【发布时间】：2014-03-28 08:22:57
【问题描述】：
对于我从 pandas 进行的线性回归，我将如何绘制我的线性回归结果？
import pandas as pd
from pandas.stats.api import ols
df = pd.read_csv('Samples.csv', index_col=0)
control = ols(y=df['Control'], x=df['Day'])
one = ols(y=df['Sample1'], x=df['Day'])
two = ols(y=df['Sample2'], x=df['Day'])
我尝试了plot()，但没有成功。我想在一个图上绘制所有三个样本是否有任何 pandas 代码或 matplotlib 代码来处理这些摘要格式的数据？
反正结果是这样的：
控制
------------------------Summary of Regression Analysis-------------------------
Formula: Y ~ <x> + <intercept>
Number of Observations: 7
Number of Degrees of Freedom: 2
R-squared: 0.5642
Adj R-squared: 0.4770
Rmse: 4.6893
F-stat (1, 5): 6.4719, p-value: 0.0516
Degrees of Freedom: model 1, resid 5
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x -0.4777 0.1878 -2.54 0.0516 -0.8457 -0.1097
intercept 41.4621 2.9518 14.05 0.0000 35.6766 47.2476
---------------------------------End of Summary---------------------------------
一个
-------------------------Summary of Regression Analysis-------------------------
Formula: Y ~ <x> + <intercept>
Number of Observations: 6
Number of Degrees of Freedom: 2
R-squared: 0.8331
Adj R-squared: 0.7914
Rmse: 2.0540
F-stat (1, 4): 19.9712, p-value: 0.0111
Degrees of Freedom: model 1, resid 4
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x -0.4379 0.0980 -4.47 0.0111 -0.6300 -0.2459
intercept 29.6731 1.6640 17.83 0.0001 26.4116 32.9345
---------------------------------End of Summary---------------------------------
两个
-------------------------Summary of Regression Analysis-------------------------
Formula: Y ~ <x> + <intercept>
Number of Observations: 5
Number of Degrees of Freedom: 2
R-squared: 0.8788
Adj R-squared: 0.8384
Rmse: 1.0774
F-stat (1, 3): 21.7542, p-value: 0.0186
Degrees of Freedom: model 1, resid 3
-----------------------Summary of Estimated Coefficients------------------------
Variable Coef Std Err t-stat p-value CI 2.5% CI 97.5%
--------------------------------------------------------------------------------
x -0.2399 0.0514 -4.66 0.0186 -0.3407 -0.1391
intercept 24.0902 0.9009 26.74 0.0001 22.3246 25.8559
---------------------------------End of Summary---------------------------------
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标签： python pandas matplotlib linear-regression statsmodels