mclust (version 5.4.3)

densityMclust: Density Estimation via Model-Based Clustering


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


densityMclust(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.


An object of class densityMclust, which inherits from Mclust, is returned with the following slot added:


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


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. 205-233.

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.

Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (2012) mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

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


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

dens <- densityMclust(faithful, modelNames = "EEE", G = 3)
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))
# }

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