simulatePilotData {powerPLS} | R Documentation |
Simulate pilot data
Description
Simulate cluster pilot data
Usage
simulatePilotData(seed = 123, nvar, clus.size, nvar_rel,m, A = 2, S1 = NULL, S2 = NULL)
Arguments
seed |
Seed value |
nvar |
Number of variables |
clus.size |
Vector of two elements, specifying the size of classes (only two classes are considered) |
nvar_rel |
Number of variables relevant to predict the dependent variable |
m |
Effect size of separation between classes |
A |
Oracle number of score components |
S1 |
Covariance matrix for the first class. Default |
S2 |
Covariance matrix for the second class. Default |
Author(s)
Angela Andreella @return List with the following objects:
- X
matrix of predictor variables with
nvar
columns and the sum ofclus.size
values as number of rows.- Y
vector of dependent variable with the sum of
clus.size
values as length
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.
Examples
datas <- simulatePilotData(nvar = 10, clus.size = c(5,5),m = 6,nvar_rel = 5,A = 2)