plot.densityMclust: Plots for Mixture-Based Density Estimate

Description Usage Arguments Details Author(s) See Also Examples

View source: R/densityMclust.R

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

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## S3 method for class '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, 
                   prob = c(0.25, 0.5, 0.75),
                   points.pch = 1, points.col = 1, points.cex = 0.8, ...)

plotDensityMclustd(x, data = NULL, nlevels = 11, levels = NULL, 
                   prob = c(0.25, 0.5, 0.75),
                   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.

prob

A vector of probability levels for computing HDR. Only used if type = "level" and supersede previous nlevels and levels arguments.

gap

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

...

Additional arguments passed to surfacePlot.

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", "level", "image", and "persp" type of graph. For type = "level" Highest Density Regions (HDRs) are plotted for probability levels prob. See link{hdrlevels} for details.

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

Author(s)

Luca Scrucca

See Also

densityMclust, surfacePlot, densityMclust.diagnostic, Mclust.

Examples

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## Not run: 
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)
summary(dens)
summary(dens, parameters = TRUE)
plot(dens, what = "density", data = faithful, 
     drawlabels = FALSE, points.pch = 20)
plot(dens, what = "density", type = "level")
plot(dens, what = "density", type = "level", prob = seq(0.1, 0.9, by = 0.1))
plot(dens, what = "density", type = "level", data = faithful)
plot(dens, what = "density", type = "persp")

dens <- densityMclust(iris[,1:4])
summary(dens, parameters = TRUE)
plot(dens, what = "density", data = iris[,1:4], 
     col = "slategrey", drawlabels = FALSE, nlevels = 7)
plot(dens, what = "density", type = "level", data = iris[,1:4])
plot(dens, what = "density", type = "persp", col = grey(0.9))

## End(Not run)

Example output

Package 'mclust' version 5.4.1
Type 'citation("mclust")' for citing this R package in publications.
------------------------------------------------------- 
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

Clustering table:
  1   2 
 99 173 
------------------------------------------------------- 
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

Clustering table:
  1   2 
 99 173 

Mixing probabilities:
        1         2 
0.3609461 0.6390539 

Means:
       1        2 
54.61675 80.09239 

Variances:
       1        2 
34.44093 34.44093 
------------------------------------------------------- 
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

Clustering table:
  1   2   3 
 40  97 135 
------------------------------------------------------- 
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

Clustering table:
  1   2   3 
 40  97 135 

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
------------------------------------------------------- 
Density estimation via Gaussian finite mixture modeling 
------------------------------------------------------- 

Mclust VEV (ellipsoidal, equal shape) model with 2 components: 

 log.likelihood   n df       BIC       ICL
       -215.726 150 26 -561.7285 -561.7289

Clustering table:
  1   2 
 50 100 

Mixing probabilities:
        1         2 
0.3333319 0.6666681 

Means:
                  [,1]     [,2]
Sepal.Length 5.0060022 6.261996
Sepal.Width  3.4280049 2.871999
Petal.Length 1.4620007 4.905992
Petal.Width  0.2459998 1.675997

Variances:
[,,1]
             Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length   0.15065114  0.13080115   0.02084463  0.01309107
Sepal.Width    0.13080115  0.17604529   0.01603245  0.01221458
Petal.Length   0.02084463  0.01603245   0.02808260  0.00601568
Petal.Width    0.01309107  0.01221458   0.00601568  0.01042365
[,,2]
             Sepal.Length Sepal.Width Petal.Length Petal.Width
Sepal.Length    0.4000438  0.10865444    0.3994018  0.14368256
Sepal.Width     0.1086544  0.10928077    0.1238904  0.07284384
Petal.Length    0.3994018  0.12389040    0.6109024  0.25738990
Petal.Width     0.1436826  0.07284384    0.2573899  0.16808182

mclust documentation built on July 2, 2018, 9:03 a.m.