52481 log-likelihood 59.2794
#> iteration= 2 diff= 15.93577 log-likelihood 75.21517
#> iteration= 3 diff= 27.66707 log-likelihood 102.8822
#> iteration= 4 diff= 17.24268 log-likelihood 120.1249
#> iteration= 5 diff= 1.238034 log-likelihood 121.363
#> iteration= 6 diff= 0.2725981 log-likelihood 121.6356
#> iteration= 7 diff= 0.1762554 log-likelihood 121.8118
#> iteration= 8 diff= 0.1116986 log-likelihood 121.9235
#> iteration= 9 diff= 0.06262589 log-likelihood 121.9861
#> iteration= 10 diff= 0.03058315 log-likelihood 122.0167
#> iteration= 11 diff= 0.0132683 log-likelihood 122.03
#> iteration= 12 diff= 0.005278647 log-likelihood 122.0353
#> iteration= 13 diff= 0.001982899 log-likelihood 122.0372
#> iteration= 14 diff= 0.0007186432 log-likelihood 122.038
#> iteration= 15 diff= 0.0002548647 log-likelihood 122.0382
#> iteration= 16 diff= 8.922635e-05 log-likelihood 122.0383
#> number of iterations= 16
df <- data.frame(out$beta)
# visualize
plot_mm(out) +
labs(title = "Mixture of Regressions",
subtitle = "Mixtools Object")
Next is a bivariate Gaussian mixture model (via
EMCluster). Note: in this case, all plots print by
default for full display of options. Use indexing (e.g.,
plot[1]) to plot a specific or preferred quantity.
library(EMCluster)
#> Loading required package: MASS
#> Loading required package: Matrix
library(patchwork)
#>
#> Attaching package: 'patchwork'
#> The following object is masked from 'package:MASS':
#>
#> area
set.seed(1234)
x <- da1$da
out <- init.EM(x, nclass = 10, method = "em.EM")
plot_mm(out, data = x) +
plot_annotation(title = "Bivariate Gaussian Mixture Model",
subtitle = "EMCluster Object")
Plot cut points (or not) via plot_cut_point() (with the
amerika color
palette)
library(mixtools)
mixmdl <- normalmixEM(faithful$waiting, k = 2)
#> number of iterations= 52
plot_cut_point(mixmdl, plot = TRUE, color = "amerika") # produces plot
#> Registered S3 method overwritten by 'wesanderson':
#> method from
#> print.palette amerika
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
plot_cut_point(mixmdl, plot = FALSE) # gives the cut point value, not the plot
#> [1] 67.35299
Customize a ggplot with plot_mix_comps()
library(mixtools)
library(magrittr)
library(ggplot2)
# Fit a univariate mixture model via mixtools
set.seed(576)
mixmdl <- normalmixEM(faithful$waiting, k = 2)
#> number of iterations= 24
# Customize a plot with `plot_mix_comps_normal()`
data.frame(x = mixmdl$x) %>%
ggplot() +
geom_histogram(aes(x, ..density..), binwidth = 1, colour = "black",
fill = "white") +
stat_function(geom = "line", fun = plot_mix_comps_normal, # here is the function
args = list(mixmdl$mu[1], mixmdl$sigma[1], lam = mixmdl$lambda[1]),
colour = "red", lwd = 1.5) +
stat_function(geom = "line", fun = plot_mix_comps_normal, # here again as k = 2
args = list(mixmdl$mu[2], mixmdl$sigma[2], lam = mixmdl$lambda[2]),
colour = "blue", lwd = 1.5) +
ylab("Density")
