jhbayes: Two functions to provide better JAGS model output
In LOGIT: Functions, Data and Code for Binary and Binomial Data

Description Usage Arguments Details Note Author(s) References Examples

For use with JAGS from within the R environment. Provides a nicer model output than comes with the default JAGS output.

1	#source("jhbayes.r")

`x`	Variable arguments based on model

Load jhbayes.r prior to running JAGS model. MyBUGSOutput and uNames functions will then be in memory. From Alain Zuur support files on highstat.com.

jhbayes.r must be loaded into memory in order to be effectve. Use the source function or paste into R editor. Code is 23 lines in length.

Alain F. Zuur, Highlands Statistics, UK. highstat@highstat.com Joseph M. Hilbe, Arizona State University, and Jet Propulsion Laboratory, California Institute of technology hilbe@asu.edu

Hilbe, Joseph M. (2015), Practical Guide to Logistic Regression, Chapman & Hall/CRC, page 137-143.

Zuur, A.F., Hilbe, J.M., and Ieno, E.N. (2013), A Beginner's Guide to GLM and GLMM with R: a frequentist and Bayesian perspective for ecologists, Highlands.

#library(R2jags)
#library(LOGIT)
#data(medpar)
#JAGS code with J0 as MCMC algorithm
#  out <- J0$BUGS$output
#  myB <- MyBUGSOutput(out, c(uNames("beta", K), "LogL", "AIC", "BIC"))
#  round(myB, 4)