bcluster.n | R Documentation |
Non-hierarchical b-cluster analysis transfers assessors iteratively to reach a local maximum in sensory differentiation retained.
bcluster.n(X, G, M = NULL, measure = "b", max.iter = 500, runs = 1,
X.input = "data", tol = exp(-32), seed = 2021)
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: |
measure |
|
max.iter |
maximum number of iteration allowed (default |
runs |
number of runs (defaults to |
X.input |
either |
tol |
algorithm stops if variance over 5 iterations is less than
|
seed |
for reproducibility (default is |
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
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")}.
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))
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