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Description:
psy 0.0.1
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pypsy
=====
`中文 <./README_ZH.rst>`_
`DINA Model and Parameter Estimation: A
Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`
psychometrics package, including structural equation model, confirmatory
factor analysis, unidimensional item response theory, multidimensional
item response theory, cognitive diagnosis model, factor analysis and
adaptive testing. The package is still a doll. will be finished in
future.
unidimensional item response theory
-----------------------------------
models
~~~~~~
- binary response data IRT (two parameters, three parameters).
- grade respone data IRT (GRM model)
Parameter estimation algorithm
------------------------------
- EM algorithm (2PL, GRM)
- MCMC algorithm (3PL）
--------------
Multidimensional item response theory (full information item factor analysis)
-----------------------------------------------------------------------------
Parameter estimation algorithm
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The initial value
^^^^^^^^^^^^^^^^^
The approximate polychoric correlation is calculated, and the slope
initial value is obtained by factor analysis of the polychoric
correlation matrix.
EM algorithm
^^^^^^^^^^^^
- E step uses GH integral.
- M step uses Newton algorithm (sparse matrix is divided into non
sparse matrix).
Factor rotation
^^^^^^^^^^^^^^^
Gradient projection algorithm
The shortcomings
~~~~~~~~~~~~~~~~
GH integrals can only estimate low dimensional parameters.
--------------
Cognitive diagnosis model
-------------------------
models
~~~~~~
- Dina
- ho-dina
parameter estimation algorithms
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- EM algorithm
- MCMC algorithm
- maximum likelihood estimation (only for estimating skill parameters
of subjects)
--------------
Structural equation model
-------------------------
- contains three parameter estimation methods(ULS, ML and GLS).
- based on gradient descent
--------------
Confirmatory factor analysis
----------------------------
- can be used for continuous data, binary data and ordered data.
- based on gradient descent
- binary and ordered data based on Polychoric correlation matrix.
--------------
Factor analysis
---------------
For the time being, only for the calculation of full information item
factor analysis, it is very simple.
The algorithm
~~~~~~~~~~~~~
principal component analysis
The rotation algorithm
~~~~~~~~~~~~~~~~~~~~~~
gradient projection
--------------
Adaptive test
-------------
model
~~~~~
Thurston IRT model (multidimensional item response theory model for
personality test)
Algorithm
~~~~~~~~~
Maximum information method for multidimensional item response theory
Require
-------
- numpy
- progressbar2
How to use it
-------------
See demo in detail
TODO LIST
---------
- theta parameterization of CCFA
- parameter estimation of structural equation models for multivariate
data
- Bayesin knowledge tracing (Bayesian knowledge tracking)
- multidimensional item response theory (full information item factor
analysis)
- high dimensional computing algorithm (adaptive integral, etc.)
- various item response models
- cognitive diagnosis model
- G-DINA model
- Q matrix correlation algorithm
- Factor analysis
- maximum likelihood estimation
- various factor rotation algorithms
- adaptive
- adaptive cognitive diagnosis
- other adaption model
- standard error and P value
- code annotation, testing and documentation.
Reference
---------
- `DINA Model and Parameter Estimation: A
Didactic <http://www.stat.cmu.edu/~brian/PIER-methods/For%202013-03-04/Readings/de%20la%20Torre-dina-est-115-30-jebs.pdf>`__
- `Higher-order latent trait models for cognitive
diagnosis <http://www.aliquote.org/pub/delatorre2004.pdf>`__
- `Full-Information Item Factor
Analysis. <http://conservancy.umn.edu/bitstream/11299/104282/1/v12n3p261.pdf>`__
- `Multidimensional adaptive
testing <http://media.metrik.de/uploads/incoming/pub/Literatur/1996_Multidimensional%20adaptive%20testing.pdf>`__
- `Derivative free gradient projection algorithms for rotation <https://cloudfront.escholarship.org/dist/prd/content/qt9938p4wc/qt9938p4wc.pdf>`__
=======
History
=======
0.0.1 (2018-09-18)
------------------
* First release on PyPI.
License
For personal and professional use. You cannot resell or redistribute these repositories in their original state.
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