simulatePilotData: Simulate pilot data

In powerPLS: Power Analysis for PLS Classification

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 NULL, i.e., the identity is considered.
S2	Covariance matrix for the second class. DefaultNULL, i.e., the identity is considered.

Author(s)

Angela Andreella @return List with the following objects:

X

matrix of predictor variables with nvar columns and the sum of clus.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)

powerPLS documentation built on April 4, 2025, 1:57 a.m.