def test_qq_plot_left_censoring2(self, block):
df = load_lcd()
fig, axes = self.plt.subplots(2, 2, figsize=(9, 5))
axes = axes.reshape(4)
for i, model in enumerate([WeibullFitter(), LogNormalFitter(), LogLogisticFitter(), ExponentialFitter()]):
model.fit_left_censoring(df["T"], df["E"])
ax = qq_plot(model, ax=axes[i])
assert ax is not None
self.plt.suptitle("test_qq_plot_left_censoring2")
self.plt.show(block=block)
def test_qq_plot_with_weights_and_entry(self, block):
from lifelines.utils import survival_events_from_table
df = pd.DataFrame(index=[60, 171, 263, 427, 505, 639])
df["death"] = [1, 1, 1, 0, 1, 0]
df["censored"] = [0, 0, 0, 3, 0, 330]
T, E, W = survival_events_from_table(df, observed_deaths_col="death", censored_col="censored")
wf = WeibullFitter().fit(T, E, weights=W, entry=0.0001 * np.ones_like(T))
ax = qq_plot(wf)
self.plt.suptitle("test_qq_plot_with_weights_and_entry")
self.plt.show(block=block)
def test_qq_plot_right_censoring_with_known_distribution(self, block):
N = 3000
T_actual = scipy.stats.fisk(8, 0, 1).rvs(N)
C = scipy.stats.fisk(8, 0, 1).rvs(N)
E = T_actual < C
T = np.minimum(T_actual, C)
fig, axes = self.plt.subplots(2, 2, figsize=(9, 5))
axes = axes.reshape(4)
for i, model in enumerate([WeibullFitter(), LogNormalFitter(), LogLogisticFitter(), ExponentialFitter()]):
model.fit(T, E)
ax = qq_plot(model, ax=axes[i])
assert ax is not None
self.plt.suptitle("test_qq_plot_right_censoring_with_known_distribution")
self.plt.show(block=block)
def test_qq_plot_left_censoring(self, block):
df = load_nh4()
fig, axes = self.plt.subplots(2, 2, figsize=(9, 5))
axes = axes.reshape(4)
for i, model in enumerate([
WeibullFitter(),
LogNormalFitter(),
LogLogisticFitter(),
ExponentialFitter()
]):
model.fit(df["NH4.mg.per.L"],
~df["Censored"],
left_censorship=True)
ax = qq_plot(model, ax=axes[i])
assert ax is not None
self.plt.suptitle("test_qq_plot_left_censoring")
self.plt.show(block=block)
def test_qq_plot_left_censoring_with_known_distribution(self, block):
N = 300
T_actual = scipy.stats.fisk(8, 0, 1).rvs(N)
MIN_0 = np.percentile(T_actual, 5)
MIN_1 = np.percentile(T_actual, 10)
T = T_actual.copy()
ix = np.random.randint(3, size=N)
T = np.where(ix == 0, np.maximum(T, MIN_0), T)
T = np.where(ix == 1, np.maximum(T, MIN_1), T)
E = T_actual == T
fig, axes = self.plt.subplots(2, 2, figsize=(9, 5))
axes = axes.reshape(4)
for i, model in enumerate([WeibullFitter(), LogNormalFitter(), LogLogisticFitter(), ExponentialFitter()]):
model.fit_left_censoring(T, E)
ax = qq_plot(model, ax=axes[i])
assert ax is not None
self.plt.suptitle("test_qq_plot_left_censoring_with_known_distribution")
self.plt.show(block=block)
T = T_actual.copy()
ix = np.random.randint(4, size=N)
T = np.where(ix == 0, np.maximum(T, MIN_0), T)
T = np.where(ix == 1, np.maximum(T, MIN_1), T)
T = np.where(ix == 2, np.maximum(T, MIN_2), T)
T = np.where(ix == 3, np.maximum(T, MIN_3), T)
E = T_actual == T
fig, axes = plt.subplots(2, 2, figsize=(9, 5))
axes = axes.reshape(4)
for i, model in enumerate([WeibullFitter(), KaplanMeierFitter(), LogNormalFitter(), LogLogisticFitter()]):
if isinstance(model, KaplanMeierFitter):
model.fit(T, E, left_censorship=True, label=model.__class__.__name__)
else:
model.fit(T, E, left_censorship=True, label=model.__class__.__name__)
model.plot_cumulative_density(ax=axes[i])
plt.tight_layout()
for i, model in enumerate([WeibullFitter(), LogNormalFitter(), LogLogisticFitter()]):
model.fit(T, E, left_censorship=True)
fig, axes = plt.subplots(2, 1, figsize=(8, 6))
left_censorship_cdf_plot(model, ax=axes[0])
qq_plot(model, ax=axes[1])
plt.show()
