statsmodels 是一个Python模块，提供了用于估计许多不同统计模型的类和函数，以及进行统计测试和统计数据探索的功能。每个估计器都有大量结果统计数据可用。结果已与现有的统计软件包进行了测试，以确保其正确性。该软件包在开源的Modified BSD（3条款）许可证下发布。在线文档托管在 statsmodels.org。
介绍¶
statsmodels 支持使用 R 风格的公式和 pandas DataFrame 来指定模型。
以下是使用普通最小二乘法的一个简单示例：
In [1]: import numpy as np
In [2]: import statsmodels.api as sm
In [3]: import statsmodels.formula.api as smf
# Load data
In [4]: dat = sm.datasets.get_rdataset("Guerry", "HistData").data
# Fit regression model (using the natural log of one of the regressors)
In [5]: results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=dat).fit()
# Inspect the results
In [6]: print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: Lottery R-squared: 0.348
Model: OLS Adj. R-squared: 0.333
Method: Least Squares F-statistic: 22.20
Date: 三, 16 10 2024 Prob (F-statistic): 1.90e-08
Time: 18:43:08 Log-Likelihood: -379.82
No. Observations: 86 AIC: 765.6
Df Residuals: 83 BIC: 773.0
Df Model: 2
Covariance Type: nonrobust
===================================================================================
coef std err t P>|t| [0.025 0.975]
-----------------------------------------------------------------------------------
Intercept 246.4341 35.233 6.995 0.000 176.358 316.510
Literacy -0.4889 0.128 -3.832 0.000 -0.743 -0.235
np.log(Pop1831) -31.3114 5.977 -5.239 0.000 -43.199 -19.424
==============================================================================
Omnibus: 3.713 Durbin-Watson: 2.019
Prob(Omnibus): 0.156 Jarque-Bera (JB): 3.394
Skew: -0.487 Prob(JB): 0.183
Kurtosis: 3.003 Cond. No. 702.
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
您也可以使用 numpy 数组来代替公式：
In [7]: import numpy as np
In [8]: import statsmodels.api as sm
# Generate artificial data (2 regressors + constant)
In [9]: nobs = 100
In [10]: X = np.random.random((nobs, 2))
In [11]: X = sm.add_constant(X)
In [12]: beta = [1, .1, .5]
In [13]: e = np.random.random(nobs)
In [14]: y = np.dot(X, beta) + e
# Fit regression model
In [15]: results = sm.OLS(y, X).fit()
# Inspect the results
In [16]: print(results.summary())
OLS Regression Results
==============================================================================
Dep. Variable: y R-squared: 0.247
Model: OLS Adj. R-squared: 0.231
Method: Least Squares F-statistic: 15.90
Date: 三, 16 10 2024 Prob (F-statistic): 1.07e-06
Time: 18:43:08 Log-Likelihood: -18.185
No. Observations: 100 AIC: 42.37
Df Residuals: 97 BIC: 50.18
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const 1.5135 0.073 20.685 0.000 1.368 1.659
x1 0.1958 0.102 1.925 0.057 -0.006 0.398
x2 0.4922 0.104 4.740 0.000 0.286 0.698
==============================================================================
Omnibus: 23.831 Durbin-Watson: 1.951
Prob(Omnibus): 0.000 Jarque-Bera (JB): 6.295
Skew: -0.262 Prob(JB): 0.0430
Kurtosis: 1.888 Cond. No. 4.95
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
查看 dir(results) 以了解可用的结果。属性在 results.__doc__ 中描述，结果方法有自己的文档字符串。
引用¶
请使用以下引文在科学出版物中引用statsmodels：
Seabold, Skipper, 和 Josef Perktold. “statsmodels: 使用 Python 进行计量经济学和统计建模。” 第九届 Python 科学会议论文集。 2010.
Bibtex 条目：
@inproceedings{seabold2010statsmodels,
title={statsmodels: Econometric and statistical modeling with python},
author={Seabold, Skipper and Perktold, Josef},
booktitle={9th Python in Science Conference},
year={2010},
}
索引¶
Last update:
Oct 16, 2024