CAT_DT {cat.dt} R Documentation

## CAT decision tree

### Description

Generates a cat.dt object containing the CAT decision tree. This object has all the necessary information to build the tree.

### Usage

CAT_DT(
bank,
model = "GRM",
crit = "MEPV",
C = 0.3,
stop = c(6, 0),
limit = 200,
inters = 0.98,
p = 0.9,
dens,
...
)


### Arguments

 bank data.frame or matrix of the item bank. Rows represent items, and columns represent parameters. If the model is "GRM", the first column represents the alpha parameters and the next columns represent the beta parameters. If the model is "NRM", odd columns represent the alpha parameters and even columns represent beta parameters model polytomous IRT model. Options: "GRM" for Graded Response Model and "NRM" for Nominal Response Model 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

### Value

An object of class cat.dt

### Examples

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, ])



[Package cat.dt version 0.3.1 Index]