mclust (version 5.3)

plot.densityMclust: Plots for Mixture-Based Density Estimate

Description

Plotting methods for an object of class 'mclustDensity'. Available graphs are plot of BIC values and density for univariate and bivariate data. For higher data dimensionality a scatterplot matrix of pairwise densities is drawn.

Usage

# S3 method for densityMclust
plot(x, data = NULL, what = c("BIC", "density", "diagnostic"), …)
plotDensityMclust1(x, data = NULL, hist.col = "lightgrey", 
                   hist.border = "white",  breaks = "Sturges", …)
plotDensityMclust2(x, data = NULL, nlevels = 11, levels = NULL, col = grey(0.6),
                   points.pch = 1, points.col = 1, points.cex = 0.8, …)
plotDensityMclustd(x, data = NULL, nlevels = 11, levels = NULL, col = grey(0.6), 
                   points.pch = 1, points.col = 1, points.cex = 0.8,
                   gap = 0.2, …)

Arguments

x

An object of class 'mclustDensity' obtained from a call to densityMclust function.

data

Optional data points.

what

The type of graph requested:

"density" =

a plot of estimated density; if data is also provided the density is plotted over data points (see Details section).

"BIC" =

a plot of BIC values for the estimated models versus the number of components.

"diagnostic" =

diagnostic plots (only available for the one-dimensional case, see densityMclust.diagnostic)

hist.col

The color to be used to fill the bars of the histogram.

hist.border

The color of the border around the bars of the histogram.

breaks

See the argument in function hist.

points.pch, points.col, points.cex

The character symbols, colors, and magnification to be used for plotting data points.

nlevels

An integer, the number of levels to be used in plotting contour densities.

levels

A vector of density levels at which to draw the contour lines.

col

Color to be used for drawing the contour lines, the perspective plot, or the image density. In the latter case can be also a vector of color values.

gap

Distance between subplots, in margin lines, for the matrix of pairwise scatterplots.

…

Additional arguments.

Details

The function plot.densityMclust allows to obtain the plot of estimated density or the graph of BIC values for evaluated models.

If what = "density" the produced plot dependes on the dimensionality of the data.

For one-dimensional data a call with no data provided produces a plot of the estimated density over a sensible range of values. If data is provided the density is over-plotted on a histogram for the observed data.

For two-dimensional data further arguments available are those accepted by the surfacePlot function. In particular, the density can be represented through "contour", "image", and "persp" type of graph.

For higher dimensionality a scatterplot matrix of pairwise densities is drawn.

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

densityMclust, densityMclust.diagnostic, Mclust.

Examples

# NOT RUN {
dens = densityMclust(faithful$waiting)
plot(dens, what = "density")
plot(dens, what = "density", data = faithful$waiting)

dens = densityMclust(faithful)
plot(dens, what = "density")
plot(dens, what = "density", type = "image", col = "steelblue")
plot(dens, what = "density", type = "persp", col = adjustcolor("steelblue", alpha.f = 0.5))

x = iris[,1:4]
dens = densityMclust(x)
plot(dens, what = "density", nlevels = 7)
# }
# NOT RUN {
  plot(dens, x, what = "density", drawlabels = FALSE,
       levels = quantile(dens$density, probs = c(0.05, 0.25, 0.5, 0.75, 0.95)))
  plot(dens, what = "density", type = "image", col = "steelblue")
  plot(dens, what = "density", type = "persp", border = adjustcolor(grey(0.1), alpha.f = 0.5))
# }