Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/continue.mcmc.R
This is a function for bairt objects. You can use continue.mcmc for continue the MCMC the Two-Parameter or Three-Parameter normal ogive item response model.
1 2 3 4 |
mcmclist |
A bairt class object (mcmc.2pnob or mcmc.3pnob). |
initial.value |
List with initial values. |
c.prior |
A two dimensional vector which defines the beta prior distribution of guessing parameters. The default is a non-informative prior, Beta(1,1). |
iter |
Total number of iterations. |
burning |
Number of burnin iterations. |
thin |
The thinning interval between consecutive observations. |
parts |
Number of splits for MCMC chain. |
... |
Further arguments. |
If any argument (final.values, c.prior, iter, burning, thin or parts) is NULL, continue.mcmc take the value of the mcmclist.
An mcmc.2pnob or mcmc.3pnob object.
Javier Mart<c3><ad>nez
Johnson, V. E., & Albert, J. H. (1999). Ordinal Data Modeling. New York: Springer.
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.
mcmc.2pnob
and mcmc.3pnob
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | # 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)
# continue the MCMC for the Two-Parameter Normal Ogive Model
model21 <- continue.mcmc(model2, iter = 100, burning = 0)
# 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)
modelAll3 <- mcmc.3pnob(MathTest, iter = 1000, burning = 0,
c.prior = cprior)
#continue the MCMC for the Three-Parameter Normal Ogive Model
# form 1
initialValues2 <- final.values.mcmc(modelAll3)
modelAll31 <- mcmc.3pnob(MathTest, initial.value = initialValues2,
iter = 2000, burning = 0, c.prior = cprior)
# form 2
modelAll32 <- continue.mcmc(modelAll3, iter = 2000, burning = 0)
## End(Not run)
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