diagnostic.mcmc: Diagnostic of _mcmc.2pnob_ or _mcmc.3pnob_ object

In bairt: Bayesian Analysis of Item Response Theory Models

Description

This function gives the summary for all MCMC chains. It including calculus of Rhat, posterior mean, posterior standard deviation and posterior quartiles.

Usage

diagnostic.mcmc(mcmclist, ...)

Arguments

mcmclist	A mcmc.2pnob or mcmc.3pnob class object.
...	Further arguments.

Value

Data frame with the summary. It including calculus of Rhat, posterior mean, posterior standard deviation and posterior quartiles.

Author(s)

Javier Mart<c3><ad>nez

References

Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, B. (2004). Bayesian Data Analysis.New York: Chapman & Hall/CRC.

See Also

mcmc.2pnob, mcmc.3pnob and continue.mcmc.bairt.

Examples

# data for model
data("MathTest")

# Only for the first 500 examinees of the data MathTest
# Two-Parameter Normal Ogive Model
model2 <- mcmc.2pnob(MathTest[1:500,], iter = 100, burning = 0)
diagnostic.mcmc(model2)


# For all examinees of the data MathTest
# Three-Parameter Normal Ogive Model
model3 <- mcmc.3pnob(MathTest, iter = 3500, burning = 500)
diagnostic.mcmc(model3)


## End(Not run)

bairt documentation built on May 1, 2019, 10:56 p.m.