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A proposal to make the correlated t-test less conservative #1

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A proposal to make the correlated t-test less conservative #1

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7 changes: 5 additions & 2 deletions src/cmpbayes/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -622,14 +622,17 @@ def fit(self, random_seed=None, num_samples=10000):

x_over = x.mean()
n = len(x)
sigma_2_hat = ((x - x_over)**2).sum() / (n - 1)
rho = self.fraction_test

sigma_2_hat = ((x - x_over) ** 2).sum() / (n - 1)
corrected_sigma_2_hat = (1 / n + rho / (1 - rho)) * sigma_2_hat # Corrected variance using Nadeau-Bengio's correction
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Please comment above the lines to keep the lines the comments refer to (to keep lines at less than 80 characters). Also, please end comments with a full stop.

scale = np.sqrt(corrected_sigma_2_hat) # Calculate the scale parameter incorporating the correlation factor

# Equation (8) from Corani and Benavoli, 2015, *A Bayesian approach for
# comparing cross-validated algorithms on multiple data sets*.
self.model_ = st.t(df=n - 1,
loc=x_over,
scale=(1 / n + rho / (1 - rho)) * sigma_2_hat)
scale=scale)
# TODO Consider splitting num_samples into chains to prevent arviz warning
self.fit_: np.ndarray = self.model_.rvs(num_samples)
self.infdata_: arviz.InferenceData = az.convert_to_inference_data(
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