文章基本信息

标题：bayesclust: An R Package for Testing and Searching for Significant Clusters
本地全文：下载
作者：Vikneswaran Gopal ; Claudio Fuentes ; George Casella 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2012
卷号：47
期号：1
页码：1-21
语种：English
出版社：University of California, Los Angeles
摘要：The detection and determination of clusters has been of special interest among researchers from different fields for a long time. In particular, assessing whether the clusters are significant is a question that has been asked by a number of experimenters. In Fuentes and Casella (2009), the authors put forth a new methodology for analyzing clusters. It tests the hypothesis H 0 : κ = 1 versus H 1 : κ = k in a Bayesian setting, where κ denotes the number of clusters in a population. The bayesclust package implements this approach in R. Here we give an overview of the algorithm and a detailed description of the functions available in the package. The routines in bayesclust allow the user to test for the existence of clusters, and then pick out optimal partitionings of the data. We demonstrate the testing procedure with simulated datasets.