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.
|Author||Matthieu Marbac and Mohammed Sedki|
|Date of publication||2016-12-14 14:16:05|
|Maintainer||Mohammed Sedki <email@example.com>|
|License||GPL (>= 2)|
ComputeEmpiricCramer: Computation of the Empiric Cramer'v.
ComputeMvBinaryCramer: Computation of the model Cramer'v.
MvBinaryEstim: Create an instance of the ['MvBinaryResult'] class
MvBinaryExample: Simulated binary data: MvBinaryExample
MvBinary-package: MvBinary a package for Multivariate Binary data
MvBinaryProbaPost: Computation of the model Cramer'v.
MvBinaryResult-class: Constructor of ['MvBinaryResult'] class
plants: Real binary data: Plants
print-methods: Summary function.
summary-methods: Summary function.