Python中的多元回归(带因子选择)

Question

我读过的关于Python中多元回归的所有线程都推荐在statmodels中使用OLS函数。这是我遇到的问题，我试图通过将其回报与14个可以解释该基金回报的独立变量进行回归来解释基金的回报(HYFAX以绿色突出显示)。这应该有一个显著的F检验，并通过逐步迭代的因素，吐出最高调整R平方的最佳拟合模型。在python中有办法做到这一点吗?

基金回报与因素对比

Answer 1

听起来你只是想看看你的模型适合的结果。下面是一个只有一个预测器的例子，但是很容易扩展到14个:

导入statmodels并指定您想要构建的模型(这是您将包含14个预测器的地方):

import statsmodels.api as sm
#read in your data however you want and assign your y, x1...x14 variables
model = sm.OLS(x, y)

适合模型:

results = model.fit()

现在显示模型拟合的摘要:

print(results.summary())

这将给你调整后的R平方值，F测试值，beta权重等。应该看起来像这样:

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      x   R-squared:                       0.601
Model:                            OLS   Adj. R-squared:                  0.594
Method:                 Least Squares   F-statistic:                     87.38
Date:                Wed, 24 Aug 2016   Prob (F-statistic):           3.56e-13
Time:                        19:51:25   Log-Likelihood:                -301.81
No. Observations:                  59   AIC:                             605.6
Df Residuals:                      58   BIC:                             607.7
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
y              0.8095      0.087      9.348      0.000         0.636     0.983
==============================================================================
Omnibus:                        0.119   Durbin-Watson:                   1.607
Prob(Omnibus):                  0.942   Jarque-Bera (JB):                0.178
Skew:                          -0.099   Prob(JB):                        0.915
Kurtosis:                       2.818   Cond. No.                         1.00
==============================================================================