GDAtools (version 1.7)

ggadd_chulls: Adds convex hulls to a cloud of individuals

Description

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

Usage

ggadd_chulls(p, resmca, var, sel=1:nlevels(var), axes=c(1,2),
col=NULL, alpha=0.2, label=TRUE, label.size=5, 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).

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.

alpha

Numerical value from 0 to 1. Transparency of the polygon's fill. Default is O.2

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 5.

legend

the position of legends ("none", "left", "right", "bottom", "top", or two-element numeric vector). Default is right.

Value

a ggplot object

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, ggadd_ellipses

Examples

# NOT RUN {
## Performs a specific MCA on 'Taste' example data set
## ignoring every 'NA' (i.e. 'not available') categories,
## then performs hierarchical clustering and
## partitions the individuals into 3 clusters, 
## draws the cloud of individuals
## and adds convex hulls for the clusters.

data(Taste)
getindexcat(Taste)
mca <- speMCA(Taste[,1:11],excl=c(3,6,9,12,15,18,21,24,27,30,33))
d <- dist(mca$ind$coord[,c(1,2)])
hca <- hclust(d, "ward.D2")
cluster <- factor(cutree(hca, 3))
p <- ggcloud_indiv(mca, col='black')
ggadd_chulls(p, mca, cluster)
# }