Repository: statsmodels/statsmodels

Issue:
discrete: numerically useless clipping
discret_model.py has at several places

`np.clip(self.cdf(q*XB), 1e-20, 1-1e-20)`

at double precision 1e-20 is too small relative to 1:

```
>>> 1-1e-20 == 1
True
```

this doesn't keep the values < 1

Since there are no test failures, we might not need to force probabilities to be strictly smaller than 1.  (cleanup not bug ?)
(In contrast, we need to bound away from zero to avoid ZeroDivision and nans)

discrete models only define deviance residuals
We should really define other residuals for the discrete model than just the deviance residuals. They give weird answers, for example, for the binary models with residuals well outside (-1,1). What does Stata do here?



Discussion:
Good point. Should replace with something like `np.finfo(dtype).eps`.

Stata reports Pearson residuals. Eg., for binary 

``` python
p = results.predict()
resid = (results.model.endog - p)/(p*(1-p))**.5
```


Resolving patch:
diff --git a/statsmodels/discrete/discrete_model.py b/statsmodels/discrete/discrete_model.py
--- a/statsmodels/discrete/discrete_model.py
+++ b/statsmodels/discrete/discrete_model.py
@@ -38,6 +38,9 @@
 except ImportError:
     have_cvxopt = False
 
+#TODO: When we eventually get user-settable precision, we need to change
+#      this
+FLOAT_EPS = np.finfo(float).eps
 
 #TODO: add options for the parameter covariance/variance
 # ie., OIM, EIM, and BHHH see Green 21.4
@@ -371,15 +374,6 @@ def predict(self, params, exog=None, linear=False):
         else:
             return np.dot(exog, params)
 
-    def fit(self, start_params=None, method='newton', maxiter=35,
-            full_output=1, disp=1, callback=None, **kwargs):
-        bnryfit = super(BinaryModel, self).fit(start_params=start_params,
-                method=method, maxiter=maxiter, full_output=full_output,
-                disp=disp, callback=callback, **kwargs)
-        discretefit = BinaryResults(self, bnryfit)
-        return BinaryResultsWrapper(discretefit)
-    fit.__doc__ = DiscreteModel.fit.__doc__
-
     def fit_regularized(self, start_params=None, method='l1',
             maxiter='defined_by_method', full_output=1, disp=1, callback=None,
             alpha=0, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=1e-4,
@@ -683,7 +677,6 @@ def predict(self, params, exog=None, exposure=None, offset=None,
             return np.exp(np.dot(exog, params) + exposure + offset) # not cdf
         else:
             return np.dot(exog, params) + exposure + offset
-            return super(CountModel, self).predict(params, exog, linear)
 
     def _derivative_predict(self, params, exog=None, transform='dydx'):
         """
@@ -1185,6 +1178,15 @@ def hessian(self, params):
         L = self.cdf(np.dot(X,params))
         return -np.dot(L*(1-L)*X.T,X)
 
+    def fit(self, start_params=None, method='newton', maxiter=35,
+            full_output=1, disp=1, callback=None, **kwargs):
+        bnryfit = super(Logit, self).fit(start_params=start_params,
+                method=method, maxiter=maxiter, full_output=full_output,
+                disp=disp, callback=callback, **kwargs)
+        discretefit = LogitResults(self, bnryfit)
+        return BinaryResultsWrapper(discretefit)
+    fit.__doc__ = DiscreteModel.fit.__doc__
+
 class Probit(BinaryModel):
     __doc__ = """
     Binary choice Probit model
@@ -1269,8 +1271,8 @@ def loglike(self, params):
 
         q = 2*self.endog - 1
         X = self.exog
-        return np.sum(np.log(np.clip(self.cdf(q*np.dot(X,params)),1e-20,
-            1)))
+        return np.sum(np.log(np.clip(self.cdf(q*np.dot(X,params)),
+            FLOAT_EPS, 1)))
 
     def loglikeobs(self, params):
         """
@@ -1299,7 +1301,7 @@ def loglikeobs(self, params):
 
         q = 2*self.endog - 1
         X = self.exog
-        return np.log(np.clip(self.cdf(q*np.dot(X,params)), 1e-20, 1))
+        return np.log(np.clip(self.cdf(q*np.dot(X,params)), FLOAT_EPS, 1))
 
