evaluateClusterQuality: Evaluate Quality of Cluster Analysis Solution

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

Evaluate Quality of Cluster Analysis Solution

Description

Evaluate the quality of cluster analysis solutions using measures related to within-cluster product discrimination, between-cluster non-redundancy, overall diversity (coverage), average RV, sensory differentiation retained, and within-cluster homogeneity.

Usage

evaluateClusterQuality(X, M, alpha = .05, M.order = NULL, 
quiet = FALSE, digits = getOption("digits"), ...)

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)
M	cluster memberships
alpha	significance level to be used for two-tailed tests
M.order	can be used to change the cluster numbers (e.g. to label cluster 1 as cluster 2 and vice versa); defaults to NULL
quiet	if FALSE (default) then it prints information quality measures; if TRUE then returns results without printing
digits	significant digits (to display)
...	other parameters for print.default (if quiet = TRUE).

Value

A list containing cluster analysis quality measures:

• $solution :

  • Pct.b = percentage of the total sensory differentiation retained in the solution

  • min(NR) = smallest observed between-cluster non-redundancy

  • Div_G = overall diversity (coverage)

  • H_G = overall homogeneity (weighted average of within-cluster homogeneity indices)

  • avRV = average RV coefficient for all between-cluster comparisons

• $clusters :

  • ng = number of cluster members

  • bg = sensory differentiation retained in cluster

  • xbarg = average citation rate in cluster

  • Hg = homogeneity index within cluster (see homogeneity)

  • Dg = within-cluster product discrimination

• $nonredundancy.clusterpairs :

  • square data frame showing non-redundancy for each pair of clusters (low values indicate high redundancy)

• $rv.clusterpairs :

  • square data frame with RV coefficient for each pair of clusters (high values indicate higher similarity in product configurations)

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")}.

See Also

homogeneity

Examples

data(bread)
evaluateClusterQuality(bread$cata[1:8,,1:5], M = rep(1:2, each = 4))

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