docs/source/sandbox.rst
.. _sandbox:
This sandbox contains code that is for various reasons not ready to be included in statsmodels proper. It contains modules from the old stats.models code that have not been tested, verified and updated to the new statsmodels structure: cox survival model, mixed effects model with repeated measures, generalized additive model and the formula framework. The sandbox also contains code that is currently being worked on until it fits the pattern of statsmodels or is sufficiently tested.
All sandbox modules have to be explicitly imported to indicate that they are not yet part of the core of statsmodels. The quality and testing of the sandbox code varies widely.
There are some examples in the sandbox.examples folder. Additional
examples are directly included in the modules and in subfolders of
the sandbox.
Time Series analysis :mod:tsa
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
In this part we develop models and functions that will be useful for time
series analysis. Most of the models and function have been moved to
:mod:statsmodels.tsa.
Moving Window Statistics """"""""""""""""""""""""
Most moving window statistics, like rolling mean, moments (up to 4th order), min,
max, mean, and variance, are covered by the functions for Moving (rolling) statistics/moments <https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html#window-functions>_ in Pandas.
.. module:: statsmodels.sandbox.tsa :synopsis: Experimental time-series analysis models
.. currentmodule:: statsmodels.sandbox.tsa
.. autosummary:: :toctree: generated/
movstat.movorder movstat.movmean movstat.movvar movstat.movmoment
Regression and ANOVA ^^^^^^^^^^^^^^^^^^^^
.. module:: statsmodels.sandbox.regression.anova_nistcertified :synopsis: Experimental ANOVA estimator
.. currentmodule:: statsmodels.sandbox.regression.anova_nistcertified
The following two ANOVA functions are fully tested against the NIST test data
for balanced one-way ANOVA. anova_oneway follows the same pattern as the
oneway anova function in scipy.stats but with higher precision for badly
scaled problems. anova_ols produces the same results as the one way anova
however using the OLS model class. It also verifies against the NIST tests,
with some problems in the worst scaled cases. It shows how to do simple ANOVA
using statsmodels in three lines and is also best taken as a recipe.
.. autosummary:: :toctree: generated/
anova_oneway anova_ols
The following are helper functions for working with dummy variables and generating ANOVA results with OLS. They are best considered as recipes since they were written with a specific use in mind. These function will eventually be rewritten or reorganized.
.. module:: statsmodels.sandbox.regression :synopsis: Experimental regression tools
.. currentmodule:: statsmodels.sandbox.regression
.. autosummary:: :toctree: generated/
try_ols_anova.data2dummy try_ols_anova.data2groupcont try_ols_anova.data2proddummy try_ols_anova.dropname try_ols_anova.form2design
The following are helper functions for group statistics where groups are defined by a label array. The qualifying comments for the previous group apply also to this group of functions.
.. autosummary:: :toctree: generated/
try_catdata.cat2dummy try_catdata.convertlabels try_catdata.groupsstats_1d try_catdata.groupsstats_dummy try_catdata.groupstatsbin try_catdata.labelmeanfilter try_catdata.labelmeanfilter_nd try_catdata.labelmeanfilter_str
Additional to these functions, sandbox regression still contains several examples, that are illustrative of the use of the regression models of statsmodels.
Systems of Regression Equations and Simultaneous Equations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
The following are for fitting systems of equations models. Though the returned parameters have been verified as accurate, this code is still very experimental, and the usage of the models will very likely change significantly before they are added to the main codebase.
.. module:: statsmodels.sandbox.sysreg :synopsis: Experimental system regression models
.. currentmodule:: statsmodels.sandbox.sysreg
.. autosummary:: :toctree: generated/
SUR Sem2SLS
Miscellaneous ^^^^^^^^^^^^^ .. module:: statsmodels.sandbox.tools.tools_tsa :synopsis: Experimental tools for working with time-series
.. currentmodule:: statsmodels.sandbox.tools.tools_tsa
Descriptive Statistics Printing """""""""""""""""""""""""""""""
.. module:: statsmodels.sandbox :synopsis: Experimental tools that have not been fully vetted
.. currentmodule:: statsmodels.sandbox
.. autosummary:: :toctree: generated/
descstats.sign_test descstats.descstats
Original stats.models ^^^^^^^^^^^^^^^^^^^^^
None of these are fully working. The formula framework is used by cox and mixed.
Mixed Effects Model with Repeated Measures using an EM Algorithm
:mod:statsmodels.sandbox.mixed
Cox Proportional Hazards Model
:mod:statsmodels.sandbox.cox
Generalized Additive Models
:mod:statsmodels.sandbox.gam
Formula
:mod:statsmodels.sandbox.formula