A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011)
Ma, W. & de la Torre, J. (2019). GDINA: The generalized DINA model framework. R package version 2.5. Retrived from https://CRAN.R-project.org/package=GDINA
Visit the package website https://wenchao-ma.github.io/GDINA for examples and tutorials.
NCME digital module 5 on the G-DINA model and the use of graphical user interface for CDM analyses
Check de la Torre and Akbay’s (2019) article on how to conduct various CDM analyses using the graphical user interface
To install this package from source:
Windows users may need to install the Rtools and include the checkbox option of installing Rtools to their path for easier command line usage. Mac users will have to download the necessary tools from the Xcode and its related command line tools (found within Xcode’s Preference Pane under Downloads/Components); most Linux distributions should already have up to date compilers (or if not they can be updated easily).
Install the devtools
package (if necessary), and install the
package from the Github source code.
# install.packages("devtools")devtools::install_github("Wenchao-Ma/GDINA")
The stable version of GDINA should be installed from R CRAN at here
ICLA()
function for attribute profile estimationprint.GDINA
()` prints valid number of individuals by default nowQval
not work when estimated number of individuals in some latent classes are 0extract
only gives valid dataGMSCDM
att.str
argument of the GDINA
function has been updatedGDINA
functiondif
functionmodelcomp
function to provide selected models directlyplot
functionecpe
datadif
function and startGDINA
functionlogLik
function to be consistent with default S3 methodsGDINA
function has been largely rewritten for both flexibility and speed.
Users are now allowed to fit MS-DINA
model, Bugs
models, and define models by providing design matrix and link functions. For joint attribute
distribution, in addition to saturated and higher-order models, users are now allowed to fit independent model and loglinear model. Code for
joint attribute distribution modelling has been restructured as well. Other major updates include model fit evaluation using M2 statistics and other
limited information measures, item-level model selection using likelihood ratio test and score test, classification accuracy evaluation indices,
bootstrap standard error estimation, etc.modelcomp
in the version 1.4.1startGDINA
to report absolute fit statisticshigher-order
modelsGDINA
function arguments were modifiednpar
, AIC
,BIC
,logLik
and deviance
functions are modifiedanova
function is changed and multiple models can be compariedprint
function for the GDINA function is changedsummary
function is changedextract
function are not roundeddif
function when DIF items are specifiedGDINA
and itemfit
initial.catprob
in extract
functionLC2LG
to find equivalent latent groupsscore
to find score functionssummary.GDINA
function for multiple group estimationGDINA
functionatt.prior
in extract
functionGDINA
functionsimGDINA
and GDINA
GDINA
ngroup
using extract.GDINA
functionitemfit
function for missing datapersonparm