 
     def score(self, params):
@@ -1329,7 +1331,7 @@ def score(self, params):
         XB = np.dot(X,params)
         q = 2*y - 1
         # clip to get rid of invalid divide complaint
-        L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), 1e-20, 1-1e-20)
+        L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS)
         return np.dot(L,X)
 
     def jac(self, params):
@@ -1361,7 +1363,7 @@ def jac(self, params):
         XB = np.dot(X,params)
         q = 2*y - 1
         # clip to get rid of invalid divide complaint
-        L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), 1e-20, 1-1e-20)
+        L = q*self.pdf(q*XB)/np.clip(self.cdf(q*XB), FLOAT_EPS, 1 - FLOAT_EPS)
         return L[:,None] * X
 
     def hessian(self, params):
@@ -1395,6 +1397,15 @@ def hessian(self, params):
         L = q*self.pdf(q*XB)/self.cdf(q*XB)
         return np.dot(-L*(L+XB)*X.T,X)
 
+    def fit(self, start_params=None, method='newton', maxiter=35,
+            full_output=1, disp=1, callback=None, **kwargs):
+        bnryfit = super(Probit, self).fit(start_params=start_params,
+                method=method, maxiter=maxiter, full_output=full_output,
+                disp=disp, callback=callback, **kwargs)
+        discretefit = ProbitResults(self, bnryfit)
+        return BinaryResultsWrapper(discretefit)
+    fit.__doc__ = DiscreteModel.fit.__doc__
+
 class MNLogit(MultinomialModel):
     __doc__ = """
     Multinomial logit model
@@ -1863,25 +1874,6 @@ def llnull(self):
         null = model.__class__(model.endog, np.ones(self.nobs)).fit(disp=0)
         return null.llf
 
-    @cache_readonly
-    def resid(self):
-        model = self.model
-        endog = model.endog
-        exog = model.exog
-        #        M = # of individuals that share a covariate pattern
-        # so M[i] = 2 for i = the two individuals who share a covariate pattern
-        # use unique row pattern?
-        #TODO: is this common to all models?  logit uses Pearson, should have options
-        #These are the deviance residuals
-        M = 1
-        p = model.predict(self.params)
-        Y_0 = np.where(exog==0)
-        Y_M = np.where(exog == M)
-        res = np.zeros_like(endog)
-        res = -(1-endog)*np.sqrt(2*M*np.abs(np.log(1-p))) + \
-                endog*np.sqrt(2*M*np.abs(np.log(p)))
-        return res
-
     @cache_readonly
     def fittedvalues(self):
         return np.dot(self.model.exog, self.params)
@@ -2187,7 +2179,21 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05,
 
 class CountResults(DiscreteResults):
     __doc__ = _discrete_results_docs % {"one_line_description" : "A results class for count data", "extra_attr" : ""}
-    pass
+    @cache_readonly
+    def resid(self):
+        """
+        Residuals
+
+        Notes
+        -----
+        The residuals for Count models are defined as
+
+        .. math:: y - p
+
+        where :math:`p = \\exp(X\\beta)`. Any exposure and offset variables
+        are also handled.
+        """
+        return self.model.endog - self.predict()
 
 class L1CountResults(DiscreteResults):
     __doc__ = _discrete_results_docs % {"one_line_description" :
@@ -2263,6 +2269,126 @@ def summary(self, yname=None, xname=None, title=None, alpha=.05,
         return smry
     summary.__doc__ = DiscreteResults.summary.__doc__
 
