Write Sphinx documentation
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__pycache__
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*.pyc
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/docs/_build
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@ -77,7 +77,7 @@ Relevant statistical functions are all directly available from the top-level *yl
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* Bayesian inference:
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* *bayesfactor_afbf*: Adjusted fractional Bayes factor for a hypothesis on parameters from regression
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Each function is documented in the respective docstring within the source code. Examples can be found in the unit tests in the *tests* directory.
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Each function is documented in the respective docstring within the source code, and Sphinx documentation is buildable from the *docs* directory. Examples can be found in the unit tests in the *tests* directory.
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## Warning
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@ -0,0 +1,20 @@
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# Minimal makefile for Sphinx documentation
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#
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# You can set these variables from the command line, and also
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# from the environment for the first two.
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SPHINXOPTS ?=
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SPHINXBUILD ?= sphinx-build
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SOURCEDIR = .
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BUILDDIR = _build
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# Put it first so that "make" without argument is like "make help".
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help:
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@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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.PHONY: help Makefile
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# Catch-all target: route all unknown targets to Sphinx using the new
|
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# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
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%: Makefile
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@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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@ -0,0 +1,13 @@
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Bayesian inference
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==================
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Functions
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---------
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.. autofunction:: yli.bayesfactor_afbf
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Result classes
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--------------
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.. autoclass:: yli.bayes_factors.BayesFactor
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:members:
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@ -0,0 +1,30 @@
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# Configuration file for the Sphinx documentation builder.
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#
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# For the full list of built-in configuration values, see the documentation:
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# https://www.sphinx-doc.org/en/master/usage/configuration.html
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import sys, os
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sys.path.append(os.path.abspath('..'))
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# -- Project information -----------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
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project = 'scipy-yli'
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copyright = '2022, RunasSudo'
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author = 'RunasSudo'
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# -- General configuration ---------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
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extensions = ['sphinx.ext.autodoc']
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templates_path = ['_templates']
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
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# -- Options for HTML output -------------------------------------------------
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
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html_theme = 'sphinx_rtd_theme'
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html_static_path = ['_static']
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@ -0,0 +1,16 @@
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Distributions
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=============
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.. autodata:: yli.beta_oddsratio
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.. automethod:: yli.distributions.betaoddsrat_gen.from_scipy
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.. automethod:: yli.distributions.betaoddsrat_gen.set_cdf_terms
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.. autodata:: yli.beta_ratio
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.. automethod:: yli.distributions.betarat_gen.from_scipy
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.. autodata:: yli.transformed_dist
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.. autofunction:: yli.hdi
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@ -0,0 +1,24 @@
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General specifications
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======================
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.. _nan-handling:
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NaN handling
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------------
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Most functions take a parameter **nan_policy** to specify how to handle *nan* values in the data. The options are:
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* **warn** (default) – Warn on *nan* values
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* **raise** – Raise an error on *nan* values
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* **omit** – Silently drop rows with *nan* values
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In determining whether there is *nan* in the data, only the columns specified in the function (if applicable) are considered.
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General result classes
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----------------------
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.. autoclass:: yli.utils.ConfidenceInterval
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:members:
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.. autoclass:: yli.utils.Estimate
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:members:
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@ -0,0 +1,12 @@
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Global options
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==============
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.. autodata:: yli.config
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.. autoattribute:: yli.config.Config.alpha
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.. autoattribute:: yli.config.Config.pvalue_leading_zero
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.. autoattribute:: yli.config.Config.pvalue_max_dps
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.. autoattribute:: yli.config.Config.pvalue_min_dps
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@ -0,0 +1,14 @@
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scipy-yli API reference
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=======================
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.. toctree::
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:maxdepth: 1
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:caption: Contents:
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general.rst
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sig_tests.rst
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regress.rst
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io.rst
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distributions.rst
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bayes_factors.rst
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global.rst
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@ -0,0 +1,10 @@
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Input/output
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============
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.. autofunction:: yli.pickle_read_compressed
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.. autofunction:: yli.pickle_read_encrypted
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.. autofunction:: yli.pickle_write_compressed
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.. autofunction:: yli.pickle_write_encrypted
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@ -0,0 +1,35 @@
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@ECHO OFF
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pushd %~dp0
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REM Command file for Sphinx documentation
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if "%SPHINXBUILD%" == "" (
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set SPHINXBUILD=sphinx-build
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)
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set SOURCEDIR=.
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set BUILDDIR=_build
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%SPHINXBUILD% >NUL 2>NUL
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if errorlevel 9009 (
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echo.
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echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
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echo.installed, then set the SPHINXBUILD environment variable to point
|
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echo.to the full path of the 'sphinx-build' executable. Alternatively you
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echo.may add the Sphinx directory to PATH.
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echo.
