In this version of HIPAM, called $HIPAM_MO$, the number k of (child) clusters is obtained by maximizing the silhouette width (asw). See Vinue et al. (2013) for more details.
The foundation and performance of the HIPAM algorithm is explained in hipamAnthropom
.
getBestPamsamMO(data,maxsplit,orness=0.7,type,ah,verbose,...)
weightsMixtureUB
and getDistMatrix
.
ah
slopes of the distance function in getDistMatrix
. Given the five variables considered, this vector is c(23,28,20,25,25). This vector would be different according to the variables considered.
Wit, E., and McClure, J., (2004). Statistics for Microarrays: Design, Analysis and Inference. John Wiley & Sons, Ltd.
Wit, E., and McClure, J., (2006). Statistics for Microarrays: Inference, Design and Analysis. R package version 0.1. http://www.math.rug.nl/~ernst/book/smida.html.
Pollard, K. S., and van der Laan, M. J., (2002). A method to identify significant clusters in gene expression data. Vol. II of SCI2002 Proceedings, 318--325.
McCulloch, C., Paal, B., and Ashdown, S., (1998). An optimization approach to apparel sizing, Journal of the Operational Research Society 49, 492--499.
hipamAnthropom