statsmodels 是一个 Python 模块，提供用于估计各种统计模型的类和函数，以及用于进行统计检验和统计数据探索的类和函数。每个估计器都提供广泛的统计结果列表。结果经过测试，与现有的统计包进行比较，以确保其正确性。该包是在开源的 Modified BSD (3-clause) 许可下发布的。在线文档托管在 statsmodels.org。
简介¶
statsmodels 支持使用 R 风格公式和 pandas DataFrame 来指定模型。以下是一个使用普通最小二乘法的简单示例
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: Thu, 03 Oct 2024 Prob (F-statistic): 1.90e-08
Time: 16:15:28 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: Thu, 03 Oct 2024 Prob (F-statistic): 1.07e-06
Time: 16:15:28 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) 以查看可用的结果。属性在 results.__doc__ 中描述，结果方法有自己的文档字符串。
引用¶
请在科学出版物中使用以下引用来引用 statsmodels
Seabold, Skipper 和 Josef Perktold。 "statsmodels：Python 的计量经济学和统计建模。” 第九届 Python in Science 大会论文集。 2010 年。
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¶
最后更新：2024 年 10 月 3 日