Print 'std err' value from statsmodels OLS results

Question

(Sorry to ask but http://statsmodels.sourceforge.net/ is currently down and I can't access the docs)

I'm doing a linear regression using statsmodels, basically:

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

I know that I can print out the full set of results with:

print results.summary()

which outputs something like:

                            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.

I need a way to print out only the values of coef and std err.

I can access coef with:

print results.params

but I've found no way to print out std err.

How can I do this?

For now temporary, but most likely permanent replacement for the documentation on sourceforge is here http://statsmodels.github.io/dev/generated/statsmodels.regression.linear_model.RegressionResults.html — Josef, Jul 20 '15 at 20:31

score 71 · Accepted Answer · edited May 23 '17 at 12:01

71

Applying the answer given here I used dir() to print all the attributes of the results object.

After that I searched for the one that contained the std err value and it turned out to be:

print results.bse

(Not sure what the b stands for in bse, but I guess the se stands for "standard error")

edited May 23 '17 at 12:01

Community

1
1

answered Jul 20 '15 at 19:58

Gabriel

37,092
64
207
369

22

The `b` is a historical artifact, when `params` where called `b` as in linear model `y = X b + u`, and should be properly called `params_se` – Josef Jul 20 '15 at 20:27
Thanks for the explanation @user333700! – Gabriel Jul 20 '15 at 20:31
@Josef Do you mean `results.params_se`. It doesn't seem to work. – steven Feb 24 '19 at 14:39
It’s still called ”bse” as in the answer. The name was never changed. – Josef Feb 25 '19 at 00:31

score 4 · Answer 2 · answered Sep 12 '21 at 19:58

results.bse provides standard errors for the coefficients, identical to those listed in results.summary().

The standard error of the regression is obtained using results.scale**.5.

Also identical to np.sqrt(np.sum(results.resid**2)/results.df_resid), where results is your fitted model.

score 0 · Answer 3 · answered Mar 05 '21 at 01:16

The following function can be used to get an overview of the regression analysis result. The parameter ols_model is the regression model generated by statsmodels.formula.api. The output is a pandas data frame saving the regression coefficient, standard errors, p values, number of observations, AIC, and adjusted rsquared. The standard errors are saved in brackets. ***, **, and * represent 0.001, 0.01, 0.1 significance level:

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

@ Bright Chang, it throws error: ```AttributeError: 'numpy.ndarray' object has no attribute 'to_dict'``` — Peshal1067, Jun 05 '21 at 05:35

score 0 · Answer 4 · edited Apr 27 '22 at 16:24

0

statistically standard error of estimate is always equal to square root of mean square error of residual. It can be obtained from results using the formula np.sqrt(results.mse_resid)

edited Apr 27 '22 at 16:24

ah bon

7,903
7
43
86

answered Aug 12 '21 at 05:49

user2925142

1

The question is for standard errors of parameter estimates, not for residual standard error. – Josef Aug 12 '21 at 15:17

Print 'std err' value from statsmodels OLS results

4 Answers4

Linked