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, digits = getOption("digits"), verbose = FALSE)

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)
digits	for rounding
verbose	set to TRUE to report counts and averages for checked and not checked conditions (default: FALSE)

Value

Penalty lift per attribute, with counts and averages if verbose is TRUE.

Author(s)

J.C. Castura

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, ], digits = 3) 

# for the first 3  attributes with counts and averages
plift(bread$cata[1:12,,1:3], bread$liking[1:12, ], digits = 3, verbose = TRUE) 

cata documentation built on April 4, 2025, 5:17 a.m.