plot.MclustMEM {mclustAddons} | R Documentation |
Plotting method for modal-clustering based on Gaussian Mixtures
Description
Plots for MclustMEM
objects.
Usage
## S3 method for class 'MclustMEM'
plot(x, dimens = NULL, addDensity = TRUE, addPoints = TRUE,
symbols = NULL, colors = NULL, cex = NULL,
labels = NULL, cex.labels = NULL, gap = 0.2,
...)
Arguments
x |
An object of class |
dimens |
A vector of integers specifying the dimensions of the coordinate projections. |
addDensity |
A logical indicating whether or not to add density estimates to the plot. |
addPoints |
A logical indicating whether or not to add data points to the plot. |
symbols |
Either an integer or character vector assigning a plotting symbol to each unique class in |
colors |
Either an integer or character vector assigning a color to each unique class in |
cex |
A vector of numerical values specifying the size of the plotting symbol for each unique class in |
labels |
A vector of character strings for labelling the variables. The default is to use the column dimension names of |
cex.labels |
A numerical value specifying the size of the text labels. |
gap |
A numerical argument specifying the distance between subplots (see |
... |
Further arguments passed to or from other methods. |
Value
No return value, called for side effects.
Author(s)
Luca Scrucca
References
Scrucca L. (2021) A fast and efficient Modal EM algorithm for Gaussian mixtures. Statistical Analysis and Data Mining, 14:4, 305–314. https://doi.org/10.1002/sam.11527
See Also
Examples
# 1-d example
GMM <- Mclust(iris$Petal.Length)
MEM <- MclustMEM(GMM)
plot(MEM)
# 2-d example
data(Baudry_etal_2010_JCGS_examples)
GMM <- Mclust(ex4.1)
MEM <- MclustMEM(GMM)
plot(MEM)
plot(MEM, addPoints = FALSE)
plot(MEM, addDensity = FALSE)
# 3-d example
GMM <- Mclust(ex4.4.2)
MEM <- MclustMEM(GMM)
plot(MEM)
plot(MEM, addPoints = FALSE)
plot(MEM, addDensity = FALSE)