title: How to use `lmplot` to plot linear regression without intercept?
body: <p>The <code>lmplot</code> in seaborn fit regression models with intercept. However, sometimes I want to fit regression models <strong>without intercept</strong>, i.e. regression through the origin. </p>
  
  <p>For example:</p>
  
  <pre><code>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]: &lt;seaborn.axisgrid.FacetGrid at 0xac88a20&gt;
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/aNFAT.png"><img src="https://i.stack.imgur.com/aNFAT.png" alt="enter image description here"></a></p>
  
  <p>The figure what I wanted:</p>
  
  <pre><code>In [9]: fig, ax = plt.subplots(figsize=(5, 5))
     ...: ax.scatter(x=x, y=y)
     ...: ax.plot(x, r.fittedvalues)
     ...: 
  Out[9]: [&lt;matplotlib.lines.Line2D at 0x5675a20&gt;]
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/fX66h.png"><img src="https://i.stack.imgur.com/fX66h.png" alt="enter image description here"></a></p>