select.c.prior {bairt} | R Documentation |
Select the c (guessing parameter) prior for mcmc.3pnob, through the application of Bayes Modal Estimation Equations.
select.c.prior(nitem, m = 20, ...)
nitem |
Number of alternatives for each item. |
m |
It is a priori weight assigned to the prior information. m = 20 by default. |
... |
Further arguments. |
Because c (guessing parameter) is bounded by 0 and 1, a Beta(α, β) prior distribution was proposed by Swaminathan and Gifford (1986). These parameters are defined as α=mp+1 and β=m(p-1)+1, where p=1/n with n = number of alternatives for each item (Harwell & Baker, 1991, p.386)
A vector length 2, this indicate the c (guessing parameter) prior for mcmc.3pnob.
Javier MartÃnez
Harwell, M. R, & Baker, F. B. (1991). The use of Prior Distributions in Marginalized Bayesian Item Parameter Estimation: A Didactic. Psychometrika, 15, 375-389.
mcmc.3pnob
and continue.mcmc.bairt
.
# data for model data("MathTest") # selection of the prior for 5 response options cprior <- select.c.prior(5) # estimate model only for the first 500 examinees of the data MathTest model3 <- mcmc.3pnob(MathTest[1:500,], iter = 300, burning = 0, c.prior = cprior) ## End(Not run)