Please use this identifier to cite or link to this item:
Title: Premium: An R package for profile regression mixture models using dirichlet processes
Authors: Liverani, S
Hastie, DI
Azizi, L
Papathomas, M
Richardson, S
Keywords: Clustering;Dirichlet process mixture model;Profile regression
Issue Date: 2015
Publisher: American Statistical Association
Citation: Journal of Statistical Software, 2015, 64 (7), pp. 1 - 30
Abstract: PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, nonparametrically 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.
ISSN: 1548-7660
Appears in Collections:Dept of Mathematics Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf613.6 kBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.