Python exponential_survival_data Examples

Example #1

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")

Example #2

File: test_generate_datasets.py Project: DGaffney/lifelines

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")

Example #3

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")

Example #4

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

Example #5

File: test_generate_datasets.py Project: DGaffney/lifelines

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")

Example #6

File: test_generate_datasets.py Project: DGaffney/lifelines

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