Python plot_ceres_residuals Examples

Example #1

    def test_ceres_poisson(self):

        np.random.seed(3446)

        n = 100
        p = 3
        exog = np.random.normal(size=(n, p))
        exog[:, 0] = 1
        lin_pred = 4 + exog[:, 1] + 0.2 * exog[:, 2]**2
        expval = np.exp(lin_pred)
        endog = np.random.poisson(expval)

        model = sm.GLM(endog, exog, family=sm.families.Poisson())
        results = model.fit()

        for focus_col in 1, 2:
            for j in 0, 1:
                if j == 0:
                    fig = plot_ceres_residuals(results, focus_col)
                else:
                    fig = results.plot_ceres_residuals(focus_col)
                ax = fig.get_axes()[0]
                add_lowess(ax)
                ax.set_position([0.1, 0.1, 0.8, 0.77])
                effect_str = [
                    "Intercept", "Linear effect, slope=1", "Quadratic effect"
                ][focus_col]
                ti = "CERES plot"
                if j == 1:
                    ti += " (called as method)"
                ax.set_title(ti + "\nPoisson regression\n" + effect_str)
                close_or_save(pdf, fig)

Example #2

File: test_regressionplots.py Project: ChadFulton/statsmodels

    def test_ceres_poisson(self, close_figures):

        np.random.seed(3446)

        n = 100
        p = 3
        exog = np.random.normal(size=(n, p))
        exog[:, 0] = 1
        lin_pred = 4 + exog[:, 1] + 0.2*exog[:, 2]**2
        expval = np.exp(lin_pred)
        endog = np.random.poisson(expval)

        model = sm.GLM(endog, exog, family=sm.families.Poisson())
        results = model.fit()

        for focus_col in 1, 2:
            for j in 0, 1:
                if j == 0:
                    fig = plot_ceres_residuals(results, focus_col)
                else:
                    fig = results.plot_ceres_residuals(focus_col)
                ax = fig.get_axes()[0]
                add_lowess(ax)
                ax.set_position([0.1, 0.1, 0.8, 0.77])
                effect_str = ["Intercept", "Linear effect, slope=1",
                              "Quadratic effect"][focus_col]
                ti = "CERES plot"
                if j == 1:
                    ti += " (called as method)"
                ax.set_title(ti + "\nPoisson regression\n" +
                             effect_str)
                close_or_save(pdf, fig)

Example #3

File: graphics_regression_ceres_residuals.py Project: timgates42/statsmodels

# -*- coding: utf-8 -*-
'''
    Using a model built from the the state crime dataset, make a CERES plot
    with the rate of Poverty as the focus variable.
'''
import matplotlib.pyplot as plt

import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.graphics.regressionplots import plot_ceres_residuals

crime_data = sm.datasets.statecrime.load_pandas()
results = smf.ols('murder ~ hs_grad + urban + poverty + single',
                  data=crime_data.data).fit()
plot_ceres_residuals(results, 'poverty')
plt.show()

Example #4

File: graphics_regression_ceres_residuals.py Project: bashtage/statsmodels

# -*- coding: utf-8 -*-
'''
    Using a model built from the the state crime dataset, make a CERES plot
    with the rate of Poverty as the focus variable.
'''
import statsmodels.api as sm
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
from statsmodels.graphics.regressionplots import plot_ceres_residuals

crime_data = sm.datasets.statecrime.load_pandas()
results = smf.ols('murder ~ hs_grad + urban + poverty + single',
                  data=crime_data.data).fit()
plot_ceres_residuals(results, 'poverty')
plt.show()