CAT_DT {cat.dt}  R Documentation 
Generates a cat.dt
object containing the CAT decision tree.
This object has all the necessary information to build the tree.
CAT_DT( bank, model = "GRM", crit = "MEPV", C = 0.3, stop = c(6, 0), limit = 200, inters = 0.98, p = 0.9, dens, ... )
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 
apriori 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?guezCuadrado
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, ])