# plot.densityMclust: Plots for Mixture-Based Density Estimate In mclust: Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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

`densityMclust`, `surfacePlot`, `densityMclust.diagnostic`, `Mclust`.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```## 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.