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()