+    @cache_readonly
+    def resid(self):
+        import warnings
+        warnings.warn("This attribute is deprecated and will be removed in "
+                      "0.6.0. Use resid_dev instead.", FutureWarning)
+        return self.resid_dev
+
+    @cache_readonly
+    def resid_dev(self):
+        """
+        Deviance residuals
+
+        Notes
+        -----
+        Deviance residuals are defined
+
+        .. math:: d_j = \\pm\\left(2\\left[Y_j\\ln\\left(\\frac{Y_j}{M_jp_j}\\right) + (M_j - Y_j\\ln\\left(\\frac{M_j-Y_j}{M_j(1-p_j)} \\right) \\right] \\right)^{1/2}
+
+        where
+
+        :math:`p_j = cdf(X\\beta)` and :math:`M_j` is the total number of
+        observations sharing the covariate pattern :math:`j`.
+
+        For now :math:`M_j` is always set to 1.
+        """
+        #These are the deviance residuals
+        #model = self.model
+        endog = self.model.endog
+        #exog = model.exog
+        # M = # of individuals that share a covariate pattern
+        # so M[i] = 2 for i = two share a covariate pattern
+        M = 1
+        p = self.predict()
+        #Y_0 = np.where(exog == 0)
+        #Y_M = np.where(exog == M)
+        #NOTE: Common covariate patterns are not yet handled
+        res = -(1-endog)*np.sqrt(2*M*np.abs(np.log(1-p))) + \
+                endog*np.sqrt(2*M*np.abs(np.log(p)))
+        return res
+
+    @cache_readonly
+    def resid_pearson(self):
+        """
+        Pearson residuals
+
+        Notes
+        -----
+        Pearson residuals are defined to be
+
+        .. math:: r_j = \\frac{(y - M_jp_j)}{\\sqrt{M_jp_j(1-p_j)}}
+
+        where :math:`p_j=cdf(X\\beta)` and :math:`M_j` is the total number of
+        observations sharing the covariate pattern :math:`j`.
+
+        For now :math:`M_j` is always set to 1.
+        """
+        # Pearson residuals
+        #model = self.model
+        endog = self.model.endog
+        #exog = model.exog
+        # M = # of individuals that share a covariate pattern
+        # so M[i] = 2 for i = two share a covariate pattern
+        # use unique row pattern?
+        M = 1
+        p = self.predict()
+        return (endog - M*p)/np.sqrt(M*p*(1-p))
+
+    @cache_readonly
+    def resid_response(self):
+        """
+        The response residuals
+
+        Notes
+        -----
+        Response residuals are defined to be
+
+        .. math:: y - p
+
+        where :math:`p=cdf(X\\beta)`.
+        """
+        return self.model.endog - self.predict()
+
+class LogitResults(BinaryResults):
+    @cache_readonly
+    def resid_generalized(self):
+        """
+        Generalized residuals
+
+        Notes
+        -----
+        The generalized residuals for the Logit model are defined
+
+        .. math:: y - p
+
+        where :math:`p=cdf(X\\beta)`. This is the same as the `resid_response`
+        for the Logit model.
+        """
+        # Generalized residuals
+        return self.model.endog - self.predict()
+
+class ProbitResults(BinaryResults):
+    @cache_readonly
+    def resid_generalized(self):
+        """
+        Generalized residuals
+
+        Notes
+        -----
+        The generalized residuals for the Probit model are defined
+
+        .. math:: y\\frac{\phi(X\\beta)}{\\Phi(X\\beta)}-(1-y)\\frac{\\phi(X\\beta)}{1-\\Phi(X\\beta)}
+        """
+        # generalized residuals
+        model = self.model
+        endog = model.endog
+        XB = self.predict(linear=True)
+        pdf = model.pdf(XB)
+        cdf = model.cdf(XB)
+        return endog * pdf/cdf - (1-endog)*pdf/(1-cdf)
+
 class L1BinaryResults(BinaryResults):
     __doc__ = _discrete_results_docs % {"one_line_description" :
     "Results instance for binary data fit by l1 regularization",
@@ -2347,6 +2473,28 @@ def conf_int(self, alpha=.05, cols=None):
     def margeff(self):
         raise NotImplementedError("Use get_margeff instead")
 
+    @cache_readonly
+    def resid_misclassified(self):
+        """
+        Residuals indicating which observations are misclassified.
+
+        Notes
+        -----
+        The residuals for the multinomial model are defined as
+
+        .. math:: argmax(y_i) \\neq argmax(p_i)
+
+        where :math:`argmax(y_i)` is the index of the category for the
+        endogenous variable and :math:`argmax(p_i)` is the index of the
+        predicted probabilities for each category. That is, the residual
+        is a binary indicator that is 0 if the category with the highest
+        predicted probability is the same as that of the observed variable
+        and 1 otherwise.
+        """
+        # it's 0 or 1 - 0 for correct prediction and 1 for a missed one
+        return (self.model.wendog.argmax(1) !=
+                self.predict().argmax(1)).astype(float)
+
 class L1MultinomialResults(MultinomialResults):
     __doc__ = _discrete_results_docs % {"one_line_description" :
         "A results class for multinomial data fit by l1 regularization",


