这是我到目前为止的代码。我正在执行加权最小二乘运算,并打印出结果。我想使用摘要中的结果,但摘要显然不可迭代。有没有办法从摘要中提取值?
self.b = np.linalg.lstsq(self.G,self.d)
w = np.asarray(self.dw)
mod_wls = sm.WLS(self.d,self.G,weights=1./np.asarray(w))
res_wls = mod_wls.fit()
report = res_wls.summary()
print report
以下是打印出的摘要。
WLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.955
Model: WLS Adj. R-squared: 0.944
Method: Least Squares F-statistic: 92.82
Date: Mon, 24 Oct 2016 Prob (F-statistic): 4.94e-14
Time: 11:38:16 Log-Likelihood: 138.19
No. Observations: 28 AIC: -264.4
Df Residuals: 22 BIC: -256.4
Df Model: 5
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1 -0.0066 0.001 -12.389 0.000 -0.008 -0.006
x2 0.0072 0.000 15.805 0.000 0.006 0.008
x3 1.853e-08 2.45e-08 0.756 0.457 -3.23e-08 6.93e-08
x4 -4.402e-09 6.58e-09 -0.669 0.511 -1.81e-08 9.25e-09
x5 -3.595e-08 1.42e-08 -2.528 0.019 -6.55e-08 -6.45e-09
x6 4.402e-09 6.58e-09 0.669 0.511 -9.25e-09 1.81e-08
x7 -6.759e-05 4.17e-05 -1.620 0.120 -0.000 1.9e-05
==============================================================================
Omnibus: 4.421 Durbin-Watson: 1.564
Prob(Omnibus): 0.110 Jarque-Bera (JB): 2.846
Skew: 0.729 Prob(JB): 0.241
Kurtosis: 3.560 Cond. No. 2.22e+16
==============================================================================
编辑:为了澄清,我想提取“标准错误”。每个x1,x2 ...... x7行的值。我似乎无法找到代表他们的属性或他们所在的行。有人知道怎么做吗?
答案 0 :(得分:1)
操作完成后,res_wls
的类型为statsmodels.regression.linear_model.RegressionResults
,其中包含您可能感兴趣的每个值的各个属性。有关这些值的详细信息,请参阅the documentation。例如,res_wls.rsquared
应该为您提供$ R ^ 2 $。