scoreTest {powerPLS} | R Documentation |
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, ...)
Arguments
X |
data matrix where columns represent the |
Y |
data matrix where columns represent the two classes and
rows the |
nperm |
number of permutations. Default to 200. |
A |
number of score components |
randomization |
Boolean value. Default to |
Y.prob |
Boolean value. Default |
eps |
Default 0.01. |
scaling |
Type of scaling, one of
|
post.transformation |
Boolean value. |
cross.validation |
Boolean value. Default |
seed |
Seed value |
... |
additional arguments related to |
Value
List with the following objects:
- pv
raw p-value. It equals
NA
ifrandomization = FALSE
- pv_adj
adjusted p-value. It equals
NA
ifrandomization = FALSE
- test
estimated test statistic
Author(s)
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