ENH: add CovDetMCD and det for regression

statsmodels/robust/norms.py

@@ -1,6 +1,6 @@
 import numpy as np
 
-# TODO: add plots to weighting functions for online docs.
+from . import tools as rtools
 
 
 def _cabs(x):
@@ -41,6 +41,9 @@
 
     continuous = 1
 
+    def __repr__(self):
+        return self.__class__.__name__
+
     def rho(self, z):
         """
         The robust criterion estimator function.
@@ -199,12 +202,16 @@
     def __init__(self, t=1.345):
         self.t = t
 
-    def _set_tuning_param(self, c):
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.t = c
+        if inplace:
+            self.t = c
+            return self
+        else:
+            return self.__class__(t=c)
 
     def max_rho(self):
         return np.inf
@@ -322,6 +329,18 @@
     def __init__(self, a=.3):
         self.a = a
 
+    def _set_tuning_param(self, c, inplace=False):
+        """Set and change the tuning parameter of the Norm.
+
+        Warning: this needs to wipe cached attributes that depend on the param.
+        """
+        # todo : change default to inplace=False, when tools are fixed
+        if inplace:
+            self.a = c
+            return self
+        else:
+            return self.__class__(a=c)
+
     def max_rho(self):
         return np.inf
 
@@ -419,12 +438,16 @@
     def __init__(self, a=1.339):
         self.a = a
 
-    def _set_tuning_param(self, a):
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.a = a
+        if inplace:
+            self.a = c
+            return self
+        else:
+            return self.__class__(a=c)
 
     def max_rho(self):
         return 2 * self.a**2
@@ -553,12 +576,16 @@
     def __init__(self, c=2.):
         self.c = c
 
-    def _set_tuning_param(self, c):
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.c = c
+        if inplace:
+            self.c = c
+            return self
+        else:
+            return self.__class__(c=c)
 
     def max_rho(self):
         return self.rho(self.c)
@@ -674,14 +701,20 @@
         self.b = b
         self.c = c
 
-    def _set_tuning_param(self, c):
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.c = c
-        self.a = c / 4
-        self.b = c / 2
+        a = c / 4
+        b = c / 2
+        if inplace:
+            self.c = c
+            self.a = a
+            self.b = b
+            return self
+        else:
+            return self.__class__(a=a, b=b, c=c)
 
     def max_rho(self):
         return self.rho(self.c)
@@ -862,12 +895,49 @@
     def __init__(self, c=4.685):
         self.c = c
 
-    def _set_tuning_param(self, c):
+    def __repr__(self):
+        return f"{self.__class__.__name__}(c={self.c})"
+
+    @classmethod
+    def get_tuning(cls, bp=None, eff=None):
+        """Tuning parameter for given breakdown point or efficiency.
+
+        This currently only return values from a table.
+
+        Parameters
+        ----------
+        bp : float in [0.05, 0.5] or None
+            Required breakdown point
+            Either bp or eff has to be specified, but not both.
+        eff : float or None
+            Required asymptotic efficiency.
+            Either bp or eff has to be specified, but not both.
+
+        Returns
+        -------
+        float : tuning parameter.
+
+        """
+        if ((bp is None and eff is None) or
+                (bp is not None and eff is not None)):
+            raise ValueError("exactly one of bp and eff needs to be provided")
+
+        if bp is not None:
+            return rtools.tukeybiweight_bp[bp]
+        elif eff is not None:
+            return rtools.tukeybiweight_eff[eff]
+
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.c = c
+        # todo : change default to inplace=False, when tools are fixed
+        if inplace:
+            self.c = c
+            return self
+        else:
+            return self.__class__(c=c)
 
     def max_rho(self):
         return self.rho(self.c)
@@ -891,7 +961,7 @@
         Returns
         -------
         rho : ndarray
-            rho(z) = -(1 - (z/c)**2)**3 * c**2/6.   for \|z\| <= R
+            rho(z) = -(1 - (z/c)**2)**3 * c**2/6 + c**2/6   for \|z\| <= R
 
             rho(z) = 0                              for \|z\| > R
         """
@@ -983,12 +1053,16 @@
         self.c = c
         self.k = k
 
-    def _set_tuning_param(self, c):
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.c = c
+        if inplace:
+            self.c = c
+            return self
+        else:
+            return self.__class__(c=c, k=self.k)
 
     def max_rho(self):
         return self.rho(self.c)
@@ -1121,12 +1195,16 @@
         self.c = c
         self.df = df
 
-    def _set_tuning_param(self, c):
+    def _set_tuning_param(self, c, inplace=False):
         """Set and change the tuning parameter of the Norm.
 
         Warning: this needs to wipe cached attributes that depend on the param.
         """
-        self.c = c
+        if inplace:
+            self.c = c
+            return self
+        else:
+            return self.__class__(c=c, df=self.df)
 
     def max_rho(self):
         return np.inf