使用Matplotlib在3D中绘制线性模型 [英] Plot linear model in 3d with Matplotlib

问题描述

我正在尝试创建适合数据集的线性模型的3d图.我能够在R中相对容易地做到这一点，但是我真的很难在Python中做到这一点.这是我在R中所做的事情:

I'm trying to create a 3d plot of a linear model fit for a data set. I was able to do this relatively easily in R, but I'm really struggling to do the same in Python. Here is what I've done in R:

这是我在Python中所做的:

Here's what I've done in Python:

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import statsmodels.formula.api as sm

csv = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
model = sm.ols(formula='Sales ~ TV + Radio', data = csv)
fit = model.fit()

fit.summary()

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

ax.scatter(csv['TV'], csv['Radio'], csv['Sales'], c='r', marker='o')

xx, yy = np.meshgrid(csv['TV'], csv['Radio'])

# Not what I expected :(
# ax.plot_surface(xx, yy, fit.fittedvalues)

ax.set_xlabel('TV')
ax.set_ylabel('Radio')
ax.set_zlabel('Sales')

plt.show()

我做错了什么，我应该怎么做?

What am I doing wrong and what should I do instead?

谢谢.

推荐答案

您正确的假设是plot_surface要使用坐标的网格，但是预测要使用适合您的数据结构("exog" ).

You were correct in assuming that plot_surface wants a meshgrid of coordinates to work with, but predict wants a data structure like the one you fitted with (the "exog").

exog = pd.core.frame.DataFrame({'TV':xx.ravel(),'Radio':yy.ravel()})
out = fit.predict(exog=exog)
ax.plot_surface(xx, yy, out.reshape(xx.shape), color='None')

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