Plot of interactions between two categorical supplementary variables

Adds the interactions between two categorical supplementary variables to a MCA cloud of variables

ggadd_interaction(p, resmca, v1, v2, sel1 = 1:nlevels(v1), sel2 = 1:nlevels(v2),
axes = c(1,2), textsize = 5, dashed = TRUE, 
legend = "none", force = 1, max.overlaps = Inf)

Arguments

p

ggplot2 object with the cloud of variables

resmca

object created with MCA, speMCA, csMCA, wcMCA, bcMCA, stMCA or multiMCA function

v1

Factor. The first categorical supplementary variable.

v2

Factor. The second categorical supplementary variable.

sel1

Numeric vector of indexes of the categories of the first supplementary variable to be used in interaction. By default, every categories are used.

sel2

Numeric vector of indexes of the categories of the second supplementary variable to be used in interaction. By default, every categories are used.

axes

numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2).

textsize

Size of the labels of categories. Default is 5.

dashed

Logical. Whether to add gray dashed lines between the points of the categories. Default is TRUE.

legend

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

force

Force of repulsion between overlapping text labels. Defaults to 1. If 0, labels are not repelled at all.

max.overlaps

Exclude text labels that overlap too many things. Defaults to Inf, which means no labels are excluded.

Value

a ggplot2 object

Note

Lines and labels are colored according to the variables, 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().

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).

Author

Nicolas Robette

See also

ggcloud_variables, ggadd_supvar, ggadd_supvars, ggadd_kellipses, ggadd_ellipses, ggadd_corr, ggsmoothed_supvar, ggadd_chulls, ggadd_density

Examples

# specific MCA of Taste example data set
data(Taste)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA",
          "Comedy.NA", "Crime.NA", "Animation.NA", "SciFi.NA", "Love.NA", 
          "Musical.NA")
mca <- speMCA(Taste[,1:11], excl = junk)
# interaction between Gender and Age
p <- ggcloud_variables(mca, col = "lightgrey", shapes = FALSE)
ggadd_interaction(p, mca, Taste$Gender, Taste$Age)