Description Usage Arguments Value Author(s) Examples
Generates a cat.dt
object containing the CAT decision tree.
This object has all the necessary information to build the tree.
1 2 3 4 5 6 7 8 9 10 11 12 |
bank |
|
model |
polytomous IRT model. Options: |
crit |
item selection criterion. Options: "MEPV" for Minimum Expected Posterior Variance and "MFI" for Maximum Fisher Information |
C |
vector of maximum item exposures. If it is an integer, this value is replicated for every item |
stop |
vector of two components that represent the decision tree stopping criterion. The first component represents the maximum level of the decision tree, and the second represents the minimum standard error of the ability level (if it is 0, this second criterion is not applied) |
limit |
maximum number of level nodes |
inters |
minimum common area between density functions in the nodes of the evaluated pair in order to join them |
p |
a-priori probability that controls the tolerance to join similar nodes |
dens |
density function (e.g. dnorm, dunif, etc.) |
... |
parameters of the density function |
An object of class cat.dt
Javier Rodr?guez-Cuadrado
1 2 3 4 5 6 7 8 9 10 11 12 | data("itemBank")
# Build the cat.dt
nodes = CAT_DT(bank = itemBank, model = "GRM", crit = "MEPV",
C = 0.3, stop = c(3,0.05), limit = 100, inters = 0.9,
p = 0.9, dens = dnorm, 0, 1)
# Estimate the ability level of a subject with responses res
CAT_ability_est(nodes, res = itemRes[1, ])
# or
nodes$predict(res = itemRes[1, ])
# or
predict(nodes, itemRes[1, ])
|
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