cata (version 0.1.0.27)

madperm: Permutation tests for CATA data

Description

Permutation tests for check-all-that-apply (CATA) data following the 'one citation, one vote' principle. Returns CATA frequency and percentage tables per condition and permutation test results specified.

Usage

madperm(X, B = 99, seed = .Random.seed, tests = 1:5, alpha = 0.05, control.fdr = FALSE, 
verbose = FALSE)

Value

list, one per condition:

• CATA.table : table of CATA citation percentages (\(P \times T\))

• CATA.freq : CATA frequency table (\(P \times T\))

• Permutation test results specified by the tests parameter

  1. Global.Results : list of multivariate (global) results

  2. Univariate.Results : list of \(T\) univariate results

  3. Elementwise.Results : list of \(PT\) elementwise results

  4. Multivariate.Paired.Results : list of \(P(P-1)/2\) multivariate paired results

  5. Univariate.Paired.Results : list of \(P(P-1)T/2\) univariate paired results

also, if verbose is TRUE:

• Null.Dist list of null distributions for tests specified

• Call : madperm function call

Arguments

X

a three-way (or four-way) array with \(I\) assessors, (\(R\) conditions/timepoints,) \(P\) products, \(T\) terms with data valued 0 (not checked) and 1 (checked)

B

permutations in null distribution; ensure B is larger than the default value (99) when conducting real analyses

seed

specify a numeric seed for reproducibility; if not provided, a random seed is generated

tests

numeric vector specifying which tests to conduct; default 1:5 (all tests): (1) multivariate (global) test; (2) univariate tests; (3) elementwise tests; (4) pairwise multivariate tests; (5) pairwise univariate tests

alpha

Type I error rate (default: \(\alpha\) = 0.05)

control.fdr

control False Discovery Rate (using Benjamini-Hochberg (BH) step-up procedure)? (default: FALSE)

verbose

return null distribution(s) and function call? (default: FALSE)

Author

J.C. Castura

References

Chaya, C., Castura, J.C., & Greenacre, M.J. (2025). One citation, one vote! A new approach for analyzing check-all-that-apply (CATA) data in sensometrics, using L1 norm methods. tools:::Rd_expr_doi("doi:10.48550/arXiv.2502.15945")

Examples

data(bread)
# add product names
X <- bread$cata[1:100,,1:5]
dimnames(X)[[2]] <- paste0("P", dimnames(X)[[2]])
# permutation tests for the first 100 consumers and 5 attributes
# will be run with default parameter values for illustrative purposes only
res <- madperm(X, B = 99, seed = 123)
print(res) # inspect results