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st: margins: (not estimatable)
From
D-Ta <[email protected]>
To
[email protected]
Subject
st: margins: (not estimatable)
Date
Tue, 12 Jul 2011 09:39:07 +0200
Dear List-Members,
I have read a similar thread
(http://www.stata.com/statalist/archive/2011-06/msg00407.html), but the
answer wouldnt solve my problem, I run a logit model where I am
interested in the marginal effect of fail (dummy) on dropout at the
value of mc_1styr_c==0 (the model is based on a regression discontinuity
research design).
Here is what I do:
. logit dropout3_en i.fail##(c.mc_1st##c.mc_1st##c.mc_1st) if sex==0,
vce(cluster mc_1st)
Iteration 0: log pseudolikelihood = -94.090863
Iteration 1: log pseudolikelihood = -64.970231
Iteration 2: log pseudolikelihood = -59.025487
Iteration 3: log pseudolikelihood = -54.402925
Iteration 4: log pseudolikelihood = -53.62672
Iteration 5: log pseudolikelihood = -53.403301
Iteration 6: log pseudolikelihood = -53.337567
Iteration 7: log pseudolikelihood = -53.329697
Iteration 8: log pseudolikelihood = -53.329635
Iteration 9: log pseudolikelihood = -53.329635
Logistic regression Number of obs =
409
Wald chi2(6) =
.
Prob > chi2 =
.
Log pseudolikelihood = -53.329635 Pseudo R2 =
0.4332
(Std. Err. adjusted for 91 clusters in
mc_1styr_centered)
------------------------------------------------------------------------------
| Robust
dropout3_e~t | Coef. Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
1.fail | 2.61074 2.755256 0.95 0.343 -2.789461
8.010942
mc_1styr_c~d | .4105334 3.229761 0.13 0.899 -5.919682
6.740749
|
c. |
mc_1styr_c~d#|
c. |
mc_1styr_c~d | .1478797 .7714279 0.19 0.848 -1.364091
1.659851
|
c. |
mc_1styr_c~d#|
c. |
mc_1styr_c~d#|
c. |
mc_1styr_c~d | .013731 .0479273 0.29 0.774 -.0802048
.1076669
|
fail#|
c. |
mc_1styr_c~d |
1 | -.3809698 3.230427 -0.12 0.906 -6.712491
5.950551
|
fail#|
c. |
mc_1styr_c~d#|
c. |
mc_1styr_c~d |
1 | -.1474152 .7714299 -0.19 0.848 -1.65939
1.36456
|
fail#|
c. |
mc_1styr_c~d#|
c. |
mc_1styr_c~d#|
c. |
mc_1styr_c~d |
1 | -.0137374 .0479273 -0.29 0.774 -.1076732
.0801984
|
_cons | -3.898577 2.719008 -1.43 0.152 -9.227734
1.43058
------------------------------------------------------------------------------
. margins ,dydx(fail) at(mc_1st==0)
Conditional marginal effects Number of obs =
409
Model VCE : Robust
Expression : Pr(dropout3_enrollment), predict()
dy/dx w.r.t. : 1.fail
at : mc_1styr_c~d = 0
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. z P>|z| [95% Conf.
Interval]
-------------+----------------------------------------------------------------
1.fail | (not estimable)
------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.
The command: margins ,dydx(fail) at(mc_1st==0) should give me the effect
and the significance level of interest. If I enlarge the sample (lets
say, not condition on sex==0) it works.
Could someone explain me the core of the problem and how to solve it?
Many thanks
Darjusch
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