fixed indexing of covar matrix in testing

Author: davidsebfischer

diffxpy/stats/stats.py

@@ -33,7 +33,7 @@ def likelihood_ratio_test(
     delta_df = df_full - df_reduced
     # Compute the deviance test statistic.
     delta_dev = 2 * (ll_full - ll_reduced)
-    # Compute the p-values based on the deviance and its expection based on the chi-square distribution.
+    # Compute the p-values based on the deviance and its expectation based on the chi-square distribution.
     pvals = 1 - scipy.stats.chi2(delta_df).cdf(delta_dev)
     return pvals
 
@@ -251,6 +251,8 @@ def wald_test_chisq(
     if theta_mle.shape[0] != theta_covar.shape[1]:
         raise ValueError(
             'stats.wald_test(): theta_mle and theta_covar have to contain the same number of parameters')
+    if len(theta_covar.shape) != 3:
+        raise ValueError('stats.wald_test(): theta_covar should have 3 dimensions but has %i' % len(theta_covar.shape))
     if theta_mle.shape[1] != theta_covar.shape[0]:
         raise ValueError('stats.wald_test(): theta_mle and theta_covar have to contain the same number of genes')
     if theta_covar.shape[1] != theta_covar.shape[2]:

diffxpy/testing/det.py

@@ -791,8 +791,7 @@ def _test(self):
         if len(self.coef_loc_totest) == 1:
             self.theta_mle = self.theta_mle[0]  # Make xarray one dimensional for stats.wald_test.
             self.theta_sd = self.model_estim.fisher_inv[:, self.coef_loc_totest[0], self.coef_loc_totest[0]]
-            self.theta_sd = np.nextafter(0, np.inf, out=self.theta_sd,
-                                         where=self.theta_sd < np.nextafter(0, np.inf))
+            self.theta_sd = np.nextafter(0, np.inf, out=self.theta_sd, where=self.theta_sd < np.nextafter(0, np.inf))
             self.theta_sd = np.sqrt(self.theta_sd)
             return stats.wald_test(
                 theta_mle=self.theta_mle,
@@ -801,12 +800,11 @@ def _test(self):
             )
         else:
             self.theta_sd = np.diagonal(self.model_estim.fisher_inv, axis1=-2, axis2=-1).copy()
-            self.theta_sd = np.nextafter(0, np.inf, out=self.theta_sd,
-                                         where=self.theta_sd < np.nextafter(0, np.inf))
+            self.theta_sd = np.nextafter(0, np.inf, out=self.theta_sd, where=self.theta_sd < np.nextafter(0, np.inf))
             self.theta_sd = np.sqrt(self.theta_sd)
             return stats.wald_test_chisq(
                 theta_mle=self.theta_mle,
-                theta_covar=self.model_estim.fisher_inv[:, self.coef_loc_totest, self.coef_loc_totest],
+                theta_covar=self.model_estim.fisher_inv[:, self.coef_loc_totest, :][:, :, self.coef_loc_totest],
                 theta0=0
             )