Description Usage Arguments Details Value Author(s) References See Also Examples
The function object.coda create a mcmc.list object. With this is possible to study the chain using the coda packet.
1 2 3 | ## S3 method for class 'bairt'
object.coda(mcmclist, parameter = "a", chain = 1,
parts = NULL, ...)
|
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. |
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).
A mcmc.list coda packet object.
Javier Mart<c3><ad>nez
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.
as.mcmc.list
and as.mcmc
.
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 | # 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)
# 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)
## End(Not run)
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