差异的原因是，当您使用Pandas读取数据集时，默认情况下将prog变量视为类型float：
df.prog.head()
0 2.0
1 2.0
2 2.0
3 2.0
4 2.0
Name: prog, dtype: float32
另一方面，在R示例中，该prog变量已显式转换为因子（类别）变量：
dat <- within(dat, {
prog <- factor(prog, levels = 1:3, labels = c("General", "Academic", "Vocational"))
id <- factor(id)
})
结果，当您查看R中的拟合摘要时，可以看到该prog变量已被拆分为n-1个二进制编码的术语：
> summary(m1 <- glm.nb(daysabs ~ math + prog, data = dat))
Call:
glm.nb(formula = daysabs ~ math + prog, data = dat, init.theta = 1.032713156,
link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1547 -1.0192 -0.3694 0.2285 2.5273
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.615265 0.197460 13.245 < 2e-16 ***
math -0.005993 0.002505 -2.392 0.0167 *
progAcademic -0.440760 0.182610 -2.414 0.0158 *
progVocational -1.278651 0.200720 -6.370 1.89e-10 ***
将此与prog变量在您发布的Python适合摘要中的显示方式进行比较。
要解决此问题，您可以使用C()函数将变量强制转换为statsmodels中的类别。这样，您将获得相同的结果：
model = smf.glm(formula = "daysabs ~ math + C(prog)", data=df, family=sm.families.NegativeBinomial()).fit()
model.summary()
<class 'statsmodels.iolib.summary.Summary'>
"""
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: daysabs No. Observations: 314
Model: GLM Df Residuals: 310
Model Family: NegativeBinomial Df Model: 3
Link Function: log Scale: 1.06830885199
Method: IRLS Log-Likelihood: -865.68
Date: Thu, 16 Feb 2017 Deviance: 350.98
Time: 10:34:16 Pearson chi2: 331.
No. Iterations: 6
==================================================================================
coef std err z P>|z| [0.025 0.975]
----------------------------------------------------------------------------------
Intercept 2.6150 0.207 12.630 0.000 2.209 3.021
C(prog)[T.2.0] -0.4408 0.192 -2.302 0.021 -0.816 -0.065
C(prog)[T.3.0] -1.2786 0.210 -6.079 0.000 -1.691 -0.866
math -0.0060 0.003 -2.281 0.023 -0.011 -0.001
==================================================================================
"""
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