Test patch:
diff --git a/statsmodels/discrete/tests/results/mnlogit_resid.csv b/statsmodels/discrete/tests/results/mnlogit_resid.csv
new file mode 100644
--- /dev/null
+++ b/statsmodels/discrete/tests/results/mnlogit_resid.csv
@@ -0,0 +1 @@
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diff --git a/statsmodels/discrete/tests/results/poisson_resid.csv b/statsmodels/discrete/tests/results/poisson_resid.csv
new file mode 100644
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-3.00, -3.00, -3.00, -3.00, -3.00, -3.00, -3.00, -2.85, -2.85, -2.85, -2.85, -1.85, -2.85, -1.85, -1.85, -1.85, -2.85, -2.74, -1.74, -2.74, 0.77, -0.23, 4.77, 8.12, 4.12, -2.88, -0.88, 0.12, 1.74, 2.74, 1.74, 3.74, -2.26, -1.63, -1.63, -1.63, -1.63, -1.63, -1.29, -1.29, -1.29, -1.29, -1.29, -1.84, -1.84, 0.16, -1.84, -1.84, -2.70, -2.70, -2.70, -0.33, -2.33, -2.33, -2.33, -1.33, 1.74, 4.74, 10.74, 4.74, 6.74, -2.05, -3.05, -3.05, -3.05, -3.05, -3.46, -0.46, -3.46, 2.14, 5.14, 9.14, -2.26, -1.26, -2.26, -3.40, -3.40, -3.40, -2.71, -2.71, -1.71, -1.30, -2.30, -2.30, -2.79, -3.79, -7.79, -3.33, -2.33, -2.33, -0.33, -1.33, 0.67, -1.76, -1.76, 0.24, 0.66, 0.66, -1.34, 0.00, -3.00, -3.00, -0.60, -2.60, -2.60, -1.33, -0.33, -2.33, 1.37, -1.63, 4.37, 1.32, -0.68, -1.68, -2.31, -1.31, -2.31, -2.35, -2.35, -1.35, -0.05, -2.05, -1.05, -1.82, -0.82, -1.82, -2.30, -2.30, -1.30, -1.35, -0.35, -0.35, -2.35, -1.35, 3.65, -1.52, -1.52, -1.52, -2.33, -2.33, -2.33, -2.33, -2.33, -1.26, -2.26, -2.26, -2.26, -2.26, -2.94, -2.94, -2.94, -2.94, 1.06, -1.82, -1.82, -0.82, -1.82, -1.82, -1.82, 0.70, -0.30, -0.30, -1.30, -0.30, -2.26, -2.26, -2.26, -2.26, -2.26, 2.33, -1.67, -1.67, -1.67, -0.67, -3.71, -3.71, -3.71, -3.71, -3.71, -5.24, -5.24, -5.24, -1.24, 3.76, -2.26, -2.26, -2.26, -2.22, -2.22, 9.78, -2.01, -2.01, -0.01, 0.12, 6.12, 3.12, 0.12, 1.12, 1.12, -2.88, 2.12, -2.33, -1.33, -2.33, -1.99, -1.99, -1.99, -1.99, -1.99, 4.53, -1.47, -1.47, -1.47, -2.17, -2.17, -2.17, -2.00, 1.00, -1.00, 1.78, -0.22, 0.78, -2.39, -2.39, -2.39, -2.39, -0.17, -2.17, -2.17, 0.12, -1.88, -2.88, -2.37, -2.37, -2.37, 4.00, 0.00, 2.00, 1.00, 1.46, 4.46, -2.54, 9.74, 0.74, -2.26, -2.26, -2.26, -2.26, 