PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes.
PReMiuMis 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.
Brunel University London.
Silvia
S
Liverani
Imperial College London.
David I
DI
Hastie
MRC Biostatistics Unit Cambridge.
Lamiae
L
Azizi
University of St Andrews.
Michail
M
Papathomas
MRC Biostatistics Unit Cambridge.
Sylvia
S
Richardson
Dirichlet process mixture model; clustering; profile regression