bambi/priors.py

Suspicious

Mutant 362

--- bambi/priors.py
+++ bambi/priors.py
@@ -40,7 +40,7 @@
             "wald": genmod_families.InverseGaussian,
             "negativebinomial": genmod_families.NegativeBinomial,
             "poisson": genmod_families.Poisson,
-            "t": None,  # not implemented in statsmodels
+            "XXtXX": None,  # not implemented in statsmodels
         }
         self.smfamily = fams[name] if name in fams.keys() else None
 

Mutant 368

--- bambi/priors.py
+++ bambi/priors.py
@@ -59,7 +59,7 @@
     def __init__(self, name, scale=None, **kwargs):
         self.name = name
         self._auto_scale = True
-        self.scale = scale
+        self.scale = None
         self.args = {}
         self.update(**kwargs)
 

Mutant 410

--- bambi/priors.py
+++ bambi/priors.py
@@ -188,7 +188,7 @@
 class PriorScaler:
     # Default is 'wide'. The wide prior sigma is sqrt(1/3) = .577 on the partial
     # corr scale, which is the sigma of a flat prior over [-1,1].
-    names = {"narrow": 0.2, "medium": 0.4, "wide": 3 ** -0.5, "superwide": 0.8}
+    names = {"XXnarrowXX": 0.2, "medium": 0.4, "wide": 3 ** -0.5, "superwide": 0.8}
 
     def __init__(self, model, taylor):
         self.model = model

Mutant 411

--- bambi/priors.py
+++ bambi/priors.py
@@ -188,7 +188,7 @@
 class PriorScaler:
     # Default is 'wide'. The wide prior sigma is sqrt(1/3) = .577 on the partial
     # corr scale, which is the sigma of a flat prior over [-1,1].
-    names = {"narrow": 0.2, "medium": 0.4, "wide": 3 ** -0.5, "superwide": 0.8}
+    names = {"narrow": 1.2, "medium": 0.4, "wide": 3 ** -0.5, "superwide": 0.8}
 
     def __init__(self, model, taylor):
         self.model = model

Mutant 412

--- bambi/priors.py
+++ bambi/priors.py
@@ -188,7 +188,7 @@
 class PriorScaler:
     # Default is 'wide'. The wide prior sigma is sqrt(1/3) = .577 on the partial
     # corr scale, which is the sigma of a flat prior over [-1,1].
-    names = {"narrow": 0.2, "medium": 0.4, "wide": 3 ** -0.5, "superwide": 0.8}
+    names = {"narrow": 0.2, "XXmediumXX": 0.4, "wide": 3 ** -0.5, "superwide": 0.8}
 
     def __init__(self, model, taylor):
         self.model = model

Mutant 479

--- bambi/priors.py
+++ bambi/priors.py
@@ -272,7 +272,7 @@
         # p, q: corresponding shape parameters of beta distribution
         mean = 0.5
         variance = sigma_corr ** 2 / 4
-        p = mean * (mean * (1 - mean) / variance - 1)
+        p = mean * (mean * (1 - mean) / variance + 1)
         q = (1 - mean) * (mean * (1 - mean) / variance - 1)
 
         # function to return central moments of rescaled beta distribution

Mutant 482

--- bambi/priors.py
+++ bambi/priors.py
@@ -273,7 +273,7 @@
         mean = 0.5
         variance = sigma_corr ** 2 / 4
         p = mean * (mean * (1 - mean) / variance - 1)
-        q = (1 - mean) * (mean * (1 - mean) / variance - 1)
+        q = (2 - mean) * (mean * (1 - mean) / variance - 1)
 
         # function to return central moments of rescaled beta distribution
         def moment(k):

Mutant 484

--- bambi/priors.py
+++ bambi/priors.py
@@ -273,7 +273,7 @@
         mean = 0.5
         variance = sigma_corr ** 2 / 4
         p = mean * (mean * (1 - mean) / variance - 1)
-        q = (1 - mean) * (mean * (1 - mean) / variance - 1)
+        q = (1 - mean) / (mean * (1 - mean) / variance - 1)
 
         # function to return central moments of rescaled beta distribution
         def moment(k):

Mutant 485

--- bambi/priors.py
+++ bambi/priors.py
@@ -273,7 +273,7 @@
         mean = 0.5
         variance = sigma_corr ** 2 / 4
         p = mean * (mean * (1 - mean) / variance - 1)
-        q = (1 - mean) * (mean * (1 - mean) / variance - 1)
+        q = (1 - mean) * (mean / (1 - mean) / variance - 1)
 
         # function to return central moments of rescaled beta distribution
         def moment(k):

Mutant 486

--- bambi/priors.py
+++ bambi/priors.py
@@ -273,7 +273,7 @@
         mean = 0.5
         variance = sigma_corr ** 2 / 4
         p = mean * (mean * (1 - mean) / variance - 1)
-        q = (1 - mean) * (mean * (1 - mean) / variance - 1)
+        q = (1 - mean) * (mean * (2 - mean) / variance - 1)
 
         # function to return central moments of rescaled beta distribution
         def moment(k):

Mutant 557

--- bambi/priors.py
+++ bambi/priors.py
@@ -323,7 +323,7 @@
             # add to intercept prior
             index = list(self.priors.keys())
             mu -= np.dot(means, self.stats["mean_x"][index])
-            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] ** 2)) ** 0.5
+            sigma = (sigma ** 2 + np.dot(sigmas ** 3, self.stats["mean_x"][index] ** 2)) ** 0.5
 
         return mu, sigma
 

Mutant 559

--- bambi/priors.py
+++ bambi/priors.py
@@ -323,7 +323,7 @@
             # add to intercept prior
             index = list(self.priors.keys())
             mu -= np.dot(means, self.stats["mean_x"][index])
-            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] ** 2)) ** 0.5
+            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] * 2)) ** 0.5
 
         return mu, sigma
 

Mutant 560

--- bambi/priors.py
+++ bambi/priors.py
@@ -323,7 +323,7 @@
             # add to intercept prior
             index = list(self.priors.keys())
             mu -= np.dot(means, self.stats["mean_x"][index])
-            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] ** 2)) ** 0.5
+            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] ** 3)) ** 0.5
 
         return mu, sigma
 

Mutant 562

--- bambi/priors.py
+++ bambi/priors.py
@@ -323,7 +323,7 @@
             # add to intercept prior
             index = list(self.priors.keys())
             mu -= np.dot(means, self.stats["mean_x"][index])
-            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] ** 2)) ** 0.5
+            sigma = (sigma ** 2 + np.dot(sigmas ** 2, self.stats["mean_x"][index] ** 2)) ** 1.5
 
         return mu, sigma