Add Results.standard_parameters_estimate

psignifit/_result.py

@@ -170,3 +170,25 @@ def slope_at_proportion_correct(self, proportion_correct: np.ndarray, unscaled:
         """
         stimulus_levels = self.threshold(proportion_correct, unscaled, return_ci=False, estimate_type=estimate_type)
         return self.slope(stimulus_levels)
+
+    def standard_parameters_estimate(self, estimate_type: Optional[EstimateType]=None):
+        """ Get the parameters of the psychometric function in the standard format.
+
+        `psignifit` uses the same intuitive parametrization, threshold and width, for all
+        sigmoid types. However, each different type of sigmoid has its own standard parametrization.
+
+        The interpretation of the standard parameters, location and scale, depends on the sigmoid class used.
+        For instance, for a Gaussian sigmoid, the location corresponds to the mean and the scale to the standard
+        deviation of the distribution.
+
+        For negative slope sigmoids, we return the same parameters as for the positive ones.
+
+        Args:
+            proportion_correct: proportion correct at the threshold you want to calculate
+        Returns:
+            Standard parameters (loc, scale) for the sigmoid subclass.
+        """
+        sigmoid = self.configuration.make_sigmoid()
+        estimate = self.get_parameters_estimate(estimate_type)
+        loc, scale = sigmoid.standard_parameters(estimate['threshold'], estimate['width'])
+        return loc, scale

psignifit/tests/test_result.py

@@ -185,3 +185,32 @@ def test_estimate_type_default(result):
     result.estimate_type = 'mean'
     estimate = result.get_parameters_estimate()
     assert _close_numpy_dict(estimate, result.parameters_estimate_mean)
+
+
+def test_standard_parameters_estimate():
+    width = 2.1
+    threshold = 0.87
+    parameter_estimate = {
+        'threshold': threshold,
+        'width': width,
+        'lambda': 0.0,
+        'gamma': 0.0,
+        'eta': 0.0,
+    }
+    confidence_intervals = {
+        'threshold': [[threshold, threshold]],
+        'width': [[width, width]],
+        'lambda': [[0.05, 0.2]],
+        'gamma': [[0.1, 0.3]],
+        'eta': [[0.0, 0.0]]
+    }
+    result = _build_result(parameter_estimate, parameter_estimate, confidence_intervals)
+
+    # For a Gaussian sigmoid with alpha=0.05, PC=0.5
+    expected_loc = threshold
+    # 1.644853626951472 is the normal PPF at alpha=0.95
+    expected_scale = width / (2 * 1.644853626951472)
+
+    loc, scale = result.standard_parameters_estimate()
+    np.testing.assert_allclose(loc, expected_loc)
+    np.testing.assert_allclose(scale, expected_scale)