sim_data: Wrapper function to generate data from a variety of...
In ramhiser/clusteval: Evaluation of Clustering Algorithms

Description Usage Arguments Details Value Examples

We provide a wrapper function to generate from three data-generating models:

sim_unif: Multivariate uniform distributions
sim_normal: Multivariate normal distributions with intraclass covariance matrices
sim_student: Multivariate Student's t distributions each with a common covariance matrix

1	sim_data(family = c("uniform", "normal", "student"), ...)

`family`	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 argument 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:

x:: A matrix whose rows are the observations generated and whose columns are the p features (variables)
y:: A vector denoting the population from which the observation in each row was generated.

set.seed(42)
uniform_data <- sim_data(family = "uniform")
normal_data <- sim_data(family = "normal", delta = 2)
student_data <- sim_data(family = "student", delta = 1, df = 1:5)