bairt (version 0.1.2)

object.coda.bairt: Creating an mcmc.list for coda package

Description

The function object.coda create a mcmc.list object. With this is possible to study the chain using the coda packet.

Usage

# S3 method for bairt
object.coda(mcmclist, parameter = "a", chain = 1,
  parts = NULL, ...)

Arguments

mcmclist

A mcmc.2pnob or mcmc.3pnob class object.

parameter

The parameter (a, b, c or theta) for graphing.

chain

The parameter's chain that will be graphed.

parts

Number of splits for MCMC chain.

...

Further arguments.

Value

A mcmc.list coda packet object.

Details

The function object.coda create a mcmc.list object of the marginal chain selectionated. The marginal chain is splited in subchains determined by parts. The aim is represent parallel chains with different starting values (Beguin & Glas, 2001, p. 547).

References

A.A. Beguin, A, A. & Glas, C.A.W. (2001). MCMC Estimation and Some Model-Fit Analysis of Multidimensional IRT Models. Psychometrika, 66, 541-562.

See Also

as.mcmc.list and as.mcmc.

Examples

# NOT RUN {
# 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 = 400, burning = 100)

chain_a1 <- object.coda(model2, parameter = "a", chain = 1)
coda::gelman.plot(chain_a1)
coda::gelman.diag(chain_a1)
plot(chain_a1)

# }
# NOT RUN {
# For all examinees of the data MathTest
# Three-Parameter Normal Ogive Model
# selection of the prior for 5 response options
cprior <- select.c.prior(5)
model3 <- mcmc.3pnob(MathTest, iter = 3500, burning = 500,
                    c.prior = cprior, parts = 3)

chain_c1 <- object.coda(model3, parameter = "c", chain = 1)
coda::gelman.plot(chain_c1)
coda::gelman.diag(chain_c1)
plot(chain_c1)
# }
# NOT RUN {
## End(Not run)


# }