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()

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)
const     NaN
GNPDEFL   NaN
GNP       NaN
UNEMP     NaN
ARMED     NaN
POP       NaN
YEAR      NaN
dtype: float64

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)
Cell In[10], line 1
----> 1 ols_model = sm.OLS(data.endog, data.exog, missing='raise')

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:996, in OLS.__init__(self, endog, exog, missing, hasconst, **kwargs)
    991     msg = (
    992         "Weights are not supported in OLS and will be ignored"
    993         "An exception will be raised in the next version."
    994     )
    995     warnings.warn(msg, ValueWarning, stacklevel=2)
--> 996 super().__init__(endog, exog, missing=missing, hasconst=hasconst, **kwargs)
    997 if "weights" in self._init_keys:
    998     self._init_keys.remove("weights")

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:806, in WLS.__init__(self, endog, exog, weights, missing, hasconst, **kwargs)
    804 else:
    805     weights = weights.squeeze()
--> 806 super().__init__(
    807     endog, exog, missing=missing, weights=weights, hasconst=hasconst, **kwargs
    808 )
    809 nobs = self.exog.shape[0]
    810 weights = self.weights

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/regression/linear_model.py:220, in RegressionModel.__init__(self, endog, exog, **kwargs)
    219 def __init__(self, endog, exog, **kwargs):
--> 220     super().__init__(endog, exog, **kwargs)
    221     self.pinv_wexog: Float64Array | None = None
    222     self._data_attr.extend(["pinv_wexog", "wendog", "wexog", "weights"])

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/base/model.py:285, in LikelihoodModel.__init__(self, endog, exog, **kwargs)
    284 def __init__(self, endog, exog=None, **kwargs):
--> 285     super().__init__(endog, exog, **kwargs)
    286     self.initialize()

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/base/model.py:103, in Model.__init__(self, endog, exog, **kwargs)
    101 missing = kwargs.pop("missing", "none")
    102 hasconst = kwargs.pop("hasconst", None)
--> 103 self.data = self._handle_data(endog, exog, missing, hasconst, **kwargs)
    104 self.k_constant = self.data.k_constant
    105 self.exog = self.data.exog

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/base/model.py:144, in Model._handle_data(self, endog, exog, missing, hasconst, **kwargs)
    143 def _handle_data(self, endog, exog, missing, hasconst, **kwargs):
--> 144     data = handle_data(endog, exog, missing, hasconst, **kwargs)
    145     # kwargs arrays could have changed, easier to just attach here
    146     for key in kwargs:

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/base/data.py:707, in handle_data(endog, exog, missing, hasconst, **kwargs)
    704     exog = np.asarray(exog)
    706 klass = handle_data_class_factory(endog, exog)
--> 707 return klass(endog, exog=exog, missing=missing, hasconst=hasconst, **kwargs)

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/base/data.py:76, in ModelData.__init__(self, endog, exog, missing, hasconst, **kwargs)
     74     self.formula = kwargs.pop("formula")
     75 if missing != "none":
---> 76     arrays, nan_idx = self.handle_missing(endog, exog, missing, **kwargs)
     77     self.missing_row_idx = nan_idx
     78     self.__dict__.update(arrays)  # attach all the data arrays

File /opt/hostedtoolcache/Python/3.10.19/x64/lib/python3.10/site-packages/statsmodels/base/data.py:296, in ModelData.handle_missing(cls, endog, exog, missing, **kwargs)
    293     return combined, []
    295 elif missing == "raise":
--> 296     raise MissingDataError("NaNs were encountered in the data")
    298 elif missing == "drop":
    299     nan_mask = ~nan_mask

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.