add _power

src/pystatpower/procedures/two_proportion.py

@@ -1,6 +1,10 @@
 """两独立样本差异性检验"""
 
 from enum import Enum
+from math import sqrt
+
+from scipy.stats import norm
+from scipy.optimize import brenth
 
 
 class SolvableParameter(Enum):
@@ -41,6 +45,55 @@ class GroupAllocation(Enum):
     PERCENT_OF_REFERENCE = 7
 
 
+def _power(
+    alpha: float,
+    treatment_n: float,
+    reference_n: float,
+    treatment_proportion: float,
+    reference_proportion: float,
+    alternative: Alternative,
+    test_type: TestType,
+):
+    n1 = treatment_n
+    n2 = reference_n
+    p1 = treatment_proportion
+    p2 = reference_proportion
+
+    # 计算标准误
+    match test_type:
+        case TestType.Z_TEST_POOLED | TestType.Z_TEST_CC_POOLED:
+            p_hat = (n1 * p1 + n2 * p2) / (n1 + n2)
+            se = sqrt(p_hat * (1 - p_hat) * (1 / n1 + 1 / n2))
+        case TestType.Z_TEST_UNPOOLED | TestType.Z_TEST_CC_UNPOOLED:
+            se = sqrt(p1 * (1 - p1) / n1 + p2 * (1 - p2) / n2)
+        case _:
+            assert False, "未知的检验类型"
+
+    # 连续性校正
+    c = 0
+    if test_type in [TestType.Z_TEST_CC_POOLED, TestType.Z_TEST_CC_UNPOOLED]:
+        c = (1 / 2) * (1 / n1 + 1 / n2)
+
+    # 计算检验效能
+    match alternative:
+        case Alternative.TWO_SIDED:
+            z_alpha = norm.ppf(1 - alpha / 2)
+            z_stat = [(p1 - p2 - c) / se, (p1 - p2 + c) / se]
+            power = norm.cdf(-z_alpha - z_stat[0]) + 1 - norm.cdf(z_alpha - z_stat[1])
+        case Alternative.ONE_SIDED:
+            z_alpha = norm.ppf(1 - alpha)
+            if p1 > p2:
+                z_stat = (p1 - p2 + c) / se
+                power = 1 - norm.cdf(z_alpha - z_stat)
+            elif p1 <= p2:
+                z_stat = (p1 - p2 - c) / se
+                power = norm.cdf(-z_alpha - z_stat)
+        case _:
+            assert False, "未知的备择假设类型"
+
+    return power
+
+
 class TwoProportionDesigner:
     def __init__(self, solve_for: SolvableParameter):
         if not isinstance(solve_for, SolvableParameter):
@@ -59,10 +112,10 @@ def __init__(self, solve_for: SolvableParameter):
                 return TwoProportionSolveForReferenceProportionDesigner()
 
 
-class TwoProportionSolveForNDesigner(TwoProportionDesigner):
+class TwoProportionSolveForNDesigner:
 
     def __init__(self):
-        self._config = {}
+        self._config: dict[str, any] = {}
 
     def set_alpha(self, alpha: float = 0.05):
         self._config["alpha"] = alpha