R: Density Estimation via Model-Based Clustering

densityMclust {mclust} | R Documentation

## 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, ...)

### 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.

### Value

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

`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., 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`.

### 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)
    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")

    ## Not run:
    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))

    ## End(Not run)

[Package mclust version 5.4.5 Index]