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Update README.md

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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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scipy/scipy.stats.fisher_exact/README.md
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@ -95,7 +95,7 @@ import numpy as np
import matplotlib.pyplot as plt
N: int = 10000
correct_a: int = N // 2
correct_b: int = N // 2
values = np.arange(0, N + 1, 100)
results_less = np.zeros((values.shape[0]))
@ -104,21 +104,21 @@ results_two_sided = np.zeros((values.shape[0]))
for i in range(0, values.shape[0]):
correct_b: int = int(values[i])
correct_a: int = int(values[i])
res = fisher_exact(
[[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="less"
)
results_less[i] = res.pvalue
for i in range(0, values.shape[0]):
correct_b = int(values[i])
correct_a = int(values[i])
res = fisher_exact(
[[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="greater"
)
results_greater[i] = res.pvalue
for i in range(0, values.shape[0]):
correct_b = int(values[i])
correct_a = int(values[i])
res = fisher_exact(
[[N - correct_a, N - correct_b], [correct_a, correct_b]],
alternative="two-sided",
@ -130,7 +130,7 @@ plt.plot(100.0 * values / N, results_two_sided, label="two-sided")
plt.plot(100.0 * values / N, results_less, label="less")
plt.plot(100.0 * values / N, results_greater, label="greater")
plt.title(f"Compared to a performance A of {100.0 * correct_a /N}%")
plt.title(f"Compared to a performance B of {100.0 * correct_b /N}%")
plt.ylabel("p-value")
plt.xlabel("Correct [%]")
plt.legend()
@ -145,7 +145,7 @@ import numpy as np
import matplotlib.pyplot as plt
N: int = 10000
correct_a: int = int(N * 0.99)
correct_b: int = int(N * 0.99)
values = np.arange(int(N * 0.98), N + 1)
results_less = np.zeros((values.shape[0]))
@ -154,21 +154,21 @@ results_two_sided = np.zeros((values.shape[0]))
for i in range(0, values.shape[0]):
correct_b: int = int(values[i])
correct_a: int = int(values[i])
res = fisher_exact(
[[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="less"
)
results_less[i] = res.pvalue
for i in range(0, values.shape[0]):
correct_b = int(values[i])
correct_a = int(values[i])
res = fisher_exact(
[[N - correct_a, N - correct_b], [correct_a, correct_b]], alternative="greater"
)
results_greater[i] = res.pvalue
for i in range(0, values.shape[0]):
correct_b = int(values[i])
correct_a = int(values[i])
res = fisher_exact(
[[N - correct_a, N - correct_b], [correct_a, correct_b]],
alternative="two-sided",
@ -180,7 +180,7 @@ plt.plot(100.0 * values / N, results_two_sided, label="two-sided")
plt.plot(100.0 * values / N, results_less, label="less")
plt.plot(100.0 * values / N, results_greater, label="greater")
plt.title(f"Compared to a performance A of {100.0 * correct_a /N}%")
plt.title(f"Compared to a performance B of {100.0 * correct_b /N}%")
plt.ylabel("p-value")
plt.xlabel("Correct [%]")
plt.legend()