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