densityMclust: Density Estimation via Model-Based Clustering

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

Density Estimation via Model-Based Clustering

Description

Produces a density estimate for each data point using a Gaussian finite mixture model from Mclust.

Usage

densityMclust(data, ..., plot = TRUE)

Arguments

data	A numeric vector, matrix, or data frame of observations. Categorical variables are not allowed. If a matrix or data frame, rows correspond to observations and columns correspond to variables.
...	Additional arguments for the Mclust function. In particular, setting the arguments G and modelNames allow to specify the number of mixture components and the type of model to be fitted. By default an "optimal" model is selected based on the BIC criterion.
plot	A logical value specifying if the estimated density should be plotted. For more contols on the resulting graph see the associated plot.densityMclust method.

Value

An object of class densityMclust, which inherits from Mclust. This contains all the components described in Mclust and the additional element:

density

The density evaluated at the input data computed from the estimated model.

Author(s)

Revised version by Luca Scrucca based on the original code by C. Fraley and A.E. Raftery.

References

Scrucca L., Fraley C., Murphy T. B. and Raftery A. E. (2023) Model-Based Clustering, Classification, and Density Estimation Using mclust in R. Chapman & Hall/CRC, ISBN: 978-1032234953, https://mclust-org.github.io/book/

Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 289-317.

Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.

See Also

plot.densityMclust, Mclust, summary.Mclust, predict.densityMclust.

Examples

dens <- densityMclust(faithful$waiting)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "BIC", legendArgs = list(x = "topright"))
plot(dens, what = "density", data = faithful$waiting)

dens <- densityMclust(faithful, modelNames = "EEE", G = 3, plot = FALSE)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data = faithful, 
     drawlabels = FALSE, points.pch = 20)
plot(dens, what = "density", type = "hdr")
plot(dens, what = "density", type = "hdr", prob = c(0.1, 0.9))
plot(dens, what = "density", type = "hdr", data = faithful)
plot(dens, what = "density", type = "persp")


dens <- densityMclust(iris[,1:4], G = 2)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data = iris[,1:4], 
     col = "slategrey", drawlabels = FALSE, nlevels = 7)
plot(dens, what = "density", type = "hdr", data = iris[,1:4])
plot(dens, what = "density", type = "persp", col = grey(0.9))

mclust documentation built on May 29, 2024, 8:06 a.m.