Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution
Computation of the Empiric Cramer'v.
Computation of the model Cramer'v.
MvBinary a package for Multivariate Binary data
Create an instance of the [MvBinaryResult] class
Computation of the model Cramer'v.
Constructor of [MvBinaryResult] class
Summary function.
Summary function.
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.