A Factor Analysis class
This is Python module to perform exploratory factor analysis, with optional varimax and promax rotations. Estimation can be performed using a minimum residual (minres) solution, or maximum likelihood estimation (MLE).
Portions of this code are ported from the excellent R library psych.
Please see the official documentation for additional details.
Exploratory factor analysis (EFA) is a statistical technique used to identify latent relationships among sets of observed variables in a dataset. In particular, EFA seeks to model a large set of observed variables as linear combinations of some smaller set of unobserved, latent factors.
The matrix of weights, or factor loadings, generated from an EFA model describes the underlying relationships between each variable and the latent factors. Typically, a number of factors (K) is selected such that is substantially smaller than the number of variables. The factor analysis model can be estimated using a variety of standard estimation methods, including but not limited to OLS, minres, or MLE.
Factor loadings are similar to standardized regression coefficients, and variables with higher loadings on a particular factor can be interpreted as explaining a larger proportion of the variation in that factor. In many cases, factor loading matrices are rotated after the factor analysis model is estimated in order to produce a simpler, more interpretable structure to identify which variables are loading on a particular factor.
Two common types of rotations are:
- The varimax rotation, which rotates the factor loading matrix so as to maximize the sum of the variance of squared loadings, while preserving the orthogonality of the loading matrix.
- The promax rotation, a method for oblique rotation, which builds upon the varimax rotation, but ultimately allows factors to become correlated.
This package includes a stand-alone Python module with a single FactorAnalyzer() class. The class includes an analyze() method that allows users to perform factor analysis using either minres or MLE, with optional promax or varimax rotations on the factor loading matrices.
In : import pandas as pd In : from factor_analyzer import FactorAnalyzer In : df_features = pd.read_csv('test02.csv') In : fa = FactorAnalyzer() In : fa.analyze(df_features, 3, rotation=None) In : fa.loadings Out: Factor1 Factor2 Factor3 sex -0.129912 -0.163982 0.738235 zygosity 0.038996 -0.046584 0.011503 moed 0.348741 -0.614523 -0.072557 faed 0.453180 -0.719267 -0.075465 faminc 0.366888 -0.443773 -0.017371 english 0.741414 0.150082 0.299775 math 0.741675 0.161230 -0.207445 socsci 0.829102 0.205194 0.049308 natsci 0.760418 0.237687 -0.120686 vocab 0.815334 0.124947 0.176397 In : fa.get_uniqueness() Out: Uniqueness sex 0.411242 zygosity 0.996177 moed 0.495476 faed 0.271588 faminc 0.668157 english 0.337916 math 0.380890 socsci 0.268054 natsci 0.350704 vocab 0.288503 In : fa.get_factor_variance() Out: Factor1 Factor2 Factor3 SS Loadings 3.510189 1.283710 0.737395 Proportion Var 0.351019 0.128371 0.073739 Cumulative Var 0.351019 0.479390 0.553129
- Python 3.4 or higher
Contributions to FactorAnalyzer are very welcome. Please file an issue on GitHub, or contact email@example.com if you would like to contribute.
You can install this package via pip with:
$ pip install factor_analyzer
Alternatively, you can install via conda with:
$ conda install -c desilinguist factor_analyzer
GNU General Public License (>= 2)
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|factor_analyzer-0.2.2-py2.py3-none-any.whl (15.8 kB) Copy SHA256 hash SHA256||Wheel||py2.py3||Dec 14, 2017|
|factor_analyzer-0.2.2.tar.gz (19.9 kB) Copy SHA256 hash SHA256||Source||None||Dec 14, 2017|