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GDINA (version 2.0.8)

The Generalized DINA Model Framework

Description

A set of psychometric tools for cognitive diagnosis modeling for both dichotomous and polytomous responses. Various cognitive diagnosis models can be estimated, include the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) , the sequential G-DINA model by Ma and de la Torre (2016) , and many other models they subsume. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.

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Install

install.packages('GDINA')

Monthly Downloads

961

Version

2.0.8

License

GPL-3

Issues

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Repository

https://github.com/Wenchao-Ma/GDINA

Maintainer

Wenchao Ma

Last Published

March 28th, 2018

Functions in GDINA (2.0.8)

Transformation between latent classes and latent groups

Q-matrix validation, model selection and calibration in one run

The Generalized DINA Model Framework

Classification Rate Evaluation

Create a block diagonal matrix

Generate hierarchical attribute structures

Calculate classification accuracy

CDM calibration under the G-DINA model framework

Tatsuoka's fraction subtraction data

Q-matrix validation

attributepattern

Generate all possible attribute patterns

Examination for the Certificate of Proficiency in English (ECPE) data

Extract log-likelihood for each individual

Extract log posterior for each individual

Calculating standard errors and variance-covariance matrix using bootstrap methods

This function checks if monotonicity is violated

extract elements from objects of various classes

Calculate the number of parameters

Combine R Objects by Columns

extract item parameters (deprecated)

Item-level model comparison using Wald, LR or LM tests

calculate person (incidental) parameters

Model fit statistics

Mesa plot for Q-matrix validation

Simulated data (21 items, sequential DINA model)

Plot item success probability

Simulated data (30 items, DINA model)

Simulated data (10 items, G-DINA model)

Generate design matrix

Data simulation based on the G-DINA models

Simulated data (20 items, sequential G-DINA model)

Differential item functioning for cognitive diagnosis models

Graphical user interface of the GDINA function

Count the frequency of a row vector in a data frame

Functions for internal use

Unique values in a vector

Item fit statistics

Generate unrestricted Qc matrix from an restricted Qc matrix

Simulated data (30 items, G-DINA model)

Simulated data (30 items, polytomous G-DINA model)