sim.data.FCGGUM: Simulate Data from the Forced-Choice GGUM Model

In ForceChoice: Forced-Choice Modeling Based on Item Response Theory and Cognitive Diagnostic Models

Simulate Data from the Forced-Choice GGUM Model

Description

Generates forced-choice ranking data using the GGUM ideal-point model as the item-level endorsement process. Within-block rankings follow the same sequential Luce/Plackett mechanism as model.FCGGUM: the utility for each statement is the logit of its binary GGUM endorsement probability.

Usage

sim.data.FCGGUM(
  N.person = 1000,
  N.block = 10,
  I.block = 2,
  D = 3,
  fc.type = "RANK",
  control = NULL
)

Arguments

N.person	Integer; number of persons (default: 1000).
N.block	Integer; number of forced-choice blocks (default: 10).
I.block	Integer; items per block (default: 2).
D	Integer; number of latent dimensions (default: 3, must be >= 2).
fc.type	Character; "RANK" (default), "MOLE", or "PICK".
control	Optional list with entries Q.matrix, block.items, and Corr.

Value

An object of class "data.FCGGUM" containing data, response, theta, par, Q.matrix, block.items, Corr, patterns, patterns.total, prob, mask, and data-generating arguments.

See Also

fit.FCGGUM, model.FCGGUM

Examples

set.seed(123)
sim <- sim.data.FCGGUM(N.person = 20, N.block = 3, I.block = 2,
                       D = 2, fc.type = "RANK")
str(sim$data)
head(sim$response)
dim(sim$prob)

ForceChoice documentation built on Sept. 13, 2026, 1:06 a.m.