0.65, -1.35, -2.35, 2.53, -0.47, -1.47, 2.53, -0.26, -2.26, -0.26, 5.65, -0.35, 2.65, -1.35, -0.98, 3.02, 0.02, -1.71, -1.71, 0.29, -1.71, 1.29, -3.71, -0.71, -2.71, 11.64, 1.64, -1.36, -1.92, -1.92, -1.92, -2.35, -1.35, -2.35, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, -2.36, 0.67, 0.67, -1.33, -3.00, -3.00, -3.00, 1.74, -0.26, 1.74, 17.66, 18.66, 25.66, -3.56, 4.44, 4.44, -1.96, -1.96, -2.96, -4.18, 1.82, 0.82, -1.62, -1.62, -1.62, -2.88, -2.88, -2.88, 0.00, -3.00, -3.00, -1.40, -3.40, -2.40, -2.54, -2.54, -0.54, -1.01, -2.01, -2.01, -1.54, -1.54, -0.54, -5.28, -5.28, -5.28, -2.35, -2.35, -2.35, -1.26, -2.26, -1.26, -1.30, -2.30, -0.30, -3.46, -2.46, -1.46, -0.26, -1.26, -2.26, -2.39, -2.39, -2.39, 10.12, 8.12, 9.12, 7.63, 7.63, 3.63, -2.71, -3.71, -1.71, 8.67, 4.67, 1.67, 2.12, -0.88, -0.88, -0.83, -2.83, -2.83, 5.04, 2.04, 0.04, -1.78, -1.78, 0.22, -2.28, -2.28, -2.28, -2.32, -0.32, -2.32, -0.58, 2.42, 1.42, 5.67, 4.67, -2.33, -1.29, 1.71, -1.50, -2.50, -3.50, -1.78, -1.78, 0.22, -2.37, -2.37, -2.37, -2.28, -2.28, -1.28, 2.37, -0.63, -1.63, -2.28, -2.28, -1.28, 1.40, -3.60, 2.40, 5.67, 1.67, -0.33, -0.23, -2.23, -2.23, 8.39, 5.39, -2.61, 17.66, 12.66, 9.66, -2.37, -2.37, -2.37, -1.37, -2.37, -2.37, 22.65, 13.65, 0.00, 5.00, -2.36, -2.36, -1.87, -1.87, 3.00, -2.35, -2.35, -2.37, -0.37, -2.37, 6.00, 0.00, -1.00, -2.00, -3.00, -3.00, -3.00, -3.00, -0.35, -1.35, 11.63, 11.63, 11.63, -1.26, -1.26, -2.26, -2.35, -1.35, 2.68, -2.32, 5.53, 1.53, 12.00, 3.00, -1.92, -1.92, 13.63, -2.37, 0.65, -2.35, -2.35, 6.64, -2.36, -0.35, -2.35, 8.38, -1.62, -2.26, -2.26, 4.26, 6.26, 3.39, 3.29, -2.71, -2.58, 18.58, -2.36, -0.54, 2.58, 5.58, 5.65, 8.00, -3.59, 3.00, 17.00, -0.44, -1.44, 1.11, -2.35, 2.58, -2.42, -1.36, -2.42, -1.63, -3.63, 5.56, 5.58, 3.58
diff --git a/statsmodels/discrete/tests/results/results_discrete.py b/statsmodels/discrete/tests/results/results_discrete.py
--- a/statsmodels/discrete/tests/results/results_discrete.py
+++ b/statsmodels/discrete/tests/results/results_discrete.py
@@ -203,6 +203,9 @@ def mnlogit_basezero(self):
             [.0318251305, .1085637405, .0062400589, .0699123044, .0201045606,
                 .3727166284]]).T
 
