Python Statasmodels 实现泊松回归 实例 代码,python实现线性回归

泊松回归，属于地理学以及GIS空间分析常用模型。适合应用于因变量为计数型变量的实例。模型基本知识移步百度，以下为亲测实例代码。一定有错漏，欢迎交流~ # _*_ coding: utf-8 _*_import pandas as pdimport numpy as npimport statsmodels.api as smfrom statsmodels.formula.api import ols #加载ols模型from statsmodels.formula.api import poissonimport matplotlib.pyplot as pltdata = pd.read_csv("C:\变量st.csv")print(data.head())y = data['工作日D']x1 = data['X_NDVI']x2 = data['X_街景绿化']x3 = data['X_道路里程']x4 = data['X_坡度']x5 = data['X_公交站']x6 = data['X_地铁站']x7 = data['X_购物点']x8 = data['X_混合']x = np.column_stack((x1, x2, x3, x4, x5, x6, x7, x8))# possion回归model = sm.GLM(y,x,family=sm.families.Poisson())# model=poisson(y,x)results = model.fit()print(results.summary())

输出模型结果：

Generalized Linear Model Regression Results ==============================================================================Dep. Variable: 工作日D No. Observations: 43Model: GLM Df Residuals: 35Model Family: Poisson Df Model: 7Link Function: log Scale: 1.0000Method: IRLS Log-Likelihood: -2.4490e+05Date: Sat, 01 Aug 2020 Deviance: 4.8933e+05Time: 17:51:09 Pearson chi2: 9.94e+05No. Iterations: 7 Covariance Type: nonrobust ============================================================================== coef std err z P>|z| [0.025 0.975]------------------------------------------------------------------------------x1 -0.2752 0.014 -19.714 0.000 -0.303 -0.248x2 5.4745 0.010 523.050 0.000 5.454 5.495x3 6.6649 0.011 618.719 0.000 6.644 6.686x4 4.8497 0.017 282.112 0.000 4.816 4.883x5 0.9419 0.013 69.895 0.000 0.915 0.968x6 0.3792 0.010 36.683 0.000 0.359 0.399x7 0.2701 0.012 22.606 0.000 0.247 0.294x8 1.4817 0.008 194.155 0.000 1.467 1.497==============================================================================