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echo.If you don't have Sphinx installed, grab it from
|
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echo.https://www.sphinx-doc.org/
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exit /b 1
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)
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if "%1" == "" goto help
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%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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goto end
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:help
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%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
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:end
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popd
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@ -0,0 +1,28 @@
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Regression
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==========
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Functions
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---------
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.. autofunction:: yli.logit_then_regress
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.. autoclass:: yli.PenalisedLogit
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.. autofunction:: yli.regress
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.. autofunction:: yli.vif
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Result classes
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--------------
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.. autoclass:: yli.regress.CategoricalTerm
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:members:
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.. autoclass:: yli.regress.LikelihoodRatioTestResult
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:members:
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.. autoclass:: yli.regress.RegressionResult
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:members:
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.. autoclass:: yli.regress.SingleTerm
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:members:
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@ -0,0 +1,36 @@
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Significance tests
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==================
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Functions
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---------
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.. autofunction:: yli.anova_oneway
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.. autofunction:: yli.chi2
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.. autofunction:: yli.mannwhitney
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.. autofunction:: yli.pearsonr
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.. autofunction:: yli.ttest_ind
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Result classes
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--------------
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.. autoclass:: yli.sig_tests.BrunnerMunzelResult
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:members:
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.. autoclass:: yli.sig_tests.FTestResult
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:members:
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.. autoclass:: yli.sig_tests.MannWhitneyResult
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:members:
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.. autoclass:: yli.sig_tests.PearsonChiSquaredResult
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:members:
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.. autoclass:: yli.sig_tests.PearsonRResult
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:members:
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.. autoclass:: yli.sig_tests.TTestResult
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:members:
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@ -18,26 +18,45 @@ class BayesFactor:
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"""A Bayes factor"""
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def __init__(self, factor, num_symbol, num_desc, denom_symbol, denom_desc):
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#: Value of the Bayes factor (*float*)
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self.factor = factor
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#: Symbol representing the hypothesis in the numerator (*str*)
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self.num_symbol = num_symbol
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#: Description of the hypothesis in the numerator (*str*)
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self.num_desc = num_desc
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#: Symbol representing the hypothesis in the denominator (*str*)
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self.denom_symbol = denom_symbol
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#: Description of the hypothesis in the denominator (*str*)
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self.denom_desc = denom_desc
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def _repr_html_(self):
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return 'BF<sub>{0}{1}</sub> = {2:.2f}, {5}<br>H<sub>{0}</sub>: {3}<br>H<sub>{1}</sub>: {4}'.format(self.num_symbol, self.denom_symbol, self.factor, self.num_desc, self.denom_desc, self.interpret_lw(html=True))
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def summary(self):
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"""
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Return a stringified summary of the Bayes factor
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:rtype: str
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"""
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return 'BF{0}{1} = {2:.2f}, {5}\nH{0}: {3}\nH{1}: {4}'.format(self.num_symbol, self.denom_symbol, self.factor, self.num_desc, self.denom_desc, self.interpret_lw(html=False))
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def invert(self):
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"""Invert the Bayes factor"""
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"""
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Invert the Bayes factor
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:rtype: :class:`BayesFactor`
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"""
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return BayesFactor(1 / self.factor, self.denom_symbol, self.denom_desc, self.num_symbol, self.num_desc)
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def interpret_lw(self, html):
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"""Interpret the Bayes factor according to the Lee & Wagenmakers classification scheme"""
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"""
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Interpret the Bayes factor according to the Lee & Wagenmakers classification scheme
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:meta private:
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"""
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if self.factor == 1:
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return 'Evidence favours neither hypothesis to the other'
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@ -64,11 +83,11 @@ class BayesFactor:
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def bayesfactor_afbf(params, cov, n, hypothesis):
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"""
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Compute an adjusted fractional Bayes factor for the hypothesis
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Using R "BFpack" library
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See R documentation for BFpack.BF
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Uses R *BFpack* library. See R documentation for *BFpack.BF*.
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Returns Bayes factor for hypothesis vs its complement
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:return: Bayes factor for the hypothesis versus its complement
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:rtype: :class:`yli.bayes_factors.BayesFactor`
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"""
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import rpy2.robjects as ro
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@ -15,18 +15,18 @@
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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class Config:
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"""Stores global configuration for the library"""
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"""Global configuration for the library"""
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def __init__(self):
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# Display at least this many decimal places for p values
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#: Display at least this many decimal places for *p* values (*int*)
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self.pvalue_min_dps = 2
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# Display at most this many decimal places for p values
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#: Display at most this many decimal places for *p* values (*int*)
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self.pvalue_max_dps = 3
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# Display a leading zero for p values
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#: Display a leading zero for *p* values (*bool*)
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self.pvalue_leading_zero = True
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# Alpha level for significance tests, confidence intervals
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#: Alpha level for significance tests, confidence intervals (*float*)
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self.alpha = 0.05
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"""Global config singleton"""
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"""Global configuration singleton"""
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config = Config()
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@ -23,17 +23,31 @@ from .utils import ConfidenceInterval
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class betarat_gen(stats.rv_continuous):
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"""
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Ratio of 2 independent beta-distributed variables
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Ratio of Beta(a1, b1) / Beta(a2, b2)
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References:
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1. Pham-Gia T. Distributions of the ratios of independent beta variables and applications. Communications in Statistics: Theory and Methods. 2000;29(12):2693–715. doi: 10.1080/03610920008832632
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2. Weekend Editor. On the ratio of Beta-distributed random variables. Some Weekend Reading. 2021 Sep 13. https://www.someweekendreading.blog/beta-ratios/
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This is a SciPy *stats.rv_continuous* distribution which takes 4 parameters, *a1*, *b1*, *a2* and *b2*,
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and gives the distribution of Beta(*a1*, *b1*) / Beta(*a2*, *b2*).
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**References:**
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1. Pham-Gia T. Distributions of the ratios of independent beta variables and applications. *Communications in Statistics: Theory and Methods*. 2000;29(12):2693–715. `doi: 10.1080/03610920008832632 <https://doi.org/10.1080/03610920008832632>`_
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2. Weekend Editor. On the ratio of Beta-distributed random variables. *Some Weekend Reading*. 2021 Sep 13. https://www.someweekendreading.blog/beta-ratios/
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"""
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# TODO: _rvs based on stats.beta
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def from_scipy(self, beta1, beta2):
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"""Construct a new beta_ratio distribution from two SciPy beta distributions"""
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"""
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Construct a new *beta_ratio* distribution from two SciPy beta distributions
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:param beta1: Distribution for the numerator of the ratio
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:type beta1: frozen SciPy stats.beta
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:param beta2: Distribution for the denominator of the ratio
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:type beta2: frozen SciPy stats.beta
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:rtype: Frozen *beta_ratio* distribution
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"""
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return self(*beta1.args, *beta2.args)
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def _do_vectorized(self, func, x, a1, b1, a2, b2):
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@ -104,20 +118,38 @@ beta_ratio = betarat_gen(name='beta_ratio', a=0) # a = lower bound of support
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class betaoddsrat_gen(stats.rv_continuous):
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"""
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Ratio of the odds of 2 independent beta-distributed variables
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Ratio of (X/(1-X)) / (Y/(1-Y)), where X ~ Beta(a1, b1), Y ~ Beta(a2, b2)
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Reference:
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Hora SC, Kelley GD. Bayesian inference on the odds and risk ratios. Communications in Statistics: Theory and Methods. 1983;12(6):725–38. doi: 10.1080/03610928308828491
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This is a SciPy *stats.rv_continuous* distribution which takes 4 parameters, *a1*, *b1*, *a2* and *b2*,
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and gives the distribution of (*X*/(1-*X*)) / (*Y*/(1-*Y*)), where *X* ~ Beta(*a1*, *b1*), and *Y* ~ Beta(*a2*, *b2*).
