具有两个随机效应的混合模型 - statsmodels [英] mixed-models with two random effects - statsmodels
本文介绍了具有两个随机效应的混合模型 - statsmodels的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
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
==============================================================
这篇关于具有两个随机效应的混合模型 - statsmodels的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!
查看全文