I'm wondering why margins (after logit) produces different results for a dummy variable (0-1) depending on whether the i-prefix is used or not. Estimates of coefficients of smoke are (logically) identical, but the marginal effect and standard error of smoke differ (0.1352669 vs.0.140626). See code and result below.
Thank you,
Mike
Code:
. webuse lbw
(Hosmer & Lemeshow data)
. logit low age lwt smoke
Iteration 0: log likelihood = -117.336
Iteration 1: log likelihood = -111.55075
Iteration 2: log likelihood = -111.44794
Iteration 3: log likelihood = -111.44776
Iteration 4: log likelihood = -111.44776
Logistic regression Number of obs = 189
LR chi2(3) = 11.78
Prob > chi2 = 0.0082
Log likelihood = -111.44776 Pseudo R2 = 0.0502
------------------------------------------------------------------------------
low | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.03902 .0327243 -1.19 0.233 -.1031585 .0251184
lwt | -.0121153 .0061336 -1.98 0.048 -.0241368 -.0000938
smoke | .6706699 .3258659 2.06 0.040 .0319845 1.309355
_cons | 1.36601 1.014251 1.35 0.178 -.6218848 3.353905
------------------------------------------------------------------------------
. margins, dydx(*)
Average marginal effects Number of obs = 189
Model VCE : OIM
Expression : Pr(low), predict()
dy/dx w.r.t. : age lwt smoke
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0078699 .0065191 -1.21 0.227 -.0206472 .0049073
lwt | -.0024435 .0011968 -2.04 0.041 -.0047892 -.0000979
smoke | .1352669 .0630567 2.15 0.032 .0116781 .2588556
------------------------------------------------------------------------------
. logit low age lwt i.smoke
Iteration 0: log likelihood = -117.336
Iteration 1: log likelihood = -111.55075
Iteration 2: log likelihood = -111.44794
Iteration 3: log likelihood = -111.44776
Iteration 4: log likelihood = -111.44776
Logistic regression Number of obs = 189
LR chi2(3) = 11.78
Prob > chi2 = 0.0082
Log likelihood = -111.44776 Pseudo R2 = 0.0502
------------------------------------------------------------------------------
low | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.03902 .0327243 -1.19 0.233 -.1031585 .0251184
lwt | -.0121153 .0061336 -1.98 0.048 -.0241368 -.0000938
|
smoke |
smoker | .6706699 .3258659 2.06 0.040 .0319845 1.309355
_cons | 1.36601 1.014251 1.35 0.178 -.6218848 3.353905
------------------------------------------------------------------------------
. margins, dydx(*)
Average marginal effects Number of obs = 189
Model VCE : OIM
Expression : Pr(low), predict()
dy/dx w.r.t. : age lwt 1.smoke
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
age | -.0078699 .0065191 -1.21 0.227 -.0206472 .0049073
lwt | -.0024435 .0011968 -2.04 0.041 -.0047892 -.0000979
|
smoke |
smoker | .140626 .0688897 2.04 0.041 .0056047 .2756473
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
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