chain.study.bairt {bairt} | R Documentation |
Convergence graphs for the study of the simulated values for an MCMC marginal chain.
## S3 method for class 'bairt' chain.study(mcmclist, parameter = "a", chain = 1, line = TRUE, ...)
mcmclist |
A mcmc.2pnob or mcmc.3pnob class object. |
parameter |
The parameter (a, b, c or theta) for graphing. |
chain |
The number of the chain that will be graphed. |
line |
A red line that represent the posterior mean of the simulated values. |
... |
Further arguments. |
The top left graph displays the sequence of simulated values and the top right graph displays the lagged correlations of the sequence as a function of the lag value. The bottom left graph is an histogram of the simulated values and the bottom right graph is the box plot of the simulated values.
Convergence graphs for the study of the simulated values for an MCMC marginal chain.
Javier MartÃnez
Johnson, V. E. & Albert, J. H. (1999). Ordinal Data Modeling. New York: Springer.
mcmc.2pnob
, mcmc.3pnob
and
continue.mcmc
.
# 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) # For all examinees of the data MathTest # 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) ## End(Not run)