bairt (version 0.1.2)

check.plot.mcmc.3pnob: Plot of the discrimination marginal posterior means against difficulty marginal posterior means

Description

Marginal Posterior means of \(b_j\) plotted against the marginal posterior means of \(a_j\). Each point is labeled with the number of the corresponding Item.

For the Three-Parameter Normal Ogive Item Response Model (3pno), the size of the numbers refers to the marginal posterior means of \(c_j\).

The Potential Scale Reduction Factor (Rhat) is calculated for each chain, bairt generates a single MCMC and evaluates convergence by breaking the chain in three sub chains and comparing the between- and within-subchain variance.

The black color suggests convergence and red items indicate convergence problems (Rhat greater than 1.1).

Usage

# S3 method for mcmc.3pnob
check.plot(mcmclist, converg.test = T, c.probs = c(0,
  0.2, 0.5, 1), legen = "topleft", ...)

Arguments

mcmclist

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

converg.test

Checking if Rhat is major that 1.1.

c.probs

Vector for assignment of intervals the Guessing (c).

legen

Coordinates to be used to position the Guessing (c) legend.

...

Further arguments.

Value

A plot of the discrimination marginal posterior means against difficulty marginal posterior means. For the Three-parameter model the guessing marginal posterior means are represented by the number size of the item.

Details

If converg.test = TRUE the items with Rhat menor that 1.1 are print in red color. It is useful for quick check of the convergence.

References

Johnson, V. E. & Albert, J. H. (1999). Ordinal Data Modeling. New York: Springer.

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

# 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)
check.plot(model2)
chain.study(model2, parameter = "b", chain = 12)
chain.study(model2, parameter = "theta", chain = 10)

# }
# NOT RUN {
# For all examinees of the data
# Two-Parameter Normal Ogive Model
modelAll2 <- mcmc.2pnob(MathTest, iter = 3500, burning = 500, thin = 10)
check.plot(modelAll2)
chain.study(modelAll2, parameter = "b", chain = 14)
chain.study(modelAll2, parameter = "theta", chain = 10)

# Three-Parameter Normal Ogive Model
modelAll3 <- mcmc.3pnob(MathTest, iter = 3500, burning = 500, thin = 10)
check.plot(modelAll3)
chain.study(modelAll3, parameter = "b", chain = 12)
chain.study(modelAll3, parameter = "c", chain = 10)
# }
# NOT RUN {
## End(Not run)

# }