Question

（很抱歉要问，但http://statsmodels.sourceforge.net/目前已关闭，我无法访问这些文档）

我使用statsmodels进行线性回归，基本上是：

import statsmodels.api as sm
model = sm.OLS(y,x)
results = model.fit()

我知道我可以打印出完整的结果集：

print results.summary()

输出如下内容：

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                      y   R-squared:                       0.952
Model:                            OLS   Adj. R-squared:                  0.951
Method:                 Least Squares   F-statistic:                     972.9
Date:                Mon, 20 Jul 2015   Prob (F-statistic):           5.55e-34
Time:                        15:35:22   Log-Likelihood:                -78.843
No. Observations:                  50   AIC:                             159.7
Df Residuals:                      49   BIC:                             161.6
Df Model:                           1                                         
Covariance Type:            nonrobust                                         
==============================================================================
                 coef    std err          t      P>|t|      [95.0% Conf. Int.]
------------------------------------------------------------------------------
x1             1.0250      0.033     31.191      0.000         0.959     1.091
==============================================================================
Omnibus:                       16.396   Durbin-Watson:                   2.166
Prob(Omnibus):                  0.000   Jarque-Bera (JB):                3.480
Skew:                          -0.082   Prob(JB):                        0.175
Kurtosis:                       1.718   Cond. No.                         1.00
==============================================================================

Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

我需要一种方法来打印仅 coef和std err的值。

我可以通过以下方式访问coef

print results.params

但我发现无法打印std err。

我该怎么做？

Answer 1

应用here给出的答案我使用dir()打印results对象的所有属性。

之后，我搜索了包含std err值的那个，结果是：

print results.bse

（不确定b代表bse代表什么，但我认为se代表＆＃34;标准错误＆＃34;）

Answer 2

以下函数可用于获取回归分析结果的概览。参数 ols_model 是由 statsmodels.formula.api 生成的回归模型。输出是一个 Pandas 数据框，其中保存了回归系数、标准误差、p 值、观察次数、AIC 和调整后的 rsquared。标准误差保存在括号中。 ***、** 和 * 表示 0.001、0.01、0.1 显着性水平：

def output_regres_result(ols_model, variable_list: list):
    """
    Create a pandas dataframe saving the regression analysis result
    :param ols_model: a linear model containing the regression result.
    type: statsmodels.regression.linear_model.RegressionResultsWrapper
    :param variable_list: a list of interested variable names
    :return: a pandas dataframe saving the regression coefficient, pvalues, standard errors, aic,
    number of observations, adjusted r squared
    """
    coef_dict = ols_model.params.to_dict()  # coefficient dictionary
    pval_dict = ols_model.pvalues.to_dict()  # pvalues dictionary
    std_error_dict = ols_model.bse.to_dict()  # standard error dictionary
    num_observs = np.int(ols_model.nobs) # number of observations
    aic_val = round(ols_model.aic, 2) # aic value
    adj_rsqured = round(ols_model.rsquared_adj, 3) # adjusted rsqured
    info_index = ['Num', 'AIC', 'Adjusted R2']
    index_list = variable_list + info_index

    for variable in variable_list:
        assert variable in coef_dict, 'Something wrong with variable name!'

    coef_vals = []

    for variable in variable_list:
        std_val = std_error_dict[variable]
        coef_val = coef_dict[variable]
        p_val = pval_dict[variable]
        if p_val <= 0.01:
            coef_vals.append('{}***({})'.format(round(coef_val, 4), round(std_val, 3)))
        elif 0.01 < p_val <= 0.05:
            coef_vals.append('{}**({})'.format(round(coef_val, 4), round(std_val, 3)))
        elif 0.05 < p_val <= 0.1:
            coef_vals.append('{}*({})'.format(round(coef_val, 4), round(std_val, 3)))
        else:
            coef_vals.append('{}({})'.format(round(coef_val, 4), round(std_val, 3)))

    coef_vals.extend([num_observs, aic_val, adj_rsqured])

    result_data = pd.DataFrame()
    result_data['coef'] = coef_vals
    result_data_reindex = result_data.set_index(pd.Index(index_list))

    return result_data_reindex

打印＆＃39; std err＆＃39;来自statsmodels的价值OLS结果

2 个答案: