Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

st: margins: (not estimatable)


From   D-Ta <altruist81@gmx.de>
To   statalist@hsphsun2.harvard.edu
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

*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/statalist/faq
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index