We provide a wrapper function to generate from three data-generating models:
Multivariate uniform distributions
Multivariate normal distributions with intraclass covariance matrices
Multivariate Student's t distributions each with a common covariance matrix
the family of distributions from which to generate data
optional arguments that are passed to the data-generating function
For each data-generating model, we generate n_k observations (k =
1, …, K_0) from each of K_0 multivariate distributions so that
the Euclidean distance between each of the population centroids and the
origin is equal and scaled by Δ ≥ 0. For each model, the
delta controls this separation.
This wrapper function is useful for simulation studies, where the efficacy of supervised and unsupervised learning methods and algorithms are evaluated as a the population separation is increased.
named list containing:
A matrix whose rows are the observations generated and whose
columns are the
p features (variables)
A vector denoting the population from which the observation in each row was generated.
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