make_item_prob_cdm: Build CDM item response probabilities for attribute mastery...

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

Build CDM item response probabilities for attribute mastery patterns

Description

Computes the probability of endorsing an item for each attribute mastery pattern under the specified CDM condensation rule (DINA, DINO, ACDM, or GDINA). p0 is the lower-bound (guessing) probability and p1 is the upper-bound (1 - slipping) probability.

Usage

make_item_prob_cdm(patterns, model, p0, p1, mono.constraint = TRUE)

Arguments

patterns	A binary matrix of attribute mastery patterns.
model	One of "DINA", "DINO", "ACDM", "GDINA".
p0	Lower-bound probability (guessing).
p1	Upper-bound probability (1 - slipping).
mono.constraint	Logical; if TRUE (default), enforce the monotonicity constraint that mastering more attributes never decreases the success probability. Only applies to model = "GDINA".

Value

A named numeric vector of probabilities, one per pattern row.

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