GDAtools (version 1.7)

ggadd_ellipses: Adds confidence ellipses to a cloud of individuals

Description

Adds confidence ellipses for a categorical variable to a MCA cloud of individuals, using the ggplot2 framework.

Usage

ggadd_ellipses(p, resmca, var, sel=1:nlevels(var), axes=c(1,2),
level=0.05, label=TRUE, label.size=3, col=NULL, size=0.5, points=TRUE, legend='right')

Arguments

p

ggplot object with the cloud of variables

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).

level

The level at which to draw an ellipse (see stat_ellipse). Default is 0.05, which means 95 percents confidence 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.

col

Colors for the ellipses and labels of the categories. Can be the name of a palette from the RcolorBrewer package, 'bw' for a black and white palette (uses scale_color_grey()), a character vector of colors for a custom palette, or the name of a color for a single color. If NULL (default), the default palette of ggplot2 is used.

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.

Value

a ggplot object

Details

A confidence ellipse aims at measuring how the "true" mean point of a category differs from its observed mean point. This is achieved by constructing a confidence zone around the observed mean point. If we choose a conventional level alpha (e.g. 0.05), a (1 - alpha) (e.g. 95 percents) confidence zone is defined as the set of possible mean points that are not significantly different from the observed mean point.

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_variables, ggcloud_indiv, ggadd_supvar, ggadd_corr, ggadd_interaction, ggadd_density, ggadd_kellipses

Examples

# NOT RUN {
## Performs a specific MCA on 'Music' example data set
## ignoring every 'NA' (i.e. 'not available') categories,
## draws the cloud of categories
## and adds confidence ellipses for Age.
data(Music)
getindexcat(Music[,1:5])
mca <- speMCA(Music[,1:5],excl=c(3,6,9,12,15))
p <- ggcloud_indiv(mca, col='lightgrey')
ggadd_ellipses(p, mca, Music$Age)
# }