+        # taken from gretl
+        self.resid = np.loadtxt(os.path.join(cur_dir,'mnlogit_resid.csv'),
+                                delimiter=",")
 
 
 class DiscreteL1(object):
@@ -382,6 +385,33 @@ def logit(self):
                 [-4.57347950298, -.346255757562, -2.35916128427,
                 24.3179625937]]
         self.bse = [1.26294114526, .141554207662, 1.06456430165, 4.93132462871]
+
+        self.resid_pearson = [-.1652382, -.2515266, -.4800059, -.1630655,
+                                .8687437, -.1900454, -.165002, -.2331563,
+                                -.3535812, .6647838, -.1583799, -.4843181,
+                                -.689527, 2.043449, -.7516119, -.1764176,
+                                -.2380445, -.2003426, -1.199277, .7164842,
+                                -.255713, .3242821, -.5646816, -2.400189,
+                                .4392082, 1.038473, .75747, -.6659256,
+                                .4336657, .2404583, -1.060033, 2.829577]
+
+        self.resid_dev = [-.2321102, -.3502712, -.6439626, -.2290982,
+                1.060478, -.2663844, -.2317827, -.3253788, -.4853875,
+                .8555557, -.2225972, -.6491808, -.8819993, 1.813269,
+                -.9463985, -.247583, -.3320177, -.2805444, -1.335131,
+                .9103027, -.3559217, .4471892, -.744005, -1.955074,
+                .5939538, 1.209638, .952332, -.8567857, .5870719, .335292,
+                -1.227311, 2.096639]
+
+        # from gretl
+        self.resid_generalized = [-0.026578, -0.059501, -0.187260,
+                -0.025902,  0.430107, -0.034858, -0.026504, -0.051559,
+                -0.111127,  0.306489, -0.024470, -0.189997, -0.322240,
+                0.806789, -0.360990, -0.030184, -0.053626, -0.038588,
+                -0.589872,  0.339214, -0.061376,  0.095153, -0.241772,
+                -0.852091,  0.161709,  0.518867,  0.364579, -0.307219,
+                0.158296,  0.054660, -0.529117,  0.888969]
+
         self.phat = np.array([ .02657799236476,
                   .05950126051903,
                   .18725991249084,
@@ -630,7 +660,7 @@ def probit(self):
                         .0619288, .9045388, .2731908, .8474501, .8341947,
                         .488726, .6424073, .3286732, .8400168, .9522446,
                         .5399595, .123544]
-        self.yhat = yhat = np.array([-2.0930860042572,
+        self.yhat = np.array([-2.0930860042572,
                   -1.615691781044,
                  -.87816804647446,
                  -2.0842070579529,
@@ -662,13 +692,27 @@ def probit(self):
                   1.6670187711716,
                   .10033150017262,
                  -1.1574513912201])
-        self.resid = [-.191509, -.3302762, -.6490455, -.1936247, 1.085867,
+        self.resid_dev = [-.191509, -.3302762, -.6490455, -.1936247, 1.085867,
                       -.2349926, -.1932698, -.3019776, -.4799906, .9064196,
                       -.1801855, -.6559291, -.8838201, 1.807661, -.9387071,
                       -.2107617, -.3058469, -.2503485, -1.341589, .9162835,
                       -.3575735, .447951, -.7988633, -1.939208, .6021435,
                       1.196623, .9407793, -.8927477, .59048, .3128364,
                       -1.246147, 2.045071]
+        # Stata doesn't have it, but I think it's just oversight
+        self.resid_pearson = None
+        # generalized residuals from gretl
+        self.resid_generalized = [-0.045452, -0.114220, -0.334908,
+                                  -0.046321,  0.712624, -0.064538,
+                                  -0.046175, -0.098447, -0.209349,
+                                  0.550593, -0.040906, -0.340339,
+                                  -0.530763,  1.413373, -0.579170,
+                                  -0.053593, -0.100556, -0.071855,
+                                  -0.954156,  0.559294, -0.130167,
+                                  0.187523, -0.457597, -1.545643,
+                                  0.298511,  0.815964,  0.581013,
+                                  -0.538579,  0.289631,  0.104405,
+                                  -0.862836,  1.652638]
         self.pred_table = np.array([[18, 3], [3, 8]])
 
 
@@ -734,4 +778,7 @@ def poisson(self):
                                            .0351582168, .0016652181,
                                            .0264611158, .0437974779,
                                            .0752099666]
+        # taken from gretl
+        self.resid = np.loadtxt(os.path.join(cur_dir,'poisson_resid.csv'),
+                                delimiter=",")
 
diff --git a/statsmodels/discrete/tests/test_discrete.py b/statsmodels/discrete/tests/test_discrete.py
--- a/statsmodels/discrete/tests/test_discrete.py
+++ b/statsmodels/discrete/tests/test_discrete.py
@@ -36,7 +36,6 @@
 DECIMAL_2 = 2
 DECIMAL_1 = 1
 DECIMAL_0 = 0
-iswindows = 'win' in platform.lower()
 
 class CheckModelResults(object):
     """
@@ -114,6 +113,18 @@ class CheckBinaryResults(CheckModelResults):
     def test_pred_table(self):
         assert_array_equal(self.res1.pred_table(), self.res2.pred_table)
 
