# ggadd_kellipses: Concentration ellipses and k-inertia ellipses In GDAtools: Geometric Data Analysis

## Concentration ellipses and k-inertia ellipses

### Description

Adds concentration ellipses and other kinds of k-inertia ellipses for a categorical variable to a MCA cloud of individuals.

### Usage

``````ggadd_kellipses(p, resmca, var, sel = 1:nlevels(var), axes = c(1,2),
kappa = 2, label = TRUE, label.size = 3, size = 0.5, points = TRUE,
legend = "right")
``````

### Arguments

 `p` `ggplot2` object with the cloud of individuals `resmca` object of class `MCA`, `speMCA`, `csMCA`, `stMCA` or `multiMCA` `var` Factor. The categorical variable used to plot ellipses. `sel` numeric vector of indexes of the categories to plot (by default, ellipses are plotted for every categories) `axes` numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2). `kappa` numeric. The kappa value (i.e. "index") of the inertia ellipses. By default, kappa = 2, which means that concentration ellipses are plotted. `label` Logical. Should the labels of the categories be plotted at the center of ellipses ? Default is TRUE. `label.size` Size of the labels of the categories at the center of ellipses. Default is 3. `size` Size of the lines of the ellipses. Default is 0.5. `points` If TRUE (default), the points are coloured according to their subcloud. `legend` the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right.

### Details

If kappa=2, ellipses are called "concentration" ellipses and, for a normally shaped subcloud, contain 86.47 percents of the points of the subcloud. If kappa=1, ellipses are "indicator" ellipses and contain 39.35 percents of the points of the subcloud. If kappa=1.177, ellipses are "median" ellipses and contain 50 percents of the points of the subcloud. This function has to be used after the cloud of individuals has been drawn.

### Value

a `ggplot2` object

### Note

Ellipses are colored according to the categories of the variable, using the default `ggplot2` palette. The palette can be customized using any `scale_color_*` function, such as `scale_color_brewer()`, `scale_color_grey()` or `scale_color_manual()`.

Nicolas Robette

### References

Le Roux B. and Rouanet H., Multiple Correspondence Analysis, SAGE, Series: Quantitative Applications in the Social Sciences, Volume 163, CA:Thousand Oaks (2010).

Le Roux B. and Rouanet H., Geometric Data Analysis: From Correspondence Analysis to Stuctured Data Analysis, Kluwer Academic Publishers, Dordrecht (June 2004).

`ggcloud_indiv`, `ggadd_supvar`, `ggadd_supvars`, `ggadd_ellipses`, `ggadd_density`, `ggadd_interaction`, `ggsmoothed_supvar`, `ggadd_chulls`, `ggadd_corr`

### Examples

``````# specific MCA of Music example data set
data(Music)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- speMCA(Music[,1:5], excl = junk)
# concentration ellipses for Age
p <- ggcloud_indiv(mca, col = "lightgrey")