def test_partial_residual_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_partial_residuals(results, focus_col)
else:
fig = results.plot_partial_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 = "Partial residual plot"
if j == 1:
ti += " (called as method)"
ax.set_title(ti + "\nPoisson regression\n" + effect_str)
close_or_save(pdf, fig)
def test_partial_residual_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_partial_residuals(results, focus_col)
else:
fig = results.plot_partial_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 = "Partial residual plot"
if j == 1:
ti += " (called as method)"
ax.set_title(ti + "\nPoisson regression\n" +
effect_str)
close_or_save(pdf, fig)
