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