def test_left_censorship_cdf_plots(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_left_censoring(df["NH4.mg.per.L"], ~df["Censored"])
ax = cdf_plot(model, ax=axes[i])
assert ax is not None
self.plt.suptitle("test_left_censorship_cdf_plots")
self.plt.show(block=block)
def test_right_censorship_cdf_plots(self, block):
df = load_rossi()
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["week"], df["arrest"])
ax = cdf_plot(model, ax=axes[i])
assert ax is not None
self.plt.suptitle("test_right_censorship_cdf_plots")
self.plt.show(block=block)
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_left_censoring(T, E, label=model.__class__.__name__)
else:
model.fit_left_censoring(T, E, label=model.__class__.__name__)
model.plot_cumulative_density(ax=axes[i])
plt.tight_layout()
for i, model in enumerate(
[WeibullFitter(), LogNormalFitter(),
LogLogisticFitter()]):
model.fit_left_censoring(T, E)
fig, axes = plt.subplots(2, 1, figsize=(8, 6))
cdf_plot(model, ax=axes[0])
qq_plot(model, ax=axes[1])
plt.show()
