bayesloglin: Bayesian analysis of contingency table data

In bayesloglin: Bayesian Analysis of Contingency Table Data

Description

Functions for Bayesian model selection and inference for log-linear models.

Details

Package:	bayesloglin
Type:	Package
Version:	1.0
Date:	2016-12-23
License:	GPL-2

The function MC3 searches for log-linear models with the highest posterior probability. The function gibbsSampler is a blocked Gibbs sampler for sampling fronm the posterior distribution of the log-linear parameters. The functions findPostMean and findPostCov compute the posterior mean and covariance matrix for decomposable models which, for these models, is available in closed form.

Author(s)

Author: Matthew Friedlander Maintainer: Matthew Friedlander <[e-mail]>

References

see vignette

Examples

data(czech)
s1 <- MC3 (init = NULL, alpha = 1, iterations = 5, 
           replicates = 1, data = czech, mode = "Decomposable")
s2 <- MC3 (init = NULL, alpha = 1, iterations = 5,   
            replicates = 1, data = czech, mode = "Graphical")
s3 <- MC3 (init = NULL, alpha = 1, iterations = 5,   
            replicates = 1, data = czech, mode = "Hierarchical")

Example output

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union


replicate [1], iteration [1].
proposal model = [a,e][b,f][c][d,e]
proposal logPostProb = 4920.34
proposal accepted
rejection rate =  0

replicate [1], iteration [2].
proposal model = [a,e][b,d][b,f][c][d,e]
proposal logPostProb = 4916.3
proposal rejected
rejection rate =  0.33

replicate [1], iteration [3].
proposal model = [a,e][b,f][c,f][d,e]
proposal logPostProb = 4916.48
proposal rejected
rejection rate =  0.5

replicate [1], iteration [4].
proposal model = [a,e][b,e][b,f][c][d,e]
proposal logPostProb = 4925.02
proposal accepted
rejection rate =  0.4

replicate [1], iteration [5].
proposal model = [a,d,e][b,e][b,f][c]
proposal logPostProb = 4924.15
proposal accepted
rejection rate =  0.33

replicate [1], iteration [1].
proposal model = [a,d][b,c][b,e][d,e,f]
proposal logPostProb = 7100.4
proposal accepted
rejection rate =  0

replicate [1], iteration [2].
proposal model = [a,c][a,d][b,c][b,e][d,e,f]
proposal logPostProb = 7110.3
proposal accepted
rejection rate =  0

replicate [1], iteration [3].
proposal model = [a,c][a,d,f][b,c][b,e][d,e,f]
proposal logPostProb = 7104.64
proposal rejected
rejection rate =  0.25

replicate [1], iteration [4].
proposal model = [a,c][b,c][b,e][d,e,f]
proposal logPostProb = 7108.62
proposal rejected
rejection rate =  0.4


[1] 15.6535
[1] 6771.347
replicate [1], iteration [1].
proposal model = [a,c][b,d][b,e][c,e][d,f]
proposal logPostProb = 6771.35
proposal rejected
rejection rate =  0.5

[1] 6770.477
replicate [1], iteration [2].
proposal model = [a,c][b,d][b,e][c,d][c,e][f]
proposal logPostProb = 6770.48
proposal rejected
rejection rate =  0.67

[1] 6770.477
replicate [1], iteration [3].
proposal model = [a,c][b,d][b,e][c,d][c,e][f]
proposal logPostProb = 6770.48
proposal rejected
rejection rate =  0.75

[1] 6770.477
replicate [1], iteration [4].
proposal model = [a,c][b,d][b,e][c,d][c,e][f]
proposal logPostProb = 6770.48
proposal rejected
rejection rate =  0.8

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