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(endog, exog, exog_idx=[0, 1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=[0])
plot_ccpr(res, exog_idx=[0, 1])
plt.close('all')
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(endog, exog, exog_idx=[0,1])
plot_regress_exog(res, exog_idx=0)
plot_ccpr(res, exog_idx=[0])
plot_ccpr(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])
fig = plot_ccpr_grid(res, exog_idx=[0,1])
for ax in fig.axes:
add_lowess(ax)
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])
fig = plot_ccpr_grid(res, exog_idx=[0, 1])
for ax in fig.axes:
add_lowess(ax)
plt.close('all')
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(y, exog0, exog_idx=[0, 1])
smrp.plot_regress_exog(res, exog_idx=0)
smrp.plot_ccpr(res, exog_idx=[0])
smrp.plot_ccpr(res, exog_idx=[0, 1])
from statsmodels.graphics.tests.test_regressionplots import TestPlot
tp = TestPlot()
tp.test_plot_fit()
fig1 = smrp.plot_partregress(y, exog0, 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]
y0 = ax.get_lines()[0]._y
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]
y0 = ax.get_lines()[0]._y
x0 = ax.get_lines()[0]._x
# fixed. Note that the origin of the vertical axis in these plots is
# not meaningful (we are not implying that anyone's blood pressure would
# be negative), but the differences along the vertical axis are
# meaningful. This plot implies that when BMI and gender are held
# fixed, the average blood pressures of an 80 and 18 year old differ by
# around 30 mm/Hg. This plot also shows, as discussed above, that the
# deviations from the mean are somewhat smaller at the low end of the
# range compared to the high end of the range. We also see that at the
# high end of the range, the deviations from the mean are somewhat
# right-skewed, with exceptionally high SBP values being more common
# than exceptionally low SBP values.
from statsmodels.graphics.regressionplots import plot_ccpr
ax = plt.axes()
plot_ccpr(result, "RIDAGEYR", ax)
ax.lines[0].set_alpha(0.2) # Reduce overplotting with transparency
_ = ax.lines[1].set_color('orange')
# Next we have a partial residual plot that shows how BMI (horizontal
# axis) and SBP (vertical axis) would be related if gender and age were
# fixed. Compared to the plot above, we see here that age is more
# uniformly distributed than BMI. Also, it appears that there is more
# scatter in the partial residuals for BMI compared to what we saw above
# for age. Thus there seems to be less information about SBP in BMI,
# although a trend certainly exists.
ax = plt.axes()
plot_ccpr(result, "BMXBMI", ax)
ax.lines[0].set_alpha(0.2)
ax.lines[1].set_color('orange')
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)
ax = fig1.axes[1]
y0 = ax.get_lines()[0]._y
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(y, exog0, exog_idx=[0,1])
smrp.plot_regress_exog(res, exog_idx=0)
smrp.plot_ccpr(res, exog_idx=[0])
smrp.plot_ccpr(res, exog_idx=[0,1])
from statsmodels.graphics.tests.test_regressionplots import TestPlot
tp = TestPlot()
tp.test_plot_fit()
fig1 = smrp.plot_partregress(y, exog0, 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]
y0 = ax.get_lines()[0]._y