本文整理汇总了Python中statsmodels.graphics.regressionplots.abline_plot函数的典型用法代码示例。如果您正苦于以下问题：Python abline_plot函数的具体用法？Python abline_plot怎么用？Python abline_plot使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了abline_plot函数的11个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_abline_remove
def test_abline_remove(self, close_figures):
mod = self.mod
intercept, slope = mod.params
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(self.X[:,1], self.y)
abline_plot(intercept=intercept, slope=slope, ax=ax)
abline_plot(intercept=intercept, slope=2*slope, ax=ax)
lines = ax.get_lines()
lines.pop(0).remove()
close_or_save(pdf, fig)示例2: plot_scatter_and_line
def plot_scatter_and_line(self, result):
'''(d)(f)'''
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(self.x, self.y, c='w')
ax.set_ylabel("y")
ax.set_xlabel("x")
rp.abline_plot(intercept=-1, slope=0.5, ax=ax, c='r', label="model fit")
rp.abline_plot(model_results=result, ax=ax, c='g', label="pop.regression")
plt.legend(loc='lower right', shadow=True, fontsize='medium')
plt.show()示例3: simple_regession
def simple_regession(self):
''' The answer of exercise03-08:
(a)
(i) Yes, from F-stat
(ii) Explain it from RSE and R^2 stat
(iii)negative
(iv) Code, no prediction interval
(b) Code
(c) Residual/fitted: non-linearity
'''
# model = smf.ols(formula="mpg ~ horsepower", data=self.df)
y = self.df['mpg']
X = self.df[['horsepower']]
X = sm.add_constant(X)
print X
res = sm.OLS(y, X).fit()
# res = model.fit()
print res.summary()
print "The prediction is: ", res.predict(exog=[[1, 98]])
print "The prediction interval is: "
'''
self.df.plot(kind="scatter", x='horsepower', y='mpg', c='w')
graph_x = np.linspace(min(self.df['horsepower']), 200)
graph_y = res.predict(sm.add_constant(graph_x))
plt.plot(graph_x, graph_y)
'''
fig = rp.abline_plot(model_results=res)
ax = fig.axes[0]
ax.scatter(X['horsepower'], y, c='w')
plt.show()
lrplot.plot_R_graphs(res)示例4: test_abline_ab_ax
def test_abline_ab_ax(self):
mod = self.mod
intercept, slope = mod.params
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(self.X[:,1], self.y)
fig = abline_plot(intercept=intercept, slope=slope, ax=ax)
plt.close(fig)示例5: test_abline_ab
def test_abline_ab(self):
mod = self.mod
intercept, slope = mod.params
fig = abline_plot(intercept=intercept, slope=slope)
plt.close(fig)示例6: test_abline_model_ax
def test_abline_model_ax(self):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(self.X[:,1], self.y)
fig = abline_plot(model_results=self.mod, ax=ax)
plt.close(fig)示例7: test_abline_model
def test_abline_model(self):
fig = abline_plot(model_results=self.mod)
ax = fig.axes[0]
ax.scatter(self.X[:,1], self.y)
plt.close(fig)示例8: test_abline_ab
def test_abline_ab(self, close_figures):
mod = self.mod
intercept, slope = mod.params
fig = abline_plot(intercept=intercept, slope=slope)
close_or_save(pdf, fig)示例9: test_abline_model_ax
def test_abline_model_ax(self, close_figures):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.scatter(self.X[:,1], self.y)
fig = abline_plot(model_results=self.mod, ax=ax)
close_or_save(pdf, fig)示例10: test_abline_model
def test_abline_model(self, close_figures):
fig = abline_plot(model_results=self.mod)
ax = fig.axes[0]
ax.scatter(self.X[:,1], self.y)
close_or_save(pdf, fig)示例11: print
col_y = 'perMnd' #'mio' #'fasteUdg' #'perMnd'
#ax=df.plot(x='m2',y='perMnd', kind='scatter',s=1.0) #0.5)
ax=df.plot(x=col_x,y=col_y, kind='scatter',s=1.0) #0.5)
#155=16.000, 200=17.700
#45m2 = 1700, 38kr/m2. 26m2 for 1000 kr mere.
if 1:
#X2 = sm.add_constant( df['m2'] )
X2 = sm.add_constant( df[col_x] )
mdl = sm.OLS(endog = df[col_y], exog = X2 )
result=mdl.fit()
print(result.summary())
pprint.pprint(result.params)
abline_plot(model_results=result, ax=ax)
#plt.xlim(100,350) #600)
=======
filename = 'C:\\Users\\jg.STATUSDK\\Downloads\\husudg - Sheet1.tsv'
df = pd.read_csv(
filename,
sep='\t',
#nrows=2
#chunksize=2,
#iterator=True,
)
#df = df.head(167)