1
你好社區我具有低於此代碼實現多回歸線性分析和顯示基於x和y值的多個統計數據:的Python numpy的statsmodels OLS迴歸特定值
import numpy as np
import statsmodels.api as sm
y = [1,2,3,4,3,4,5,4,5,5,4,5,4,5,4,5,6,5,4,5,4,3,4]
x = [
[4,2,3,4,5,4,5,6,7,4,8,9,8,8,6,6,5,5,5,5,5,5,5], #<-x1
[4,1,2,3,4,5,6,7,5,8,7,8,7,8,7,8,7,7,7,7,7,6,5], #<-x2
[4,1,2,5,6,7,8,9,7,8,7,8,7,7,7,7,7,7,6,6,4,4,4] #<-x3
]
x = np.array(x).T
x = sm.add_constant(x)
results = sm.OLS(endog=y, exog=x).fit()
print results.summary()
它返回:
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.535
Model: OLS Adj. R-squared: 0.461
Method: Least Squares F-statistic: 7.281
Date: Tue, 19 Feb 2013 Prob (F-statistic): 0.00191
Time: 21:51:28 Log-Likelihood: -26.025
No. Observations: 23 AIC: 60.05
Df Residuals: 19 BIC: 64.59
Df Model: 3
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1 0.2424 0.139 1.739 0.098 -0.049 0.534
x2 0.2360 0.149 1.587 0.129 -0.075 0.547
x3 -0.0618 0.145 -0.427 0.674 -0.365 0.241
const 1.5704 0.633 2.481 0.023 0.245 2.895
==============================================================================
Omnibus: 6.904 Durbin-Watson: 1.905
Prob(Omnibus): 0.032 Jarque-Bera (JB): 4.708
Skew: -0.849 Prob(JB): 0.0950
Kurtosis: 4.426 Cond. No. 38.6
如何才能實現statsmodel api只是作爲一個整數返回/打印R^2值(0.535)?我想利用這個值進行多元迴歸分析,並在分析完成時獲得最高的R^2值。 感謝
如果包含一個常量,rsquared是介於0和1之間,請參閱J Kelly關於如何訪問它的答案。更一般地說,rsquared不會懲罰包含更多變量,並且如果包含每個可用變量,則最大化。如果您只比較具有相同數量解釋變量的模型,但對於評估具有不同數量變量AIC,BIC或調整rsquared的模型會更好,那麼這不是問題。 – user333700