ENH: added OLS.predict method

pandas/stats/ols.py

@@ -9,7 +9,7 @@
 
 import numpy as np
 
-from pandas.core.api import DataFrame, Series
+from pandas.core.api import DataFrame, Series, isnull
 from pandas.core.common import _ensure_float64
 from pandas.core.index import MultiIndex
 from pandas.core.panel import Panel
@@ -381,6 +381,58 @@ def y_predict(self):
         For in-sample, this is same as y_fitted."""
         return self.y_fitted
 
+    def predict(self, beta=None, x=None, fill_value=None,
+                fill_method=None, axis=0):
+        """
+        Parameters
+        ----------
+        beta : Series
+        x : Series or DataFrame
+        fill_value : scalar or dict, default None
+        fill_method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None
+        axis : {0, 1}, default 0
+            See DataFrame.fillna for more details
+
+        Notes
+        -----
+        1. If both fill_value and fill_method are None then NaNs are dropped
+        (this is the default behavior)
+        2. An intercept will be automatically added to the new_y_values if
+           the model was fitted using an intercept
+
+        Returns
+        -------
+        Series of predicted values
+        """
+        if beta is None and x is None:
+            return self.y_predict
+
+        if beta is None:
+            beta = self.beta
+        else:
+            beta = beta.reindex(self.beta.index)
+            if isnull(beta).any():
+                raise ValueError('Must supply betas for same variables')
+
+        if x is None:
+            x = self._x
+            orig_x = x
+        else:
+            orig_x = x
+            if fill_value is None and fill_method is None:
+                x = x.dropna(how='any')
+            else:
+                x = x.fillna(value=fill_value, method=fill_method, axis=axis)
+            if isinstance(x, Series):
+                x = DataFrame({'x' : x})
+            if self._intercept:
+                x['intercept'] = 1.
+
+            x = x.reindex(columns=self._x.columns)
+
+        rs = x.values.dot(beta.values)
+        return Series(rs, x.index).reindex(orig_x.index)
+
     RESULT_FIELDS = ['r2', 'r2_adj', 'df', 'df_model', 'df_resid', 'rmse',
                      'f_stat', 'beta', 'std_err', 't_stat', 'p_value', 'nobs']
 

pandas/stats/tests/test_ols.py

@@ -287,6 +287,33 @@ def test_y_predict(self):
         assert_series_equal(model1.y_predict, model1.y_fitted)
         assert_almost_equal(model1._y_predict_raw, model1._y_fitted_raw)
 
+    def test_predict(self):
+        y = tm.makeTimeSeries()
+        x = tm.makeTimeDataFrame()
+        model1 = ols(y=y, x=x)
+        assert_series_equal(model1.predict(), model1.y_predict)
+        assert_series_equal(model1.predict(x=x), model1.y_predict)
+        assert_series_equal(model1.predict(beta=model1.beta), model1.y_predict)
+
+        exog = x.copy()
+        exog['intercept'] = 1.
+        rs = Series(exog.values.dot(model1.beta.values), x.index)
+        assert_series_equal(model1.y_predict, rs)
+
+        x2 = x.reindex(columns=x.columns[::-1])
+        assert_series_equal(model1.predict(x=x2), model1.y_predict)
+
+        x3 = x2 + 10
+        pred3 = model1.predict(x=x3)
+        x3['intercept'] = 1.
+        x3 = x3.reindex(columns = model1.beta.index)
+        expected = Series(x3.values.dot(model1.beta.values), x3.index)
+        assert_series_equal(expected, pred3)
+
+        beta = Series(0., model1.beta.index)
+        pred4 = model1.predict(beta=beta)
+        assert_series_equal(Series(0., pred4.index), pred4)
+
     def test_longpanel_series_combo(self):
         wp = tm.makePanel()
         lp = wp.to_frame()