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|
||||
**Reference:** Hora SC, Kelley GD. Bayesian inference on the odds and risk ratios. *Communications in Statistics: Theory and Methods*. 1983;12(6):725–38. `doi: 10.1080/03610928308828491 <https://doi.org/10.1080/03610928308828491>`_
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"""
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# TODO: _rvs based on stats.beta
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def from_scipy(self, beta1, beta2):
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"""Construct a new beta_ratio distribution from two SciPy beta distributions"""
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"""
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Construct a new *beta_oddsratio* distribution from two SciPy beta distributions
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:param beta1: Distribution for the numerator of the ratio
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:type beta1: frozen SciPy stats.beta
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:param beta2: Distribution for the denominator of the ratio
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:type beta2: frozen SciPy stats.beta
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||||
:rtype: Frozen *beta_oddsratio* distribution
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"""
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||||
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||||
return self(*beta1.args, *beta2.args)
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||||
|
||||
def set_cdf_terms(self, cdf_terms):
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"""Set the number of terms to use when calculating CDF (see _cdf)"""
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"""
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Set the number of terms to use when calculating the CDF
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If *cdf_terms* = *np.inf* (default), the CDF will be computed by numerical integration of the PDF.
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Otherwise, the CDF will be computed by truncating the infinite sum given by Hora & Kelley to the specified number of terms.
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"""
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BETA_ODDSRATIO_CDF_TERMS[0] = cdf_terms
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def _do_vectorized(self, func, x, a1, b1, a2, b2):
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@ -179,12 +211,7 @@ class betaoddsrat_gen(stats.rv_continuous):
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return 1 - k * inf_sum
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def _cdf(self, w, a1, b1, a2, b2):
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"""
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CDF for the distribution
|
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||||
If BETA_ODDSRATIO_CDF_TERMS = np.inf, compute the CDF by integrating the PDF
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||||
Otherwise, compute the CDF by truncating the infinite sum given by Hora & Kelley
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||||
"""
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"""CDF for the distribution"""
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w = np.atleast_1d(w)
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a1 = np.atleast_1d(a1)
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@ -259,10 +286,24 @@ beta_oddsratio = betaoddsrat_gen(name='beta_oddsratio', a=0) # a = lower bound
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class transformed_gen(stats.rv_continuous):
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"""
|
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Represents a transformation, Y, of a "base" random variable, X, where f(Y) = X
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||||
Hence Y.pdf(x) = X.pdf(f(x)) * f'(x)
|
||||
Represents a transformation, *Y*, of a "base" random variable, *X*
|
||||
|
||||
e.g. if X is a model parameter, then transformed_dist(X, f=np.exp, fprime=np.exp, finv=np.log) is distribution of the log-parameter
|
||||
This is a SciPy *stats.rv_continuous* distribution which takes parameters as described below.
|
||||
|
||||
The transformation is *f*\ (*Y*) = *X*.
|
||||
Hence *Y*.pdf\ (*x*) = *X*.pdf\ (*f*\ (*x*)) \* *f*'\ (*x*).
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||||
|
||||
For example, if *X* is a model parameter, then ``transformed_dist(X, f=np.exp, fprime=np.exp, finv=np.log)`` is the distribution of the log-parameter.
|
||||
|
||||
**Parameters:**
|
||||
|
||||
* **base** (*frozen SciPy distribution*) – Distribution of the base random variable
|
||||
|
||||
* **f** (*callable*) – Function *f* representing the transformation, which takes values of *Y* to values of *X*
|
||||
|
||||
* **fprime** (*callable*) – Derivative of the function *f*
|
||||
|
||||
* **finv** (*callable*) – Inverse of the function *f*, which takes values of *X* to values of *Y*
|
||||
"""
|
||||
|
||||
def _pdf(self, x, base, f, fprime, finv):
|
||||
|
|
@ -297,7 +338,12 @@ def hdi(distribution, level=None):
|
|||
"""
|
||||
Get the highest density interval for the distribution, e.g. for a Bayesian posterior, the highest posterior density interval (HPD/HDI)
|
||||
|
||||
level: Coverage/confidence probability, default (None) is 1 - config.alpha
|
||||
:param distribution: Distribution to compute the interval for
|
||||
:type distribution: frozen SciPy distribution
|
||||
:param level: Coverage/confidence probability, default (*None*) is 1 − *config.alpha*
|
||||
:type level: float
|
||||
|
||||
:rtype: :class:`yli.utils.ConfidenceInterval`
|
||||
"""
|
||||
|
||||
if level is None:
|
||||
|
|
|
|||
36
yli/io.py
36
yli/io.py
|
|
@ -23,9 +23,16 @@ def pickle_write_encrypted(df, fname):
|
|||
"""
|
||||
Write the DataFrame to an encrypted file
|
||||
|
||||
DataFrame is serialised with Pickle, compressed with LZMA, then encrypted
|
||||
Encryption is AES-256-CTR
|
||||
Password is derived using scrypt KDF
|
||||
Prompts the user for a password.
