ILCA: Iterative latent-class analysis

In Wenchao-Ma/GDINA: The Generalized DINA Model Framework

Iterative latent-class analysis

Description

This function implements an iterative latent class analysis (ILCA; Jiang, 2019) approach to estimating attributes for cognitive diagnosis.

Usage

ILCA(dat, Q, seed.num = 5)

Arguments

dat	A required binary item response matrix.
Q	A required binary item and attribute association matrix.
seed.num	seed number; Default = 5.

Value

Estimated attribute profiles.

Author(s)

Zhehan Jiang, The University of Alabama

References

Jiang, Z. (2019). Using the iterative latent-class analysis approach to improve attribute accuracy in diagnostic classification models. Behavior research methods, 1-10.

Examples

## Not run: 
ILCA(sim10GDINA$simdat, sim10GDINA$simQ)

## End(Not run)

Wenchao-Ma/GDINA documentation built on Nov. 13, 2022, 5:35 a.m.