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)
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)
# -*- 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()
# -*- 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()