dQ2Test(X, Y, nperm = 200, A, randomization = FALSE,
Y.prob = FALSE, eps = 0.01, scaling = 'auto-scaling',
post.transformation = TRUE, class = 1, cross.validation = FALSE, ...)
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 TRUEY 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
class
Numeric value. Specifiy the reference class. Default 1
cross.validation
Boolean value. Default FALSE. TRUE if you want to compute the observed test statistic by Nested cross-validation
...
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, scoreTest,
sensitivityTest, specificityTest,AUCTest, R2Test,
FMTest, F1Test.