def test_added_variable():
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
np.random.seed(3446)
n = 100
p = 3
exog = np.random.normal(size=(n, p))
lin_pred = 4 + exog[:, 0] + 0.2*exog[:, 1]**2
expval = np.exp(lin_pred)
endog = np.random.poisson(expval)
model = sm.GLM(endog, exog, family=sm.families.Poisson())
results = model.fit()
focus_col = 0
use_glm_weights = False#, True:
resid_type = ["resid_deviance", "resid_response"]
fig = rplots.plot_added_variable(results, focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type[0], ax=ax)
ti = "Added variable plot"
#fig.savefig('test_added_variable-expected.pdf')
fig2 = plt.figure()
ax = fig2.add_subplot(1,1,1)
fig2 = rplots.plot_added_variable(results, focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type[1], ax=ax)
return fig, fig2
def test_added_variable():
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
np.random.seed(3446)
n = 100
p = 3
exog = np.random.normal(size=(n, p))
lin_pred = 4 + exog[:, 0] + 0.2 * exog[:, 1]**2
expval = np.exp(lin_pred)
endog = np.random.poisson(expval)
model = sm.GLM(endog, exog, family=sm.families.Poisson())
results = model.fit()
focus_col = 0
use_glm_weights = False #, True:
resid_type = ["resid_deviance", "resid_response"]
fig = rplots.plot_added_variable(results,
focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type[0],
ax=ax)
ti = "Added variable plot"
#fig.savefig('test_added_variable-expected.pdf')
fig2 = plt.figure()
ax = fig2.add_subplot(1, 1, 1)
fig2 = rplots.plot_added_variable(results,
focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type[1],
ax=ax)
return fig, fig2
def test_added_variable_poisson(self, close_figures):
np.random.seed(3446)
n = 100
p = 3
exog = np.random.normal(size=(n, p))
lin_pred = 4 + exog[:, 0] + 0.2 * exog[:, 1]**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 0, 1, 2:
for use_glm_weights in False, True:
for resid_type in "resid_deviance", "resid_response":
weight_str = ["Unweighted", "Weighted"][use_glm_weights]
# Run directly and called as a results method.
for j in 0, 1:
if j == 0:
fig = plot_added_variable(
results,
focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type)
ti = "Added variable plot"
else:
fig = results.plot_added_variable(
focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type)
ti = "Added variable plot (called as method)"
ax = fig.get_axes()[0]
add_lowess(ax)
ax.set_position([0.1, 0.1, 0.8, 0.7])
effect_str = [
"Linear effect, slope=1", "Quadratic effect",
"No effect"
][focus_col]
ti += "\nPoisson regression\n"
ti += effect_str + "\n"
ti += weight_str + "\n"
ti += "Using '%s' residuals" % resid_type
ax.set_title(ti)
close_or_save(pdf, fig)
close_figures()
def test_added_variable_poisson(self, close_figures):
np.random.seed(3446)
n = 100
p = 3
exog = np.random.normal(size=(n, p))
lin_pred = 4 + exog[:, 0] + 0.2 * exog[:, 1]**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 0, 1, 2:
for use_glm_weights in False, True:
for resid_type in "resid_deviance", "resid_response":
weight_str = ["Unweighted", "Weighted"][use_glm_weights]
# Run directly and called as a results method.
for j in 0, 1:
if j == 0:
fig = plot_added_variable(results, focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type)
ti = "Added variable plot"
else:
fig = results.plot_added_variable(focus_col,
use_glm_weights=use_glm_weights,
resid_type=resid_type)
ti = "Added variable plot (called as method)"
ax = fig.get_axes()[0]
add_lowess(ax)
ax.set_position([0.1, 0.1, 0.8, 0.7])
effect_str = ["Linear effect, slope=1",
"Quadratic effect", "No effect"][focus_col]
ti += "\nPoisson regression\n"
ti += effect_str + "\n"
ti += weight_str + "\n"
ti += "Using '%s' residuals" % resid_type
ax.set_title(ti)
close_or_save(pdf, fig)
close_figures()