banknote

The data set contain 6 measures of 100 genuine and 100 counterfeit Swiss franc banknotes.

data sets

Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the generalized hyperbolic distribution. The first model 'MGHD' (Browne and McNicholas (2015) <doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016) <doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD' is the mixture of multiple scaled generalized hyperbolic distributions, the 'cMSGHD' is a 'MSGHD' with convex contour plots and the 'MCGHD', mixture of coalesced generalized hyperbolic distributions is a new more flexible model (Tortora et al. (2019)<doi:10.1007/s00357-019-09319-3>. The paper related to the software can be found at <doi:10.18637/jss.v098.i03>.

Cristina Tortora

MixGHD

Model Based Clustering, Classification and Discriminant Analysis
Using the Mixture of Generalized Hyperbolic Distributions

banknote function

A data frame with the following variables:<dl class="dl-horizontal">
<dt>Status</dt><dd>the status of the banknote: genuine or counterfeit</dd>
<dt>Length</dt><dd>Length of bill (mm)</dd>
<dt>Left</dt><dd>Width of left edge (mm)</dd>
<dt>Right</dt><dd>Width of right edge (mm)</dd>
<dt>Bottom</dt><dd>Bottom margin width (mm)</dd>
<dt>Top</dt><dd>Top margin width (mm)</dd>
<dt>Diagonal</dt><dd>Length of diagonal (mm)</dd></dl>

banknote: Swiss Banknote data

Description

Usage

Arguments

Format

References