parameter.plot.bairt: Graph of marginal posterior densities
In bairt: Bayesian Analysis of Item Response Theory Models

Description Usage Arguments Details Value Author(s) References See Also Examples

Graph of marginal posterior densities for the item parameters (a, b or c).

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## S3 method for class 'bairt'
parameter.plot(mcmclist, items = NULL, parameter = NULL,
  prob = c(0.05, 0.95), ...)

`mcmclist`	A mcmc.2pnob or mcmc.3pnob class object.
`items`	A vector to indicate the item to be plotted.
`parameter`	The parameter (a, b, c or theta) for graphing.
`prob`	A vector of length two for defined the percentiles of the posterior density.
`...`	Further arguments.

Graph of marginal posterior densities of the item parameter a, b for mcmc.2pnob object or a, b, c for mcmc.3pnob object. The center of error bar corresponds to the marginal posterior mean and the extremes correspond to percentiles of the marginal posterior density (These are delimited by prob). For example, prob = c(0.05, 0.95) is equivalent to the 5th and 95th percentiles of the marginal posterior density.

Graph of posterior densities of the item parameter (a, b or c).

Javier Mart<c3><ad>nez

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

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

# 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)
parameter.plot(model2)
parameter.plot(model2, items = c(2, 10:15))
parameter.plot(model2, items = 1:100, parameter = "theta" )


# For all examinees of the data MathTest
# Three-Parameter Normal Ogive Model
model3 <- mcmc.3pnob(MathTest, iter = 3500, burning = 500)
parameter.plot(model3)
parameter.plot(model3, items = c(2, 10:15))
parameter.plot(model3, items = 1:100, parameter = c("c", "theta"))


## End(Not run)