Missing Data

All of the models can handle missing data. For performance reasons, the default is not to do any checking for missing data. If, however, you would like for missing data to be handled internally, you can do so by using the missing keyword argument. The default is to do nothing

In [1]: import statsmodels.api as sm

In [2]: data = sm.datasets.longley.load(as_pandas=False)

In [3]: data.exog = sm.add_constant(data.exog)

# add in some missing data
In [4]: missing_idx = np.array([False] * len(data.endog))

In [5]: missing_idx[[4, 10, 15]] = True

In [6]: data.endog[missing_idx] = np.nan

In [7]: ols_model = sm.OLS(data.endog, data.exog)

In [8]: ols_fit = ols_model.fit()

In [9]: print(ols_fit.params)
[nan nan nan nan nan nan nan]

This silently fails and all of the model parameters are NaN, which is probably not what you expected. If you are not sure whether or not you have missing data you can use missing = ‘raise’. This will raise a MissingDataError during model instantiation if missing data is present so that you know something was wrong in your input data.

In [10]: ols_model = sm.OLS(data.endog, data.exog, missing='raise')
---------------------------------------------------------------------------
MissingDataError                          Traceback (most recent call last)
<ipython-input-10-5debd60362bf> in <module>
----> 1 ols_model = sm.OLS(data.endog, data.exog, missing='raise')

/usr/lib/python3/dist-packages/statsmodels/regression/linear_model.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
    870     def __init__(self, endog, exog=None, missing='none', hasconst=None,
    871                  **kwargs):
--> 872         super(OLS, self).__init__(endog, exog, missing=missing,
    873                                   hasconst=hasconst, **kwargs)
    874         if "weights" in self._init_keys:

/usr/lib/python3/dist-packages/statsmodels/regression/linear_model.py in __init__(self, endog, exog, weights, missing, hasconst, **kwargs)
    701         else:
    702             weights = weights.squeeze()
--> 703         super(WLS, self).__init__(endog, exog, missing=missing,
    704                                   weights=weights, hasconst=hasconst, **kwargs)
    705         nobs = self.exog.shape[0]

/usr/lib/python3/dist-packages/statsmodels/regression/linear_model.py in __init__(self, endog, exog, **kwargs)
    188     """
    189     def __init__(self, endog, exog, **kwargs):
--> 190         super(RegressionModel, self).__init__(endog, exog, **kwargs)
    191         self._data_attr.extend(['pinv_wexog', 'weights'])
    192 

/usr/lib/python3/dist-packages/statsmodels/base/model.py in __init__(self, endog, exog, **kwargs)
    235 
    236     def __init__(self, endog, exog=None, **kwargs):
--> 237         super(LikelihoodModel, self).__init__(endog, exog, **kwargs)
    238         self.initialize()
    239 

/usr/lib/python3/dist-packages/statsmodels/base/model.py in __init__(self, endog, exog, **kwargs)
     75         missing = kwargs.pop('missing', 'none')
     76         hasconst = kwargs.pop('hasconst', None)
---> 77         self.data = self._handle_data(endog, exog, missing, hasconst,
     78                                       **kwargs)
     79         self.k_constant = self.data.k_constant

/usr/lib/python3/dist-packages/statsmodels/base/model.py in _handle_data(self, endog, exog, missing, hasconst, **kwargs)
     99 
    100     def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
--> 101         data = handle_data(endog, exog, missing, hasconst, **kwargs)
    102         # kwargs arrays could have changed, easier to just attach here
    103         for key in kwargs:

/usr/lib/python3/dist-packages/statsmodels/base/data.py in handle_data(endog, exog, missing, hasconst, **kwargs)
    670 
    671     klass = handle_data_class_factory(endog, exog)
--> 672     return klass(endog, exog=exog, missing=missing, hasconst=hasconst,
    673                  **kwargs)

/usr/lib/python3/dist-packages/statsmodels/base/data.py in __init__(self, endog, exog, missing, hasconst, **kwargs)
     69             self.formula = kwargs.pop('formula')
     70         if missing != 'none':
---> 71             arrays, nan_idx = self.handle_missing(endog, exog, missing,
     72                                                   **kwargs)
     73             self.missing_row_idx = nan_idx

/usr/lib/python3/dist-packages/statsmodels/base/data.py in handle_missing(cls, endog, exog, missing, **kwargs)
    283 
    284         elif missing == 'raise':
--> 285             raise MissingDataError("NaNs were encountered in the data")
    286 
    287         elif missing == 'drop':

MissingDataError: NaNs were encountered in the data

If you want statsmodels to handle the missing data by dropping the observations, use missing = ‘drop’.

In [11]: ols_model = sm.OLS(data.endog, data.exog, missing='drop')

We are considering adding a configuration framework so that you can set the option with a global setting.