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

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.

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. 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.

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.

Author

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

See also

Examples

dens <- densityMclust(faithful$waiting) summary(dens)
#> ------------------------------------------------------- #> Density estimation via Gaussian finite mixture modeling #> ------------------------------------------------------- #> #> Mclust E (univariate, equal variance) model with 2 components: #> #> log-likelihood n df BIC ICL #> -1034.002 272 4 -2090.427 -2099.576
summary(dens, parameters = TRUE)
#> ------------------------------------------------------- #> Density estimation via Gaussian finite mixture modeling #> ------------------------------------------------------- #> #> Mclust E (univariate, equal variance) model with 2 components: #> #> log-likelihood n df BIC ICL #> -1034.002 272 4 -2090.427 -2099.576 #> #> Mixing probabilities: #> 1 2 #> 0.3609461 0.6390539 #> #> Means: #> 1 2 #> 54.61675 80.09239 #> #> Variances: #> 1 2 #> 34.44093 34.44093
plot(dens, what = "BIC", legendArgs = list(x = "topright"))
plot(dens, what = "density", data = faithful$waiting)
dens <- densityMclust(faithful, modelNames = "EEE", G = 3) summary(dens)
#> ------------------------------------------------------- #> Density estimation via Gaussian finite mixture modeling #> ------------------------------------------------------- #> #> Mclust EEE (ellipsoidal, equal volume, shape and orientation) model with 3 #> components: #> #> log-likelihood n df BIC ICL #> -1126.326 272 11 -2314.316 -2357.824
summary(dens, parameters = TRUE)
#> ------------------------------------------------------- #> Density estimation via Gaussian finite mixture modeling #> ------------------------------------------------------- #> #> Mclust EEE (ellipsoidal, equal volume, shape and orientation) model with 3 #> components: #> #> log-likelihood n df BIC ICL #> -1126.326 272 11 -2314.316 -2357.824 #> #> Mixing probabilities: #> 1 2 3 #> 0.1656784 0.3563696 0.4779520 #> #> Means: #> [,1] [,2] [,3] #> eruptions 3.793066 2.037596 4.463245 #> waiting 77.521051 54.491158 80.833439 #> #> Variances: #> [,,1] #> eruptions waiting #> eruptions 0.07825448 0.4801979 #> waiting 0.48019785 33.7671464 #> [,,2] #> eruptions waiting #> eruptions 0.07825448 0.4801979 #> waiting 0.48019785 33.7671464 #> [,,3] #> eruptions waiting #> eruptions 0.07825448 0.4801979 #> waiting 0.48019785 33.7671464
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")
if (FALSE) { 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)) }