def test_kmf_minimum_observation_bias(): N = 250 kmf = KaplanMeierFitter() T, C = exponential_survival_data(N, 0.1, scale=10) B = 0.01 * T kmf.fit(T, C, entry=B) kmf.plot() plt.title("Should have larger variances in the tails")
def test_kmf_minimum_observation_bias(): N = 250 kmf = KaplanMeierFitter() T, C = exponential_survival_data(N, 0.1, scale=10) B = 0.01 * T kmf.fit(T, C, entry=B) kmf.plot() plt.title("Should have larger variances in the tails")
def test_exponential_data_sets_fit(): N = 20000 T, C = exponential_survival_data(N, 0.2, scale=10) naf = NelsonAalenFitter() naf.fit(T, C).plot() plt.title("Should be a linear with slope = 0.1")
def test_exponential_data_sets_correct_censor(): N = 20000 censorship = 0.2 T, C = exponential_survival_data(N, censorship, scale=10) assert abs(C.mean() - (1 - censorship)) < 0.02
def test_exponential_data_sets_fit(): N = 20000 T, C = exponential_survival_data(N, 0.2, scale=10) naf = NelsonAalenFitter() naf.fit(T, C).plot() plt.title("Should be a linear with slope = 0.1")
def test_exponential_data_sets_correct_censor(): print(os.environ) N = 20000 censorship = 0.2 T, C = exponential_survival_data(N, censorship, scale=10) assert abs(C.mean() - (1 - censorship)) < 0.02