cata (version 0.1.0.27)

inspect: Inspect/summarize many b-cluster analysis runs

Description

Inspect many runs of b-cluster analysis. Calculate sensory differentiation retained and recurrence rate.

Usage

inspect(X, G = 2, bestB = NULL, bestM = NULL, inspect.plot = TRUE)

Value

A data frame with unique solutions in rows and the following columns:

• B : Sensory differentiation retained

• PctB : Percentage of the total sensory differentiation retained

• B.prop : Proportion of sensory differentiation retained compared to best solution

• Raw.agree : raw agreement with best solution

• Count : number of runs for which this solution was observed

• Index : list index (i.e., run number) of first solution solution in X corresponding to this row

Arguments

X

list of multiple runs of b-cluster analysis results from bcluster.n or bcluster.h

G

number of clusters (required for non-hierarchical algorithm)

bestB

total sensory differentiation retained in the best solution. If not provided, then bestB is determined from best solution in the runs provided (in X).

bestM

cluster memberships for best solution. If not provided, then the best solution is determined from the runs provided (in X).

inspect.plot

default (TRUE) plots results from the inspect function

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)

res <- bcluster.n(bread$cata[1:8, , 1:5], G = 3, runs = 3)
(ires <- inspect(res))
# get index of solution retaining the most sensory differentiation (in these runs)
indx <- ires$Index[1]
# cluster memberships for solution of this solution
res[[indx]]$cluster