Linear mixed effects regressions

Linear mixed effects regressions are great, but if you’re here, you probably already agree. You can find more infomration about them elsewhere, the links lme4 aren’t a bad place to start.

Installation

Mixing r and python used to be a bit more fraught, but rpy2 and conda seem to be working together better these days. To install first get a conda environment with the dependencies:

>>> conda create -n sklmer -c conda-forge numpy scipy rpy2 r-lme4 r-lmertest r-lsmeans tzlocal

Then pip install sklearn-lmer:

>>> pip install sklearn-lmer

Usage

It can be imported as:

>>> from sklmer import LmerRegressor

Now the mostly part of that compatiblity is that init does have two required paramters: a formula and the names of the columns holding independent variables and grouping variables (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

>>> df = pd.read_csv(os.path.join(get_resource_path(),'sample_data.csv'))
>>> lreg = LmerRegressor('DV ~ IV2 + (IV2|Group)', X_cols=df.columns)

If you want the best compatibility with sklearn it probably makes sense to split out the dataframe into X, y, and group variables, though since you’ve defined a formula it’s ok if the y and group columns are in X

>>> X = df.values
>>> y = df.DV.values
>>> groups = df.Group.values

Once you’ve done that, it seems to work fine with other sklearn tools, like cross_val_score

>>> logo = LeaveOneGroupOut()
>>> cross_val_score(lreg, X=X, y=y, cv=logo.split(X, groups=groups), scoring='neg_mean_squared_error')