Liverani Silvia, Hastie David I, Azizi Lamiae, Papathomas Michail, Richardson Sylvia
Brunel University London.
Imperial College London.
J Stat Softw. 2015 Mar 20;64(7):1-30. doi: 10.18637/jss.v064.i07.
is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership (Molitor, Papathomas, Jerrett, and Richardson 2010). The package allows binary, categorical, count and continuous response, as well as continuous and discrete covariates. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.
是一个最近开发的用于使用狄利克雷过程混合模型进行贝叶斯聚类的R包。该模型是回归模型的替代方案，通过聚类成员资格将响应向量与协变量数据进行非参数链接（莫利托、帕帕托马斯、杰雷特和理查森，2010年）。该包允许二元、分类、计数和连续响应，以及连续和离散协变量。此外，可以对响应进行预测，并处理协变量的缺失值。实现了几个采样器和标签切换移动以及用于评估收敛的诊断工具。还提供了许多用于输出后处理的R函数。除了拟合混合模型外，确定哪些协变量积极驱动混合成分可能也很有意义。这在包中作为变量选择来实现。