|
||||
|
||||
The DataFrame is serialised with *pickle*, compressed with LZMA, then encrypted.
|
||||
Encryption uses AES-256-CTR.
|
||||
Encryption key is derived from the password using the scrypt KDF.
|
||||
|
||||
:param df: Data to serialise
|
||||
:type df: DataFrame
|
||||
:param fname: Filename to write data to
|
||||
:type fname: str
|
||||
"""
|
||||
|
||||
from Crypto.Cipher import AES
|
||||
|
|
@ -56,7 +63,12 @@ def pickle_write_compressed(df, fname):
|
|||
"""
|
||||
Write the DataFrame to a compressed file
|
||||
|
||||
DataFrame is serialised with Pickle and compressed with LZMA
|
||||
The DataFrame is serialised with *pickle*, then compressed with LZMA.
|
||||
|
||||
:param df: Data to serialise
|
||||
:type df: DataFrame
|
||||
:param fname: Filename to write data to
|
||||
:type fname: str
|
||||
"""
|
||||
|
||||
# Serialise Pandas and compress
|
||||
|
|
@ -67,6 +79,13 @@ def pickle_write_compressed(df, fname):
|
|||
def pickle_read_encrypted(fname):
|
||||
"""
|
||||
Read a DataFrame from an encrypted file
|
||||
|
||||
See :meth:`yli.pickle_write_encrypted`.
|
||||
|
||||
:param fname: Filename to read data from
|
||||
:type fname: str
|
||||
|
||||
:rtype: DataFrame
|
||||
"""
|
||||
|
||||
from Crypto.Cipher import AES
|
||||
|
|
@ -91,7 +110,14 @@ def pickle_read_encrypted(fname):
|
|||
|
||||
def pickle_read_compressed(fname):
|
||||
"""
|
||||
Read a DataFrame from an compressed (but not encrypted) file
|
||||
Read a DataFrame from a compressed file
|
||||
|
||||
See :meth:`yli.pickle_write_compressed`.
|
||||
|
||||
:param fname: Filename to read data from
|
||||
:type fname: str
|
||||
|
||||
:rtype: DataFrame
|
||||
"""
|
||||
|
||||
# Uncompress and deserialise to Pandas
|
||||
|
|
|
|||
145
yli/regress.py
145
yli/regress.py
|
|
@ -30,11 +30,17 @@ from .config import config
|
|||
from .sig_tests import FTestResult
|
||||
from .utils import Estimate, check_nan, cols_for_formula, fmt_p, formula_factor_ref_category, parse_patsy_term
|
||||
|
||||
def vif(df, formula=None, nan_policy='warn'):
|
||||
def vif(df, formula=None, *, nan_policy='warn'):
|
||||
"""
|
||||
Calculate the variance inflation factor for each variable in df
|
||||
Calculate the variance inflation factor (VIF) for each variable in *df*
|
||||
|
||||
formula: If specified, calculate the VIF only for the variables in the formula
|
||||
:param df: Data to calculate the VIF for
|
||||
:type df: DataFrame
|
||||
:param formula: If specified, calculate the VIF only for the variables in the formula
|
||||
:type formula: str
|
||||
|
||||
:return: The variance inflation factor
|
||||
:rtype: float
|
||||
"""
|
||||
|
||||
if formula:
|
||||
|
|
@ -63,24 +69,37 @@ def vif(df, formula=None, nan_policy='warn'):
|
|||
# Regression
|
||||
|
||||
class LikelihoodRatioTestResult:
|
||||
"""Result of a likelihood ratio test for regression"""
|
||||
"""
|
||||
Result of a likelihood ratio test for regression
|
||||
|
||||
See :meth:`RegressionResult.lrtest_null`.
|
||||
"""
|
||||
|
||||
def __init__(self, statistic, dof, pvalue):
|
||||
#: Likelihood ratio test statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: Degrees of freedom for the likelihood ratio test statistic (*int*)
|
||||
self.dof = dof
|
||||
#: *p* value for the likelihood ratio test (*float*)
|
||||
self.pvalue = pvalue
|
||||
|
||||
def _repr_html_(self):
|
||||
return 'LR({}) = {:.2f}; <i>p</i> {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=True))
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the likelihood ratio test
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return 'LR({}) = {:.2f}; p {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=False))
|
||||
|
||||
class RegressionResult:
|
||||
"""
|
||||
Result of a regression
|
||||
|
||||
llf/llnull: Log-likelihood of model/null model
|
||||
See :func:`yli.regress`.
