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        "description": ".. -*- mode: rst -*-\n\n|Travis|_ |AppVeyor|_ |Codecov|_ |CircleCI|_ |ReadTheDocs|_\n\n.. |Travis| image:: https://travis-ci.org/nimh-mbdu/sklearn-lmer.svg?branch=master\n.. _Travis: https://travis-ci.org/nimh-mbdu/sklearn-lmer\n\n.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/pifxyfnev94kbej4/branch/master?svg=true\n.. _AppVeyor: https://ci.appveyor.com/project/Shotgunosine/sklearn-lmer/branch/master\n\n.. |Codecov| image:: https://codecov.io/gh/nimh-mbdu/sklearn-lmer/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/nimh-mbdu/sklearn-lmer\n.. _Codecov: https://codecov.io/gh/nimh-mbdu/sklearn-lmer\n\n.. |CircleCI| image:: https://circleci.com/gh/nimh-mbdu/sklearn-lmer.svg?style=shield&circle-token=:circle-token\n.. _CircleCI: https://circleci.com/gh/nimh-mbdu/sklearn-lmer/tree/master\n\n.. |ReadTheDocs| image:: https://readthedocs.org/projects/sklearn-lmer/badge/?version=latest\n.. _ReadTheDocs: https://sklearn-lmer.readthedocs.io/en/latest/?badge=latest\n\nsklearn-lmer - Scikit-learn estimator wrappers for pymer4 wrapped LME4 mixed effects models\n===========================================================================================\n\n.. _sklearn: https://scikit-learn.org\n.. _pymer4: http://eshinjolly.com/pymer4/\n.. _lme4: https://cran.r-project.org/web/packages/lme4/index.html\n.. _documentation: https://sklearn-lmer.readthedocs.io/en/latest/\n\nsklearn-lmer is a simple package to wrap the convienience \nof pymer4_'s lme4_ wrapping in a *mostly* sklearn_ compatible regressor class.\n\nRefer to the documentation_ for examples and api.\n\nLinear mixed effects regressions\n--------------------------------\n\nLinear mixed effects regressions are great, but if you're here,\nyou probably already agree. You can find more infomration about\nthem elsewhere, the links lme4_ aren't a bad place to start. \n\nInstallation\n------------\n\nMixing r and python used to be a bit more fraught, but rpy2 and conda\nseem to be working together better these days.\nTo install first get a conda environment with the dependencies::\n\n   >>> conda create -n sklmer numpy scipy rpy2=2.9.4 r-lme4 r-lmertest r-lsmeans tzlocal\n\nThen pip install sklearn-lmer::\n\n   >>> pip install sklearn-lmer\n\nUsage\n-----\nIt can be imported as::\n\n    >>> from sklmer import LmerRegressor\n\nNow the *mostly* part of that compatiblity is that init does have two required paramters:\na formula and the names of the columns holding independent variables and grouping variables\n(I've called this parameter ``X_cols`` even though it is more than just X). When I use this I've got my data in a dataframe and just pass ``dataframe.columns`` with ``X_cols`` like so ::\n\n    >>> df = pd.read_csv(os.path.join(get_resource_path(),'sample_data.csv'))\n    >>> lreg = LmerRegressor('DV ~ IV2 + (IV2|Group)', X_cols=df.columns)\n\nIf you want the best compatibility with sklearn it probably makes sense to split\nout the dataframe into X, y, and group variables, though since you've defined a formula\nit's ok if the y and group columns are in X ::\n\n   >>> X = df.values\n   >>> y = df.DV.values\n   >>> groups = df.Group.values\n\nOnce you've done that, it seems to work fine with other sklearn tools, like ``cross_val_score`` ::\n\n   >>> logo = LeaveOneGroupOut()\n   >>> cross_val_score(lreg, X=X, y=y, cv=logo.split(X, groups=groups), scoring='neg_mean_squared_error')\n\n\n",
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