findPostCov: Posterior covariance matrix for a decomposable model.

In bayesloglin: Bayesian Analysis of Contingency Table Data

Description

Computes the posterior covariance matrix of the log-linear parameters, which for decomposable models, is known in closed form.

Usage

findPostCov(formula, alpha, data)

Arguments

formula	A decomposable model formula.
alpha	The value of the hyperparameter alpha.
data	A data frame containing the contingency table. All cells must be included in data and the last column must be the cell counts. The number of variables in the contingency table must be at least 2.

Value

theta

An array giving the posterior covariance matrix of the log-linear parameters.

Author(s)

Matthew Friedlander

References

see vignette

Examples

data(czech)
formula <- freq ~ b*c + a*c*e + d*e + f
s <- findPostCov (formula, alpha = 1, data = czech)
print(s)

bayesloglin documentation built on May 1, 2019, 9:45 p.m.