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
|
|
@ -92,44 +111,63 @@ class RegressionResult:
|
|||
dof_resid, rsquared, f_statistic,
|
||||
exp
|
||||
):
|
||||
# A copy of the raw result so we can access it
|
||||
#: Raw result from statsmodels *model.fit*
|
||||
self.raw_result = raw_result
|
||||
|
||||
# Information for display
|
||||
#: Full name of the regression model type (*str*)
|
||||
self.full_name = full_name
|
||||
#: Short name of the regression model type (*str*)
|
||||
self.model_name = model_name
|
||||
#: Method for fitting the regression model (*str*)
|
||||
self.fit_method = fit_method
|
||||
|
||||
# Basic fitted model information
|
||||
#: Name of the dependent variable (*str*)
|
||||
self.dep = dep
|
||||
#: Number of observations (*int*)
|
||||
self.nobs = nobs
|
||||
#: Degrees of freedom for the model (*int*)
|
||||
self.dof_model = dof_model
|
||||
#: Date and time of fitting the model (Python *datetime*)
|
||||
self.fitted_dt = fitted_dt
|
||||
#: Method for computing the covariance matrix (*str*)
|
||||
self.cov_type = cov_type
|
||||
|
||||
# Regression coefficients/p values
|
||||
#: Coefficients and *p* values for each term in the model (*dict* of :class:`SingleTerm` or :class:`CategoricalTerm`)
|
||||
self.terms = terms
|
||||
|
||||
# Model log-likelihood
|
||||
#: Log-likelihood of fitted model (*float*)
|
||||
self.llf = llf
|
||||
#: Log-likelihood of null model (*float*)
|
||||
self.llnull = llnull
|
||||
|
||||
# Extra statistics (not all regression models have these)
|
||||
#: Degrees of freedom for the residuals (*int*; *None* if N/A)
|
||||
self.dof_resid = dof_resid
|
||||
#: *R*:sup:`2` statistic (*float*; *None* if N/A)
|
||||
self.rsquared = rsquared
|
||||
#: *F* statistic (*float*; *None* if N/A)
|
||||
self.f_statistic = f_statistic
|
||||
|
||||
# Config for display style
|
||||
#: See :func:`yli.regress`
|
||||
self.exp = exp
|
||||
|
||||
@property
|
||||
def pseudo_rsquared(self):
|
||||
"""McFadden's pseudo R-squared"""
|
||||
"""McFadden's pseudo *R*:sup:`2` statistic"""
|
||||
|
||||
return 1 - self.llf/self.llnull
|
||||
|
||||
def lrtest_null(self):
|
||||
"""Compute the likelihood ratio test comparing the model to the null model"""
|
||||
"""
|
||||
Compute the likelihood ratio test comparing the model to the null model
|
||||
|
||||
:rtype: :class:`LikelihoodRatioTestResult`
|
||||
"""
|
||||
|
||||
statistic = -2 * (self.llnull - self.llf)
|
||||
pvalue = 1 - stats.chi2.cdf(statistic, self.dof_model)
|
||||
|
|
@ -137,17 +175,28 @@ class RegressionResult:
|
|||
return LikelihoodRatioTestResult(statistic, self.dof_model, pvalue)
|
||||
|
||||
def ftest(self):
|
||||
"""Perform the F test that all slopes are 0"""
|
||||
"""
|
||||
Perform the *F* test that all slopes are 0
|
||||
|
||||
:rtype: :class:`yli.sig_tests.FTestResult`
|
||||
"""
|
||||
|
||||
pvalue = 1 - stats.f(self.dof_model, self.dof_resid).cdf(self.f_statistic)
|
||||
return FTestResult(self.f_statistic, self.dof_model, self.dof_resid, pvalue)
|
||||
|
||||
def bayesfactor_beta_zero(self, term):
|
||||
"""
|
||||
Compute Bayes factor testing the hypothesis that the given beta is 0
|
||||
Requires statsmodels regression
|
||||
Compute a Bayes factor testing the hypothesis that the given beta is 0
|
||||
|
||||
term: Raw name (from statsmodels, available via RegressionResult.raw_result) of term to be tested
|
||||
Uses the R *BFpack* library.
|
||||
|
||||
Requires the regression to be from statsmodels.
|
||||
The term must be specified as the *raw name* from the statsmodels regression, available via :attr:`RegressionResult.raw_result`.
|
||||
|
||||
:param term: Raw name of the term to be tested
|
||||
:type term: str
|
||||
|
||||
:rtype: :class:`yli.bayes_factors.BayesFactor`
|
||||
"""
|
||||
|
||||
# FIXME: Allow specifying our renamed terms
|
||||
|
|
@ -264,6 +313,12 @@ class RegressionResult:
|
|||
return out
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the regression model
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
# Render header table
|
||||
left_col, right_col = self._header_table(html=False)
|
||||
|
||||
|
|
@ -325,24 +380,23 @@ class RegressionResult:
|
|||
return out
|
||||
|
||||
class SingleTerm:
|
||||
"""
|
||||
A term in a RegressionResult which is a single term
|
||||
|
||||
raw_name: The raw name of the term (e.g. in RegressionResult.raw_result data)
|
||||
beta: An Estimate of the coefficient
|
||||
pvalue: The p value
|
||||
"""
|
||||
"""A term in a :class:`RegressionResult` which is a single term"""
|
||||
|
||||
def __init__(self, raw_name, beta, pvalue):
|
||||
#: Raw name of the term (*str*; e.g. in :attr:`RegressionResult.raw_result`)
|
||||
self.raw_name = raw_name
|
||||
#: :class:`yli.utils.Estimate` of the coefficient
|
||||
self.beta = beta
|
||||
#: *p* value for the coefficient (*float*)
|
||||
self.pvalue = pvalue
|
||||
|
||||
class CategoricalTerm:
|
||||
"""A group of terms in a RegressionResult corresponding to a categorical variable"""
|
||||
"""A group of terms in a :class:`RegressionResult` corresponding to a categorical variable"""
|
||||
|
||||
def __init__(self, categories, ref_category):
|
||||
#: Terms for each of the categories, excluding the reference category (*dict* of :class:`SingleTerm`)
|
||||
self.categories = categories
|
||||
#: Name of the reference category (*str*)
|
||||
self.ref_category = ref_category
|
||||
|
||||
def regress(
|
||||
|
|
@ -356,8 +410,30 @@ def regress(
