# A quick reference to GDINA R package

#### Wenchao Ma

#### 2025-07-02

## What can this package do?

This package provides a framework for conducting a series of cognitive diagnosis analysis for dichotomous and polytomous responses.

For more details, see here

For an example of how to use the package, see here

## How to use this package?

- To estimate various CDMs, use GDINA() function
- To extract item and person parameters from GDINA estimates, use coef() and personparm() functions, respectively
- To extract other estimation components, use extract() function
- To evaluate absolute model fit, use modelfit(), itemfit() or itemfitPD() function
- To evaluate relative model fit, use AIC(), BIC() or deviance() function
- To validate Q-matrix, use Qval() function
- To detect differential item functioning, use dif() function
- To run graphical user interface, use startGDINA() function
- To simulate data from various CDMs, use simGDINA() function

Visit here for an online reference

## A quick example

Below is an illustration showing some analyses that you could do using the package. You can copy the code, paste and run it in R console. For a more comprehensive example, see this tutorial.

1. Load the package and assume we have the following item response matrix and Q-matrix:

    library(GDINA)
    dat <- sim10GDINA$simdat
    Q <- matrix(c(1,0,0,
                  0,1,0,
                  0,0,1,
                  1,0,1,
                  0,1,1,
                  1,1,0,
                  0,0,1,
                  1,0,0,
                  1,1,1,
                  1,0,1),byrow = T,ncol = 3)

2. Estimate the G-DINA model:

    est <- GDINA(dat = dat, Q = Q, model = "GDINA")

3. Conduct the Q-matrix validation. By default, it implements de la Torre and Chiu’s (2016) algorithm using a fixed cutoff. This is fast but sometimes suggests too many modifications.

    Qv <- Qval(est)
    Qv

To avoid using fixed cutoffs and also take uncertainty in item parameter estimation into account, you may consider the stepwise method:

    Qv2 <- Qval(est,method = "Wald")
    Qv2

To further examine the q-vectors that are suggested to be modified, you can draw mesa plots:

    plot(Qv, item = 9)

4. Perform item-level model selection to see if the saturated G-DINA model can be simplified:

    mc <- modelcomp(est)
    mc

5. Assess model-data fit at test and item levels:

    # test level absolute fit
    mft <- modelfit(est)
    mft
    # item level absolute fit
    ift <- itemfit(est)
    ift
    summary(ift)
    plot(ift)

6. Calculate classification accuracy

    CA(est)

## More Examples

- G-DINA model estimation
- Model estimation and diagnostics
- Sequential model estimation and diagnostics
- LCDM model estimation
- Higher-order GDINA model estimation

If you would like to contribute an example to this website, please send me your .Rmd file.