Learn R Programming

emdbook (version 1.3.2.1)

credint: Calculate Bayesian credible intervals

Description

Calculate Bayesian credible intervals based on various types of information about the posterior distribution

Usage

tcredint(dist, parlist, ranges, level = 0.95, eps = 1e-05,verbose=FALSE)
ncredint(pvec,npost,level=0.95,tol=0.01,verbose=FALSE)

Arguments

dist
character string giving the name of a distribution for which "d", "q", and "p" function exist, e.g. "beta"
parlist
list of parameters to pass to distribution functions
ranges
lower, middle, and upper values to bracket lower and upper boundaries of the credible interval
level
confidence level
eps
if ranges is missing, set lower and upper brackets to the eps and 1-eps quantiles of the distribution
tol
tolerance on credible interval
verbose
if TRUE, return detailed information on the probability cutoff and realized area of the credible interval; if FALSE, just lower and upper bounds of the credible region
...
additional arguments to density
pvec
numeric vector of parameter values
npost
numeric vector of posterior density values corresponding to pvec

Value

  • A numeric vector giving the credible interval. If verbose=FALSE, gives just lower and upper bounds; if verbose=TRUE, also gives information on the probability cutoff and realized area of the credible interval

Details

tcredint gives credible intervals for a theoretical posterior density with defined density, cumulative density, and quantile functions; ncredint gives credible intervals for a numerical posterior density.

Examples

Run this code
tcredint("beta",list(shape1=5,shape2=10),verbose=TRUE)
pvec = seq(0,1,length=100)
postvec = dbeta(pvec,shape1=5,shape2=10)
ncredint(pvec,postvec,verbose=TRUE)
set.seed(1001)

Run the code above in your browser using DataLab