inspect {cata} | R Documentation |
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)
Arguments
X |
list of multiple runs of b-cluster analysis results from
|
G |
number of clusters (required for non-hierarchical algorithm) |
bestB |
total sensory differentiation retained in the best solution. If
not provided, then |
bestM |
cluster memberships for best solution. If not provided, then
the best solution is determined from the runs provided (in |
inspect.plot |
default ( |
Value
A data frame with unique solutions in rows and the following columns:
B
: Sensory differentiation retainedPctB
: Percentage of the total sensory differentiation retainedB.prop
: Proportion of sensory differentiation retained compared to best solutionRaw.agree
: raw agreement with best solutionCount
: number of runs for which this solution was observedIndex
: list index (i.e., run number) of first solution solution inX
corresponding to this rowc.1, c.2, ...
: remaining columns gives index of the cluster to which the consumers (columns) are allocated
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. doi:10.1016/j.foodqual.2022.104564.
Examples
data(bread)
res <- bcluster.n(bread$cata[1:8, , 1:5], G = 2, runs = 3)
inspect(res)