|
|||
"""
|
||||
Fit a statsmodels regression model
|
||||
|
||||
bool_baselevels: Show reference categories for boolean independent variables even if reference category is False
|
||||
exp: Report exponentiated parameters rather than raw parameters
|
||||
:param model_class: Type of regression model to fit
|
||||
:type model_class: statsmodels model class
|
||||
:param df: Data to perform regression on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (numeric)
|
||||
:type dep: str
|
||||
:param formula: Patsy formula for the regression model
|
||||
:type formula: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
:param model_kwargs: Keyword arguments to pass to *model_class* constructor
|
||||
:type model_kwargs: dict
|
||||
:param fit_kwargs: Keyword arguments to pass to *model.fit*
|
||||
:type fit_kwargs: dict
|
||||
:param family: See statsmodels *GLM* constructor
|
||||
:param cov_type: See statsmodels *model.fit*
|
||||
:param maxiter: See statsmodels *model.fit*
|
||||
:param start_params: See statsmodels *model.fit*
|
||||
:param bool_baselevels: Show reference categories for boolean independent variables even if reference category is *False*
|
||||
:type bool_baselevels: bool
|
||||
:param exp: Report exponentiated parameters rather than raw parameters, default (*None*) is to autodetect based on *model_class*
|
||||
:type exp: bool
|
||||
|
||||
:rtype: :class:`yli.regress.RegressionResult`
|
||||
"""
|
||||
|
||||
# Populate model_kwargs
|
||||
|
|
@ -473,7 +549,23 @@ def regress(
|
|||
)
|
||||
|
||||
def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kwargs):
|
||||
"""Perform logistic regression, then use parameters as start parameters for desired regression"""
|
||||
"""
|
||||
Perform logistic regression, then use parameters as start parameters for desired regression
|
||||
|
||||
:param model_class: Type of regression model to fit
|
||||
:type model_class: statsmodels model class
|
||||
:param df: Data to perform regression on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (numeric)
|
||||
:type dep: str
|
||||
:param formula: Patsy formula for the regression model
|
||||
:type formula: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
:param kwargs: Passed through to :func:`yli.regress`
|
||||
|
||||
:rtype: :class:`yli.regress.RegressionResult`
|
||||
"""
|
||||
|
||||
# Check for/clean NaNs
|
||||
# Do this once here so we only get 1 warning
|
||||
|
|
@ -496,10 +588,11 @@ def logit_then_regress(model_class, df, dep, formula, *, nan_policy='warn', **kw
|
|||
|
||||
class PenalisedLogit(statsmodels.discrete.discrete_model.BinaryModel):
|
||||
"""
|
||||
Statsmodel-compatible model for computing Firth penalised logistic regression
|
||||
Uses R "logistf" library
|
||||
statsmodel-compatible model for computing Firth penalised logistic regression
|
||||
|
||||
NB: This class expects to be used in the context of yli.regress()
|
||||
Uses the R *logistf* library.
|
||||
|
||||
This class should be used in conjunction with :func:`yli.regress`.
|
||||
"""
|
||||
|
||||
def fit(self, disp=False):
|
||||
|
|
|
|||
179
yli/sig_tests.py
179
yli/sig_tests.py
|
|
@ -30,26 +30,50 @@ from .utils import Estimate, as_2groups, check_nan, fmt_p
|
|||
|
||||
class TTestResult:
|
||||
"""
|
||||
Result of a Student's t test
|
||||
Result of a Student's *t* test
|
||||
|
||||
delta: Mean difference
|
||||
See :func:`yli.ttest_ind`.
|
||||
"""
|
||||
|
||||
def __init__(self, statistic, dof, pvalue, delta, delta_direction):
|
||||
#: *t* statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: Degrees of freedom of the *t* distribution (*int*)
|
||||
self.dof = dof
|
||||
#: *p* value for the *t* statistic (*float*)
|
||||
self.pvalue = pvalue
|
||||
#: Absolute value of the mean difference (:class:`yli.utils.Estimate`)
|
||||
self.delta = delta
|
||||
#: Description of the direction of the effect (*str*)
|
||||
self.delta_direction = delta_direction
|
||||
|
||||
def _repr_html_(self):
|
||||
return '<i>t</i>({:.0f}) = {:.2f}; <i>p</i> {}<br>Δ<i>μ</i> ({:g}% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=True), (1-config.alpha)*100, self.delta.summary(), self.delta_direction)
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the *t* test
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return 't({:.0f}) = {:.2f}; p {}\nΔμ ({:g}% CI) = {}, {}'.format(self.dof, self.statistic, fmt_p(self.pvalue, html=False), (1-config.alpha)*100, self.delta.summary(), self.delta_direction)
|
||||
|
||||
def ttest_ind(df, dep, ind, *, nan_policy='warn'):
|
||||
"""Perform an independent-sample Student's t test"""
|
||||
"""
|
||||
Perform an independent 2-sample Student's *t* test
|
||||
|
||||
:param df: Data to perform the test on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (numeric)
|
||||
:type dep: str
|
||||
:param ind: Column in *df* for the independent variable (dichotomous)
|
||||
:type ind: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
|
||||
:rtype: :class:`yli.sig_tests.TTestResult`
|
||||
"""
|
||||
|
||||
# Check for/clean NaNs
|
||||
df = check_nan(df[[ind, dep]], nan_policy)
|
||||
|
|
@ -78,22 +102,49 @@ def ttest_ind(df, dep, ind, *, nan_policy='warn'):
|
|||
# One-way ANOVA
|
||||
|
||||
class FTestResult:
|
||||
"""Result of an F test for ANOVA/regression"""
|
||||
"""
|
||||
Result of an *F* test for ANOVA/regression
|
||||
|
||||
See :func:`yli.anova_oneway` and :meth:`yli.regress.RegressionResult.ftest`.
