mclust (version 4.1)

densityMclust: 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 m

Value

• An object of class densityMclust, which inherits from Mclust, is returned with the following slots added:
• varname
  A character string denoting the variables names.
• range
  The range of the input data (used as a default for plotting).
• density
  The density of the input data x according to the estimated model.

References

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631. C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (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

x = faithful$waiting
dens = densityMclust(x)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "BIC")
plot(dens)
plot(dens, x)

x = as.matrix(faithful)
dens = densityMclust(x)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "BIC")
plot(dens)
plot(dens, x, col = "cadetblue", drawlabels = FALSE, pch = 20,
     levels = quantile(dens$density, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)))
plot(dens, x, col = "grey", 
     points.col = mclust.options()$classPlotColors[dens$classification], 
     pch = dens$classification)
plot(dens, type = "image", col = topo.colors(50))
plot(dens, type = "persp")

x = iris[,1:4]
dens = densityMclust(x)
summary(dens, parameters = TRUE)
plot(dens)
plot(dens, x, col = "cadetblue", drawlabels = FALSE,
     levels = quantile(dens$density, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)))
plot(dens, type = "image", col = gray.colors(50))
plot(dens, type = "persp", col = gray(0.5), border = NA)