具有两个随机效应的混合模型 - statsmodels [英] mixed-models with two random effects - statsmodels

问题描述

import pandas as pd
import statsmodels.formula.api as smf

df = pd.read_csv('http://www.bodowinter.com/tutorial/politeness_data.csv')
df = df.drop(38)

在 R 中我会这样做:

lmer(frequency ~ attitude + (1|subject) + (1|scenario), data=df)

在 R 中给了我:

Random effects:
 Groups   Name        Variance Std.Dev.
 scenario (Intercept)  219     14.80   
 subject  (Intercept) 4015     63.36   
 Residual              646     25.42   
Fixed effects:
            Estimate Std. Error t value
(Intercept)  202.588     26.754   7.572
attitudepol  -19.695      5.585  -3.527

我尝试对 statsmodels 做同样的事情:

I tried to do the same with statsmodels:

model = smf.mixedlm("frequency ~ attitude", data=df, groups=df[["subject","scenario"]]).fit()

但是 model.summary() 给了我一个不同的输出:

But model.summary() gives me a different output:

      Mixed Linear Model Regression Results
=======================================================
Model:            MixedLM Dependent Variable: frequency
No. Observations: 83      Method:             REML     
No. Groups:       2       Scale:              0.0000   
Min. group size:  1       Likelihood:         inf      
Max. group size:  1       Converged:          Yes      
Mean group size:  1.0                                  
-------------------------------------------------------
                  Coef.  Std.Err. z P>|z| [0.025 0.975]
-------------------------------------------------------
Intercept        204.500                               
attitude[T.pol]    8.800                               
groups RE          0.000                               
=======================================================

推荐答案

下面的代码重现了 R 结果.由于这是一个没有独立组的交叉模型，您需要将每个人都放在同一个组中，并使用方差分量指定随机效应.

The code below reproduces the R results. Since this is a crossed model with no independent groups, you need to put everyone in the same group and specify the random effects using variance components.

import pandas as pd                                                                                                        
import statsmodels.api as sm                                                                                               
                                                                                                                           
df = pd.read_csv('http://www.bodowinter.com/uploads/1/2/9/3/129362560/politeness_data.csv')                                                 
df = df.dropna()                                                                                                           
df["group"] = 1                                                                                                            
                                                                                                                           
vcf = {"scenario": "0 + C(scenario)", "subject": "0 + C(subject)"}                                                         
model = sm.MixedLM.from_formula("frequency ~ attitude", groups="group",                                                    
                                vc_formula=vcf, re_formula="0", data=df)                                                   
result = model.fit()  

结果如下:

            Mixed Linear Model Regression Results
==============================================================
Model:               MixedLM   Dependent Variable:   frequency
No. Observations:    83        Method:               REML     
No. Groups:          1         Scale:                646.0163 
Min. group size:     83        Likelihood:           -396.7268
Max. group size:     83        Converged:            Yes      
Mean group size:     83.0                                     
--------------------------------------------------------------
                 Coef.   Std.Err.   z    P>|z|  [0.025  0.975]
--------------------------------------------------------------
Intercept        202.588   26.754  7.572 0.000 150.152 255.025
attitude[T.pol]  -19.695    5.585 -3.526 0.000 -30.641  -8.748
scenario Var     218.991    6.476                             
subject Var     4014.616  104.614                             
==============================================================

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