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GDINA (version 1.4.2)

The Generalized DINA Model Framework

Description

A set of psychometric tools for cognitive diagnostic analyses 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 saturated, higher-order 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

1.4.2

License

GPL-3

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Repository

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

Maintainer

Wenchao Ma

Last Published

April 13th, 2017

Functions in GDINA (1.4.2)

Count the frequency of a row vector in a data frame

Unique values in a vector

Generate unrestricted Qc matrix from an restricted Qc matrix

Classification Rate Evaluation

The Generalized DINA Model Framework

Extract log posterior for each individual

Item fit statistics

Simulated data and Qc-matrix based on the sequential DINA model

Simulated data Q-matrix and item parameters (30 items, DINA model)

Q-matrix validation

Generate hierarchical attribute structures

extract higher-order parameters

Extract log-likelihood for each individual

Plot item success probability

Count and label unique rows in a data frame

Calibrate dichotomous and polytomous responses

Transformation between latent classes and latent groups

Design matrix for parameter transformation

Differential item functioning for cognitive diagnosis models

Item-level model comparison using Wald test

This function checks if monotonicity is violated

attributepattern

Generate all possible attribute patterns

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

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

extract elements from objects of various classes

Calculate the number of parameters

calculate lower-order incidental (person) parameters

Simulated data, Q-matrix and item parameters (30 items, G-DINA model)

Data simulation based on the G-DINA models

Graphical user interface of the GDINA function

Simulated data, Q-matrix and item parameters (30 items, polytomous G-DINA model)

Create a block diagonal matrix

Combine R Objects by Columns

Tatsuoka's fraction subtraction data

extract lower-order structural (item) parameters

Mesa plot for Q-matrix validation

Simulated data, Q-matrix and item parameters (10 items, G-DINA model)

Simulated data, Q-matrix and item parameters (20 items, sequential DINA model)