我正在尝试使用公式在包含分类变量的数据集上运行通用线性模型。当我列出参数时，结果汇总表似乎漏掉了一个变量？
我找不到针对glm显示分类变量输出的特定文档，但是我已经为OLS做过这方面的文档，看起来它应该单独列出每个分类变量。无论是使用GLM还是OLS，它都会留下每个类别的一个值。例如：
我找不到针对glm显示分类变量输出的特定文档，但是我已经为OLS做过这方面的文档，看起来它应该单独列出每个分类变量。无论是使用GLM还是OLS，它都会留下每个类别的一个值。例如：
import statsmodels.formula.api as smf
import pandas as pd
Data = pd.read_csv(root+'/Illisarvik/TestData.csv')
formula = 'Response~Day+Class+Var'
gm = sm.GLM.from_formula(formula=formula, data=Data,
family=sm.families.Gaussian()).fit()
ls = smf.ols(formula=formula,data=Data).fit()
print (Data)
print(gm.params)
print(ls.params)
Day Class Var Response
0 D A 0.533088 0.582931
1 D B 0.839837 0.075011
2 D C 1.454716 0.505442
3 D A 1.455503 0.188945
4 D B 1.163155 0.144176
5 N A 1.072238 0.918962
6 N B 0.815384 0.249160
7 N C 1.182626 0.520460
8 N A 1.448843 0.870644
9 N B 0.653531 0.460177
Intercept 0.625111
Day[T.N] 0.298084
Class[T.B] -0.439025
Class[T.C] -0.104725
Var -0.118662
dtype: float64
Intercept 0.625111
Day[T.N] 0.298084
Class[T.B] -0.439025
Class[T.C] -0.104725
Var -0.118662
dtype: float64
C:/Users/wesle/Dropbox/PhD_Work/Figures/SkeeterEtAlAnalysis.py:55: FutureWarning: sort is deprecated, use sort_values(inplace=True) for INPLACE sorting
P.sort()
我的模型有问题吗？当我打印完整的汇总表时，同样的问题出现了：
print(gm.summary())
print(ls.summary())
Generalized Linear Model Regression Results
==============================================================================
Dep. Variable: Response No. Observations: 10
Model: GLM Df Residuals: 5
Model Family: Gaussian Df Model: 4
Link Function: identity Scale: 0.0360609978309
Method: IRLS Log-Likelihood: 5.8891
Date: Sun, 05 Mar 2017 Deviance: 0.18030
Time: 23:26:48 Pearson chi2: 0.180
No. Iterations: 2
==============================================================================
coef std err z P>|z| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 0.6251 0.280 2.236 0.025 0.077 1.173
Day[T.N] 0.2981 0.121 2.469 0.014 0.061 0.535
Class[T.B] -0.4390 0.146 -3.005 0.003 -0.725 -0.153
Class[T.C] -0.1047 0.170 -0.617 0.537 -0.438 0.228
Var -0.1187 0.222 -0.535 0.593 -0.553 0.316
==============================================================================
OLS Regression Results
==============================================================================
Dep. Variable: Response R-squared: 0.764
Model: OLS Adj. R-squared: 0.576
Method: Least Squares F-statistic: 4.055
Date: Sun, 05 Mar 2017 Prob (F-statistic): 0.0784
Time: 23:26:48 Log-Likelihood: 5.8891
No. Observations: 10 AIC: -1.778
Df Residuals: 5 BIC: -0.2652
Df Model: 4
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
Intercept 0.6251 0.280 2.236 0.076 -0.094 1.344
Day[T.N] 0.2981 0.121 2.469 0.057 -0.012 0.608
Class[T.B] -0.4390 0.146 -3.005 0.030 -0.815 -0.064
Class[T.C] -0.1047 0.170 -0.617 0.564 -0.541 0.332
Var -0.1187 0.222 -0.535 0.615 -0.689 0.451
==============================================================================
Omnibus: 1.493 Durbin-Watson: 2.699
Prob(Omnibus): 0.474 Jarque-Bera (JB): 1.068
Skew: -0.674 Prob(JB): 0.586
Kurtosis: 2.136 Cond. No. 9.75
==============================================================================