powerPLS (version 0.2.1)

scoreTest: Score test

Description

Performs permutation-based test based on predictive score vector

Usage

scoreTest(X, Y, nperm = 200, A, randomization = FALSE,
Y.prob = FALSE, eps = 0.01, scaling = 'auto-scaling',
post.transformation = TRUE, cross.validation = FALSE, seed = 123, ...)

Value

List with the following objects:

pv

raw p-value. It equals NA if randomization = FALSE

pv_adj

adjusted p-value. It equals NA if randomization = FALSE

test

estimated test statistic

Arguments

X

data matrix where columns represent the \(p\) variables and rows the \(n\) observations.

Y

data matrix where columns represent the two classes and rows the \(n\) observations.

nperm

number of permutations. Default to 200.

A

number of score components

randomization

Boolean value. Default to FALSE. If TRUE the permutation p-value is computed

Y.prob

Boolean value. Default FALSE. IF TRUE Y is a probability vector

eps

Default 0.01. eps is used when Y.prob = FALSE to transform Y in a probability vector

scaling

Type of scaling, one of c('auto-scaling', 'pareto-scaling', 'mean-centering'). Default 'auto-scaling'.

post.transformation

Boolean value. TRUE if you want to apply post transformation. Default TRUE

cross.validation

Boolean value. Default FALSE. TRUE if you want to compute the observed test statistic by Nested cross-validation

seed

Seed value

...

additional arguments related to cross.validation. See repeatedCV_test

Author

Angela Andreella

References

For the general framework of power analysis for PLS-based methods see:

Andreella, A., Fino, L., Scarpa, B., & Stocchero, M. (2024). Towards a power analysis for PLS-based methods. arXiv preprint https://arxiv.org/abs/2403.10289.

See Also

Other test statistics implemented: mccTest, R2Test, sensitivityTest, specificityTest,AUCTest, dQ2Test, FMTest, F1Test.

Examples

datas <- simulatePilotData(nvar = 30, clus.size = c(5,5),m = 6,nvar_rel = 5,A = 2)
out <- scoreTest(X = datas$X, Y = datas$Y, A = 1)
out