cata (version 0.1.0.27)

selectionPlot: Plot variation in retained sensory differentiation

Description

Plot variation in retained sensory differentiation of cluster memberships obtained from b-cluster analysis. This plot can be used to help the decision of how many clusters to retain.

Usage

selectionPlot(x, pctB = NULL, x.input = "deltaB", indx = NULL, 
ylab = "change in B (K to G)", xlab = NULL)

Arguments

x

input vector which is either deltaB (default; change in sensory differentiation retained) or B (sensory differentiation retained) if x.input is "B"

pctB

vector of percentage of the total sensory differentiation retained

x.input

indicates what x is; either "deltaB" (default) or B.

indx

numeric value indicating which point(s) to emphasize

ylab

label shown on y axis and at selection point

xlab

label for points along x axis

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

set.seed(123)
G2 <- bcluster.n(bread$cata[1:8, , 1:5], G = 2, runs = 3)
G3 <- bcluster.n(bread$cata[1:8, , 1:5], G = 3, runs = 3)
G4 <- bcluster.n(bread$cata[1:8, , 1:5], G = 4, runs = 3)

best.indx <- c(which.max(unlist(lapply(G2, function(x) x$retainedB))),
               which.max(unlist(lapply(G3, function(x) x$retainedB))),
               which.max(unlist(lapply(G4, function(x) x$retainedB))))
               
G1.bc <- barray(bread$cata[1:8, , 1:5])
G1.B <- getb(G1.bc[,,1,], G1.bc[,,2,])
BpctB <- data.frame(retainedB = c(G1.B, 
                                  G2[[best.indx[1]]]$retainedB, 
                                  G3[[best.indx[2]]]$retainedB,
                                  G4[[best.indx[3]]]$retainedB))
BpctB$pctB <- 100*BpctB$retainedB / G2[[1]]$totalB
BpctB$deltaB <- 
           c(100*(1-BpctB$retainedB[-nrow(BpctB)] / BpctB$retainedB[-1]), NA)
BpctB <- BpctB[-nrow(BpctB),]

opar <- par(no.readonly=TRUE)
par(mar = rep(5,4))
selectionPlot(BpctB$deltaB, BpctB$pctB, indx = 2)
par(opar)