在使用xtlogit命令运行多级回归后，我尝试计算Stata 12中变量的边距。但是，虽然我在运行回归后立即使用margins命令，但仍然收到错误，说我的变量在协变量列表中找不到。这是我的代码的简化版本：
. use http://url.com/file.dta, clear
. xtset country
. xtlogit dv iv1 iv2 iv3 iv4 iv5
. margins iv1, at(iv2==(0(1)6))
'iv1' not found in list of covariates
r(322);
有趣的是，当我以一种需要逗号的格式使用margins命令时，Stata不会出现任何错误。例如，以下两行代码没有任何问题：
margins, at(iv2=(0(1)6)) over(iv1)
margins, dydx(iv1) at(iv2=(0(1)6))
我已经看过2013年3月的上一篇文章，但我仍然无法弄清楚如何解决这个问题：Stata error: not found in list of covariates
答案 0 :(得分：1)
您可以使用公共数据集重现错误吗？这是我的尝试（在底部使用因子变量解决方案）：
. use http://www.stata-press.com/data/r13/union
(NLS Women 14-24 in 1968)
. xtlogit union age grade not_smsa south##c.year
Fitting comparison model:
Iteration 0: log likelihood = -13864.23
Iteration 1: log likelihood = -13547.326
Iteration 2: log likelihood = -13542.493
Iteration 3: log likelihood = -13542.49
Iteration 4: log likelihood = -13542.49
Fitting full model:
tau = 0.0 log likelihood = -13542.49
tau = 0.1 log likelihood = -12923.751
tau = 0.2 log likelihood = -12417.651
tau = 0.3 log likelihood = -12001.665
tau = 0.4 log likelihood = -11655.586
tau = 0.5 log likelihood = -11366.441
tau = 0.6 log likelihood = -11128.749
tau = 0.7 log likelihood = -10946.399
tau = 0.8 log likelihood = -10844.833
Iteration 0: log likelihood = -10946.488
Iteration 1: log likelihood = -10557.39
Iteration 2: log likelihood = -10540.493
Iteration 3: log likelihood = -10540.274
Iteration 4: log likelihood = -10540.274 (backed up)
Iteration 5: log likelihood = -10540.274
Random-effects logistic regression Number of obs = 26200
Group variable: idcode Number of groups = 4434
Random effects u_i ~ Gaussian Obs per group: min = 1
avg = 5.9
max = 12
Integration method: mvaghermite Integration points = 12
Wald chi2(6) = 227.46
Log likelihood = -10540.274 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
union | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | .0156732 .0149895 1.05 0.296 -.0137056 .045052
grade | .0870851 .0176476 4.93 0.000 .0524965 .1216738
not_smsa | -.2511884 .0823508 -3.05 0.002 -.4125929 -.0897839
1.south | -2.839112 .6413116 -4.43 0.000 -4.096059 -1.582164
year | -.0068604 .0156575 -0.44 0.661 -.0375486 .0238277
|
south#c.year |
1 | .0238506 .0079732 2.99 0.003 .0082235 .0394777
|
_cons | -3.009365 .8414963 -3.58 0.000 -4.658667 -1.360062
-------------+----------------------------------------------------------------
/lnsig2u | 1.749366 .0470017 1.657245 1.841488
-------------+----------------------------------------------------------------
sigma_u | 2.398116 .0563577 2.290162 2.511158
rho | .6361098 .0108797 .6145307 .6571548
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) = 6004.43 Prob >= chibar2 = 0.000
. margins not_smsa, at(age=(10(5)20))
'not_smsa' not found in list of covariates
r(322);
. xtlogit union age grade i.not_smsa i.south##c.year
Fitting comparison model:
Iteration 0: log likelihood = -13864.23
Iteration 1: log likelihood = -13547.326
Iteration 2: log likelihood = -13542.493
Iteration 3: log likelihood = -13542.49
Iteration 4: log likelihood = -13542.49
Fitting full model:
tau = 0.0 log likelihood = -13542.49
tau = 0.1 log likelihood = -12923.751
tau = 0.2 log likelihood = -12417.651
tau = 0.3 log likelihood = -12001.665
tau = 0.4 log likelihood = -11655.586
tau = 0.5 log likelihood = -11366.441
tau = 0.6 log likelihood = -11128.749
tau = 0.7 log likelihood = -10946.399
tau = 0.8 log likelihood = -10844.833
Iteration 0: log likelihood = -10946.488
Iteration 1: log likelihood = -10557.39
Iteration 2: log likelihood = -10540.493
Iteration 3: log likelihood = -10540.274
Iteration 4: log likelihood = -10540.274 (backed up)
Iteration 5: log likelihood = -10540.274
Random-effects logistic regression Number of obs = 26200
Group variable: idcode Number of groups = 4434
Random effects u_i ~ Gaussian Obs per group: min = 1
avg = 5.9
max = 12
Integration method: mvaghermite Integration points = 12
Wald chi2(6) = 227.46
Log likelihood = -10540.274 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
union | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | .0156732 .0149895 1.05 0.296 -.0137056 .045052
grade | .0870851 .0176476 4.93 0.000 .0524965 .1216738
1.not_smsa | -.2511884 .0823508 -3.05 0.002 -.4125929 -.0897839
1.south | -2.839112 .6413116 -4.43 0.000 -4.096059 -1.582164
year | -.0068604 .0156575 -0.44 0.661 -.0375486 .0238277
|
south#c.year |
1 | .0238506 .0079732 2.99 0.003 .0082235 .0394777
|
_cons | -3.009365 .8414963 -3.58 0.000 -4.658667 -1.360062
-------------+----------------------------------------------------------------
/lnsig2u | 1.749366 .0470017 1.657245 1.841488
-------------+----------------------------------------------------------------
sigma_u | 2.398116 .0563577 2.290162 2.511158
rho | .6361098 .0108797 .6145307 .6571548
------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) = 6004.43 Prob >= chibar2 = 0.000
. margins not_smsa, at(age=(10(5)20))
Predictive margins Number of obs = 26200
Model VCE : OIM
Expression : Linear prediction, predict()
1._at : age = 10
2._at : age = 15
3._at : age = 20
------------------------------------------------------------------------------
| Delta-method
| Margin Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_at#not_smsa |
1 0 | -2.674903 .3107206 -8.61 0.000 -3.283905 -2.065902
1 1 | -2.926092 .3148551 -9.29 0.000 -3.543196 -2.308987
2 0 | -2.596538 .2375601 -10.93 0.000 -3.062147 -2.130928
2 1 | -2.847726 .2432156 -11.71 0.000 -3.32442 -2.371032
3 0 | -2.518172 .1660016 -15.17 0.000 -2.843529 -2.192814
3 1 | -2.76936 .1743793 -15.88 0.000 -3.111137 -2.427583
------------------------------------------------------------------------------
答案 1 :(得分：0)
我在尝试计算虚拟变量的边际时遇到了同样的问题 - 错误消息：“在协变量列表中找不到因子变量”。我必须在 logit 模型中指定 i.dummyvar 才能计算边距。