sensory.mrCA: Multiple-response Correspondence Analysis (MR-CA) for sensory...

In MahieuB/MultiResponseR: Analysis of Data from Multiple-Response Questionnaires

Multiple-response Correspondence Analysis (MR-CA) for sensory data

Description

This function performs the MR-CA of the data as well as the total bootstrap procedure (Cadoret & Husson, 2013) and the pairwise total bootstrap tests as proposed in Castura et al. (2023). The difference with mrCA used with ellipse=TRUE is that the total bootstrap procedure is stratified with respect to subjects in sensory.mrCA

Usage

sensory.mrCA(data, nboot = 2000, nbaxes.sig = Inf)

Arguments

data	A data.frame of evaluations in rows whose first two columns are factors (subject and product) and subsequent columns are binary numeric or integer, each column being a descriptor
nboot	The number of bootstrapped panel of the total bootstrap procedure
nbaxes.sig	The number of significant axes retuned by sensory.mr.dimensionality.test. By default, all axes are considered significant. See details

Details

• nbaxes.sig: The number of significant axes determines the number of axes accounted for while performing the Procrustes rotations of the total bootstrap procedure (Mahieu, Schlich, Visalli, & Cardot, 2021). These same axes are accounted for the pairwise total bootstrap tests.

Value

A list with the following elements:

eigen

Eigenvalues of the MR-CA and their corresponding percentages of inertia

prod.coord

Products coordinates

desc.coord

Descriptors coordinates

svd

Results of the singular value decomposition

bootstrap.replicate.coord

Coordinates of the rotated bootstrap replicates

total.bootstrap.test.pvalues

P-values of the pairwise total bootstrap tests

References

Cadoret, M., & Husson, F. (2013). Construction and evaluation of confidence ellipses applied at sensory data. Food Quality and Preference, 28(1), 106-115.

Castura, J. C., Varela, P., & Næs, T. (2023). Evaluation of complementary numerical and visual approaches for investigating pairwise comparisons after principal component analysis. Food Quality and Preference, 107.

Mahieu, B., Schlich, P., Visalli, M., & Cardot, H. (2021). A multiple-response chi-square framework for the analysis of Free-Comment and Check-All-That-Apply data. Food Quality and Preference, 93.

Examples

data(milkchoc)

dim.sig=sensory.mr.dimensionality.test(milkchoc)$dim.sig

res=sensory.mrCA(milkchoc,nbaxes.sig=dim.sig)

plot(res)

MahieuB/MultiResponseR documentation built on June 22, 2024, 8:08 a.m.