is.wholenumber

Function to check if a number is whole, accounting for a rounding error.

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>.

Silvia Liverani

PReMiuM

Dirichlet Process Bayesian Clustering, Profile Regression

is.wholenumber function

<dl><dt>x</dt>
<dd>The number to be checked.</dd><dt>tol</dt>
<dd>Tolerance level.</dd></dl>

Arguments

David Hastie, Department of Epidemiology and Biostatistics, Imperial College London, UK
Silvia Liverani, Department of Epidemiology and Biostatistics, Imperial College London and MRC Biostatistics Unit, Cambridge, UK
Maintainer: Silvia Liverani &lt;liveranis@gmail.com&gt;

Authors

Function to check if a number is a whole number — is.wholenumber

<dl>

<dt>x</dt>
<dd>The number to be checked.</dd>

<dt>tol</dt>
<dd>Tolerance level.</dd>

</dl>

is.wholenumber: Function to check if a number is a whole number

Description

Usage

Value

Arguments

Authors

References

Examples