conting (version 1.4)

inter_probs: Calculate Posterior Probability of Each Term

Description

This function computes the posterior probability of each term using the MCMC output of "bcct" and "bict" objects.

Usage

inter_probs(object, cutoff = 0.75, n.burnin = 0, thin = 1)

Arguments

object

An object of class "bcct" or "bict".

cutoff

An optional argument giving the cutoff posterior probability for displaying posterior summary statistics of the log-linear parameters. Only those log-linear parameters with a posterior probability greater than cutoff will be returned as p

n.burnin

An optional argument giving the number of iterations to use as burn-in. The default value is 0.

thin

An optional argument giving the amount of thinning to use, i.e. the computations are based on every thin-th value in the MCMC sample. The default value is 1, i.e. no thinning.

Value

• This function returns an object of class "interprob" which is a list with the following components.
• term
  A vector of term labels.
• prob
  A vector of posterior probabilities.
• thin
  The value of the argument thin.
• The function will only return elements in the above list if prob > cutoff.

Details

This function provides a scaled back version of what inter_stats provides. The use of thinning is recommended when the number of MCMC iterations and/or the number of log-linear parameters in the maximal model are/is large, which may cause problems with comuter memory storage.

See Also

bcct, bict, print.interprob, inter_stats.

Examples

set.seed(1)
## Set seed for reproducibility
data(AOH)
## Load AOH data

test1<-bcct(formula=y~(alc+hyp+obe)^3,data=AOH,n.sample=100,prior="UIP")
## Starting from maximal model of saturated model do 100 iterations of MCMC
## algorithm.

inter_probs(test1,n.burnin=10,cutoff=0)
## Calculate posterior probabilities having used a burn-in phase of 
## 10 iterations and a cutoff of 0 (i.e. display all terms with 
## non-zero posterior probability). Will get the following:

#Posterior probabilities of log-linear parameters:
#            post_prob
#(Intercept)    1.0000
#alc            1.0000
#hyp            1.0000
#obe            1.0000
#alc:hyp        0.1778
#alc:obe        0.0000
#hyp:obe        0.4444
#alc:hyp:obe    0.0000

## Note that the MCMC chain (after burn-in) does not visit any models 
## with the alc:obe or alc:hyp:obe interactions.