|
||||
"""
|
||||
|
||||
def __init__(self, statistic, dof1, dof2, pvalue):
|
||||
#: *F* statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: Degrees of freedom in the *F* distribution numerator (*int*)
|
||||
self.dof1 = dof1
|
||||
#: Degrees of freedom in the *F* distribution denominator (*int*)
|
||||
self.dof2 = dof2
|
||||
#: *p* value for the *F* statistic (*float*)
|
||||
self.pvalue = pvalue
|
||||
|
||||
def _repr_html_(self):
|
||||
return '<i>F</i>({}, {}) = {:.2f}; <i>p</i> {}'.format(self.dof1, self.dof2, self.statistic, fmt_p(self.pvalue, html=True))
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the *F* test
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return 'F({}, {}) = {:.2f}; p {}'.format(self.dof1, self.dof2, self.statistic, fmt_p(self.pvalue, html=False))
|
||||
|
||||
def anova_oneway(df, dep, ind, *, nan_policy='omit'):
|
||||
"""Perform one-way ANOVA"""
|
||||
"""
|
||||
Perform one-way ANOVA
|
||||
|
||||
:param df: Data to perform the test on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (numeric)
|
||||
:type dep: str
|
||||
:param ind: Column in *df* for the independent variable (categorical)
|
||||
:type ind: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
|
||||
:rtype: :class:`yli.sig_tests.FTestResult`
|
||||
"""
|
||||
|
||||
# Check for/clean NaNs
|
||||
df = check_nan(df[[ind, dep]], nan_policy)
|
||||
|
|
@ -115,16 +166,21 @@ def anova_oneway(df, dep, ind, *, nan_policy='omit'):
|
|||
|
||||
class MannWhitneyResult:
|
||||
"""
|
||||
Result of a Mann-Whitney test
|
||||
Result of a Mann-Whitney *U* test
|
||||
|
||||
brunnermunzel: BrunnerMunzelResult on same data
|
||||
See :func:`yli.mannwhitney`.
|
||||
"""
|
||||
|
||||
def __init__(self, statistic, pvalue, rank_biserial, direction, brunnermunzel=None):
|
||||
#: Mann–Whitney *U* statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: *p* value for the *U* statistic (*float*)
|
||||
self.pvalue = pvalue
|
||||
#: Absolute value of the rank-biserial correlation (*float*)
|
||||
self.rank_biserial = rank_biserial
|
||||
#: Description of the direction of the effect (*str*)
|
||||
self.direction = direction
|
||||
#: :class:`BrunnerMunzelResult` on the same data, or *None* if N/A
|
||||
self.brunnermunzel = brunnermunzel
|
||||
|
||||
def _repr_html_(self):
|
||||
|
|
@ -135,6 +191,12 @@ class MannWhitneyResult:
|
|||
return line1
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the Mann–Whitney test
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
line1 = 'U = {:.1f}; p {}\nr = {}, {}'.format(self.statistic, fmt_p(self.pvalue, html=False), self.rank_biserial, self.direction)
|
||||
if self.brunnermunzel:
|
||||
return line1 + '\n' + self.brunnermunzel.summary()
|
||||
|
|
@ -142,24 +204,55 @@ class MannWhitneyResult:
|
|||
return line1
|
||||
|
||||
class BrunnerMunzelResult:
|
||||
"""
|
||||
Result of a Brunner–Munzel test
|
||||
|
||||
See :func:`yli.mannwhitney`. This library calls the Brunner–Munzel test statistic *W*.
|
||||
"""
|
||||
|
||||
"""Result of a Brunner-Munzel test"""
|
||||
|
||||
def __init__(self, statistic, pvalue):
|
||||
#: *W* statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: *p* value for the *W* statistic (*float*)
|
||||
self.pvalue = pvalue
|
||||
|
||||
def _repr_html_(self):
|
||||
return '<i>W</i> = {:.1f}; <i>p</i> {}'.format(self.statistic, fmt_p(self.pvalue, html=True))
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the Brunner–Munzel test
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return 'W = {:.1f}; p {}'.format(self.statistic, fmt_p(self.pvalue, html=False))
|
||||
|
||||
def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_continuity=False, alternative='two-sided', method='auto'):
|
||||
"""
|
||||
Perform a Mann-Whitney test
|
||||
Perform a Mann-Whitney *U* test
|
||||
|
||||
brunnermunzel: Set to False to skip the Brunner-Munzel test
|
||||
use_continuity, alternative, method: See scipy.stats.mannwhitneyu
|
||||
By default, this function performs a Brunner–Munzel test if the Mann–Whitney test is significant.
|
||||
If the Mann–Whitney test is significant but the Brunner–Munzel test is not, a warning is raised.
|
||||
The Brunner–Munzel test is returned only if non-significant.
|
||||
|
||||
:param df: Data to perform the test on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (numeric)
|
||||
:type dep: str
|
||||
:param ind: Column in *df* for the independent variable (dichotomous)
|
||||
:type ind: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
:param brunnermunzel: Whether to compute the Brunner–Munzel test if the Mann–Whitney test is significant
|
||||
:type brunnermunzel: bool
|
||||
:param use_continuity: See *scipy.stats.mannwhitneyu*
|
||||
:param alternative: See *scipy.stats.mannwhitneyu*
|
||||
:param method: See *scipy.stats.mannwhitneyu*
|
||||
|
||||
:rtype: :class:`yli.sig_tests.MannWhitneyResult`
|
||||
"""
|
||||
|
||||
# Check for/clean NaNs
|
||||
|
|
@ -198,14 +291,24 @@ def mannwhitney(df, dep, ind, *, nan_policy='warn', brunnermunzel=True, use_cont
|
|||
# Pearson chi-squared test
|
||||
|
||||
class PearsonChiSquaredResult:
|
||||
"""Result of a Pearson chi-squared test"""
|
||||
"""
|
||||
Result of a Pearson *χ*:sup:`2` test
|
||||
|
||||
See :func:`yli.chi2`.
