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Qval (version 1.1.1)

The Q-Matrix Validation Methods Framework

Description

Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) discrimination index (the GDI method) by de la Torre and Chiu (2016) , the step-wise Wald test method (the Wald method) by Ma and de la Torre (2020) , the Hull method by Najera et al. (2021) , the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) , the beta method based on signal detection theory by Li and Chen (2024) and Q-matrix validation based on relative fit index by Chen et la. (2013) . Different research methods and iterative procedures during Q-matrix validating are available.

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Version

Install

install.packages('Qval')

Monthly Downloads

266

Version

1.1.1

License

GPL-3

Maintainer

Haijiang Qin

Last Published

January 9th, 2025

Functions in Qval (1.1.1)

zUSR

Caculate under-specifcation rate (USR)
zTPR

Caculate true-positive rate (TPR)
zTNR

Calculate true negative rate (TNR)
zOSR

Caculate over-specifcation rate (OSR)
zQRR

Caculate Q-matrix recovery rate (QRR)
sim.Q

generate a random Q-matrix
sim.data

generate response data
zVRR

Caculate vector recovery ratio (VRR)
sim.MQ

Simulate mis-specifications
validation

Perform Q-matrix validation methods
parallel_iter

A tool for the \(\beta\) Method
plot.Hull

Hull Plot
fit

Calculate data fit indeces
CDM

Parameter estimation for cognitive diagnosis models (CDMs) by MMLE/EM or MMLE/BM algorithm.
get.Mmatrix

Calculate \(\mathbf{M}\) matrix
get.Rmatrix

Restriction matrix
get.PVAF

Calculate \(PVAF\)
get.priority

Priority of Attribute
Wald.test

Wald.test for two q-vecotrs
get.R2

Calculate McFadden pseudo-\(R^{2}\)