CODE: ddof

Add delta degrees of freedom input in `normalise_phenotype` function.

tests/test_simulate_phenotype.py

@@ -312,6 +312,27 @@ def test_negative_var(self, sample_ts, var):
         with pytest.raises(ValueError, match="Variance must be greater than 0."):
             tstrait.normalise_phenotypes(phenotype_df, var=var)
 
+    @pytest.mark.parametrize("ddof", [0, 1])
+    def test_ddof(self, sample_ts, ddof):
+        model = tstrait.trait_model(distribution="normal", mean=2, var=6)
+        sim_result = tstrait.sim_phenotype(
+            ts=sample_ts, num_causal=100, model=model, h2=0.3, random_seed=1
+        )
+        phenotype_df = sim_result.phenotype
+        normalised_df = tstrait.normalise_phenotypes(
+            phenotype_df, mean=0, var=1, ddof=ddof
+        )
+        normalised_phenotype_array = normalised_df["phenotype"].values
+
+        phenotype_array = phenotype_df["phenotype"].values
+        phenotype_array = (phenotype_array - np.mean(phenotype_array)) / np.std(
+            phenotype_array, ddof=ddof
+        )
+
+        np.testing.assert_array_almost_equal(
+            normalised_phenotype_array, phenotype_array
+        )
+
     def test_pleiotropy(self, sample_ts):
         mean = 0
         var = 1

tstrait/simulate_phenotype.py

@@ -138,7 +138,7 @@ def sim_phenotype(
     return result
 
 
-def normalise_phenotypes(phenotype_df, mean=0, var=1):
+def normalise_phenotypes(phenotype_df, mean=0, var=1, ddof=1):
     """Normalise phenotype dataframe.
 
     Parameters
@@ -149,6 +149,9 @@ def normalise_phenotypes(phenotype_df, mean=0, var=1):
         Mean of the resulting phenotype.
     var : float, default 1
         Variance of the resulting phenotype.
+    ddof : int, default 1
+        Delta degrees of freedom. The divisor used in computing the variance
+        is N - ddof, where N represents the number of elements.
 
     Returns
     -------
@@ -184,7 +187,9 @@ def normalise_phenotypes(phenotype_df, mean=0, var=1):
         phenotype_df, ["individual_id", "trait_id", "phenotype"], "phenotype_df"
     )
     grouped = phenotype_df.groupby("trait_id")[["phenotype"]]
-    transformed_phenotype = grouped.transform(lambda x: (x - x.mean()) / x.std())
+    transformed_phenotype = grouped.transform(
+        lambda x: (x - x.mean()) / x.std(ddof=ddof)
+    )
     transformed_phenotype = transformed_phenotype * np.sqrt(var) + mean
     phenotype_df.loc[:, "phenotype"] = transformed_phenotype