|
||||
"""
|
||||
|
||||
def __init__(self, ct, statistic, dof, pvalue, oddsratio=None, riskratio=None):
|
||||
#: Contingency table for the observations (*DataFrame*)
|
||||
self.ct = ct
|
||||
#: *χ*:sup:`2` statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: Degrees of freedom for the *χ*:sup:`2` distribution (*int*)
|
||||
self.dof = dof
|
||||
#: *p* value for the *χ*:sup:`2` test (*float*)
|
||||
self.pvalue = pvalue
|
||||
#: Odds ratio (*float*; *None* if not a 2×2 table)
|
||||
self.oddsratio = oddsratio
|
||||
#: Risk ratio (*float*; *None* if not a 2×2 table)
|
||||
self.riskratio = riskratio
|
||||
|
||||
def _repr_html_(self):
|
||||
|
|
@ -217,6 +320,12 @@ class PearsonChiSquaredResult:
|
|||
self.ct._repr_html_(), self.dof, self.statistic, fmt_p(self.pvalue, html=True))
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the *χ*:sup:`2` test
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
if self.oddsratio is not None:
|
||||
return '{0}\nχ²({1}) = {2:.2f}; p {3}\nOR ({4:g}% CI) = {5}\nRR ({4:g}% CI) = {6}'.format(
|
||||
self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False), (1-config.alpha)*100, self.oddsratio.summary(), self.riskratio.summary())
|
||||
|
|
@ -225,7 +334,24 @@ class PearsonChiSquaredResult:
|
|||
self.ct, self.dof, self.statistic, fmt_p(self.pvalue, html=False))
|
||||
|
||||
def chi2(df, dep, ind, *, nan_policy='warn'):
|
||||
"""Perform a Pearson chi-squared test"""
|
||||
"""
|
||||
Perform a Pearson *χ*:sup:`2` test
|
||||
|
||||
If a 2×2 contingency table is obtained (i.e. if both variables are dichotomous), an odds ratio and risk ratio are calculated.
|
||||
The ratios are calculated for the higher-valued value in each variable (i.e. ``True`` compared with ``False`` for a boolean).
|
||||
The risk ratio is calculated relative to the independent variable.
|
||||
|
||||
:param df: Data to perform the test on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (categorical)
|
||||
:type dep: str
|
||||
:param ind: Column in *df* for the independent variable (categorical)
|
||||
:type ind: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
|
||||
:rtype: :class:`yli.sig_tests.PearsonChiSquaredResult`
|
||||
"""
|
||||
|
||||
# Check for/clean NaNs
|
||||
df = check_nan(df[[ind, dep]], nan_policy)
|
||||
|
|
@ -266,20 +392,45 @@ def chi2(df, dep, ind, *, nan_policy='warn'):
|
|||
# Pearson correlation
|
||||
|
||||
class PearsonRResult:
|
||||
"""Result of Pearson correlation"""
|
||||
"""
|
||||
Result of Pearson correlation
|
||||
|
||||
See :func:`yli.pearsonr`.
|
||||
"""
|
||||
|
||||
def __init__(self, statistic, pvalue):
|
||||
#: Pearson *r* correlation statistic (*float*)
|
||||
self.statistic = statistic
|
||||
#: *p* value for the *r* statistic (*float*)
|
||||
self.pvalue = pvalue
|
||||
|
||||
def _repr_html_(self):
|
||||
return '<i>r</i> ({:g}% CI) = {}; <i>p</i> {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, html=True))
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the Pearson correlation
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return 'r ({:g}% CI) = {}; p {}'.format((1-config.alpha)*100, self.statistic.summary(), fmt_p(self.pvalue, html=False))
|
||||
|
||||
def pearsonr(df, dep, ind, *, nan_policy='warn'):
|
||||
"""Compute the Pearson correlation coefficient (Pearson's r)"""
|
||||
"""
|
||||
Compute the Pearson correlation coefficient (Pearson's r)
|
||||
|
||||
:param df: Data to perform the test on
|
||||
:type df: DataFrame
|
||||
:param dep: Column in *df* for the dependent variable (numerical)
|
||||
:type dep: str
|
||||
:param ind: Column in *df* for the independent variable (numerical)
|
||||
:type ind: str
|
||||
:param nan_policy: How to handle *nan* values (see :ref:`nan-handling`)
|
||||
:type nan_policy: str
|
||||
|
||||
:rtype: :class:`yli.sig_tests.PearsonRResult`
|
||||
"""
|
||||
|
||||
# Check for/clean NaNs
|
||||
df = check_nan(df[[ind, dep]], nan_policy)
|
||||
|
|
|
|||
18
yli/utils.py
18
yli/utils.py
|
|
@ -143,26 +143,44 @@ class ConfidenceInterval:
|
|||
"""A confidence interval"""
|
||||
|
||||
def __init__(self, lower, upper):
|
||||
#: Lower confidence limit (*float*)
|
||||
self.lower = lower
|
||||
#: Upper confidence limit (*float*)
|
||||
self.upper = upper
|
||||
|
||||
def _repr_html_(self):
|
||||
return self.summary()
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the confidence interval
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return '{:.2f}–{:.2f}'.format(self.lower, self.upper)
|
||||
|
||||
class Estimate:
|
||||
"""A point estimate and surrounding confidence interval"""
|
||||
|
||||
def __init__(self, point, ci_lower, ci_upper):
|
||||
#: Point estimate (*float*)
|
||||
self.point = point
|
||||
#: Lower confidence limit (*float*)
|
||||
self.ci_lower = ci_lower
|
||||
#: Upper confidence limit (*float*)
|
||||
self.ci_upper = ci_upper
|
||||
|
||||
def _repr_html_(self):
|
||||
return self.summary()
|
||||
|
||||
def summary(self):
|
||||
"""
|
||||
Return a stringified summary of the estimate and confidence interval
|
||||
|
||||
:rtype: str
|
||||
"""
|
||||
|
||||
return '{:.2f} ({:.2f}–{:.2f})'.format(self.point, self.ci_lower, self.ci_upper)
|
||||
|
||||
def __neg__(self):
|
||||
|
|
|
|||
Loading…
Reference in New Issue