+    def test_resid_dev(self):
+        assert_almost_equal(self.res1.resid_dev, self.res2.resid_dev,
+                DECIMAL_4)
+
+    def test_resid_generalized(self):
+        assert_almost_equal(self.res1.resid_generalized,
+                            self.res2.resid_generalized, DECIMAL_4)
+
+    def smoke_test_resid_response(self):
+        self.res1.resid_response
+
+
 class CheckMargEff(object):
     """
     Test marginal effects (margeff) and its options
@@ -302,10 +313,6 @@ def setupClass(cls):
     #    assert_almost_equal(self.res1.model.predict(self.res1.params),
     #            self.res2.predict, DECIMAL_4)
 
-    def test_resid(self):
-        assert_almost_equal(self.res1.resid, self.res2.resid, DECIMAL_4)
-
-
 class TestProbitBFGS(CheckBinaryResults):
 
     @classmethod
@@ -344,8 +351,6 @@ def setupClass(cls):
 class TestProbitCG(CheckBinaryResults):
     @classmethod
     def setupClass(cls):
-        if iswindows:   # does this work with classmethod?
-            raise SkipTest("fmin_cg sometimes fails to converge on windows")
         data = sm.datasets.spector.load()
         data.exog = sm.add_constant(data.exog, prepend=False)
         res2 = Spector()
@@ -588,7 +593,7 @@ def setupClass(cls):
         exog_no_PSI = data.exog[:, :cls.m]
         cls.res_unreg = MNLogit(data.endog, exog_no_PSI).fit(
             disp=0, tol=1e-15)
-#
+
     def test_t_test(self):
         m = self.m
         kvars = self.kvars
@@ -698,6 +703,10 @@ def setupClass(cls):
         res2.logit()
         cls.res2 = res2
 
+    def test_resid_pearson(self):
+        assert_almost_equal(self.res1.resid_pearson,
+                            self.res2.resid_pearson, 5)
+
     def test_nodummy_exog1(self):
         me = self.res1.get_margeff(atexog={0 : 2.0, 2 : 1.})
         assert_almost_equal(me.margeff,
@@ -730,10 +739,10 @@ def test_dummy_exog2(self):
 class TestLogitBFGS(CheckBinaryResults, CheckMargEff):
     @classmethod
     def setupClass(cls):
-#        import scipy
-#        major, minor, micro = scipy.__version__.split('.')[:3]
-#        if int(minor) < 9:
-#            raise SkipTest
+        #import scipy
+        #major, minor, micro = scipy.__version__.split('.')[:3]
+        #if int(minor) < 9:
+        #    raise SkipTest
         #Skip this unconditionally for release 0.3.0
         #since there are still problems with scipy 0.9.0 on some machines
         #Ralf on mailing list 2011-03-26
@@ -772,6 +781,9 @@ def test_margeff_dummy_overall(self):
         assert_almost_equal(me.margeff_se,
                 self.res2.margeff_dummy_overall_se, DECIMAL_4)
 
+    def test_resid(self):
+        assert_almost_equal(self.res1.resid, self.res2.resid, 2)
+
 class TestMNLogitNewtonBaseZero(CheckModelResults):
     @classmethod
     def setupClass(cls):
@@ -885,6 +897,9 @@ def test_pred_table(self):
                 [   9.,    7.,    1.,    0.,    0.,   18.,  140.]]
         assert_array_equal(self.res1.pred_table(), pred)
 
+    def test_resid(self):
+        assert_array_equal(self.res1.resid_misclassified, self.res2.resid)
+
 def test_perfect_prediction():
     cur_dir = os.path.dirname(os.path.abspath(__file__))
     iris_dir = os.path.join(cur_dir, '..', '..', 'genmod', 'tests', 'results')