Merge c6d82f8 into 4f675c7

docs/source/release/version0.6.rst

@@ -20,7 +20,7 @@ Other important new features
 ----------------------------
 
 * Other new changes can go
-* In a 
+* In a
 * Bullet list
 
 Major Bugs fixed
@@ -33,14 +33,14 @@ Major Bugs fixed
 Backwards incompatible changes and deprecations
 -----------------------------------------------
 
-* Note any backwards incompatible changes here
+* RegressionResults.norm_resid is now a readonly property, rather than a function.
 
 Development summary and credits
 -------------------------------
 
 A blurb about the number of changes and the contributors list.
 
-.. note:: 
+.. note::
 
    Obtained by running ``git log v0.5.0..HEAD --format='* %aN <%aE>' | sed 's/@/\-at\-/' | sed 's/<>//' | sort -u``.
 

statsmodels/regression/linear_model.py

@@ -114,6 +114,9 @@ def df_resid(self):
     def df_resid(self, value):
         self._df_resid = value
 
+    def whiten(self, X):
+        raise NotImplementedError("Subclasses should implement.")
+
     def fit(self, method="pinv", **kwargs):
         """
         Full fit of the model.
@@ -1122,18 +1125,14 @@ def HC3_se(self):
             self._HC3_se = np.sqrt(np.diag(self.cov_HC3))
         return self._HC3_se
 
-    #TODO: this needs a test
+    @cache_readonly
     def norm_resid(self):
         """
         Residuals, normalized to have unit length and unit variance.
 
         Returns
         -------
         An array wresid/sqrt(scale)
-
-        Notes
-        -----
-        This method is untested
         """
         if not hasattr(self, 'resid'):
             raise ValueError('need normalized residuals to estimate standard '
@@ -1691,19 +1690,19 @@ class RegressionResultsWrapper(wrap.ResultsWrapper):
         'HC0_se' : 'columns',
         'HC1_se' : 'columns',
         'HC2_se' : 'columns',
-        'HC3_se' : 'columns'
+        'HC3_se' : 'columns',
+        'norm_resid' : 'rows',
     }
 
     _wrap_attrs = wrap.union_dicts(base.LikelihoodResultsWrapper._attrs,
                                    _attrs)
 
-    _methods = {
-        'norm_resid' : 'rows',
-    }
+    _methods = {}
 
     _wrap_methods = wrap.union_dicts(
                         base.LikelihoodResultsWrapper._wrap_methods,
                         _methods)
+
 wrap.populate_wrapper(RegressionResultsWrapper,
                       RegressionResults)
 

statsmodels/regression/tests/results/results_regression.py

@@ -43,6 +43,14 @@ def __init__(self):
             -17.26893, -39.05504, -155.54997, -85.67131, 341.93151,
             -206.75783))
 
+        # Obtained from R using m$residuals / sqrt(sum(m$residuals * m$residuals) / m$df.residual)
+        self.norm_resid = np.array((0.87694426, -0.30838998,
+                                    0.15183385, -1.34528175, 1.01594375,
+                                    -0.81780510, -0.53812289, -0.04323497,
+                                    0.04692334, 1.49381010, -0.05664654,
+                                    -0.12811061, -0.51024404, -0.28102399,
+                                    1.12162357, -0.67821900))
+
     def conf_int(self): # a method to be consistent with sm
         return [(-177.0291,207.1524), (-.111581,.0399428),(-3.125065,
                 -.9153928),(-1.517948,-.5485049),(-.5625173,.4603083),
@@ -130,6 +138,20 @@ def __init__(self):
                       -100.632293701, 34.9342498779, -96.6685943604,
                       -367.32925415, -40.1300048828, -72.8692245483,
                       -60.8728256226, -35.9937324524, -222.944747925]
+        self.norm_resid = \
+            [-0.90569581, -0.75496938, -1.28663890, -0.11309411, -0.24746253, -0.47181831,
+             -1.02062293, -0.31403683,  1.62862142, -0.84973225, -0.42919669, -0.78007426,
+             -0.63913772,  0.29787637, -0.38364568, -0.21381846, -0.85577361,  0.54156452,
+             -0.48496031, -1.15374603,  0.41145856,  0.23996158, 2.70305838, -0.53171027,
+              0.79057028,  1.82433320, -0.04150362,  0.97048328,  0.13667658,  0.26750667,
+             -0.12690810,  0.11703354, -0.72689772,  0.34160874, 0.71332338, -0.75079661,
+              1.73137185, -0.39477348,  0.04107215, -0.11332274, 0.22952063, -0.88580496,
+             -0.67239515, -0.17656300,  4.48867723,  2.61499898, -0.16988320, -0.63136893,
+             -0.68135396,  0.06351572, -0.64467367, -0.37800911, -0.64304809,  1.88607184,
+              0.57624742,  0.60875207, -0.78636761, -0.17897383, -0.21716827, -0.07885570,
+             -0.57566752,  0.25202879, -0.29176531, -0.54378274,  0.30203654, -0.57460072,
+             -0.72378394, -0.23853382, -0.17325464, -0.24121979, -0.10269489, -0.57826451]
+
 
     def conf_int(self): # a method to be consistent with sm
         return [( -10.2936,  4.90523), ( 5.949595, 310.9044),
@@ -165,6 +187,13 @@ def __init__(self):
                     201.321121216, 215.908889771, 73.0936813354, 913.216918945,
                     424.824859619, -8.56475830078, -361.329742432,
                     27.3456058502, 151.28956604, -492.499359131]
+        # Obtained from R using m$residuals / sqrt(sum(m$residuals * m$residuals) / m$df.residual)
+        self.norm_resid = [0.58906369, -0.27427213, 0.19094881,
+                                    -0.84457419, -0.92697740, -1.14390695,
+                                    0.42368630, 0.45438671, 0.15382784,
+                                    1.92189233, 0.89405658, -0.01802479,
+                                    -0.76042924, 0.05754964, 0.31839340,
+                                    -1.03647964]
 
     def conf_int(self):
         return [(-341.6373,   235.6502), ( .0038583,   .1382881),

statsmodels/regression/tests/test_regression.py

@@ -135,6 +135,11 @@ def test_resids(self):
         assert_almost_equal(self.res1.resid, self.res2.resid,
             self.decimal_resids)
 
+    decimal_norm_resids = DECIMAL_4
+    def test_norm_resids(self):
+        assert_almost_equal(self.res1.norm_resid, self.res2.norm_resid,
+                            self.decimal_norm_resids)
+
 #TODO: test fittedvalues and what else?
 
 class TestOLS(CheckRegressionResults):