pLift: Penalty-Lift Analysis

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

Penalty-Lift Analysis

Description

Penalty-Lift analysis for CATA variables, which is the difference between the average hedonic response when CATA attribute is checked vs. the average hedonic response when CATA attribute is not checked.

Usage

pLift(X, Y)

Arguments

X	either a matrix of CATA data with I consumers (rows) and J products (columns) or an array of CATA data with I consumers, J products, and M attributes.
Y	matrix of hedonic data with I consumers (rows) and J products (columns)

Value

Penalty lift for the attribute if X is a matrix; otherwise, penalty-lift for each attribute if X is a 3d array. If an attributes is only checked or not check then NA is returned.

References

Meyners, M., Castura, J.C., & Carr, B.T. (2013). Existing and new approaches for the analysis of CATA data. Food Quality and Preference, 30, 309-319, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.foodqual.2013.06.010")}

Examples

data(bread)

# penalty lift, based only on the first 12 consumers

# for the first attribute ("Fresh")
pLift(bread$cata[1:12,,1], bread$liking[1:12, ]) 

# for the first 3  attributes
pLift(bread$cata[1:12,,1:3], bread$liking[1:12, ]) 

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