carcinoma

<p>The carcinoma data from Agresti (2002, 542) consist of seven dichotomous variables representing
the ratings by seven pathologists of 118 slides on the presence or absence of carcinoma.
The purpose of studying this data is to model "interobserver agreement" by examining how
subjects might be divided into groups depending upon the consistency of their diagnoses.</p>

datasets

Fits finite Bayesian mixture models with a random number of components. The MCMC algorithm implemented is based on point processes as proposed by Argiento and De Iorio (2019) <arXiv:1904.09733> and offers a more computationally efficient alternative to reversible jump. Different mixture kernels can be specified: univariate Gaussian, multivariate Gaussian, univariate Poisson, and multivariate Bernoulli (latent class analysis). For the parameters characterising the mixture kernel, we specify conjugate priors, with possibly user specified hyper-parameters. We allow for different choices for the prior on the number of components: shifted Poisson, negative binomial, and point masses (i.e. mixtures with fixed number of components).

Bruno Bodin

AntMAN

Anthology of Mixture Analysis Tools

Priscilla Ong

Raffaele Argiento

Maria De Iorio

carcinoma function

<p>A data frame with 118 rows and 7 variables (from A to G).</p>

Format

Carcinoma dataset — carcinoma

Carcinoma dataset

carcinoma: Carcinoma dataset

Description

Usage

Arguments

Format

References

Examples