Visualizing Class Specific Heterogeneous Tendencies in Categorical Data

Provides functions for performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the package are described in Mariko Takagishi and Michel van de Velden (2022).

mccca

The R package mccca implements multiple-class cluster correspondence analysis (MCCCA; Takagishi and van de Velden, 2022). MCCCA is a statistical method that identifies and visualizes heterogeneous tendencies specific to certain "classes", specified by the researcher, in a low dimensional space. In MCCCA, two kinds of variables, external and active variables, are distinguished. External variables directly define the classes whereas the active variables are used to derive, for each class, multiple clusters of observations exhibiting similar tendencies. The class-specific clusters are displayed together with the categories of the active variables in a single biplot.

Installation

Install the released version from CRAN:

install.packages("mccca")

Or install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("mtakagishi0/mccca")

Example

library(mccca)
data(mealDrink)

# Create the classes from the external variables (nationality and gender)
mcccadata <- create.MCCCAdata(mealDrink[, 1:2], mealDrink[, 3:4])

# Apply MCCCA with the numbers of class-specific clusters
set.seed(1)
res <- MCCCA(mcccadata, K.vec = c(2, 1, 2, 2))

# Show the MCCCA biplot and a numerical summary
plot(res, classlabel = c("AF", "AM", "JF", "JM"))
summary(res)

For real data, the numbers of class-specific clusters can be chosen with decideK(). See vignette("mccca") for a full example.

Reference

Takagishi, M. and van de Velden, M. (2022). Visualizing class specific heterogeneous tendencies in categorical data. Journal of Computational and Graphical Statistics, 31(3), 790–801. https://doi.org/10.1080/10618600.2022.2035737