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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