emulate_r_plot_9.py

plot_lm_4 = plt.figure(4)
plot_lm_4.set_figheight(8)
plot_lm_4.set_figwidth(12)

plt.scatter(model_leverage, model_norm_residuals, alpha=0.5)
sns.regplot(model_leverage, model_norm_residuals, 
            scatter=False, 
            ci=False, 
            lowess=True,
            line_kws={'color': 'red', 'lw': 1, 'alpha': 0.8})

plot_lm_4.axes[0].set_xlim(0, 0.20)
plot_lm_4.axes[0].set_ylim(-3, 5)
plot_lm_4.axes[0].set_title('Residuals vs Leverage')
plot_lm_4.axes[0].set_xlabel('Leverage')
plot_lm_4.axes[0].set_ylabel('Standardized Residuals')

# annotations
leverage_top_3 = np.flip(np.argsort(model_cooks), 0)[:3]

for i in leverage_top_3:
    plot_lm_4.axes[0].annotate(i, 
                               xy=(model_leverage[i], 
                                   model_norm_residuals[i]))
    
# shenanigans for cook's distance contours
def graph(formula, x_range, label=None):
    x = x_range
    y = formula(x)
    plt.plot(x, y, label=label, lw=1, ls='--', color='red')

p = len(model_fit.params) # number of model parameters

graph(lambda x: np.sqrt((0.5 * p * (1 - x)) / x), 
      np.linspace(0.001, 0.200, 50), 
      'Cook\'s distance') # 0.5 line
graph(lambda x: np.sqrt((1 * p * (1 - x)) / x), 
      np.linspace(0.001, 0.200, 50)) # 1 line
plt.legend(loc='upper right');