GDAtools (version 2.1)

ggadd_kellipses: 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")

Value

a ggplot2 object

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.

Author

Nicolas Robette

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.

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).

See Also

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")
ggadd_ellipses(p, mca, Music$Age)