madperm {cata} | R Documentation |
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)
Arguments
X |
a three-way (or four-way) array with |
B |
permutations in null distribution; ensure |
seed |
specify a numeric seed for reproducibility; if not provided, a random seed is generated |
tests |
numeric vector specifying which tests to conduct; default
|
alpha |
Type I error rate (default: |
control.fdr |
control False Discovery Rate (using Benjamini-Hochberg (BH) step-up
procedure)? (default: |
verbose |
return null distribution(s) and function call? (default: |
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
parameterGlobal.Results
: list of multivariate (global) resultsUnivariate.Results
: list ofT
univariate resultsElementwise.Results
: list ofPT
elementwise resultsMultivariate.Paired.Results
: list ofP(P-1)/2
multivariate paired resultsUnivariate.Paired.Results
: list ofP(P-1)T/2
univariate paired results
also, if verbose
is TRUE
:
Null.Dist
list of null distributions for tests specifiedCall
:madperm
function call
Author(s)
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. 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