mclust: plot.MclustDA – R documentation

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plot.MclustDA

Plotting method for MclustDA discriminant analysis

Description

Plots for model-based mixture discriminant analysis results, such as scatterplot of training and test data, classification of train and test data, and errors.

Usage

## S3 method for class 'MclustDA'
plot(x, what = c("scatterplot", "classification", "train&test", "error"), 
     newdata, newclass, dimens = NULL, 
     symbols, colors, main = NULL, ...)

Arguments

`x`	An object of class `'MclustDA'` resulting from a call to `MclustDA`.
`what`	A string specifying the type of graph requested. Available choices are: `"scatterplot"` = a plot of training data with points marked based on the known classification. Ellipses corresponding to covariances of mixture components are also drawn. `"classification"` = a plot of data with points marked on based the predicted classification; if `newdata` is provided then the test set is shown otherwise the training set. `"train&test"` = a plot of training and test data with points marked according to the type of set. `"error"` = a plot of training set (or test set if `newdata` and `newclass` are provided) with misclassified points marked. If not specified, in interactive sessions a menu of choices is proposed.
`newdata`	A data frame or matrix for test data.
`newclass`	A vector giving the class labels for the observations in the test data (if known).
`dimens`	A vector of integers giving the dimensions of the desired coordinate projections for multivariate data. The default is to take all the the available dimensions for plotting.
`symbols`	Either an integer or character vector assigning a plotting symbol to each unique class. Elements in `colors` correspond to classes in order of appearance in the sequence of observations (the order used by the function `factor`). The default is given by `mclust.options("classPlotSymbols")`.
`colors`	Either an integer or character vector assigning a color to each unique class in `classification`. Elements in `colors` correspond to classes in order of appearance in the sequence of observations (the order used by the function `factor`). The default is given by `mclust.options("classPlotColors")`.
`main`	A logical, a character string, or `NULL` (default) for the main title. If `NULL` or `FALSE` no title is added to a plot. If `TRUE` a default title is added identifying the type of plot drawn. If a character string is provided, this is used for the title.
`...`	further arguments passed to or from other methods.

Details

For more flexibility in plotting, use mclust1Dplot, mclust2Dplot, surfacePlot, coordProj, or randProj.

Author(s)

Luca Scrucca

Examples

odd <- seq(from = 1, to = nrow(iris), by = 2)
even <- odd + 1
X.train <- iris[odd,-5]
Class.train <- iris[odd,5]
X.test <- iris[even,-5]
Class.test <- iris[even,5]

# common EEE covariance structure (which is essentially equivalent to linear discriminant analysis)
irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA", modelNames = "EEE")
summary(irisMclustDA, parameters = TRUE)
summary(irisMclustDA, newdata = X.test, newclass = Class.test)

# common covariance structure selected by BIC
irisMclustDA <- MclustDA(X.train, Class.train, modelType = "EDDA")
summary(irisMclustDA, parameters = TRUE)
summary(irisMclustDA, newdata = X.test, newclass = Class.test)

# general covariance structure selected by BIC
irisMclustDA <- MclustDA(X.train, Class.train)
summary(irisMclustDA, parameters = TRUE)
summary(irisMclustDA, newdata = X.test, newclass = Class.test)

plot(irisMclustDA)
plot(irisMclustDA, dimens = 3:4)
plot(irisMclustDA, dimens = 4)

plot(irisMclustDA, what = "classification")
plot(irisMclustDA, what = "classification", newdata = X.test)
plot(irisMclustDA, what = "classification", dimens = 3:4)
plot(irisMclustDA, what = "classification", newdata = X.test, dimens = 3:4)
plot(irisMclustDA, what = "classification", dimens = 4)
plot(irisMclustDA, what = "classification", dimens = 4, newdata = X.test)

plot(irisMclustDA, what = "train&test", newdata = X.test)
plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 3:4)
plot(irisMclustDA, what = "train&test", newdata = X.test, dimens = 4)

plot(irisMclustDA, what = "error")
plot(irisMclustDA, what = "error", dimens = 3:4)
plot(irisMclustDA, what = "error", dimens = 4)
plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test)
plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 3:4)
plot(irisMclustDA, what = "error", newdata = X.test, newclass = Class.test, dimens = 4)

# simulated 1D data
n <- 250 
set.seed(1)
triModal <- c(rnorm(n,-5), rnorm(n,0), rnorm(n,5))
triClass <- c(rep(1,n), rep(2,n), rep(3,n))
odd <- seq(from = 1, to = length(triModal), by = 2)
even <- odd + 1
triMclustDA <- MclustDA(triModal[odd], triClass[odd])
summary(triMclustDA, parameters = TRUE)
summary(triMclustDA, newdata = triModal[even], newclass = triClass[even])
plot(triMclustDA)
plot(triMclustDA, what = "classification")
plot(triMclustDA, what = "classification", newdata = triModal[even])
plot(triMclustDA, what = "train&test", newdata = triModal[even])
plot(triMclustDA, what = "error")
plot(triMclustDA, what = "error", newdata = triModal[even], newclass = triClass[even])

# simulated 2D cross data
data(cross)
odd <- seq(from = 1, to = nrow(cross), by = 2)
even <- odd + 1
crossMclustDA <- MclustDA(cross[odd,-1], cross[odd,1])
summary(crossMclustDA, parameters = TRUE)
summary(crossMclustDA, newdata = cross[even,-1], newclass = cross[even,1])
plot(crossMclustDA)
plot(crossMclustDA, what = "classification")
plot(crossMclustDA, what = "classification", newdata = cross[even,-1])
plot(crossMclustDA, what = "train&test", newdata = cross[even,-1])
plot(crossMclustDA, what = "error")
plot(crossMclustDA, what = "error", newdata =cross[even,-1], newclass = cross[even,1])

mclust

Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

v5.4.10

GPL (>= 2)

Authors

Chris Fraley [aut], Adrian E. Raftery [aut] (<https://orcid.org/0000-0002-6589-301X>), Luca Scrucca [aut, cre] (<https://orcid.org/0000-0003-3826-0484>), Thomas Brendan Murphy [ctb] (<https://orcid.org/0000-0002-5668-7046>), Michael Fop [ctb] (<https://orcid.org/0000-0003-3936-2757>)

Initial release

2022-05-20