Fix a bug.

Fix a bug that displayed an incorrect rho squared metric when a Nested 
Model was used.

Author: JoseAngelMartinB

PyKernelLogit/nested_logit.py

@@ -541,8 +541,18 @@ def fit_mle(self,
             fixed_params.extend(constrained_pos)
         final_constrained_pos = sorted(list(set(fixed_params)))
 
+
+        num_nests = len(self.nest_spec) # Obtain the number of nests
+        # Set the initial vector of the nest parameters to 40. Note that the 40
+        # corresponds to scale parameter that is numericalle indistinguishable
+        # from 1.0
+        nest_initial_vector = np.ones(num_nests)*40
+        initial_parm_vector = np.zeros(init_vals.shape[0] - num_nests)
+
         # Create the estimation object
-        zero_vector = np.zeros(init_vals.shape)
+        zero_vector = np.concatenate((nest_initial_vector, initial_parm_vector),
+                                     axis=0)
+
         estimator_args = [self,
                           mapping_res,
                           PMLE,

setup.py

@@ -21,7 +21,7 @@
     # Versions should comply with PEP440.  For a discussion on single-sourcing
     # the version across setup.py and the project code, see
     # https://packaging.python.org/en/latest/single_source_version.html
-    version='0.1.3',
+    version='0.1.4',
 
     description='Maximum likelihood estimation of conditional logit models  \
         using Kernel Logistic Regression (KLR)',