Dirichlet Process Bayesian Clustering, Profile Regression
Calculation of the average risks and profiles
Calculates the dissimilarity matrix
Calculation of the optimal clustering
Calculates the predictions
Sample datasets for profile regression
computeRatioOfVariance
Generate sample data files for profile regression
Plot of the trace of some of the global parameters
Plot the heatmap of the dissimilarity matrix
Function to check if a number is a whole number
Map generated data
Marginal Model Posterior
Plot the conditional density using the predicted scenarios
Plot the Risk Profiles
Dirichlet Process Bayesian Clustering
Profile Regression
Asymmetric Laplace Distribution
Definition of characteristics of sample datasets for profile regressio...
Benchmark for simulated examples
summariseVarSelectRho
Vector to upper triangular matrix
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. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. 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. The main reference for the package is Liverani, Hastie, Azizi, Papathomas and Richardson (2015) <doi:10.18637/jss.v064.i07>.