Statistics 估计边际效应时的误差
我在Stata 15中检索拟合逻辑回归模型的边际效应时遇到问题。结果变量mathtsbv是二元变量,性别变量sex也是虚拟变量,并且eth变量是分类变量,其值范围为0到5。已排除所有缺少的值 以下是我的do文件的摘录:Statistics 估计边际效应时的误差,statistics,stata,logistic-regression,mlogit,Statistics,Stata,Logistic Regression,Mlogit,我在Stata 15中检索拟合逻辑回归模型的边际效应时遇到问题。结果变量mathtsbv是二元变量,性别变量sex也是虚拟变量,并且eth变量是分类变量,其值范围为0到5。已排除所有缺少的值 以下是我的do文件的摘录: logit mathtsbv sex eth sex##i.eth if (mathtsbv>=0&mathtsbv<.)&(sex>=0&sex<.)&(eth>=0ð<.) margins, d
logit mathtsbv sex eth sex##i.eth if (mathtsbv>=0&mathtsbv<.)&(sex>=0&sex<.)&(eth>=0ð<.)
margins, dydx(sex eth sex##i.eth) atmeans
我花了一个多小时在谷歌上搜索和试验:从模型中删除性别,只保留eth,并在预测因子列表中添加一个连续变量。不幸的是,这些都没有解决问题。你可以计算平均边际效应的对比度,这将使你得到与你想要的相似的东西:当你改变一个变量时,成功概率的变化如何随着第二个变量的变化而变化 以下是Stata中的一个可复制示例:
. webuse lbw, clear
(Hosmer & Lemeshow data)
. qui logit low i.smoke##i.race
. margins r.smoke#r.race
Contrasts of adjusted predictions
Model VCE : OIM
Expression : Pr(low), predict()
---------------------------------------------------------------------------
| df chi2 P>chi2
----------------------------------------+----------------------------------
smoke#race |
(smoker vs nonsmoker) (black vs white) | 1 0.00 0.9504
(smoker vs nonsmoker) (other vs white) | 1 1.59 0.2070
Joint | 2 1.67 0.4332
---------------------------------------------------------------------------
-----------------------------------------------------------------------------------------
| Delta-method
| Contrast Std. Err. [95% Conf. Interval]
----------------------------------------+------------------------------------------------
smoke#race |
(smoker vs nonsmoker) (black vs white) | .0130245 .2092014 -.3970027 .4230517
(smoker vs nonsmoker) (other vs white) | -.2214452 .1754978 -.5654146 .1225242
-----------------------------------------------------------------------------------------
例如,与白人相比,吸烟对低体重儿童出生概率的影响要低22个百分点。这一差异并不显著
这些结果与完全饱和OLS模型的结果相同,在该模型中,可以直接解释相互作用系数:
. reg low i.smoke##i.race, robust
Linear regression Number of obs = 189
F(5, 183) = 5.09
Prob > F = 0.0002
R-squared = 0.0839
Root MSE = .45072
-------------------------------------------------------------------------------
| Robust
low | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------+----------------------------------------------------------------
smoke |
smoker | .2744755 .0809029 3.39 0.001 .1148531 .4340979
|
race |
black | .2215909 .1257293 1.76 0.080 -.0264745 .4696563
other | .2727273 .0792791 3.44 0.001 .1163086 .4291459
|
smoke#race |
smoker#black | .0130245 .2126033 0.06 0.951 -.4064443 .4324933
smoker#other | -.2214452 .1783516 -1.24 0.216 -.5733351 .1304447
|
_cons | .0909091 .044044 2.06 0.040 .0040098 .1778083
-------------------------------------------------------------------------------
在在线复查详细信息后,我发现塔塔不能对交互项产生边际效应:交互项的值取决于其组件的值,因此不可能对其进行单独的效应估计。然而,我仍然不确定。无论如何,希望这能帮助社区成员在未来。你可能会发现。
. reg low i.smoke##i.race, robust
Linear regression Number of obs = 189
F(5, 183) = 5.09
Prob > F = 0.0002
R-squared = 0.0839
Root MSE = .45072
-------------------------------------------------------------------------------
| Robust
low | Coef. Std. Err. t P>|t| [95% Conf. Interval]
--------------+----------------------------------------------------------------
smoke |
smoker | .2744755 .0809029 3.39 0.001 .1148531 .4340979
|
race |
black | .2215909 .1257293 1.76 0.080 -.0264745 .4696563
other | .2727273 .0792791 3.44 0.001 .1163086 .4291459
|
smoke#race |
smoker#black | .0130245 .2126033 0.06 0.951 -.4064443 .4324933
smoker#other | -.2214452 .1783516 -1.24 0.216 -.5733351 .1304447
|
_cons | .0909091 .044044 2.06 0.040 .0040098 .1778083
-------------------------------------------------------------------------------