bcluster.h: b-cluster analysis by hierarchical agglomerative strategy

In cata: Analysis of Check-All-that-Apply (CATA) Data

b-cluster analysis by hierarchical agglomerative strategy

Description

Perform b-clustering using the hierarchical agglomerative clustering strategy.

Usage

bcluster.h(X, measure = "b", runs = 1, seed = 2021)

Arguments

X	three-way array; the I, J, M array has I assessors, J products, M attributes where CATA data have values 0 (not checked) and 1 (checked)
measure	currently only b (the b-measure) is implemented
runs	number of runs (defaults to 1; use a higher number of runs for a real application)
seed	for reproducibility (default is 2021)

Value

An object of class hclust from hierarchical b-cluster analysis results (a list of such objects if runs>1), where each hclust object has the structure described in hclust as well as the item retainedB (a vector indicating the retained sensory differentiation at each iteration (merger)).

References

Castura, J.C., Meyners, M., Varela, P., & Næs, T. (2022). Clustering consumers based on product discrimination in check-all-that-apply (CATA) data. Food Quality and Preference, 104564. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.foodqual.2022.104564")}.

Examples

data(bread)

# hierarchical b-cluster analysis on first 8 consumers and first 5 attributes
b <- bcluster.h(bread$cata[1:8,,1:5])

plot(as.dendrogram(b), 
  main = "Hierarchical b-cluster analysis", 
  sub = "8 bread consumers on 5 attributes")

cata documentation built on Oct. 8, 2024, 9:07 a.m.