Yamm (version 1.3.2)

clusters3d: Four Clusters of 3-dimensional Data

Description

This dataset with 105 observations contains four clusters, which are generated from different Laplace distributions randomly, and five outliers (located in the last five rows).

Usage

data("clusters3d")

Format

The four clusters are generated from different multivariate Laplace distributions. The first cluster has 20 observations, where the mean values \(\mu\) of the Laplace distribution are equal to \((-8, -8, -8)\) and the covariance matrix \(\Sigma\) is the product of two times identity matrix. The second cluster has 35 observations, where \(\mu = (-5, 5, 5)\) and \(\Sigma\) is the identity matrix. The third cluster has 30 observations, where \(\mu = (12, -12, 12)\) and \(\Sigma\) is the identity matrix. The fourth cluster has 30 observations, where \(\mu = (18, 18, -18)\) and \(\Sigma\) is the identity matrix. The five outliers are from the \(\mu = (100, 100, -100)\) and \(\Sigma\) is the product of ten times identity matrix.

References

Chen, F. and Nason, Guy P. (2020) A new method for computing the projection medi an, its influence curve and techniques for the production of projected quantile plots. PLOS One, 10.1371/journal.pone.0229845

Examples

# NOT RUN {
data(clusters3d)

# }