def test_plot_oth(self, close_figures):
#just test that they run
res = self.res
plot_fit(res, 0, y_true=None)
plot_partregress_grid(res, exog_idx=[0, 1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=0)
plot_ccpr_grid(res, exog_idx=[0])
fig = plot_ccpr_grid(res, exog_idx=[0, 1])
for ax in fig.axes:
add_lowess(ax)
close_or_save(pdf, fig)
def test_plot_oth(self, close_figures):
#just test that they run
res = self.res
plot_fit(res, 0, y_true=None)
plot_partregress_grid(res, exog_idx=[0,1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=0)
plot_ccpr_grid(res, exog_idx=[0])
fig = plot_ccpr_grid(res, exog_idx=[0,1])
for ax in fig.axes:
add_lowess(ax)
close_or_save(pdf, fig)
def test_plot_oth(self):
#just test that they run
res = self.res
endog = res.model.endog
exog = res.model.exog
plot_fit(res, 0, y_true=None)
plot_partregress_grid(res, exog_idx=[0, 1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=0)
plot_ccpr_grid(res, exog_idx=[0])
plot_ccpr_grid(res, exog_idx=[0, 1])
plt.close('all')
def test_plot_oth(self):
#just test that they run
res = self.res
endog = res.model.endog
exog = res.model.exog
plot_fit(res, 0, y_true=None)
plot_partregress_grid(res, exog_idx=[0,1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=0)
plot_ccpr_grid(res, exog_idx=[0])
plot_ccpr_grid(res, exog_idx=[0,1])
plt.close('all')
# -*- coding: utf-8 -*-
'''
Using the state crime dataset separately plot the effect of the each
variable on the on the outcome, murder rate while accounting for the effect
of all other variables in the model visualized with a grid of partial
regression plots.
'''
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
from statsmodels.graphics.regressionplots import plot_partregress_grid
fig = plt.figure(figsize=(8, 6))
crime_data = sm.datasets.statecrime.load_pandas()
results = smf.ols('murder ~ hs_grad + urban + poverty + single',
data=crime_data.data).fit()
plot_partregress_grid(results, fig=fig)
plt.show()
x1beta = x1 * res.params[1]
x2beta = x2 * res.params[2]
plt.plot(x1, x1beta + res.resid, 'o')
plt.plot(x1, x1beta, '-')
ax.set_title('X_i beta_i plus residuals versus exog (CCPR)') # + namestr)
ax = fig6.add_subplot(2, 1, 2)
plt.plot(x2, x2beta + res.resid, 'o')
plt.plot(x2, x2beta, '-')
#print res.summary()
doplots = 1
if doplots:
fig1 = smrp.plot_fit(res, 0, y_true=None)
smrp.plot_fit(res, 1, y_true=None)
smrp.plot_partregress_grid(res, exog_idx=[0, 1])
smrp.plot_regress_exog(res, exog_idx=0)
smrp.plot_ccpr(res, exog_idx=0)
smrp.plot_ccpr_grid(res, exog_idx=[0, 1])
tp = TestPlot()
tp.test_plot_fit()
fig1 = smrp.plot_partregress_grid(res, exog_idx=[0, 1])
#add lowess
ax = fig1.axes[0]
y0 = ax.get_lines()[0]._y
x0 = ax.get_lines()[0]._x
lres = sm.nonparametric.lowess(y0, x0, frac=0.2)
ax.plot(lres[:, 0], lres[:, 1], 'r', lw=1.5)
ax = fig1.axes[1]
x2beta = x2*res.params[2]
plt.plot(x1, x1beta + res.resid, 'o')
plt.plot(x1, x1beta, '-')
ax.set_title('X_i beta_i plus residuals versus exog (CCPR)')# + namestr)
ax = fig6.add_subplot(2,1,2)
plt.plot(x2, x2beta + res.resid, 'o')
plt.plot(x2, x2beta, '-')
#print res.summary()
doplots = 1
if doplots:
fig1 = smrp.plot_fit(res, 0, y_true=None)
smrp.plot_fit(res, 1, y_true=None)
smrp.plot_partregress_grid(res, exog_idx=[0,1])
smrp.plot_regress_exog(res, exog_idx=0)
smrp.plot_ccpr(res, exog_idx=0)
smrp.plot_ccpr_grid(res, exog_idx=[0,1])
from statsmodels.graphics.tests.test_regressionplots import TestPlot
tp = TestPlot()
tp.test_plot_fit()
fig1 = smrp.plot_partregress_grid(res, exog_idx=[0,1])
#add lowess
ax = fig1.axes[0]
y0 = ax.get_lines()[0]._y
x0 = ax.get_lines()[0]._x
lres = sm.nonparametric.lowess(y0, x0, frac=0.2)
ax.plot(lres[:,0], lres[:,1], 'r', lw=1.5)
# -*- coding: utf-8 -*-
'''
Using the state crime dataset seperately plot the effect of the each
variable on the on the outcome, murder rate while accounting for the effect
of all other variables in the model visualized with a grid of partial
regression plots.
'''
from statsmodels.graphics.regressionplots import plot_partregress_grid
import statsmodels.api as sm
import matplotlib.pyplot as plt
import statsmodels.formula.api as smf
fig = plt.figure(figsize=(8, 6))
crime_data = sm.datasets.statecrime.load_pandas()
results = smf.ols('murder ~ hs_grad + urban + poverty + single',
data=crime_data.data).fit()
plot_partregress_grid(results, fig=fig)
plt.show()
