plot.csMCA: Plot of class specific MCA

In GDAtools: Geometric Data Analysis

Plot of class specific MCA

Description

Plots a class specific Multiple Correspondence Analysis (resulting from csMCA function), i.e. the clouds of individuals or categories.

Usage

## S3 method for class 'csMCA'
plot(x, type = "v", axes = 1:2, points = "all",
col = "dodgerblue4", app = 0, ...)

Arguments

x	object of class csMCA
type	character string: 'v' to plot the categories (default), 'i' to plot individuals' points, 'inames' to plot individuals' names
axes	numeric vector of length 2, specifying the components (axes) to plot (c(1,2) is default)
points	character string. If 'all' all points are plotted (default); if 'besth' only those who contribute most to horizontal axis are plotted; if 'bestv' only those who contribute most to vertical axis are plotted; if 'besthv' only those who contribute most to horizontal or vertical axis are plotted.
col	color for the points of the individuals or for the labels of the categories (default is 'dodgerblue4')
app	numerical value. If 0 (default), only the labels of the categories are plotted and their size is constant; if 1, only the labels are plotted and their size is proportional to the weights of the categories; if 2, points (triangles) and labels are plotted, and points size is proportional to the weight of the categories.
...	further arguments passed to or from other methods, such as cex, cex.main, ...

Details

A category is considered to be one of the most contributing to a given axis if its contribution is higher than the average contribution, i.e. 100 divided by the total number of categories.

Author(s)

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).

See Also

csMCA, textvarsup, conc.ellipse

Examples

# class specific MCA on Music example data set
# ignoring every NA values categories 
# and focusing on the subset of women,
data(Music)
female <- Music$Gender=="Women"
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA")
mca <- csMCA(Music[,1:5], subcloud = female, excl = junk)
# cloud of categories
plot(mca)
# cloud of most contributing categories
plot(mca,axes=c(2,3), points = "besthv", col = "darkred", app = 1)

GDAtools documentation built on Oct. 6, 2023, 5:07 p.m.