cata (version 0.1.0.27)

bcluster.n: b-cluster analysis by non-hierarchical iterative ascent clustering strategy

Description

Non-hierarchical b-cluster analysis transfers assessors iteratively to reach a local maximum in sensory differentiation retained.

Usage

bcluster.n(X, G, M = NULL, measure = "b", max.iter = 500, runs = 1,
X.input = "data", tol = exp(-32), seed = 2021)

Value

An object of class bclust.n (or a list of such objects if runs>1), where each such object has the following components:

• cluster : vector of the final cluster memberships

• totalB : value of the total sensory differentiation in data set

• retainedB : value of sensory differentiation retained in b-cluster analysis solution

• progression : vector of sensory differentiation retained in each iteration

• iter : number of iterations completed

• finished : boolean indicates whether the algorithm converged before max.iter

Arguments

X

CATA data organized in a three-way array (assessors, products, attributes)

G

number of clusters (required for non-hierarchical algorithm)

M

initial cluster memberships (default: NULL), but can be a vector (one run) or a matrix (consumers in rows; runs in columns)

measure

b (default) for the b-measure is implemented

max.iter

maximum number of iteration allowed (default 500)

runs

number of runs (defaults to 1)

X.input

either "data" (default) or "bc" if X is obtained from the function barray

tol

algorithm stops if variance over 5 iterations is less than tol (default: exp(-32))

seed

for reproducibility (default is 2021)

Author

J.C. Castura

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. tools:::Rd_expr_doi("10.1016/j.foodqual.2022.104564").

Examples

data(bread)

# b-cluster analysis on the first 8 consumers and the first 5 attributes
(b <- bcluster.n(bread$cata[1:8, , 1:5], G=2))