将lmplot
在seaborn拟合回归模型的截距.但是,有时我想要在没有截距的情况下拟合回归模型,即通过原点进行回归.
例如:
In [1]: import numpy as np ...: import pandas as pd ...: import seaborn as sns ...: import matplotlib.pyplot as plt ...: import statsmodels.formula.api as sfa ...: In [2]: %matplotlib inline In [3]: np.random.seed(2016) In [4]: x = np.linspace(0, 10, 32) In [5]: y = 0.3 * x + np.random.randn(len(x)) In [6]: df = pd.DataFrame({'x': x, 'y': y}) In [7]: r = sfa.ols('y ~ x + 0', data=df).fit() In [8]: sns.lmplot(x='x', y='y', data=df, fit_reg=True) Out[8]:
这个数字我想要的是:
In [9]: fig, ax = plt.subplots(figsize=(5, 5)) ...: ax.scatter(x=x, y=y) ...: ax.plot(x, r.fittedvalues) ...: Out[9]: []