I am estimating, for example, the following double-difference / difference-in-difference logistic regression where opt_dum, depstat (Mild or None), and treat (HT or Con) are all binary variables.
The output of the model is as follows:
Code:
logit opt_dum i.depstat##i.treat, or
Iteration 0: log likelihood = -166.14693
Iteration 1: log likelihood = -121.37891
Iteration 2: log likelihood = -120.88007
Iteration 3: log likelihood = -120.87909
Iteration 4: log likelihood = -120.87909
Logistic regression Number of obs = 240
LR chi2(3) = 90.54
Prob > chi2 = 0.0000
Log likelihood = -120.87909 Pseudo R2 = 0.2725
-------------------------------------------------------------------------------
opt_dum | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
--------------+----------------------------------------------------------------
depstat |
Mild | .3891403 .1851221 -1.98 0.047 .1531694 .9886448
|
treat |
HT | 19.15126 9.456233 5.98 0.000 7.276179 50.40706
|
depstat#treat |
Mild#HT | .5459965 .3697249 -0.89 0.372 .1448083 2.058668
|
_cons | .3953488 .1132654 -3.24 0.001 .2254839 .693179
-------------------------------------------------------------------------------Code:
. margins i.depstat##i.treat, post coeflegend
Predictive margins Number of obs = 240
Model VCE : OIM
Expression : Pr(opt_dum), predict()
-------------------------------------------------------------------------------
| Margin Legend
--------------+----------------------------------------------------------------
depstat |
Non | .5833333 _b[1bn.depstat]
Mild | .375 _b[2.depstat]
|
treat |
Con | .2083333 _b[1bn.treat]
HT | .75 _b[2.treat]
|
depstat#treat |
Non#Con | .2833333 _b[1bn.depstat#1bn.treat]
Non#HT | .8833333 _b[1bn.depstat#2.treat]
Mild#Con | .1333333 _b[2.depstat#1bn.treat]
Mild#HT | .6166667 _b[2.depstat#2.treat]
-------------------------------------------------------------------------------Code:
. lincom ( _b[2.depstat#2.treat]- _b[2.depstat#1bn.treat])-(_b[1bn.depstat#2.treat]-_b[1bn.depstat#1bn.treat])
( 1) 1bn.depstat#1bn.treat - 1bn.depstat#2.treat - 2.depstat#1bn.treat + 2.depstat#2.treat = 0
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
(1) | -.1166667 .1047263 -1.11 0.265 -.3219264 .0885931
------------------------------------------------------------------------------Caroline
0 Response to Lincom for interpreting interactions in logistic regression
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